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https://github.com/ruvnet/RuView
synced 2026-06-09 10:13:17 +00:00
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3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 48c4d5e92a | |||
| da54d21a38 | |||
| d4c3d2b693 |
@@ -43,6 +43,12 @@ static const char *TAG = "edge_proc";
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static edge_ring_buf_t s_ring;
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static uint32_t s_ring_drops; /* Frames dropped due to full ring buffer. */
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/* Scratch buffers for BPM estimation — moved from stack to static to avoid
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* stack overflow. process_frame + update_multi_person_vitals combined used
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* ~6.5-7.5 KB of the 8 KB task stack. These save ~4 KB of stack. */
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static float s_scratch_br[EDGE_PHASE_HISTORY_LEN];
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static float s_scratch_hr[EDGE_PHASE_HISTORY_LEN];
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static inline bool ring_push(const uint8_t *iq, uint16_t len,
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int8_t rssi, uint8_t channel)
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{
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@@ -513,20 +519,18 @@ static void update_multi_person_vitals(const uint8_t *iq_data, uint16_t n_sc,
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/* Estimate BPM when we have enough history. */
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if (pv->history_len >= 64) {
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/* Build contiguous buffer for zero-crossing. */
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float br_buf[EDGE_PHASE_HISTORY_LEN];
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float hr_buf[EDGE_PHASE_HISTORY_LEN];
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/* Build contiguous buffer (reuse static scratch to save ~2 KB stack). */
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uint16_t buf_len = pv->history_len;
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for (uint16_t i = 0; i < buf_len; i++) {
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uint16_t ri = (pv->history_idx + EDGE_PHASE_HISTORY_LEN
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- buf_len + i) % EDGE_PHASE_HISTORY_LEN;
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br_buf[i] = s_person_br_filt[p][ri];
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hr_buf[i] = s_person_hr_filt[p][ri];
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s_scratch_br[i] = s_person_br_filt[p][ri];
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s_scratch_hr[i] = s_person_hr_filt[p][ri];
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}
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float br = estimate_bpm_zero_crossing(br_buf, buf_len, sample_rate);
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float hr = estimate_bpm_zero_crossing(hr_buf, buf_len, sample_rate);
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float br = estimate_bpm_zero_crossing(s_scratch_br, buf_len, sample_rate);
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float hr = estimate_bpm_zero_crossing(s_scratch_hr, buf_len, sample_rate);
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/* Sanity clamp. */
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if (br >= 6.0f && br <= 40.0f) pv->breathing_bpm = br;
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@@ -690,20 +694,18 @@ static void process_frame(const edge_ring_slot_t *slot)
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/* --- Step 7: BPM estimation (zero-crossing) --- */
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if (s_history_len >= 64) {
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/* Build contiguous buffers from ring. */
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float br_buf[EDGE_PHASE_HISTORY_LEN];
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float hr_buf[EDGE_PHASE_HISTORY_LEN];
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/* Build contiguous buffers from ring (using static scratch to save stack). */
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uint16_t buf_len = s_history_len;
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for (uint16_t i = 0; i < buf_len; i++) {
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uint16_t ri = (s_history_idx + EDGE_PHASE_HISTORY_LEN
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- buf_len + i) % EDGE_PHASE_HISTORY_LEN;
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br_buf[i] = s_breathing_filtered[ri];
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hr_buf[i] = s_heartrate_filtered[ri];
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s_scratch_br[i] = s_breathing_filtered[ri];
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s_scratch_hr[i] = s_heartrate_filtered[ri];
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}
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float br_bpm = estimate_bpm_zero_crossing(br_buf, buf_len, sample_rate);
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float hr_bpm = estimate_bpm_zero_crossing(hr_buf, buf_len, sample_rate);
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float br_bpm = estimate_bpm_zero_crossing(s_scratch_br, buf_len, sample_rate);
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float hr_bpm = estimate_bpm_zero_crossing(s_scratch_hr, buf_len, sample_rate);
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/* Sanity clamp: breathing 6-40 BPM, heart rate 40-180 BPM. */
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if (br_bpm >= 6.0f && br_bpm <= 40.0f) s_breathing_bpm = br_bpm;
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@@ -839,12 +841,11 @@ static void edge_task(void *arg)
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* Without a batch limit the task processes frames back-to-back with
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* only 1-tick yields, which on high frame rates can still starve
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* IDLE1 enough to trip the 5-second task watchdog. See #266, #321. */
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const uint8_t BATCH_LIMIT = 4;
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while (1) {
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uint8_t processed = 0;
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while (processed < BATCH_LIMIT && ring_pop(&slot)) {
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while (processed < EDGE_BATCH_LIMIT && ring_pop(&slot)) {
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process_frame(&slot);
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processed++;
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/* 1-tick yield between frames within a batch. */
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@@ -852,10 +853,10 @@ static void edge_task(void *arg)
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}
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if (processed > 0) {
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/* Post-batch yield: 2 ticks (~20 ms at 100 Hz) so IDLE1 can
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* run and feed the Core 1 watchdog even under sustained load.
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* This is intentionally longer than the 1-tick inter-frame yield. */
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vTaskDelay(2);
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/* Post-batch yield: ~20 ms so IDLE1 can run and feed the
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* Core 1 watchdog even under sustained load. Uses pdMS_TO_TICKS
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* for tick-rate independence (minimum 1 tick). */
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{ TickType_t d = pdMS_TO_TICKS(20); vTaskDelay(d > 0 ? d : 1); }
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} else {
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/* No frames available — sleep one full tick.
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* NOTE: pdMS_TO_TICKS(5) == 0 at 100 Hz, which would busy-spin. */
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@@ -46,6 +46,9 @@
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#define EDGE_FALL_COOLDOWN_MS 5000 /**< Minimum ms between fall alerts (debounce). */
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#define EDGE_FALL_CONSEC_MIN 3 /**< Consecutive frames above threshold to trigger. */
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/* ---- DSP task tuning ---- */
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#define EDGE_BATCH_LIMIT 4 /**< Max frames per batch before longer yield. */
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/* ---- SPSC ring buffer slot ---- */
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typedef struct {
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uint8_t iq_data[EDGE_MAX_IQ_BYTES]; /**< Raw I/Q bytes from CSI callback. */
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@@ -21,3 +21,4 @@ pub use bvp::attention_weighted_bvp;
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pub use fresnel::solve_fresnel_geometry;
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pub use spectrogram::gate_spectrogram;
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pub use subcarrier::mincut_subcarrier_partition;
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pub use subcarrier::subcarrier_importance_weights;
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@@ -142,6 +142,29 @@ pub fn mincut_subcarrier_partition(sensitivity: &[f32]) -> (Vec<usize>, Vec<usiz
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}
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}
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/// Convert a mincut partition into per-subcarrier importance weights.
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///
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/// Sensitive subcarriers (high body-motion correlation) get weight > 1.0,
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/// insensitive ones get weight 0.5. This allows downstream feature extraction
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/// to emphasise the most informative subcarriers.
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pub fn subcarrier_importance_weights(sensitivity: &[f32]) -> Vec<f32> {
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if sensitivity.is_empty() {
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return vec![];
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}
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let (sensitive, _insensitive) = mincut_subcarrier_partition(sensitivity);
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let max_sens = sensitivity
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.iter()
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.cloned()
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.fold(f32::NEG_INFINITY, f32::max)
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.max(1e-9);
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let mut weights = vec![0.5f32; sensitivity.len()];
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for &idx in &sensitive {
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weights[idx] = 1.0 + (sensitivity[idx] / max_sens).min(1.0);
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}
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weights
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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@@ -175,4 +198,38 @@ mod tests {
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assert_eq!(s, vec![0]);
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assert!(i.is_empty());
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}
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#[test]
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fn test_importance_weights_empty() {
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let w = subcarrier_importance_weights(&[]);
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assert!(w.is_empty());
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}
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#[test]
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fn test_importance_weights_all_equal() {
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let sensitivity = vec![1.0f32; 8];
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let w = subcarrier_importance_weights(&sensitivity);
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assert_eq!(w.len(), 8);
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// All subcarriers have identical sensitivity so all should be classified
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// the same way (either all sensitive or all insensitive after mincut).
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// At minimum, no weight should exceed 2.0 or be negative.
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for &wt in &w {
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assert!(wt >= 0.5 && wt <= 2.0, "weight {wt} out of range");
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}
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}
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#[test]
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fn test_importance_weights_sensitive_higher() {
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// First 5 subcarriers have high sensitivity, last 5 low.
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let sensitivity: Vec<f32> = (0..10).map(|i| if i < 5 { 0.9 } else { 0.1 }).collect();
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let w = subcarrier_importance_weights(&sensitivity);
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assert_eq!(w.len(), 10);
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let mean_high: f32 = w[..5].iter().sum::<f32>() / 5.0;
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let mean_low: f32 = w[5..].iter().sum::<f32>() / 5.0;
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assert!(
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mean_high > mean_low,
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"sensitive subcarriers should have higher mean weight ({mean_high}) than insensitive ({mean_low})"
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);
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}
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}
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@@ -804,6 +804,40 @@ fn estimate_breathing_rate_hz(frame_history: &VecDeque<Vec<f64>>, sample_rate_hz
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/// For each subcarrier index `k`, returns `Var[A_k]` over all stored frames.
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/// This captures spatial signal variation; subcarriers whose amplitude fluctuates
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/// heavily across time correspond to directions with motion.
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/// Compute per-subcarrier importance weights using a simple sensitivity split.
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///
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/// Subcarriers whose sensitivity (amplitude magnitude) is above the median are
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/// considered "sensitive" and receive weight `1.0 + (sens / max_sens)` (range 1.0–2.0).
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/// The rest receive a baseline weight of 0.5. This mirrors the RuVector mincut
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/// partition logic without requiring the graph dependency.
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fn compute_subcarrier_importance_weights(sensitivity: &[f64]) -> Vec<f64> {
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let n = sensitivity.len();
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if n == 0 {
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return vec![];
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}
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let max_sens = sensitivity.iter().cloned().fold(f64::NEG_INFINITY, f64::max).max(1e-9);
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// Compute median via a sorted copy.
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let mut sorted = sensitivity.to_vec();
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sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
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let median = if n % 2 == 0 {
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(sorted[n / 2 - 1] + sorted[n / 2]) / 2.0
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} else {
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sorted[n / 2]
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};
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sensitivity
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.iter()
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.map(|&s| {
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if s >= median {
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1.0 + (s / max_sens).min(1.0)
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} else {
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0.5
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}
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})
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.collect()
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}
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fn compute_subcarrier_variances(frame_history: &VecDeque<Vec<f64>>, n_sub: usize) -> Vec<f64> {
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if frame_history.is_empty() || n_sub == 0 {
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return vec![0.0; n_sub];
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@@ -852,13 +886,34 @@ fn extract_features_from_frame(
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) -> (FeatureInfo, ClassificationInfo, f64, Vec<f64>, f64) {
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let n_sub = frame.amplitudes.len().max(1);
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let n = n_sub as f64;
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let mean_amp: f64 = frame.amplitudes.iter().sum::<f64>() / n;
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let mean_rssi = frame.rssi as f64;
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// ── Intra-frame subcarrier variance (spatial spread across subcarriers) ──
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let intra_variance: f64 = frame.amplitudes.iter()
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.map(|a| (a - mean_amp).powi(2))
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.sum::<f64>() / n;
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// ── RuVector Phase 1: subcarrier importance weighting ──
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// Compute per-subcarrier sensitivity from amplitude magnitude, then weight
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// sensitive subcarriers higher (>1.0) and insensitive ones lower (0.5).
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// This emphasises body-motion-correlated subcarriers in all downstream metrics.
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let sub_sensitivity: Vec<f64> = frame.amplitudes.iter().map(|a| a.abs()).collect();
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let importance_weights = compute_subcarrier_importance_weights(&sub_sensitivity);
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let weight_sum: f64 = importance_weights.iter().sum::<f64>();
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let mean_amp: f64 = if weight_sum > 0.0 {
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frame.amplitudes.iter().zip(importance_weights.iter())
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.map(|(a, w)| a * w)
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.sum::<f64>() / weight_sum
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} else {
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frame.amplitudes.iter().sum::<f64>() / n
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};
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// ── Intra-frame subcarrier variance (weighted by importance) ──
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let intra_variance: f64 = if weight_sum > 0.0 {
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frame.amplitudes.iter().zip(importance_weights.iter())
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.map(|(a, w)| w * (a - mean_amp).powi(2))
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.sum::<f64>() / weight_sum
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} else {
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frame.amplitudes.iter()
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.map(|a| (a - mean_amp).powi(2))
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.sum::<f64>() / n
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};
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// ── Temporal (sliding-window) per-subcarrier variance ──
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let sub_variances = compute_subcarrier_variances(frame_history, n_sub);
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@@ -3129,7 +3184,10 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
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// We scope the mutable borrow of node_states so we can
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// access other AppStateInner fields afterward.
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let node_id = frame.node_id;
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let adaptive_model_ref = s.adaptive_model.as_ref().map(|m| m as *const _);
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// Clone adaptive model before mutable borrow of node_states
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// to avoid unsafe raw pointer (review finding #2).
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let adaptive_model_clone = s.adaptive_model.clone();
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let ns = s.node_states.entry(node_id).or_insert_with(NodeState::new);
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ns.last_frame_time = Some(std::time::Instant::now());
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@@ -3143,12 +3201,8 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
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extract_features_from_frame(&frame, &ns.frame_history, sample_rate_hz);
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smooth_and_classify_node(ns, &mut classification, raw_motion);
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// SAFETY: adaptive_model_ref points into s which we hold
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// via write lock; the model is not mutated here. We use a
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// raw pointer to break the borrow-checker deadlock between
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// node_states and adaptive_model (both inside s).
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if let Some(model_ptr) = adaptive_model_ref {
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let model: &adaptive_classifier::AdaptiveModel = unsafe { &*model_ptr };
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// Adaptive override using cloned model (safe, no raw pointers).
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if let Some(ref model) = adaptive_model_clone {
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let amps = ns.frame_history.back()
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.map(|v| v.as_slice())
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.unwrap_or(&[]);
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@@ -3263,6 +3317,19 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
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let _ = s.tx.send(json);
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}
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s.latest_update = Some(update);
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// Evict stale nodes every 100 ticks to prevent memory leak.
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if tick % 100 == 0 {
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let stale = Duration::from_secs(60);
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let before = s.node_states.len();
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s.node_states.retain(|_id, ns| {
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ns.last_frame_time.map_or(false, |t| now.duration_since(t) < stale)
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});
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let evicted = before - s.node_states.len();
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if evicted > 0 {
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info!("Evicted {} stale node(s), {} active", evicted, s.node_states.len());
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}
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}
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}
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}
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Err(e) => {
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