mirror of
https://github.com/ruvnet/RuView
synced 2026-06-12 10:43:19 +00:00
Compare commits
12 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| d0a7690f8f | |||
| 8487192d0f | |||
| d120cc2278 | |||
| 8ad0d0f91c | |||
| 36af09a4a8 | |||
| 772ece4568 | |||
| 48b002fa7e | |||
| 8d9c5994db | |||
| 6b5fd3cf25 | |||
| 2400216920 | |||
| 98bf8c4726 | |||
| 2e4461d64d |
@@ -0,0 +1,75 @@
|
||||
# PROOF — reproduce every claim, or find the one we can't yet
|
||||
|
||||
This project (RuView / wifi-densepose) has been publicly called "AI slop" and
|
||||
"fake." This document is the answer: **a skeptic can clone the repo, run one
|
||||
script, and have every headline claim either verified on their own machine or
|
||||
shown — explicitly — as "CLAIMED, not yet reproduced (here's exactly what it
|
||||
needs)."** Nothing below is asserted without a command you can run.
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ruvnet/RuView && cd RuView
|
||||
bash scripts/prove.sh # core gate + the anti-slop assertion tests
|
||||
bash scripts/prove.sh --full # also attempt the feature-gated subset
|
||||
```
|
||||
|
||||
`prove.sh` exits 0 only if every **non-gated** claim passes. Gated claims never
|
||||
fail the run; they print the prerequisite (a GPU, a dataset, real hardware, a
|
||||
trained checkpoint) so you can reproduce them yourself.
|
||||
|
||||
## Grading
|
||||
|
||||
- **MEASURED** — reproduced on our hardware, with the exact command recorded, and
|
||||
pinned by a test that *fails on the pre-fix code*. `prove.sh` re-runs these.
|
||||
- **CLAIMED** — cited from a source, or measured by the source, but not
|
||||
reproduced in this repo's automated harness.
|
||||
- **DATA-GATED / HARDWARE-GATED** — the *code path* is real and tested, but the
|
||||
*accuracy/throughput claim* needs data or hardware we don't ship. We never
|
||||
fabricate the number; the code carries a typed error or a `weights_trained`/
|
||||
provenance flag instead.
|
||||
|
||||
## The hard gate (run on any machine with Rust + Python)
|
||||
|
||||
| Claim | Grade | Reproduce |
|
||||
|---|---|---|
|
||||
| Rust workspace: 3,128 tests, 0 failed | **MEASURED** | `cd v2 && cargo test --workspace --no-default-features` |
|
||||
| Deterministic CSI pipeline proof (bit-exact SHA-256) | **MEASURED** | `python archive/v1/data/proof/verify.py` → `VERDICT: PASS` |
|
||||
|
||||
## Anti-slop assertion tests (each fails on the pre-fix code)
|
||||
|
||||
| Claim | Grade | Test (run via `cargo test -p <crate> <name>`) |
|
||||
|---|---|---|
|
||||
| Fusion crafted-input DoS panics are closed (ADR-156 §2.2) | **MEASURED** | `wifi-densepose-ruvector :: triangulation_out_of_range_index_returns_none_no_panic` |
|
||||
| **The "Soul Signature" identity claim, honestly bounded:** on WiFi-only cardiac+respiratory channels two people are **not separable** (gap ≈ 0.0005) | **MEASURED** | `wifi-densepose-bfld :: cardiac_alone_cannot_separate_identity_matches_audit` |
|
||||
| OccWorld `predict()` is real (input-dependent), not random noise | **MEASURED** | `wifi-densepose-occworld-candle :: predict_is_deterministic_for_same_input` |
|
||||
| Pose runtime emits frames under its own default config (ADR-159 A1) | **MEASURED** | `cog-pose-estimation :: default_config_emits_frames_with_real_model` |
|
||||
| Person-count flags untrained classes — no count inflation (ADR-159 A2) | **MEASURED** | `cog-person-count :: untrained_class_argmax_is_flagged_low_confidence` |
|
||||
| Medical edge skills carry a "not a medical device" disclaimer (ADR-160 A1) | **MEASURED** | `wifi-densepose-wasm-edge :: a1_med_modules_have_clinical_disclaimer` (`--features std`) |
|
||||
| Survivor dedup 3→1, count-inflation killed (ADR-158 §2) | **MEASURED** | `wifi-densepose-mat :: test_identical_vitals_no_location_dedup_to_one` (`--features mat`) |
|
||||
|
||||
## Measured performance (criterion; reproduce on your machine)
|
||||
|
||||
| Claim | Grade | Reproduce |
|
||||
|---|---|---|
|
||||
| PSD FFT-planner cache 2.0–3.1×, DTW band 2.4–4.1× (ADR-154) | **MEASURED** | `cd v2 && cargo bench -p wifi-densepose-signal` |
|
||||
| fuse() double-clone removed ~2.17× marshalling (ADR-156) | **MEASURED** | `cd v2 && cargo bench -p wifi-densepose-ruvector --bench fusion_bench` |
|
||||
| zero-copy ORT input ~1.48× (ADR-155) | **MEASURED** | `cd v2 && cargo bench -p wifi-densepose-nn --features onnx --bench onnx_bench` |
|
||||
| pointcloud splats 9→2 passes ~1.24× (ADR-160 research) | **MEASURED** | `cd v2 && cargo bench -p wifi-densepose-pointcloud --bench splats_bench` |
|
||||
| native wlanapi multi-BSSID scan 9.74 Hz (vs netsh ~2 Hz) | **MEASURED (Windows)** | `cd v2 && cargo test -p wifi-densepose-wifiscan -- --ignored measure_native_scan_rate` |
|
||||
|
||||
## What we do NOT claim (the honest negatives — the strongest anti-slop signal)
|
||||
|
||||
| Capability | Status |
|
||||
|---|---|
|
||||
| **Named person-identity from WiFi** | **NOT achieved, and measured why.** The §3.6 matcher is real, but identity does not lock on WiFi-only channels (gap 0.0005). DATA-GATED on a real enrollment feeding the AETHER/body-resonance channel — never done. No named-identity claim is made. |
|
||||
| WiFlow-STD ~96% PCK@20 | **CLAIMED-reproduced** on our RTX 5080 (`benchmarks/wiflow-std/RESULTS.md`); HARDWARE-GATED for you (needs an NVIDIA GPU + the MM-Fi dataset). The upstream *shipped checkpoint* was **REFUTED** (0.08% PCK) — we publish that. |
|
||||
| OccWorld trajectory accuracy | DATA-GATED on a trained checkpoint; `predict()` carries `weights_trained=false` until one is loaded — never silently faked. |
|
||||
| Edge-skill detection accuracy (seizure, weapon, affect, …) | UNVALIDATED — every such module is now disclaimer-gated as experimental/research; the DSP is real, the accuracy is not claimed. |
|
||||
| 802.11bf-2025 OTA conformance | No commodity silicon ships a conformant interface as of 2026; ours is a simulation-tested forward-compat protocol model, not a certified implementation. |
|
||||
|
||||
## Provenance
|
||||
|
||||
Every claim above traces to a committed ADR (`docs/adr/ADR-154`…`ADR-160`), a
|
||||
test, a criterion bench, or `benchmarks/wiflow-std/RESULTS.md`. The history
|
||||
includes published **retractions** (the 92.9% PCK retraction; the WiFlow-STD
|
||||
shipped-checkpoint refutation; the NV-diamond BOM reality check) — a faker hides
|
||||
failures; we commit them.
|
||||
@@ -0,0 +1,242 @@
|
||||
# ADR-159: Cognitum Appliance Cluster — Beyond-SOTA Sweep, Anti-"AI-Slop" Hardening
|
||||
|
||||
- **Status**: accepted
|
||||
- **Date**: 2026-06-11
|
||||
- **Deciders**: ruv
|
||||
- **Tags**: cognitum, cogs, person-count, pose-estimation, ha-matter, drone-swarm, remote-id, manifest, prove-everything
|
||||
|
||||
## Context
|
||||
|
||||
This ADR records the beyond-SOTA sweep over the Cognitum appliance cluster
|
||||
(`cog-person-count`, `cog-pose-estimation`, `cog-ha-matter`, `ruview-swarm`),
|
||||
executed under the project's **prove-everything / anti-"AI-slop"** directive: the
|
||||
claim surface every cog presents (manifests, descriptions, runtime events,
|
||||
broadcast fields) must match what the code and the shipped weights actually do.
|
||||
|
||||
### Headline — the "never identified anyone" accusation is REFUTED
|
||||
|
||||
A read-only audit raised the worst-class accusation: that these cogs are slop that
|
||||
"never identified anyone." That accusation is **refuted by byte-level evidence**:
|
||||
|
||||
- `cog-pose-estimation` and `cog-person-count` ship **real, trained Candle models**
|
||||
(`pose_v1.safetensors`, `count_v1.safetensors`), not placeholders. The forward
|
||||
passes (`PoseNet`, `CountNet`) mirror the training scripts exactly and run on
|
||||
real CSI bytes.
|
||||
- The artifacts are **SHA-pinned and Ed25519-signed**: the on-disk
|
||||
`manifests/x86_64/manifest.json` carries a real `binary_sha256`
|
||||
(`051614ce…388b3` for person-count, `a434739a…71fa` for pose), a real
|
||||
`weights_sha256`, and a `binary_signature` over `sig_algo: Ed25519`.
|
||||
- The manifests are **brutally honest about accuracy**: person-count's
|
||||
`build_metadata` ships `training_class1_accuracy = 0.343` and a candid
|
||||
`training_caveat`; pose ships `training_pck20 = 3.0` / `training_pck50 = 18.5`.
|
||||
Nothing is inflated. That honesty *is* the anti-slop win — the models are weak
|
||||
in the field, and the manifests say so.
|
||||
|
||||
So the cogs **do** run real trained inference and **do** disclose how weak it is.
|
||||
What the audit correctly found were not fabrications but **claim-surface
|
||||
overclaims** — four places where the surface said more than the weights deliver.
|
||||
This ADR tightens those four (A1–A4) and cites the already-correct subsystems as
|
||||
NO-ACTION positives.
|
||||
|
||||
Grading vocabulary follows ADR-152 / ADR-158:
|
||||
- **MEASURED** — reproduced in this worktree, command + failing-on-old test recorded.
|
||||
- **DATA-GATED** — real code path present; honestly flagged where data/hardware is absent.
|
||||
- **NO-ACTION (already-SOTA)** — audited, found correct, cited as a positive.
|
||||
- **ACCEPTED-FUTURE** — deliberately deferred, nothing dropped.
|
||||
|
||||
## Graded SOTA Landscape
|
||||
|
||||
| Capability | Grade | Note |
|
||||
|------------|-------|------|
|
||||
| CSI person counting (`cog-person-count`) | **DATA-GATED** | Real Candle count head + Bayesian fusion; weights trained only on classes 0/1 (presence). Multi-occupant accuracy is genuinely unproven and is **not fabricated** — counts above the trained range are now flagged `low_confidence` and clamped. |
|
||||
| CSI pose estimation (`cog-pose-estimation`) | **DATA-GATED** | Real Candle encoder + 17-keypoint head; field accuracy honestly weak (PCK@50 = 18.5%, disclosed in the manifest). The default-install gate bug (A1) is fixed so it actually emits frames. |
|
||||
| Signed cog manifests (Ed25519 + SHA-256) | **NO-ACTION (already-SOTA)** | On-disk manifests are real, signed, SHA-pinned, and honest about accuracy. The CLI now emits them verbatim (A4). |
|
||||
| HA bridge (`cog-ha-matter`) MQTT + witness | **NO-ACTION (already-SOTA)** | Real Ed25519 hash-chain witness, mDNS, embedded broker. Matter commissioning is honestly deferred to v0.8 (TLS off, LAN-only) — description softened to stop claiming Matter (honest-absence). |
|
||||
| Drone-swarm MARL (`ruview-swarm`) | **DATA-GATED / honest** | `candle_ppo.rs` is real autodiff PPO; it is **untrained at runtime** (random init) by design — the swarm must be trained before deploy, which the code does not hide. |
|
||||
| ASTM F3411 Remote ID | **MEASURED (A3)** | Basic ID message is real; the Location/Vector message is honestly *not* implemented (NED metres are no longer mislabelled as WGS84 lat/lon). |
|
||||
|
||||
## Decision — Fixes Landed (MEASURED)
|
||||
|
||||
### §A1 Pose runtime emitted ZERO frames under default config (HIGH)
|
||||
|
||||
**Overclaim (silent correctness bug):** `inference.rs` hardcoded
|
||||
`confidence: 0.185` for every inference, `config.rs default_min_confidence()`
|
||||
returned `0.3`, and `runtime.rs` gated emission on `confidence >= min_confidence`.
|
||||
A default install therefore **never emitted a single `pose.frame`** while
|
||||
`health` reported healthy — the cog *claimed* to be a running pose estimator but
|
||||
silently produced nothing.
|
||||
|
||||
**Real fix:** `pose_v1` has **no confidence head** (the head emits 34 keypoint
|
||||
coordinates only), so a real per-frame confidence is genuinely unavailable. We
|
||||
took the disclosed "ok" path rather than silently lowering the threshold:
|
||||
- Introduced `inference::MODEL_TYPICAL_CONFIDENCE = 0.185` (the validation PCK@50)
|
||||
as the single published per-frame confidence, used by both `infer()` and the
|
||||
config default.
|
||||
- Pinned `default_min_confidence()` to `MODEL_TYPICAL_CONFIDENCE` so a default
|
||||
install clears its own gate and emits.
|
||||
- Documented the trade-off in the config field doc, the JSON schema
|
||||
(`default` 0.3 → 0.185, with a description), **and** added a `run.started`
|
||||
warning in `main.rs` that fires when an operator raises `min_confidence` above
|
||||
the model's typical confidence — so a deliberately-high threshold is loud, not
|
||||
silent.
|
||||
|
||||
**Failing-on-old test:** `cog_pose_estimation` smoke
|
||||
`default_config_emits_frames_with_real_model` — parses a default config and
|
||||
asserts `min_confidence <= MODEL_TYPICAL_CONFIDENCE` (and, with the real model
|
||||
loaded, that `infer().confidence >= min_confidence`). **Proven to fail** on the
|
||||
old `default_min_confidence()=0.3`:
|
||||
`default min_confidence 0.3 exceeds model typical confidence 0.185 — a default
|
||||
install would emit zero pose.frame events`.
|
||||
|
||||
**Grade: MEASURED.**
|
||||
|
||||
### §A2 8-class count head on a 2-class-trained model (MEDIUM)
|
||||
|
||||
**Overclaim:** `inference.rs COUNT_CLASSES = 8` with argmax over {0..7}, but
|
||||
`count_train_results.json` has support only for classes 0 and 1 (`per_class_accuracy`
|
||||
keys `"0"`/`"1"`). The model is a **presence detector**, not a calibrated
|
||||
multi-occupant counter; an argmax on classes 2..=7 is out-of-distribution, yet the
|
||||
cog would emit it as a confident headcount. The Cargo.toml billed it as a
|
||||
"learned multi-person counter."
|
||||
|
||||
**Real fix (no network change — DATA-GATED, accuracy not fabricated):**
|
||||
- Added `inference::MAX_TRAINED_CLASS = 1`, plus `CountPrediction::is_low_confidence()`
|
||||
(argmax beyond the trained ceiling) and `clamped_count()` (report clamped to the
|
||||
trained range, raw argmax kept for audit).
|
||||
- `person.count` events now carry `low_confidence` + `raw_count`, and downgrade to
|
||||
`level: "warn"` when out-of-distribution; the reported `count` is clamped so we
|
||||
never emit a fabricated headcount the weights can't back.
|
||||
- `run.started` discloses `count_max_trained_class` and `count_classes`.
|
||||
- Cargo.toml description changed from "learned multi-person counter" to
|
||||
"presence detector + (data-gated) person count".
|
||||
|
||||
**Failing-on-old test:** `cog_person_count` smoke
|
||||
`untrained_class_argmax_is_flagged_low_confidence` — a prediction whose argmax is
|
||||
class 5 is asserted `is_low_confidence() == true` and `clamped_count() ==
|
||||
MAX_TRAINED_CLASS`; a class-1 prediction is asserted *not* flagged. Fails on old
|
||||
code (no such methods/flag existed).
|
||||
|
||||
**Grade: MEASURED (mechanism); multi-occupant accuracy DATA-GATED.**
|
||||
|
||||
### §A3 Remote ID broadcast NED metres as WGS84 lat/lon (MEDIUM — safety/compliance)
|
||||
|
||||
**Overclaim (compliance hazard):** `security/remote_id.rs update()` stored
|
||||
`state.position.x/.y` (NED **metres**) into `drone_lat`/`drone_lon`, so the Remote
|
||||
ID broadcast would carry physically-impossible coordinates (e.g. "latitude =
|
||||
37.5 m"). The module doc claimed a "Basic ID + Location/Vector message," but only
|
||||
`encode_basic_id()` exists.
|
||||
|
||||
**Real fix (honest naming — never broadcast impossible coordinates):**
|
||||
- Renamed `drone_lat`/`drone_lon` → `drone_north_m`/`drone_east_m` (NED metres
|
||||
relative to the operator/takeoff datum), with field docs stating they are *not*
|
||||
geodetic. `operator_lat`/`operator_lon` remain true WGS84 (from the operator's
|
||||
GNSS).
|
||||
- Corrected the module doc to claim **Basic ID only**; the Location/Vector encoder
|
||||
is explicitly deferred until a datum-anchored NED→WGS84 transform lands
|
||||
(ACCEPTED-FUTURE), rather than removing a real feature.
|
||||
|
||||
**Failing-on-old test:** `security::remote_id::tests::test_ned_offset_stored_as_metres_not_latlon`
|
||||
— a 37.5 m north / −12.0 m east NED offset is asserted to land in
|
||||
`drone_north_m`/`drone_east_m`; the operator's real WGS84 fix stays in range. Fails
|
||||
on old code, where these values were stored into `drone_lat`/`drone_lon`.
|
||||
|
||||
**Grade: MEASURED.**
|
||||
|
||||
### §A4 Hollow CLI manifest (LOW)
|
||||
|
||||
**Overclaim:** `cog-person-count main.rs cmd_manifest` emitted a null skeleton
|
||||
(`binary_sha256: null`, no training metadata), making the CLI look unsigned even
|
||||
though the **real signed manifest** existed at
|
||||
`cog/artifacts/manifests/x86_64/manifest.json`.
|
||||
|
||||
**Real fix:** new `cog_person_count::manifest` module `include_str!`-embeds the
|
||||
real signed manifests (x86_64 + arm), selected by build target arch.
|
||||
`cmd_manifest` now parses-then-emits the embedded signed manifest — exactly the
|
||||
pattern `cog-pose-estimation`'s `manifest_roundtrips` test demonstrates. The CLI
|
||||
now reports the real `binary_sha256`, `weights_sha256`, Ed25519 signature, and
|
||||
honest `build_metadata` (`training_class1_accuracy = 0.343`).
|
||||
|
||||
**Failing-on-old test:** `manifest::tests::embedded_manifest_has_non_null_binary_sha256`
|
||||
asserts a 64-hex-char `binary_sha256`; companions assert the embedded manifest is
|
||||
signed (`sig_algo == Ed25519`) and `id == COG_ID`. End-to-end verified:
|
||||
`cog-person-count manifest` prints `binary_sha256:
|
||||
051614ce6ba63df704fae848a67ad095df4bb88862fdff05ef3c0419cc8388b3`.
|
||||
|
||||
**Grade: MEASURED.**
|
||||
|
||||
### §A5 cog-ha-matter description claimed Matter before it exists (LOW — honest-labeling)
|
||||
|
||||
**Overclaim:** the Cargo.toml description said "Home Assistant + Matter
|
||||
integration," but Matter commissioning is deferred to v0.8 (`TlsConfig::Off`,
|
||||
LAN-only, asserted by `runtime.rs tls_defaults_to_off_for_v1_lan_only`).
|
||||
|
||||
**Real fix (no code change):** softened the description to "Home Assistant (MQTT)
|
||||
integration … LAN-only (no TLS); Matter Bridge commissioning is deferred to v0.8
|
||||
and not yet implemented." Mirrors ADR-158 §6 honest-absence: state what isn't
|
||||
there rather than implying it is.
|
||||
|
||||
**Grade: MEASURED (label).**
|
||||
|
||||
## Negative Results (Confirmed — NO-ACTION positives)
|
||||
|
||||
Audited and found genuinely correct; cited as positives, not edited:
|
||||
|
||||
- **`cog-ha-matter` witness chain** (`witness.rs` / `witness_signing.rs`) — real
|
||||
Ed25519 hash-chained witness log. Already-SOTA.
|
||||
- **`cog-person-count` fusion** (`fusion.rs`) — real Bayesian product-of-experts
|
||||
multi-node fusion (Stoer-Wagner-bounded clip), not a heuristic. Already-SOTA.
|
||||
- **`ruview-swarm` PPO** (`marl/candle_ppo.rs`) — real Candle autodiff PPO with a
|
||||
genuine policy-gradient update; its `randn` uses (init, action sampling,
|
||||
exploration) are all legitimate, not fake-output substitutes. Untrained at
|
||||
runtime by design (the swarm must be trained before deploy), which the code
|
||||
does not hide. Already-SOTA / honest.
|
||||
|
||||
## Deferred Backlog (Nothing Dropped)
|
||||
|
||||
- **Multi-occupant count accuracy** — DATA-GATED on labelled multi-occupant CSI.
|
||||
The `low_confidence` flag + clamp (§A2) is the honest stand-in until then.
|
||||
- **Remote ID Location/Vector message** — ACCEPTED-FUTURE; requires a
|
||||
datum-anchored local-tangent-plane NED→WGS84 transform with an operator datum.
|
||||
Basic ID ships today.
|
||||
- **Matter Bridge commissioning** — ACCEPTED-FUTURE (v0.8); LAN-only MQTT ships today.
|
||||
- **Criterion benches** for cog inference latency and `mesh_guard` — ACCEPTED-FUTURE
|
||||
(cold-start timings are recorded in the manifests' `build_metadata`, not yet a
|
||||
regression bench).
|
||||
- **`wasm-edge` skill accuracy** — unvalidated; **now honestly labelled, not
|
||||
claimed** (done in ADR-160: medical/affect/security/exotic claim surfaces
|
||||
disclaimed, renamed, and feature-gated; per-skill accuracy remains DATA-GATED).
|
||||
|
||||
## Consequences
|
||||
|
||||
- A default pose-estimation install now actually emits `pose.frame` events;
|
||||
raising the threshold above the model's reach is a loud `run.started` warning,
|
||||
not a silent dropout.
|
||||
- A person-count reading on an untrained class is flagged `low_confidence`,
|
||||
clamped, and downgraded to `warn` — no fabricated headcounts.
|
||||
- The Remote ID broadcast can never carry physically-impossible coordinates; NED
|
||||
metres live in honestly-named metre fields.
|
||||
- `cog-person-count manifest` now reports the real signed manifest instead of a
|
||||
hollow null skeleton.
|
||||
- No cog Cargo.toml description claims a capability (multi-person counting, Matter)
|
||||
the code/weights don't yet deliver.
|
||||
|
||||
## Reproduction (MEASURED)
|
||||
|
||||
```bash
|
||||
cd v2
|
||||
cargo test -p cog-person-count -p cog-pose-estimation -p cog-ha-matter -p ruview-swarm \
|
||||
--no-default-features
|
||||
# ruview-swarm train path compiles (PPO autodiff)
|
||||
cargo check -p ruview-swarm --features train
|
||||
# A4 end-to-end — real signed manifest, non-null binary_sha256
|
||||
cargo run -q -p cog-person-count --no-default-features -- manifest
|
||||
```
|
||||
|
||||
Result at time of writing (all 0 failed):
|
||||
- `cog-person-count` — **19 passed** (lib 10 incl. 3 manifest; smoke 9)
|
||||
- `cog-pose-estimation` — **8 passed** (smoke)
|
||||
- `cog-ha-matter` — **64 passed** (unchanged; description-only edit)
|
||||
- `ruview-swarm` — **117 passed** (default features); `--features train` compiles clean.
|
||||
|
||||
Scope was limited to the four named crates. NO-ACTION positives (witness chain,
|
||||
fusion, PPO + randn audit) were verified by inspection and left untouched.
|
||||
@@ -0,0 +1,228 @@
|
||||
# ADR-160: Edge Skill Library (`wifi-densepose-wasm-edge`) — Honest Labeling & Soundness Cleanup
|
||||
|
||||
- **Status**: accepted
|
||||
- **Date**: 2026-06-11
|
||||
- **Deciders**: ruv
|
||||
- **Tags**: wasm-edge, esp32, edge-skills, claim-surface, medical-overclaim, affect, prove-everything, soundness, static-mut
|
||||
- **Amends**: ADR-159 (deferred-backlog line for wasm-edge now TRUE)
|
||||
|
||||
## Context
|
||||
|
||||
Beyond-SOTA sweep Milestone 6, over `v2/crates/wifi-densepose-wasm-edge` only,
|
||||
executed under the project's **prove-everything / anti-"AI-slop"** directive.
|
||||
|
||||
### Headline — 0 stubs, 0 theater, all real DSP (REFUTES the slop accusation)
|
||||
|
||||
A read-only audit found this crate has **zero stubs and zero fake-output theater:
|
||||
every one of the ~70 edge skills runs real DSP** (Welford statistics,
|
||||
autocorrelation, DTW, sliced-Wasserstein, ISTA-style recovery, Kalman/HNSW, etc.).
|
||||
The forward paths are genuine signal processing on real CSI-derived inputs. That
|
||||
is the anti-slop win and it is cited here as a positive, not a fabrication.
|
||||
|
||||
What the audit correctly found was **not fake code but an over-confident claim
|
||||
surface**: skill *names* and doc-comments asserting clinical/affective/security
|
||||
capabilities that the **unvalidated** code cannot back, concentrated in the
|
||||
medical (`med_*`) and affect (`exo_happiness`/`exo_emotion`) skills. The fix is
|
||||
**honest labeling — making the labels TRUE — NOT making the claimed capability
|
||||
real.** You cannot validate seizure detection, affect inference, or weapon
|
||||
discrimination without clinical/labelled data and reference standards; this ADR
|
||||
does not pretend to. It disclaims, renames, softens, and feature-gates so the
|
||||
surface matches what the DSP actually delivers.
|
||||
|
||||
Grading vocabulary follows ADR-152 / ADR-158 / ADR-159:
|
||||
- **MEASURED** — reproduced in this worktree, command + failing-on-old test recorded.
|
||||
- **DATA-GATED** — real code path present; honestly flagged where data is absent.
|
||||
- **NO-ACTION (already-honest)** — audited, found correct, cited as a positive.
|
||||
- **ACCEPTED-FUTURE** — deliberately deferred, nothing dropped.
|
||||
|
||||
## Per-prefix classification
|
||||
|
||||
| Prefix | Class | Note |
|
||||
|--------|-------|------|
|
||||
| `sig_*` (signal intelligence) | **REAL-DSP, honest** | Algorithm-named (flash-attention, sparse-recovery, optimal-transport, temporal-compress, mincut). Names describe the math, not an overclaimed outcome. NO-ACTION on labels; A5 soundness applied. |
|
||||
| `lrn_*` (adaptive learning) | **REAL-DSP, honest** | DTW/EWC/meta-adapt/attractor — algorithm-named. NO-ACTION on labels; A5 applied. |
|
||||
| `spt_*` / `tmp_*` | **REAL-DSP, honest** | PageRank/HNSW/spiking-tracker; LTL-guard/GOAP/pattern-sequence. Algorithm-named. NO-ACTION on labels; A5 applied. |
|
||||
| `qnt_*` | **REAL-DSP, honest (disclosed analogy)** | "quantum-**inspired**" / Grover-**inspired** are already disclosed analogies. NO-ACTION (DO-NOT-touch); A5 applied (mechanical, no label/behavior change). |
|
||||
| `bld_*` / `ret_*` / `ind_*` / `occupancy`/`intrusion` | **REAL-DSP, honest** | Occupancy/queue/forklift/clean-room etc. describe physical observables. NO-ACTION on labels; A5 applied. |
|
||||
| `sec_weapon_detect` | **REAL-DSP, overclaiming NAME** → fixed (A3) | Variance-ratio reflectivity renamed off "weapon". |
|
||||
| `med_*` (5) | **REAL-DSP, overclaiming NAME/DOC** → fixed (A1) | Clinical detection asserted as fact; now disclaimed + softened + feature-gated. |
|
||||
| `exo_happiness` / `exo_emotion` | **REAL-DSP, overclaiming NAME/DOC** → fixed (A2) | Affect outputs reframed as proxies; uncited stat removed. |
|
||||
| `exo_dream_stage` / `exo_gesture_language` | **REAL-DSP, quasi-medical/over-named** → fixed (A4) | Disclaimers added; Research tag promoted to header. |
|
||||
| `exo_time_crystal` / `exo_ghost_hunter` | **REAL-DSP, honest novelty** | Disclosed exploratory/novelty skills. NO-ACTION (DO-NOT-touch); A5 applied. |
|
||||
| `nvsim` | out of scope | Disclaimer gold standard; copied its tone. |
|
||||
|
||||
## Decision — Fixes Landed
|
||||
|
||||
### §A1 Medical overclaim (HIGH) — MEASURED
|
||||
|
||||
The five `med_*` modules (`med_seizure_detect`, `med_cardiac_arrhythmia`,
|
||||
`med_respiratory_distress`, `med_sleep_apnea`, `med_gait_analysis`) stated clinical
|
||||
detection as fact with no disclaimer ("Detects tonic-clonic seizures…").
|
||||
|
||||
**Real fix (honest labeling — the DSP is kept, untouched):**
|
||||
- **(a)** Every module's `//!` header now carries a mandatory disclaimer block,
|
||||
modelled on `sec_weapon_detect.rs` and `nvsim/src/lib.rs`: *"EXPERIMENTAL
|
||||
RESEARCH MODULE — NOT VALIDATED AGAINST CLINICAL DATA. NOT A MEDICAL DEVICE.
|
||||
Flags candidate <X>-like signatures only,"* citing ADR-160.
|
||||
- **(b)** Doc verbs softened: *"Detects tonic-clonic seizures"* →
|
||||
*"Flags candidate tonic-clonic-seizure-like motion signatures (experimental)"*;
|
||||
similarly for cardiac/respiratory/apnea/gait.
|
||||
- **(c)** All five gated behind a new **non-default** cargo feature
|
||||
`medical-experimental` (`#[cfg(feature = "medical-experimental")]` in `lib.rs`,
|
||||
`medical-experimental = []` in `Cargo.toml`, **not** in `default`) so they cannot
|
||||
be silently built into a shipping artifact.
|
||||
|
||||
**Failing-on-old tests** (`tests/honest_labeling.rs`):
|
||||
`a1_med_modules_have_clinical_disclaimer`,
|
||||
`a1_med_modules_gated_behind_medical_experimental`,
|
||||
`a1_seizure_verbs_softened`. All fail on the old, undisclaimed, ungated source.
|
||||
**Grade: MEASURED (label); per-skill clinical accuracy DATA-GATED.**
|
||||
|
||||
### §A2 Affect overclaim (HIGH) — MEASURED
|
||||
|
||||
`exo_happiness_score.rs` carried an **uncited** "Happy people walk ~12% faster"
|
||||
statistic and emits `HAPPINESS_SCORE`; `exo_emotion_detect.rs` emits
|
||||
`STRESS_INDEX`/`CALM_DETECTED`/`AGITATION_DETECTED`.
|
||||
|
||||
**Real fix (honest labeling — math kept):**
|
||||
- Deleted the uncited "12% faster" / "~12% above" / "Happy people walk" statements.
|
||||
- Added a prominent *"speculative, unvalidated affect heuristic; outputs are NOT
|
||||
measurements of emotion"* disclaimer to both `//!` headers, citing ADR-160.
|
||||
- Reframed `HAPPINESS_SCORE` in the docs as a **"gait-energy proxy, not a validated
|
||||
affect measure."**
|
||||
|
||||
**Failing-on-old tests:** `a2_affect_modules_have_unvalidated_disclaimer`,
|
||||
`a2_uncited_12_percent_stat_removed`, `a2_happiness_reframed_as_proxy`.
|
||||
**Grade: MEASURED (label); affect validity DATA-GATED.**
|
||||
|
||||
### §A3 Security event-name overclaim (MEDIUM) — MEASURED
|
||||
|
||||
`sec_weapon_detect.rs`'s module doc was already honest (research-grade,
|
||||
calibration-required), but the event/const names claimed weapon-grade
|
||||
discrimination a variance ratio cannot deliver.
|
||||
|
||||
**Real fix (honest physical-quantity naming — behavior unchanged):**
|
||||
- `EVENT_WEAPON_ALERT` → `EVENT_HIGH_METAL_REFLECTIVITY` (event id 221 unchanged).
|
||||
- `WEAPON_RATIO_THRESH` → `HIGH_REFLECTIVITY_THRESH`.
|
||||
- Internal fields/consts renamed (`weapon_run`→`high_refl_run`,
|
||||
`cd_weapon`→`cd_high_refl`, `WEAPON_DEBOUNCE`→`HIGH_REFLECTIVITY_DEBOUNCE`).
|
||||
- `lib.rs` `event_types` registry: `WEAPON_ALERT` → `HIGH_METAL_REFLECTIVITY`.
|
||||
- A reflectivity-vs-weapons honest-naming note added to the header.
|
||||
The detector still flags a high amplitude-variance/phase-variance ratio (real RF
|
||||
reflectivity); it just no longer *names* that "weapon".
|
||||
|
||||
**Failing-on-old tests:** `a3_weapon_names_renamed_to_reflectivity`,
|
||||
`a3_registry_no_longer_exports_weapon_alert` (registry no longer exports a
|
||||
`WEAPON_ALERT` name). **Grade: MEASURED.**
|
||||
|
||||
### §A4 Quasi-medical / sign-language exotic modules (MEDIUM) — MEASURED
|
||||
|
||||
`exo_dream_stage.rs` ("sleep stage classification", quasi-medical) and
|
||||
`exo_gesture_language.rs` ("sign language letter recognition").
|
||||
|
||||
**Real fix (honest labeling — DSP kept):** added an experimental "NOT VALIDATED"
|
||||
disclaimer to each `//!` header (citing ADR-160) and promoted the
|
||||
**Exotic/Research** registry tag into the header where a reader sees it.
|
||||
`exo_gesture_language` additionally states it is a coarse gesture-cluster
|
||||
classifier that **does not recognize true sign language** (never evaluated on a
|
||||
labelled ASL set).
|
||||
|
||||
**Failing-on-old test:** `a4_exotic_modules_have_experimental_disclaimer`.
|
||||
**Grade: MEASURED (label); accuracy DATA-GATED.**
|
||||
|
||||
### §A5 `static mut` event-buffer soundness (MEDIUM) — the one real code fix — MEASURED
|
||||
|
||||
~61 per-call event scratch buffers across the crate used a module-level
|
||||
`static mut EVENTS: [(i32,f32); N]` (a handful named `EV`/`TE`/`EMPTY`) and returned
|
||||
`&EVENTS[..n]`. On a `cdylib`+`rlib` linkable into multithreaded/reentrant host
|
||||
code this is latent aliasing UB, and `static_mut_refs` is deny-by-default on newer
|
||||
Rust.
|
||||
|
||||
**Real fix (mechanical, behavior-preserving):** moved each scratch buffer off
|
||||
`static mut` into an **owned per-instance field** (`events: [(i32,f32); N]` on the
|
||||
detector struct, written via `&mut self` and returned as `&self.events[..n]`). The
|
||||
public `-> &[(i32, f32)]` signature is **unchanged**, so no caller (in-module
|
||||
tests, `ghost_hunter` bin, `budget_compliance`) needed editing. Two helper methods
|
||||
that built events under `&self` (`spt_pagerank_influence::build_events`,
|
||||
`spt_spiking_tracker::build_events`) and `sig_temporal_compress::on_timer` were
|
||||
promoted to `&mut self`. Leftover now-redundant `unsafe { }` wrappers were removed.
|
||||
|
||||
**Count: 61 scratch buffers across 60 module files fixed** (the only `static mut`
|
||||
left in `src/` are the two **legitimate WASM module singletons** — `lib.rs STATE`
|
||||
and `bin/ghost_hunter.rs DETECTOR` — `#[cfg(target_arch="wasm32")]`,
|
||||
`#[no_mangle]`, accessed via `core::ptr::addr_of_mut!`, single-threaded by the
|
||||
wasm runtime contract; these are *not* the aliasing-UB scratch pattern and are
|
||||
left as-is).
|
||||
|
||||
**Verification:** the full host build (`--features std` and
|
||||
`std,medical-experimental`) compiles with **0 warnings** — there is no longer any
|
||||
`static mut <name>` + `&<name>` source for `static_mut_refs` to fire on in the 60
|
||||
fixed modules. (The pure-`wasm32-unknown-unknown` build, where the lint is
|
||||
deny-by-default, could not be run in this worktree because the `wasm32` target is
|
||||
not installed on the build toolchain; the source-level elimination is the
|
||||
evidence, asserted per-module by `a5_claim_bearing_modules_have_no_static_mut_event_buffer`.)
|
||||
**Grade: MEASURED (source-eliminated; residual = 2 legitimate singletons).**
|
||||
|
||||
## Negative Results (NO-ACTION positives — cited, not edited for labels)
|
||||
|
||||
Audited and found genuinely honest; cited as positives:
|
||||
- **`qnt_quantum_coherence.rs`** — discloses "quantum-**inspired**" analogy.
|
||||
- **`exo_time_crystal.rs`**, **`exo_ghost_hunter.rs`** — disclosed exploratory/novelty.
|
||||
- **`qnt_interference_search.rs`** — disclosed "Grover-**inspired**".
|
||||
- **`sig_*` / `lrn_*`** algorithm-named skills — names describe the DSP, not an outcome.
|
||||
- **`nvsim`** — out of scope; the project's disclaimer gold standard (its tone was
|
||||
copied into the A1/A2/A4 disclaimers).
|
||||
|
||||
(These were A5-soundness-fixed mechanically where they used `static mut`, with no
|
||||
label or behavior change, consistent with leaving their claim surface intact.)
|
||||
|
||||
## Deferred Backlog (Nothing Dropped)
|
||||
|
||||
- **Per-skill accuracy validation** — **DATA-GATED**. Validating any med_*/affect/
|
||||
sign-language claim requires labelled clinical/affective/ASL data and reference
|
||||
standards that do not exist in this repo. The disclaimers + feature gate are the
|
||||
honest stand-in. Nothing is claimed that is not measured.
|
||||
- **Criterion benches for `process_frame` budget claims** — **ACCEPTED-FUTURE**.
|
||||
`tests/budget_compliance.rs` asserts L/S/H tier wall-clock budgets (25 tests,
|
||||
passing), but a regression-grade criterion bench is not yet wired.
|
||||
- **`wasm32-unknown-unknown` `static_mut_refs` confirmation** — **ACCEPTED-FUTURE**
|
||||
(toolchain): the source pattern is eliminated; a CI job on the wasm target should
|
||||
assert zero `static_mut_refs` once the target is added to the build image.
|
||||
- **The 2 residual `static mut` singletons** (`lib.rs STATE`, `ghost_hunter DETECTOR`)
|
||||
— **ACCEPTED-FUTURE**: these are the canonical wasm module-state pattern; migrating
|
||||
them to a safe cell is a separate, larger change with no current UB (single-threaded
|
||||
wasm runtime, `addr_of_mut!` access).
|
||||
|
||||
## Reproduction (MEASURED)
|
||||
|
||||
```bash
|
||||
cd v2/crates/wifi-densepose-wasm-edge # excluded from the v2 workspace; build here
|
||||
cargo test --features std # default
|
||||
cargo test --features std,medical-experimental # med_* skills enabled
|
||||
cargo test --no-default-features --features std # no default-pipeline
|
||||
cargo test --features std --test honest_labeling # A1–A5 label invariants
|
||||
```
|
||||
|
||||
(`std` is required for host tests — the crate is `no_std` for `wasm32`; pure
|
||||
`--no-default-features` builds only on `wasm32-unknown-unknown`, where it
|
||||
intentionally has no panic handler on the host.)
|
||||
|
||||
Result at time of writing (all 0 failed):
|
||||
- **DEFAULT** (`--features std`) — **615 passed** (lib 504; budget 25; honest_labeling 10; bench 1; vendor 75)
|
||||
- **MEDICAL** (`--features std,medical-experimental`) — **653 passed** (lib 542; +38 med_* tests; others unchanged)
|
||||
- **NO-DEFAULT** (`--no-default-features --features std`) — **615 passed**
|
||||
- Full host build emits **0 warnings**; **61** `static mut` scratch buffers eliminated, **2** legitimate wasm singletons remain.
|
||||
|
||||
## Consequences
|
||||
|
||||
- No edge skill's name or doc-comment claims a clinical, affective, security, or
|
||||
sign-language capability the unvalidated DSP cannot back.
|
||||
- The five medical skills cannot be silently compiled into a shipping artifact
|
||||
(non-default `medical-experimental` gate).
|
||||
- The security skill can never emit a "weapon alert" — it reports
|
||||
`HIGH_METAL_REFLECTIVITY`, the physical quantity it actually measures.
|
||||
- The latent `static mut` aliasing-UB / `static_mut_refs` exposure is removed from
|
||||
60 modules; the public API and all runtime behavior are unchanged (615/653 tests
|
||||
prove behavior preservation).
|
||||
- ADR-159's deferred-backlog statement *"wasm-edge … honestly labelled, not
|
||||
claimed"* is now actually TRUE.
|
||||
@@ -0,0 +1,146 @@
|
||||
#!/usr/bin/env bash
|
||||
# prove.sh — one-command reproduction harness for RuView / wifi-densepose.
|
||||
#
|
||||
# Mission: this project has been publicly accused of being "AI slop / fake."
|
||||
# The answer is reproducibility. Clone the repo, run THIS script, and every
|
||||
# headline claim is either VERIFIED on your machine (MEASURED) or printed as
|
||||
# "CLAIMED — not reproduced here (why)". Nothing is asserted without a command.
|
||||
#
|
||||
# Usage:
|
||||
# bash scripts/prove.sh # core gate + anti-slop assertion tests
|
||||
# bash scripts/prove.sh --full # also run the tch/GPU/dataset-gated claims
|
||||
#
|
||||
# Exit code 0 only if every NON-gated claim passes. Gated claims never fail the
|
||||
# run; they print exactly what they need (libtorch, a GPU, a dataset) so you can
|
||||
# reproduce them yourself.
|
||||
set -uo pipefail
|
||||
|
||||
ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
|
||||
cd "$ROOT"
|
||||
FULL=0; [ "${1:-}" = "--full" ] && FULL=1
|
||||
|
||||
pass=0; fail=0; skip=0
|
||||
PASS(){ echo " [PASS] $1"; pass=$((pass+1)); }
|
||||
FAIL(){ echo " [FAIL] $1"; fail=$((fail+1)); }
|
||||
SKIP(){ echo " [CLAIMED — not reproduced here] $1"; skip=$((skip+1)); }
|
||||
hr(){ echo "------------------------------------------------------------"; }
|
||||
|
||||
echo "RuView / wifi-densepose — PROOF harness"
|
||||
echo "repo: $ROOT"
|
||||
echo "date: $(date -u +%Y-%m-%dT%H:%M:%SZ)"
|
||||
hr
|
||||
|
||||
# ── 1. HARD GATE: Rust workspace tests (no native libs required) ────────────
|
||||
echo "[1] Rust workspace tests (cargo test --workspace --no-default-features)"
|
||||
if command -v cargo >/dev/null 2>&1; then
|
||||
if ( cd v2 && cargo test --workspace --no-default-features ) > /tmp/prove_ws.log 2>&1; then
|
||||
n=$(grep -oE "result: ok\. [0-9]+ passed" /tmp/prove_ws.log | grep -oE "[0-9]+" | awk '{s+=$1} END {print s}')
|
||||
PASS "workspace tests green — ${n:-?} passed, 0 failed (CARGO exit 0)"
|
||||
else
|
||||
FAIL "workspace tests — see /tmp/prove_ws.log (grep 'test result: FAILED')"
|
||||
fi
|
||||
else
|
||||
SKIP "cargo not installed — install Rust to run the workspace gate"
|
||||
fi
|
||||
hr
|
||||
|
||||
# ── 2. HARD GATE: deterministic Python pipeline proof (SHA-256) ─────────────
|
||||
echo "[2] Deterministic CSI pipeline proof (archive/v1/data/proof/verify.py)"
|
||||
if command -v python >/dev/null 2>&1; then
|
||||
if python archive/v1/data/proof/verify.py > /tmp/prove_py.log 2>&1 && grep -q "VERDICT: PASS" /tmp/prove_py.log; then
|
||||
PASS "Python proof VERDICT: PASS (bit-exact SHA-256 of reference features)"
|
||||
else
|
||||
FAIL "Python proof — see /tmp/prove_py.log"
|
||||
fi
|
||||
else
|
||||
SKIP "python not installed — install Python 3.10+ to run the deterministic proof"
|
||||
fi
|
||||
hr
|
||||
|
||||
# ── 3. ANTI-SLOP ASSERTION TESTS — each encodes a headline MEASURED claim ────
|
||||
# Format: claim_test <crate> <test-name-filter> <human claim> [extra cargo args]
|
||||
claim_test(){
|
||||
local crate="$1" filt="$2" desc="$3"; shift 3
|
||||
if ! command -v cargo >/dev/null 2>&1; then SKIP "$desc (cargo missing)"; return; fi
|
||||
if ( cd v2 && cargo test -p "$crate" "$@" "$filt" ) > /tmp/prove_claim.log 2>&1 \
|
||||
&& grep -qE "test result: ok\. [1-9]" /tmp/prove_claim.log; then
|
||||
PASS "$desc"
|
||||
else
|
||||
# distinguish "didn't run" (feature/lib gated) from real failure
|
||||
if grep -qE "0 passed|filtered out;? finished|error: no test target" /tmp/prove_claim.log \
|
||||
&& ! grep -q "test result: FAILED" /tmp/prove_claim.log; then
|
||||
SKIP "$desc (test gated/absent in this build — see /tmp/prove_claim.log)"
|
||||
else
|
||||
FAIL "$desc — see /tmp/prove_claim.log"
|
||||
fi
|
||||
fi
|
||||
}
|
||||
|
||||
# Variant for workspace-excluded crates (e.g. wasm-edge): run from the crate dir.
|
||||
claim_test_indir(){
|
||||
local dir="$1" filt="$2" desc="$3"; shift 3
|
||||
if ! command -v cargo >/dev/null 2>&1; then SKIP "$desc (cargo missing)"; return; fi
|
||||
if ( cd "$dir" && cargo test "$@" "$filt" ) > /tmp/prove_claim.log 2>&1 \
|
||||
&& grep -qE "test result: ok\. [1-9]" /tmp/prove_claim.log; then
|
||||
PASS "$desc"
|
||||
else
|
||||
if grep -qE "0 passed|error: no test target" /tmp/prove_claim.log \
|
||||
&& ! grep -q "test result: FAILED" /tmp/prove_claim.log; then
|
||||
SKIP "$desc (test gated/absent — see /tmp/prove_claim.log)"
|
||||
else
|
||||
FAIL "$desc — see /tmp/prove_claim.log"
|
||||
fi
|
||||
fi
|
||||
}
|
||||
|
||||
echo "[3] Anti-slop assertion tests (each fails on the pre-fix code)"
|
||||
echo " ADR-156 §2.2 — fusion crafted-input DoS panics are closed:"
|
||||
claim_test wifi-densepose-ruvector triangulation_out_of_range_index_returns_none_no_panic \
|
||||
"crafted out-of-range index returns None, no panic" --no-default-features
|
||||
|
||||
echo " Soul Signature §3.6 — the audit's 'identity does not lock' claim, MEASURED:"
|
||||
claim_test wifi-densepose-bfld cardiac_alone_cannot_separate_identity_matches_audit \
|
||||
"WiFi-only cardiac+respiratory channels CANNOT separate two people (gap ~0.0005)"
|
||||
|
||||
echo " OccWorld — predict() is real (input-dependent), not random:"
|
||||
claim_test wifi-densepose-occworld-candle predict_is_deterministic_for_same_input \
|
||||
"same occupancy input -> identical prediction (no randn stub)"
|
||||
|
||||
echo " ADR-159 A1 — pose runtime actually emits under its own default config:"
|
||||
claim_test cog-pose-estimation default_config_emits_frames_with_real_model \
|
||||
"default install emits pose frames (confidence >= min_confidence)" --no-default-features
|
||||
|
||||
echo " ADR-159 A2 — person-count flags untrained classes (no count inflation):"
|
||||
claim_test cog-person-count untrained_class_argmax_is_flagged_low_confidence \
|
||||
"argmax on an untrained class is flagged low_confidence" --no-default-features
|
||||
|
||||
echo " ADR-160 A1 — medical edge skills carry a not-a-medical-device disclaimer:"
|
||||
# wasm-edge is a workspace-excluded crate → run from its own directory.
|
||||
claim_test_indir v2/crates/wifi-densepose-wasm-edge a1_med_modules_have_clinical_disclaimer \
|
||||
"every med_* module carries the experimental/non-clinical disclaimer" --features std
|
||||
hr
|
||||
|
||||
# ── 4. DATA/HARDWARE-GATED claims — honestly NOT reproduced by this script ───
|
||||
echo "[4] DATA/HARDWARE-GATED claims (reproduce instructions, not asserted here)"
|
||||
if [ "$FULL" = "1" ]; then
|
||||
echo " (--full) attempting the gated claims; missing prereqs are reported, not failed:"
|
||||
claim_test wifi-densepose-mat test_identical_vitals_no_location_dedup_to_one \
|
||||
"ADR-158 §2 survivor dedup 3->1 (count-inflation fix)" --features mat
|
||||
else
|
||||
SKIP "WiFlow-STD ~96% PCK@20 reproduction — needs an NVIDIA GPU + MM-Fi dataset; see benchmarks/wiflow-std/RESULTS.md"
|
||||
SKIP "named person-identity — DATA-GATED: needs a real enrollment feeding the AETHER/body-resonance channel (see docs/research/soul/)"
|
||||
SKIP "OccWorld trained accuracy — needs a trained checkpoint (predict() carries weights_trained=false until then)"
|
||||
SKIP "native wlanapi 9.74 Hz scan — Windows-only; run: cargo test -p wifi-densepose-wifiscan -- --ignored measure_native_scan_rate"
|
||||
echo " (re-run with --full to attempt the feature-gated subset where prereqs exist)"
|
||||
fi
|
||||
hr
|
||||
|
||||
# ── verdict ──────────────────────────────────────────────────────────────────
|
||||
echo "VERDICT: $pass verified · $fail failed · $skip claimed-not-reproduced-here"
|
||||
if [ "$fail" -eq 0 ]; then
|
||||
echo "RESULT: PASS — every reproducible claim verified on this machine."
|
||||
exit 0
|
||||
else
|
||||
echo "RESULT: FAIL — $fail claim(s) did not reproduce. See the /tmp/prove_*.log files."
|
||||
exit 1
|
||||
fi
|
||||
@@ -5,7 +5,7 @@ edition.workspace = true
|
||||
authors.workspace = true
|
||||
license.workspace = true
|
||||
repository.workspace = true
|
||||
description = "Cognitum Cog: Home Assistant + Matter integration for the Seed (ADR-116). Wraps ADR-115's HA-DISCO + HA-MIND publisher as a Seed-installable artifact with mDNS, embedded broker, RuVector-backed thresholds, and Ed25519 witness."
|
||||
description = "Cognitum Cog: Home Assistant (MQTT) integration for the Seed (ADR-116). Wraps ADR-115's HA-DISCO + HA-MIND publisher as a Seed-installable artifact with mDNS, embedded broker, RuVector-backed thresholds, and Ed25519 witness. LAN-only (no TLS); Matter Bridge commissioning is deferred to v0.8 and not yet implemented."
|
||||
|
||||
[[bin]]
|
||||
name = "cog-ha-matter"
|
||||
|
||||
@@ -5,7 +5,7 @@ edition.workspace = true
|
||||
authors.workspace = true
|
||||
license.workspace = true
|
||||
repository.workspace = true
|
||||
description = "Cognitum Cog: learned multi-person counter from WiFi CSI (ADR-103). Replaces the PR #491 slot heuristic with a Candle-based count head + Stoer-Wagner multi-node fusion."
|
||||
description = "Cognitum Cog: WiFi-CSI presence detector + (data-gated) person count (ADR-103). Candle-based head trained on classes 0/1 (presence); the 8-class count head ships but counts above the trained range are flagged low_confidence. Stoer-Wagner multi-node fusion."
|
||||
|
||||
[[bin]]
|
||||
name = "cog-person-count"
|
||||
|
||||
@@ -24,6 +24,17 @@ pub const INPUT_TIMESTEPS: usize = 20;
|
||||
/// Count classification over {0, 1, ..., 7} persons.
|
||||
pub const COUNT_CLASSES: usize = 8;
|
||||
|
||||
/// Highest class the shipped `count_v1` weights were actually **trained** on.
|
||||
///
|
||||
/// The count head has 8 logits, but `count_train_results.json` only has support
|
||||
/// for classes 0 and 1 (`per_class_accuracy` keys are `"0"` and `"1"`). The model
|
||||
/// is a presence detector (0 vs ≥1 person), **not** a calibrated multi-occupant
|
||||
/// counter. An argmax landing on classes 2..=7 is out-of-distribution: the logits
|
||||
/// there were never supervised against labelled data. We flag such outputs
|
||||
/// `low_confidence` so downstream consumers don't trust a fabricated headcount.
|
||||
/// (Multi-occupant *accuracy* is DATA-GATED — not fabricated here.)
|
||||
pub const MAX_TRAINED_CLASS: usize = 1;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CsiWindow {
|
||||
pub data: Vec<f32>,
|
||||
@@ -45,6 +56,23 @@ impl CountPrediction {
|
||||
self.probs.iter().all(|v| v.is_finite()) && self.confidence.is_finite()
|
||||
}
|
||||
|
||||
/// True when the maximum-likelihood class is beyond what the shipped weights
|
||||
/// were trained on ([`MAX_TRAINED_CLASS`]). Such a prediction is out-of-
|
||||
/// distribution — the count head's logits for classes 2..=7 were never
|
||||
/// supervised, so the headcount is not trustworthy. Surfaced as the
|
||||
/// `low_confidence` field on the `person.count` event (honest-clip pattern).
|
||||
pub fn is_low_confidence(&self) -> bool {
|
||||
self.argmax() > MAX_TRAINED_CLASS
|
||||
}
|
||||
|
||||
/// Argmax clamped to [`MAX_TRAINED_CLASS`]. When the raw argmax is an
|
||||
/// untrained class we clamp the *reported* count to the highest trained
|
||||
/// class rather than emit a fabricated multi-occupant headcount. The raw
|
||||
/// distribution is still available in `probs` for diagnostics.
|
||||
pub fn clamped_count(&self) -> usize {
|
||||
self.argmax().min(MAX_TRAINED_CLASS)
|
||||
}
|
||||
|
||||
/// Maximum-likelihood class.
|
||||
pub fn argmax(&self) -> usize {
|
||||
let mut best_i = 0;
|
||||
|
||||
@@ -9,6 +9,7 @@
|
||||
|
||||
pub mod fusion;
|
||||
pub mod inference;
|
||||
pub mod manifest;
|
||||
pub mod publisher;
|
||||
pub mod runtime;
|
||||
|
||||
|
||||
@@ -12,7 +12,6 @@ use cog_person_count::{
|
||||
publisher, COG_ID, COG_VERSION,
|
||||
};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use serde_json::{json, Value};
|
||||
use std::path::PathBuf;
|
||||
|
||||
#[derive(Parser)]
|
||||
@@ -83,19 +82,11 @@ fn cmd_version() -> Result<(), Box<dyn std::error::Error>> {
|
||||
}
|
||||
|
||||
fn cmd_manifest() -> Result<(), Box<dyn std::error::Error>> {
|
||||
println!(
|
||||
"{}",
|
||||
serde_json::to_string_pretty(&json!({
|
||||
"id": COG_ID,
|
||||
"version": COG_VERSION,
|
||||
"binary_url": Value::Null,
|
||||
"binary_bytes": Value::Null,
|
||||
"binary_sha256": Value::Null,
|
||||
"binary_signature": Value::Null,
|
||||
"installed_at": Value::Null,
|
||||
"status": Value::Null,
|
||||
}))?
|
||||
);
|
||||
// Emit the real, signed manifest embedded at compile time (ADR-159 §A4) —
|
||||
// not the old hollow null skeleton. Parse-then-emit so a malformed embedded
|
||||
// artifact fails loudly and the output is canonical JSON.
|
||||
let spec = cog_person_count::manifest::embedded_manifest_value()?;
|
||||
println!("{}", serde_json::to_string_pretty(&spec)?);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,77 @@
|
||||
//! Embedded signed cog manifest (ADR-100 §"manifest.json", ADR-159 §A4).
|
||||
//!
|
||||
//! The `cog-person-count manifest` subcommand emits the **real, signed**
|
||||
//! manifest the release pipeline produced — byte-for-byte the artifact served
|
||||
//! from GCS, with a real `binary_sha256`, `weights_sha256`, Ed25519
|
||||
//! `binary_signature`, and honest `build_metadata` (e.g. `training_class1_accuracy
|
||||
//! = 0.343`, not inflated). The previous implementation printed a hollow
|
||||
//! skeleton with `binary_sha256: null`, which made the CLI look unsigned even
|
||||
//! though the signed manifest existed on disk.
|
||||
//!
|
||||
//! The matching manifest for the build's target arch is selected via `cfg!`.
|
||||
|
||||
/// Real signed manifest for `x86_64-unknown-linux-gnu`.
|
||||
pub const MANIFEST_X86_64: &str =
|
||||
include_str!("../cog/artifacts/manifests/x86_64/manifest.json");
|
||||
|
||||
/// Real signed manifest for `aarch64`/`arm` (the Seed appliance).
|
||||
pub const MANIFEST_ARM: &str = include_str!("../cog/artifacts/manifests/arm/manifest.json");
|
||||
|
||||
/// The embedded signed manifest matching the build's target arch.
|
||||
pub fn embedded_manifest_str() -> &'static str {
|
||||
if cfg!(any(target_arch = "aarch64", target_arch = "arm")) {
|
||||
MANIFEST_ARM
|
||||
} else {
|
||||
MANIFEST_X86_64
|
||||
}
|
||||
}
|
||||
|
||||
/// Parse the embedded manifest into canonical JSON. Returns an error if the
|
||||
/// embedded artifact is malformed (so the CLI fails loudly rather than printing
|
||||
/// garbage).
|
||||
pub fn embedded_manifest_value() -> Result<serde_json::Value, serde_json::Error> {
|
||||
serde_json::from_str(embedded_manifest_str())
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
/// ADR-159 §A4 — the embedded manifest the CLI emits must carry a real
|
||||
/// `binary_sha256` (the field the old hollow `cmd_manifest` left null).
|
||||
#[test]
|
||||
fn embedded_manifest_has_non_null_binary_sha256() {
|
||||
let v = embedded_manifest_value().expect("embedded manifest parses");
|
||||
let sha = v.get("binary_sha256").and_then(|s| s.as_str());
|
||||
assert!(
|
||||
sha.is_some(),
|
||||
"embedded manifest must have a non-null binary_sha256 (got {:?})",
|
||||
v.get("binary_sha256")
|
||||
);
|
||||
let sha = sha.unwrap();
|
||||
assert_eq!(sha.len(), 64, "binary_sha256 must be a 32-byte hex digest");
|
||||
assert!(
|
||||
sha.chars().all(|c| c.is_ascii_hexdigit()),
|
||||
"binary_sha256 must be hex"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn embedded_manifest_is_signed() {
|
||||
let v = embedded_manifest_value().expect("parse");
|
||||
assert!(
|
||||
v.get("binary_signature").and_then(|s| s.as_str()).is_some(),
|
||||
"embedded manifest must carry an Ed25519 binary_signature"
|
||||
);
|
||||
assert_eq!(
|
||||
v.get("sig_algo").and_then(|s| s.as_str()),
|
||||
Some("Ed25519")
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn embedded_manifest_id_matches_cog() {
|
||||
let v = embedded_manifest_value().expect("parse");
|
||||
assert_eq!(v.get("id").and_then(|s| s.as_str()), Some(crate::COG_ID));
|
||||
}
|
||||
}
|
||||
@@ -45,20 +45,35 @@ pub fn run_started(cog_id: &str, sensing_url: &str, poll_ms: u64, model_path: &s
|
||||
"sensing_url": sensing_url,
|
||||
"poll_ms": poll_ms,
|
||||
"model_path": model_path,
|
||||
// Honest disclosure: the count head has 8 classes but the shipped
|
||||
// weights were only trained on classes 0..=MAX_TRAINED_CLASS
|
||||
// (presence, not multi-occupant counting). Counts above this are
|
||||
// flagged `low_confidence` on each person.count event.
|
||||
"count_max_trained_class": crate::inference::MAX_TRAINED_CLASS,
|
||||
"count_classes": crate::inference::COUNT_CLASSES,
|
||||
}),
|
||||
});
|
||||
}
|
||||
|
||||
pub fn person_count(tick: u64, fused: &CountPrediction, n_nodes: usize) {
|
||||
let (lo, hi) = fused.p95_range();
|
||||
let low_confidence = fused.is_low_confidence();
|
||||
emit_event(&Event {
|
||||
ts: now_secs(),
|
||||
level: "info",
|
||||
// An out-of-distribution count (argmax beyond the trained classes) is
|
||||
// a warning, not a clean info reading.
|
||||
level: if low_confidence { "warn" } else { "info" },
|
||||
event: "person.count",
|
||||
fields: json!({
|
||||
"tick": tick,
|
||||
"count": fused.argmax(),
|
||||
// Reported count is clamped to the trained range — we never emit a
|
||||
// fabricated multi-occupant headcount the weights can't back.
|
||||
"count": fused.clamped_count(),
|
||||
// Raw argmax kept for diagnostics/audit.
|
||||
"raw_count": fused.argmax(),
|
||||
"confidence": fused.confidence,
|
||||
// True when argmax > MAX_TRAINED_CLASS (untrained class).
|
||||
"low_confidence": low_confidence,
|
||||
"count_p95_low": lo,
|
||||
"count_p95_high": hi,
|
||||
"n_nodes": n_nodes,
|
||||
|
||||
@@ -4,7 +4,7 @@ use cog_person_count::{
|
||||
fusion::{fuse_confidence_weighted, fuse_with_mincut_clip},
|
||||
inference::{
|
||||
CountPrediction, CsiWindow, InferenceEngine, SyntheticInput, COUNT_CLASSES,
|
||||
INPUT_SUBCARRIERS, INPUT_TIMESTEPS,
|
||||
INPUT_SUBCARRIERS, INPUT_TIMESTEPS, MAX_TRAINED_CLASS,
|
||||
},
|
||||
};
|
||||
|
||||
@@ -83,6 +83,51 @@ fn fusion_passes_through_single_node() {
|
||||
assert!((out.confidence - 0.6).abs() < 1e-6);
|
||||
}
|
||||
|
||||
/// ADR-159 §A2 — the 8-class count head ships, but the weights were only
|
||||
/// trained on classes 0/1 (presence). A prediction whose argmax lands on an
|
||||
/// UNTRAINED class (2..=7) must be flagged `low_confidence` and the reported
|
||||
/// count clamped to the trained range, so we never emit a fabricated
|
||||
/// multi-occupant headcount. Fails on old code (no such flag/clamp existed).
|
||||
#[test]
|
||||
fn untrained_class_argmax_is_flagged_low_confidence() {
|
||||
// Sanity: the trained ceiling is below the head width.
|
||||
assert!(MAX_TRAINED_CLASS < COUNT_CLASSES - 1);
|
||||
|
||||
// Mass on an untrained class (5 persons) — out-of-distribution.
|
||||
let mut probs = [0.0_f32; COUNT_CLASSES];
|
||||
probs[5] = 0.9;
|
||||
probs[1] = 0.1;
|
||||
let oodp = CountPrediction {
|
||||
probs,
|
||||
confidence: 0.95, // even a "confident" softmax must be flagged
|
||||
};
|
||||
assert_eq!(oodp.argmax(), 5);
|
||||
assert!(
|
||||
oodp.is_low_confidence(),
|
||||
"argmax beyond MAX_TRAINED_CLASS must be flagged low_confidence"
|
||||
);
|
||||
assert_eq!(
|
||||
oodp.clamped_count(),
|
||||
MAX_TRAINED_CLASS,
|
||||
"reported count must clamp to the trained ceiling, not fabricate a headcount"
|
||||
);
|
||||
|
||||
// A trained-range prediction (1 person) is NOT flagged.
|
||||
let mut probs2 = [0.0_f32; COUNT_CLASSES];
|
||||
probs2[1] = 0.8;
|
||||
probs2[0] = 0.2;
|
||||
let inp = CountPrediction {
|
||||
probs: probs2,
|
||||
confidence: 0.8,
|
||||
};
|
||||
assert_eq!(inp.argmax(), 1);
|
||||
assert!(
|
||||
!inp.is_low_confidence(),
|
||||
"a trained-range count must not be flagged"
|
||||
);
|
||||
assert_eq!(inp.clamped_count(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mincut_clip_with_high_cap_is_noop() {
|
||||
let mut probs = [0.0_f32; COUNT_CLASSES];
|
||||
|
||||
@@ -26,8 +26,8 @@
|
||||
"type": "number",
|
||||
"minimum": 0,
|
||||
"maximum": 1,
|
||||
"default": 0.3,
|
||||
"description": "Drop frames where the inferred pose confidence is below this threshold."
|
||||
"default": 0.185,
|
||||
"description": "Drop frames where the inferred pose confidence is below this threshold. pose_v1 has no confidence head, so every frame carries the model's published per-frame confidence (0.185 = validation PCK@50); the default is pinned to that value so a default install actually emits frames. Raising it above 0.185 suppresses ALL pose.frame events (the runtime warns when this happens)."
|
||||
}
|
||||
},
|
||||
"required": ["model_path"]
|
||||
|
||||
@@ -23,6 +23,13 @@ pub struct CogConfig {
|
||||
pub poll_ms: u64,
|
||||
|
||||
/// Confidence threshold below which a frame's keypoints are not emitted.
|
||||
///
|
||||
/// Defaults to [`crate::inference::MODEL_TYPICAL_CONFIDENCE`] (0.185) — the
|
||||
/// model's published per-frame confidence. `pose_v1` has no confidence head,
|
||||
/// so every frame carries this same value; a default above it would silently
|
||||
/// suppress *all* `pose.frame` events while health still reports healthy.
|
||||
/// The runtime warns at `run.started` if this is raised above the model's
|
||||
/// typical confidence rather than dropping frames quietly.
|
||||
#[serde(default = "default_min_confidence")]
|
||||
pub min_confidence: f32,
|
||||
}
|
||||
@@ -36,7 +43,9 @@ fn default_poll_ms() -> u64 {
|
||||
}
|
||||
|
||||
fn default_min_confidence() -> f32 {
|
||||
0.3
|
||||
// Pinned to the model's typical/published confidence so a default install
|
||||
// actually emits frames. See `min_confidence` doc and ADR-159 §A1.
|
||||
crate::inference::MODEL_TYPICAL_CONFIDENCE
|
||||
}
|
||||
|
||||
impl CogConfig {
|
||||
|
||||
@@ -27,6 +27,16 @@ pub const INPUT_SUBCARRIERS: usize = 56;
|
||||
pub const INPUT_TIMESTEPS: usize = 20;
|
||||
pub const OUTPUT_KEYPOINTS: usize = 17;
|
||||
|
||||
/// The model's typical self-reported confidence. `pose_v1` has **no confidence
|
||||
/// head** (the head emits 34 keypoint coordinates only), so per-frame confidence
|
||||
/// is not available from the network. This is the validation-set PCK@50 (18.5%)
|
||||
/// the training run reported, used as the published per-frame confidence floor.
|
||||
///
|
||||
/// Surfaced as a public constant so the runtime can warn when a configured
|
||||
/// `min_confidence` threshold exceeds it — otherwise a default install would
|
||||
/// silently emit zero `pose.frame` events while health reports healthy.
|
||||
pub const MODEL_TYPICAL_CONFIDENCE: f32 = 0.185;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CsiWindow {
|
||||
pub data: Vec<f32>, // length INPUT_SUBCARRIERS * INPUT_TIMESTEPS
|
||||
@@ -283,12 +293,15 @@ impl InferenceEngine {
|
||||
let out = model.net.forward(&t)?; // [1, 34]
|
||||
let flat: Vec<f32> = out.flatten_all()?.to_vec1()?;
|
||||
// Confidence from pose_v1 is a published constant rather than per-frame —
|
||||
// the trained model didn't emit a confidence head. Use the validation-set
|
||||
// PCK@50 (18.5%) as the published self-reported confidence so downstream
|
||||
// consumers can gate display decisions on it.
|
||||
// the trained model has no confidence head (the head emits 34 keypoint
|
||||
// coordinates only), so a real per-frame value is genuinely unavailable.
|
||||
// We surface the validation-set PCK@50 (`MODEL_TYPICAL_CONFIDENCE`) as the
|
||||
// honest self-reported confidence. The runtime's `min_confidence` default
|
||||
// is pinned at or below this so a default install actually emits frames
|
||||
// (and warns if an operator raises the threshold above the model's reach).
|
||||
Ok(PoseOutput {
|
||||
keypoints: flat,
|
||||
confidence: 0.185,
|
||||
confidence: MODEL_TYPICAL_CONFIDENCE,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
@@ -113,6 +113,18 @@ fn cmd_run(
|
||||
let cfg = CogConfig::load(&config_path)?;
|
||||
emit_event(&Event::run_started(COG_ID, &cfg));
|
||||
|
||||
// Disclosure: pose_v1 has no confidence head, so every frame carries the
|
||||
// same `MODEL_TYPICAL_CONFIDENCE`. A `min_confidence` above that silently
|
||||
// suppresses *all* pose.frame events. Warn loudly rather than drop quietly.
|
||||
if cfg.min_confidence > cog_pose_estimation::inference::MODEL_TYPICAL_CONFIDENCE {
|
||||
tracing::warn!(
|
||||
min_confidence = cfg.min_confidence,
|
||||
model_typical_confidence = cog_pose_estimation::inference::MODEL_TYPICAL_CONFIDENCE,
|
||||
"configured min_confidence exceeds the model's typical confidence; \
|
||||
no pose.frame events will be emitted until this is lowered"
|
||||
);
|
||||
}
|
||||
|
||||
let engine = InferenceEngine::with_adapter(adapter.as_deref())?;
|
||||
if engine.is_calibrated() {
|
||||
tracing::info!("per-room calibration adapter loaded");
|
||||
|
||||
@@ -172,3 +172,56 @@ fn manifest_roundtrips() {
|
||||
assert_eq!(back.id, "pose-estimation");
|
||||
assert_eq!(back.version, "0.0.1");
|
||||
}
|
||||
|
||||
/// ADR-159 §A1 — the default-config min_confidence threshold must not silently
|
||||
/// suppress every `pose.frame`. With the old `default_min_confidence()=0.3` and
|
||||
/// the model's per-frame confidence pinned at 0.185, the runtime gate
|
||||
/// (`out.confidence >= cfg.min_confidence`) never fired, so a default install
|
||||
/// emitted ZERO frames while health reported healthy. This asserts the default
|
||||
/// install actually clears its own gate.
|
||||
#[test]
|
||||
fn default_config_emits_frames_with_real_model() {
|
||||
use cog_pose_estimation::config::CogConfig;
|
||||
|
||||
// A minimal config (only the required model_path) exercises every
|
||||
// `#[serde(default)]` path — i.e. the *default* install threshold.
|
||||
let cfg: CogConfig =
|
||||
serde_json::from_value(serde_json::json!({ "model_path": "pose_v1.safetensors" }))
|
||||
.expect("default config parse");
|
||||
|
||||
// Real model when present; stub otherwise. Either way the per-frame
|
||||
// confidence the runtime gates on must clear the default threshold,
|
||||
// OR (stub case) the gate must still let the model's typical confidence
|
||||
// through. We assert against the same value the runtime emits.
|
||||
let weights = std::path::Path::new("cog/artifacts/pose_v1.safetensors");
|
||||
let engine = if weights.exists() {
|
||||
InferenceEngine::with_weights(Some(weights)).expect("load real weights")
|
||||
} else {
|
||||
InferenceEngine::new().expect("engine init")
|
||||
};
|
||||
|
||||
// Core regression assertion (fails on the old `default_min_confidence()=0.3`):
|
||||
// the default threshold must not exceed the model's published per-frame
|
||||
// confidence (0.185), which is the exact value `infer()` emits for the real
|
||||
// model. With 0.3 the runtime gate `out.confidence >= min_confidence` never
|
||||
// fired → zero pose.frame events on a default install.
|
||||
assert!(
|
||||
cfg.min_confidence <= cog_pose_estimation::inference::MODEL_TYPICAL_CONFIDENCE,
|
||||
"default min_confidence {} exceeds model typical confidence {} — \
|
||||
a default install would emit zero pose.frame events",
|
||||
cfg.min_confidence,
|
||||
cog_pose_estimation::inference::MODEL_TYPICAL_CONFIDENCE
|
||||
);
|
||||
|
||||
// End-to-end: when the real model is loaded, the value it actually emits
|
||||
// must clear the default gate (i.e. the runtime would emit this frame).
|
||||
if engine.backend().starts_with("candle-") {
|
||||
let out = engine.infer(&SyntheticInput.as_window()).expect("infer");
|
||||
assert!(
|
||||
out.confidence >= cfg.min_confidence,
|
||||
"default install must emit: infer confidence {} < default min_confidence {}",
|
||||
out.confidence,
|
||||
cfg.min_confidence
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,16 +1,38 @@
|
||||
//! ASTM F3411 Remote ID broadcast (Basic ID + Location/Vector message).
|
||||
//! ASTM F3411 Remote ID — **Basic ID message only** (ADR-159 §A3).
|
||||
//!
|
||||
//! Only the Basic ID message (`encode_basic_id`) is implemented. The
|
||||
//! Location/Vector message is **not** encoded yet because the drone position is
|
||||
//! tracked in a local NED frame (north/east metres relative to a takeoff datum),
|
||||
//! and a compliant Location/Vector message requires WGS84 latitude/longitude.
|
||||
//! Broadcasting NED metres in lat/lon fields would emit physically-impossible
|
||||
//! coordinates (e.g. "latitude = 12.4 metres"), so we deliberately keep the
|
||||
//! drone position in honest `drone_north_m` / `drone_east_m` fields until a real
|
||||
//! local-tangent-plane NED→WGS84 transform (with an operator datum) lands. See
|
||||
//! the `ACCEPTED-FUTURE` note in ADR-159 §A3.
|
||||
|
||||
use crate::types::DroneState;
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
/// Remote ID broadcast state for one drone.
|
||||
///
|
||||
/// Drone position is stored as **NED metres** (`drone_north_m` / `drone_east_m`)
|
||||
/// relative to the operator/takeoff datum — *not* WGS84 lat/lon — because no
|
||||
/// datum-anchored geodetic transform is wired yet. The operator position is true
|
||||
/// WGS84 (it comes from the operator's GNSS, not the local frame).
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct RemoteIdBroadcast {
|
||||
pub uas_id: [u8; 20], // 20-byte UAS ID (ANSI/CTA-2063-A)
|
||||
/// Operator latitude (WGS84 degrees) — real geodetic position.
|
||||
pub operator_lat: f64,
|
||||
/// Operator longitude (WGS84 degrees) — real geodetic position.
|
||||
pub operator_lon: f64,
|
||||
pub drone_lat: f64,
|
||||
pub drone_lon: f64,
|
||||
/// Drone north offset in **metres** from the operator/takeoff datum (NED x).
|
||||
/// NOT a latitude. See module docs — Location/Vector encoding is deferred
|
||||
/// until a real NED→WGS84 transform exists.
|
||||
pub drone_north_m: f64,
|
||||
/// Drone east offset in **metres** from the operator/takeoff datum (NED y).
|
||||
/// NOT a longitude.
|
||||
pub drone_east_m: f64,
|
||||
pub altitude_msl_m: f32,
|
||||
pub speed_ms: f32,
|
||||
pub heading_deg: f32,
|
||||
@@ -24,8 +46,8 @@ impl RemoteIdBroadcast {
|
||||
uas_id,
|
||||
operator_lat: 0.0,
|
||||
operator_lon: 0.0,
|
||||
drone_lat: 0.0,
|
||||
drone_lon: 0.0,
|
||||
drone_north_m: 0.0,
|
||||
drone_east_m: 0.0,
|
||||
altitude_msl_m: 0.0,
|
||||
speed_ms: 0.0,
|
||||
heading_deg: 0.0,
|
||||
@@ -35,11 +57,15 @@ impl RemoteIdBroadcast {
|
||||
}
|
||||
|
||||
/// Update from a drone state and operator position.
|
||||
///
|
||||
/// The drone position is stored as honest NED metres — we do **not** fake a
|
||||
/// lat/lon from a local-frame offset. The operator position is true WGS84.
|
||||
pub fn update(&mut self, state: &DroneState, operator_pos: (f64, f64)) {
|
||||
// Convert NED position to approximate lat/lon (placeholder — real impl uses WGS84).
|
||||
// We store the NED metres as placeholder values here.
|
||||
self.drone_lat = state.position.x; // placeholder: x ≈ north offset
|
||||
self.drone_lon = state.position.y; // placeholder: y ≈ east offset
|
||||
// NED metres, stored as-is in metre-typed fields (no fabricated geodetic
|
||||
// coordinates). A future Location/Vector encoder must transform these
|
||||
// through a datum-anchored NED→WGS84 projection before broadcast.
|
||||
self.drone_north_m = state.position.x; // NED x = north offset, metres
|
||||
self.drone_east_m = state.position.y; // NED y = east offset, metres
|
||||
self.altitude_msl_m = state.altitude_agl_m as f32;
|
||||
self.speed_ms = state.velocity.magnitude() as f32;
|
||||
self.heading_deg = state.heading_rad.to_degrees() as f32;
|
||||
@@ -80,4 +106,38 @@ mod tests {
|
||||
let buf = rid.encode_basic_id();
|
||||
assert_eq!(buf[2], 0xFF);
|
||||
}
|
||||
|
||||
/// ADR-159 §A3 — a known NED offset must land in honest **metre** fields,
|
||||
/// never in WGS84 lat/lon fields (which would broadcast physically-impossible
|
||||
/// coordinates like "latitude = 37.5 m"). Fails on old code, where the same
|
||||
/// values were stored into `drone_lat`/`drone_lon`.
|
||||
#[test]
|
||||
fn test_ned_offset_stored_as_metres_not_latlon() {
|
||||
use crate::types::{DroneState, NodeId, Position3D};
|
||||
|
||||
let mut state = DroneState::default_at_origin(NodeId(7));
|
||||
// 37.5 m north, -12.0 m east of the takeoff datum.
|
||||
state.position = Position3D {
|
||||
x: 37.5,
|
||||
y: -12.0,
|
||||
z: 5.0,
|
||||
};
|
||||
let mut rid = RemoteIdBroadcast::new([0x41u8; 20]);
|
||||
// Operator at a real WGS84 fix (San Francisco-ish).
|
||||
rid.update(&state, (37.7749, -122.4194));
|
||||
|
||||
// Drone offset is honest NED metres.
|
||||
assert_eq!(rid.drone_north_m, 37.5);
|
||||
assert_eq!(rid.drone_east_m, -12.0);
|
||||
|
||||
// Operator position is the real geodetic fix and is plausibly a lat/lon.
|
||||
assert!((-90.0..=90.0).contains(&rid.operator_lat));
|
||||
assert!((-180.0..=180.0).contains(&rid.operator_lon));
|
||||
assert!((rid.operator_lat - 37.7749).abs() < 1e-9);
|
||||
|
||||
// The drone NED metres would have been an out-of-range "latitude" only
|
||||
// if a value happened to exceed 90 — but the contract is the field name
|
||||
// itself: these are metres, not degrees. A future Location/Vector
|
||||
// encoder must project them through a real NED→WGS84 transform.
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "wifi-densepose-hardware"
|
||||
version.workspace = true
|
||||
version = "0.3.1"
|
||||
edition.workspace = true
|
||||
description = "Hardware interface abstractions for WiFi CSI sensors (ESP32, Intel 5300, Atheros)"
|
||||
license = "MIT OR Apache-2.0"
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "wifi-densepose-mat"
|
||||
version = "0.3.0"
|
||||
version = "0.3.1"
|
||||
edition = "2021"
|
||||
authors = ["rUv <ruv@ruv.net>", "WiFi-DensePose Contributors"]
|
||||
description = "Mass Casualty Assessment Tool - WiFi-based disaster survivor detection"
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "wifi-densepose-nn"
|
||||
version.workspace = true
|
||||
version = "0.3.1"
|
||||
edition.workspace = true
|
||||
authors.workspace = true
|
||||
license.workspace = true
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "wifi-densepose-ruvector"
|
||||
version = "0.3.1" # ADR-138: ClockQualityGate / clock-quality coherence gate
|
||||
version = "0.3.2"
|
||||
edition.workspace = true
|
||||
authors.workspace = true
|
||||
license.workspace = true
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "wifi-densepose-sensing-server"
|
||||
version = "0.3.1"
|
||||
version = "0.3.2"
|
||||
edition.workspace = true
|
||||
description = "Lightweight Axum server for WiFi sensing UI with RuVector signal processing"
|
||||
license.workspace = true
|
||||
|
||||
@@ -894,7 +894,7 @@ mod tests {
|
||||
|
||||
#[test]
|
||||
fn file_round_trip() {
|
||||
let dir = std::env::temp_dir().join("rvf_test");
|
||||
let dir = std::env::temp_dir().join(format!("rvf_test_{}", std::process::id()));
|
||||
std::fs::create_dir_all(&dir).unwrap();
|
||||
let path = dir.join("test_model.rvf");
|
||||
|
||||
|
||||
@@ -1002,7 +1002,7 @@ mod tests {
|
||||
|
||||
#[test]
|
||||
fn rvf_model_file_round_trip() {
|
||||
let dir = std::env::temp_dir().join("rvf_pipeline_test");
|
||||
let dir = std::env::temp_dir().join(format!("rvf_pipeline_test_{}", std::process::id()));
|
||||
std::fs::create_dir_all(&dir).unwrap();
|
||||
let path = dir.join("pipeline_model.rvf");
|
||||
|
||||
|
||||
@@ -1318,7 +1318,7 @@ mod tests {
|
||||
let mut t = Trainer::new(TrainerConfig::default());
|
||||
t.train_epoch(&[sample()]);
|
||||
let ckpt = t.checkpoint();
|
||||
let dir = std::env::temp_dir().join("trainer_ckpt_test");
|
||||
let dir = std::env::temp_dir().join(format!("trainer_ckpt_test_{}", std::process::id()));
|
||||
std::fs::create_dir_all(&dir).unwrap();
|
||||
let path = dir.join("ckpt.json");
|
||||
ckpt.save_to_file(&path).unwrap();
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "wifi-densepose-signal"
|
||||
version = "0.3.2" # ADR-137/138/142/143: fuse_scored_calibrated, ArrayCoordinator, evolution, rf_slam, calibration apply
|
||||
version = "0.3.3"
|
||||
edition.workspace = true
|
||||
description = "WiFi CSI signal processing for DensePose estimation"
|
||||
license.workspace = true
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "wifi-densepose-train"
|
||||
version = "0.3.1"
|
||||
version = "0.3.2"
|
||||
edition = "2021"
|
||||
authors = ["rUv <ruv@ruv.net>", "WiFi-DensePose Contributors"]
|
||||
license = "MIT OR Apache-2.0"
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "wifi-densepose-vitals"
|
||||
version.workspace = true
|
||||
version = "0.3.1"
|
||||
edition.workspace = true
|
||||
description = "ESP32 CSI-grade vital sign extraction (ADR-021): heart rate and respiratory rate from WiFi Channel State Information"
|
||||
license.workspace = true
|
||||
|
||||
@@ -22,6 +22,15 @@ sha2 = { version = "0.10", optional = true, default-features = false }
|
||||
default = ["default-pipeline"]
|
||||
# Enable std for testing on host + RVF builder
|
||||
std = ["sha2/std"]
|
||||
# Experimental medical skills (med_seizure_detect, med_cardiac_arrhythmia,
|
||||
# med_respiratory_distress, med_sleep_apnea, med_gait_analysis).
|
||||
#
|
||||
# ⚠️ NON-DEFAULT BY DESIGN. These modules run real DSP but are NOT validated
|
||||
# against clinical data and are NOT medical devices (ADR-160 §A1). They are
|
||||
# gated behind this feature so they cannot be silently built into a shipping
|
||||
# artifact. Build/test with:
|
||||
# cargo test -p wifi-densepose-wasm-edge --features std,medical-experimental
|
||||
medical-experimental = []
|
||||
# Include the default combined pipeline (gesture+coherence+adversarial) entry points.
|
||||
# Disable this when building standalone module binaries (ghost_hunter, etc.)
|
||||
default-pipeline = []
|
||||
|
||||
@@ -111,6 +111,8 @@ pub struct BehavioralProfiler {
|
||||
obs_cycles: u32,
|
||||
cooldown: u16,
|
||||
anomaly_count: u32,
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
}
|
||||
|
||||
impl BehavioralProfiler {
|
||||
@@ -118,6 +120,7 @@ impl BehavioralProfiler {
|
||||
Self {
|
||||
stats: [Welford::new(); N_DIM], obs: ObsWindow::new(),
|
||||
mature: false, frame_count: 0, obs_cycles: 0, cooldown: 0, anomaly_count: 0,
|
||||
events: [(0, 0.0); 4],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -127,7 +130,6 @@ impl BehavioralProfiler {
|
||||
self.cooldown = self.cooldown.saturating_sub(1);
|
||||
self.obs.push(present, motion, n_persons);
|
||||
|
||||
static mut EV: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut ne = 0usize;
|
||||
|
||||
if self.frame_count % (OBS_WIN as u32) == 0 && self.obs.len == OBS_WIN {
|
||||
@@ -139,7 +141,7 @@ impl BehavioralProfiler {
|
||||
if self.obs_cycles >= LEARNING_FRAMES / (OBS_WIN as u32) {
|
||||
self.mature = true;
|
||||
let days = self.frame_count as f32 / (20.0 * 86400.0);
|
||||
unsafe { EV[ne] = (EVENT_PROFILE_MATURITY, days); }
|
||||
self.events[ne] = (EVENT_PROFILE_MATURITY, days);
|
||||
ne += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -159,12 +161,12 @@ impl BehavioralProfiler {
|
||||
if self.cooldown == 0 {
|
||||
if cz > ANOMALY_Z {
|
||||
self.anomaly_count += 1;
|
||||
unsafe { EV[ne] = (EVENT_BEHAVIOR_ANOMALY, cz); } ne += 1;
|
||||
if ne < 4 { unsafe { EV[ne] = (EVENT_PROFILE_DEVIATION, max_d as f32); } ne += 1; }
|
||||
self.events[ne] = (EVENT_BEHAVIOR_ANOMALY, cz); ne += 1;
|
||||
if ne < 4 { self.events[ne] = (EVENT_PROFILE_DEVIATION, max_d as f32); ne += 1; }
|
||||
self.cooldown = COOLDOWN;
|
||||
}
|
||||
if hi_z >= NOVEL_MIN && ne < 4 {
|
||||
unsafe { EV[ne] = (EVENT_NOVEL_PATTERN, hi_z as f32); } ne += 1;
|
||||
self.events[ne] = (EVENT_NOVEL_PATTERN, hi_z as f32); ne += 1;
|
||||
if self.cooldown == 0 { self.cooldown = COOLDOWN; }
|
||||
}
|
||||
}
|
||||
@@ -173,10 +175,10 @@ impl BehavioralProfiler {
|
||||
|
||||
// Periodic maturity report.
|
||||
if self.mature && self.frame_count % MATURITY_INTERVAL == 0 && ne < 4 {
|
||||
unsafe { EV[ne] = (EVENT_PROFILE_MATURITY, self.frame_count as f32 / (20.0 * 86400.0)); }
|
||||
self.events[ne] = (EVENT_PROFILE_MATURITY, self.frame_count as f32 / (20.0 * 86400.0));
|
||||
ne += 1;
|
||||
}
|
||||
unsafe { &EV[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
pub fn is_mature(&self) -> bool { self.mature }
|
||||
|
||||
@@ -48,6 +48,8 @@ pub struct PromptShield {
|
||||
cd_replay: u16,
|
||||
cd_inject: u16,
|
||||
cd_jam: u16,
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
}
|
||||
|
||||
impl PromptShield {
|
||||
@@ -58,6 +60,7 @@ impl PromptShield {
|
||||
baseline_snr: 0.0, cal_amp: 0.0, cal_var: 0.0, cal_n: 0,
|
||||
calibrated: false, low_snr_run: 0, frame_count: 0,
|
||||
cd_replay: 0, cd_inject: 0, cd_jam: 0,
|
||||
events: [(0, 0.0); 4],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -70,7 +73,6 @@ impl PromptShield {
|
||||
self.cd_inject = self.cd_inject.saturating_sub(1);
|
||||
self.cd_jam = self.cd_jam.saturating_sub(1);
|
||||
|
||||
static mut EV: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut ne = 0usize;
|
||||
|
||||
// Frame features: mean phase, mean amp, amp variance.
|
||||
@@ -98,7 +100,7 @@ impl PromptShield {
|
||||
}
|
||||
let h = self.fnv1a(m_ph, m_a, a_var);
|
||||
self.push_hash(h);
|
||||
return unsafe { &EV[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// ── 1. Replay ───────────────────────────────────────────────────
|
||||
@@ -106,7 +108,7 @@ impl PromptShield {
|
||||
let replay = self.has_hash(h);
|
||||
self.push_hash(h);
|
||||
if replay && self.cd_replay == 0 {
|
||||
unsafe { EV[ne] = (EVENT_REPLAY_ATTACK, 1.0); }
|
||||
self.events[ne] = (EVENT_REPLAY_ATTACK, 1.0);
|
||||
ne += 1; self.cd_replay = COOLDOWN;
|
||||
}
|
||||
|
||||
@@ -121,7 +123,7 @@ impl PromptShield {
|
||||
jc as f32 / n as f32
|
||||
} else { 0.0 };
|
||||
if inj_f >= INJECTION_FRAC && self.cd_inject == 0 && ne < 4 {
|
||||
unsafe { EV[ne] = (EVENT_INJECTION_DETECTED, inj_f); }
|
||||
self.events[ne] = (EVENT_INJECTION_DETECTED, inj_f);
|
||||
ne += 1; self.cd_inject = COOLDOWN;
|
||||
}
|
||||
|
||||
@@ -133,7 +135,7 @@ impl PromptShield {
|
||||
} else { self.low_snr_run = 0; }
|
||||
if self.low_snr_run >= JAMMING_CONSEC && self.cd_jam == 0 && ne < 4 {
|
||||
let r = if cur_snr > 0.0001 { self.baseline_snr / cur_snr } else { 1000.0 };
|
||||
unsafe { EV[ne] = (EVENT_JAMMING_DETECTED, 10.0 * log10f(r)); }
|
||||
self.events[ne] = (EVENT_JAMMING_DETECTED, 10.0 * log10f(r));
|
||||
ne += 1; self.cd_jam = COOLDOWN;
|
||||
}
|
||||
|
||||
@@ -146,12 +148,12 @@ impl PromptShield {
|
||||
let r = cur_snr / self.baseline_snr;
|
||||
if r < 0.5 { s -= (1.0 - r * 2.0).min(0.3); }
|
||||
}
|
||||
unsafe { EV[ne] = (EVENT_SIGNAL_INTEGRITY, if s < 0.0 { 0.0 } else { s }); }
|
||||
self.events[ne] = (EVENT_SIGNAL_INTEGRITY, if s < 0.0 { 0.0 } else { s });
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
for i in 0..n { self.prev_amps[i] = amps[i]; }
|
||||
unsafe { &EV[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
fn fnv1a(&self, ph: f32, amp: f32, var: f32) -> u32 {
|
||||
|
||||
@@ -290,6 +290,8 @@ static KNOWLEDGE_BASE: [Rule; MAX_RULES] = build_knowledge_base();
|
||||
|
||||
/// Psycho-symbolic inference engine.
|
||||
pub struct PsychoSymbolicEngine {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); MAX_EVENTS],
|
||||
/// Bitmap of rules that fired in the current frame.
|
||||
fired_rules: u16,
|
||||
/// Previous frame's winning conclusion ID.
|
||||
@@ -307,6 +309,7 @@ pub struct PsychoSymbolicEngine {
|
||||
impl PsychoSymbolicEngine {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); MAX_EVENTS],
|
||||
fired_rules: 0,
|
||||
prev_conclusion: 0,
|
||||
contradiction_count: 0,
|
||||
@@ -340,7 +343,6 @@ impl PsychoSymbolicEngine {
|
||||
n_persons: f32,
|
||||
time_bucket: f32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); MAX_EVENTS] = [(0, 0.0); MAX_EVENTS];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -372,7 +374,7 @@ impl PsychoSymbolicEngine {
|
||||
|
||||
// Emit RULE_FIRED event (up to budget).
|
||||
if n_events < MAX_EVENTS {
|
||||
unsafe { EVENTS[n_events] = (EVENT_RULE_FIRED, i as f32); }
|
||||
self.events[n_events] = (EVENT_RULE_FIRED, i as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
@@ -394,7 +396,7 @@ impl PsychoSymbolicEngine {
|
||||
self.contradiction_count += 1;
|
||||
if n_events < MAX_EVENTS {
|
||||
let encoded = (a as f32) * 100.0 + (b as f32);
|
||||
unsafe { EVENTS[n_events] = (EVENT_CONTRADICTION, encoded); }
|
||||
self.events[n_events] = (EVENT_CONTRADICTION, encoded);
|
||||
n_events += 1;
|
||||
}
|
||||
// Suppress the weaker conclusion.
|
||||
@@ -414,10 +416,10 @@ impl PsychoSymbolicEngine {
|
||||
|
||||
// Emit winning inference.
|
||||
if best_confidence > 0.0 && n_events < MAX_EVENTS {
|
||||
unsafe { EVENTS[n_events] = (EVENT_INFERENCE_RESULT, best_conclusion as f32); }
|
||||
self.events[n_events] = (EVENT_INFERENCE_RESULT, best_conclusion as f32);
|
||||
n_events += 1;
|
||||
if n_events < MAX_EVENTS {
|
||||
unsafe { EVENTS[n_events] = (EVENT_INFERENCE_CONFIDENCE, best_confidence); }
|
||||
self.events[n_events] = (EVENT_INFERENCE_CONFIDENCE, best_confidence);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -426,7 +428,7 @@ impl PsychoSymbolicEngine {
|
||||
self.prev_motion = motion;
|
||||
self.prev_conclusion = best_conclusion;
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Get the bitmap of rules that fired in the last frame.
|
||||
|
||||
@@ -28,6 +28,8 @@ pub const EVENT_HEALING_COMPLETE: i32 = 888;
|
||||
|
||||
/// Self-healing mesh monitor with Stoer-Wagner min-cut analysis.
|
||||
pub struct SelfHealingMesh {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); MAX_EVENTS],
|
||||
/// EMA-smoothed quality score per node [0, 1].
|
||||
node_quality: [f32; MAX_NODES],
|
||||
/// Whether each node quality has received its first sample.
|
||||
@@ -49,6 +51,7 @@ pub struct SelfHealingMesh {
|
||||
impl SelfHealingMesh {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); MAX_EVENTS],
|
||||
node_quality: [0.0; MAX_NODES],
|
||||
node_init: [false; MAX_NODES],
|
||||
adj: [[0.0; MAX_NODES]; MAX_NODES],
|
||||
@@ -76,7 +79,6 @@ impl SelfHealingMesh {
|
||||
/// per active node (length clamped to 8).
|
||||
/// Returns a slice of (event_id, value) pairs.
|
||||
pub fn process_frame(&mut self, node_qualities: &[f32]) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); MAX_EVENTS] = [(0, 0.0); MAX_EVENTS];
|
||||
let mut ne = 0usize;
|
||||
self.frame_count += 1;
|
||||
|
||||
@@ -84,7 +86,7 @@ impl SelfHealingMesh {
|
||||
self.n_active = n;
|
||||
for i in 0..n { self.update_node_quality(i, node_qualities[i]); }
|
||||
|
||||
if n < 2 { return unsafe { &EVENTS[..0] }; }
|
||||
if n < 2 { return &self.events[..0]; }
|
||||
|
||||
// Build adjacency: edge weight = min(quality_i, quality_j).
|
||||
for i in 0..n {
|
||||
@@ -101,7 +103,7 @@ impl SelfHealingMesh {
|
||||
for i in 0..n { sum += self.node_quality[i]; }
|
||||
let coverage = sum / (n as f32);
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe { EVENTS[ne] = (EVENT_COVERAGE_SCORE, coverage); }
|
||||
self.events[ne] = (EVENT_COVERAGE_SCORE, coverage);
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
@@ -112,24 +114,24 @@ impl SelfHealingMesh {
|
||||
if !self.healing { self.healing = true; }
|
||||
self.weakest = cut_node;
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe { EVENTS[ne] = (EVENT_NODE_DEGRADED, cut_node as f32); }
|
||||
self.events[ne] = (EVENT_NODE_DEGRADED, cut_node as f32);
|
||||
ne += 1;
|
||||
}
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe { EVENTS[ne] = (EVENT_MESH_RECONFIGURE, mincut); }
|
||||
self.events[ne] = (EVENT_MESH_RECONFIGURE, mincut);
|
||||
ne += 1;
|
||||
}
|
||||
} else if self.healing && mincut >= MINCUT_HEALTHY {
|
||||
self.healing = false;
|
||||
self.weakest = NO_NODE;
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe { EVENTS[ne] = (EVENT_HEALING_COMPLETE, mincut); }
|
||||
self.events[ne] = (EVENT_HEALING_COMPLETE, mincut);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
|
||||
self.prev_mincut = mincut;
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
/// Simplified Stoer-Wagner min-cut for n <= 8 nodes.
|
||||
|
||||
@@ -59,6 +59,8 @@ pub enum DoorState {
|
||||
|
||||
/// Elevator occupancy counter.
|
||||
pub struct ElevatorCounter {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Baseline amplitude per subcarrier (empty cabin).
|
||||
baseline_amp: [f32; MAX_SC],
|
||||
/// Baseline variance per subcarrier.
|
||||
@@ -93,6 +95,7 @@ pub struct ElevatorCounter {
|
||||
impl ElevatorCounter {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
baseline_amp: [0.0; MAX_SC],
|
||||
baseline_var: [0.0; MAX_SC],
|
||||
prev_amp: [0.0; MAX_SC],
|
||||
@@ -268,15 +271,12 @@ impl ElevatorCounter {
|
||||
}
|
||||
|
||||
// ── Build events ────────────────────────────────────────────────
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// Door events (immediate).
|
||||
if let Some(evt) = door_event {
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (evt, self.count as f32);
|
||||
}
|
||||
self.events[n_events] = (evt, self.count as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -284,22 +284,18 @@ impl ElevatorCounter {
|
||||
// Periodic count and overload.
|
||||
if self.frame_count % EMIT_INTERVAL == 0 {
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_ELEVATOR_COUNT, self.count as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_ELEVATOR_COUNT, self.count as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
// Overload warning.
|
||||
if self.count >= self.overload_thresh && n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_OVERLOAD_WARNING, self.count as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_OVERLOAD_WARNING, self.count as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Get current occupant count estimate.
|
||||
|
||||
@@ -77,6 +77,8 @@ impl HourBin {
|
||||
|
||||
/// Energy audit analyzer.
|
||||
pub struct EnergyAuditor {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
/// Weekly histogram: [day][hour].
|
||||
histogram: [[HourBin; HOURS_PER_DAY]; DAYS_PER_WEEK],
|
||||
/// Current simulated hour (0-23). In production, derived from host timestamp.
|
||||
@@ -98,6 +100,7 @@ impl EnergyAuditor {
|
||||
const BIN_INIT: HourBin = HourBin::new();
|
||||
const DAY_INIT: [HourBin; HOURS_PER_DAY] = [BIN_INIT; HOURS_PER_DAY];
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
histogram: [DAY_INIT; DAYS_PER_WEEK],
|
||||
current_hour: 8, // Default start: 8 AM.
|
||||
current_day: 0, // Monday.
|
||||
@@ -161,14 +164,11 @@ impl EnergyAuditor {
|
||||
}
|
||||
|
||||
// Build events.
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// After-hours alert.
|
||||
if self.after_hours_presence >= AFTER_HOURS_ALERT_FRAMES && n_events < 3 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_AFTER_HOURS_ALERT, self.current_hour as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_AFTER_HOURS_ALERT, self.current_hour as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
@@ -177,23 +177,19 @@ impl EnergyAuditor {
|
||||
// Emit current hour's occupancy rate.
|
||||
let rate = self.histogram[d][h].occupancy_rate();
|
||||
if n_events < 3 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_SCHEDULE_SUMMARY, rate);
|
||||
}
|
||||
self.events[n_events] = (EVENT_SCHEDULE_SUMMARY, rate);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
// Emit overall utilization rate.
|
||||
if n_events < 3 {
|
||||
let util = self.utilization_rate();
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_UTILIZATION_RATE, util);
|
||||
}
|
||||
self.events[n_events] = (EVENT_UTILIZATION_RATE, util);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Check if a given hour is after-hours.
|
||||
|
||||
@@ -57,6 +57,8 @@ pub enum ActivityLevel {
|
||||
|
||||
/// HVAC-optimized presence detector.
|
||||
pub struct HvacPresenceDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
state: HvacState,
|
||||
/// Smoothed motion energy (EMA).
|
||||
motion_ema: f32,
|
||||
@@ -73,6 +75,7 @@ pub struct HvacPresenceDetector {
|
||||
impl HvacPresenceDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
state: HvacState::Vacant,
|
||||
motion_ema: 0.0,
|
||||
activity: ActivityLevel::Sedentary,
|
||||
@@ -159,7 +162,6 @@ impl HvacPresenceDetector {
|
||||
}
|
||||
|
||||
// Build output events.
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut n = 0usize;
|
||||
|
||||
if self.frame_count % EMIT_INTERVAL == 0 {
|
||||
@@ -168,9 +170,7 @@ impl HvacPresenceDetector {
|
||||
HvacState::Occupied | HvacState::DeparturePending => 1.0,
|
||||
_ => 0.0,
|
||||
};
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_HVAC_OCCUPIED, occupied_val);
|
||||
}
|
||||
self.events[n] = (EVENT_HVAC_OCCUPIED, occupied_val);
|
||||
n += 1;
|
||||
|
||||
// Activity level: 0.0 = sedentary, 1.0 = active, plus raw EMA.
|
||||
@@ -178,9 +178,7 @@ impl HvacPresenceDetector {
|
||||
ActivityLevel::Sedentary => 0.0 + self.motion_ema.min(0.99),
|
||||
ActivityLevel::Active => 1.0,
|
||||
};
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_ACTIVITY_LEVEL, activity_val);
|
||||
}
|
||||
self.events[n] = (EVENT_ACTIVITY_LEVEL, activity_val);
|
||||
n += 1;
|
||||
}
|
||||
|
||||
@@ -191,13 +189,11 @@ impl HvacPresenceDetector {
|
||||
{
|
||||
let remaining = DEPARTURE_TIMEOUT.saturating_sub(self.absence_frames);
|
||||
let fraction = remaining as f32 / DEPARTURE_TIMEOUT as f32;
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_DEPARTURE_COUNTDOWN, fraction);
|
||||
}
|
||||
self.events[n] = (EVENT_DEPARTURE_COUNTDOWN, fraction);
|
||||
n += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
/// Get current HVAC state.
|
||||
|
||||
@@ -76,6 +76,8 @@ struct ZoneLight {
|
||||
|
||||
/// Lighting zone controller.
|
||||
pub struct LightingZoneController {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 8],
|
||||
zones: [ZoneLight; MAX_ZONES],
|
||||
n_zones: usize,
|
||||
/// Calibration accumulators.
|
||||
@@ -99,6 +101,7 @@ impl LightingZoneController {
|
||||
vacant_frames: 0,
|
||||
};
|
||||
Self {
|
||||
events: [(0, 0.0); 8],
|
||||
zones: [ZONE_INIT; MAX_ZONES],
|
||||
n_zones: 0,
|
||||
calib_sum: [0.0; MAX_ZONES],
|
||||
@@ -230,7 +233,6 @@ impl LightingZoneController {
|
||||
}
|
||||
|
||||
// Build output events.
|
||||
static mut EVENTS: [(i32, f32); 8] = [(0, 0.0); 8];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// Emit transitions immediately.
|
||||
@@ -241,9 +243,7 @@ impl LightingZoneController {
|
||||
LightState::Dim => EVENT_LIGHT_DIM,
|
||||
LightState::Off => EVENT_LIGHT_OFF,
|
||||
};
|
||||
unsafe {
|
||||
EVENTS[n_events] = (event_id, z as f32);
|
||||
}
|
||||
self.events[n_events] = (event_id, z as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -259,15 +259,13 @@ impl LightingZoneController {
|
||||
};
|
||||
// Encode zone_id + confidence in value.
|
||||
let val = z as f32 + self.zones[z].score.min(0.99);
|
||||
unsafe {
|
||||
EVENTS[n_events] = (event_id, val);
|
||||
}
|
||||
self.events[n_events] = (event_id, val);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Get the lighting state of a specific zone.
|
||||
|
||||
@@ -54,6 +54,8 @@ pub enum MeetingState {
|
||||
|
||||
/// Meeting room tracker.
|
||||
pub struct MeetingRoomTracker {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
state: MeetingState,
|
||||
/// Frames in current state.
|
||||
state_frames: u32,
|
||||
@@ -76,6 +78,7 @@ pub struct MeetingRoomTracker {
|
||||
impl MeetingRoomTracker {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
state: MeetingState::Empty,
|
||||
state_frames: 0,
|
||||
n_persons: 0,
|
||||
@@ -116,7 +119,6 @@ impl MeetingRoomTracker {
|
||||
self.multi_person_frames += 1;
|
||||
}
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
let _prev_state = self.state;
|
||||
@@ -146,9 +148,7 @@ impl MeetingRoomTracker {
|
||||
self.meeting_count += 1;
|
||||
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_MEETING_START, self.n_persons as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_MEETING_START, self.n_persons as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
} else if self.state_frames >= PRE_MEETING_TIMEOUT {
|
||||
@@ -175,17 +175,13 @@ impl MeetingRoomTracker {
|
||||
// Emit meeting end with duration.
|
||||
let duration_mins = self.total_meeting_frames as f32 / (20.0 * 60.0);
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_MEETING_END, duration_mins);
|
||||
}
|
||||
self.events[n_events] = (EVENT_MEETING_END, duration_mins);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
// Emit peak headcount.
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_PEAK_HEADCOUNT, self.peak_headcount as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_PEAK_HEADCOUNT, self.peak_headcount as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -204,9 +200,7 @@ impl MeetingRoomTracker {
|
||||
self.multi_person_frames = 0;
|
||||
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_ROOM_AVAILABLE, 1.0);
|
||||
}
|
||||
self.events[n_events] = (EVENT_ROOM_AVAILABLE, 1.0);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -216,14 +210,12 @@ impl MeetingRoomTracker {
|
||||
// Periodic status emission.
|
||||
if self.frame_count % EMIT_INTERVAL == 0 && self.state == MeetingState::Active {
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_PEAK_HEADCOUNT, self.peak_headcount as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_PEAK_HEADCOUNT, self.peak_headcount as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Get current meeting room state.
|
||||
|
||||
@@ -151,6 +151,8 @@ impl PairState {
|
||||
/// group assignment, then computes pairwise cross-correlation to detect
|
||||
/// phase-locked breathing.
|
||||
pub struct BreathingSyncDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Per-person breathing channels (max 4).
|
||||
channels: [BreathingChannel; MAX_PERSONS],
|
||||
/// Pairwise synchronization states (max 6).
|
||||
@@ -170,6 +172,7 @@ pub struct BreathingSyncDetector {
|
||||
impl BreathingSyncDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
channels: [
|
||||
BreathingChannel::new(), BreathingChannel::new(),
|
||||
BreathingChannel::new(), BreathingChannel::new(),
|
||||
@@ -201,7 +204,6 @@ impl BreathingSyncDetector {
|
||||
_breathing_bpm: f32,
|
||||
n_persons: i32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -214,14 +216,12 @@ impl BreathingSyncDetector {
|
||||
if n_pers < 2 {
|
||||
// Reset pair states when fewer than 2 persons.
|
||||
if self.any_synced {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_SYNC_LOST, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_SYNC_LOST, 1.0);
|
||||
n_ev += 1;
|
||||
self.any_synced = false;
|
||||
self.prev_sync_count = 0;
|
||||
}
|
||||
return unsafe { &EVENTS[..n_ev] };
|
||||
return &self.events[..n_ev];
|
||||
}
|
||||
|
||||
let n_sc = core::cmp::min(phases.len(), MAX_SC);
|
||||
@@ -331,36 +331,28 @@ impl BreathingSyncDetector {
|
||||
|
||||
// Emit events.
|
||||
if self.any_synced && !was_any_synced {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_SYNC_DETECTED, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_SYNC_DETECTED, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
if was_any_synced && !self.any_synced {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_SYNC_LOST, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_SYNC_LOST, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
if sync_count != self.prev_sync_count && sync_count > 0 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_SYNC_PAIR_COUNT, sync_count as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_SYNC_PAIR_COUNT, sync_count as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
self.prev_sync_count = sync_count;
|
||||
|
||||
// Emit coherence periodically (every 10 frames).
|
||||
if self.frame_count % 10 == 0 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_GROUP_COHERENCE, self.group_coherence);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_GROUP_COHERENCE, self.group_coherence);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Compute normalized cross-correlation between two person channels
|
||||
|
||||
@@ -1,4 +1,12 @@
|
||||
//! Non-contact sleep stage classification — ADR-041 exotic module.
|
||||
//! Non-contact sleep-stage-like classification — ADR-041 exotic / research module.
|
||||
//!
|
||||
//! ⚠️ EXPERIMENTAL RESEARCH MODULE — NOT VALIDATED. Quasi-medical sleep-stage
|
||||
//! ⚠️ classification here is a *candidate* heuristic only: it has never been
|
||||
//! ⚠️ compared against polysomnography or any sleep-staging reference standard,
|
||||
//! ⚠️ and its accuracy is unproven (see ADR-160 §A4). NOT a medical device. Do
|
||||
//! ⚠️ NOT use for sleep diagnosis or any clinical decision. (Registry tag:
|
||||
//! ⚠️ Exotic / Research.) The DSP is real; the sleep-stage labels are not
|
||||
//! ⚠️ validated.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//!
|
||||
@@ -113,6 +121,8 @@ pub enum SleepStage {
|
||||
|
||||
/// Non-contact sleep stage classifier using WiFi CSI physiological signatures.
|
||||
pub struct DreamStageDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Rolling breathing BPM values.
|
||||
breath_hist: CircularBuffer<BREATH_HIST_LEN>,
|
||||
/// Rolling heart rate BPM values.
|
||||
@@ -152,6 +162,7 @@ pub struct DreamStageDetector {
|
||||
impl DreamStageDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
breath_hist: CircularBuffer::new(),
|
||||
hr_hist: CircularBuffer::new(),
|
||||
phase_buf: CircularBuffer::new(),
|
||||
@@ -192,7 +203,6 @@ impl DreamStageDetector {
|
||||
_variance: f32,
|
||||
presence: i32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -282,33 +292,25 @@ impl DreamStageDetector {
|
||||
};
|
||||
|
||||
// Emit events.
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_SLEEP_STAGE, self.current_stage as u8 as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_SLEEP_STAGE, self.current_stage as u8 as f32);
|
||||
n_ev += 1;
|
||||
|
||||
// Emit quality periodically (every 20 frames).
|
||||
if self.frame_count % 20 == 0 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_SLEEP_QUALITY, efficiency);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_SLEEP_QUALITY, efficiency);
|
||||
n_ev += 1;
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_DEEP_SLEEP_RATIO, deep_ratio);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_DEEP_SLEEP_RATIO, deep_ratio);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
// Emit REM episode when in REM or just exited.
|
||||
if rem_ep > 0 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_REM_EPISODE, rem_ep as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_REM_EPISODE, rem_ep as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Classify the sleep stage from physiological features.
|
||||
|
||||
@@ -1,4 +1,13 @@
|
||||
//! Affect computing from physiological CSI signatures — ADR-041 exotic module.
|
||||
//! Affect-proxy heuristic from physiological CSI signatures — ADR-041 exotic module.
|
||||
//!
|
||||
//! ⚠️ SPECULATIVE, UNVALIDATED AFFECT HEURISTIC. The outputs of this module
|
||||
//! ⚠️ (`AROUSAL_LEVEL`, `STRESS_INDEX`, `CALM_DETECTED`, `AGITATION_DETECTED`)
|
||||
//! ⚠️ are NOT measurements of emotion. They are threshold-based proxies over
|
||||
//! ⚠️ breathing/motion/heart-rate estimates that have never been correlated
|
||||
//! ⚠️ against self-report, physiological ground truth, or any reference standard
|
||||
//! ⚠️ (see ADR-160 §A2). Do NOT use for affect inference, stress screening, or
|
||||
//! ⚠️ any decision about a person's emotional state. The DSP (rolling statistics
|
||||
//! ⚠️ + weighted scoring) is real; the affect interpretation of its output is not.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//!
|
||||
@@ -153,6 +162,8 @@ pub struct EmotionDetector {
|
||||
agitation_detected: bool,
|
||||
/// Total frames processed.
|
||||
frame_count: u32,
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
}
|
||||
|
||||
impl EmotionDetector {
|
||||
@@ -171,6 +182,7 @@ impl EmotionDetector {
|
||||
calm_detected: false,
|
||||
agitation_detected: false,
|
||||
frame_count: 0,
|
||||
events: [(0, 0.0); 4],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -192,7 +204,6 @@ impl EmotionDetector {
|
||||
_phase: f32,
|
||||
variance: f32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -251,31 +262,23 @@ impl EmotionDetector {
|
||||
|| breath_cv > STRESS_BREATH_CV_THRESH);
|
||||
|
||||
// ── Emit events ──
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_AROUSAL_LEVEL, self.arousal);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_AROUSAL_LEVEL, self.arousal);
|
||||
n_ev += 1;
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_STRESS_INDEX, self.stress_index);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_STRESS_INDEX, self.stress_index);
|
||||
n_ev += 1;
|
||||
|
||||
if self.calm_detected {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_CALM_DETECTED, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_CALM_DETECTED, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
if self.agitation_detected {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_AGITATION_DETECTED, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_AGITATION_DETECTED, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Compute breathing rate score [0, 1].
|
||||
|
||||
@@ -1,4 +1,13 @@
|
||||
//! Sign language letter recognition from CSI signatures — ADR-041 exotic module.
|
||||
//! Sign-language-letter-like recognition from CSI signatures — ADR-041 exotic / research module.
|
||||
//!
|
||||
//! ⚠️ EXPERIMENTAL RESEARCH MODULE — NOT VALIDATED. This is a *candidate*
|
||||
//! ⚠️ coarse gesture-cluster classifier, NOT a validated sign-language
|
||||
//! ⚠️ recognizer: it has never been evaluated against a labelled ASL (or any
|
||||
//! ⚠️ sign-language) dataset, accuracy is unproven, and it does not recognize
|
||||
//! ⚠️ true sign language (see ADR-160 §A4). Do NOT rely on its letter labels
|
||||
//! ⚠️ for communication or accessibility. (Registry tag: Exotic / Research.)
|
||||
//! ⚠️ The DSP (feature extraction + template matching) is real; the
|
||||
//! ⚠️ sign-language interpretation is not validated.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//!
|
||||
@@ -87,6 +96,8 @@ pub const EVENT_GESTURE_REJECTED: i32 = 623;
|
||||
/// Supports up to 26 letter templates loaded via `set_template()`.
|
||||
/// Uses DTW matching on compact feature sequences.
|
||||
pub struct GestureLanguageDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Template feature sequences: [template_idx][frame][feature].
|
||||
templates: [[[f32; FEAT_DIM]; GESTURE_WIN_LEN]; MAX_TEMPLATES],
|
||||
/// Length of each template (0 = not loaded).
|
||||
@@ -118,6 +129,7 @@ pub struct GestureLanguageDetector {
|
||||
impl GestureLanguageDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
templates: [[[0.0; FEAT_DIM]; GESTURE_WIN_LEN]; MAX_TEMPLATES],
|
||||
template_lens: [0; MAX_TEMPLATES],
|
||||
n_templates: 0,
|
||||
@@ -201,7 +213,6 @@ impl GestureLanguageDetector {
|
||||
motion_energy: f32,
|
||||
presence: i32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -223,29 +234,21 @@ impl GestureLanguageDetector {
|
||||
if self.gesture_fill >= MIN_GESTURE_FILL && self.gesture_active {
|
||||
let (letter, confidence) = self.match_gesture();
|
||||
if letter < MAX_TEMPLATES as u8 && self.since_last_letter >= DEBOUNCE_FRAMES {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_LETTER_RECOGNIZED, letter as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_LETTER_RECOGNIZED, letter as f32);
|
||||
n_ev += 1;
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_LETTER_CONFIDENCE, confidence);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_LETTER_CONFIDENCE, confidence);
|
||||
n_ev += 1;
|
||||
self.last_letter = letter;
|
||||
self.last_confidence = confidence;
|
||||
self.since_last_letter = 0;
|
||||
} else {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_GESTURE_REJECTED, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_GESTURE_REJECTED, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
|
||||
// Emit word boundary.
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_WORD_BOUNDARY, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_WORD_BOUNDARY, 1.0);
|
||||
n_ev += 1;
|
||||
self.word_boundary_emitted = true;
|
||||
self.reset_gesture();
|
||||
@@ -264,7 +267,7 @@ impl GestureLanguageDetector {
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Match the current gesture buffer against all loaded templates.
|
||||
|
||||
@@ -123,6 +123,8 @@ pub enum AnomalyClass {
|
||||
|
||||
/// Environmental anomaly detector for empty-room CSI monitoring.
|
||||
pub struct GhostHunterDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Noise floor per subcarrier group (slow EWMA of variance).
|
||||
noise_floor: [Ema; N_GROUPS],
|
||||
/// Anomaly energy buffer per group.
|
||||
@@ -158,6 +160,7 @@ pub struct GhostHunterDetector {
|
||||
impl GhostHunterDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
noise_floor: [
|
||||
Ema::new(NOISE_ALPHA), Ema::new(NOISE_ALPHA),
|
||||
Ema::new(NOISE_ALPHA), Ema::new(NOISE_ALPHA),
|
||||
@@ -203,7 +206,6 @@ impl GhostHunterDetector {
|
||||
presence: i32,
|
||||
motion_energy: f32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -336,35 +338,27 @@ impl GhostHunterDetector {
|
||||
let norm_energy = if energy > 1.0 { 1.0 } else { energy };
|
||||
|
||||
if anomaly_active {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_ANOMALY_DETECTED, norm_energy);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_ANOMALY_DETECTED, norm_energy);
|
||||
n_ev += 1;
|
||||
|
||||
if self.current_class != AnomalyClass::None {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_ANOMALY_CLASS, self.current_class as u8 as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_ANOMALY_CLASS, self.current_class as u8 as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
|
||||
if self.hidden_presence_score > HIDDEN_PRESENCE_THRESHOLD {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_HIDDEN_PRESENCE, self.hidden_presence_score);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_HIDDEN_PRESENCE, self.hidden_presence_score);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
if self.drift_frames >= DRIFT_MIN_FRAMES {
|
||||
let drift_mag = fabsf(amp_delta) * self.drift_frames as f32;
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_ENVIRONMENTAL_DRIFT, drift_mag);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_ENVIRONMENTAL_DRIFT, drift_mag);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Check periodicity in the phase buffer via short autocorrelation.
|
||||
|
||||
@@ -1,12 +1,21 @@
|
||||
//! Happiness score from WiFi CSI physiological proxies -- ADR-041 exotic module.
|
||||
//! Gait-energy / affect-proxy scoring from WiFi CSI -- ADR-041 exotic module.
|
||||
//!
|
||||
//! ⚠️ SPECULATIVE, UNVALIDATED AFFECT HEURISTIC. The outputs of this module are
|
||||
//! ⚠️ NOT measurements of emotion. `HAPPINESS_SCORE` is a gait-energy / movement
|
||||
//! ⚠️ proxy, not a validated affect measure; it has never been correlated
|
||||
//! ⚠️ against self-report, facial-affect, or any reference standard, and its
|
||||
//! ⚠️ relationship to actual mood is unproven (see ADR-160 §A2). Do NOT use for
|
||||
//! ⚠️ affect inference, screening, or any decision about a person's emotional
|
||||
//! ⚠️ state. The DSP (rolling statistics + weighted scoring) is real; the affect
|
||||
//! ⚠️ interpretation of its output is not.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//!
|
||||
//! Combines six physiological proxies extracted from CSI into a composite
|
||||
//! happiness score [0, 1]:
|
||||
//! Combines six movement/physiology proxies extracted from CSI into a composite
|
||||
//! gait-energy score [0, 1] (labelled `HAPPINESS_SCORE` for the event registry,
|
||||
//! but it is a proxy, not an affect measurement):
|
||||
//!
|
||||
//! 1. **Gait speed** -- Doppler proxy from phase rate-of-change. Happy people
|
||||
//! walk approximately 12% faster than neutral baseline.
|
||||
//! 1. **Gait speed** -- Doppler proxy from phase rate-of-change.
|
||||
//!
|
||||
//! 2. **Stride regularity** -- Variance of step intervals from successive phase
|
||||
//! differences. Regular strides correlate with confidence and positive affect.
|
||||
@@ -31,7 +40,9 @@
|
||||
//!
|
||||
//! # Events (690-694: Exotic / Research)
|
||||
//!
|
||||
//! - `HAPPINESS_SCORE` (690): Composite happiness [0.0 = sad, 0.5 = neutral, 1.0 = happy].
|
||||
//! - `HAPPINESS_SCORE` (690): Composite **gait-energy proxy** [0, 1], NOT a
|
||||
//! validated affect measure. Higher = more energetic/fluid movement, which is
|
||||
//! only speculatively (unvalidated) associated with positive affect.
|
||||
//! - `GAIT_ENERGY` (691): Normalized gait speed/stride score [0, 1].
|
||||
//! - `AFFECT_VALENCE` (692): Emotional valence from breathing + motion [0, 1].
|
||||
//! - `SOCIAL_ENERGY` (693): Group animation/interaction level [0, 1].
|
||||
@@ -97,7 +108,7 @@ const MAX_SC: usize = 32;
|
||||
const EVENT_DECIMATION: u32 = 4;
|
||||
|
||||
/// Baseline gait speed (phase rate-of-change, arbitrary units).
|
||||
/// Happy gait is ~12% above this.
|
||||
/// Used only as a normalization reference for the gait-energy proxy.
|
||||
const BASELINE_GAIT_SPEED: f32 = 0.5;
|
||||
|
||||
/// Maximum expected gait speed for normalization.
|
||||
@@ -184,6 +195,9 @@ pub struct HappinessScoreDetector {
|
||||
|
||||
/// Total frames processed.
|
||||
frame_count: u32,
|
||||
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 5],
|
||||
}
|
||||
|
||||
impl HappinessScoreDetector {
|
||||
@@ -209,6 +223,7 @@ impl HappinessScoreDetector {
|
||||
happiness_vector: [0.0; HAPPINESS_VECTOR_DIM],
|
||||
|
||||
frame_count: 0,
|
||||
events: [(0, 0.0); 5],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -234,7 +249,6 @@ impl HappinessScoreDetector {
|
||||
breathing_bpm: f32,
|
||||
heart_rate_bpm: f32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 5] = [(0, 0.0); 5];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -341,34 +355,24 @@ impl HappinessScoreDetector {
|
||||
|
||||
// ── Emit events (decimated for ESP32 bandwidth) ──
|
||||
// Always emit happiness score; other events only every Nth frame.
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_HAPPINESS_SCORE, self.happiness);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_HAPPINESS_SCORE, self.happiness);
|
||||
n_ev += 1;
|
||||
|
||||
if self.frame_count % EVENT_DECIMATION == 0 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_GAIT_ENERGY, gait_energy);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_GAIT_ENERGY, gait_energy);
|
||||
n_ev += 1;
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_AFFECT_VALENCE, affect_valence);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_AFFECT_VALENCE, affect_valence);
|
||||
n_ev += 1;
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_SOCIAL_ENERGY, social_energy);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_SOCIAL_ENERGY, social_energy);
|
||||
n_ev += 1;
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_TRANSIT_DIRECTION, transit);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_TRANSIT_DIRECTION, transit);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Average phase rate-of-change over the rolling window.
|
||||
|
||||
@@ -88,6 +88,8 @@ pub const EVENT_LOCATION_LABEL: i32 = 687;
|
||||
/// Pre-configured with 16 reference points (4 rooms, 12 zones) and a
|
||||
/// linear projection from 8D CSI features to 2D Poincare disk.
|
||||
pub struct HyperbolicEmbedder {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
/// Reference embeddings on the Poincare disk [N_REFS][DIM].
|
||||
references: [[f32; DIM]; N_REFS],
|
||||
/// Linear projection matrix W: [DIM][FEAT_DIM] (2x8).
|
||||
@@ -111,6 +113,7 @@ pub struct HyperbolicEmbedder {
|
||||
impl HyperbolicEmbedder {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
references: Self::default_references(),
|
||||
projection_w: Self::default_projection(),
|
||||
prev_label: 0,
|
||||
@@ -166,7 +169,6 @@ impl HyperbolicEmbedder {
|
||||
///
|
||||
/// Returns events as `(event_id, value)` pairs.
|
||||
pub fn process_frame(&mut self, amplitudes: &[f32]) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
if amplitudes.len() < FEAT_DIM {
|
||||
@@ -250,22 +252,16 @@ impl HyperbolicEmbedder {
|
||||
let level: u8 = if radius < LEVEL_RADIUS_THRESHOLD { 0 } else { 1 };
|
||||
|
||||
// Emit events.
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_HIERARCHY_LEVEL, level as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_HIERARCHY_LEVEL, level as f32);
|
||||
n_ev += 1;
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_HYPERBOLIC_RADIUS, radius);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_HYPERBOLIC_RADIUS, radius);
|
||||
n_ev += 1;
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_LOCATION_LABEL, best_label as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_LOCATION_LABEL, best_label as f32);
|
||||
n_ev += 1;
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Set a reference embedding. `index` must be < N_REFS.
|
||||
|
||||
@@ -99,6 +99,8 @@ pub const EVENT_GESTURE_FERMATA: i32 = 634;
|
||||
/// Extracts tempo, beat position, dynamics, and special gestures from
|
||||
/// WiFi CSI motion patterns.
|
||||
pub struct MusicConductorDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 5],
|
||||
/// Circular buffer of motion energy samples.
|
||||
motion_buf: CircularBuffer<BUF_LEN>,
|
||||
/// Autocorrelation values at lags MIN_LAG..MAX_LAG.
|
||||
@@ -132,6 +134,7 @@ pub struct MusicConductorDetector {
|
||||
impl MusicConductorDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 5],
|
||||
motion_buf: CircularBuffer::new(),
|
||||
autocorr: [0.0; MAX_LAG],
|
||||
tempo_ema: Ema::new(TEMPO_ALPHA),
|
||||
@@ -165,7 +168,6 @@ impl MusicConductorDetector {
|
||||
motion_energy: f32,
|
||||
_variance: f32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 5] = [(0, 0.0); 5];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -277,37 +279,27 @@ impl MusicConductorDetector {
|
||||
|
||||
// ── Emit events ──
|
||||
if self.tempo_ema.is_initialized() {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_CONDUCTOR_BPM, self.tempo_ema.value);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_CONDUCTOR_BPM, self.tempo_ema.value);
|
||||
n_ev += 1;
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_BEAT_POSITION, beat_position as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_BEAT_POSITION, beat_position as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_DYNAMIC_LEVEL, dynamic_level);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_DYNAMIC_LEVEL, dynamic_level);
|
||||
n_ev += 1;
|
||||
|
||||
if self.cutoff_detected {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_GESTURE_CUTOFF, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_GESTURE_CUTOFF, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
if self.fermata_active {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_GESTURE_FERMATA, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_GESTURE_FERMATA, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Compute buffer mean and variance (single-pass).
|
||||
|
||||
@@ -95,6 +95,8 @@ pub const EVENT_WATERING_EVENT: i32 = 643;
|
||||
/// and phase to detect growth drift, circadian oscillation, wilting,
|
||||
/// and watering events.
|
||||
pub struct PlantGrowthDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Slow EWMA of amplitude per subcarrier group.
|
||||
amp_baseline: [Ema; N_GROUPS],
|
||||
/// Fast EWMA of amplitude per subcarrier group.
|
||||
@@ -124,6 +126,7 @@ pub struct PlantGrowthDetector {
|
||||
impl PlantGrowthDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
amp_baseline: [
|
||||
Ema::new(BASELINE_ALPHA), Ema::new(BASELINE_ALPHA),
|
||||
Ema::new(BASELINE_ALPHA), Ema::new(BASELINE_ALPHA),
|
||||
@@ -174,7 +177,6 @@ impl PlantGrowthDetector {
|
||||
variance: &[f32],
|
||||
presence: i32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -264,9 +266,7 @@ impl PlantGrowthDetector {
|
||||
self.drift_interval_count = 0;
|
||||
|
||||
if fabsf(avg_drift) > GROWTH_THRESHOLD {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_GROWTH_RATE, avg_drift);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_GROWTH_RATE, avg_drift);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
@@ -288,9 +288,7 @@ impl PlantGrowthDetector {
|
||||
if avg_osc > CIRCADIAN_MIN_MAGNITUDE {
|
||||
// Normalize to [0, 1] range (cap at 1.0).
|
||||
let normalized = if avg_osc > 1.0 { 1.0 } else { avg_osc };
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_CIRCADIAN_PHASE, normalized);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_CIRCADIAN_PHASE, normalized);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
@@ -315,9 +313,7 @@ impl PlantGrowthDetector {
|
||||
}
|
||||
// Need majority of groups to agree.
|
||||
if amp_rise_count >= (N_GROUPS / 2) as u8 && var_drop_count >= 2 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_WILT_DETECTED, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_WILT_DETECTED, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
@@ -333,14 +329,12 @@ impl PlantGrowthDetector {
|
||||
}
|
||||
}
|
||||
if drop_count >= (N_GROUPS / 2) as u8 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_WATERING_EVENT, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_WATERING_EVENT, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Get the number of empty-room frames accumulated.
|
||||
|
||||
@@ -99,6 +99,8 @@ pub enum RainIntensity {
|
||||
|
||||
/// Detects rain from broadband CSI phase variance perturbations.
|
||||
pub struct RainDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
/// Baseline variance per subcarrier group (slow EWMA).
|
||||
baseline_var: [Ema; N_GROUPS],
|
||||
/// Short-term variance per subcarrier group (fast EWMA).
|
||||
@@ -122,6 +124,7 @@ pub struct RainDetector {
|
||||
impl RainDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
baseline_var: [
|
||||
Ema::new(BASELINE_ALPHA), Ema::new(BASELINE_ALPHA),
|
||||
Ema::new(BASELINE_ALPHA), Ema::new(BASELINE_ALPHA),
|
||||
@@ -159,7 +162,6 @@ impl RainDetector {
|
||||
amplitudes: &[f32],
|
||||
presence: i32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.frame_count += 1;
|
||||
@@ -250,9 +252,7 @@ impl RainDetector {
|
||||
// Onset: was not raining, now have enough consecutive rain frames.
|
||||
if !self.raining && self.rain_frames >= ONSET_FRAMES {
|
||||
self.raining = true;
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_RAIN_ONSET, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_RAIN_ONSET, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
@@ -260,9 +260,7 @@ impl RainDetector {
|
||||
if was_raining && self.quiet_frames >= CESSATION_FRAMES {
|
||||
self.raining = false;
|
||||
self.intensity = RainIntensity::None;
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_RAIN_CESSATION, 1.0);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_RAIN_CESSATION, 1.0);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
@@ -277,13 +275,11 @@ impl RainDetector {
|
||||
RainIntensity::Heavy
|
||||
};
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_RAIN_INTENSITY, self.intensity as u8 as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_RAIN_INTENSITY, self.intensity as u8 as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Whether rain is currently detected.
|
||||
|
||||
@@ -74,6 +74,8 @@ pub const EVENT_COORDINATION_INDEX: i32 = 682;
|
||||
/// Samples `motion_energy` into a circular buffer and runs autocorrelation
|
||||
/// to detect period doubling and multi-person temporal coordination.
|
||||
pub struct TimeCrystalDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
/// Circular buffer of motion energy samples.
|
||||
motion_buf: CircularBuffer<BUF_LEN>,
|
||||
/// Autocorrelation values at lags 1..MAX_LAG.
|
||||
@@ -101,6 +103,7 @@ pub struct TimeCrystalDetector {
|
||||
impl TimeCrystalDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
motion_buf: CircularBuffer::new(),
|
||||
autocorr: [0.0; MAX_LAG],
|
||||
last_multiplier: 0,
|
||||
@@ -119,7 +122,6 @@ impl TimeCrystalDetector {
|
||||
///
|
||||
/// Returns events as `(event_id, value)` pairs in a static buffer.
|
||||
pub fn process_frame(&mut self, motion_energy: f32) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
// Push sample into circular buffer.
|
||||
@@ -216,25 +218,19 @@ impl TimeCrystalDetector {
|
||||
|
||||
// Emit events.
|
||||
if detected_multiplier > 0 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_CRYSTAL_DETECTED, detected_multiplier as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_CRYSTAL_DETECTED, detected_multiplier as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_CRYSTAL_STABILITY, self.stability_ema.value);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_CRYSTAL_STABILITY, self.stability_ema.value);
|
||||
n_ev += 1;
|
||||
|
||||
if coordination > 0 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_COORDINATION_INDEX, coordination as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_COORDINATION_INDEX, coordination as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Compute mean and variance of the circular buffer contents.
|
||||
|
||||
@@ -41,6 +41,8 @@ pub const EVENT_COMPLIANCE_REPORT: i32 = 523;
|
||||
|
||||
/// Clean room monitor.
|
||||
pub struct CleanRoomMonitor {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Maximum allowed occupancy.
|
||||
max_occupancy: u8,
|
||||
/// Current smoothed person count.
|
||||
@@ -70,6 +72,7 @@ pub struct CleanRoomMonitor {
|
||||
impl CleanRoomMonitor {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
max_occupancy: DEFAULT_MAX_OCCUPANCY,
|
||||
current_count: 0,
|
||||
prev_count: 0,
|
||||
@@ -88,6 +91,7 @@ impl CleanRoomMonitor {
|
||||
/// Create with custom maximum occupancy.
|
||||
pub const fn with_max_occupancy(max: u8) -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
max_occupancy: max,
|
||||
current_count: 0,
|
||||
prev_count: 0,
|
||||
@@ -146,12 +150,11 @@ impl CleanRoomMonitor {
|
||||
}
|
||||
}
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// --- Step 1: Emit count changes ---
|
||||
if count != self.prev_count && n_events < 4 {
|
||||
unsafe { EVENTS[n_events] = (EVENT_OCCUPANCY_COUNT, count as f32); }
|
||||
self.events[n_events] = (EVENT_OCCUPANCY_COUNT, count as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
@@ -166,7 +169,7 @@ impl CleanRoomMonitor {
|
||||
self.violation_cooldown = VIOLATION_COOLDOWN;
|
||||
// Value encodes: count * 10 + max_allowed.
|
||||
let val = count as f32;
|
||||
unsafe { EVENTS[n_events] = (EVENT_OCCUPANCY_VIOLATION, val); }
|
||||
self.events[n_events] = (EVENT_OCCUPANCY_VIOLATION, val);
|
||||
n_events += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -182,7 +185,7 @@ impl CleanRoomMonitor {
|
||||
{
|
||||
self.total_turbulent += 1;
|
||||
self.turbulent_cooldown = TURBULENT_COOLDOWN;
|
||||
unsafe { EVENTS[n_events] = (EVENT_TURBULENT_MOTION, motion_energy); }
|
||||
self.events[n_events] = (EVENT_TURBULENT_MOTION, motion_energy);
|
||||
n_events += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -196,11 +199,11 @@ impl CleanRoomMonitor {
|
||||
} else {
|
||||
100.0
|
||||
};
|
||||
unsafe { EVENTS[n_events] = (EVENT_COMPLIANCE_REPORT, compliance_pct); }
|
||||
self.events[n_events] = (EVENT_COMPLIANCE_REPORT, compliance_pct);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Current occupancy count.
|
||||
|
||||
@@ -55,6 +55,8 @@ pub enum WorkerState {
|
||||
|
||||
/// Confined space monitor.
|
||||
pub struct ConfinedSpaceMonitor {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Current worker state.
|
||||
state: WorkerState,
|
||||
/// Presence debounce counters.
|
||||
@@ -79,6 +81,7 @@ pub struct ConfinedSpaceMonitor {
|
||||
impl ConfinedSpaceMonitor {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
state: WorkerState::Empty,
|
||||
present_count: 0,
|
||||
absent_count: 0,
|
||||
@@ -110,7 +113,6 @@ impl ConfinedSpaceMonitor {
|
||||
) -> &[(i32, f32)] {
|
||||
self.frame_count += 1;
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// --- Step 1: Debounced presence detection ---
|
||||
@@ -141,7 +143,7 @@ impl ConfinedSpaceMonitor {
|
||||
self.extraction_alerted = false;
|
||||
self.immobile_alerted = false;
|
||||
if n_events < 4 {
|
||||
unsafe { EVENTS[n_events] = (EVENT_WORKER_ENTRY, 1.0); }
|
||||
self.events[n_events] = (EVENT_WORKER_ENTRY, 1.0);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -150,7 +152,7 @@ impl ConfinedSpaceMonitor {
|
||||
if !self.worker_inside && was_inside {
|
||||
self.state = WorkerState::Empty;
|
||||
if n_events < 4 {
|
||||
unsafe { EVENTS[n_events] = (EVENT_WORKER_EXIT, 1.0); }
|
||||
self.events[n_events] = (EVENT_WORKER_EXIT, 1.0);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -169,7 +171,7 @@ impl ConfinedSpaceMonitor {
|
||||
|
||||
// Periodic breathing confirmation.
|
||||
if self.frame_count % BREATHING_REPORT_INTERVAL == 0 && n_events < 4 {
|
||||
unsafe { EVENTS[n_events] = (EVENT_BREATHING_OK, breathing_bpm); }
|
||||
self.events[n_events] = (EVENT_BREATHING_OK, breathing_bpm);
|
||||
n_events += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -197,7 +199,7 @@ impl ConfinedSpaceMonitor {
|
||||
self.state = WorkerState::BreathingCeased;
|
||||
self.extraction_alerted = true;
|
||||
let seconds = self.no_breathing_frames as f32 / 20.0;
|
||||
unsafe { EVENTS[n_events] = (EVENT_EXTRACTION_ALERT, seconds); }
|
||||
self.events[n_events] = (EVENT_EXTRACTION_ALERT, seconds);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
@@ -209,12 +211,12 @@ impl ConfinedSpaceMonitor {
|
||||
self.state = WorkerState::Immobile;
|
||||
self.immobile_alerted = true;
|
||||
let seconds = self.no_motion_frames as f32 / 20.0;
|
||||
unsafe { EVENTS[n_events] = (EVENT_IMMOBILE_ALERT, seconds); }
|
||||
self.events[n_events] = (EVENT_IMMOBILE_ALERT, seconds);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Current worker state.
|
||||
|
||||
@@ -59,6 +59,8 @@ pub const EVENT_HUMAN_NEAR_VEHICLE: i32 = 502;
|
||||
|
||||
/// Forklift proximity detector.
|
||||
pub struct ForkliftProximityDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Per-subcarrier baseline amplitude (calibrated).
|
||||
baseline_amp: [f32; MAX_SC],
|
||||
/// Phase history ring buffer for frequency analysis.
|
||||
@@ -83,6 +85,7 @@ pub struct ForkliftProximityDetector {
|
||||
impl ForkliftProximityDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
baseline_amp: [0.0; MAX_SC],
|
||||
phase_history: [[0.0; MAX_SC]; PHASE_HISTORY],
|
||||
phase_hist_idx: 0,
|
||||
@@ -139,7 +142,6 @@ impl ForkliftProximityDetector {
|
||||
self.phase_hist_len += 1;
|
||||
}
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// Calibration phase: 100 frames (~5 seconds).
|
||||
@@ -158,7 +160,7 @@ impl ForkliftProximityDetector {
|
||||
}
|
||||
self.calibrated = true;
|
||||
}
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// --- Step 1: Detect forklift/AGV signature ---
|
||||
@@ -182,9 +184,7 @@ impl ForkliftProximityDetector {
|
||||
|
||||
// Emit vehicle detected on transition.
|
||||
if self.vehicle_present && !was_vehicle && n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_VEHICLE_DETECTED, amp_ratio);
|
||||
}
|
||||
self.events[n_events] = (EVENT_VEHICLE_DETECTED, amp_ratio);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
@@ -197,9 +197,7 @@ impl ForkliftProximityDetector {
|
||||
|
||||
// Emit human-near-vehicle event on transition (debounce threshold reached).
|
||||
if self.proximity_debounce == PROXIMITY_DEBOUNCE && n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_HUMAN_NEAR_VEHICLE, motion_energy);
|
||||
}
|
||||
self.events[n_events] = (EVENT_HUMAN_NEAR_VEHICLE, motion_energy);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
@@ -215,9 +213,7 @@ impl ForkliftProximityDetector {
|
||||
} else {
|
||||
2.0 // caution
|
||||
};
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_PROXIMITY_WARNING, dist_cat);
|
||||
}
|
||||
self.events[n_events] = (EVENT_PROXIMITY_WARNING, dist_cat);
|
||||
n_events += 1;
|
||||
self.cooldown = ALERT_COOLDOWN;
|
||||
}
|
||||
@@ -225,7 +221,7 @@ impl ForkliftProximityDetector {
|
||||
self.proximity_debounce = 0;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Compute mean amplitude ratio vs baseline across subcarriers.
|
||||
|
||||
@@ -72,6 +72,8 @@ impl Species {
|
||||
|
||||
/// Livestock monitor.
|
||||
pub struct LivestockMonitor {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Configured species.
|
||||
species: Species,
|
||||
/// Whether animal is currently detected (debounced).
|
||||
@@ -97,6 +99,7 @@ pub struct LivestockMonitor {
|
||||
impl LivestockMonitor {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
species: Species::Cattle,
|
||||
animal_present: false,
|
||||
presence_frames: 0,
|
||||
@@ -113,6 +116,7 @@ impl LivestockMonitor {
|
||||
/// Create with a specific species.
|
||||
pub const fn with_species(species: Species) -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
species,
|
||||
animal_present: false,
|
||||
presence_frames: 0,
|
||||
@@ -148,7 +152,6 @@ impl LivestockMonitor {
|
||||
self.escape_cooldown -= 1;
|
||||
}
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
let raw_present = presence > 0 || motion_energy > MIN_MOTION_ACTIVE;
|
||||
@@ -177,7 +180,7 @@ impl LivestockMonitor {
|
||||
{
|
||||
self.escape_cooldown = ESCAPE_COOLDOWN;
|
||||
let minutes_present = self.presence_frames as f32 / (20.0 * 60.0);
|
||||
unsafe { EVENTS[n_events] = (EVENT_ESCAPE_ALERT, minutes_present); }
|
||||
self.events[n_events] = (EVENT_ESCAPE_ALERT, minutes_present);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
@@ -190,7 +193,7 @@ impl LivestockMonitor {
|
||||
&& self.frame_count % PRESENCE_REPORT_INTERVAL == 0
|
||||
&& n_events < 4
|
||||
{
|
||||
unsafe { EVENTS[n_events] = (EVENT_ANIMAL_PRESENT, breathing_bpm); }
|
||||
self.events[n_events] = (EVENT_ANIMAL_PRESENT, breathing_bpm);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
@@ -209,7 +212,7 @@ impl LivestockMonitor {
|
||||
{
|
||||
self.stillness_alerted = true;
|
||||
let minutes_still = self.still_frames as f32 / (20.0 * 60.0);
|
||||
unsafe { EVENTS[n_events] = (EVENT_ABNORMAL_STILLNESS, minutes_still); }
|
||||
self.events[n_events] = (EVENT_ABNORMAL_STILLNESS, minutes_still);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -226,7 +229,7 @@ impl LivestockMonitor {
|
||||
if is_labored {
|
||||
self.labored_debounce = self.labored_debounce.saturating_add(1);
|
||||
if self.labored_debounce >= LABORED_DEBOUNCE && n_events < 4 {
|
||||
unsafe { EVENTS[n_events] = (EVENT_LABORED_BREATHING, breathing_bpm); }
|
||||
self.events[n_events] = (EVENT_LABORED_BREATHING, breathing_bpm);
|
||||
n_events += 1;
|
||||
self.labored_debounce = 0; // Reset to allow repeated alerts.
|
||||
}
|
||||
@@ -235,7 +238,7 @@ impl LivestockMonitor {
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Whether an animal is currently detected.
|
||||
|
||||
@@ -72,6 +72,8 @@ pub const EVENT_VIBRATION_SPECTRUM: i32 = 543;
|
||||
|
||||
/// Structural vibration monitor.
|
||||
pub struct StructuralVibrationMonitor {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Phase history ring buffer [time][subcarrier].
|
||||
phase_history: [[f32; MAX_SC]; PHASE_HISTORY_LEN],
|
||||
hist_idx: usize,
|
||||
@@ -104,6 +106,7 @@ pub struct StructuralVibrationMonitor {
|
||||
impl StructuralVibrationMonitor {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
phase_history: [[0.0; MAX_SC]; PHASE_HISTORY_LEN],
|
||||
hist_idx: 0,
|
||||
hist_len: 0,
|
||||
@@ -162,7 +165,6 @@ impl StructuralVibrationMonitor {
|
||||
self.hist_len += 1;
|
||||
}
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// --- Calibration: establish baseline when space is empty ---
|
||||
@@ -180,7 +182,7 @@ impl StructuralVibrationMonitor {
|
||||
self.baseline_set = true;
|
||||
}
|
||||
}
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// Only analyze when unoccupied (human presence masks structural signals).
|
||||
@@ -191,7 +193,7 @@ impl StructuralVibrationMonitor {
|
||||
self.drift_direction[i] = 0;
|
||||
self.drift_accumulator[i] = 0.0;
|
||||
}
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// --- Step 1: Compute phase deviation RMS ---
|
||||
@@ -209,7 +211,7 @@ impl StructuralVibrationMonitor {
|
||||
&& n_events < 4
|
||||
{
|
||||
self.seismic_cooldown = SEISMIC_COOLDOWN;
|
||||
unsafe { EVENTS[n_events] = (EVENT_SEISMIC_DETECTED, rms); }
|
||||
self.events[n_events] = (EVENT_SEISMIC_DETECTED, rms);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -235,7 +237,7 @@ impl StructuralVibrationMonitor {
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
unsafe { EVENTS[n_events] = (EVENT_MECHANICAL_RESONANCE, freq); }
|
||||
self.events[n_events] = (EVENT_MECHANICAL_RESONANCE, freq);
|
||||
n_events += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -253,7 +255,7 @@ impl StructuralVibrationMonitor {
|
||||
if fabsf(avg_drift) > DRIFT_RATE_THRESH {
|
||||
self.drift_cooldown = DRIFT_COOLDOWN;
|
||||
// Value is drift rate in rad/second.
|
||||
unsafe { EVENTS[n_events] = (EVENT_STRUCTURAL_DRIFT, avg_drift * 20.0); }
|
||||
self.events[n_events] = (EVENT_STRUCTURAL_DRIFT, avg_drift * 20.0);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -263,11 +265,11 @@ impl StructuralVibrationMonitor {
|
||||
&& self.hist_len >= MAX_LAGS + 1
|
||||
&& n_events < 4
|
||||
{
|
||||
unsafe { EVENTS[n_events] = (EVENT_VIBRATION_SPECTRUM, rms); }
|
||||
self.events[n_events] = (EVENT_VIBRATION_SPECTRUM, rms);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Compute RMS phase deviation from baseline.
|
||||
|
||||
@@ -57,6 +57,8 @@ pub enum DetectorState {
|
||||
|
||||
/// Intrusion detector.
|
||||
pub struct IntrusionDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Per-subcarrier baseline amplitude.
|
||||
baseline_amp: [f32; MAX_SC],
|
||||
/// Per-subcarrier baseline variance.
|
||||
@@ -86,6 +88,7 @@ pub struct IntrusionDetector {
|
||||
impl IntrusionDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
baseline_amp: [0.0; MAX_SC],
|
||||
baseline_var: [0.0; MAX_SC],
|
||||
prev_phases: [0.0; MAX_SC],
|
||||
@@ -119,7 +122,6 @@ impl IntrusionDetector {
|
||||
self.cooldown -= 1;
|
||||
}
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
match self.state {
|
||||
@@ -165,9 +167,7 @@ impl IntrusionDetector {
|
||||
if self.quiet_frames >= ARM_FRAMES {
|
||||
self.state = DetectorState::Armed;
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_INTRUSION_ARMED, 1.0);
|
||||
}
|
||||
self.events[n_events] = (EVENT_INTRUSION_ARMED, 1.0);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -190,18 +190,14 @@ impl IntrusionDetector {
|
||||
self.cooldown = ALERT_COOLDOWN;
|
||||
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_INTRUSION_ALERT, disturbance);
|
||||
}
|
||||
self.events[n_events] = (EVENT_INTRUSION_ALERT, disturbance);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
// Find the most disturbed zone.
|
||||
let zone = self.find_disturbed_zone(amplitudes, n_sc);
|
||||
if n_events < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_INTRUSION_ZONE, zone as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_INTRUSION_ZONE, zone as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -235,7 +231,7 @@ impl IntrusionDetector {
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Compute overall disturbance score.
|
||||
|
||||
@@ -46,10 +46,20 @@ pub mod vital_trend;
|
||||
pub mod intrusion;
|
||||
|
||||
// ── Category 1: Medical & Health (ADR-041, event IDs 100-199) ───────────────
|
||||
//
|
||||
// ⚠️ EXPERIMENTAL — NOT clinically validated, NOT medical devices (ADR-160 §A1).
|
||||
// Gated behind the non-default `medical-experimental` feature so they cannot be
|
||||
// silently built into a shipping artifact. The DSP is real; the clinical claim
|
||||
// surface is not. See each module's header disclaimer.
|
||||
#[cfg(feature = "medical-experimental")]
|
||||
pub mod med_sleep_apnea;
|
||||
#[cfg(feature = "medical-experimental")]
|
||||
pub mod med_cardiac_arrhythmia;
|
||||
#[cfg(feature = "medical-experimental")]
|
||||
pub mod med_respiratory_distress;
|
||||
#[cfg(feature = "medical-experimental")]
|
||||
pub mod med_gait_analysis;
|
||||
#[cfg(feature = "medical-experimental")]
|
||||
pub mod med_seizure_detect;
|
||||
|
||||
// ── Category 2: Security & Safety (ADR-041, event IDs 200-299) ──────────────
|
||||
@@ -228,9 +238,11 @@ pub mod event_types {
|
||||
pub const DEPARTURE_DETECTED: i32 = 212;
|
||||
pub const SEC_ZONE_TRANSITION: i32 = 213;
|
||||
|
||||
// sec_weapon_detect (220-222)
|
||||
// sec_weapon_detect (220-222) — ADR-160 §A3: honest physical-quantity names.
|
||||
// `WEAPON_ALERT` was renamed to `HIGH_METAL_REFLECTIVITY`: a variance ratio
|
||||
// measures RF reflectivity, not weapon-grade discrimination.
|
||||
pub const METAL_ANOMALY: i32 = 220;
|
||||
pub const WEAPON_ALERT: i32 = 221;
|
||||
pub const HIGH_METAL_REFLECTIVITY: i32 = 221;
|
||||
pub const CALIBRATION_NEEDED: i32 = 222;
|
||||
|
||||
// sec_tailgating (230-232)
|
||||
|
||||
@@ -71,6 +71,8 @@ type StateVec = [f32; STATE_DIM];
|
||||
|
||||
/// Attractor-based anomaly detector.
|
||||
pub struct AttractorDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Circular trajectory buffer.
|
||||
trajectory: [StateVec; TRAJ_LEN],
|
||||
/// Write index into trajectory buffer.
|
||||
@@ -108,6 +110,7 @@ pub struct AttractorDetector {
|
||||
impl AttractorDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
trajectory: [[0.0; STATE_DIM]; TRAJ_LEN],
|
||||
traj_idx: 0,
|
||||
traj_len: 0,
|
||||
@@ -137,7 +140,6 @@ impl AttractorDetector {
|
||||
amplitudes: &[f32],
|
||||
motion_energy: f32,
|
||||
) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
let n_sc = phases.len().min(amplitudes.len());
|
||||
@@ -200,16 +202,14 @@ impl AttractorDetector {
|
||||
self.radius = 0.01;
|
||||
}
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_LEARNING_COMPLETE, 1.0);
|
||||
n_ev += 1;
|
||||
EVENTS[n_ev] = (EVENT_ATTRACTOR_TYPE, self.attractor_type as u8 as f32);
|
||||
n_ev += 1;
|
||||
EVENTS[n_ev] = (EVENT_LYAPUNOV_EXPONENT, lambda);
|
||||
n_ev += 1;
|
||||
}
|
||||
self.events[n_ev] = (EVENT_LEARNING_COMPLETE, 1.0);
|
||||
n_ev += 1;
|
||||
self.events[n_ev] = (EVENT_ATTRACTOR_TYPE, self.attractor_type as u8 as f32);
|
||||
n_ev += 1;
|
||||
self.events[n_ev] = (EVENT_LYAPUNOV_EXPONENT, lambda);
|
||||
n_ev += 1;
|
||||
|
||||
return unsafe { &EVENTS[..n_ev] };
|
||||
return &self.events[..n_ev];
|
||||
}
|
||||
|
||||
return &[];
|
||||
@@ -221,10 +221,8 @@ impl AttractorDetector {
|
||||
|
||||
if dist > departure_threshold && self.cooldown == 0 {
|
||||
self.cooldown = DEPARTURE_COOLDOWN;
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_BASIN_DEPARTURE, dist / self.radius);
|
||||
n_ev += 1;
|
||||
}
|
||||
self.events[n_ev] = (EVENT_BASIN_DEPARTURE, dist / self.radius);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
// ── Periodic attractor update (every 200 frames) ────────────────
|
||||
@@ -234,16 +232,14 @@ impl AttractorDetector {
|
||||
|
||||
if new_type != self.attractor_type && n_ev < 3 {
|
||||
self.attractor_type = new_type;
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_ATTRACTOR_TYPE, new_type as u8 as f32);
|
||||
n_ev += 1;
|
||||
EVENTS[n_ev] = (EVENT_LYAPUNOV_EXPONENT, lambda);
|
||||
n_ev += 1;
|
||||
}
|
||||
self.events[n_ev] = (EVENT_ATTRACTOR_TYPE, new_type as u8 as f32);
|
||||
n_ev += 1;
|
||||
self.events[n_ev] = (EVENT_LYAPUNOV_EXPONENT, lambda);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Compute the current largest Lyapunov exponent estimate.
|
||||
|
||||
@@ -85,6 +85,8 @@ impl Template {
|
||||
|
||||
/// User-teachable gesture learner and recognizer.
|
||||
pub struct GestureLearner {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
// ── Stored templates ─────────────────────────────────────────────────
|
||||
templates: [Template; MAX_TEMPLATES],
|
||||
template_count: usize,
|
||||
@@ -117,6 +119,7 @@ pub struct GestureLearner {
|
||||
impl GestureLearner {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
templates: [Template::empty(); MAX_TEMPLATES],
|
||||
template_count: 0,
|
||||
learn_phase: LearnPhase::Idle,
|
||||
@@ -143,7 +146,6 @@ impl GestureLearner {
|
||||
///
|
||||
/// Returns events as `(event_id, value)` pairs in a static buffer.
|
||||
pub fn process_frame(&mut self, phases: &[f32], motion_energy: f32) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
if phases.is_empty() {
|
||||
@@ -228,12 +230,10 @@ impl GestureLearner {
|
||||
// Check if all 3 rehearsals are mutually similar.
|
||||
if self.rehearsals_are_similar() {
|
||||
if let Some(id) = self.commit_template() {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_GESTURE_LEARNED, id as f32);
|
||||
n_ev += 1;
|
||||
EVENTS[n_ev] = (EVENT_TEMPLATE_COUNT, self.template_count as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
self.events[n_ev] = (EVENT_GESTURE_LEARNED, id as f32);
|
||||
n_ev += 1;
|
||||
self.events[n_ev] = (EVENT_TEMPLATE_COUNT, self.template_count as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
// Reset learning state regardless.
|
||||
@@ -284,18 +284,16 @@ impl GestureLearner {
|
||||
|
||||
if let Some(id) = best_id {
|
||||
self.cooldown = MATCH_COOLDOWN;
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_GESTURE_MATCHED, id as f32);
|
||||
self.events[n_ev] = (EVENT_GESTURE_MATCHED, id as f32);
|
||||
n_ev += 1;
|
||||
if n_ev < 4 {
|
||||
self.events[n_ev] = (EVENT_MATCH_DISTANCE, best_dist);
|
||||
n_ev += 1;
|
||||
if n_ev < 4 {
|
||||
EVENTS[n_ev] = (EVENT_MATCH_DISTANCE, best_dist);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Check if all rehearsals are pairwise similar (DTW distance < threshold).
|
||||
|
||||
@@ -99,6 +99,8 @@ pub const EVENT_FORGETTING_RISK: i32 = 748;
|
||||
|
||||
/// Elastic Weight Consolidation lifelong on-device learner.
|
||||
pub struct EwcLifelong {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Current learnable parameters [N_PARAMS] (flattened [N_OUTPUT][N_INPUT]).
|
||||
params: [f32; N_PARAMS],
|
||||
/// Fisher Information diagonal [N_PARAMS].
|
||||
@@ -128,6 +130,7 @@ pub struct EwcLifelong {
|
||||
impl EwcLifelong {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
params: Self::default_params(),
|
||||
fisher: [0.0; N_PARAMS],
|
||||
theta_star: [0.0; N_PARAMS],
|
||||
@@ -169,7 +172,6 @@ impl EwcLifelong {
|
||||
///
|
||||
/// Returns events as `(event_id, value)` pairs.
|
||||
pub fn process_frame(&mut self, features: &[f32], target_zone: i32) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
if features.len() < N_INPUT {
|
||||
@@ -217,17 +219,13 @@ impl EwcLifelong {
|
||||
&& self.task_count < MAX_TASKS
|
||||
{
|
||||
self.commit_task();
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_NEW_TASK_LEARNED, self.task_count as f32);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_NEW_TASK_LEARNED, self.task_count as f32);
|
||||
n_ev += 1;
|
||||
|
||||
// Emit mean Fisher value.
|
||||
let mean_fisher = self.mean_fisher();
|
||||
if n_ev < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_FISHER_UPDATE, mean_fisher);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_FISHER_UPDATE, mean_fisher);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
@@ -235,9 +233,7 @@ impl EwcLifelong {
|
||||
// Periodic reporting.
|
||||
if self.frame_count % REPORT_INTERVAL == 0 {
|
||||
if n_ev < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_KNOWLEDGE_RETAINED, ewc_penalty);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_KNOWLEDGE_RETAINED, ewc_penalty);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
@@ -248,15 +244,13 @@ impl EwcLifelong {
|
||||
0.0
|
||||
};
|
||||
if n_ev < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_FORGETTING_RISK, risk);
|
||||
}
|
||||
self.events[n_ev] = (EVENT_FORGETTING_RISK, risk);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Forward pass: linear classifier `output = params * features`.
|
||||
|
||||
@@ -85,6 +85,8 @@ enum OptPhase {
|
||||
|
||||
/// Meta-learning parameter optimizer.
|
||||
pub struct MetaAdapter {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Tunable parameters.
|
||||
params: [TunableParam; NUM_PARAMS],
|
||||
|
||||
@@ -140,6 +142,7 @@ impl MetaAdapter {
|
||||
/// 7: intrusion_sensitivity (0.30, range 0.05-0.9)
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
params: [
|
||||
TunableParam::new(0.05, 0.01, 0.50, 0.01),
|
||||
TunableParam::new(0.10, 0.02, 1.00, 0.02),
|
||||
@@ -198,7 +201,6 @@ impl MetaAdapter {
|
||||
///
|
||||
/// Returns events as `(event_id, value)` pairs.
|
||||
pub fn on_timer(&mut self) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
self.eval_ticks += 1;
|
||||
@@ -228,16 +230,14 @@ impl MetaAdapter {
|
||||
self.consecutive_failures = 0;
|
||||
self.success_count += 1;
|
||||
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (
|
||||
EVENT_PARAM_ADJUSTED,
|
||||
self.current_param as f32
|
||||
+ self.params[self.current_param].value / 1000.0,
|
||||
);
|
||||
n_ev += 1;
|
||||
EVENTS[n_ev] = (EVENT_ADAPTATION_SCORE, score);
|
||||
n_ev += 1;
|
||||
}
|
||||
self.events[n_ev] = (
|
||||
EVENT_PARAM_ADJUSTED,
|
||||
self.current_param as f32
|
||||
+ self.params[self.current_param].value / 1000.0,
|
||||
);
|
||||
n_ev += 1;
|
||||
self.events[n_ev] = (EVENT_ADAPTATION_SCORE, score);
|
||||
n_ev += 1;
|
||||
} else {
|
||||
// Revert the perturbation.
|
||||
self.params[self.current_param].value =
|
||||
@@ -248,10 +248,8 @@ impl MetaAdapter {
|
||||
// ── Safety rollback ──────────────────────────────────
|
||||
if self.consecutive_failures >= MAX_CONSECUTIVE_FAILURES {
|
||||
self.safety_rollback();
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_ROLLBACK_TRIGGERED, self.meta_level as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
self.events[n_ev] = (EVENT_ROLLBACK_TRIGGERED, self.meta_level as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
|
||||
// ── Advance to next parameter ────────────────────────
|
||||
@@ -261,16 +259,14 @@ impl MetaAdapter {
|
||||
|
||||
// ── Emit meta level periodically ─────────────────────
|
||||
if self.sweep_idx == 0 && n_ev < 4 {
|
||||
unsafe {
|
||||
EVENTS[n_ev] = (EVENT_META_LEVEL, self.meta_level as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
self.events[n_ev] = (EVENT_META_LEVEL, self.meta_level as f32);
|
||||
n_ev += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
/// Compute the performance score from accumulated feedback.
|
||||
|
||||
@@ -1,10 +1,20 @@
|
||||
//! Cardiac arrhythmia detection — ADR-041 Category 1 Medical module.
|
||||
//! Cardiac-rhythm anomaly flagging — ADR-041 Category 1 Medical module.
|
||||
//!
|
||||
//! Monitors heart rate from host CSI pipeline and detects:
|
||||
//! - Tachycardia: sustained HR > 100 BPM
|
||||
//! - Bradycardia: sustained HR < 50 BPM
|
||||
//! - Missed beats: sudden HR dips > 30% below running average
|
||||
//! - HRV anomaly: RMSSD outside normal range over 30-second window
|
||||
//! ⚠️ EXPERIMENTAL RESEARCH MODULE — NOT VALIDATED AGAINST CLINICAL DATA.
|
||||
//! ⚠️ NOT A MEDICAL DEVICE. Do NOT use for diagnosis or patient monitoring.
|
||||
//! ⚠️ This module flags *candidate* arrhythmia-like heart-rate signatures only
|
||||
//! ⚠️ (sustained high/low rate estimates, abrupt drops, variability proxies);
|
||||
//! ⚠️ it has never been compared against ECG or any reference standard, and its
|
||||
//! ⚠️ accuracy is unproven (see ADR-160 §A1). Gated behind the non-default
|
||||
//! ⚠️ `medical-experimental` cargo feature.
|
||||
//!
|
||||
//! Monitors a heart-rate estimate from the host CSI pipeline and flags:
|
||||
//! - Tachycardia-like: sustained rate estimate > 100 BPM
|
||||
//! - Bradycardia-like: sustained rate estimate < 50 BPM
|
||||
//! - Missed-beat-like: sudden rate dips > 30% below running average
|
||||
//! - HRV-like anomaly: RMSSD proxy outside a coarse band over 30 seconds
|
||||
//!
|
||||
//! These are experimental signal proxies, NOT clinical measurements.
|
||||
//!
|
||||
//! Events:
|
||||
//! TACHYCARDIA (110) — sustained high heart rate
|
||||
@@ -87,6 +97,8 @@ pub struct CardiacArrhythmiaDetector {
|
||||
cd_hrv: u16,
|
||||
/// Frame counter.
|
||||
frame_count: u32,
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
}
|
||||
|
||||
impl CardiacArrhythmiaDetector {
|
||||
@@ -106,6 +118,7 @@ impl CardiacArrhythmiaDetector {
|
||||
cd_missed: 0,
|
||||
cd_hrv: 0,
|
||||
frame_count: 0,
|
||||
events: [(0, 0.0); 4],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -122,14 +135,13 @@ impl CardiacArrhythmiaDetector {
|
||||
self.cd_missed = self.cd_missed.saturating_sub(1);
|
||||
self.cd_hrv = self.cd_hrv.saturating_sub(1);
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n = 0usize;
|
||||
|
||||
// Ignore invalid / zero / NaN readings.
|
||||
// NaN comparisons return false, so we must check explicitly to prevent
|
||||
// NaN from contaminating the EMA and RMSSD calculations.
|
||||
if !(hr_bpm >= 1.0) {
|
||||
return unsafe { &EVENTS[..n] };
|
||||
return &self.events[..n];
|
||||
}
|
||||
|
||||
// ── EMA update ──────────────────────────────────────────────────
|
||||
@@ -156,7 +168,7 @@ impl CardiacArrhythmiaDetector {
|
||||
if hr_bpm > TACHY_THRESH {
|
||||
self.tachy_count = self.tachy_count.saturating_add(1);
|
||||
if self.tachy_count >= SUSTAINED_SECS && self.cd_tachy == 0 && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_TACHYCARDIA, hr_bpm); }
|
||||
self.events[n] = (EVENT_TACHYCARDIA, hr_bpm);
|
||||
n += 1;
|
||||
self.cd_tachy = COOLDOWN_SECS;
|
||||
}
|
||||
@@ -168,7 +180,7 @@ impl CardiacArrhythmiaDetector {
|
||||
if hr_bpm < BRADY_THRESH {
|
||||
self.brady_count = self.brady_count.saturating_add(1);
|
||||
if self.brady_count >= SUSTAINED_SECS && self.cd_brady == 0 && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_BRADYCARDIA, hr_bpm); }
|
||||
self.events[n] = (EVENT_BRADYCARDIA, hr_bpm);
|
||||
n += 1;
|
||||
self.cd_brady = COOLDOWN_SECS;
|
||||
}
|
||||
@@ -180,7 +192,7 @@ impl CardiacArrhythmiaDetector {
|
||||
if self.ema_init && self.hr_ema > 1.0 {
|
||||
let drop_frac = (self.hr_ema - hr_bpm) / self.hr_ema;
|
||||
if drop_frac > MISSED_BEAT_DROP && self.cd_missed == 0 && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_MISSED_BEAT, hr_bpm); }
|
||||
self.events[n] = (EVENT_MISSED_BEAT, hr_bpm);
|
||||
n += 1;
|
||||
self.cd_missed = COOLDOWN_SECS;
|
||||
}
|
||||
@@ -190,13 +202,13 @@ impl CardiacArrhythmiaDetector {
|
||||
if self.rr_len >= HRV_WINDOW && n < 4 {
|
||||
let rmssd = self.compute_rmssd();
|
||||
if (rmssd < RMSSD_LOW || rmssd > RMSSD_HIGH) && self.cd_hrv == 0 {
|
||||
unsafe { EVENTS[n] = (EVENT_HRV_ANOMALY, rmssd); }
|
||||
self.events[n] = (EVENT_HRV_ANOMALY, rmssd);
|
||||
n += 1;
|
||||
self.cd_hrv = COOLDOWN_SECS;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
/// Compute RMSSD from the RR-diff ring buffer.
|
||||
|
||||
@@ -1,7 +1,15 @@
|
||||
//! Gait analysis — ADR-041 Category 1 Medical module.
|
||||
//! Gait-parameter proxies & fall-risk-like scoring — ADR-041 Category 1 Medical module.
|
||||
//!
|
||||
//! Extracts gait parameters from CSI phase variance periodicity to assess
|
||||
//! mobility and fall risk:
|
||||
//! ⚠️ EXPERIMENTAL RESEARCH MODULE — NOT VALIDATED AGAINST CLINICAL DATA.
|
||||
//! ⚠️ NOT A MEDICAL DEVICE. Do NOT use for diagnosis, fall-risk assessment, or
|
||||
//! ⚠️ any clinical decision. This module computes *candidate* gait-parameter
|
||||
//! ⚠️ proxies and a fall-risk-like score only; it has never been compared
|
||||
//! ⚠️ against gait labs, clinical fall-risk instruments, or any reference
|
||||
//! ⚠️ standard, and its accuracy is unproven (see ADR-160 §A1). Gated behind
|
||||
//! ⚠️ the non-default `medical-experimental` cargo feature.
|
||||
//!
|
||||
//! Extracts candidate gait-parameter proxies from CSI phase-variance
|
||||
//! periodicity (experimental, NOT clinical measurements):
|
||||
//! - Step cadence (steps/min) from dominant phase variance frequency
|
||||
//! - Gait asymmetry from left/right step interval ratio
|
||||
//! - Stride variability (coefficient of variation)
|
||||
@@ -109,6 +117,9 @@ pub struct GaitAnalyzer {
|
||||
|
||||
/// Frame counter.
|
||||
frame_count: u32,
|
||||
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 5],
|
||||
}
|
||||
|
||||
impl GaitAnalyzer {
|
||||
@@ -132,6 +143,7 @@ impl GaitAnalyzer {
|
||||
last_asymmetry: 0.0,
|
||||
last_fall_risk: 0.0,
|
||||
frame_count: 0,
|
||||
events: [(0, 0.0); 5],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -162,7 +174,6 @@ impl GaitAnalyzer {
|
||||
self.var_idx = (self.var_idx + 1) % GAIT_WINDOW;
|
||||
if self.var_len < GAIT_WINDOW { self.var_len += 1; }
|
||||
|
||||
static mut EVENTS: [(i32, f32); 5] = [(0, 0.0); 5];
|
||||
let mut n = 0usize;
|
||||
|
||||
// ── Step detection (peak in variance) ───────────────────────────
|
||||
@@ -201,13 +212,13 @@ impl GaitAnalyzer {
|
||||
|
||||
// Emit cadence.
|
||||
if n < 5 {
|
||||
unsafe { EVENTS[n] = (EVENT_STEP_CADENCE, cadence); }
|
||||
self.events[n] = (EVENT_STEP_CADENCE, cadence);
|
||||
n += 1;
|
||||
}
|
||||
|
||||
// Emit asymmetry if above threshold.
|
||||
if fabsf(asymmetry - 1.0) > ASYMMETRY_THRESH && n < 5 {
|
||||
unsafe { EVENTS[n] = (EVENT_GAIT_ASYMMETRY, asymmetry); }
|
||||
self.events[n] = (EVENT_GAIT_ASYMMETRY, asymmetry);
|
||||
n += 1;
|
||||
}
|
||||
|
||||
@@ -215,7 +226,7 @@ impl GaitAnalyzer {
|
||||
if cadence > SHUFFLE_CADENCE_HIGH && avg_energy < SHUFFLE_ENERGY_LOW
|
||||
&& self.cd_shuffle == 0 && n < 5
|
||||
{
|
||||
unsafe { EVENTS[n] = (EVENT_SHUFFLING_DETECTED, cadence); }
|
||||
self.events[n] = (EVENT_SHUFFLING_DETECTED, cadence);
|
||||
n += 1;
|
||||
self.cd_shuffle = COOLDOWN_SECS;
|
||||
}
|
||||
@@ -223,7 +234,7 @@ impl GaitAnalyzer {
|
||||
// Festination: accelerating cadence.
|
||||
if self.cadence_len >= 3 && self.cd_festination == 0 && n < 5 {
|
||||
if self.detect_festination() {
|
||||
unsafe { EVENTS[n] = (EVENT_FESTINATION, cadence); }
|
||||
self.events[n] = (EVENT_FESTINATION, cadence);
|
||||
n += 1;
|
||||
self.cd_festination = COOLDOWN_SECS;
|
||||
}
|
||||
@@ -233,7 +244,7 @@ impl GaitAnalyzer {
|
||||
let risk = self.compute_fall_risk(cadence, asymmetry, variability, avg_energy);
|
||||
self.last_fall_risk = risk;
|
||||
if n < 5 {
|
||||
unsafe { EVENTS[n] = (EVENT_FALL_RISK_SCORE, risk); }
|
||||
self.events[n] = (EVENT_FALL_RISK_SCORE, risk);
|
||||
n += 1;
|
||||
}
|
||||
|
||||
@@ -241,7 +252,7 @@ impl GaitAnalyzer {
|
||||
self.step_count = 0;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
/// Compute cadence in steps/min from step intervals.
|
||||
|
||||
@@ -1,11 +1,20 @@
|
||||
//! Respiratory distress detection — ADR-041 Category 1 Medical module.
|
||||
//! Respiratory-distress-like pattern flagging — ADR-041 Category 1 Medical module.
|
||||
//!
|
||||
//! Detects pathological breathing patterns from host CSI pipeline:
|
||||
//! - Tachypnea: sustained breathing rate > 25 BPM
|
||||
//! - Labored breathing: high amplitude variance relative to baseline
|
||||
//! - Cheyne-Stokes respiration: crescendo-decrescendo periodicity (30-90 s)
|
||||
//! detected via autocorrelation of the breathing amplitude envelope
|
||||
//! - Overall respiratory distress level: composite severity score 0-100
|
||||
//! ⚠️ EXPERIMENTAL RESEARCH MODULE — NOT VALIDATED AGAINST CLINICAL DATA.
|
||||
//! ⚠️ NOT A MEDICAL DEVICE. Do NOT use for diagnosis or patient monitoring.
|
||||
//! ⚠️ This module flags *candidate* respiratory-distress-like breathing
|
||||
//! ⚠️ signatures only; it has never been compared against capnography,
|
||||
//! ⚠️ spirometry, or any reference standard, and its accuracy is unproven
|
||||
//! ⚠️ (see ADR-160 §A1). Gated behind the non-default `medical-experimental`
|
||||
//! ⚠️ cargo feature.
|
||||
//!
|
||||
//! Flags candidate pathological-breathing-like patterns from the host CSI
|
||||
//! pipeline (experimental proxies, NOT clinical measurements):
|
||||
//! - Tachypnea-like: sustained breathing-rate estimate > 25 BPM
|
||||
//! - Labored-breathing-like: high amplitude variance relative to baseline
|
||||
//! - Cheyne-Stokes-like: crescendo-decrescendo periodicity (30-90 s)
|
||||
//! flagged via autocorrelation of the breathing-rate envelope
|
||||
//! - Composite distress-level proxy: severity score 0-100
|
||||
//!
|
||||
//! Events:
|
||||
//! TACHYPNEA (120) — sustained high respiratory rate
|
||||
@@ -97,6 +106,9 @@ pub struct RespiratoryDistressDetector {
|
||||
|
||||
/// Frame counter.
|
||||
frame_count: u32,
|
||||
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
}
|
||||
|
||||
impl RespiratoryDistressDetector {
|
||||
@@ -116,6 +128,7 @@ impl RespiratoryDistressDetector {
|
||||
cd_cs: 0,
|
||||
last_distress: 0.0,
|
||||
frame_count: 0,
|
||||
events: [(0, 0.0); 4],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -163,14 +176,13 @@ impl RespiratoryDistressDetector {
|
||||
self.var_mean += d / self.var_count as f32;
|
||||
}
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n = 0usize;
|
||||
|
||||
// ── Tachypnea ───────────────────────────────────────────────────
|
||||
if breathing_bpm > TACHYPNEA_THRESH {
|
||||
self.tachy_count = self.tachy_count.saturating_add(1);
|
||||
if self.tachy_count >= SUSTAINED_SECS && self.cd_tachy == 0 && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_TACHYPNEA, breathing_bpm); }
|
||||
self.events[n] = (EVENT_TACHYPNEA, breathing_bpm);
|
||||
n += 1;
|
||||
self.cd_tachy = COOLDOWN_SECS;
|
||||
}
|
||||
@@ -183,7 +195,7 @@ impl RespiratoryDistressDetector {
|
||||
let current_var = self.recent_var_mean();
|
||||
let ratio = current_var / self.var_mean;
|
||||
if ratio > LABORED_VAR_RATIO && self.cd_labored == 0 && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_LABORED_BREATHING, ratio); }
|
||||
self.events[n] = (EVENT_LABORED_BREATHING, ratio);
|
||||
n += 1;
|
||||
self.cd_labored = COOLDOWN_SECS;
|
||||
}
|
||||
@@ -192,7 +204,7 @@ impl RespiratoryDistressDetector {
|
||||
// ── Cheyne-Stokes (autocorrelation) ─────────────────────────────
|
||||
if self.bpm_len >= AC_WINDOW && self.cd_cs == 0 && n < 4 {
|
||||
if let Some(period) = self.detect_cheyne_stokes() {
|
||||
unsafe { EVENTS[n] = (EVENT_CHEYNE_STOKES, period as f32); }
|
||||
self.events[n] = (EVENT_CHEYNE_STOKES, period as f32);
|
||||
n += 1;
|
||||
self.cd_cs = COOLDOWN_SECS;
|
||||
}
|
||||
@@ -202,11 +214,11 @@ impl RespiratoryDistressDetector {
|
||||
if self.frame_count % DISTRESS_REPORT_INTERVAL == 0 && n < 4 {
|
||||
let score = self.compute_distress_score(breathing_bpm, variance);
|
||||
self.last_distress = score;
|
||||
unsafe { EVENTS[n] = (EVENT_RESP_DISTRESS_LEVEL, score); }
|
||||
self.events[n] = (EVENT_RESP_DISTRESS_LEVEL, score);
|
||||
n += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
/// Mean of recent variance samples.
|
||||
|
||||
@@ -1,7 +1,17 @@
|
||||
//! Seizure detection — ADR-041 Category 1 Medical module.
|
||||
//! Seizure-like motion-signature flagging — ADR-041 Category 1 Medical module.
|
||||
//!
|
||||
//! Detects tonic-clonic seizures via high-energy rhythmic motion in the
|
||||
//! 3-8 Hz band, discriminating from:
|
||||
//! ⚠️ EXPERIMENTAL RESEARCH MODULE — NOT VALIDATED AGAINST CLINICAL DATA.
|
||||
//! ⚠️ NOT A MEDICAL DEVICE. Do NOT use for diagnosis, seizure monitoring, or any
|
||||
//! ⚠️ clinical decision. This module flags *candidate* seizure-like motion
|
||||
//! ⚠️ signatures (high-energy rhythmic 3-8 Hz motion) only; it has never been
|
||||
//! ⚠️ validated against EEG/video-EEG or any reference standard, and its
|
||||
//! ⚠️ accuracy is unproven (see ADR-160 §A1). Seizure detection cannot be
|
||||
//! ⚠️ validated without clinical data — this module does not claim to do so.
|
||||
//! ⚠️ Gated behind the non-default `medical-experimental` cargo feature.
|
||||
//!
|
||||
//! Flags candidate tonic-clonic-seizure-like motion signatures (experimental)
|
||||
//! via high-energy rhythmic motion in the 3-8 Hz band, attempting to
|
||||
//! discriminate from:
|
||||
//! - Falls: single impulse followed by stillness
|
||||
//! - Tremor: lower amplitude, higher regularity
|
||||
//!
|
||||
@@ -125,6 +135,9 @@ pub struct SeizureDetector {
|
||||
|
||||
/// Frame counter.
|
||||
frame_count: u32,
|
||||
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
}
|
||||
|
||||
impl SeizureDetector {
|
||||
@@ -143,6 +156,7 @@ impl SeizureDetector {
|
||||
cooldown: 0,
|
||||
seizure_count: 0,
|
||||
frame_count: 0,
|
||||
events: [(0, 0.0); 4],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -172,7 +186,6 @@ impl SeizureDetector {
|
||||
self.amp_idx = (self.amp_idx + 1) % PHASE_WINDOW;
|
||||
if self.amp_len < PHASE_WINDOW { self.amp_len += 1; }
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n = 0usize;
|
||||
|
||||
// No detection without presence.
|
||||
@@ -182,7 +195,7 @@ impl SeizureDetector {
|
||||
self.state_frames = 0;
|
||||
self.high_energy_frames = 0;
|
||||
}
|
||||
return unsafe { &EVENTS[..n] };
|
||||
return &self.events[..n];
|
||||
}
|
||||
|
||||
// Tick cooldown.
|
||||
@@ -192,7 +205,7 @@ impl SeizureDetector {
|
||||
self.phase = SeizurePhase::Monitoring;
|
||||
self.state_frames = 0;
|
||||
}
|
||||
return unsafe { &EVENTS[..n] };
|
||||
return &self.events[..n];
|
||||
}
|
||||
|
||||
// ── State machine ───────────────────────────────────────────────
|
||||
@@ -222,7 +235,7 @@ impl SeizureDetector {
|
||||
self.phase = SeizurePhase::Monitoring;
|
||||
self.state_frames = 0;
|
||||
self.high_energy_frames = 0;
|
||||
return unsafe { &EVENTS[..n] };
|
||||
return &self.events[..n];
|
||||
}
|
||||
}
|
||||
|
||||
@@ -232,7 +245,7 @@ impl SeizureDetector {
|
||||
self.phase = SeizurePhase::Tonic;
|
||||
self.state_frames = 0;
|
||||
self.seizure_count += 1;
|
||||
unsafe { EVENTS[n] = (EVENT_SEIZURE_ONSET, motion_energy); }
|
||||
self.events[n] = (EVENT_SEIZURE_ONSET, motion_energy);
|
||||
n += 1;
|
||||
}
|
||||
|
||||
@@ -244,10 +257,10 @@ impl SeizureDetector {
|
||||
self.phase = SeizurePhase::Clonic;
|
||||
self.state_frames = 0;
|
||||
self.seizure_count += 1;
|
||||
unsafe { EVENTS[n] = (EVENT_SEIZURE_ONSET, motion_energy); }
|
||||
self.events[n] = (EVENT_SEIZURE_ONSET, motion_energy);
|
||||
n += 1;
|
||||
if n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_SEIZURE_CLONIC, period as f32); }
|
||||
self.events[n] = (EVENT_SEIZURE_CLONIC, period as f32);
|
||||
n += 1;
|
||||
}
|
||||
}
|
||||
@@ -271,13 +284,13 @@ impl SeizureDetector {
|
||||
if energy_var > TONIC_VAR_CEIL {
|
||||
if let Some(period) = self.detect_rhythm() {
|
||||
if self.state_frames >= TONIC_MIN_FRAMES && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_SEIZURE_TONIC, self.state_frames as f32); }
|
||||
self.events[n] = (EVENT_SEIZURE_TONIC, self.state_frames as f32);
|
||||
n += 1;
|
||||
}
|
||||
self.phase = SeizurePhase::Clonic;
|
||||
self.state_frames = 0;
|
||||
if n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_SEIZURE_CLONIC, period as f32); }
|
||||
self.events[n] = (EVENT_SEIZURE_CLONIC, period as f32);
|
||||
n += 1;
|
||||
}
|
||||
}
|
||||
@@ -289,7 +302,7 @@ impl SeizureDetector {
|
||||
self.low_energy_frames += 1;
|
||||
if self.low_energy_frames >= POST_ICTAL_MIN_FRAMES {
|
||||
if self.state_frames >= TONIC_MIN_FRAMES && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_SEIZURE_TONIC, self.state_frames as f32); }
|
||||
self.events[n] = (EVENT_SEIZURE_TONIC, self.state_frames as f32);
|
||||
n += 1;
|
||||
}
|
||||
self.phase = SeizurePhase::PostIctal;
|
||||
@@ -318,7 +331,7 @@ impl SeizureDetector {
|
||||
SeizurePhase::PostIctal => {
|
||||
self.state_frames += 1;
|
||||
if self.state_frames == 1 && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_POST_ICTAL, 1.0); }
|
||||
self.events[n] = (EVENT_POST_ICTAL, 1.0);
|
||||
n += 1;
|
||||
}
|
||||
|
||||
@@ -337,7 +350,7 @@ impl SeizureDetector {
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
/// Compute variance of recent motion energy.
|
||||
|
||||
@@ -1,10 +1,19 @@
|
||||
//! Sleep apnea detection — ADR-041 Category 1 Medical module.
|
||||
//! Apnea-like breathing-pause flagging — ADR-041 Category 1 Medical module.
|
||||
//!
|
||||
//! Detects obstructive and central sleep apnea by monitoring breathing BPM
|
||||
//! from the host CSI pipeline. When breathing drops below 4 BPM for more
|
||||
//! than 10 seconds the detector flags an apnea event. It also tracks the
|
||||
//! Apnea-Hypopnea Index (AHI) — the number of apnea events per hour of
|
||||
//! monitored sleep time.
|
||||
//! ⚠️ EXPERIMENTAL RESEARCH MODULE — NOT VALIDATED AGAINST CLINICAL DATA.
|
||||
//! ⚠️ NOT A MEDICAL DEVICE. Do NOT use for diagnosis, monitoring of patients,
|
||||
//! ⚠️ or any clinical decision. This module flags *candidate* apnea-like
|
||||
//! ⚠️ breathing-pause signatures (sustained low breathing-rate estimates)
|
||||
//! ⚠️ only; it has never been compared against polysomnography or any
|
||||
//! ⚠️ reference standard, and its accuracy is unproven (see ADR-160 §A1).
|
||||
//! ⚠️ Gated behind the non-default `medical-experimental` cargo feature so it
|
||||
//! ⚠️ cannot be silently built into a shipping artifact.
|
||||
//!
|
||||
//! Monitors breathing-rate estimates from the host CSI pipeline. When the
|
||||
//! estimate drops below 4 BPM for more than 10 seconds the detector flags a
|
||||
//! candidate apnea-like event. It also tracks a candidate Apnea-Hypopnea
|
||||
//! Index (AHI) proxy — the number of flagged events per hour of monitored
|
||||
//! time. These are experimental proxies, NOT clinical measurements.
|
||||
//!
|
||||
//! Events:
|
||||
//! APNEA_START (100) — breathing ceased or fell below threshold
|
||||
@@ -77,6 +86,8 @@ pub struct SleepApneaDetector {
|
||||
timer_count: u32,
|
||||
/// Most recently computed AHI.
|
||||
last_ahi: f32,
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
}
|
||||
|
||||
impl SleepApneaDetector {
|
||||
@@ -90,6 +101,7 @@ impl SleepApneaDetector {
|
||||
monitoring_secs: 0,
|
||||
timer_count: 0,
|
||||
last_ahi: 0.0,
|
||||
events: [(0, 0.0); 4],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -104,7 +116,6 @@ impl SleepApneaDetector {
|
||||
) -> &[(i32, f32)] {
|
||||
self.timer_count += 1;
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n = 0usize;
|
||||
|
||||
// Only monitor when subject is present.
|
||||
@@ -115,11 +126,11 @@ impl SleepApneaDetector {
|
||||
self.record_episode(self.current_start, dur);
|
||||
self.in_apnea = false;
|
||||
self.low_breath_secs = 0;
|
||||
unsafe { EVENTS[n] = (EVENT_APNEA_END, dur as f32); }
|
||||
self.events[n] = (EVENT_APNEA_END, dur as f32);
|
||||
n += 1;
|
||||
}
|
||||
self.low_breath_secs = 0;
|
||||
return unsafe { &EVENTS[..n] };
|
||||
return &self.events[..n];
|
||||
}
|
||||
|
||||
self.monitoring_secs += 1;
|
||||
@@ -129,7 +140,7 @@ impl SleepApneaDetector {
|
||||
// Treat NaN as invalid — skip detection for this frame.
|
||||
if breathing_bpm != breathing_bpm {
|
||||
// NaN: f32::NAN != f32::NAN is true.
|
||||
return unsafe { &EVENTS[..n] };
|
||||
return &self.events[..n];
|
||||
}
|
||||
|
||||
// ── Apnea detection ─────────────────────────────────────────────
|
||||
@@ -140,7 +151,7 @@ impl SleepApneaDetector {
|
||||
// Apnea onset — backdate start to when breathing first dropped.
|
||||
self.in_apnea = true;
|
||||
self.current_start = self.timer_count.saturating_sub(self.low_breath_secs);
|
||||
unsafe { EVENTS[n] = (EVENT_APNEA_START, breathing_bpm); }
|
||||
self.events[n] = (EVENT_APNEA_START, breathing_bpm);
|
||||
n += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -149,7 +160,7 @@ impl SleepApneaDetector {
|
||||
let dur = self.timer_count.saturating_sub(self.current_start);
|
||||
self.record_episode(self.current_start, dur);
|
||||
self.in_apnea = false;
|
||||
unsafe { EVENTS[n] = (EVENT_APNEA_END, dur as f32); }
|
||||
self.events[n] = (EVENT_APNEA_END, dur as f32);
|
||||
n += 1;
|
||||
}
|
||||
self.low_breath_secs = 0;
|
||||
@@ -163,11 +174,11 @@ impl SleepApneaDetector {
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
unsafe { EVENTS[n] = (EVENT_AHI_UPDATE, self.last_ahi); }
|
||||
self.events[n] = (EVENT_AHI_UPDATE, self.last_ahi);
|
||||
n += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
fn record_episode(&mut self, start: u32, duration: u32) {
|
||||
|
||||
@@ -42,6 +42,8 @@ struct ZoneState {
|
||||
|
||||
/// Occupancy zone detector.
|
||||
pub struct OccupancyDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 12],
|
||||
zones: [ZoneState; MAX_ZONES],
|
||||
n_zones: usize,
|
||||
/// Calibration accumulators.
|
||||
@@ -61,6 +63,7 @@ impl OccupancyDetector {
|
||||
prev_occupied: false,
|
||||
};
|
||||
Self {
|
||||
events: [(0, 0.0); 12],
|
||||
zones: [ZONE_INIT; MAX_ZONES],
|
||||
n_zones: 0,
|
||||
calib_sum: [0.0; MAX_ZONES],
|
||||
@@ -163,7 +166,6 @@ impl OccupancyDetector {
|
||||
|
||||
// Build output events in a static buffer.
|
||||
// We re-use a static to avoid allocation in no_std.
|
||||
static mut EVENTS: [(i32, f32); 12] = [(0, 0.0); 12];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// Emit per-zone occupancy (every 10 frames to limit bandwidth).
|
||||
@@ -172,18 +174,14 @@ impl OccupancyDetector {
|
||||
if self.zones[z].occupied && n_events < 10 {
|
||||
// Encode zone_id in integer part, confidence in fractional.
|
||||
let val = z as f32 + self.zones[z].score.min(0.99);
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_ZONE_OCCUPIED, val);
|
||||
}
|
||||
self.events[n_events] = (EVENT_ZONE_OCCUPIED, val);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
|
||||
// Emit total occupied zone count.
|
||||
if n_events < 11 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_ZONE_COUNT, total_occupied as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_ZONE_COUNT, total_occupied as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -192,14 +190,12 @@ impl OccupancyDetector {
|
||||
for z in 0..zone_count {
|
||||
if self.zones[z].occupied != self.zones[z].prev_occupied && n_events < 12 {
|
||||
let val = z as f32 + if self.zones[z].occupied { 0.5 } else { 0.0 };
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_ZONE_TRANSITION, val);
|
||||
}
|
||||
self.events[n_events] = (EVENT_ZONE_TRANSITION, val);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Get the number of currently occupied zones.
|
||||
|
||||
@@ -112,6 +112,8 @@ impl Hypothesis {
|
||||
|
||||
/// Grover-inspired room state search engine.
|
||||
pub struct InterferenceSearch {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
/// Amplitude for each of the 16 hypotheses.
|
||||
amplitudes: [f32; N_HYPO],
|
||||
/// Total Grover iterations applied.
|
||||
@@ -130,6 +132,7 @@ impl InterferenceSearch {
|
||||
pub const fn new() -> Self {
|
||||
// 1/sqrt(16) = 0.25
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
amplitudes: [0.25; N_HYPO],
|
||||
iteration_count: 0,
|
||||
converged: false,
|
||||
@@ -178,37 +181,30 @@ impl InterferenceSearch {
|
||||
self.converged = winner_prob > CONVERGENCE_PROB;
|
||||
|
||||
// ── Build output events ──
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// Emit winner periodically or on change.
|
||||
let winner_changed = winner_idx as u8 != self.prev_winner;
|
||||
if winner_changed || self.frame_count % WINNER_EMIT_INTERVAL == 0 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_HYPOTHESIS_WINNER, winner_idx as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_HYPOTHESIS_WINNER, winner_idx as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
// Emit amplitude periodically.
|
||||
if self.frame_count % AMPLITUDE_EMIT_INTERVAL == 0 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_HYPOTHESIS_AMPLITUDE, winner_prob);
|
||||
}
|
||||
self.events[n_events] = (EVENT_HYPOTHESIS_AMPLITUDE, winner_prob);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
// Emit iteration count periodically.
|
||||
if self.frame_count % ITERATION_EMIT_INTERVAL == 0 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_SEARCH_ITERATIONS, self.iteration_count as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_SEARCH_ITERATIONS, self.iteration_count as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
self.prev_winner = winner_idx as u8;
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Apply the oracle: set boost/dampen factors based on CSI evidence.
|
||||
|
||||
@@ -58,6 +58,8 @@ pub const EVENT_BLOCH_DRIFT: i32 = 852;
|
||||
|
||||
/// Quantum-inspired coherence monitor using Bloch sphere representation.
|
||||
pub struct QuantumCoherenceMonitor {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
/// Previous aggregate Bloch vector [x, y, z].
|
||||
prev_bloch: [f32; 3],
|
||||
/// EMA-smoothed Von Neumann entropy.
|
||||
@@ -74,6 +76,7 @@ impl QuantumCoherenceMonitor {
|
||||
/// Create a new monitor. Const-evaluable for static initialization.
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
prev_bloch: [0.0, 0.0, 1.0],
|
||||
smoothed_entropy: 0.0,
|
||||
prev_entropy: 0.0,
|
||||
@@ -129,34 +132,27 @@ impl QuantumCoherenceMonitor {
|
||||
self.prev_bloch = bloch;
|
||||
|
||||
// ── Build output events ──
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// Entropy (periodic).
|
||||
if self.frame_count % ENTROPY_EMIT_INTERVAL == 0 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_ENTANGLEMENT_ENTROPY, self.smoothed_entropy);
|
||||
}
|
||||
self.events[n_events] = (EVENT_ENTANGLEMENT_ENTROPY, self.smoothed_entropy);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
// Decoherence event (immediate).
|
||||
if entropy_jump > DECOHERENCE_THRESHOLD {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_DECOHERENCE_EVENT, entropy_jump);
|
||||
}
|
||||
self.events[n_events] = (EVENT_DECOHERENCE_EVENT, entropy_jump);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
// Bloch drift (periodic).
|
||||
if self.frame_count % DRIFT_EMIT_INTERVAL == 0 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_BLOCH_DRIFT, drift);
|
||||
}
|
||||
self.events[n_events] = (EVENT_BLOCH_DRIFT, drift);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Compute the mean Bloch vector from subcarrier phases.
|
||||
|
||||
@@ -72,6 +72,8 @@ const MAX_EVENTS: usize = 4;
|
||||
|
||||
/// Tracks directional foot traffic using phase gradient analysis.
|
||||
pub struct CustomerFlowTracker {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); MAX_EVENTS],
|
||||
/// Previous phase values per subcarrier.
|
||||
prev_phases: [f32; MAX_SC],
|
||||
/// Previous amplitude values per subcarrier.
|
||||
@@ -101,6 +103,7 @@ pub struct CustomerFlowTracker {
|
||||
impl CustomerFlowTracker {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); MAX_EVENTS],
|
||||
prev_phases: [0.0; MAX_SC],
|
||||
prev_amplitudes: [0.0; MAX_SC],
|
||||
gradient_ema: Ema::new(GRADIENT_EMA_ALPHA),
|
||||
@@ -200,7 +203,6 @@ impl CustomerFlowTracker {
|
||||
}
|
||||
|
||||
// Build events.
|
||||
static mut EVENTS: [(i32, f32); MAX_EVENTS] = [(0, 0.0); MAX_EVENTS];
|
||||
let mut ne = 0usize;
|
||||
|
||||
// Crossing detection: look for gradient peak + motion + amplitude spike.
|
||||
@@ -218,9 +220,7 @@ impl CustomerFlowTracker {
|
||||
self.ingress_count += 1;
|
||||
self.hourly_ingress += 1;
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_INGRESS, self.ingress_count as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_INGRESS, self.ingress_count as f32);
|
||||
ne += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -228,9 +228,7 @@ impl CustomerFlowTracker {
|
||||
self.egress_count += 1;
|
||||
self.hourly_egress += 1;
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_EGRESS, self.egress_count as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_EGRESS, self.egress_count as f32);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
@@ -238,9 +236,7 @@ impl CustomerFlowTracker {
|
||||
// Emit net occupancy on each crossing.
|
||||
let net = self.net_occupancy();
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_NET_OCCUPANCY, net as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_NET_OCCUPANCY, net as f32);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
@@ -248,9 +244,7 @@ impl CustomerFlowTracker {
|
||||
// Periodic net occupancy report.
|
||||
if self.frame_count % OCCUPANCY_REPORT_INTERVAL == 0 && ne < MAX_EVENTS {
|
||||
let net = self.net_occupancy();
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_NET_OCCUPANCY, net as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_NET_OCCUPANCY, net as f32);
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
@@ -259,16 +253,14 @@ impl CustomerFlowTracker {
|
||||
// Encode: ingress * 1000 + egress.
|
||||
let summary = self.hourly_ingress as f32 * 1000.0 + self.hourly_egress as f32;
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_HOURLY_TRAFFIC, summary);
|
||||
}
|
||||
self.events[ne] = (EVENT_HOURLY_TRAFFIC, summary);
|
||||
ne += 1;
|
||||
}
|
||||
self.hourly_ingress = 0;
|
||||
self.hourly_egress = 0;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
/// Get net occupancy (ingress - egress), clamped to 0.
|
||||
|
||||
@@ -80,6 +80,8 @@ const ZONE_INIT: ZoneState = ZoneState {
|
||||
|
||||
/// Tracks dwell time across a 3x3 spatial zone grid.
|
||||
pub struct DwellHeatmapTracker {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); MAX_EVENTS],
|
||||
zones: [ZoneState; NUM_ZONES],
|
||||
/// Frame counter.
|
||||
frame_count: u32,
|
||||
@@ -96,6 +98,7 @@ pub struct DwellHeatmapTracker {
|
||||
impl DwellHeatmapTracker {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); MAX_EVENTS],
|
||||
zones: [ZONE_INIT; NUM_ZONES],
|
||||
frame_count: 0,
|
||||
any_present: false,
|
||||
@@ -176,7 +179,6 @@ impl DwellHeatmapTracker {
|
||||
self.any_present = is_present || any_zone_occupied;
|
||||
|
||||
// Build events.
|
||||
static mut EVENTS: [(i32, f32); MAX_EVENTS] = [(0, 0.0); MAX_EVENTS];
|
||||
let mut ne = 0usize;
|
||||
|
||||
// Periodic zone updates.
|
||||
@@ -186,9 +188,7 @@ impl DwellHeatmapTracker {
|
||||
if self.zones[z].dwell_seconds > 0.0 && ne < MAX_EVENTS - 3 {
|
||||
// Encode zone_id in integer part, dwell seconds in value.
|
||||
let val = z as f32 * 1000.0 + self.zones[z].dwell_seconds;
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_DWELL_ZONE_UPDATE, val);
|
||||
}
|
||||
self.events[ne] = (EVENT_DWELL_ZONE_UPDATE, val);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
@@ -211,16 +211,12 @@ impl DwellHeatmapTracker {
|
||||
}
|
||||
|
||||
if hot_dwell > 0.0 && ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_HOT_ZONE, hot_zone as f32 + hot_dwell / 1000.0);
|
||||
}
|
||||
self.events[ne] = (EVENT_HOT_ZONE, hot_zone as f32 + hot_dwell / 1000.0);
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
if cold_dwell < f32::MAX && ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_COLD_ZONE, cold_zone as f32 + cold_dwell / 1000.0);
|
||||
}
|
||||
self.events[ne] = (EVENT_COLD_ZONE, cold_zone as f32 + cold_dwell / 1000.0);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
@@ -230,14 +226,12 @@ impl DwellHeatmapTracker {
|
||||
self.session_active = false;
|
||||
let session_duration = (self.frame_count - self.session_start_frame) as f32 / FRAME_RATE;
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_SESSION_SUMMARY, session_duration);
|
||||
}
|
||||
self.events[ne] = (EVENT_SESSION_SUMMARY, session_duration);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
/// Get dwell time (seconds) for a specific zone in the current session.
|
||||
|
||||
@@ -62,6 +62,8 @@ const RATE_HISTORY: usize = 1200;
|
||||
|
||||
/// Estimates queue length from CSI presence and person-count data.
|
||||
pub struct QueueLengthEstimator {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Smoothed queue length estimate.
|
||||
queue_ema: Ema,
|
||||
/// Smoothed arrival rate (persons/minute).
|
||||
@@ -91,6 +93,7 @@ pub struct QueueLengthEstimator {
|
||||
impl QueueLengthEstimator {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
queue_ema: Ema::new(QUEUE_EMA_ALPHA),
|
||||
arrival_rate_ema: Ema::new(RATE_EMA_ALPHA),
|
||||
service_rate_ema: Ema::new(RATE_EMA_ALPHA),
|
||||
@@ -161,14 +164,11 @@ impl QueueLengthEstimator {
|
||||
}
|
||||
|
||||
// Build events.
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut ne = 0usize;
|
||||
|
||||
// Periodic queue length report.
|
||||
if self.frame_count % REPORT_INTERVAL == 0 {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_QUEUE_LENGTH, self.current_queue as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_QUEUE_LENGTH, self.current_queue as f32);
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
@@ -184,9 +184,7 @@ impl QueueLengthEstimator {
|
||||
|
||||
// Service rate event.
|
||||
if ne < 4 {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_SERVICE_RATE, self.service_rate_ema.value);
|
||||
}
|
||||
self.events[ne] = (EVENT_SERVICE_RATE, self.service_rate_ema.value);
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
@@ -199,9 +197,7 @@ impl QueueLengthEstimator {
|
||||
};
|
||||
|
||||
if ne < 4 {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_WAIT_TIME_ESTIMATE, wait_time);
|
||||
}
|
||||
self.events[ne] = (EVENT_WAIT_TIME_ESTIMATE, wait_time);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
@@ -216,16 +212,14 @@ impl QueueLengthEstimator {
|
||||
if self.current_queue as f32 >= QUEUE_ALERT_THRESH && !self.alert_active {
|
||||
self.alert_active = true;
|
||||
if ne < 4 {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_QUEUE_ALERT, self.current_queue as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_QUEUE_ALERT, self.current_queue as f32);
|
||||
ne += 1;
|
||||
}
|
||||
} else if (self.current_queue as f32) < QUEUE_ALERT_THRESH - 1.0 {
|
||||
self.alert_active = false;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
/// Get the current smoothed queue length.
|
||||
|
||||
@@ -96,6 +96,8 @@ pub enum EngagementLevel {
|
||||
|
||||
/// Detects and classifies customer shelf engagement from CSI data.
|
||||
pub struct ShelfEngagementDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); MAX_EVENTS],
|
||||
/// Previous phase values for perturbation calculation.
|
||||
prev_phases: [f32; MAX_SC],
|
||||
/// Phase perturbation EMA (high-frequency component).
|
||||
@@ -133,6 +135,7 @@ pub struct ShelfEngagementDetector {
|
||||
impl ShelfEngagementDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); MAX_EVENTS],
|
||||
prev_phases: [0.0; MAX_SC],
|
||||
perturbation_ema: Ema::new(PERTURBATION_EMA_ALPHA),
|
||||
motion_ema: Ema::new(MOTION_EMA_ALPHA),
|
||||
@@ -221,7 +224,6 @@ impl ShelfEngagementDetector {
|
||||
self.phase_diff_history.push(perturbation);
|
||||
|
||||
// Build events.
|
||||
static mut EVENTS: [(i32, f32); MAX_EVENTS] = [(0, 0.0); MAX_EVENTS];
|
||||
let mut ne = 0usize;
|
||||
|
||||
if !is_present {
|
||||
@@ -234,7 +236,7 @@ impl ShelfEngagementDetector {
|
||||
self.still_frames = 0;
|
||||
self.level = EngagementLevel::None;
|
||||
self.prev_emitted_level = EngagementLevel::None;
|
||||
unsafe { return &EVENTS[..ne]; }
|
||||
return &self.events[..ne];
|
||||
}
|
||||
|
||||
// Detect stillness (low translational motion).
|
||||
@@ -249,7 +251,7 @@ impl ShelfEngagementDetector {
|
||||
self.engagement_frames = 0;
|
||||
self.level = EngagementLevel::None;
|
||||
self.prev_emitted_level = EngagementLevel::None;
|
||||
unsafe { return &EVENTS[..ne]; }
|
||||
return &self.events[..ne];
|
||||
}
|
||||
|
||||
// Only start engagement counting after debounce.
|
||||
@@ -284,9 +286,7 @@ impl ShelfEngagementDetector {
|
||||
};
|
||||
|
||||
if event_id != 0 && ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (event_id, duration);
|
||||
}
|
||||
self.events[ne] = (event_id, duration);
|
||||
ne += 1;
|
||||
self.prev_emitted_level = self.level;
|
||||
self.cooldown = ENGAGEMENT_COOLDOWN;
|
||||
@@ -297,13 +297,11 @@ impl ShelfEngagementDetector {
|
||||
// Reach detection: sudden high-frequency phase burst while still.
|
||||
if self.still_frames > STILL_DEBOUNCE && perturbation > REACH_BURST_THRESH && ne < MAX_EVENTS {
|
||||
self.total_reaches += 1;
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_REACH_DETECTED, perturbation);
|
||||
}
|
||||
self.events[ne] = (EVENT_REACH_DETECTED, perturbation);
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
/// Emit engagement end event based on current level.
|
||||
|
||||
@@ -80,6 +80,8 @@ pub enum TableState {
|
||||
|
||||
/// Tracks table occupancy state transitions and turnover metrics.
|
||||
pub struct TableTurnoverTracker {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); MAX_EVENTS],
|
||||
/// Current table state.
|
||||
state: TableState,
|
||||
/// Smoothed motion energy.
|
||||
@@ -109,6 +111,7 @@ pub struct TableTurnoverTracker {
|
||||
impl TableTurnoverTracker {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); MAX_EVENTS],
|
||||
state: TableState::Empty,
|
||||
motion_ema: Ema::new(MOTION_EMA_ALPHA),
|
||||
presence_frames: 0,
|
||||
@@ -143,7 +146,6 @@ impl TableTurnoverTracker {
|
||||
let smoothed_motion = self.motion_ema.update(motion_energy);
|
||||
let n = if n_persons < 0 { 0 } else { n_persons };
|
||||
|
||||
static mut EVENTS: [(i32, f32); MAX_EVENTS] = [(0, 0.0); MAX_EVENTS];
|
||||
let mut ne = 0usize;
|
||||
|
||||
match self.state {
|
||||
@@ -158,9 +160,7 @@ impl TableTurnoverTracker {
|
||||
self.absence_frames = 0;
|
||||
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_TABLE_SEATED, n as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_TABLE_SEATED, n as f32);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
@@ -202,9 +202,7 @@ impl TableTurnoverTracker {
|
||||
let duration_s = self.session_frames as f32 / FRAME_RATE;
|
||||
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_TABLE_VACATED, duration_s);
|
||||
}
|
||||
self.events[ne] = (EVENT_TABLE_VACATED, duration_s);
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
@@ -241,9 +239,7 @@ impl TableTurnoverTracker {
|
||||
|
||||
let duration_s = self.session_frames as f32 / FRAME_RATE;
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_TABLE_VACATED, duration_s);
|
||||
}
|
||||
self.events[ne] = (EVENT_TABLE_VACATED, duration_s);
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
@@ -270,9 +266,7 @@ impl TableTurnoverTracker {
|
||||
self.peak_persons = 0;
|
||||
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_TABLE_AVAILABLE, 1.0);
|
||||
}
|
||||
self.events[ne] = (EVENT_TABLE_AVAILABLE, 1.0);
|
||||
ne += 1;
|
||||
}
|
||||
} else if is_present {
|
||||
@@ -285,9 +279,7 @@ impl TableTurnoverTracker {
|
||||
self.presence_frames = 0;
|
||||
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_TABLE_SEATED, n as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_TABLE_SEATED, n as f32);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
@@ -301,14 +293,12 @@ impl TableTurnoverTracker {
|
||||
if self.frame_count % TURNOVER_REPORT_INTERVAL == 0 && self.frame_count > 0 {
|
||||
let rate = self.turnover_rate();
|
||||
if ne < MAX_EVENTS {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_TURNOVER_RATE, rate);
|
||||
}
|
||||
self.events[ne] = (EVENT_TURNOVER_RATE, rate);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
/// Compute turnovers per hour (rolling window).
|
||||
|
||||
@@ -46,6 +46,8 @@ pub enum LoiterState {
|
||||
|
||||
/// Loitering detector.
|
||||
pub struct LoiteringDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 2],
|
||||
state: LoiterState,
|
||||
/// Consecutive frames with presence detected.
|
||||
presence_frames: u32,
|
||||
@@ -65,6 +67,7 @@ pub struct LoiteringDetector {
|
||||
impl LoiteringDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 2],
|
||||
state: LoiterState::Absent,
|
||||
presence_frames: 0,
|
||||
dwell_frames: 0,
|
||||
@@ -88,7 +91,6 @@ impl LoiteringDetector {
|
||||
self.frame_count += 1;
|
||||
self.post_end_cd = self.post_end_cd.saturating_sub(1);
|
||||
|
||||
static mut EVENTS: [(i32, f32); 2] = [(0, 0.0); 2];
|
||||
let mut ne = 0usize;
|
||||
|
||||
// Determine if someone is present and roughly stationary.
|
||||
@@ -133,9 +135,7 @@ impl LoiteringDetector {
|
||||
|
||||
if ne < 2 {
|
||||
let dwell_seconds = self.dwell_frames as f32 / 20.0;
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_LOITERING_START, dwell_seconds);
|
||||
}
|
||||
self.events[ne] = (EVENT_LOITERING_START, dwell_seconds);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
@@ -161,9 +161,7 @@ impl LoiteringDetector {
|
||||
self.ongoing_timer = 0;
|
||||
if ne < 2 {
|
||||
let total_seconds = self.dwell_frames as f32 / 20.0;
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_LOITERING_ONGOING, total_seconds);
|
||||
}
|
||||
self.events[ne] = (EVENT_LOITERING_ONGOING, total_seconds);
|
||||
ne += 1;
|
||||
}
|
||||
}
|
||||
@@ -177,9 +175,7 @@ impl LoiteringDetector {
|
||||
|
||||
if ne < 2 {
|
||||
let total_seconds = self.dwell_frames as f32 / 20.0;
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_LOITERING_END, total_seconds);
|
||||
}
|
||||
self.events[ne] = (EVENT_LOITERING_END, total_seconds);
|
||||
ne += 1;
|
||||
}
|
||||
|
||||
@@ -191,7 +187,7 @@ impl LoiteringDetector {
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
pub fn state(&self) -> LoiterState { self.state }
|
||||
|
||||
@@ -54,6 +54,8 @@ pub const EVENT_FLEEING_DETECTED: i32 = 252;
|
||||
|
||||
/// Panic/erratic motion detector.
|
||||
pub struct PanicMotionDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
/// Circular buffer of motion energy values.
|
||||
energy_buf: [f32; WINDOW],
|
||||
/// Circular buffer of phase variance values (for direction estimation).
|
||||
@@ -75,6 +77,7 @@ pub struct PanicMotionDetector {
|
||||
impl PanicMotionDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
energy_buf: [0.0; WINDOW],
|
||||
variance_buf: [0.0; WINDOW],
|
||||
buf_idx: 0,
|
||||
@@ -102,7 +105,6 @@ impl PanicMotionDetector {
|
||||
self.cd_struggle = self.cd_struggle.saturating_sub(1);
|
||||
self.cd_fleeing = self.cd_fleeing.saturating_sub(1);
|
||||
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut ne = 0usize;
|
||||
|
||||
// Store in circular buffer.
|
||||
@@ -117,13 +119,13 @@ impl PanicMotionDetector {
|
||||
if !self.buf_filled {
|
||||
self.prev_energy = motion_energy;
|
||||
self.prev_energy_init = true;
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// Require presence.
|
||||
if presence < MIN_PRESENCE {
|
||||
self.prev_energy = motion_energy;
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// Compute jerk (absolute rate of change of motion energy).
|
||||
@@ -142,7 +144,7 @@ impl PanicMotionDetector {
|
||||
|
||||
// Skip if not enough motion.
|
||||
if mean_energy < MIN_MOTION {
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// Panic detection: high jerk AND high entropy over threshold fraction of window.
|
||||
@@ -152,7 +154,7 @@ impl PanicMotionDetector {
|
||||
|
||||
if is_panic && self.cd_panic == 0 && ne < 3 {
|
||||
let severity = (mean_jerk / JERK_THRESH) * (entropy / ENTROPY_THRESH);
|
||||
unsafe { EVENTS[ne] = (EVENT_PANIC_DETECTED, severity.min(10.0)); }
|
||||
self.events[ne] = (EVENT_PANIC_DETECTED, severity.min(10.0));
|
||||
ne += 1;
|
||||
self.cd_panic = COOLDOWN;
|
||||
self.panic_count += 1;
|
||||
@@ -167,7 +169,7 @@ impl PanicMotionDetector {
|
||||
&& entropy > ENTROPY_THRESH * 0.5;
|
||||
|
||||
if is_struggle && !is_panic && self.cd_struggle == 0 && ne < 3 {
|
||||
unsafe { EVENTS[ne] = (EVENT_STRUGGLE_PATTERN, mean_jerk); }
|
||||
self.events[ne] = (EVENT_STRUGGLE_PATTERN, mean_jerk);
|
||||
ne += 1;
|
||||
self.cd_struggle = COOLDOWN;
|
||||
}
|
||||
@@ -179,12 +181,12 @@ impl PanicMotionDetector {
|
||||
&& entropy < FLEE_MAX_ENTROPY;
|
||||
|
||||
if is_fleeing && !is_panic && self.cd_fleeing == 0 && ne < 3 {
|
||||
unsafe { EVENTS[ne] = (EVENT_FLEEING_DETECTED, mean_energy); }
|
||||
self.events[ne] = (EVENT_FLEEING_DETECTED, mean_energy);
|
||||
ne += 1;
|
||||
self.cd_fleeing = COOLDOWN;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
/// Compute window-level statistics.
|
||||
|
||||
@@ -92,6 +92,8 @@ impl ZoneState {
|
||||
|
||||
/// Multi-zone perimeter breach detector.
|
||||
pub struct PerimeterBreachDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
zones: [ZoneState; MAX_ZONES],
|
||||
/// Calibration accumulators per zone: sum of gradient magnitudes.
|
||||
cal_grad_sum: [f32; MAX_ZONES],
|
||||
@@ -118,6 +120,7 @@ pub struct PerimeterBreachDetector {
|
||||
impl PerimeterBreachDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
zones: [ZoneState::new(); MAX_ZONES],
|
||||
cal_grad_sum: [0.0; MAX_ZONES],
|
||||
cal_var_sum: [0.0; MAX_ZONES],
|
||||
@@ -155,7 +158,6 @@ impl PerimeterBreachDetector {
|
||||
self.cd_departure = self.cd_departure.saturating_sub(1);
|
||||
self.cd_transition = self.cd_transition.saturating_sub(1);
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut ne = 0usize;
|
||||
|
||||
let subs_per_zone = n_sc / MAX_ZONES;
|
||||
@@ -196,7 +198,7 @@ impl PerimeterBreachDetector {
|
||||
}
|
||||
if !self.phase_init {
|
||||
self.phase_init = true;
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// Calibration phase.
|
||||
@@ -214,7 +216,7 @@ impl PerimeterBreachDetector {
|
||||
}
|
||||
self.calibrated = true;
|
||||
}
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// Detect breaches and direction per zone.
|
||||
@@ -262,7 +264,7 @@ impl PerimeterBreachDetector {
|
||||
if self.approach_run[z] >= DIRECTION_DEBOUNCE && is_breach
|
||||
&& self.cd_approach == 0 && ne < 4
|
||||
{
|
||||
unsafe { EVENTS[ne] = (EVENT_APPROACH_DETECTED, z as f32); }
|
||||
self.events[ne] = (EVENT_APPROACH_DETECTED, z as f32);
|
||||
ne += 1;
|
||||
self.cd_approach = COOLDOWN;
|
||||
self.approach_run[z] = 0;
|
||||
@@ -272,7 +274,7 @@ impl PerimeterBreachDetector {
|
||||
if self.departure_run[z] >= DIRECTION_DEBOUNCE
|
||||
&& self.cd_departure == 0 && ne < 4
|
||||
{
|
||||
unsafe { EVENTS[ne] = (EVENT_DEPARTURE_DETECTED, z as f32); }
|
||||
self.events[ne] = (EVENT_DEPARTURE_DETECTED, z as f32);
|
||||
ne += 1;
|
||||
self.cd_departure = COOLDOWN;
|
||||
self.departure_run[z] = 0;
|
||||
@@ -281,7 +283,7 @@ impl PerimeterBreachDetector {
|
||||
|
||||
// Perimeter breach event.
|
||||
if most_disturbed_zone >= 0 && self.cd_breach == 0 && ne < 4 {
|
||||
unsafe { EVENTS[ne] = (EVENT_PERIMETER_BREACH, max_energy); }
|
||||
self.events[ne] = (EVENT_PERIMETER_BREACH, max_energy);
|
||||
ne += 1;
|
||||
self.cd_breach = COOLDOWN;
|
||||
}
|
||||
@@ -296,7 +298,7 @@ impl PerimeterBreachDetector {
|
||||
// Encode as from*10 + to.
|
||||
let transition_code = self.last_active_zone as f32 * 10.0
|
||||
+ most_disturbed_zone as f32;
|
||||
unsafe { EVENTS[ne] = (EVENT_ZONE_TRANSITION, transition_code); }
|
||||
self.events[ne] = (EVENT_ZONE_TRANSITION, transition_code);
|
||||
ne += 1;
|
||||
self.cd_transition = COOLDOWN;
|
||||
}
|
||||
@@ -305,7 +307,7 @@ impl PerimeterBreachDetector {
|
||||
self.last_active_zone = most_disturbed_zone;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
pub fn is_calibrated(&self) -> bool { self.calibrated }
|
||||
|
||||
@@ -50,6 +50,8 @@ enum PeakState {
|
||||
|
||||
/// Tailgating detector.
|
||||
pub struct TailgateDetector {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
state: PeakState,
|
||||
/// Current peak's maximum energy.
|
||||
peak_max: f32,
|
||||
@@ -80,6 +82,7 @@ pub struct TailgateDetector {
|
||||
impl TailgateDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
state: PeakState::Idle,
|
||||
peak_max: 0.0,
|
||||
peak_frames: 0,
|
||||
@@ -110,7 +113,6 @@ impl TailgateDetector {
|
||||
self.cd_tailgate = self.cd_tailgate.saturating_sub(1);
|
||||
self.cd_passage = self.cd_passage.saturating_sub(1);
|
||||
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut ne = 0usize;
|
||||
|
||||
// Update noise floor estimate (exponential moving average of variance).
|
||||
@@ -168,7 +170,7 @@ impl TailgateDetector {
|
||||
self.state = PeakState::InPeak;
|
||||
self.peak_max = motion_energy;
|
||||
self.peak_frames = 1;
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// Window expired — evaluate passage.
|
||||
@@ -176,9 +178,7 @@ impl TailgateDetector {
|
||||
if self.peaks_in_window >= 2 {
|
||||
// Multiple peaks detected = tailgating.
|
||||
if self.cd_tailgate == 0 && ne < 3 {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_TAILGATE_DETECTED, self.peaks_in_window as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_TAILGATE_DETECTED, self.peaks_in_window as f32);
|
||||
ne += 1;
|
||||
self.cd_tailgate = COOLDOWN;
|
||||
self.tailgate_count += 1;
|
||||
@@ -186,18 +186,14 @@ impl TailgateDetector {
|
||||
|
||||
// Also emit multi-passage.
|
||||
if self.cd_passage == 0 && ne < 3 {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_MULTI_PASSAGE, self.peaks_in_window as f32);
|
||||
}
|
||||
self.events[ne] = (EVENT_MULTI_PASSAGE, self.peaks_in_window as f32);
|
||||
ne += 1;
|
||||
self.cd_passage = COOLDOWN;
|
||||
}
|
||||
} else if self.peaks_in_window == 1 {
|
||||
// Single passage.
|
||||
if self.cd_passage == 0 && ne < 3 {
|
||||
unsafe {
|
||||
EVENTS[ne] = (EVENT_SINGLE_PASSAGE, self.peak_energies[0]);
|
||||
}
|
||||
self.events[ne] = (EVENT_SINGLE_PASSAGE, self.peak_energies[0]);
|
||||
ne += 1;
|
||||
self.cd_passage = COOLDOWN;
|
||||
self.single_passages += 1;
|
||||
@@ -212,7 +208,7 @@ impl TailgateDetector {
|
||||
}
|
||||
|
||||
self.prev_energy = motion_energy;
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
pub fn frame_count(&self) -> u32 { self.frame_count }
|
||||
|
||||
@@ -11,7 +11,14 @@
|
||||
//! variance ratio compared to a person without metal, because metal strongly
|
||||
//! reflects RF energy while producing less phase dispersion than diffuse tissue.
|
||||
//!
|
||||
//! Events: METAL_ANOMALY(220), WEAPON_ALERT(221), CALIBRATION_NEEDED(222).
|
||||
//! ⚠️ HONEST-NAMING NOTE (ADR-160 §A3): this module measures RF **reflectivity**
|
||||
//! ⚠️ (an amplitude-variance / phase-variance ratio), not weapons. A variance
|
||||
//! ⚠️ ratio cannot discriminate a weapon from any other highly-reflective metal
|
||||
//! ⚠️ object (keys, laptop, belt buckle). The high-ratio event is therefore named
|
||||
//! ⚠️ `HIGH_METAL_REFLECTIVITY`, NOT a weapon alert — the physical quantity the
|
||||
//! ⚠️ code can actually back.
|
||||
//!
|
||||
//! Events: METAL_ANOMALY(220), HIGH_METAL_REFLECTIVITY(221), CALIBRATION_NEEDED(222).
|
||||
//! Budget: S (<5 ms).
|
||||
|
||||
#[cfg(not(feature = "std"))]
|
||||
@@ -26,16 +33,17 @@ const MAX_SC: usize = 32;
|
||||
const BASELINE_FRAMES: u32 = 100;
|
||||
/// Amplitude variance / phase variance ratio threshold for metal detection.
|
||||
const METAL_RATIO_THRESH: f32 = 4.0;
|
||||
/// Elevated ratio for weapon-grade alert (very high reflectivity).
|
||||
const WEAPON_RATIO_THRESH: f32 = 8.0;
|
||||
/// Elevated reflectivity-ratio threshold (very high RF reflectivity).
|
||||
/// NOTE (ADR-160 §A3): a variance ratio measures reflectivity, not weapons.
|
||||
const HIGH_REFLECTIVITY_THRESH: f32 = 8.0;
|
||||
/// Minimum motion energy to consider detection valid (ignore static scenes).
|
||||
const MIN_MOTION_ENERGY: f32 = 0.5;
|
||||
/// Minimum presence required (person must be present).
|
||||
const MIN_PRESENCE: i32 = 1;
|
||||
/// Consecutive frames for metal anomaly debounce.
|
||||
const METAL_DEBOUNCE: u8 = 4;
|
||||
/// Consecutive frames for weapon alert debounce.
|
||||
const WEAPON_DEBOUNCE: u8 = 6;
|
||||
/// Consecutive frames for high-reflectivity debounce.
|
||||
const HIGH_REFLECTIVITY_DEBOUNCE: u8 = 6;
|
||||
/// Cooldown frames after event emission.
|
||||
const COOLDOWN: u16 = 60;
|
||||
/// Re-calibration trigger: if baseline drift exceeds this ratio.
|
||||
@@ -44,7 +52,9 @@ const RECALIB_DRIFT_THRESH: f32 = 3.0;
|
||||
const VAR_WINDOW: usize = 16;
|
||||
|
||||
pub const EVENT_METAL_ANOMALY: i32 = 220;
|
||||
pub const EVENT_WEAPON_ALERT: i32 = 221;
|
||||
/// High RF reflectivity (formerly mislabelled `EVENT_WEAPON_ALERT`, ADR-160 §A3).
|
||||
/// A variance ratio measures reflectivity, not weapon-grade discrimination.
|
||||
pub const EVENT_HIGH_METAL_REFLECTIVITY: i32 = 221;
|
||||
pub const EVENT_CALIBRATION_NEEDED: i32 = 222;
|
||||
|
||||
/// Concealed metallic object detector.
|
||||
@@ -74,12 +84,14 @@ pub struct WeaponDetector {
|
||||
run_count: u32,
|
||||
/// Debounce counters.
|
||||
metal_run: u8,
|
||||
weapon_run: u8,
|
||||
high_refl_run: u8,
|
||||
/// Cooldowns.
|
||||
cd_metal: u16,
|
||||
cd_weapon: u16,
|
||||
cd_high_refl: u16,
|
||||
cd_recalib: u16,
|
||||
frame_count: u32,
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
}
|
||||
|
||||
impl WeaponDetector {
|
||||
@@ -101,11 +113,12 @@ impl WeaponDetector {
|
||||
run_phase_m2: [0.0; MAX_SC],
|
||||
run_count: 0,
|
||||
metal_run: 0,
|
||||
weapon_run: 0,
|
||||
high_refl_run: 0,
|
||||
cd_metal: 0,
|
||||
cd_weapon: 0,
|
||||
cd_high_refl: 0,
|
||||
cd_recalib: 0,
|
||||
frame_count: 0,
|
||||
events: [(0, 0.0); 3],
|
||||
}
|
||||
}
|
||||
|
||||
@@ -125,10 +138,9 @@ impl WeaponDetector {
|
||||
|
||||
self.frame_count += 1;
|
||||
self.cd_metal = self.cd_metal.saturating_sub(1);
|
||||
self.cd_weapon = self.cd_weapon.saturating_sub(1);
|
||||
self.cd_high_refl = self.cd_high_refl.saturating_sub(1);
|
||||
self.cd_recalib = self.cd_recalib.saturating_sub(1);
|
||||
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut ne = 0usize;
|
||||
|
||||
// Calibration phase: collect baseline statistics in empty room.
|
||||
@@ -153,7 +165,7 @@ impl WeaponDetector {
|
||||
}
|
||||
self.calibrated = true;
|
||||
}
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// Update running Welford statistics.
|
||||
@@ -176,7 +188,7 @@ impl WeaponDetector {
|
||||
// Only detect when someone is present and moving.
|
||||
if presence < MIN_PRESENCE || motion_energy < MIN_MOTION_ENERGY {
|
||||
self.metal_run = 0;
|
||||
self.weapon_run = 0;
|
||||
self.high_refl_run = 0;
|
||||
// Reset running stats periodically when no one is present.
|
||||
if self.run_count > 200 {
|
||||
self.run_count = 0;
|
||||
@@ -187,12 +199,12 @@ impl WeaponDetector {
|
||||
self.run_phase_m2[i] = 0.0;
|
||||
}
|
||||
}
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
// Compute current amplitude variance / phase variance ratio.
|
||||
if self.run_count < 4 {
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
let mut ratio_sum = 0.0f32;
|
||||
@@ -221,14 +233,14 @@ impl WeaponDetector {
|
||||
}
|
||||
|
||||
if valid_sc < 2 {
|
||||
return unsafe { &EVENTS[..0] };
|
||||
return &self.events[..0];
|
||||
}
|
||||
|
||||
let mean_ratio = ratio_sum / valid_sc as f32;
|
||||
|
||||
// Check for re-calibration need.
|
||||
if max_drift > RECALIB_DRIFT_THRESH && self.cd_recalib == 0 && ne < 3 {
|
||||
unsafe { EVENTS[ne] = (EVENT_CALIBRATION_NEEDED, max_drift); }
|
||||
self.events[ne] = (EVENT_CALIBRATION_NEEDED, max_drift);
|
||||
ne += 1;
|
||||
self.cd_recalib = COOLDOWN * 5; // Less frequent recalibration alerts.
|
||||
}
|
||||
@@ -240,28 +252,28 @@ impl WeaponDetector {
|
||||
self.metal_run = self.metal_run.saturating_sub(1);
|
||||
}
|
||||
|
||||
// Weapon-grade detection (higher threshold).
|
||||
if mean_ratio > WEAPON_RATIO_THRESH {
|
||||
self.weapon_run = self.weapon_run.saturating_add(1);
|
||||
// High-reflectivity detection (higher threshold). NOT weapon discrimination.
|
||||
if mean_ratio > HIGH_REFLECTIVITY_THRESH {
|
||||
self.high_refl_run = self.high_refl_run.saturating_add(1);
|
||||
} else {
|
||||
self.weapon_run = self.weapon_run.saturating_sub(1);
|
||||
self.high_refl_run = self.high_refl_run.saturating_sub(1);
|
||||
}
|
||||
|
||||
// Emit metal anomaly.
|
||||
if self.metal_run >= METAL_DEBOUNCE && self.cd_metal == 0 && ne < 3 {
|
||||
unsafe { EVENTS[ne] = (EVENT_METAL_ANOMALY, mean_ratio); }
|
||||
self.events[ne] = (EVENT_METAL_ANOMALY, mean_ratio);
|
||||
ne += 1;
|
||||
self.cd_metal = COOLDOWN;
|
||||
}
|
||||
|
||||
// Emit weapon alert (supersedes metal anomaly in severity).
|
||||
if self.weapon_run >= WEAPON_DEBOUNCE && self.cd_weapon == 0 && ne < 3 {
|
||||
unsafe { EVENTS[ne] = (EVENT_WEAPON_ALERT, mean_ratio); }
|
||||
// Emit high-reflectivity event (supersedes metal anomaly in severity).
|
||||
if self.high_refl_run >= HIGH_REFLECTIVITY_DEBOUNCE && self.cd_high_refl == 0 && ne < 3 {
|
||||
self.events[ne] = (EVENT_HIGH_METAL_REFLECTIVITY, mean_ratio);
|
||||
ne += 1;
|
||||
self.cd_weapon = COOLDOWN;
|
||||
self.cd_high_refl = COOLDOWN;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
pub fn is_calibrated(&self) -> bool { self.calibrated }
|
||||
@@ -311,7 +323,7 @@ mod tests {
|
||||
let ev = det.process_frame(&p, &[20.0; 16], &[0.01; 16], 0.0, 0);
|
||||
for &(et, _) in ev {
|
||||
assert_ne!(et, EVENT_METAL_ANOMALY);
|
||||
assert_ne!(et, EVENT_WEAPON_ALERT);
|
||||
assert_ne!(et, EVENT_HIGH_METAL_REFLECTIVITY);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -369,7 +381,7 @@ mod tests {
|
||||
}
|
||||
let ev = det.process_frame(&p, &a, &[0.01; 16], 1.0, 1);
|
||||
for &(et, _) in ev {
|
||||
assert_ne!(et, EVENT_WEAPON_ALERT, "normal person should not trigger weapon alert");
|
||||
assert_ne!(et, EVENT_HIGH_METAL_REFLECTIVITY, "normal person should not trigger weapon alert");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -73,6 +73,8 @@ impl WelfordStats {
|
||||
|
||||
/// Coherence-gated frame filter.
|
||||
pub struct CoherenceGate {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
prev_phases: [f32; MAX_SC],
|
||||
stats: WelfordStats,
|
||||
initial_variance: f32,
|
||||
@@ -89,6 +91,7 @@ pub struct CoherenceGate {
|
||||
impl CoherenceGate {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
prev_phases: [0.0; MAX_SC],
|
||||
stats: WelfordStats::new(),
|
||||
initial_variance: 0.0,
|
||||
@@ -105,7 +108,6 @@ impl CoherenceGate {
|
||||
let n_sc = if phases.len() > MAX_SC { MAX_SC } else { phases.len() };
|
||||
if n_sc < 2 { return &[]; }
|
||||
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut n_ev = 0usize;
|
||||
|
||||
if !self.initialized {
|
||||
@@ -146,7 +148,7 @@ impl CoherenceGate {
|
||||
self.gate = GateDecision::Recalibrate;
|
||||
self.low_count = 0;
|
||||
self.high_count = 0;
|
||||
unsafe { EVENTS[n_ev] = (EVENT_RECALIBRATE_NEEDED, variance); }
|
||||
self.events[n_ev] = (EVENT_RECALIBRATE_NEEDED, variance);
|
||||
n_ev += 1;
|
||||
} else {
|
||||
let below = coherence < LOW_THRESHOLD;
|
||||
@@ -178,11 +180,11 @@ impl CoherenceGate {
|
||||
};
|
||||
}
|
||||
|
||||
unsafe { EVENTS[n_ev] = (EVENT_GATE_DECISION, self.gate.as_f32()); }
|
||||
self.events[n_ev] = (EVENT_GATE_DECISION, self.gate.as_f32());
|
||||
n_ev += 1;
|
||||
unsafe { EVENTS[n_ev] = (EVENT_COHERENCE_SCORE, coherence); }
|
||||
self.events[n_ev] = (EVENT_COHERENCE_SCORE, coherence);
|
||||
n_ev += 1;
|
||||
unsafe { &EVENTS[..n_ev] }
|
||||
&self.events[..n_ev]
|
||||
}
|
||||
|
||||
pub fn gate(&self) -> GateDecision { self.gate }
|
||||
|
||||
@@ -25,6 +25,8 @@ pub const EVENT_SPATIAL_FOCUS_ZONE: i32 = 702;
|
||||
|
||||
/// Flash Attention spatial focus estimator.
|
||||
pub struct FlashAttention {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
prev_group_phases: [f32; N_GROUPS],
|
||||
attention_weights: [f32; N_GROUPS],
|
||||
smoothed_entropy: f32,
|
||||
@@ -37,6 +39,7 @@ pub struct FlashAttention {
|
||||
impl FlashAttention {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
prev_group_phases: [0.0; N_GROUPS],
|
||||
attention_weights: [0.0; N_GROUPS],
|
||||
smoothed_entropy: MAX_ENTROPY,
|
||||
@@ -50,7 +53,6 @@ impl FlashAttention {
|
||||
let n_sc = phases.len().min(amplitudes.len()).min(MAX_SC);
|
||||
if n_sc < N_GROUPS { return &[]; }
|
||||
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
|
||||
// Per-group means for Q and V.
|
||||
let subs_per = n_sc / N_GROUPS;
|
||||
@@ -117,12 +119,10 @@ impl FlashAttention {
|
||||
for g in 0..N_GROUPS { self.prev_group_phases[g] = q[g]; }
|
||||
|
||||
// Emit events.
|
||||
unsafe {
|
||||
EVENTS[0] = (EVENT_ATTENTION_PEAK_SC, peak_idx as f32);
|
||||
EVENTS[1] = (EVENT_ATTENTION_SPREAD, self.smoothed_entropy);
|
||||
EVENTS[2] = (EVENT_SPATIAL_FOCUS_ZONE, centroid);
|
||||
&EVENTS[..3]
|
||||
}
|
||||
self.events[0] = (EVENT_ATTENTION_PEAK_SC, peak_idx as f32);
|
||||
self.events[1] = (EVENT_ATTENTION_SPREAD, self.smoothed_entropy);
|
||||
self.events[2] = (EVENT_SPATIAL_FOCUS_ZONE, centroid);
|
||||
&self.events[..3]
|
||||
}
|
||||
|
||||
pub fn weights(&self) -> &[f32; N_GROUPS] { &self.attention_weights }
|
||||
|
||||
@@ -59,6 +59,8 @@ impl PersonSlot {
|
||||
|
||||
/// Min-cut person identity matcher.
|
||||
pub struct PersonMatcher {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 8],
|
||||
slots: [PersonSlot; MAX_PERSONS],
|
||||
active_count: u8,
|
||||
prev_assignment: [u8; MAX_PERSONS],
|
||||
@@ -69,6 +71,7 @@ pub struct PersonMatcher {
|
||||
impl PersonMatcher {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 8],
|
||||
slots: [
|
||||
PersonSlot::new(0),
|
||||
PersonSlot::new(1),
|
||||
@@ -98,7 +101,6 @@ impl PersonMatcher {
|
||||
self.frame_count += 1;
|
||||
let n_det = n_persons.min(MAX_PERSONS);
|
||||
|
||||
static mut EVENTS: [(i32, f32); 8] = [(0, 0.0); 8];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// Extract per-person feature vectors (spatial region -> top-8 variances).
|
||||
@@ -134,9 +136,7 @@ impl PersonMatcher {
|
||||
self.swap_count += 1;
|
||||
if n_events < 7 {
|
||||
let swap_val = (prev as f32) * 16.0 + (curr as f32);
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_PERSON_ID_SWAP, swap_val);
|
||||
}
|
||||
self.events[n_events] = (EVENT_PERSON_ID_SWAP, swap_val);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -177,9 +177,7 @@ impl PersonMatcher {
|
||||
0.0
|
||||
};
|
||||
let val = slot.person_id as f32 + confidence.min(0.99) * 0.01;
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_PERSON_ID_ASSIGNED, val);
|
||||
}
|
||||
self.events[n_events] = (EVENT_PERSON_ID_ASSIGNED, val);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -213,9 +211,7 @@ impl PersonMatcher {
|
||||
avg_conf /= n_det as f32;
|
||||
|
||||
if n_events < 8 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_MATCH_CONFIDENCE, avg_conf);
|
||||
}
|
||||
self.events[n_events] = (EVENT_MATCH_CONFIDENCE, avg_conf);
|
||||
n_events += 1;
|
||||
}
|
||||
}
|
||||
@@ -223,7 +219,7 @@ impl PersonMatcher {
|
||||
// Save current assignment for next-frame swap detection.
|
||||
self.prev_assignment = assignment;
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Extract top-FEAT_DIM variance values (descending) from a subcarrier range.
|
||||
|
||||
@@ -84,12 +84,15 @@ pub struct OptimalTransportDetector {
|
||||
frame_count: u32,
|
||||
shift_streak: u8,
|
||||
subtle_streak: u8,
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
}
|
||||
|
||||
impl OptimalTransportDetector {
|
||||
pub const fn new() -> Self {
|
||||
Self { prev_amps: [0.0; MAX_SC], smoothed_dist: 0.0, smoothed_var: 0.0, prev_var: 0.0,
|
||||
initialized: false, frame_count: 0, shift_streak: 0, subtle_streak: 0 }
|
||||
initialized: false, frame_count: 0, shift_streak: 0, subtle_streak: 0,
|
||||
events: [(0, 0.0); 4] }
|
||||
}
|
||||
|
||||
fn w1_sorted(a: &[f32], b: &[f32], n: usize) -> f32 {
|
||||
@@ -150,16 +153,15 @@ impl OptimalTransportDetector {
|
||||
|
||||
i = 0; while i < n { self.prev_amps[i] = cur[i]; i += 1; }
|
||||
|
||||
static mut EV: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut ne = 0usize;
|
||||
|
||||
if self.frame_count % 5 == 0 && ne < 4 {
|
||||
unsafe { EV[ne] = (EVENT_WASSERSTEIN_DISTANCE, self.smoothed_dist); } ne += 1;
|
||||
self.events[ne] = (EVENT_WASSERSTEIN_DISTANCE, self.smoothed_dist); ne += 1;
|
||||
}
|
||||
if self.smoothed_dist > WASS_SHIFT {
|
||||
self.shift_streak = self.shift_streak.saturating_add(1);
|
||||
if self.shift_streak >= SHIFT_DEB && ne < 4 {
|
||||
unsafe { EV[ne] = (EVENT_DISTRIBUTION_SHIFT, self.smoothed_dist); } ne += 1;
|
||||
self.events[ne] = (EVENT_DISTRIBUTION_SHIFT, self.smoothed_dist); ne += 1;
|
||||
self.shift_streak = 0;
|
||||
}
|
||||
} else { self.shift_streak = 0; }
|
||||
@@ -167,12 +169,12 @@ impl OptimalTransportDetector {
|
||||
if self.smoothed_dist > WASS_SUBTLE && vc < VAR_STABLE {
|
||||
self.subtle_streak = self.subtle_streak.saturating_add(1);
|
||||
if self.subtle_streak >= SUBTLE_DEB && ne < 4 {
|
||||
unsafe { EV[ne] = (EVENT_SUBTLE_MOTION, self.smoothed_dist); } ne += 1;
|
||||
self.events[ne] = (EVENT_SUBTLE_MOTION, self.smoothed_dist); ne += 1;
|
||||
self.subtle_streak = 0;
|
||||
}
|
||||
} else { self.subtle_streak = 0; }
|
||||
|
||||
unsafe { &EV[..ne] }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
pub fn distance(&self) -> f32 { self.smoothed_dist }
|
||||
|
||||
@@ -64,6 +64,8 @@ fn soft_threshold(x: f32, t: f32) -> f32 {
|
||||
|
||||
/// Sparse subcarrier recovery engine.
|
||||
pub struct SparseRecovery {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 3],
|
||||
/// Compact correlation estimate: [MAX_SC][NEIGHBORS].
|
||||
/// For subcarrier i: [corr(i,i-1), corr(i,i), corr(i,i+1)].
|
||||
/// Edge entries (i=0 left neighbor, i=31 right neighbor) are zero.
|
||||
@@ -87,6 +89,7 @@ pub struct SparseRecovery {
|
||||
impl SparseRecovery {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 3],
|
||||
correlation: [[0.0; NEIGHBORS]; MAX_SC],
|
||||
recent_valid: [0.0; MAX_SC],
|
||||
initialized: false,
|
||||
@@ -135,20 +138,17 @@ impl SparseRecovery {
|
||||
}
|
||||
|
||||
// -- Build event output -----------------------------------------------
|
||||
static mut EVENTS: [(i32, f32); 3] = [(0, 0.0); 3];
|
||||
let mut n_events = 0usize;
|
||||
|
||||
// Always emit dropout rate periodically (every 20 frames).
|
||||
if self.frame_count % 20 == 0 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_DROPOUT_RATE, dropout_rate);
|
||||
}
|
||||
self.events[n_events] = (EVENT_DROPOUT_RATE, dropout_rate);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
// -- Skip recovery if dropout too low or model not ready ---------------
|
||||
if dropout_rate < MIN_DROPOUT_RATE || !self.initialized {
|
||||
unsafe { return &EVENTS[..n_events]; }
|
||||
return &self.events[..n_events];
|
||||
}
|
||||
|
||||
// -- ISTA recovery ----------------------------------------------------
|
||||
@@ -158,19 +158,15 @@ impl SparseRecovery {
|
||||
|
||||
// Emit recovery results.
|
||||
if n_events < 3 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_RECOVERY_COMPLETE, recovered as f32);
|
||||
}
|
||||
self.events[n_events] = (EVENT_RECOVERY_COMPLETE, recovered as f32);
|
||||
n_events += 1;
|
||||
}
|
||||
if n_events < 3 {
|
||||
unsafe {
|
||||
EVENTS[n_events] = (EVENT_RECOVERY_ERROR, residual);
|
||||
}
|
||||
self.events[n_events] = (EVENT_RECOVERY_ERROR, residual);
|
||||
n_events += 1;
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n_events] }
|
||||
&self.events[..n_events]
|
||||
}
|
||||
|
||||
/// Update the compact correlation model from a fully valid frame.
|
||||
|
||||
@@ -54,12 +54,16 @@ pub struct TemporalCompressor {
|
||||
prev_ts: u32,
|
||||
has_ts: bool,
|
||||
ratio: f32,
|
||||
/// Per-call event scratch buffers (owned; replace former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
timer_events: [(i32, f32); 2],
|
||||
}
|
||||
|
||||
impl TemporalCompressor {
|
||||
pub const fn new() -> Self {
|
||||
const E: Snap = Snap::empty();
|
||||
Self { buf: [E; CAP], w_idx: 0, total: 0, frame_rate: 20.0, prev_ts: 0, has_ts: false, ratio: 1.0 }
|
||||
Self { buf: [E; CAP], w_idx: 0, total: 0, frame_rate: 20.0, prev_ts: 0, has_ts: false, ratio: 1.0,
|
||||
events: [(0, 0.0); 4], timer_events: [(0, 0.0); 2] }
|
||||
}
|
||||
|
||||
fn occ(&self) -> usize { if (self.total as usize) < CAP { self.total as usize } else { CAP } }
|
||||
@@ -97,7 +101,6 @@ impl TemporalCompressor {
|
||||
}
|
||||
self.prev_ts = ts_ms; self.has_ts = true;
|
||||
|
||||
static mut EV: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut ne = 0usize;
|
||||
let occ = self.occ();
|
||||
|
||||
@@ -113,23 +116,22 @@ impl TemporalCompressor {
|
||||
let mut j = 0;
|
||||
while j < VALS { let d = dequantize(self.buf[slot].data[j], s, old_l); self.buf[slot].data[j] = quantize(d, s, new_l); j += 1; }
|
||||
self.buf[slot].tier = new_t;
|
||||
if ne < 4 { unsafe { EV[ne] = (EVENT_TIER_TRANSITION, new_t as i32 as f32); } ne += 1; }
|
||||
if ne < 4 { self.events[ne] = (EVENT_TIER_TRANSITION, new_t as i32 as f32); ne += 1; }
|
||||
}
|
||||
}
|
||||
}
|
||||
self.ratio = self.calc_ratio(occ);
|
||||
if self.total % 64 == 0 && ne < 4 { unsafe { EV[ne] = (EVENT_COMPRESSION_RATIO, self.ratio); } ne += 1; }
|
||||
unsafe { &EV[..ne] }
|
||||
if self.total % 64 == 0 && ne < 4 { self.events[ne] = (EVENT_COMPRESSION_RATIO, self.ratio); ne += 1; }
|
||||
&self.events[..ne]
|
||||
}
|
||||
|
||||
/// Periodic timer events.
|
||||
pub fn on_timer(&self) -> &[(i32, f32)] {
|
||||
static mut TE: [(i32, f32); 2] = [(0, 0.0); 2];
|
||||
pub fn on_timer(&mut self) -> &[(i32, f32)] {
|
||||
let mut n = 0;
|
||||
let h = self.history_hours();
|
||||
if h > 0.0 { unsafe { TE[n] = (EVENT_HISTORY_DEPTH_HOURS, h); } n += 1; }
|
||||
unsafe { TE[n] = (EVENT_COMPRESSION_RATIO, self.ratio); } n += 1;
|
||||
unsafe { &TE[..n] }
|
||||
if h > 0.0 { self.timer_events[n] = (EVENT_HISTORY_DEPTH_HOURS, h); n += 1; }
|
||||
self.timer_events[n] = (EVENT_COMPRESSION_RATIO, self.ratio); n += 1;
|
||||
&self.timer_events[..n]
|
||||
}
|
||||
|
||||
fn calc_ratio(&self, occ: usize) -> f32 {
|
||||
|
||||
@@ -56,6 +56,8 @@ fn l2_query(stored: &[f32; DIM], query: &[f32]) -> f32 {
|
||||
|
||||
/// Micro-HNSW on-device vector index.
|
||||
pub struct MicroHnsw {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
nodes: [HnswNode; MAX_VECTORS],
|
||||
n_vectors: usize,
|
||||
entry_point: usize,
|
||||
@@ -68,6 +70,7 @@ impl MicroHnsw {
|
||||
pub const fn new() -> Self {
|
||||
const EMPTY: HnswNode = HnswNode::empty();
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
nodes: [EMPTY; MAX_VECTORS], n_vectors: 0, entry_point: usize::MAX,
|
||||
frame_count: 0, last_nearest: 0, last_distance: f32::MAX,
|
||||
}
|
||||
@@ -194,9 +197,8 @@ impl MicroHnsw {
|
||||
pub fn process_frame(&mut self, features: &[f32]) -> &[(i32, f32)] {
|
||||
self.frame_count += 1;
|
||||
if self.n_vectors == 0 {
|
||||
static mut EMPTY: [(i32, f32); 1] = [(0, 0.0); 1];
|
||||
unsafe { EMPTY[0] = (EVENT_LIBRARY_SIZE, 0.0); }
|
||||
return unsafe { &EMPTY[..1] };
|
||||
self.events[0] = (EVENT_LIBRARY_SIZE, 0.0);
|
||||
return &self.events[..1];
|
||||
}
|
||||
let (nearest_id, distance) = self.search(features);
|
||||
self.last_nearest = nearest_id;
|
||||
@@ -205,14 +207,11 @@ impl MicroHnsw {
|
||||
self.nodes[nearest_id].label
|
||||
} else { CLASS_UNKNOWN };
|
||||
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
unsafe {
|
||||
EVENTS[0] = (EVENT_NEAREST_MATCH_ID, nearest_id as f32);
|
||||
EVENTS[1] = (EVENT_MATCH_DISTANCE, distance);
|
||||
EVENTS[2] = (EVENT_CLASSIFICATION, label as f32);
|
||||
EVENTS[3] = (EVENT_LIBRARY_SIZE, self.n_vectors as f32);
|
||||
}
|
||||
unsafe { &EVENTS[..4] }
|
||||
self.events[0] = (EVENT_NEAREST_MATCH_ID, nearest_id as f32);
|
||||
self.events[1] = (EVENT_MATCH_DISTANCE, distance);
|
||||
self.events[2] = (EVENT_CLASSIFICATION, label as f32);
|
||||
self.events[3] = (EVENT_LIBRARY_SIZE, self.n_vectors as f32);
|
||||
&self.events[..4]
|
||||
}
|
||||
|
||||
pub fn size(&self) -> usize { self.n_vectors }
|
||||
|
||||
@@ -48,6 +48,8 @@ pub const EVENT_INFLUENCE_CHANGE: i32 = 762;
|
||||
|
||||
/// PageRank influence tracker.
|
||||
pub struct PageRankInfluence {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 8],
|
||||
/// Weighted adjacency matrix (row-major, adj[i][j] = correlation i<->j).
|
||||
adj: [[f32; MAX_PERSONS]; MAX_PERSONS],
|
||||
/// Current PageRank vector.
|
||||
@@ -63,6 +65,7 @@ pub struct PageRankInfluence {
|
||||
impl PageRankInfluence {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 8],
|
||||
adj: [[0.0; MAX_PERSONS]; MAX_PERSONS],
|
||||
rank: [0.25; MAX_PERSONS],
|
||||
prev_rank: [0.25; MAX_PERSONS],
|
||||
@@ -190,9 +193,8 @@ impl PageRankInfluence {
|
||||
}
|
||||
}
|
||||
|
||||
/// Build output events into a static buffer.
|
||||
fn build_events(&self, np: usize) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 8] = [(0, 0.0); 8];
|
||||
/// Build output events into the owned per-call buffer.
|
||||
fn build_events(&mut self, np: usize) -> &[(i32, f32)] {
|
||||
let mut n = 0usize;
|
||||
|
||||
// Find dominant person.
|
||||
@@ -206,15 +208,11 @@ impl PageRankInfluence {
|
||||
}
|
||||
|
||||
// Emit dominant person every frame.
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_DOMINANT_PERSON, best_idx as f32);
|
||||
}
|
||||
self.events[n] = (EVENT_DOMINANT_PERSON, best_idx as f32);
|
||||
n += 1;
|
||||
|
||||
// Emit influence score every frame.
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_INFLUENCE_SCORE, best_rank);
|
||||
}
|
||||
self.events[n] = (EVENT_INFLUENCE_SCORE, best_rank);
|
||||
n += 1;
|
||||
|
||||
// Emit change events for persons whose rank shifted significantly.
|
||||
@@ -223,14 +221,12 @@ impl PageRankInfluence {
|
||||
if fabsf(delta) > CHANGE_THRESHOLD && n < 8 {
|
||||
// Encode: integer part = person_id, fractional = clamped delta.
|
||||
let encoded = i as f32 + delta.clamp(-0.49, 0.49);
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_INFLUENCE_CHANGE, encoded);
|
||||
}
|
||||
self.events[n] = (EVENT_INFLUENCE_CHANGE, encoded);
|
||||
n += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
/// Get the current PageRank score for a person.
|
||||
|
||||
@@ -69,6 +69,8 @@ pub const EVENT_TRACK_LOST: i32 = 773;
|
||||
|
||||
/// Spiking neural network person tracker.
|
||||
pub struct SpikingTracker {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Membrane potential of each input neuron.
|
||||
membrane: [f32; N_INPUT],
|
||||
/// Synaptic weights from input to output neurons.
|
||||
@@ -109,6 +111,7 @@ impl SpikingTracker {
|
||||
}
|
||||
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
membrane: [0.0; N_INPUT],
|
||||
weights,
|
||||
input_spike_time: [0; N_INPUT],
|
||||
@@ -242,8 +245,7 @@ impl SpikingTracker {
|
||||
}
|
||||
|
||||
/// Construct event output.
|
||||
fn build_events(&self, zone: i8, was_active: bool) -> &[(i32, f32)] {
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
fn build_events(&mut self, zone: i8, was_active: bool) -> &[(i32, f32)] {
|
||||
let mut n = 0usize;
|
||||
|
||||
// Mean spike rate across all zones.
|
||||
@@ -255,29 +257,29 @@ impl SpikingTracker {
|
||||
|
||||
if zone >= 0 {
|
||||
// TRACK_UPDATE with zone ID.
|
||||
unsafe { EVENTS[n] = (EVENT_TRACK_UPDATE, zone as f32); }
|
||||
self.events[n] = (EVENT_TRACK_UPDATE, zone as f32);
|
||||
n += 1;
|
||||
|
||||
// TRACK_VELOCITY.
|
||||
unsafe { EVENTS[n] = (EVENT_TRACK_VELOCITY, self.velocity_ema); }
|
||||
self.events[n] = (EVENT_TRACK_VELOCITY, self.velocity_ema);
|
||||
n += 1;
|
||||
|
||||
// SPIKE_RATE.
|
||||
unsafe { EVENTS[n] = (EVENT_SPIKE_RATE, mean_rate); }
|
||||
self.events[n] = (EVENT_SPIKE_RATE, mean_rate);
|
||||
n += 1;
|
||||
} else {
|
||||
// SPIKE_RATE even when no track.
|
||||
unsafe { EVENTS[n] = (EVENT_SPIKE_RATE, mean_rate); }
|
||||
self.events[n] = (EVENT_SPIKE_RATE, mean_rate);
|
||||
n += 1;
|
||||
|
||||
// TRACK_LOST if we had a track before.
|
||||
if was_active {
|
||||
unsafe { EVENTS[n] = (EVENT_TRACK_LOST, self.prev_zone as f32); }
|
||||
self.events[n] = (EVENT_TRACK_LOST, self.prev_zone as f32);
|
||||
n += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
/// Get the current tracked zone (-1 if lost).
|
||||
|
||||
@@ -73,6 +73,8 @@ impl PlanNode {
|
||||
|
||||
/// GOAP autonomy planner.
|
||||
pub struct GoapPlanner {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
world_state: WorldState,
|
||||
current_goal: u8,
|
||||
plan: [u8; MAX_PLAN_DEPTH],
|
||||
@@ -89,6 +91,7 @@ impl GoapPlanner {
|
||||
let mut p = [0.0f32; NUM_GOALS];
|
||||
p[0]=0.9; p[1]=0.8; p[2]=0.7; p[3]=0.5; p[4]=0.3; p[5]=0.1;
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
world_state: 0, current_goal: 0xFF,
|
||||
plan: [0xFF; MAX_PLAN_DEPTH], plan_len: 0, plan_step: 0,
|
||||
goal_priorities: p, timer_count: 0, replan_interval: 60,
|
||||
@@ -112,17 +115,16 @@ impl GoapPlanner {
|
||||
/// Called at ~1 Hz. Replans periodically and executes plan steps.
|
||||
pub fn on_timer(&mut self) -> &[(i32, f32)] {
|
||||
self.timer_count += 1;
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n = 0usize;
|
||||
// Replan at interval.
|
||||
if self.timer_count % self.replan_interval == 0 {
|
||||
let g = self.select_goal();
|
||||
if g < NUM_GOALS as u8 {
|
||||
self.current_goal = g;
|
||||
if n < 4 { unsafe { EVENTS[n] = (EVENT_GOAL_SELECTED, g as f32); } n += 1; }
|
||||
if n < 4 { self.events[n] = (EVENT_GOAL_SELECTED, g as f32); n += 1; }
|
||||
let cost = self.plan_for_goal(g as usize);
|
||||
if cost < 255 && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_PLAN_COST, cost as f32); } n += 1;
|
||||
self.events[n] = (EVENT_PLAN_COST, cost as f32); n += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -135,16 +137,16 @@ impl GoapPlanner {
|
||||
let old = self.world_state;
|
||||
self.world_state = action.apply(self.world_state);
|
||||
if (self.world_state & !old) != 0 && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_MODULE_ACTIVATED, aid as f32); } n += 1;
|
||||
self.events[n] = (EVENT_MODULE_ACTIVATED, aid as f32); n += 1;
|
||||
}
|
||||
if (old & !self.world_state) != 0 && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_MODULE_DEACTIVATED, aid as f32); } n += 1;
|
||||
self.events[n] = (EVENT_MODULE_DEACTIVATED, aid as f32); n += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
self.plan_step += 1;
|
||||
}
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
fn select_goal(&self) -> u8 {
|
||||
|
||||
@@ -38,6 +38,8 @@ impl PatternEntry { const fn empty() -> Self { Self { symbols: [0; PATTERN_LEN],
|
||||
|
||||
/// Temporal pattern sequence analyzer.
|
||||
pub struct PatternSequenceAnalyzer {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 4],
|
||||
/// Two-day history: [0..DAY_LEN)=yesterday, [DAY_LEN..2*DAY_LEN)=today.
|
||||
history: [u8; DAY_LEN * 2],
|
||||
minute_counter: u16,
|
||||
@@ -55,6 +57,7 @@ pub struct PatternSequenceAnalyzer {
|
||||
impl PatternSequenceAnalyzer {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 4],
|
||||
history: [0; DAY_LEN * 2], minute_counter: 0, day_offset: 0,
|
||||
pattern_lib: [PatternEntry::empty(); MAX_PATTERNS], n_patterns: 0,
|
||||
routine_confidence: 0.0, frame_votes: [0; 5], frames_in_minute: 0,
|
||||
@@ -72,7 +75,6 @@ impl PatternSequenceAnalyzer {
|
||||
/// Called at ~1 Hz. Commits symbols and runs hourly LCS comparison.
|
||||
pub fn on_timer(&mut self) -> &[(i32, f32)] {
|
||||
self.timer_count += 1;
|
||||
static mut EVENTS: [(i32, f32); 4] = [(0, 0.0); 4];
|
||||
let mut n = 0usize;
|
||||
|
||||
if self.timer_count % 60 == 0 && self.frames_in_minute > 0 {
|
||||
@@ -83,12 +85,12 @@ impl PatternSequenceAnalyzer {
|
||||
if self.day_offset > 0 {
|
||||
let predicted = self.history[self.minute_counter as usize];
|
||||
if sym as u8 != predicted && n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_ROUTINE_DEVIATION, self.minute_counter as f32); }
|
||||
self.events[n] = (EVENT_ROUTINE_DEVIATION, self.minute_counter as f32);
|
||||
n += 1;
|
||||
}
|
||||
let next_min = (self.minute_counter + 1) % DAY_LEN as u16;
|
||||
if n < 4 {
|
||||
unsafe { EVENTS[n] = (EVENT_PREDICTION_NEXT, self.history[next_min as usize] as f32); }
|
||||
self.events[n] = (EVENT_PREDICTION_NEXT, self.history[next_min as usize] as f32);
|
||||
n += 1;
|
||||
}
|
||||
}
|
||||
@@ -104,14 +106,14 @@ impl PatternSequenceAnalyzer {
|
||||
if wlen >= MIN_PATTERN_LEN {
|
||||
let lcs = self.compute_lcs(start, wlen);
|
||||
self.routine_confidence = if wlen > 0 { lcs as f32 / wlen as f32 } else { 0.0 };
|
||||
if n < 4 { unsafe { EVENTS[n] = (EVENT_PATTERN_CONFIDENCE, self.routine_confidence); } n += 1; }
|
||||
if n < 4 { self.events[n] = (EVENT_PATTERN_CONFIDENCE, self.routine_confidence); n += 1; }
|
||||
if lcs >= MIN_PATTERN_LEN {
|
||||
self.store_pattern(start, wlen);
|
||||
if n < 4 { unsafe { EVENTS[n] = (EVENT_PATTERN_DETECTED, lcs as f32); } n += 1; }
|
||||
if n < 4 { self.events[n] = (EVENT_PATTERN_DETECTED, lcs as f32); n += 1; }
|
||||
}
|
||||
}
|
||||
}
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
fn majority_symbol(&self) -> Symbol {
|
||||
|
||||
@@ -45,18 +45,19 @@ pub struct TemporalLogicGuard {
|
||||
vio_counts: [u32; NUM_RULES],
|
||||
frame_idx: u32,
|
||||
report_interval: u32,
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 12],
|
||||
}
|
||||
|
||||
impl TemporalLogicGuard {
|
||||
pub const fn new() -> Self {
|
||||
Self { rules: [Rule::new(); NUM_RULES], vio_counts: [0; NUM_RULES],
|
||||
frame_idx: 0, report_interval: 200 }
|
||||
frame_idx: 0, report_interval: 200, events: [(0, 0.0); 12] }
|
||||
}
|
||||
|
||||
/// Process one frame. Returns events to emit.
|
||||
pub fn on_frame(&mut self, input: &FrameInput) -> &[(i32, f32)] {
|
||||
self.frame_idx += 1;
|
||||
static mut EV: [(i32, f32); 12] = [(0, 0.0); 12];
|
||||
let mut n = 0usize;
|
||||
|
||||
// G-rules (0-3, 6): violated when condition holds on any frame.
|
||||
@@ -75,10 +76,10 @@ impl TemporalLogicGuard {
|
||||
self.rules[rid].state = RuleState::Violated;
|
||||
self.rules[rid].vio_frame = self.frame_idx;
|
||||
self.vio_counts[rid] += 1;
|
||||
if n + 1 < 12 { unsafe {
|
||||
EV[n] = (EVENT_LTL_VIOLATION, rid as f32);
|
||||
EV[n+1] = (EVENT_COUNTEREXAMPLE, self.frame_idx as f32);
|
||||
} n += 2; }
|
||||
if n + 1 < 12 {
|
||||
self.events[n] = (EVENT_LTL_VIOLATION, rid as f32);
|
||||
self.events[n+1] = (EVENT_COUNTEREXAMPLE, self.frame_idx as f32);
|
||||
n += 2; }
|
||||
}
|
||||
} else { self.rules[rid].state = RuleState::Satisfied; }
|
||||
g += 1;
|
||||
@@ -86,18 +87,18 @@ impl TemporalLogicGuard {
|
||||
|
||||
// Rule 4: F(motion_start -> motion_end within 300s).
|
||||
if self.check_deadline_rule(4, input.motion_energy > 0.1, MOTION_STOP_DEADLINE) {
|
||||
if n + 1 < 12 { unsafe {
|
||||
EV[n] = (EVENT_LTL_VIOLATION, 4.0);
|
||||
EV[n+1] = (EVENT_COUNTEREXAMPLE, self.frame_idx as f32);
|
||||
} n += 2; }
|
||||
if n + 1 < 12 {
|
||||
self.events[n] = (EVENT_LTL_VIOLATION, 4.0);
|
||||
self.events[n+1] = (EVENT_COUNTEREXAMPLE, self.frame_idx as f32);
|
||||
n += 2; }
|
||||
}
|
||||
|
||||
// Rule 5: G(breathing>40 -> alert within 5s).
|
||||
if self.check_deadline_rule(5, input.breathing_bpm > 40.0, FAST_BREATH_DEADLINE) {
|
||||
if n + 1 < 12 { unsafe {
|
||||
EV[n] = (EVENT_LTL_VIOLATION, 5.0);
|
||||
EV[n+1] = (EVENT_COUNTEREXAMPLE, self.frame_idx as f32);
|
||||
} n += 2; }
|
||||
if n + 1 < 12 {
|
||||
self.events[n] = (EVENT_LTL_VIOLATION, 5.0);
|
||||
self.events[n+1] = (EVENT_COUNTEREXAMPLE, self.frame_idx as f32);
|
||||
n += 2; }
|
||||
}
|
||||
|
||||
// Rule 7: G(seizure -> !normal_gait within 60s).
|
||||
@@ -113,10 +114,10 @@ impl TemporalLogicGuard {
|
||||
self.rules[7].state = RuleState::Violated;
|
||||
self.rules[7].vio_frame = self.frame_idx;
|
||||
self.vio_counts[7] += 1;
|
||||
if n + 1 < 12 { unsafe {
|
||||
EV[n] = (EVENT_LTL_VIOLATION, 7.0);
|
||||
EV[n+1] = (EVENT_COUNTEREXAMPLE, self.frame_idx as f32);
|
||||
} n += 2; }
|
||||
if n + 1 < 12 {
|
||||
self.events[n] = (EVENT_LTL_VIOLATION, 7.0);
|
||||
self.events[n+1] = (EVENT_COUNTEREXAMPLE, self.frame_idx as f32);
|
||||
n += 2; }
|
||||
} else if self.frame_idx >= self.rules[7].deadline {
|
||||
self.rules[7].state = RuleState::Satisfied;
|
||||
}
|
||||
@@ -129,10 +130,10 @@ impl TemporalLogicGuard {
|
||||
}
|
||||
|
||||
if self.frame_idx % self.report_interval == 0 && n < 12 {
|
||||
unsafe { EV[n] = (EVENT_LTL_SATISFACTION, self.satisfied_count() as f32); }
|
||||
self.events[n] = (EVENT_LTL_SATISFACTION, self.satisfied_count() as f32);
|
||||
n += 1;
|
||||
}
|
||||
unsafe { &EV[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
/// Generic deadline rule: condition triggers pending, expiry = violation,
|
||||
|
||||
@@ -137,6 +137,8 @@ impl VitalHistory {
|
||||
|
||||
/// Vital trend analyzer.
|
||||
pub struct VitalTrendAnalyzer {
|
||||
/// Per-call event scratch buffer (owned; replaces former `static mut`).
|
||||
events: [(i32, f32); 8],
|
||||
breathing: VitalHistory,
|
||||
heartrate: VitalHistory,
|
||||
/// Debounce counters for each alert type.
|
||||
@@ -153,6 +155,7 @@ pub struct VitalTrendAnalyzer {
|
||||
impl VitalTrendAnalyzer {
|
||||
pub const fn new() -> Self {
|
||||
Self {
|
||||
events: [(0, 0.0); 8],
|
||||
breathing: VitalHistory::new(),
|
||||
heartrate: VitalHistory::new(),
|
||||
bradypnea_count: 0,
|
||||
@@ -172,16 +175,13 @@ impl VitalTrendAnalyzer {
|
||||
self.breathing.push(breathing_bpm);
|
||||
self.heartrate.push(heartrate_bpm);
|
||||
|
||||
static mut EVENTS: [(i32, f32); 8] = [(0, 0.0); 8];
|
||||
let mut n = 0usize;
|
||||
|
||||
// ── Apnea detection (highest priority) ──────────────────────────
|
||||
if breathing_bpm < 1.0 {
|
||||
self.apnea_counter += 1;
|
||||
if self.apnea_counter >= APNEA_SECONDS {
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_APNEA, self.apnea_counter as f32);
|
||||
}
|
||||
self.events[n] = (EVENT_APNEA, self.apnea_counter as f32);
|
||||
n += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -192,9 +192,7 @@ impl VitalTrendAnalyzer {
|
||||
if breathing_bpm > 0.0 && breathing_bpm < BRADYPNEA_THRESH {
|
||||
self.bradypnea_count = self.bradypnea_count.saturating_add(1);
|
||||
if self.bradypnea_count >= ALERT_DEBOUNCE && n < 7 {
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_BRADYPNEA, breathing_bpm);
|
||||
}
|
||||
self.events[n] = (EVENT_BRADYPNEA, breathing_bpm);
|
||||
n += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -205,9 +203,7 @@ impl VitalTrendAnalyzer {
|
||||
if breathing_bpm > TACHYPNEA_THRESH {
|
||||
self.tachypnea_count = self.tachypnea_count.saturating_add(1);
|
||||
if self.tachypnea_count >= ALERT_DEBOUNCE && n < 7 {
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_TACHYPNEA, breathing_bpm);
|
||||
}
|
||||
self.events[n] = (EVENT_TACHYPNEA, breathing_bpm);
|
||||
n += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -218,9 +214,7 @@ impl VitalTrendAnalyzer {
|
||||
if heartrate_bpm > 0.0 && heartrate_bpm < BRADYCARDIA_THRESH {
|
||||
self.bradycardia_count = self.bradycardia_count.saturating_add(1);
|
||||
if self.bradycardia_count >= ALERT_DEBOUNCE && n < 7 {
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_BRADYCARDIA, heartrate_bpm);
|
||||
}
|
||||
self.events[n] = (EVENT_BRADYCARDIA, heartrate_bpm);
|
||||
n += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -231,9 +225,7 @@ impl VitalTrendAnalyzer {
|
||||
if heartrate_bpm > TACHYCARDIA_THRESH {
|
||||
self.tachycardia_count = self.tachycardia_count.saturating_add(1);
|
||||
if self.tachycardia_count >= ALERT_DEBOUNCE && n < 7 {
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_TACHYCARDIA, heartrate_bpm);
|
||||
}
|
||||
self.events[n] = (EVENT_TACHYCARDIA, heartrate_bpm);
|
||||
n += 1;
|
||||
}
|
||||
} else {
|
||||
@@ -245,20 +237,16 @@ impl VitalTrendAnalyzer {
|
||||
let br_avg = self.breathing.mean_last(WINDOW_1M);
|
||||
let hr_avg = self.heartrate.mean_last(WINDOW_1M);
|
||||
if n < 7 {
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_BREATHING_AVG, br_avg);
|
||||
}
|
||||
self.events[n] = (EVENT_BREATHING_AVG, br_avg);
|
||||
n += 1;
|
||||
}
|
||||
if n < 8 {
|
||||
unsafe {
|
||||
EVENTS[n] = (EVENT_HEARTRATE_AVG, hr_avg);
|
||||
}
|
||||
self.events[n] = (EVENT_HEARTRATE_AVG, hr_avg);
|
||||
n += 1;
|
||||
}
|
||||
}
|
||||
|
||||
unsafe { &EVENTS[..n] }
|
||||
&self.events[..n]
|
||||
}
|
||||
|
||||
/// Get the 1-minute breathing average.
|
||||
|
||||
@@ -0,0 +1,259 @@
|
||||
//! Honest-labeling source-presence tests (ADR-160).
|
||||
//!
|
||||
//! These tests assert that the claim-surface fixes A1–A4 are physically present
|
||||
//! in the source (disclaimers added, uncited stats removed, overclaiming names
|
||||
//! renamed). They are deliberately source-text assertions (`include_str!`),
|
||||
//! mirroring ADR-159 §A5 / `cog-pose-estimation`'s `manifest_roundtrips` pattern:
|
||||
//! the win here is making the *labels* true, which is a documentation invariant,
|
||||
//! not a runtime capability. Each test is designed to FAIL on the pre-fix source.
|
||||
|
||||
// ── A1: medical modules carry the mandatory disclaimer + feature gate ─────────
|
||||
|
||||
const MED_SEIZURE: &str = include_str!("../src/med_seizure_detect.rs");
|
||||
const MED_CARDIAC: &str = include_str!("../src/med_cardiac_arrhythmia.rs");
|
||||
const MED_RESP: &str = include_str!("../src/med_respiratory_distress.rs");
|
||||
const MED_APNEA: &str = include_str!("../src/med_sleep_apnea.rs");
|
||||
const MED_GAIT: &str = include_str!("../src/med_gait_analysis.rs");
|
||||
|
||||
const MED_MODULES: &[(&str, &str)] = &[
|
||||
("med_seizure_detect", MED_SEIZURE),
|
||||
("med_cardiac_arrhythmia", MED_CARDIAC),
|
||||
("med_respiratory_distress", MED_RESP),
|
||||
("med_sleep_apnea", MED_APNEA),
|
||||
("med_gait_analysis", MED_GAIT),
|
||||
];
|
||||
|
||||
|
||||
/// Char-boundary-safe prefix of up to `max` bytes (module headers are ASCII-ish
|
||||
/// but contain box-drawing chars, so a naive byte slice can split a UTF-8 char).
|
||||
fn char_safe_prefix(s: &str, max: usize) -> &str {
|
||||
let mut end = s.len().min(max);
|
||||
while end > 0 && !s.is_char_boundary(end) { end -= 1; }
|
||||
&s[..end]
|
||||
}
|
||||
|
||||
/// A1(a): every med_* module's `//!` header must carry the mandatory disclaimer
|
||||
/// stating it is experimental, not clinically validated, and not a medical device.
|
||||
#[test]
|
||||
fn a1_med_modules_have_clinical_disclaimer() {
|
||||
for (name, src) in MED_MODULES {
|
||||
// Search the whole module doc-comment region (first ~2KB) for robustness.
|
||||
let scan = char_safe_prefix(src, 2048);
|
||||
assert!(
|
||||
scan.contains("NOT VALIDATED AGAINST CLINICAL DATA"),
|
||||
"{name}: missing 'NOT VALIDATED AGAINST CLINICAL DATA' disclaimer"
|
||||
);
|
||||
assert!(
|
||||
scan.contains("NOT A MEDICAL DEVICE"),
|
||||
"{name}: missing 'NOT A MEDICAL DEVICE' disclaimer"
|
||||
);
|
||||
assert!(
|
||||
scan.contains("EXPERIMENTAL"),
|
||||
"{name}: missing 'EXPERIMENTAL' marker"
|
||||
);
|
||||
// ADR cross-reference so the disclaimer is traceable.
|
||||
assert!(
|
||||
scan.contains("ADR-160"),
|
||||
"{name}: disclaimer should cite ADR-160"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/// A1(c): all five med_* modules must be gated behind the non-default
|
||||
/// `medical-experimental` cargo feature in lib.rs (cannot be silently shipped).
|
||||
#[test]
|
||||
fn a1_med_modules_gated_behind_medical_experimental() {
|
||||
const LIB: &str = include_str!("../src/lib.rs");
|
||||
for (name, _) in MED_MODULES {
|
||||
// Each module declaration must be immediately preceded by the cfg gate.
|
||||
let decl = format!("pub mod {name};");
|
||||
let idx = LIB
|
||||
.find(&decl)
|
||||
.unwrap_or_else(|| panic!("{name}: `{decl}` not found in lib.rs"));
|
||||
let preceding = &LIB[idx.saturating_sub(80)..idx];
|
||||
assert!(
|
||||
preceding.contains("#[cfg(feature = \"medical-experimental\")]"),
|
||||
"{name}: `{decl}` not gated behind medical-experimental in lib.rs"
|
||||
);
|
||||
}
|
||||
// The feature itself must exist in Cargo.toml.
|
||||
const CARGO: &str = include_str!("../Cargo.toml");
|
||||
assert!(
|
||||
CARGO.contains("medical-experimental = []"),
|
||||
"Cargo.toml missing `medical-experimental` feature definition"
|
||||
);
|
||||
// And it must NOT be in the default feature set.
|
||||
let default_line = CARGO
|
||||
.lines()
|
||||
.find(|l| l.trim_start().starts_with("default = ["))
|
||||
.expect("Cargo.toml missing default features");
|
||||
assert!(
|
||||
!default_line.contains("medical-experimental"),
|
||||
"medical-experimental must be NON-default; found in: {default_line}"
|
||||
);
|
||||
}
|
||||
|
||||
/// A1(b): the seizure module must no longer assert detection as fact
|
||||
/// ("Detects tonic-clonic seizures") and must use softened "candidate"/"flags"
|
||||
/// language instead.
|
||||
#[test]
|
||||
fn a1_seizure_verbs_softened() {
|
||||
assert!(
|
||||
!MED_SEIZURE.contains("Detects tonic-clonic seizures"),
|
||||
"med_seizure_detect still asserts 'Detects tonic-clonic seizures' as fact"
|
||||
);
|
||||
assert!(
|
||||
MED_SEIZURE.contains("candidate") && MED_SEIZURE.contains("signature"),
|
||||
"med_seizure_detect should describe 'candidate ... signatures' (experimental)"
|
||||
);
|
||||
}
|
||||
|
||||
// ── A2: affect modules carry the speculative/unvalidated disclaimer ───────────
|
||||
|
||||
const EXO_HAPPINESS: &str = include_str!("../src/exo_happiness_score.rs");
|
||||
const EXO_EMOTION: &str = include_str!("../src/exo_emotion_detect.rs");
|
||||
|
||||
/// A2: both affect modules must declare outputs are NOT measurements of emotion
|
||||
/// and cite ADR-160.
|
||||
#[test]
|
||||
fn a2_affect_modules_have_unvalidated_disclaimer() {
|
||||
for (name, src) in [("exo_happiness_score", EXO_HAPPINESS), ("exo_emotion_detect", EXO_EMOTION)] {
|
||||
let scan = char_safe_prefix(src, 2048);
|
||||
assert!(
|
||||
scan.contains("NOT measurements of emotion") || scan.contains("NOT a")
|
||||
&& scan.contains("affect"),
|
||||
"{name}: missing 'NOT measurements of emotion' style disclaimer"
|
||||
);
|
||||
assert!(
|
||||
scan.to_lowercase().contains("speculative")
|
||||
|| scan.to_lowercase().contains("unvalidated"),
|
||||
"{name}: missing speculative/unvalidated qualifier"
|
||||
);
|
||||
assert!(scan.contains("ADR-160"), "{name}: disclaimer should cite ADR-160");
|
||||
}
|
||||
}
|
||||
|
||||
/// A2: the uncited "Happy people walk ~12% faster" statistic must be deleted.
|
||||
#[test]
|
||||
fn a2_uncited_12_percent_stat_removed() {
|
||||
assert!(
|
||||
!EXO_HAPPINESS.contains("12% faster"),
|
||||
"exo_happiness_score still contains the uncited '12% faster' claim"
|
||||
);
|
||||
assert!(
|
||||
!EXO_HAPPINESS.contains("~12% above"),
|
||||
"exo_happiness_score still contains the uncited '~12% above' claim"
|
||||
);
|
||||
assert!(
|
||||
!EXO_HAPPINESS.contains("Happy people walk"),
|
||||
"exo_happiness_score still contains the uncited 'Happy people walk' claim"
|
||||
);
|
||||
}
|
||||
|
||||
/// A2: HAPPINESS_SCORE must be documented as a gait-energy proxy, not an affect
|
||||
/// measurement.
|
||||
#[test]
|
||||
fn a2_happiness_reframed_as_proxy() {
|
||||
assert!(
|
||||
EXO_HAPPINESS.contains("gait-energy proxy"),
|
||||
"exo_happiness_score should document HAPPINESS_SCORE as a 'gait-energy proxy'"
|
||||
);
|
||||
}
|
||||
|
||||
// ── A3: weapon-detect renamed to honest physical quantities ───────────────────
|
||||
|
||||
const SEC_WEAPON: &str = include_str!("../src/sec_weapon_detect.rs");
|
||||
const LIB_RS: &str = include_str!("../src/lib.rs");
|
||||
|
||||
/// A3: the weapon-grade overclaim must be gone from the event/const names.
|
||||
#[test]
|
||||
fn a3_weapon_names_renamed_to_reflectivity() {
|
||||
// The module must no longer *define* a WEAPON_ALERT event or WEAPON_RATIO_THRESH
|
||||
// const. (A doc-comment may still reference the old name historically, e.g.
|
||||
// "formerly `EVENT_WEAPON_ALERT`" — we assert on the definitions, not mentions.)
|
||||
assert!(
|
||||
!SEC_WEAPON.contains("pub const EVENT_WEAPON_ALERT"),
|
||||
"sec_weapon_detect still defines/exports EVENT_WEAPON_ALERT"
|
||||
);
|
||||
assert!(
|
||||
!SEC_WEAPON.contains("const WEAPON_RATIO_THRESH"),
|
||||
"sec_weapon_detect still defines WEAPON_RATIO_THRESH"
|
||||
);
|
||||
// Honest replacements must be present.
|
||||
assert!(
|
||||
SEC_WEAPON.contains("EVENT_HIGH_METAL_REFLECTIVITY"),
|
||||
"sec_weapon_detect missing renamed EVENT_HIGH_METAL_REFLECTIVITY"
|
||||
);
|
||||
assert!(
|
||||
SEC_WEAPON.contains("HIGH_REFLECTIVITY_THRESH"),
|
||||
"sec_weapon_detect missing renamed HIGH_REFLECTIVITY_THRESH"
|
||||
);
|
||||
}
|
||||
|
||||
/// A3: the lib.rs event registry must no longer export a `WEAPON_ALERT` name.
|
||||
#[test]
|
||||
fn a3_registry_no_longer_exports_weapon_alert() {
|
||||
assert!(
|
||||
!LIB_RS.contains("pub const WEAPON_ALERT"),
|
||||
"event_types registry still exports WEAPON_ALERT"
|
||||
);
|
||||
assert!(
|
||||
LIB_RS.contains("pub const HIGH_METAL_REFLECTIVITY"),
|
||||
"event_types registry missing HIGH_METAL_REFLECTIVITY (id 221)"
|
||||
);
|
||||
}
|
||||
|
||||
// ── A4: quasi-medical / sign-language exotic modules carry the disclaimer ──────
|
||||
|
||||
const EXO_DREAM: &str = include_str!("../src/exo_dream_stage.rs");
|
||||
const EXO_SIGN: &str = include_str!("../src/exo_gesture_language.rs");
|
||||
|
||||
/// A4: dream-stage and gesture-language modules promote the Exotic/Research tag
|
||||
/// into a header disclaimer and state they are not validated.
|
||||
#[test]
|
||||
fn a4_exotic_modules_have_experimental_disclaimer() {
|
||||
for (name, src) in [("exo_dream_stage", EXO_DREAM), ("exo_gesture_language", EXO_SIGN)] {
|
||||
let scan = char_safe_prefix(src, 2048);
|
||||
assert!(
|
||||
scan.contains("EXPERIMENTAL") && scan.contains("NOT VALIDATED"),
|
||||
"{name}: missing EXPERIMENTAL / NOT VALIDATED disclaimer"
|
||||
);
|
||||
assert!(scan.contains("ADR-160"), "{name}: disclaimer should cite ADR-160");
|
||||
assert!(
|
||||
scan.contains("Research"),
|
||||
"{name}: should surface the Exotic/Research registry tag in the header"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// ── A5: the static_mut soundness fix is present (no per-call static mut bufs) ──
|
||||
|
||||
/// A5: claim-bearing modules must no longer use a `static mut` event scratch
|
||||
/// buffer (latent aliasing UB). They now own a per-instance `events` field.
|
||||
#[test]
|
||||
fn a5_claim_bearing_modules_have_no_static_mut_event_buffer() {
|
||||
let modules: &[(&str, &str)] = &[
|
||||
("med_seizure_detect", MED_SEIZURE),
|
||||
("med_cardiac_arrhythmia", MED_CARDIAC),
|
||||
("med_respiratory_distress", MED_RESP),
|
||||
("med_sleep_apnea", MED_APNEA),
|
||||
("med_gait_analysis", MED_GAIT),
|
||||
("exo_happiness_score", EXO_HAPPINESS),
|
||||
("exo_emotion_detect", EXO_EMOTION),
|
||||
("sec_weapon_detect", SEC_WEAPON),
|
||||
("exo_dream_stage", EXO_DREAM),
|
||||
("exo_gesture_language", EXO_SIGN),
|
||||
];
|
||||
for (name, src) in modules {
|
||||
assert!(
|
||||
!src.contains("static mut EVENTS")
|
||||
&& !src.contains("static mut EV:")
|
||||
&& !src.contains("static mut EMPTY"),
|
||||
"{name}: still uses a `static mut` event scratch buffer (A5 not applied)"
|
||||
);
|
||||
assert!(
|
||||
src.contains("events: [(i32, f32);"),
|
||||
"{name}: missing owned `events` scratch buffer field (A5)"
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "wifi-densepose-wifiscan"
|
||||
version.workspace = true
|
||||
version = "0.3.1"
|
||||
edition.workspace = true
|
||||
description = "Multi-BSSID WiFi scanning domain layer for enhanced Windows WiFi DensePose sensing (ADR-022)"
|
||||
license.workspace = true
|
||||
|
||||
Reference in New Issue
Block a user