ruvnet--RuView/v2/crates/wifi-densepose-ruvector

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