37b54d649b
12,126 lines of new Rust code across 22 modules with 285 tests: ADR-029 RuvSense Core (signal crate, 10 modules): - multiband.rs: Multi-band CSI frame fusion from channel hopping - phase_align.rs: Cross-channel LO phase rotation correction - multistatic.rs: Attention-weighted cross-node viewpoint fusion - coherence.rs: Z-score per-subcarrier coherence scoring - coherence_gate.rs: Accept/PredictOnly/Reject/Recalibrate gating - pose_tracker.rs: 17-keypoint Kalman tracker with re-ID - mod.rs: Pipeline orchestrator ADR-030 Persistent Field Model (signal crate, 7 modules): - field_model.rs: SVD-based room eigenstructure, Welford stats - tomography.rs: Coarse RF tomography from link attenuations (ISTA) - longitudinal.rs: Personal baseline drift detection over days - intention.rs: Pre-movement prediction (200-500ms lead signals) - cross_room.rs: Cross-room identity continuity - gesture.rs: Gesture classification via DTW template matching - adversarial.rs: Physically impossible signal detection ADR-031 RuView (ruvector crate, 5 modules): - attention.rs: Scaled dot-product with geometric bias - geometry.rs: Geometric Diversity Index, Cramer-Rao bounds - coherence.rs: Phase phasor coherence gating - fusion.rs: MultistaticArray aggregate, fusion orchestrator - mod.rs: Module exports Training & Hardware: - ruview_metrics.rs: 3-metric acceptance test (PCK/OKS, MOTA, vitals) - esp32/tdm.rs: TDM sensing protocol, sync beacons, drift compensation - Firmware: channel hopping, NDP injection, NVS config extensions Security fixes: - field_model.rs: saturating_sub prevents timestamp underflow - longitudinal.rs: FIFO eviction note for bounded buffer README updated with RuvSense section, new feature badges, changelog v3.1.0. Co-Authored-By: claude-flow <ruv@ruv.net>
wifi-densepose-ruvector
RuVector v2.0.4 integration layer for WiFi-DensePose — ADR-017.
This crate implements all 7 ADR-017 ruvector integration points for the signal-processing pipeline and the Multi-AP Triage (MAT) disaster-detection module.
Integration Points
| File | ruvector crate | What it does | Benefit |
|---|---|---|---|
signal/subcarrier |
ruvector-mincut | Graph min-cut partitions subcarriers into sensitive / insensitive groups based on body-motion correlation | Automatic subcarrier selection without hand-tuned thresholds |
signal/spectrogram |
ruvector-attn-mincut | Attention-guided min-cut gating suppresses noise frames, amplifies body-motion periods | Cleaner Doppler spectrogram input to DensePose head |
signal/bvp |
ruvector-attention | Scaled dot-product attention aggregates per-subcarrier STFT rows weighted by sensitivity | Robust body velocity profile even with missing subcarriers |
signal/fresnel |
ruvector-solver | Sparse regularized least-squares estimates TX-body (d1) and body-RX (d2) distances from multi-subcarrier Fresnel amplitude observations | Physics-grounded geometry without extra hardware |
mat/triangulation |
ruvector-solver | Neumann series solver linearises TDoA hyperbolic equations to estimate 2-D survivor position across multi-AP deployments | Sub-5 m accuracy from ≥3 TDoA pairs |
mat/breathing |
ruvector-temporal-tensor | Tiered quantized streaming buffer: hot ~10 frames at 8-bit, warm at 5–7-bit, cold at 3-bit | 13.4 MB raw → 3.4–6.7 MB for 56 sc × 60 s × 100 Hz |
mat/heartbeat |
ruvector-temporal-tensor | Per-frequency-bin tiered compressor for heartbeat spectrogram; band_power() extracts mean squared energy in any band |
Independent tiering per bin; no cross-bin quantization coupling |
Usage
Add to your Cargo.toml (workspace member or direct dependency):
[dependencies]
wifi-densepose-ruvector = { path = "../wifi-densepose-ruvector" }
Signal processing
use wifi_densepose_ruvector::signal::{
mincut_subcarrier_partition,
gate_spectrogram,
attention_weighted_bvp,
solve_fresnel_geometry,
};
// Partition 56 subcarriers by body-motion sensitivity.
let (sensitive, insensitive) = mincut_subcarrier_partition(&sensitivity_scores);
// Gate a 32×64 Doppler spectrogram (mild).
let gated = gate_spectrogram(&flat_spectrogram, 32, 64, 0.1);
// Aggregate 56 STFT rows into one BVP vector.
let bvp = attention_weighted_bvp(&stft_rows, &sensitivity_scores, 128);
// Solve TX-body / body-RX geometry from 5-subcarrier Fresnel observations.
if let Some((d1, d2)) = solve_fresnel_geometry(&observations, d_total) {
println!("d1={d1:.2} m, d2={d2:.2} m");
}
MAT disaster detection
use wifi_densepose_ruvector::mat::{
solve_triangulation,
CompressedBreathingBuffer,
CompressedHeartbeatSpectrogram,
};
// Localise a survivor from 4 TDoA measurements.
let pos = solve_triangulation(&tdoa_measurements, &ap_positions);
// Stream 6000 breathing frames at < 50% memory cost.
let mut buf = CompressedBreathingBuffer::new(56, zone_id);
for frame in frames {
buf.push_frame(&frame);
}
// 128-bin heartbeat spectrogram with band-power extraction.
let mut hb = CompressedHeartbeatSpectrogram::new(128);
hb.push_column(&freq_column);
let cardiac_power = hb.band_power(10, 30); // ~0.8–2.0 Hz range
Memory Reduction
Breathing buffer for 56 subcarriers × 60 s × 100 Hz:
| Tier | Bits/value | Size |
|---|---|---|
| Raw f32 | 32 | 13.4 MB |
| Hot (8-bit) | 8 | 3.4 MB |
| Mixed hot/warm/cold | 3–8 | 3.4–6.7 MB |