[dependencies]
wifi-densepose-ruvector = { path = "../wifi-densepose-ruvector" }

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");
}

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