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feat(adr-017): Complete all 7 ruvector integrations across signal and MAT crates
All ADR-017 integration points now implemented: --- wifi-densepose-signal --- 1. subcarrier_selection.rs — ruvector-mincut: mincut_subcarrier_partition uses DynamicMinCut to dynamically partition sensitive/insensitive subcarriers via O(n^1.5 log n) graph bisection. Tests: 8 passed. 2. spectrogram.rs — ruvector-attn-mincut: gate_spectrogram applies self-attention (Q=K=V, configurable lambda) over STFT time frames to suppress noise/multipath interference. Tests: 2 added. 3. bvp.rs — ruvector-attention: attention_weighted_bvp uses ScaledDotProductAttention for sensitivity-weighted BVP aggregation across subcarriers (vs uniform sum). Tests: 2 added. 4. fresnel.rs — ruvector-solver: solve_fresnel_geometry estimates unknown TX-body-RX geometry from multi-subcarrier Fresnel observations via NeumannSolver. Regularization scaled to inv_w_sq_sum * 0.5 for guaranteed convergence (spectral radius = 0.667). Tests: 10 passed. --- wifi-densepose-mat --- 5. localization/triangulation.rs — ruvector-solver: solve_tdoa_triangulation solves multi-AP TDoA positioning via 2×2 NeumannSolver normal equations (Cramer's rule fallback). O(1) in AP count. Tests: 2 added. 6. detection/breathing.rs — ruvector-temporal-tensor: CompressedBreathingBuffer uses TemporalTensorCompressor with tiered quantization for 50-75% CSI amplitude memory reduction (13.4→3.4-6.7 MB/zone). Tests: 2 added. 7. detection/heartbeat.rs — ruvector-temporal-tensor: CompressedHeartbeatSpectrogram stores per-bin TemporalTensorCompressor for micro-Doppler spectrograms with hot/warm/cold tiers. Tests: 1 added. Cargo.toml: ruvector deps optional in MAT crate (feature = "ruvector"), enabled by default. Prevents --no-default-features regressions. Pre-existing MAT --no-default-features failures are unrelated (api/dto.rs serde gating, pre-existed before this PR). Test summary: 144 MAT lib tests + 91 signal tests = all passed. cargo check wifi-densepose-mat (default features): 0 errors. cargo check wifi-densepose-signal: 0 errors. https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
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‎.claude-flow/daemon-state.json‎

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"startedAt": "2026-02-28T15:54:19.353Z",
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"lastRun": "2026-02-28T15:58:19.175Z",
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"averageDurationMs": 0,
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"nextRun": "2026-02-28T16:15:19.082Z",
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"isRunning": false
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},
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"averageDurationMs": 0,
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"lastRun": "2026-02-28T16:03:19.360Z",
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"isRunning": true
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},
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"consolidate": {
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"runCount": 23,
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}
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]
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},
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"savedAt": "2026-02-28T16:08:19.369Z"
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"savedAt": "2026-02-28T16:20:19.184Z"
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}

‎.claude-flow/metrics/codebase-map.json‎

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{
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"timestamp": "2026-02-28T15:58:19.170Z",
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"timestamp": "2026-02-28T16:13:19.193Z",
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"projectRoot": "/home/user/wifi-densepose",
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"structure": {
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"hasPackageJson": false,
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"hasTsConfig": false,
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"hasClaudeConfig": true,
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"hasClaudeFlow": true
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},
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"scannedAt": 1772294299171
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"scannedAt": 1772295199193
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}

‎rust-port/wifi-densepose-rs/Cargo.lock‎

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‎rust-port/wifi-densepose-rs/crates/wifi-densepose-mat/Cargo.toml‎

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@@ -10,7 +10,8 @@ keywords = ["wifi", "disaster", "rescue", "detection", "vital-signs"]
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categories = ["science", "algorithms"]
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[features]
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default = ["std", "api"]
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default = ["std", "api", "ruvector"]
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ruvector = ["dep:ruvector-solver", "dep:ruvector-temporal-tensor"]
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std = []
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api = ["dep:serde", "chrono/serde", "geo/use-serde"]
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portable = ["low-power"]
@@ -24,6 +25,8 @@ serde = ["dep:serde", "chrono/serde", "geo/use-serde"]
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wifi-densepose-core = { path = "../wifi-densepose-core" }
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wifi-densepose-signal = { path = "../wifi-densepose-signal" }
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wifi-densepose-nn = { path = "../wifi-densepose-nn" }
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ruvector-solver = { workspace = true, optional = true }
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ruvector-temporal-tensor = { workspace = true, optional = true }
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# Async runtime
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tokio = { version = "1.35", features = ["rt", "sync", "time"] }

‎rust-port/wifi-densepose-rs/crates/wifi-densepose-mat/src/detection/breathing.rs‎

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use crate::domain::{BreathingPattern, BreathingType, ConfidenceScore};
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// ---------------------------------------------------------------------------
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// Integration 6: CompressedBreathingBuffer (ADR-017, ruvector feature)
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// ---------------------------------------------------------------------------
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#[cfg(feature = "ruvector")]
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use ruvector_temporal_tensor::segment;
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#[cfg(feature = "ruvector")]
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use ruvector_temporal_tensor::{TemporalTensorCompressor, TierPolicy};
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/// Memory-efficient breathing waveform buffer using tiered temporal compression.
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///
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/// Compresses CSI amplitude time-series by 50-75% using tiered quantization:
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/// - Hot tier (recent): 8-bit precision
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/// - Warm tier: 5-7-bit precision
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/// - Cold tier (historical): 3-bit precision
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///
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/// For 60-second window at 100 Hz, 56 subcarriers:
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/// Before: 13.4 MB/zone → After: 3.4-6.7 MB/zone
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#[cfg(feature = "ruvector")]
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pub struct CompressedBreathingBuffer {
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compressor: TemporalTensorCompressor,
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encoded: Vec<u8>,
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n_subcarriers: usize,
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frame_count: u64,
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}
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#[cfg(feature = "ruvector")]
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impl CompressedBreathingBuffer {
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pub fn new(n_subcarriers: usize, zone_id: u64) -> Self {
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Self {
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compressor: TemporalTensorCompressor::new(
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TierPolicy::default(),
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n_subcarriers as u32,
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zone_id as u32,
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),
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encoded: Vec::new(),
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n_subcarriers,
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frame_count: 0,
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}
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}
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/// Push one frame of CSI amplitudes (one time step, all subcarriers).
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pub fn push_frame(&mut self, amplitudes: &[f32]) {
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assert_eq!(amplitudes.len(), self.n_subcarriers);
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let ts = self.frame_count as u32;
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// Synchronize last_access_ts with current timestamp so that the tier
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// policy's age computation (now_ts - last_access_ts + 1) never wraps to
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// zero (which would cause a divide-by-zero in wrapping_div).
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self.compressor.set_access(ts, ts);
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self.compressor.push_frame(amplitudes, ts, &mut self.encoded);
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self.frame_count += 1;
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}
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/// Flush pending compressed data.
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pub fn flush(&mut self) {
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self.compressor.flush(&mut self.encoded);
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}
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/// Decode all frames for breathing frequency analysis.
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/// Returns flat Vec<f32> of shape [n_frames × n_subcarriers].
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pub fn to_flat_vec(&self) -> Vec<f32> {
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let mut out = Vec::new();
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segment::decode(&self.encoded, &mut out);
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out
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}
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/// Get a single frame for real-time display.
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pub fn get_frame(&self, frame_idx: usize) -> Option<Vec<f32>> {
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segment::decode_single_frame(&self.encoded, frame_idx)
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}
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/// Number of frames stored.
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pub fn frame_count(&self) -> u64 {
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self.frame_count
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}
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/// Number of subcarriers per frame.
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pub fn n_subcarriers(&self) -> usize {
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self.n_subcarriers
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}
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}
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/// Configuration for breathing detection
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#[derive(Debug, Clone)]
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pub struct BreathingDetectorConfig {
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}
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}
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#[cfg(all(test, feature = "ruvector"))]
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mod breathing_buffer_tests {
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use super::*;
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#[test]
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fn compressed_breathing_buffer_push_and_decode() {
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let n_sc = 56_usize;
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let mut buf = CompressedBreathingBuffer::new(n_sc, 1);
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for t in 0..10_u64 {
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let frame: Vec<f32> = (0..n_sc).map(|i| (i as f32 + t as f32) * 0.01).collect();
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buf.push_frame(&frame);
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}
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buf.flush();
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assert_eq!(buf.frame_count(), 10);
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// Decoded data should be non-empty
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let flat = buf.to_flat_vec();
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assert!(!flat.is_empty());
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}
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#[test]
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fn compressed_breathing_buffer_get_frame() {
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let n_sc = 8_usize;
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let mut buf = CompressedBreathingBuffer::new(n_sc, 2);
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let frame = vec![0.1_f32; n_sc];
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buf.push_frame(&frame);
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buf.flush();
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// Frame 0 should be decodable
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let decoded = buf.get_frame(0);
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assert!(decoded.is_some() || buf.to_flat_vec().len() == n_sc);
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;

‎rust-port/wifi-densepose-rs/crates/wifi-densepose-mat/src/detection/heartbeat.rs‎

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use crate::domain::{HeartbeatSignature, SignalStrength};
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// ---------------------------------------------------------------------------
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// Integration 7: CompressedHeartbeatSpectrogram (ADR-017, ruvector feature)
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// ---------------------------------------------------------------------------
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#[cfg(feature = "ruvector")]
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use ruvector_temporal_tensor::segment;
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#[cfg(feature = "ruvector")]
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use ruvector_temporal_tensor::{TemporalTensorCompressor, TierPolicy};
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/// Memory-efficient heartbeat micro-Doppler spectrogram using tiered temporal compression.
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///
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/// Stores one TemporalTensorCompressor per frequency bin, each compressing
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/// that bin's time-evolution. Hot tier (recent 10 seconds) at 8-bit,
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/// warm at 5-7-bit, cold at 3-bit — preserving recent heartbeat cycles.
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#[cfg(feature = "ruvector")]
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pub struct CompressedHeartbeatSpectrogram {
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bin_buffers: Vec<TemporalTensorCompressor>,
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encoded: Vec<Vec<u8>>,
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n_freq_bins: usize,
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frame_count: u64,
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}
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#[cfg(feature = "ruvector")]
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impl CompressedHeartbeatSpectrogram {
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pub fn new(n_freq_bins: usize) -> Self {
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let bin_buffers: Vec<_> = (0..n_freq_bins)
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.map(|i| TemporalTensorCompressor::new(TierPolicy::default(), 1, i as u32))
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.collect();
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let encoded = vec![Vec::new(); n_freq_bins];
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Self { bin_buffers, encoded, n_freq_bins, frame_count: 0 }
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}
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/// Push one column of the spectrogram (one time step, all frequency bins).
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pub fn push_column(&mut self, column: &[f32]) {
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assert_eq!(column.len(), self.n_freq_bins);
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let ts = self.frame_count as u32;
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for (i, &val) in column.iter().enumerate() {
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// Synchronize last_access_ts with current timestamp so that the
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// tier policy's age computation (now_ts - last_access_ts + 1) never
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// wraps to zero (which would cause a divide-by-zero in wrapping_div).
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self.bin_buffers[i].set_access(ts, ts);
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self.bin_buffers[i].push_frame(&[val], ts, &mut self.encoded[i]);
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}
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self.frame_count += 1;
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}
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/// Flush all bin buffers.
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pub fn flush(&mut self) {
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for (buf, enc) in self.bin_buffers.iter_mut().zip(self.encoded.iter_mut()) {
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buf.flush(enc);
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}
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}
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/// Compute mean power in a frequency bin range (e.g., heartbeat 0.8-1.5 Hz).
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/// Uses most recent `n_recent` frames for real-time triage.
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pub fn band_power(&self, low_bin: usize, high_bin: usize, n_recent: usize) -> f32 {
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let high = high_bin.min(self.n_freq_bins.saturating_sub(1));
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if low_bin > high {
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return 0.0;
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}
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let mut total = 0.0_f32;
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let mut count = 0_usize;
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for b in low_bin..=high {
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let mut out = Vec::new();
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segment::decode(&self.encoded[b], &mut out);
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let recent: f32 = out.iter().rev().take(n_recent).map(|x| x * x).sum();
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total += recent;
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count += 1;
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}
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if count == 0 { 0.0 } else { total / count as f32 }
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}
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pub fn frame_count(&self) -> u64 { self.frame_count }
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pub fn n_freq_bins(&self) -> usize { self.n_freq_bins }
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}
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/// Configuration for heartbeat detection
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#[derive(Debug, Clone)]
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pub struct HeartbeatDetectorConfig {
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}
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}
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#[cfg(all(test, feature = "ruvector"))]
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mod heartbeat_buffer_tests {
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use super::*;
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#[test]
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fn compressed_heartbeat_push_and_band_power() {
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let n_bins = 32_usize;
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let mut spec = CompressedHeartbeatSpectrogram::new(n_bins);
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for t in 0..20_u64 {
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let col: Vec<f32> = (0..n_bins)
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.map(|b| if b < 16 { 1.0 } else { 0.1 })
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.collect();
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let _ = t;
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spec.push_column(&col);
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}
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spec.flush();
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assert_eq!(spec.frame_count(), 20);
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// Low bins (0..15) should have higher power than high bins (16..31)
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let low_power = spec.band_power(0, 15, 20);
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let high_power = spec.band_power(16, 31, 20);
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assert!(low_power >= high_power,
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"low_power={low_power} should >= high_power={high_power}");
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;

‎rust-port/wifi-densepose-rs/crates/wifi-densepose-mat/src/detection/mod.rs‎

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mod movement;
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mod pipeline;
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pub use breathing::{BreathingDetector, BreathingDetectorConfig};
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pub use breathing::{BreathingDetector, BreathingDetectorConfig, CompressedBreathingBuffer};
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pub use ensemble::{EnsembleClassifier, EnsembleConfig, EnsembleResult, SignalConfidences};
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pub use heartbeat::{HeartbeatDetector, HeartbeatDetectorConfig};
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pub use heartbeat::{HeartbeatDetector, HeartbeatDetectorConfig, CompressedHeartbeatSpectrogram};
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pub use movement::{MovementClassifier, MovementClassifierConfig};
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pub use pipeline::{DetectionPipeline, DetectionConfig, VitalSignsDetector, CsiDataBuffer};

‎rust-port/wifi-densepose-rs/crates/wifi-densepose-mat/src/localization/mod.rs‎

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mod depth;
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mod fusion;
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pub use triangulation::{Triangulator, TriangulationConfig};
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pub use triangulation::{Triangulator, TriangulationConfig, solve_tdoa_triangulation};
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pub use depth::{DepthEstimator, DepthEstimatorConfig};
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pub use fusion::{PositionFuser, LocalizationService};

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