88 lines
3.6 KiB
Markdown
88 lines
3.6 KiB
Markdown
# wifi-densepose-ruvector
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RuVector v2.0.4 integration layer for WiFi-DensePose — ADR-017.
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This crate implements all 7 ADR-017 ruvector integration points for the
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signal-processing pipeline and the Multi-AP Triage (MAT) disaster-detection
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module.
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## Integration Points
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| File | ruvector crate | What it does | Benefit |
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|------|----------------|--------------|---------|
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| `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 |
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| `signal/spectrogram` | ruvector-attn-mincut | Attention-guided min-cut gating suppresses noise frames, amplifies body-motion periods | Cleaner Doppler spectrogram input to DensePose head |
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| `signal/bvp` | ruvector-attention | Scaled dot-product attention aggregates per-subcarrier STFT rows weighted by sensitivity | Robust body velocity profile even with missing subcarriers |
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| `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 |
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| `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 |
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| `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 |
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| `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 |
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## Usage
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Add to your `Cargo.toml` (workspace member or direct dependency):
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```toml
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[dependencies]
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wifi-densepose-ruvector = { path = "../wifi-densepose-ruvector" }
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```
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### Signal processing
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```rust
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use wifi_densepose_ruvector::signal::{
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mincut_subcarrier_partition,
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gate_spectrogram,
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attention_weighted_bvp,
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solve_fresnel_geometry,
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};
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// Partition 56 subcarriers by body-motion sensitivity.
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let (sensitive, insensitive) = mincut_subcarrier_partition(&sensitivity_scores);
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// Gate a 32×64 Doppler spectrogram (mild).
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let gated = gate_spectrogram(&flat_spectrogram, 32, 64, 0.1);
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// Aggregate 56 STFT rows into one BVP vector.
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let bvp = attention_weighted_bvp(&stft_rows, &sensitivity_scores, 128);
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// Solve TX-body / body-RX geometry from 5-subcarrier Fresnel observations.
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if let Some((d1, d2)) = solve_fresnel_geometry(&observations, d_total) {
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println!("d1={d1:.2} m, d2={d2:.2} m");
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}
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```
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### MAT disaster detection
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```rust
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use wifi_densepose_ruvector::mat::{
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solve_triangulation,
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CompressedBreathingBuffer,
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CompressedHeartbeatSpectrogram,
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};
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// Localise a survivor from 4 TDoA measurements.
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let pos = solve_triangulation(&tdoa_measurements, &ap_positions);
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// Stream 6000 breathing frames at < 50% memory cost.
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let mut buf = CompressedBreathingBuffer::new(56, zone_id);
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for frame in frames {
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buf.push_frame(&frame);
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}
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// 128-bin heartbeat spectrogram with band-power extraction.
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let mut hb = CompressedHeartbeatSpectrogram::new(128);
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hb.push_column(&freq_column);
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let cardiac_power = hb.band_power(10, 30); // ~0.8–2.0 Hz range
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```
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## Memory Reduction
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Breathing buffer for 56 subcarriers × 60 s × 100 Hz:
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| Tier | Bits/value | Size |
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|------|-----------|------|
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| Raw f32 | 32 | 13.4 MB |
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| Hot (8-bit) | 8 | 3.4 MB |
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| Mixed hot/warm/cold | 3–8 | 3.4–6.7 MB |
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