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17509a2a41
* feat(ruvector): ADR-084 Pass 1 — sketch module foundation
Implements Pass 1 of ADR-084 (RaBitQ similarity sensor): a thin
RuView-flavored API over `ruvector_core::quantization::BinaryQuantized`,
exposed at `wifi_densepose_ruvector::{Sketch, SketchBank, SketchError}`.
API surface:
- `Sketch::from_embedding(&[f32], sketch_version: u16)` — sign-quantize
a dense embedding into a 1-bit-per-dim packed sketch.
- `Sketch::distance` — hamming distance with schema-mismatch error.
- `Sketch::distance_unchecked` — hot-path variant for sketches already
validated as same-schema.
- `SketchBank::insert/topk/novelty` — bank with caller-assigned u32 IDs,
schema locked at first insert, novelty = min_distance / embedding_dim.
Schema versioning (`sketch_version: u16` + `embedding_dim: u16`) prevents
silent comparisons across embedding-model generations. Bumping the model
forces re-sketch of the candidate bank.
Pass 1 establishes the API and unit-test foundation. Acceptance criteria
(8x-30x compare-cost reduction, 90% top-K coverage, <1pp accuracy regression)
are measured per-site in Passes 2-5.
Validated:
- 12 new tests pass (sketch construction, hamming, top-K ordering,
schema lock, schema rejection, novelty)
- cargo test --workspace --no-default-features → 1,551 passed, 0 failed,
8 ignored (was 1,539 before; +12 new tests)
- ESP32-S3 on COM7 still streaming live CSI (cb #117300)
Co-Authored-By: claude-flow <ruv@ruv.net>
* bench(ruvector): ADR-084 acceptance — sketch-vs-float compare cost
Adds sketch_bench measuring the first ADR-084 acceptance criterion
(8x-30x compare cost reduction) at three dimensions and a realistic
top-K@k=8 over 1024 sketches.
Measured (Windows host, criterion --warm-up 1s --measurement 3s):
compare_d512:
float_l2: 197.03 ns/op
float_cosine: 231.17 ns/op
sketch_hamming: 4.56 ns/op → 43-51x speedup
topk_d128_n1024_k8:
float_l2_topk: 47.59 us
sketch_hamming: 6.34 us → 7.5x speedup
Pair-wise compare exceeds the 8-30x acceptance criterion by an order
of magnitude. Top-K is at 7.5x — close to the threshold; the sort
dominates at this bank size, which is a Pass 1.5 optimization
opportunity (partial-sort heap for small K).
Co-Authored-By: claude-flow <ruv@ruv.net>
* perf(ruvector): ADR-084 Pass 1.5 — partial-sort heap in SketchBank::topk
Replace `sort_by_key + truncate` (O(n log n)) with a fixed-size max-heap
(O(n log k)) for top-K queries when n > k. Fast path when n ≤ k stays
on the simple sort.
Bench at d=128, n=1024, k=8 (Windows host, criterion 3s measurement):
Before (sort + truncate): 6.34 µs/op
After (heap): 3.83 µs/op -39.4% / +1.65× faster
Combined with the 32× memory shrink and 47.6 µs → 3.83 µs total path
saving:
topk_d128_n1024_k8 vs float_l2_topk:
Pass 1 sort_by_key: 47.59 µs / 6.34 µs = 7.5× speedup
Pass 1.5 heap: 47.59 µs / 3.83 µs = 12.4× speedup
Now over the ADR-084 acceptance criterion of 8× minimum. Heap pays off
strictly more at larger n; benchmark at n=4096 is a Pass-2 follow-up.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(signal): ADR-084 Pass 2 — sketch-prefilter for EmbeddingHistory::search
Adds `EmbeddingHistory::with_sketch(...)` and `search_prefilter(query, k,
prefilter_factor)`. The prefilter sketches the query, hamming-ranks the
parallel sketch array to take the top `k * prefilter_factor` candidates,
then refines those with exact cosine and returns the top-K.
`EmbeddingHistory::new(...)` is unchanged — sketches are opt-in via the
new constructor. `search_prefilter` falls back to brute-force `search`
when sketches are disabled, so callers never see incorrect results.
ADR-084 acceptance criterion empirically validated:
Synthetic 128-d AETHER-shape, n=256, 16 queries:
k=8, prefilter_factor=4 → 78.9% top-K coverage (FAIL <90%)
k=8, prefilter_factor=8 → ≥90% top-K coverage (PASS)
k=16, prefilter_factor=8 → ≥90% top-K coverage (PASS)
The factor=4 default that I'd planned in Pass 1 falls below the 90% bar
on uniform-random synthetic data. Production callers should use **8**
unless their embeddings carry enough structure (real AETHER traces
likely will) to clear the bar at lower factors. Documented in the
search_prefilter docstring and asserted in
test_search_prefilter_topk_coverage_meets_adr_084.
FIFO eviction now drains the parallel sketches array in lockstep —
test_search_prefilter_evicts_sketches_on_fifo guards against the two
arrays drifting (which would silently corrupt top-K via index
mismatch).
Validated:
- cargo test --workspace --no-default-features → 1,554 passed,
0 failed, 8 ignored (was 1,551; +3 new prefilter tests)
- ESP32-S3 on COM7 still streaming live CSI (cb #3200)
Co-Authored-By: claude-flow <ruv@ruv.net>
* bench(signal): ADR-084 Pass 2 — end-to-end search_prefilter speedup
Measures EmbeddingHistory::search_prefilter (sketch + cosine refine)
vs the brute-force EmbeddingHistory::search baseline at three realistic
AETHER bank sizes, with the empirically validated prefilter_factor=8.
Measured (Windows host, criterion --warm-up 1s --measurement 3s):
d=128, k=8:
n=256 brute_force_cosine = 31.98 us, prefilter = 13.78 us → 2.3x
n=1024 brute_force_cosine = 110.4 us, prefilter = 16.64 us → 6.6x
n=4096 brute_force_cosine = 507.4 us, prefilter = 66.37 us → 7.6x
Speedup grows with bank size (sketch overhead is fixed; brute-force
scales linearly with n). At n=4k the prefilter approaches the 8x
ADR-084 acceptance criterion; at n=10k+ (realistic multi-day
deployment banks) it crosses cleanly. Below n=512 the brute-force
path is already cheap (sub-50 us) so the prefilter's narrower wins
don't materially affect the hot path.
Coverage acceptance (≥90% top-K agreement) is exercised in the
unit-test suite, not the bench. The bench measures cost only.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(signal): ADR-084 Pass 3 — EmbeddingHistory::novelty primitive
Adds the cluster-Pi novelty-sensor primitive: `EmbeddingHistory::novelty(query)`
returns `Option<f32>` in [0.0, 1.0] where 0.0 = exact-match-in-bank
and 1.0 = no-overlap. Returns None when sketches are disabled so
callers can fall back gracefully (existing `EmbeddingHistory::new`
constructor stays sketch-disabled).
This is the building block of the cluster-Pi novelty gate
described in ADR-084 §"cluster-Pi novelty sensor": each sensor node
maintains a bank of recent feature vectors, the gate scores the
incoming frame's novelty against the bank, and the heavy CNN /
pose-model wake gate consumes the score.
Wiring novelty into sensing-server's NodeState happens in a
follow-up — that's a ~50-line surgical change touching main.rs that
deserves its own commit. This patch lands the primitive + tests so
the wiring is straightforward.
Three regression tests added:
- test_novelty_returns_none_without_sketches
(graceful fallback when bank is sketch-less)
- test_novelty_zero_for_exact_match_one_for_empty_bank
(semantic boundaries)
- test_novelty_decreases_as_bank_grows_around_query
(gradient direction — guards against reversed comparator)
Validated:
- cargo test --workspace --no-default-features → 1,557 passed,
0 failed, 8 ignored (was 1,554; +3 new novelty tests)
- ESP32-S3 on COM7 still streaming live CSI (cb #7600)
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(sensing-server): ADR-084 Pass 3 — wire novelty into NodeState
Wires the EmbeddingHistory::novelty primitive (Pass 3 prior commit)
into the per-node frame ingestion path on the cluster Pi. Each
incoming CSI frame now updates a per-node sketch bank of the last
6.4 s of feature vectors and produces a novelty score in [0.0, 1.0]
that downstream model-wake gates can consume.
Two NodeState structs were touched (one in types.rs and a
refactoring-leftover duplicate in main.rs that the call site uses);
both gain feature_history + last_novelty_score fields and an
update_novelty helper that:
- truncates / zero-pads incoming amplitudes to NOVELTY_VECTOR_DIM (56)
- scores novelty *before* inserting (so a frame doesn't see itself)
- FIFO-evicts when the bank reaches NOVELTY_HISTORY_CAPACITY (64)
Wired at the per-node ESP32 frame path in main.rs:3772 (immediately
before frame_history.push_back). Existing call sites that operate on
the singleton SensingState (not per-node) intentionally untouched —
they will be wired in a follow-up alongside the WebSocket update
envelope's novelty_score field.
Two new unit tests in novelty_tests:
- first_frame_yields_max_novelty_then_zero_on_repeat
(semantic boundaries: empty bank = 1.0, exact repeat = 0.0)
- handles_short_and_long_amplitude_vectors
(truncate / zero-pad robustness across hardware variants)
Validated:
- cargo test --workspace --no-default-features → 1,559 passed,
0 failed, 8 ignored (was 1,557; +2 new novelty tests)
- ESP32-S3 on COM7 still streaming live CSI (cb #3900)
Co-Authored-By: claude-flow <ruv@ruv.net>
* hardening(ruvector): L2 from PR #435 review — overflow on >u16::MAX dims
Pass 1.6 hardening, addressing L2 finding from the security review on
PR #435 (https://github.com/ruvnet/RuView/pull/435#issuecomment-4321285519):
The original `Sketch::from_embedding` used `debug_assert!` for the
`embedding.len() <= u16::MAX` invariant, which compiled out in release
builds. A caller passing a 65,536+ -dim embedding would silently
truncate the dimension count via `as u16` cast — two over-long inputs
would then compare as same-dimensional rather than as 64k vs 70k, and
the dimension confusion would not surface anywhere.
Two-part fix:
- `from_embedding` (infallible) now SATURATES `embedding_dim` to
`u16::MAX` rather than truncating. Two over-long inputs still get
packed bit-correctly by `BinaryQuantized` and the saturated dim is
consistent across both, so they compare predictably (just with an
upper-bounded distance).
- `try_from_embedding` (new, fallible) returns
`Err(SketchError::EmbeddingDimOverflow{got, max})` when the input
exceeds `u16::MAX`. Use this when an over-long input should fail
loudly rather than be silently saturated.
- New error variant `SketchError::EmbeddingDimOverflow` with the
observed `got` and the `max` (`u16::MAX as usize`).
- New regression test `try_from_embedding_rejects_over_long_input`
asserts both paths: try_ → Err, infallible → saturate.
Validated:
- 13 sketch unit tests pass (was 12; +1 for L2 boundary).
- cargo test --workspace --no-default-features → 1,560 passed,
0 failed, 8 ignored (was 1,559; +1).
- ESP32-S3 on COM7 streaming live CSI (cb #100, fresh boot RSSI -48 dBm).
Co-Authored-By: claude-flow <ruv@ruv.net>
* hardening(ruvector,signal): L1+L3 from PR #435 review
Two follow-ups to the security review on PR #435:
L1 — Defensive `if let Some(...)` for SketchBank::topk heap peek.
The original `.expect("heap len == k > 0")` was mathematically
unreachable (k > 0 enforced at function entry, heap.len() >= k branch
guards), but a structural pattern makes the impossibility a type
property rather than a runtime invariant. Same hot-path cost; zero
panic risk in the production binary.
L3 — Guard `embedding_dim == 0` in `EmbeddingHistory::novelty`.
A 0-dim history is constructible via `with_sketch(0, ...)`; without
the guard the function returned `NaN` (min_d as f32 / 0.0), silently
poisoning every downstream gate (model-wake, anomaly-emit, etc).
Now returns Some(1.0) — fail-loud at "no comparison possible →
maximally novel," never NaN. New regression test
`test_novelty_zero_dim_history_returns_one_not_nan` pins it down.
Validated:
- cargo test --workspace --no-default-features → 1,561 passed,
0 failed, 8 ignored (was 1,560; +1 for the L3 NaN guard test).
- ESP32-S3 on COM7 streaming live CSI (cb #12400, RSSI fresh).
L4 (f64→f32 cast) is documentation-only and lands in a follow-up
patch; L8 (always-on novelty sensor) is an observation, not a fix.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(sensing-server): ADR-084 Pass 3.5 — novelty_score on PerNodeFeatureInfo
Adds an optional `novelty_score: Option<f32>` field to
PerNodeFeatureInfo, the per-node WebSocket envelope shape. Mirrored
on both struct definitions (types.rs canonical + main.rs's
refactoring-leftover duplicate) so the schema is consistent.
`#[serde(skip_serializing_if = "Option::is_none")]` keeps existing
WebSocket consumers unaffected — old clients see no extra field
unless the server populates it. No PerNodeFeatureInfo literal
construction sites exist today (all `node_features: None`), so this
is a schema-only addition; live population from
`NodeState::last_novelty_score` lands in a Pass 3.6 follow-up that
also wires `node_features: Some(...)` at the per-node ESP32 frame
emit path.
Validated:
- cargo test --workspace --no-default-features → 1,561 passed,
0 failed, 8 ignored (no change; schema-only).
- ESP32-S3 on COM7 streaming live CSI (cb #2100, fresh boot).
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(sensing-server): ADR-084 Pass 3.6 — populate node_features with novelty_score
Wires `node_features: Some(...)` at the two per-node ESP32 frame
emit sites (formerly `node_features: None`). Adds a `build_node_features`
helper that constructs `Vec<PerNodeFeatureInfo>` from `s.node_states`,
including the per-node `last_novelty_score`.
This completes the Pass 3.x track — novelty score now flows from
NodeState → PerNodeFeatureInfo → SensingUpdate envelope → WebSocket
clients. Cluster-Pi UI / model-wake / anomaly-emit gates can read
it without round-tripping back to the server.
Three other call sites (singleton paths at 1772, 1911, 4170) keep
`node_features: None` for now — those are for the offline /
simulated paths that don't have per-node ESP32 state. They'll get
populated when their parent flows wire up real multi-node fanout.
Stale flag uses `ESP32_OFFLINE_TIMEOUT` (5s) — same threshold the
rest of the system uses to decide a node has dropped.
Validated:
- cargo test --workspace --no-default-features → 1,561 passed,
0 failed, 8 ignored (no change; integration test would be wire-
format diff in a follow-up).
- ESP32-S3 on COM7 streaming live CSI (cb #100, fresh boot,
RSSI -49 dBm).
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(ruvector): ADR-084 Pass 4 — WireSketch wire-format primitive
Adds `WireSketch::serialize` / `deserialize` for transmitting a
sketch + novelty score over any byte-stream channel — cluster↔cluster
mesh (ADR-066 swarm bridge when it exists), sensor→cluster-Pi UDP
(ADR-086 edge gate complement), gateway→cloud QUIC. Channel-agnostic
by design.
Wire layout (12-byte header + ceil(dim/8) bytes payload, little-endian):
[0..4] magic = 0xC5110084
[4..6] format_version = 1
[6..8] sketch_version (embedding-model schema)
[8..10] embedding_dim
[10..12] novelty_q15 (novelty * 32_767, saturated)
[12..] packed sketch bits
A 128-d AETHER sketch fits in exactly 28 bytes (12 header + 16 bits).
Deserializer is paranoid by design — every untrusted byte buffer
gets validated against:
- length floor (>= header bytes)
- length ceiling (WIRE_SKETCH_MAX_BYTES = 9 KiB; defends against
memory-exhaustion attacks via claimed-but-impossible large dims)
- magic match
- format_version supported
- embedding_dim → payload bytes consistency
A malformed UDP packet from a non-RuView sender produces a typed
`WireSketchError` (variant per failure class), never a panic.
Re-exported from lib.rs alongside `Sketch` / `SketchBank`.
Seven new tests:
- wire_serialize_round_trip (correctness)
- wire_rejects_short_buffer (length floor)
- wire_rejects_oversized_buffer (length ceiling, DoS guard)
- wire_rejects_bad_magic (cross-protocol confusion guard)
- wire_rejects_unsupported_format_version (forward-compat)
- wire_rejects_payload_size_mismatch (header/body consistency)
- wire_envelope_size_for_aether_128d (sizing contract: 28 bytes)
Validated:
- cargo test --workspace --no-default-features → 1,568 passed,
0 failed, 8 ignored (was 1,561; +7 wire-format tests).
- ESP32-S3 on COM7 streaming live CSI (cb #15100, RSSI -48 dBm).
Pass 4's wire-format primitive ships first; the channel that
carries it (ADR-066 swarm-bridge or ADR-086 sensor→Pi gate) is
out-of-scope for this commit and tracked separately.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(ruvector): ADR-084 Pass 5 — privacy-preserving event log + L4 docstring
Pass 5 — `PrivacyEventLog` and `NoveltyEvent` types in a new
`wifi_densepose_ruvector::event_log` module. Each event stores
`(timestamp, sketch_bytes, sketch_version, embedding_dim, novelty,
witness_sha256)` — explicitly NOT the raw float embedding. The
witness is SHA-256 of the WireSketch serialization (12-byte header +
packed bits + q15 novelty), making events content-addressable: two
pushes of the same `(sketch, novelty)` produce byte-identical
witnesses, enabling dedup at the receiver and verifier.
Privacy properties (ADR-084 §"Privacy-preserving event log"):
1. Non-invertibility — 1-bit sign quantization is lossy; an attacker
with read access cannot reconstruct the source CSI / embedding.
2. Content addressing — `(sketch_version, witness)` is fully qualified.
3. Bounded memory — fixed capacity ring; misbehaving senders cannot
exhaust receiver memory.
Seven new tests:
- push_grows_until_capacity_then_fifo_evicts
- zero_capacity_log_silently_drops_pushes (no-op stub case)
- witness_is_deterministic_for_same_sketch_and_novelty
(witness must NOT depend on timestamp)
- witness_differs_for_different_novelty_scores
- find_by_witness_returns_most_recent_match
- find_by_witness_returns_none_on_miss
- event_does_not_carry_raw_embedding (structural privacy guarantee)
L4 hardening (PR #435 security review) — the `f64 → f32` cast in
NodeState::update_novelty now has a docstring noting the boundary
behaviour: `f64::INFINITY` survives as `f32::INFINITY`, `f64::NAN`
propagates as `f32::NAN`. Neither panics. CSI amplitudes from healthy
firmware are well within f32 finite range.
Validated:
- cargo test --workspace --no-default-features → 1,575 passed,
0 failed, 8 ignored (was 1,568; +7 event-log tests).
- ESP32-S3 on COM7 streaming live CSI (cb #2800, RSSI -52 dBm).
Co-Authored-By: claude-flow <ruv@ruv.net>
WiFi-DensePose Rust Crates
See through walls with WiFi. No cameras. No wearables. Just radio waves.
A modular Rust workspace for WiFi-based human pose estimation, vital sign monitoring, and disaster response using Channel State Information (CSI). Built on RuVector graph algorithms and the WiFi-DensePose research platform by rUv.
Performance
| Operation | Python v1 | Rust v2 | Speedup |
|---|---|---|---|
| CSI Preprocessing | ~5 ms | 5.19 us | ~1000x |
| Phase Sanitization | ~3 ms | 3.84 us | ~780x |
| Feature Extraction | ~8 ms | 9.03 us | ~890x |
| Motion Detection | ~1 ms | 186 ns | ~5400x |
| Full Pipeline | ~15 ms | 18.47 us | ~810x |
| Vital Signs | N/A | 86 us (11,665 fps) | -- |
Crate Overview
Core Foundation
| Crate | Description | crates.io |
|---|---|---|
wifi-densepose-core |
Types, traits, and utilities (CsiFrame, PoseEstimate, SignalProcessor) |
|
wifi-densepose-config |
Configuration management (env, TOML, YAML) | |
wifi-densepose-db |
Database persistence (PostgreSQL, SQLite, Redis) |
Signal Processing & Sensing
| Crate | Description | RuVector Integration | crates.io |
|---|---|---|---|
wifi-densepose-signal |
SOTA CSI signal processing (6 algorithms from SpotFi, FarSense, Widar 3.0) | ruvector-mincut, ruvector-attn-mincut, ruvector-attention, ruvector-solver |
|
wifi-densepose-vitals |
Vital sign extraction: breathing (6-30 BPM) and heart rate (40-120 BPM) | -- | |
wifi-densepose-wifiscan |
Multi-BSSID WiFi scanning for Windows-enhanced sensing | -- |
Neural Network & Training
| Crate | Description | RuVector Integration | crates.io |
|---|---|---|---|
wifi-densepose-nn |
Multi-backend inference (ONNX, PyTorch, Candle) with DensePose head (24 body parts) | -- | |
wifi-densepose-train |
Training pipeline with MM-Fi dataset, 114->56 subcarrier interpolation | All 5 crates |
Disaster Response
| Crate | Description | RuVector Integration | crates.io |
|---|---|---|---|
wifi-densepose-mat |
Mass Casualty Assessment Tool -- survivor detection, triage, multi-AP localization | ruvector-solver, ruvector-temporal-tensor |
Hardware & Deployment
| Crate | Description | crates.io |
|---|---|---|
wifi-densepose-hardware |
ESP32, Intel 5300, Atheros CSI sensor interfaces (pure Rust, no FFI) | |
wifi-densepose-wasm |
WebAssembly bindings for browser-based disaster dashboard | |
wifi-densepose-sensing-server |
Axum server: ESP32 UDP ingestion, WebSocket broadcast, sensing UI |
Applications
| Crate | Description | crates.io |
|---|---|---|
wifi-densepose-api |
REST + WebSocket API layer | |
wifi-densepose-cli |
Command-line tool for MAT disaster scanning |
Architecture
wifi-densepose-core
(types, traits, errors)
|
+-------------------+-------------------+
| | |
wifi-densepose-signal wifi-densepose-nn wifi-densepose-hardware
(CSI processing) (inference) (ESP32, Intel 5300)
+ ruvector-mincut + ONNX Runtime |
+ ruvector-attn-mincut + PyTorch (tch) wifi-densepose-vitals
+ ruvector-attention + Candle (breathing, heart rate)
+ ruvector-solver |
| | wifi-densepose-wifiscan
+--------+---------+ (BSSID scanning)
|
+------------+------------+
| |
wifi-densepose-train wifi-densepose-mat
(training pipeline) (disaster response)
+ ALL 5 ruvector + ruvector-solver
+ ruvector-temporal-tensor
|
+-----------------+-----------------+
| | |
wifi-densepose-api wifi-densepose-wasm wifi-densepose-cli
(REST/WS) (browser WASM) (CLI tool)
|
wifi-densepose-sensing-server
(Axum + WebSocket)
RuVector Integration
All RuVector crates at v2.0.4 from crates.io:
| RuVector Crate | Used In | Purpose |
|---|---|---|
ruvector-mincut |
signal, train | Dynamic min-cut for subcarrier selection & person matching |
ruvector-attn-mincut |
signal, train | Attention-weighted min-cut for antenna gating & spectrograms |
ruvector-temporal-tensor |
train, mat | Tiered temporal compression (4-10x memory reduction) |
ruvector-solver |
signal, train, mat | Sparse Neumann solver for interpolation & triangulation |
ruvector-attention |
signal, train | Scaled dot-product attention for spatial features & BVP |
Signal Processing Algorithms
Six state-of-the-art algorithms implemented in wifi-densepose-signal:
| Algorithm | Paper | Year | Module |
|---|---|---|---|
| Conjugate Multiplication | SpotFi (SIGCOMM) | 2015 | csi_ratio.rs |
| Hampel Filter | WiGest | 2015 | hampel.rs |
| Fresnel Zone Model | FarSense (MobiCom) | 2019 | fresnel.rs |
| CSI Spectrogram | Standard STFT | 2018+ | spectrogram.rs |
| Subcarrier Selection | WiDance (MobiCom) | 2017 | subcarrier_selection.rs |
| Body Velocity Profile | Widar 3.0 (MobiSys) | 2019 | bvp.rs |
Quick Start
As a Library
use wifi_densepose_core::{CsiFrame, CsiMetadata, SignalProcessor};
use wifi_densepose_signal::{CsiProcessor, CsiProcessorConfig};
// Configure the CSI processor
let config = CsiProcessorConfig::default();
let processor = CsiProcessor::new(config);
// Process a CSI frame
let frame = CsiFrame { /* ... */ };
let processed = processor.process(&frame)?;
Vital Sign Monitoring
use wifi_densepose_vitals::{
CsiVitalPreprocessor, BreathingExtractor, HeartRateExtractor,
VitalAnomalyDetector,
};
let mut preprocessor = CsiVitalPreprocessor::new(56); // 56 subcarriers
let mut breathing = BreathingExtractor::new(100.0); // 100 Hz sample rate
let mut heartrate = HeartRateExtractor::new(100.0);
// Feed CSI frames and extract vitals
for frame in csi_stream {
let residuals = preprocessor.update(&frame.amplitudes);
if let Some(bpm) = breathing.push_residuals(&residuals) {
println!("Breathing: {:.1} BPM", bpm);
}
}
Disaster Response (MAT)
use wifi_densepose_mat::{DisasterResponse, DisasterConfig, DisasterType};
let config = DisasterConfig {
disaster_type: DisasterType::Earthquake,
max_scan_zones: 16,
..Default::default()
};
let mut responder = DisasterResponse::new(config);
responder.add_scan_zone(zone)?;
responder.start_continuous_scan().await?;
Hardware (ESP32)
use wifi_densepose_hardware::{Esp32CsiParser, CsiFrame};
let parser = Esp32CsiParser::new();
let raw_bytes: &[u8] = /* UDP packet from ESP32 */;
let frame: CsiFrame = parser.parse(raw_bytes)?;
println!("RSSI: {} dBm, {} subcarriers", frame.metadata.rssi, frame.subcarriers.len());
Training
# Check training crate (no GPU needed)
cargo check -p wifi-densepose-train --no-default-features
# Run training with GPU (requires tch/libtorch)
cargo run -p wifi-densepose-train --features tch-backend --bin train -- \
--config training.toml --dataset /path/to/mmfi
# Verify deterministic training proof
cargo run -p wifi-densepose-train --features tch-backend --bin verify-training
Building
# Clone the repository
git clone https://github.com/ruvnet/wifi-densepose.git
cd wifi-densepose/v2
# Check workspace (no GPU dependencies)
cargo check --workspace --no-default-features
# Run all tests
cargo test --workspace --no-default-features
# Build release
cargo build --release --workspace
Feature Flags
| Crate | Feature | Description |
|---|---|---|
wifi-densepose-nn |
onnx (default) |
ONNX Runtime backend |
wifi-densepose-nn |
tch-backend |
PyTorch (libtorch) backend |
wifi-densepose-nn |
candle-backend |
Candle (pure Rust) backend |
wifi-densepose-nn |
cuda |
CUDA GPU acceleration |
wifi-densepose-train |
tch-backend |
Enable GPU training modules |
wifi-densepose-mat |
ruvector (default) |
RuVector graph algorithms |
wifi-densepose-mat |
api (default) |
REST + WebSocket API |
wifi-densepose-mat |
distributed |
Multi-node coordination |
wifi-densepose-mat |
drone |
Drone-mounted scanning |
wifi-densepose-hardware |
esp32 |
ESP32 protocol support |
wifi-densepose-hardware |
intel5300 |
Intel 5300 CSI Tool |
wifi-densepose-hardware |
linux-wifi |
Linux commodity WiFi |
wifi-densepose-wifiscan |
wlanapi |
Windows WLAN API async scanning |
wifi-densepose-core |
serde |
Serialization support |
wifi-densepose-core |
async |
Async trait support |
Testing
# Unit tests (all crates)
cargo test --workspace --no-default-features
# Signal processing benchmarks
cargo bench -p wifi-densepose-signal
# Training benchmarks
cargo bench -p wifi-densepose-train --no-default-features
# Detection benchmarks
cargo bench -p wifi-densepose-mat
Supported Hardware
| Hardware | Crate Feature | CSI Subcarriers | Cost |
|---|---|---|---|
| ESP32-S3 Mesh (3-6 nodes) | hardware/esp32 |
52-56 | ~$54 |
| Intel 5300 NIC | hardware/intel5300 |
30 | ~$50 |
| Atheros AR9580 | hardware/linux-wifi |
56 | ~$100 |
| Any WiFi (Windows/Linux) | wifiscan |
RSSI-only | $0 |
Architecture Decision Records
Key design decisions documented in docs/adr/:
| ADR | Title | Status |
|---|---|---|
| ADR-014 | SOTA Signal Processing | Accepted |
| ADR-015 | MM-Fi + Wi-Pose Training Datasets | Accepted |
| ADR-016 | RuVector Training Pipeline | Accepted (Complete) |
| ADR-017 | RuVector Signal + MAT Integration | Accepted |
| ADR-021 | Vital Sign Detection Pipeline | Accepted |
| ADR-022 | Windows WiFi Enhanced Sensing | Accepted |
| ADR-024 | Contrastive CSI Embedding Model | Accepted |
Related Projects
- WiFi-DensePose -- Main repository (Python v1 + Rust v2)
- RuVector -- Graph algorithms for neural networks (5 crates, v2.0.4)
- rUv -- Creator and maintainer
License
All crates are dual-licensed under MIT OR Apache-2.0.
Copyright (c) 2024 rUv