chore(deps): bump actions/checkout from 4 to 6

Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 6.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v4...v6)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-version: '6'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

.github/workflows/aether-arena-harness.yml

@@ -32,7 +32,7 @@ jobs:
       run:
         working-directory: v2
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/bench-regression.yml

@@ -71,7 +71,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout (recursive — wifi-densepose-rufield path-deps vendor/rufield)
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           # The workspace includes `wifi-densepose-rufield`, which path-deps the
           # `vendor/rufield` submodule crates. Without a recursive checkout the
@@ -150,7 +150,7 @@ jobs:
     needs: [bench-compile]
     steps:
       - name: Checkout (recursive)
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/bfld-mqtt-integration.yml

@@ -52,7 +52,7 @@ jobs:
 
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/cd.yml

@@ -41,7 +41,7 @@ jobs:
       image_tag: ${{ steps.determine-tag.outputs.tag }}
     steps:
     - name: Checkout code
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 
@@ -87,7 +87,7 @@ jobs:
       url: https://staging.wifi-densepose.com
     steps:
     - name: Checkout code
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 
@@ -135,7 +135,7 @@ jobs:
       url: https://wifi-densepose.com
     steps:
     - name: Checkout code
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 

.github/workflows/ci.yml

@@ -27,7 +27,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
         fetch-depth: 0
@@ -82,7 +82,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Checkout code
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
       # ADR-262 P1: `wifi-densepose-rufield` path-deps the `vendor/rufield`
@@ -209,7 +209,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 
@@ -276,7 +276,7 @@ jobs:
     if: github.event_name == 'push' && github.ref == 'refs/heads/main'
     steps:
     - name: Checkout code
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 
@@ -346,7 +346,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 
@@ -420,7 +420,7 @@ jobs:
       contents: write   # gh-pages deploy needs write (GITHUB_TOKEN is read-only by default -> 403)
     steps:
     - name: Checkout code
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 

.github/workflows/clone-tracking.yml

@@ -34,7 +34,7 @@ jobs:
   snapshot:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/cog-ha-matter-release.yml

@@ -27,7 +27,7 @@ jobs:
     name: Build x86_64
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -79,7 +79,7 @@ jobs:
     name: Build aarch64 (arm)
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -148,7 +148,7 @@ jobs:
       github.event_name == 'push' &&
       vars.HAS_GCP_CREDENTIALS == 'true'
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/dashboard-a11y.yml

@@ -19,7 +19,7 @@ jobs:
   a11y:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/dashboard-pages.yml

@@ -25,7 +25,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout main
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/desktop-release.yml

@@ -27,7 +27,7 @@ jobs:
         target: [aarch64-apple-darwin, x86_64-apple-darwin]
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -84,7 +84,7 @@ jobs:
     runs-on: windows-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -134,7 +134,7 @@ jobs:
       contents: write
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/firmware-ci.yml

@@ -21,7 +21,7 @@ jobs:
     runs-on: ubuntu-latest
     if: github.ref_type == 'tag'
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
       - name: Check firmware version.txt == tag
@@ -72,7 +72,7 @@ jobs:
             artifact_pt: partition-table-c6.bin
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/firmware-qemu.yml

@@ -99,7 +99,7 @@ jobs:
           - boundary-min
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -215,7 +215,7 @@ jobs:
     name: Fuzz Testing (ADR-061 Layer 6)
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -266,7 +266,7 @@ jobs:
     name: NVS Matrix Generation
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -322,7 +322,7 @@ jobs:
       image: espressif/idf:v5.4
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/fix-regression-guard.yml

@@ -21,7 +21,7 @@ jobs:
     name: Verify fix markers
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/mqtt-integration.yml

@@ -40,7 +40,7 @@ jobs:
       RUST_BACKTRACE: 1
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/nvsim-server-docker.yml

@@ -25,7 +25,7 @@ jobs:
   build-and-publish:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/pip-release.yml

@@ -75,7 +75,7 @@ jobs:
             arch: AMD64
     runs-on: ${{ matrix.os }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -122,7 +122,7 @@ jobs:
       startsWith(github.ref, 'refs/tags/v2.')
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
       - name: Install maturin
@@ -147,7 +147,7 @@ jobs:
       startsWith(github.ref, 'refs/tags/v1.99')
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
       - uses: actions/setup-python@v5

.github/workflows/pointcloud-pages.yml

@@ -28,7 +28,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout main
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/ruview-swarm-ci.yml

@@ -39,7 +39,7 @@ jobs:
           - { label: 'ruflo',            flags: '--features ruflo' }
           - { label: 'full+train',       flags: '--features full,train' }
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
       - uses: dtolnay/rust-toolchain@stable
@@ -61,7 +61,7 @@ jobs:
     name: clippy (-D warnings, --no-deps)
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
       # v2/rust-toolchain.toml pins channel "1.89" with profile "minimal" (no
@@ -96,7 +96,7 @@ jobs:
     name: build train_marl bin
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
       - uses: dtolnay/rust-toolchain@stable
@@ -132,7 +132,7 @@ jobs:
     name: ITAR / publish guard
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
       - name: publish = false is present (no accidental crates.io publish)

.github/workflows/security-scan.yml

@@ -26,7 +26,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
         fetch-depth: 0
@@ -97,7 +97,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 
@@ -166,7 +166,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 
@@ -249,7 +249,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 
@@ -312,7 +312,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
         fetch-depth: 0
@@ -348,7 +348,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 
@@ -387,7 +387,7 @@ jobs:
     steps:
     - name: Checkout code
       continue-on-error: true
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         submodules: recursive
 

.github/workflows/sensing-server-docker.yml

@@ -48,7 +48,7 @@ jobs:
     name: build · push · smoke-test
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/threejs-pages.yml

@@ -29,7 +29,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout main
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/update-submodules.yml

@@ -13,7 +13,7 @@ jobs:
   update:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: true
           fetch-depth: 0

.github/workflows/verify-pipeline.yml

@@ -29,7 +29,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 

.gitignore

@@ -277,6 +277,3 @@ aether-arena/staging/
 # MM-Fi benchmark dataset archives — large data, fetch separately, never commit
 assets/MM-Fi/E0*.zip
 assets/MM-Fi/*.zip
-
-# through-wall demo: regenerable trained model artifact
-examples/through-wall/model/

CHANGELOG.md

@@ -7,9 +7,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
-### Fixed
-- **Multistatic fusion guard interval is now operator-configurable — fixes permanent trust demotion with WiFi-synced ESP32 nodes (#1049).** Two independently-clocked ESP32-S3 boards on ESP-NOW sync drift 10–150 ms (typ. ~70 ms) — the 100 ms beacon + WiFi-MAC jitter cannot hold them within the published 60 ms default guard, so the governed-trust cycle permanently demoted to `Restricted`, suppressed all pose output, and spun the error counter to 200k+ with **no escape hatch but a container restart**. Added a **direct `WDP_GUARD_INTERVAL_US` override** (+ optional `WDP_SOFT_GUARD_US`) to `multistatic_guard_config_from_env`, so a deployment can lift the hard guard past its measured spread (e.g. `WDP_GUARD_INTERVAL_US=200000`) without having to know its exact TDM schedule. Precedence is most-specific-wins: a direct override beats the existing `WDP_TDM_SLOTS`+`WDP_TDM_SLOT_US` schedule-derived guard, which beats the 60 ms/20 ms default; the override is applied on top of whichever base is selected, the soft band is always clamped strictly below the hard guard, and a malformed/zero value is ignored (falls back to the base rather than breaking fusion). The effective guard is now logged at startup. Pinned by 6 new tests (`multistatic_guard_config_tests`): direct-override-wins / beats-TDM-derived / soft-clamped-below-hard / lowering-hard-pulls-soft-down / malformed-or-zero-falls-back / default-when-unset. `wifi-densepose-sensing-server` bin tests **449 → 455**, 0 failed; Python proof VERDICT PASS, hash unchanged (off the signal proof path).
-
 ### Security
 - **`wifi-densepose-occworld-candle` — beyond-SOTA security + correctness review (Milestone #9, crate 4/4).** (1) **HIGH (MEASURED) — checkpoint-load crash on any int32 tensor** (`model.rs::safetensor_dtype_to_candle`). `safetensors::Dtype::I32` was mapped to `candle_core::DType::I64` and the raw int32 byte buffer (4 bytes/elem) was then handed to `Tensor::from_raw_buffer(.., I64, shape, ..)`. Candle derives `elem_count = data.len() / dtype.size_in_bytes()`, so the I64 path halved the element count while keeping the *original* shape — yielding a tensor whose declared shape claims twice as many elements as its backing storage holds. Reading it **panics** (`range end index 6 out of range for slice of length 3` — slice OOB inside candle-core) on any attacker-supplied or PyTorch-exported checkpoint containing an int32 tensor (common: index/buffer tensors). Fixed by mapping `I32 → DType::I32` (and `I16 → DType::I16`), both first-class candle dtypes. Reproduction recorded on old code; pinned by `tests/checkpoint_loading.rs::int32_tensor_loads_with_consistent_shape_and_values` (panics on old, passes on new) plus F32/I64/corrupt-file control cases. (2) **LOW (MEASURED) — `predict()` lacked frame/batch validation at the input boundary** (`inference.rs`). It validated H/W/D but not the externally-supplied frame count; an `f_in > num_frames*2` over-indexed the temporal positional embedding deep in the transformer and surfaced as a cryptic candle "gather" `InvalidIndex` (returned error, not a panic — candle bounds-checks), and a zero frame/batch dim fed a zero-element tensor into the pipeline. Now rejected at the boundary with a clear `ShapeMismatch`. Pinned by `predict_rejects_zero_frames` / `predict_rejects_too_many_frames` / `predict_accepts_frame_count_at_capacity`. (3) **LOW (MEASURED) — divide-by-zero panic on a degenerate input to the public `VQCodebook::encode`** (`vqvae.rs`): a rank-0 / empty-last-dim tensor made `last == 0` and panicked on `elem_count() / last`. Now fails closed with a clear error. Pinned by `encode_rejects_scalar_without_panicking`. **Dimensions confirmed CLEAN with evidence:** panic surface — zero `unwrap()`/`expect()`/`panic!`/`unreachable!` in production code paths (grep evidence; all error handling via `?`/`map_err`); NaN-state-poisoning — N/A (engine is stateless between `predict` calls, input is `u8` class indices so non-finite input is structurally impossible, no persistent world-model buffer to latch into); unbounded-alloc / shape-data mismatch from malformed weights — defended upstream by `safetensors::validate()` (overflow-checked `nelements*dtype.size()` vs declared byte range, rejected before reaching candle); secrets — none (grep clean, only `token_h`/`token_w` config fields match). `unsafe_code = forbid` in the crate manifest. **Build/validation status (MEASURED on Windows):** crate builds and tests under `cargo test -p wifi-densepose-occworld-candle --no-default-features` — **29/29 pass** (20 unit + 4 checkpoint_loading + 3 predict_honesty + 2 doc) after fixes; `cargo test --workspace --no-default-features` = 0 failed across all crates (lone `wifi-densepose-desktop` `api_integration` failure was a Windows "Access is denied (os error 5)" file-lock flake — re-ran in isolation **21/21 pass**); Python proof VERDICT PASS, hash `f8e76f21…446f7a` unchanged. *Warrants ADR slot 179 (parent to author).*
 - **`wifi-densepose-wasm-edge` beyond-SOTA closing review — boundary NaN-state-poisoning guard + clean-with-evidence attestation (ADR-040 edge crate, ~70 modules).** Closing pass of the security campaign over the last untouched sizeable crate. **One real finding fixed (LOW / source-analysis + reproduced):** the two WASM↔host frame boundaries (`lib.rs::on_frame`/`on_timer` and `bin/ghost_hunter.rs::on_frame`) read raw IEEE-754 `f32` from the `csi_get_phase`/`csi_get_amplitude`/`csi_get_variance`/`csi_get_motion_energy` host imports **without any finiteness check** — the entire crate had **zero** `is_finite`/`is_nan` guards, and the in-crate `clamp` helpers propagate NaN (`NaN < lo` and `NaN > hi` are both false). A single non-finite value (firmware DSP bug, uninitialised buffer, or hostile host) latches NaN into the long-lived per-module accumulators (EMA, Welford, phasor sums, anomaly baselines); once latched, every downstream comparison evaluates `false`, so detectors fail **degraded** (stuck gate state, silently-disabled anomaly checks) — silent corruption, not a crash (WASM `panic=abort` is *not* tripped: no indexing/`unwrap` on the poisoned value). Threat model is a **semi-trusted** boundary (the Tier-2 DSP firmware supplies the imports, not direct network/JS), hence LOW severity / defense-in-depth. **Fix:** added `sanitize_host_f32()` (maps non-finite→`0.0`, `core`-only so it holds in `no_std`) applied at every `host_get_*` float read — a single chokepoint covering all ~70 downstream modules, mirroring the existing M-01 negative-`n_subcarriers` boundary clamp. **Pinned by** `boundary_tests::{sanitize_passes_finite_values_through, sanitize_maps_non_finite_to_zero, coherence_monitor_nan_latches_without_sanitize_but_not_with}` — the last asserts on the *current* `CoherenceMonitor` that a raw NaN frame latches the smoothed score (documents the hazard) while the boundary-sanitized path stays finite. **Dimensions attested CLEAN with evidence (source-analysis):** (a) **panic-on-input** — every non-test `unwrap()`/`expect()` is either `#[cfg(test)]` or in the `std`-gated RVF *builder* host tool writing to an in-memory `Vec` (infallible); no `panic!`/`unreachable!`/`todo!`/`get_unchecked` in any hot path. (b) **shape/bounds** — all frame-buffer access is `min()`-clamped (`MAX_SC=32`, `DTW_MAX_LEN`, `LCS_WINDOW`, `PATTERN_LEN`), all index-by-cast sites (`feature_id as usize`, `conclusion_id`, `minute_counter`, `plan_step`) are either compile-time-const-bounded or `if idx <`/`%`-guarded; negative `n_subcarriers` already mapped to 0 (M-01). (c) **memory/leak** — no `move ||` closures, no `mem::forget`/`Box::leak`/`.leak()`; the only `Box::new` is in the `std`-gated `skill_registry` (one-time init, bounded). (d) **secrets** — none (grep clean). **MEASURED build/test evidence:** host `cargo test --features std,medical-experimental` = **672 passed / 0 failed** (was 669 pre-fix; +3 new tests); the real deployment artifacts all build clean on the actual target — `cargo build --target wasm32-unknown-unknown --release` (no_std/panic=abort default lib), `--bin ghost_hunter --no-default-features --features standalone-bin`, and `--features medical-experimental` (toolchain 1.89 per `rust-toolchain.toml`). No ADR slot needed — a single LOW defense-in-depth boundary fix; CHANGELOG attestation suffices.

examples/through-wall/pose.html

@@ -1,159 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-<meta charset="UTF-8"/>
-<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
-<title>WiFlow · live WiFi-inferred pose</title>
-<style>
-  :root{--bg:#0a0c10;--panel:#11151c;--amber:#ffb840;--green:#46e08a;--red:#ff5a5a;--mute:#7d8796;--line:#1d2430}
-  *{box-sizing:border-box}
-  body{margin:0;background:var(--bg);color:#dfe6ee;font:14px/1.5 'JetBrains Mono',ui-monospace,Menlo,monospace}
-  header{padding:14px 18px;border-bottom:1px solid var(--line);display:flex;align-items:center;gap:14px;flex-wrap:wrap}
-  h1{font-size:15px;margin:0;letter-spacing:1px;text-transform:uppercase;font-weight:600}
-  h1 span{color:var(--amber)}
-  #banner{margin-left:auto;padding:5px 12px;border-radius:5px;font-weight:600;font-size:12px;letter-spacing:.5px}
-  .live{background:rgba(70,224,138,.15);color:var(--green);border:1px solid var(--green)}
-  .sim{background:rgba(255,184,64,.15);color:var(--amber);border:1px solid var(--amber)}
-  .down{background:rgba(255,90,90,.15);color:var(--red);border:1px solid var(--red)}
-  main{display:flex;gap:18px;padding:18px;flex-wrap:wrap}
-  .card{background:var(--panel);border:1px solid var(--line);border-radius:10px;padding:14px}
-  canvas{background:#070a0e;border-radius:8px;display:block}
-  .label{font-size:11px;text-transform:uppercase;letter-spacing:1.5px;color:var(--mute);margin-bottom:8px}
-  .stats{min-width:240px}
-  .row{display:flex;justify-content:space-between;padding:3px 0;border-bottom:1px dashed var(--line)}
-  .row .k{color:var(--mute)} .row .v{color:var(--amber);font-variant-numeric:tabular-nums}
-  .v.green{color:var(--green)}
-  .note{margin-top:12px;font-size:11px;color:var(--mute);line-height:1.6;max-width:300px}
-  .note b{color:#dfe6ee}
-</style>
-</head>
-<body>
-<header>
-  <h1>WiFlow · <span>live WiFi-inferred pose</span></h1>
-  <div id="banner" class="down">CONNECTING…</div>
-</header>
-<main>
-  <div class="card">
-    <div class="label">CSI → pose (skeleton) overlaid on your laptop camera</div>
-    <div id="stage" style="width:420px;height:560px;border-radius:8px;overflow:hidden;background:#070a0e">
-      <video id="cam" autoplay muted playsinline style="position:absolute;width:2px;height:2px;opacity:0;pointer-events:none"></video>
-      <canvas id="cv" width="420" height="560"></canvas>
-    </div>
-    <div style="margin-top:10px;display:flex;gap:8px;align-items:center;flex-wrap:wrap">
-      <button id="camBtn" style="background:var(--amber);color:#0a0c10;border:0;border-radius:6px;padding:7px 14px;font:inherit;font-weight:600;cursor:pointer">enable laptop camera</button>
-      <select id="camSel" style="display:none;background:var(--panel);color:#dfe6ee;border:1px solid var(--line);border-radius:6px;padding:6px;font:inherit;max-width:220px"></select>
-    </div>
-    <div id="camStatus" style="margin-top:6px;font-size:11px;color:var(--mute)">camera: off</div>
-    <div class="note" style="margin-top:8px">Camera is a <b>visual reference only</b> — it is NOT fed to the model. Overlay alignment is approximate (model trained in a different camera's frame).</div>
-  </div>
-  <div class="card stats">
-    <div class="label">live</div>
-    <div class="row"><span class="k">CSI source</span><span class="v" id="src">—</span></div>
-    <div class="row"><span class="k">nodes</span><span class="v" id="nodes">—</span></div>
-    <div class="row"><span class="k">presence</span><span class="v" id="pres">—</span></div>
-    <div class="row"><span class="k">motion</span><span class="v" id="motion">—</span></div>
-    <div class="row"><span class="k">pose fps</span><span class="v" id="fps">—</span></div>
-    <div class="note">
-      This skeleton is inferred <b>from WiFi CSI only</b> — no camera in the loop here. A model was
-      trained on paired (camera-pose, CSI) data in this room (ADR-079/180).
-      <br/><br/>
-      <b>Honest accuracy:</b> ~<b>59.5% PCK@0.10</b> on held-out data (vs a 50% mean-pose baseline →
-      <b>+9.4 pp real signal</b>). It captures <b>coarse</b> pose; fine detail is weak (PCK@0.05 ≈ 24%).
-      Same person / room / session — not validated cross-day or through-wall.
-    </div>
-  </div>
-</main>
-<script>
-const POSE_WS = (new URLSearchParams(location.search)).get('ws') || `ws://${location.hostname||'localhost'}:8770/pose`;
-const cv = document.getElementById('cv'), ctx = cv.getContext('2d');
-const $ = id => document.getElementById(id);
-let edges = [[5,7],[7,9],[6,8],[8,10],[5,6],[11,12],[5,11],[6,12],[11,13],[13,15],[12,14],[14,16],[0,1],[0,2],[1,3],[2,4],[0,5],[0,6]];
-let last = null, frames = 0, t0 = performance.now();
-
-function banner(state, txt){ const b=$('banner'); b.className=state; b.textContent=txt; }
-
-// per-joint smoothing (EMA) so dropped/jittery CSI frames render fluidly (ADR-180 dead-reckoning, lite)
-let sm = null;
-function smooth(kps){
-  if(!sm){ sm = kps.map(p=>[p[0],p[1]]); return sm; }
-  const a=0.35; for(let i=0;i<kps.length;i++){ sm[i][0]+=a*(kps[i][0]-sm[i][0]); sm[i][1]+=a*(kps[i][1]-sm[i][1]); }
-  return sm;
-}
-const camEl=document.getElementById('cam');
-function draw(p){
-  const W=cv.width, H=cv.height;
-  // paint the live camera frame onto the canvas (robust — no z-index/overlay tricks)
-  if(camEl && camEl.videoWidth>0){
-    ctx.save(); ctx.globalAlpha=0.9;
-    // cover-fit the camera frame into the canvas
-    const vr=camEl.videoWidth/camEl.videoHeight, cr=W/H;
-    let dw=W, dh=H, dx=0, dy=0;
-    if(vr>cr){ dh=H; dw=H*vr; dx=(W-dw)/2; } else { dw=W; dh=W/vr; dy=(H-dh)/2; }
-    ctx.drawImage(camEl, dx, dy, dw, dh); ctx.restore();
-  } else {
-    ctx.fillStyle='#070a0e'; ctx.fillRect(0,0,W,H);
-  }
-  if(!p || !p.kps){ return; }
-  const s = smooth(p.kps);
-  const k = s.map(([x,y])=>[x*W, y*H]);
-  ctx.lineWidth=5; ctx.strokeStyle=p.presence?'rgba(70,224,138,.95)':'rgba(125,135,150,.8)'; ctx.lineCap='round';
-  ctx.shadowColor='rgba(70,224,138,.6)'; ctx.shadowBlur=8;
-  for(const [a,b] of edges){ ctx.beginPath(); ctx.moveTo(k[a][0],k[a][1]); ctx.lineTo(k[b][0],k[b][1]); ctx.stroke(); }
-  ctx.shadowBlur=0;
-  for(const [x,y] of k){ ctx.beginPath(); ctx.arc(x,y,5,0,7); ctx.fillStyle=p.presence?'#ffb840':'#667'; ctx.fill(); }
-}
-
-// ---- laptop webcam (visual reference only; NOT fed to the model) ----
-let camStream=null;
-async function startCam(deviceId){
-  if(camStream){ camStream.getTracks().forEach(t=>t.stop()); }
-  const constraints = deviceId ? {video:{deviceId:{exact:deviceId}}} : {video:true};
-  const st=document.getElementById('camStatus');
-  try{
-    st.textContent='camera: requesting…';
-    camStream = await navigator.mediaDevices.getUserMedia(constraints);
-    const v=document.getElementById('cam'); v.muted=true; v.srcObject=camStream;
-    v.onloadedmetadata=()=>{ v.play().catch(err=>st.textContent='camera: play() blocked '+err.name); };
-    await v.play().catch(()=>{});
-    const tr=camStream.getVideoTracks()[0]; const ss=tr.getSettings();
-    // live readout: shows if real frames are flowing (videoWidth>0) and which device
-    const tick=()=>{ st.textContent = `camera: "${tr.label}" ${v.videoWidth}x${v.videoHeight} ${tr.readyState} ${v.paused?'PAUSED':'playing'}`; };
-    tick(); setInterval(tick, 1000);
-    document.getElementById('camBtn').textContent='switch camera ↻';
-    // populate the picker now that we have permission (labels need permission)
-    const devs = (await navigator.mediaDevices.enumerateDevices()).filter(d=>d.kind==='videoinput');
-    const sel=document.getElementById('camSel'); sel.style.display = devs.length>1?'inline-block':'none';
-    sel.innerHTML = devs.map((d,i)=>`<option value="${d.deviceId}">${d.label||('camera '+(i+1))}</option>`).join('');
-    const cur = camStream.getVideoTracks()[0].getSettings().deviceId; if(cur) sel.value=cur;
-  }catch(e){
-    document.getElementById('camBtn').textContent = 'camera error: '+e.name+(e.name==='NotReadableError'?' (in use by Zoom/Teams?)':'');
-    console.error('getUserMedia', e);
-  }
-}
-document.getElementById('camBtn').addEventListener('click', ()=>startCam());
-document.getElementById('camSel').addEventListener('change', e=>startCam(e.target.value));
-
-function connect(){
-  banner('down','CONNECTING…');
-  const ws = new WebSocket(POSE_WS);
-  ws.onopen = ()=> banner('sim','WAITING FOR POSE…');
-  ws.onmessage = ev => {
-    const d = JSON.parse(ev.data);
-    if(d.type==='meta'){ edges = d.edges; return; }
-    if(d.type!=='pose') return;
-    last=d; frames++;
-    if(d.src==='esp32') banner('live','LIVE — WiFi-inferred pose (real ESP32 CSI)');
-    else banner('sim','SIMULATED CSI — not real ('+d.src+')');
-    $('src').textContent=d.src; $('src').className = d.src==='esp32'?'v green':'v';
-    $('nodes').textContent=(d.nodes||[]).join(', ')||'—';
-    $('pres').textContent=d.presence?'PRESENT':'—';
-    $('motion').textContent=(d.motion!=null?Math.round(d.motion):'—');
-  };
-  ws.onclose = ()=>{ banner('down','NO BRIDGE — start wiflow_infer.py'); setTimeout(connect,1500); };
-  ws.onerror = ()=> ws.close();
-}
-function loop(){ draw(last); const now=performance.now(); if(now-t0>1000){ $('fps').textContent=frames; frames=0; t0=now; } requestAnimationFrame(loop); }
-connect(); loop();
-</script>
-</body>
-</html>

examples/through-wall/wiflow_ab.py

@@ -1,126 +0,0 @@
-#!/usr/bin/env python3
-"""Rigorous A/B for WiFlow CSI->pose: is the held-out PCK real signal or split leakage?
-
-For a dataset of {csi:[D], kps:17x[x,y,vis]} pairs, train the SAME small MLP under
-several train/val SPLITS and report held-out PCK@0.10 vs the mean-pose baseline:
-
-  - chronological_80_20 : last 20% in time (val temporally ADJACENT to train -> leaks
-                          via CSI/pose autocorrelation; this is what gave us +9.4)
-  - random_80_20        : shuffled (val frames interleaved with train -> MAX leak)
-  - blocked_gap         : hold out a contiguous MIDDLE block with a time GAP buffer on
-                          each side so val is NOT adjacent to any train frame -> the
-                          honest, leakage-controlled test
-
-If the model beats baseline on chronological/random but COLLAPSES to ~baseline on
-blocked_gap, the apparent signal was temporal leakage, not generalizable CSI->pose.
-
-Usage (ruvultra venv): python wiflow_ab.py --data ~/wiflow-room/dataset.jsonl
-"""
-import argparse, json, sys
-import numpy as np, torch, torch.nn as nn
-
-def _rec(r, X, Y, V, B):
-    X.append(r["csi"]); kp=r["kps"]
-    if kp and isinstance(kp[0], (list,tuple)):       # 17 x [x,y(,vis)]
-        Y.append([c for k in kp for c in (k[0],k[1])]); V.append([(k[2] if len(k)>2 else 1.0) for k in kp])
-    else:                                            # flat 34 (browser export, no vis)
-        Y.append(list(kp)); V.append([1.0]*17)
-    B.append(r.get("bucket"))
-
-def load(path):
-    X,Y,V,B=[],[],[],[]
-    txt=open(path).read().strip()
-    if txt[:1] in "[{":                               # JSON (browser export: dict{samples:[]} or bare array)
-        d=json.loads(txt)
-        rows = d if isinstance(d,list) else d.get("samples", d.get("data", []))
-        for r in rows: _rec(r,X,Y,V,B)
-    else:                                             # JSONL (python capture)
-        for line in txt.splitlines():
-            if line.strip(): _rec(json.loads(line),X,Y,V,B)
-    return np.array(X,np.float32), np.array(Y,np.float32), np.array(V,np.float32), B
-
-class Net(nn.Module):
-    def __init__(s,din,dout):
-        super().__init__()
-        s.n=nn.Sequential(nn.Linear(din,384),nn.ReLU(),nn.Dropout(.35),
-                          nn.Linear(384,192),nn.ReLU(),nn.Dropout(.35),
-                          nn.Linear(192,96),nn.ReLU(),nn.Linear(96,dout),nn.Sigmoid())
-    def forward(s,x): return s.n(x)
-
-def pck(pred,gt,vis,thr=0.10):
-    p=pred.reshape(-1,17,2); g=gt.reshape(-1,17,2)
-    d=np.linalg.norm(p-g,axis=2); m=vis>0.5
-    return float((d[m]<thr).mean()) if m.any() else 0.0
-
-def split_idx(n, kind, B=None):
-    idx=np.arange(n)
-    if kind=="chronological_80_20":
-        c=int(n*.8); return idx[:c], idx[c:]
-    if kind=="random_80_20":
-        rng=np.random.default_rng(0); p=rng.permutation(n); c=int(n*.8); return p[:c], p[c:]
-    if kind=="blocked_gap":
-        # val = contiguous middle 20%; a WIDE 10% time gap each side guarantees no train
-        # frame is temporally adjacent to a val frame (kills frame-autocorrelation leakage).
-        v0=int(n*.4); v1=int(n*.6); gap=int(n*.10)
-        val=idx[v0:v1]; train=np.concatenate([idx[:max(0,v0-gap)], idx[min(n,v1+gap):]])
-        return train, val
-    if kind=="grouped_bucket":
-        # hold out ENTIRE activity buckets -> val poses/activities never seen in train.
-        # the strictest leakage-free test (only when bucket labels exist).
-        b=np.array([x if x is not None else -1 for x in B])
-        uniq=[u for u in sorted(set(b.tolist())) if u!=-1]
-        if len(uniq)<3: raise ValueError("too few buckets")
-        hold=set(uniq[::max(1,len(uniq)//3)][:max(1,len(uniq)//3)])  # ~1/3 of activities held out
-        val=idx[np.isin(b,list(hold))]; train=idx[~np.isin(b,list(hold))]
-        return train, val
-    raise ValueError(kind)
-
-def run(X,Y,V,tr,va,epochs=250,seed=0):
-    torch.manual_seed(seed); np.random.seed(seed)   # seed weight init + batch shuffle
-    dev="cuda" if torch.cuda.is_available() else "cpu"
-    mu,sd=X[tr].mean(0),X[tr].std(0)+1e-6
-    Xtr=torch.tensor((X[tr]-mu)/sd).to(dev); Ytr=torch.tensor(Y[tr]).to(dev)
-    Xva=torch.tensor((X[va]-mu)/sd).to(dev)
-    net=Net(X.shape[1],Y.shape[1]).to(dev)
-    opt=torch.optim.Adam(net.parameters(),lr=1e-3,weight_decay=1e-4); lf=nn.MSELoss()
-    best=(1e9,None)
-    for ep in range(epochs):
-        net.train(); perm=torch.randperm(len(Xtr),device=dev)
-        for i in range(0,len(Xtr),64):
-            j=perm[i:i+64]; opt.zero_grad(); loss=lf(net(Xtr[j]),Ytr[j]); loss.backward(); opt.step()
-        net.eval()
-        with torch.no_grad(): pv=net(Xva).cpu().numpy()
-        vl=float(((pv-Y[va])**2).mean())
-        if vl<best[0]: best=(vl,pv)
-    base=np.tile(Y[tr].mean(0),(len(va),1))
-    return pck(best[1],Y[va],V[va]), pck(base,Y[va],V[va])
-
-def main():
-    ap=argparse.ArgumentParser(); ap.add_argument("--data",required=True)
-    ap.add_argument("--epochs",type=int,default=250); ap.add_argument("--seeds",type=int,default=3)
-    a=ap.parse_args()
-    X,Y,V,B=load(a.data); n=len(X)
-    has_buckets=any(x is not None for x in B)
-    print(f"[ab] {n} samples, X={X.shape}, buckets={'yes' if has_buckets else 'no'}, "
-          f"seeds={a.seeds}, epochs={a.epochs}\n")
-    print(f"{'split':<22}{'model PCK@0.10':>16}{'baseline':>11}{'delta (mean±sd)':>20}   verdict")
-    print("-"*86)
-    splits=["chronological_80_20","random_80_20","blocked_gap"]+(["grouped_bucket"] if has_buckets else [])
-    for kind in splits:
-        try:
-            tr,va=split_idx(n,kind,B)
-            ms=[]; bs=[]
-            for s in range(a.seeds):
-                m,b=run(X,Y,V,tr,va,a.epochs,seed=s); ms.append(m); bs.append(b)
-            ms=np.array(ms)*100; bs=np.array(bs)*100; ds=ms-bs
-            dm,dsd=ds.mean(),ds.std()
-            # REAL only if the mean delta minus 1 sd still clears the 1.5pp threshold (robust to seed variance)
-            verdict = "REAL signal" if dm-dsd>1.5 else ("weak/uncertain" if dm>1.5 else "no signal (==baseline)")
-            print(f"{kind:<22}{ms.mean():>13.1f}±{ms.std():>3.1f}{bs.mean():>10.1f}%{dm:>+12.1f}±{dsd:>4.1f}pp   {verdict}")
-        except Exception as e:
-            print(f"{kind:<22}  skipped: {e}")
-    print(f"\nmean±sd over {a.seeds} seeds (weight init + batch order). blocked_gap = 10% time gap each")
-    print("side; grouped_bucket holds out ENTIRE activities (strictest). If only the LEAKY splits")
-    print("(chronological/random) beat baseline, the apparent signal is leakage, not generalizable pose.")
-
-if __name__=="__main__": main()

examples/through-wall/wiflow_browser.html

@@ -112,11 +112,7 @@
         <div class="label">empty-room baseline (ADR-151) — step OUT of the space</div>
         <canvas id="calCv" width="420" height="300"></canvas>
         <div style="margin-top:10px;display:flex;gap:8px;align-items:center;flex-wrap:wrap">
-          <button id="detBtn" class="btn">① detect ESP32 sensors</button>
-          <span id="detNodes" class="v">not detected</span>
-        </div>
-        <div style="margin-top:10px;display:flex;gap:8px;align-items:center;flex-wrap:wrap">
-          <button id="calBtn" class="btn">② calibrate baseline (10 s)</button>
+          <button id="calBtn" class="btn">calibrate baseline (10 s)</button>
           <button id="recalBtn" class="ghost btn">recalibrate</button>
           <label class="note" style="margin:0">get-ready countdown
             <input id="calReady" type="number" value="5" min="3" max="15" style="width:64px"> s</label>
@@ -289,15 +285,9 @@
 // wss when served over https (mobile/secure-context safe), else ws; ?ws= overrides
 const CSI_WS = (new URLSearchParams(location.search)).get('ws')
              || `${location.protocol === 'https:' ? 'wss' : 'ws'}://${location.hostname || 'localhost'}:8765/ws/sensing`;
-// Per-node feature schema — AUTO-DETECTED from the live stream (see detectSensors).
-// [9,13] is only the fallback until detection runs. ORDER is fixed (sorted ascending)
-// so the model's input layout is stable across capture / train / infer.
-let NODE_IDS = [9, 13];
+const NODE_IDS = [9, 13];          // per-node features in this fixed order (matches Python pipeline)
 const FIELD_LEN = 400;             // signal_field.values padded/truncated to 400
-let CSI_DIM = 4 + NODE_IDS.length * 3 + FIELD_LEN;   // 4 global + 3/node + 400 field
-function recomputeCsiDim(){ CSI_DIM = 4 + NODE_IDS.length * 3 + FIELD_LEN; }
-let sensorsDetected = false;       // true once a detect (auto/manual/restored) has locked the node set
-let autoDetectStarted = false;     // one-shot guard for the auto-detect on first live frame
+const CSI_DIM = 4 + NODE_IDS.length * 3 + FIELD_LEN;  // 4 + 6 + 400 = 410
 const N_KP = 17, OUT_DIM = N_KP * 2;                  // 17 COCO keypoints -> 34 coords
 const BASELINE_SECONDS = 10;       // empty-room calibration window
 const EPS = 1e-6;
@@ -343,9 +333,9 @@ async function selectBackend(){
 // ============================================================================
 //  CSI vector construction — MUST match wiflow_capture.py csi_vector() exactly.
 //  [mean_rssi, variance, motion_band_power, breathing_band_power]            (4 global)
-//  + for each node in NODE_IDS order: [mean_rssi, variance, motion_band_power] (3 per-node)
+//  + for node 9 then node 13: [mean_rssi, variance, motion_band_power]       (6 per-node)
 //  + signal_field.values padded/truncated to 400                            (400 field)
-//  = CSI_DIM-d (RAW — baseline-normalization applied separately, see baselineNorm)
+//  = 410-d (RAW — baseline-normalization applied separately, see baselineNorm)
 // ============================================================================
 function csiVector(frame){
   const f = frame.features || {};
@@ -378,87 +368,6 @@ function baselineNorm(vecRaw){
   return out;
 }
 
-// ============================================================================
-//  ESP32 sensor auto-detection
-//  Sniff the live /ws/sensing stream, find which node_ids are actually present
-//  and healthy, and lock that ordered set as the per-node schema (NODE_IDS/CSI_DIM).
-//  The node set defines the model's input dimension, so detection must run BEFORE
-//  calibration + capture; changing it invalidates a baseline/dataset built on a
-//  different set (we confirm, then reset, on a manual re-detect).
-// ============================================================================
-async function detectSensors(ms = 3000){
-  const tally = {};            // node_id -> { seen, fps, rssi }
-  let frames = 0;
-  const t0 = performance.now();
-  const el = $('detNodes'); if (el){ el.textContent = 'scanning…'; el.className = 'v'; }
-  while (performance.now() - t0 < ms){
-    if (latestCSI.frame && latestCSI.source === 'esp32'){
-      frames++;
-      for (const nf of (latestCSI.frame.node_features || [])){
-        const id = nf.node_id; if (id == null) continue;
-        const f = nf.features || {};
-        const t = (tally[id] || (tally[id] = { seen:0, fps:0, rssi:0 }));
-        t.seen++; t.fps += (+nf.frame_rate_hz || 0);
-        t.rssi += (+f.mean_rssi || +nf.rssi_dbm || 0);
-      }
-    }
-    await new Promise(r => setTimeout(r, 100));
-  }
-  // healthy = seen in >40% of sampled frames (filters transient / duplicate ids)
-  const healthy = Object.keys(tally).map(k => ({
-      id:+k, seen:tally[k].seen, fps:tally[k].fps/tally[k].seen, rssi:tally[k].rssi/tally[k].seen }))
-    .filter(n => n.seen >= Math.max(2, frames * 0.4))
-    .sort((a,b)=> a.id - b.id);
-  return { healthy, frames };
-}
-
-function renderDetectedSensors(list){
-  const el = $('detNodes'); if (!el) return;
-  el.textContent = list.length
-    ? list.map(n => `#${n.id} (${Math.round(n.fps)}fps, ${Math.round(n.rssi)}dB)`).join('  ·  ')
-    : 'none found';
-  el.className = list.length ? 'v green' : 'v red';
-}
-
-async function runDetect(manual){
-  const { healthy, frames } = await detectSensors(manual ? 4000 : 3000);
-  if (!healthy.length){
-    const el = $('detNodes');
-    if (el){ el.textContent = frames ? 'no healthy nodes' : 'no live CSI (start sensing-server / esp32)';
-             el.className = 'v red'; }
-    return;
-  }
-  const ids = healthy.map(n => n.id);
-  const changed = ids.length !== NODE_IDS.length || ids.some((v,i)=> v !== NODE_IDS[i]);
-  if (changed && (baseline || SAMPLES.length)){
-    const ok = confirm(
-      `Detected sensors [${ids.join(', ')}] differ from the current set [${NODE_IDS.join(', ')}].\n\n` +
-      `The node set defines the model input, so switching invalidates the existing baseline` +
-      (SAMPLES.length ? ` and ${SAMPLES.length} captured samples` : ``) +
-      `. Reset and use the detected set?`);
-    if (!ok){ renderDetectedSensors(healthy); return; }
-    if (baseline){ baseline = null; stageDone.calibrate = false; idbDel('baseline');
-      $('calStatus').textContent = 'NOT CALIBRATED'; $('calStatus').className = 'v'; $('calBar').style.width = '0%'; }
-    if (SAMPLES.length){ SAMPLES = []; covCounts = new Array(BUCKETS.length).fill(0);
-      idbPut('samples', []); $('capN').textContent = '0'; $('trN').textContent = '0'; renderCoverage(); }
-  }
-  NODE_IDS = ids; recomputeCsiDim(); sensorsDetected = true;
-  idbPut('nodeIds', NODE_IDS);
-  renderDetectedSensors(healthy);
-  refreshGates();
-}
-
-async function restoreNodeIds(){
-  try{
-    const ids = await idbGet('nodeIds');
-    if (Array.isArray(ids) && ids.length){
-      NODE_IDS = ids.slice(); recomputeCsiDim(); sensorsDetected = true;
-      const el = $('detNodes');
-      if (el){ el.textContent = 'restored: ' + NODE_IDS.map(i => '#' + i).join(' '); el.className = 'v'; }
-    }
-  }catch(e){ /* ignore */ }
-}
-
 // ============================================================================
 //  CSI WebSocket
 // ============================================================================
@@ -479,11 +388,6 @@ function connectCSI(){
       source: src,
       nodes: (d.nodes || []).map(n => n.node_id).filter(x => x != null).sort((a,b)=>a-b)
     };
-    // auto-detect the sensor set once, on the first live frame, only when starting fresh
-    // (no baseline / no samples) so we never silently change a schema work is built on.
-    if (src === 'esp32' && !sensorsDetected && !autoDetectStarted && !baseline && SAMPLES.length === 0){
-      autoDetectStarted = true; runDetect(false);
-    }
     if (src === 'esp32') banner('live','LIVE — real ESP32 CSI');
     else banner('sim',`SIMULATED — not real (source=${src})`);
   };
@@ -695,7 +599,6 @@ function finishCalibration(){
   refreshGates();
 }
 $('calBtn').addEventListener('click', startCalibration);
-$('detBtn').addEventListener('click', ()=> runDetect(true));
 $('recalBtn').addEventListener('click', ()=>{ baseline = null; stageDone.calibrate = false;
   $('calStatus').textContent = 'NOT CALIBRATED'; $('calStatus').className = 'v';
   $('calBar').style.width = '0%'; $('calN').textContent = '0'; idbDel('baseline'); refreshGates(); startCalibration(); });
@@ -833,7 +736,7 @@ $('clrBtn').addEventListener('click', async ()=>{
 $('expBtn').addEventListener('click', ()=>{
   const out = {
     format: 'wiflow-browser-dataset', version: 1, exported: new Date().toISOString(),
-    csi_dim: CSI_DIM, out_dim: OUT_DIM, buckets: BUCKETS, nodes: NODE_IDS.slice(),
+    csi_dim: CSI_DIM, out_dim: OUT_DIM, buckets: BUCKETS,
     note: 'csi is baseline-normalized (ADR-151 deviation-from-baseline); kps are 17 COCO keypoints in [0,1] image coords',
     samples: SAMPLES.map((s,i)=>({ csi: Array.from(s.csi), kps: Array.from(s.kps), bucket: s.bucket, t: (s.t!=null?s.t:i) }))
   };
@@ -1249,7 +1152,6 @@ function inferLoop(){
 (async function boot(){
   connectCSI();
   await selectBackend();
-  await restoreNodeIds();    // restore a previously-detected sensor set (fixes CSI_DIM before baseline)
   await loadBaseline();
   await idbLoad();
   await loadModel();

examples/through-wall/wiflow_capture.py

@@ -1,161 +0,0 @@
-#!/usr/bin/env python3
-"""WiFlow-style camera-supervised capture (ADR-079 / ADR-180).
-
-Runs on a box with BOTH a camera (ground truth) and reachable live CSI:
-  - opens a camera, runs MediaPipe Pose -> 17 COCO keypoints (the LABEL),
-  - subscribes to the sensing-server /ws/sensing (the INPUT: CSI features +
-    20x20 signal-field),
-  - writes timestamp-aligned (csi -> pose) pairs to a JSONL dataset.
-
-This is the *collect* phase of camera-supervised CSI->pose training. The camera
-and the CSI nodes MUST see the same person in the same space at the same time,
-or the pairs are meaningless. Honest by construction: we only emit a pair when
-BOTH a confident camera pose AND a live (source=esp32) CSI frame are present in
-the same ~100 ms window.
-
-Usage (on ruvultra, with the CSI tunneled to localhost:8765):
-    python3 wiflow_capture.py --ws ws://localhost:8765/ws/sensing \
-        --cam 0 --out ~/wiflow-room/dataset.jsonl --seconds 180
-"""
-import argparse, asyncio, json, time, threading, sys, os
-from collections import deque
-
-import urllib.request
-import cv2
-import numpy as np
-import mediapipe as mp
-from mediapipe.tasks.python import BaseOptions
-from mediapipe.tasks.python.vision import PoseLandmarker, PoseLandmarkerOptions, RunningMode
-import websockets
-
-_MODEL_URL = ("https://storage.googleapis.com/mediapipe-models/pose_landmarker/"
-              "pose_landmarker_lite/float16/latest/pose_landmarker_lite.task")
-
-def ensure_model(path: str) -> str:
-    if not os.path.exists(path):
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-        print(f"[capture] downloading pose model -> {path}", flush=True)
-        urllib.request.urlretrieve(_MODEL_URL, path)
-    return path
-
-# MediaPipe Pose (33 landmarks) -> 17 COCO keypoints (same mapping as
-# scripts/collect-ground-truth.py, ADR-079).
-COCO_FROM_MP = [0, 2, 5, 7, 8, 11, 12, 13, 14, 15, 16, 23, 24, 25, 26, 27, 28]
-COCO_NAMES = ["nose","l_eye","r_eye","l_ear","r_ear","l_sho","r_sho","l_elb",
-              "r_elb","l_wri","r_wri","l_hip","r_hip","l_knee","r_knee","l_ank","r_ank"]
-
-# ---- shared state between the CSI (async) thread and the camera (sync) loop ----
-_latest_csi = {"t": 0.0, "frame": None}
-_csi_lock = threading.Lock()
-_stop = threading.Event()
-
-
-def csi_thread(ws_url: str):
-    """Background thread: keep the most recent LIVE csi frame in _latest_csi."""
-    async def run():
-        while not _stop.is_set():
-            try:
-                async with websockets.connect(ws_url, open_timeout=8, ping_interval=20) as ws:
-                    while not _stop.is_set():
-                        msg = await asyncio.wait_for(ws.recv(), timeout=8)
-                        d = json.loads(msg)
-                        with _csi_lock:
-                            _latest_csi["t"] = time.time()
-                            _latest_csi["frame"] = d
-            except Exception as e:
-                print(f"[csi] reconnect ({e})", flush=True)
-                await asyncio.sleep(1.0)
-    asyncio.new_event_loop().run_until_complete(run())
-
-
-def csi_vector(frame: dict):
-    """Flatten a csi frame to a fixed-length input vector: features + field."""
-    f = frame.get("features", {}) or {}
-    feats = [f.get("mean_rssi", 0.0), f.get("variance", 0.0),
-             f.get("motion_band_power", 0.0), f.get("breathing_band_power", 0.0)]
-    # per-node mean_rssi/variance/motion for up to the 2 nodes (9, 13)
-    pernode = {nf.get("node_id"): (nf.get("features") or {}) for nf in (frame.get("node_features") or [])}
-    for nid in (9, 13):
-        nf = pernode.get(nid, {})
-        feats += [nf.get("mean_rssi", 0.0), nf.get("variance", 0.0), nf.get("motion_band_power", 0.0)]
-    field = (frame.get("signal_field", {}) or {}).get("values") or []
-    field = (field + [0.0] * 400)[:400]
-    return feats + field   # 4 + 6 + 400 = 410-d
-
-
-def main():
-    ap = argparse.ArgumentParser(description="WiFlow camera-supervised CSI<->pose capture (ADR-180).")
-    ap.add_argument("--ws", default="ws://localhost:8765/ws/sensing")
-    ap.add_argument("--cam", type=int, default=0)
-    ap.add_argument("--out", default=os.path.expanduser("~/wiflow-room/dataset.jsonl"))
-    ap.add_argument("--seconds", type=int, default=180)
-    ap.add_argument("--min-vis", type=float, default=0.5, help="min mean landmark visibility to accept a pose label")
-    ap.add_argument("--max-skew-ms", type=float, default=150, help="max csi/pose time skew to pair")
-    ap.add_argument("--require-esp32", action="store_true", default=True,
-                    help="only pair when csi source==esp32 (real). Default on.")
-    args = ap.parse_args()
-
-    os.makedirs(os.path.dirname(args.out), exist_ok=True)
-    th = threading.Thread(target=csi_thread, args=(args.ws,), daemon=True)
-    th.start()
-
-    cap = cv2.VideoCapture(args.cam)
-    if not cap.isOpened():
-        print(f"ERROR: cannot open camera {args.cam}", file=sys.stderr); sys.exit(2)
-    W = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) or 640
-    H = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) or 480
-    model_path = ensure_model(os.path.expanduser("~/wiflow-room/pose_landmarker_lite.task"))
-    landmarker = PoseLandmarker.create_from_options(PoseLandmarkerOptions(
-        base_options=BaseOptions(model_asset_path=model_path),
-        running_mode=RunningMode.IMAGE, min_pose_detection_confidence=0.5))
-
-    n_pairs = 0; n_nopose = 0; n_nocsi = 0; n_skew = 0; n_sim = 0
-    t0 = time.time()
-    print(f"[capture] camera {args.cam} {W}x{H} -> {args.out} for {args.seconds}s")
-    print("[capture] stand in view AND in the CSI field; move/walk so poses vary. Ctrl-C to stop.")
-    with open(args.out, "a") as out:
-        try:
-            while time.time() - t0 < args.seconds:
-                ok, frame = cap.read()
-                if not ok:
-                    continue
-                now = time.time()
-                rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                res = landmarker.detect(mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb))
-                if not res.pose_landmarks:
-                    n_nopose += 1; continue
-                lm = res.pose_landmarks[0]
-                kps = [[lm[i].x, lm[i].y, lm[i].visibility] for i in COCO_FROM_MP]
-                vis = float(np.mean([k[2] for k in kps]))
-                if vis < args.min_vis:
-                    n_nopose += 1; continue
-                with _csi_lock:
-                    ct = _latest_csi["t"]; cf = _latest_csi["frame"]
-                if cf is None:
-                    n_nocsi += 1; continue
-                if (now - ct) * 1000.0 > args.max_skew_ms:
-                    n_skew += 1; continue
-                if args.require_esp32 and cf.get("source") != "esp32":
-                    n_sim += 1; continue
-                rec = {"t": now, "vis": round(vis, 3),
-                       "kps": [[round(x, 4), round(y, 4), round(v, 3)] for x, y, v in kps],
-                       "csi": csi_vector(cf),
-                       "src": cf.get("source"),
-                       "nodes": sorted(n.get("node_id") for n in cf.get("nodes", []) if n.get("node_id") is not None)}
-                out.write(json.dumps(rec) + "\n")
-                n_pairs += 1
-                if n_pairs % 30 == 0:
-                    out.flush()
-                    el = int(now - t0)
-                    print(f"[capture] t+{el:3d}s pairs={n_pairs} (skip: nopose={n_nopose} nocsi={n_nocsi} skew={n_skew} sim={n_sim})", flush=True)
-        except KeyboardInterrupt:
-            print("\n[capture] stopped by user")
-    _stop.set(); cap.release()
-    print(f"[capture] DONE. wrote {n_pairs} paired samples to {args.out}")
-    print(f"[capture] skipped: no-pose={n_nopose} no-csi={n_nocsi} skew={n_skew} simulated={n_sim}")
-    if n_pairs == 0:
-        print("[capture] WARNING: 0 pairs — check camera sees you AND csi source==esp32 (live).")
-
-
-if __name__ == "__main__":
-    main()

examples/through-wall/wiflow_infer.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python3
-"""Live CSI->pose inference bridge (ADR-180).
-
-Runs on the box with the live CSI. Loads the camera-supervised model (numpy,
-no torch needed), subscribes to /ws/sensing, runs a forward pass per frame, and
-broadcasts the predicted 17-keypoint pose to HTML clients on ws://:8770/pose.
-
-  python wiflow_infer.py --model model/model.npz \
-      --in ws://localhost:8765/ws/sensing --port 8770
-"""
-import argparse, asyncio, json, os
-import numpy as np
-import websockets
-
-# COCO skeleton edges (for the client; sent once in 'meta')
-EDGES = [[5,7],[7,9],[6,8],[8,10],[5,6],[11,12],[5,11],[6,12],
-         [11,13],[13,15],[12,14],[14,16],[0,1],[0,2],[1,3],[2,4],[0,5],[0,6]]
-
-def csi_vector(frame):
-    f = frame.get("features", {}) or {}
-    feats = [f.get("mean_rssi",0.0), f.get("variance",0.0),
-             f.get("motion_band_power",0.0), f.get("breathing_band_power",0.0)]
-    pernode = {nf.get("node_id"): (nf.get("features") or {}) for nf in (frame.get("node_features") or [])}
-    for nid in (9,13):
-        nf = pernode.get(nid,{}); feats += [nf.get("mean_rssi",0.0), nf.get("variance",0.0), nf.get("motion_band_power",0.0)]
-    field = (frame.get("signal_field",{}) or {}).get("values") or []
-    field = (field + [0.0]*400)[:400]
-    return np.array(feats + field, np.float32)
-
-class Model:
-    def __init__(self, path):
-        z = np.load(path)
-        self.mu, self.sd = z["mu"], z["sd"]
-        self.W = [z["net_0_weight"], z["net_3_weight"], z["net_6_weight"], z["net_8_weight"]]
-        self.b = [z["net_0_bias"],   z["net_3_bias"],   z["net_6_bias"],   z["net_8_bias"]]
-    def __call__(self, x):
-        h = (x - self.mu) / self.sd
-        for i in range(3):
-            h = np.maximum(0.0, h @ self.W[i].T + self.b[i])     # Linear+ReLU
-        out = 1.0/(1.0+np.exp(-(h @ self.W[3].T + self.b[3])))   # Linear+Sigmoid -> 34
-        return out.reshape(17,2)
-
-CLIENTS = set()
-LATEST = {"pose": None}
-
-async def serve_client(ws):
-    CLIENTS.add(ws)
-    try:
-        await ws.send(json.dumps({"type":"meta","edges":EDGES}))
-        async for _ in ws:    # client is read-only; just keep alive
-            pass
-    except Exception:
-        pass
-    finally:
-        CLIENTS.discard(ws)
-
-async def infer_loop(model, in_url):
-    while True:
-        try:
-            async with websockets.connect(in_url, open_timeout=8, ping_interval=20) as ws:
-                async for msg in ws:
-                    d = json.loads(msg)
-                    kp = model(csi_vector(d))
-                    cls = d.get("classification",{})
-                    payload = {"type":"pose","src":d.get("source"),
-                               "presence":bool(cls.get("presence")),
-                               "motion":(d.get("features",{}) or {}).get("motion_band_power"),
-                               "kps":[[round(float(x),4),round(float(y),4)] for x,y in kp],
-                               "nodes":sorted(n.get("node_id") for n in d.get("nodes",[]) if n.get("node_id") is not None)}
-                    LATEST["pose"]=payload
-                    if CLIENTS:
-                        dead=[]
-                        for c in list(CLIENTS):
-                            try: await c.send(json.dumps(payload))
-                            except Exception: dead.append(c)
-                        for c in dead: CLIENTS.discard(c)
-        except Exception as e:
-            print(f"[infer] reconnect ({e})", flush=True); await asyncio.sleep(1.0)
-
-async def main():
-    ap = argparse.ArgumentParser()
-    ap.add_argument("--model", default=os.path.join(os.path.dirname(__file__),"model","model.npz"))
-    ap.add_argument("--in", dest="in_url", default="ws://localhost:8765/ws/sensing")
-    ap.add_argument("--port", type=int, default=8770)
-    args = ap.parse_args()
-    model = Model(args.model)
-    print(f"[infer] model {args.model} loaded; serving predicted poses on ws://0.0.0.0:{args.port}/pose")
-    async with websockets.serve(serve_client, "0.0.0.0", args.port):
-        await infer_loop(model, args.in_url)
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/through-wall/wiflow_train.py

@@ -1,102 +0,0 @@
-#!/usr/bin/env python3
-"""Train a CSI->pose model on the camera-supervised dataset (ADR-079/180).
-
-Input  : 410-d CSI vector (4 global feats + 6 per-node + 400 signal-field).
-Target : 17 COCO keypoints (x,y), normalized 0..1 from the camera (ground truth).
-Reports HONEST held-out PCK@k + MPJPE on a chronological val split (the last
-20% of the session — never trained on), so the number is not leaked.
-
-Usage (ruvultra venv):
-    python wiflow_train.py --data ~/wiflow-room/dataset.jsonl --out ~/wiflow-room/model.pt
-"""
-import argparse, json, math, os, sys
-import numpy as np
-import torch, torch.nn as nn
-
-
-def load(path):
-    X, Y, V = [], [], []
-    with open(path) as f:
-        for line in f:
-            r = json.loads(line)
-            X.append(r["csi"])                       # 410
-            kp = r["kps"]                            # 17 x [x,y,vis]
-            Y.append([c for k in kp for c in (k[0], k[1])])   # 34
-            V.append([k[2] for k in kp])             # 17 visibilities
-    return np.array(X, np.float32), np.array(Y, np.float32), np.array(V, np.float32)
-
-
-class Net(nn.Module):
-    def __init__(self, din, dout):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(din, 512), nn.ReLU(), nn.Dropout(0.3),
-            nn.Linear(512, 256), nn.ReLU(), nn.Dropout(0.3),
-            nn.Linear(256, 128), nn.ReLU(),
-            nn.Linear(128, dout), nn.Sigmoid())   # coords in 0..1
-    def forward(self, x): return self.net(x)
-
-
-def pck(pred, gt, vis, thr):
-    # pred/gt: [N,34] -> [N,17,2]; PCK@thr in normalized image units, visible kps only
-    p = pred.reshape(-1, 17, 2); g = gt.reshape(-1, 17, 2)
-    d = np.linalg.norm(p - g, axis=2)             # [N,17]
-    m = vis > 0.5
-    return float((d[m] < thr).mean()) if m.any() else 0.0, float(d[m].mean()) if m.any() else float("nan")
-
-
-def main():
-    ap = argparse.ArgumentParser()
-    ap.add_argument("--data", required=True)
-    ap.add_argument("--out", default=os.path.expanduser("~/wiflow-room/model.pt"))
-    ap.add_argument("--epochs", type=int, default=300)
-    ap.add_argument("--bs", type=int, default=64)
-    args = ap.parse_args()
-
-    X, Y, V = load(args.data)
-    n = len(X)
-    print(f"[train] {n} samples, X={X.shape} Y={Y.shape}")
-    if n < 200:
-        print("[train] too few samples"); sys.exit(2)
-
-    # chronological split (NOT shuffled) so val is a held-out time segment -> honest
-    cut = int(n * 0.8)
-    mu, sd = X[:cut].mean(0), X[:cut].std(0) + 1e-6           # standardize on train only
-    Xn = (X - mu) / sd
-    dev = "cuda" if torch.cuda.is_available() else "cpu"
-    Xtr = torch.tensor(Xn[:cut]).to(dev); Ytr = torch.tensor(Y[:cut]).to(dev)
-    Xva = torch.tensor(Xn[cut:]).to(dev); Yva = Y[cut:]; Vva = V[cut:]
-
-    # mean-pose baseline (predict the train-mean pose for everything) — the bar to beat
-    mean_pose = Y[:cut].mean(0)
-    base_pck, base_mpjpe = pck(np.tile(mean_pose, (len(Yva), 1)), Yva, Vva, 0.10)
-
-    net = Net(X.shape[1], Y.shape[1]).to(dev)
-    opt = torch.optim.Adam(net.parameters(), lr=1e-3, weight_decay=1e-4)
-    lossf = nn.MSELoss()
-    best = (1e9, None)
-    for ep in range(args.epochs):
-        net.train(); perm = torch.randperm(len(Xtr), device=dev)
-        for i in range(0, len(Xtr), args.bs):
-            idx = perm[i:i+args.bs]
-            opt.zero_grad(); out = net(Xtr[idx]); loss = lossf(out, Ytr[idx]); loss.backward(); opt.step()
-        if (ep + 1) % 20 == 0 or ep == args.epochs - 1:
-            net.eval()
-            with torch.no_grad(): pv = net(Xva).cpu().numpy()
-            p10, mpj = pck(pv, Yva, Vva, 0.10); p05, _ = pck(pv, Yva, Vva, 0.05)
-            vloss = float(((pv - Yva) ** 2).mean())
-            print(f"[train] ep{ep+1:3d} val_mse={vloss:.4f} PCK@0.10={p10*100:.1f}% PCK@0.05={p05*100:.1f}% MPJPE={mpj:.4f}")
-            if vloss < best[0]: best = (vloss, {"sd": net.state_dict(), "p10": p10, "p05": p05, "mpj": mpj})
-
-    torch.save({"model": best[1]["sd"], "mu": mu, "sd": sd, "din": X.shape[1]}, args.out)
-    print("\n==================== HONEST RESULT (held-out 20%, never trained) ====================")
-    print(f"  MEAN-POSE BASELINE : PCK@0.10 = {base_pck*100:.1f}%  MPJPE = {base_mpjpe:.4f}  (the bar to beat)")
-    print(f"  CSI->POSE MODEL    : PCK@0.10 = {best[1]['p10']*100:.1f}%  PCK@0.05 = {best[1]['p05']*100:.1f}%  MPJPE = {best[1]['mpj']:.4f}")
-    delta = (best[1]['p10'] - base_pck) * 100
-    print(f"  VERDICT: model {'BEATS' if delta>1 else 'does NOT beat'} mean-pose baseline by {delta:+.1f} pp "
-          f"-> {'real CSI->pose signal' if delta>1 else 'NO usable CSI->pose signal (honest negative)'}")
-    print(f"  saved -> {args.out}")
-
-
-if __name__ == "__main__":
-    main()

firmware/esp32-csi-node/main/display_task.c

@@ -114,19 +114,6 @@ esp_err_t display_task_start(void)
     /* Init touch (optional) */
     esp_err_t touch_ret = display_hal_init_touch();
 
-    /* The SH8601 QSPI panel is write-only — display_hal_init_panel() above "succeeds"
-     * even on a bare board with no panel attached, so it cannot detect absence. The
-     * FT3168 touch controller is an I2C device with readback and is always present on
-     * the Touch-AMOLED board. If touch is absent, the panel "success" was a false-
-     * positive on a display-less DevKit: bail to headless so display_is_active() stays
-     * false and CSI upgrades to MGMT+DATA capture instead of starving at MGMT-only
-     * (RuView#1000). */
-    if (touch_ret != ESP_OK) {
-        ESP_LOGW(TAG, "No FT3168 touch readback — SH8601 probe was a false-positive on a "
-                      "display-less board; running headless so CSI captures (#1000)");
-        return ESP_OK;
-    }
-
     /* Initialize LVGL */
     lv_init();
 

firmware/esp32-csi-node/main/mmwave_sensor.c

@@ -387,21 +387,11 @@ static mmwave_type_t probe_at_baud(uint32_t baud)
         if (len <= 0) continue;
 
         for (int i = 0; i < len; i++) {
-            /* MR60BHA2: require a *validated* 8-byte header — SOF (0x01) + a valid
-             * header checksum (over bytes 0..6) + a known frame type (0x0A__ or
-             * 0x0F09) — NOT a bare 0x01 byte. A floating UART1 with no sensor reads
-             * noise full of 0x01s, which the old `buf[i] == MR60_SOF` check mistook
-             * for a real sensor (false "Detected MR60BHA2", #1107). */
-            if (buf[i] == MR60_SOF && baud == MMWAVE_MR60_BAUD && i + 7 < len) {
-                const uint8_t *h = &buf[i];
-                if (mr60_calc_checksum(h, 7) == h[7]) {
-                    uint16_t type = ((uint16_t)h[5] << 8) | h[6];
-                    if ((type >> 8) == 0x0A || type == 0x0F09) {
-                        mr60_sof_seen++;
-                    }
-                }
+            /* MR60BHA2: SOF = 0x01, followed by valid-looking frame_id bytes */
+            if (buf[i] == MR60_SOF && baud == MMWAVE_MR60_BAUD) {
+                mr60_sof_seen++;
             }
-            /* LD2410: 4-byte header 0xF4F3F2F1 (already specific enough). */
+            /* LD2410: 4-byte header 0xF4F3F2F1 */
             if (i + 3 < len && buf[i] == 0xF4 && buf[i+1] == 0xF3
                 && buf[i+2] == 0xF2 && buf[i+3] == 0xF1
                 && baud == MMWAVE_LD2410_BAUD) {
@@ -413,8 +403,9 @@ static mmwave_type_t probe_at_baud(uint32_t baud)
         if (ld2410_header_seen >= 2) return MMWAVE_TYPE_LD2410;
     }
 
-    /* No weak single-hit fallback: line noise can produce a stray match, so a real
-     * sensor must clear the ≥3 (MR60) / ≥2 (LD2410) validated-frame thresholds. */
+    if (mr60_sof_seen > 0) return MMWAVE_TYPE_MR60BHA2;
+    if (ld2410_header_seen > 0) return MMWAVE_TYPE_LD2410;
+
     return MMWAVE_TYPE_NONE;
 }
 

scripts/align-ground-truth.js

@@ -184,9 +184,7 @@ function loadGroundTruth(filePath) {
   const raw = loadJsonl(filePath);
   const frames = [];
   for (const r of raw) {
-    // Skip non-detection frames (empty keypoints []) — they must not dilute window
-    // confidence; confidence stats are over actual detections only (#1007 Bug 2).
-    if (r.ts_ns == null || !r.keypoints || r.keypoints.length === 0) continue;
+    if (r.ts_ns == null || !r.keypoints) continue;
     frames.push({
       tsMs: cameraTsToMs(r.ts_ns),
       keypoints: r.keypoints,
@@ -268,29 +266,7 @@ function loadCsi(filePath) {
   // Sort by timestamp
   rawCsi.sort((a, b) => a.tsMs - b.tsMs);
   features.sort((a, b) => a.tsMs - b.tsMs);
-
-  // Bug 3 (#1007): keep only frames at the session's MODAL subcarrier count so windows
-  // are homogeneous; never silently zero-pad/truncate the off-format frames the ESP32
-  // emits (HT20/HT40/fragments). extractCsiMatrix then sees uniform-width frames.
-  return { rawCsi: filterToModalSubcarriers(rawCsi), features };
-}
-
-/**
- * Keep only frames whose subcarrier count equals the session's modal (most common)
- * count. Off-format frames are dropped (logged), not padded — prevents the silent
- * zero-padding that corrupted windows in #1007.
- */
-function filterToModalSubcarriers(frames) {
-  if (frames.length === 0) return frames;
-  const counts = new Map();
-  for (const f of frames) counts.set(f.subcarriers, (counts.get(f.subcarriers) || 0) + 1);
-  let modal = frames[0].subcarriers, best = 0;
-  for (const [sc, n] of counts) if (n > best) { best = n; modal = sc; }
-  const kept = frames.filter((f) => f.subcarriers === modal);
-  if (kept.length !== frames.length) {
-    console.error(`[align] #1007: kept ${kept.length}/${frames.length} CSI frames at modal subcarrier count ${modal} (dropped ${frames.length - kept.length} off-format; no silent padding)`);
-  }
-  return kept;
+  return { rawCsi, features };
 }
 
 // ---------------------------------------------------------------------------
@@ -367,8 +343,7 @@ function averageKeypoints(cameraFrames) {
 
 /**
  * Extract CSI amplitude matrix from raw_csi window.
- * Fill is frame-major (matrix[f*nSc + s]), so shape is [windowFrames, subcarriers]
- * (#1007 Bug 4 — was mislabeled [subcarriers, windowFrames], transposing consumers).
+ * Returns { data: flat Float32Array, shape: [subcarriers, windowFrames] }.
  */
 function extractCsiMatrix(window) {
   const nFrames = window.length;
@@ -388,13 +363,12 @@ function extractCsiMatrix(window) {
     }
   }
 
-  return { data: Array.from(matrix), shape: [nFrames, nSc] };
+  return { data: Array.from(matrix), shape: [nSc, nFrames] };
 }
 
 /**
  * Extract feature matrix from feature-type window.
- * Fill is frame-major (matrix[f*dim + d]), so shape is [windowFrames, featureDim]
- * (#1007 Bug 4 — was mislabeled [featureDim, windowFrames]).
+ * Returns { data: flat array, shape: [featureDim, windowFrames] }.
  */
 function extractFeatureMatrix(window) {
   const nFrames = window.length;
@@ -408,7 +382,7 @@ function extractFeatureMatrix(window) {
     }
   }
 
-  return { data: Array.from(matrix), shape: [nFrames, dim] };
+  return { data: Array.from(matrix), shape: [dim, nFrames] };
 }
 
 // ---------------------------------------------------------------------------

scripts/record-csi-udp.py

@@ -15,7 +15,6 @@ import os
 import socket
 import struct
 import time
-from datetime import datetime, timezone
 
 
 def parse_csi_packet(data):
@@ -42,8 +41,7 @@ def parse_csi_packet(data):
 
     return {
         "type": "raw_csi",
-        # true UTC, not local-time-labeled-Z (#1007 Bug 1) — e.g. "2026-06-17T01:23:45.678Z"
-        "timestamp": datetime.now(timezone.utc).isoformat(timespec="milliseconds").replace("+00:00", "Z"),
+        "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S.") + f"{int(time.time() * 1000) % 1000:03d}Z",
         "ts_ns": time.time_ns(),
         "node_id": node_id,
         "rssi": rssi,

v2/crates/wifi-densepose-sensing-server/src/main.rs

@@ -6391,71 +6391,32 @@ fn vitals_snapshots_from_sensing_json(
     }
 }
 
-/// Build the multistatic guard config from the environment (#1031, #1049).
+/// Build the multistatic guard config, optionally derived from the TDM schedule
+/// declared in the environment (#1031).
 ///
-/// Three precedence layers, most-specific wins:
-/// 1. `WDP_GUARD_INTERVAL_US` (+ optional `WDP_SOFT_GUARD_US`) — a **direct**
-///    hard-guard override. This is the #1049 escape hatch: WiFi/ESP-NOW-synced
-///    ESP32 nodes drift 10–150 ms (the 100 ms beacon + WiFi-MAC jitter cannot
-///    hold two independently-clocked boards within the published default), so a
-///    deployment can simply lift the guard past its measured spread (e.g.
-///    `WDP_GUARD_INTERVAL_US=200000`) without knowing its exact TDM schedule.
-/// 2. `WDP_TDM_SLOTS` + `WDP_TDM_SLOT_US` (both positive) — derive the guard
-///    from the declared schedule via [`MultistaticConfig::for_tdm_schedule`].
-/// 3. Otherwise the published default (60 ms hard / 20 ms soft).
-///
-/// The direct override (1) is applied **on top of** whichever base (2 or 3) is
-/// selected, so `WDP_GUARD_INTERVAL_US` always wins for the hard guard while a
-/// TDM-derived soft band is preserved unless it would exceed the new hard guard.
-/// `min_nodes` is *not* set here — the caller overrides it for single-node
-/// passthrough.
+/// When both `WDP_TDM_SLOTS` and `WDP_TDM_SLOT_US` parse as positive integers,
+/// the guard is derived via [`MultistaticConfig::for_tdm_schedule`] so a
+/// deployment can match its exact schedule. Otherwise the published default
+/// (60 ms hard / 20 ms soft) is returned. `min_nodes` is *not* set here — the
+/// caller overrides it for single-node passthrough.
 fn multistatic_guard_config_from_env() -> MultistaticConfig {
     multistatic_guard_config_from(
         std::env::var("WDP_TDM_SLOTS").ok().as_deref(),
         std::env::var("WDP_TDM_SLOT_US").ok().as_deref(),
-        std::env::var("WDP_GUARD_INTERVAL_US").ok().as_deref(),
-        std::env::var("WDP_SOFT_GUARD_US").ok().as_deref(),
     )
 }
 
 /// Pure core of [`multistatic_guard_config_from_env`] for testability.
-fn multistatic_guard_config_from(
-    slots: Option<&str>,
-    slot_us: Option<&str>,
-    guard_us: Option<&str>,
-    soft_us: Option<&str>,
-) -> MultistaticConfig {
-    // Base: TDM-schedule-derived when both slot params are valid, else default.
-    let mut cfg = match (
+fn multistatic_guard_config_from(slots: Option<&str>, slot_us: Option<&str>) -> MultistaticConfig {
+    match (
         slots.and_then(|s| s.trim().parse::<usize>().ok()),
         slot_us.and_then(|s| s.trim().parse::<u64>().ok()),
     ) {
-        (Some(n), Some(us)) if n >= 1 && us >= 1 => MultistaticConfig::for_tdm_schedule(n, us),
-        _ => MultistaticConfig::default(),
-    };
-
-    // Direct hard-guard override (#1049). Ignored when unset/zero/unparseable so
-    // a malformed env var falls back to the base rather than breaking fusion.
-    if let Some(g) = guard_us
-        .and_then(|s| s.trim().parse::<u64>().ok())
-        .filter(|&g| g >= 1)
-    {
-        cfg.guard_interval_us = g;
-        // Keep the soft band strictly below the (possibly lowered) hard guard.
-        if cfg.soft_guard_us >= g {
-            cfg.soft_guard_us = g.saturating_sub(1).max(1);
+        (Some(n), Some(us)) if n >= 1 && us >= 1 => {
+            MultistaticConfig::for_tdm_schedule(n, us)
         }
+        _ => MultistaticConfig::default(),
     }
-
-    // Optional explicit soft-guard override, always clamped strictly below hard.
-    if let Some(s) = soft_us
-        .and_then(|s| s.trim().parse::<u64>().ok())
-        .filter(|&s| s >= 1)
-    {
-        cfg.soft_guard_us = s.min(cfg.guard_interval_us.saturating_sub(1).max(1));
-    }
-
-    cfg
 }
 
 /// Turn a `ProgressiveLoader::new` failure into an actionable diagnostic (#894).
@@ -7524,16 +7485,11 @@ async fn main() {
         pose_tracker: PoseTracker::new(),
         last_tracker_instant: None,
         multistatic_fuser: {
-            // #1031/#1049: the default guard (60 ms hard / 20 ms soft)
-            // accommodates a real TDM slot offset. A deployment overrides it via
-            // WDP_GUARD_INTERVAL_US (direct, e.g. 200000 for WiFi/ESP-NOW sync —
-            // #1049) or WDP_TDM_SLOTS + WDP_TDM_SLOT_US (derive from schedule).
+            // #1031: the default guard (60 ms hard / 20 ms soft) accommodates a
+            // real TDM slot offset. A deployment can override it to match its
+            // own schedule via WDP_TDM_SLOTS + WDP_TDM_SLOT_US (both set ⇒ derive
+            // from the schedule), else the published default is used.
             let cfg = multistatic_guard_config_from_env();
-            info!(
-                "Multistatic fusion guard: {} µs hard / {} µs soft (override via \
-                 WDP_GUARD_INTERVAL_US / WDP_SOFT_GUARD_US, or WDP_TDM_SLOTS+WDP_TDM_SLOT_US)",
-                cfg.guard_interval_us, cfg.soft_guard_us
-            );
             let mut fuser = MultistaticFuser::with_config(MultistaticConfig {
                 min_nodes: 1, // single-node passthrough
                 ..cfg
@@ -7841,72 +7797,6 @@ async fn main() {
     info!("Server shut down cleanly");
 }
 
-#[cfg(test)]
-mod multistatic_guard_config_tests {
-    //! #1049 — the multistatic guard interval must be operator-configurable so a
-    //! WiFi/ESP-NOW deployment (10–150 ms inter-node clock drift) can lift the
-    //! guard past its measured timestamp spread instead of being permanently
-    //! demoted to Restricted with no escape hatch.
-    use super::*;
-
-    #[test]
-    fn default_guard_when_nothing_set() {
-        let cfg = multistatic_guard_config_from(None, None, None, None);
-        assert_eq!(cfg.guard_interval_us, MultistaticConfig::default().guard_interval_us);
-        assert_eq!(cfg.soft_guard_us, MultistaticConfig::default().soft_guard_us);
-    }
-
-    #[test]
-    fn direct_guard_override_wins_and_unblocks_wifi_spread() {
-        // The #1049 reporter's measured ~70 ms spread exceeds the 60 ms default
-        // → permanent demotion. A direct 200 ms override accepts it.
-        let cfg = multistatic_guard_config_from(None, None, Some("200000"), None);
-        assert_eq!(cfg.guard_interval_us, 200_000);
-        assert!(cfg.soft_guard_us < cfg.guard_interval_us);
-        // 70 ms spread now sits inside the guard.
-        assert!(70_000 < cfg.guard_interval_us);
-    }
-
-    #[test]
-    fn direct_guard_override_beats_tdm_derived() {
-        // Both TDM params AND a direct override set → the direct hard guard wins,
-        // the TDM-derived soft band is preserved (still strictly below hard).
-        let cfg = multistatic_guard_config_from(Some("2"), Some("18000"), Some("200000"), None);
-        assert_eq!(cfg.guard_interval_us, 200_000);
-        assert!(cfg.soft_guard_us < cfg.guard_interval_us);
-        assert!(cfg.soft_guard_us >= 1);
-    }
-
-    #[test]
-    fn soft_override_is_clamped_strictly_below_hard() {
-        // A soft guard ≥ hard would be nonsensical → clamped below the hard guard.
-        let cfg = multistatic_guard_config_from(None, None, Some("50000"), Some("999999"));
-        assert_eq!(cfg.guard_interval_us, 50_000);
-        assert!(cfg.soft_guard_us < 50_000);
-    }
-
-    #[test]
-    fn lowering_hard_below_default_soft_pulls_soft_down() {
-        // Override hard to 10 ms (< default 20 ms soft) → soft drops below it.
-        let cfg = multistatic_guard_config_from(None, None, Some("10000"), None);
-        assert_eq!(cfg.guard_interval_us, 10_000);
-        assert!(cfg.soft_guard_us < 10_000);
-    }
-
-    #[test]
-    fn malformed_or_zero_override_falls_back_to_base() {
-        // Garbage / zero must not break fusion — fall back to the base config.
-        for bad in ["", "abc", "0", "-5", "12.5"] {
-            let cfg = multistatic_guard_config_from(None, None, Some(bad), None);
-            assert_eq!(
-                cfg.guard_interval_us,
-                MultistaticConfig::default().guard_interval_us,
-                "override {bad:?} should be ignored"
-            );
-        }
-    }
-}
-
 #[cfg(test)]
 mod node_sync_snapshot_serialization_tests {
     //! ADR-110 iter 24 — JSON public-API contract for the iter 23