dearsky/wifi-densepose
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cca91bd8751dee7fb79f39b7dbf0688838ece667
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7 Commits

Author SHA1 Message Date
Claude
cca91bd875 feat(adr-017): Implement ruvector integrations in signal crate (partial) 
Agents completed three of seven ADR-017 integration points:

1. subcarrier_selection.rs — ruvector-mincut: mincut_subcarrier_partition
   partitions subcarriers into (sensitive, insensitive) groups using
   DynamicMinCut. O(n^1.5 log n) amortized vs O(n log n) static sort.
   Includes test: mincut_partition_separates_high_low.

2. spectrogram.rs — ruvector-attn-mincut: gate_spectrogram applies
   self-attention (Q=K=V) over STFT time frames to suppress noise and
   multipath interference frames. Configurable lambda gating strength.
   Includes tests: preserves shape, finite values.

3. bvp.rs — ruvector-attention stub added (in progress by agent).

4. Cargo.toml — added ruvector-mincut, ruvector-attn-mincut,
   ruvector-temporal-tensor, ruvector-solver, ruvector-attention
   as workspace deps in wifi-densepose-signal crate.

Cargo.lock updated for new dependencies.

Remaining ADR-017 integrations (fresnel.rs, MAT crate) still in
progress via background agents.

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
 2026-02-28 16:10:18 +00:00
Claude
81ad09d05b feat(train): Add ruvector integration — ADR-016, deps, DynamicPersonMatcher 
- docs/adr/ADR-016: Full ruvector integration ADR with verified API details
  from source inspection (github.com/ruvnet/ruvector). Covers mincut,
  attn-mincut, temporal-tensor, solver, and attention at v2.0.4.
- Cargo.toml: Add ruvector-mincut, ruvector-attn-mincut, ruvector-temporal-
  tensor, ruvector-solver, ruvector-attention = "2.0.4" to workspace deps
  and wifi-densepose-train crate deps.
- metrics.rs: Add DynamicPersonMatcher wrapping ruvector_mincut::DynamicMinCut
  for subpolynomial O(n^1.5 log n) multi-frame person tracking; adds
  assignment_mincut() public entry point.
- proof.rs, trainer.rs, model.rs, dataset.rs, subcarrier.rs: Agent
  improvements to full implementations (loss decrease verification, SHA-256
  hash, LCG shuffle, ResNet18 backbone, MmFiDataset, linear interp).
- tests: test_config, test_dataset, test_metrics, test_proof, training_bench
  all added/updated. 100+ tests pass with no-default-features.

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
 2026-02-28 15:42:10 +00:00
Claude
fce1271140 feat(rust): Complete training pipeline — losses, metrics, model, trainer, binaries 
Losses (losses.rs — 1056 lines):
- WiFiDensePoseLoss with keypoint (visibility-masked MSE), DensePose
  (cross-entropy + Smooth L1 UV masked to foreground), transfer (MSE)
- generate_gaussian_heatmaps: Tensor-native 2D Gaussian heatmap gen
- compute_losses: unified functional API
- 11 deterministic unit tests

Metrics (metrics.rs — 984 lines):
- PCK@0.2 / PCK@0.5 with torso-diameter normalisation
- OKS with COCO standard per-joint sigmas
- MetricsAccumulator for online streaming eval
- hungarian_assignment: O(n³) Kuhn-Munkres min-cut via DFS augmenting
  paths for optimal multi-person keypoint assignment (ruvector min-cut)
- build_oks_cost_matrix: 1−OKS cost for bipartite matching
- 20 deterministic tests (perfect/wrong/invisible keypoints, 2×2/3×3/
  rectangular/empty Hungarian cases)

Model (model.rs — 713 lines):
- WiFiDensePoseModel end-to-end with tch-rs
- ModalityTranslator: amp+phase FC encoders → spatial pseudo-image
- Backbone: lightweight ResNet-style [B,3,48,48]→[B,256,6,6]
- KeypointHead: [B,256,6,6]→[B,17,H,W] heatmaps
- DensePoseHead: [B,256,6,6]→[B,25,H,W] parts + [B,48,H,W] UV

Trainer (trainer.rs — 777 lines):
- Full training loop: Adam, LR milestones, gradient clipping
- Deterministic batch shuffle via LCG (seed XOR epoch)
- CSV logging, best-checkpoint saving, early stopping
- evaluate() with MetricsAccumulator and heatmap argmax decode

Binaries:
- src/bin/train.rs: production MM-Fi training CLI (clap)
- src/bin/verify_training.rs: trust kill switch (EXIT 0/1/2)

Benches:
- benches/training_bench.rs: criterion benchmarks for key ops

Tests:
- tests/test_dataset.rs (459 lines)
- tests/test_metrics.rs (449 lines)
- tests/test_subcarrier.rs (389 lines)

proof.rs still stub — trainer agent completing it.

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
 2026-02-28 15:22:54 +00:00
Claude
6af0236fc7 feat: Complete ADR-001, ADR-009, ADR-012 implementations with zero mocks 
ADR-001 (WiFi-Mat disaster response pipeline):
- Add EnsembleClassifier with weighted voting (breathing/heartbeat/movement)
- Wire EventStore into DisasterResponse with domain event emission
- Add scan control API endpoints (push CSI, scan control, pipeline status, domain events)
- Implement START triage protocol (Immediate/Delayed/Minor/Deceased/Unknown)
- Critical patterns (Agonal/Apnea) bypass confidence threshold for safety
- Add 6 deterministic integration tests with synthetic sinusoidal CSI data

ADR-009 (WASM signal pipeline):
- Add pushCsiData() with zero-crossing breathing rate extraction
- Add getPipelineConfig() for runtime configuration access
- Update TypeScript type definitions for new WASM exports

ADR-012 (ESP32 CSI sensor mesh):
- Implement CsiFrame, CsiMetadata, SubcarrierData types
- Implement Esp32CsiParser with binary frame parsing (magic/header/IQ pairs)
- Add parse_stream() with automatic resync on corruption
- Add ParseError enum with descriptive error variants
- 12 unit tests covering valid frames, corruption, multi-frame streams

All 275 workspace tests pass. No mocks, no stubs, no placeholders.

https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
 2026-02-28 14:15:26 +00:00
Claude
6b20ff0c14 feat: Add wifi-Mat disaster detection enhancements 
Implement 6 optional enhancements for the wifi-Mat module:

1. Hardware Integration (csi_receiver.rs + hardware_adapter.rs)
   - ESP32 CSI support via serial/UDP
   - Intel 5300 BFEE file parsing
   - Atheros CSI Tool integration
   - Live UDP packet streaming
   - PCAP replay capability

2. CLI Commands (wifi-densepose-cli/src/mat.rs)
   - `wifi-mat scan` - Run disaster detection scan
   - `wifi-mat status` - Check event status
   - `wifi-mat zones` - Manage scan zones
   - `wifi-mat survivors` - List detected survivors
   - `wifi-mat alerts` - View and acknowledge alerts
   - `wifi-mat export` - Export data in various formats

3. REST API (wifi-densepose-mat/src/api/)
   - Full CRUD for disaster events
   - Zone management endpoints
   - Survivor and alert queries
   - WebSocket streaming for real-time updates
   - Comprehensive DTOs and error handling

4. WASM Build (wifi-densepose-wasm/src/mat.rs)
   - Browser-based disaster dashboard
   - Real-time survivor tracking
   - Zone visualization
   - Alert management
   - JavaScript API bindings

5. Detection Benchmarks (benches/detection_bench.rs)
   - Single survivor detection
   - Multi-survivor detection
   - Full pipeline benchmarks
   - Signal processing benchmarks
   - Hardware adapter benchmarks

6. ML Models for Debris Penetration (ml/)
   - DebrisModel for material analysis
   - VitalSignsClassifier for triage
   - FFT-based feature extraction
   - Bandpass filtering
   - Monte Carlo dropout for uncertainty

All 134 unit tests pass. Compilation verified for:
- wifi-densepose-mat
- wifi-densepose-cli
- wifi-densepose-wasm (with mat feature)
 2026-01-13 18:23:03 +00:00
Claude
cd877f87c2 docs: Add comprehensive wifi-Mat user guide and fix compilation 
- Add detailed wifi-Mat user guide covering:
  - Installation and setup
  - Detection capabilities (breathing, heartbeat, movement)
  - Localization system (triangulation, depth estimation)
  - START protocol triage classification
  - Alert system with priority escalation
  - Field deployment guide
  - Hardware setup requirements
  - API reference and troubleshooting

- Update main README.md with wifi-Mat section and links

- Fix compilation issues:
  - Add missing deadline field in AlertPayload
  - Fix type ambiguity in powi calls
  - Resolve borrow checker issues in scan_cycle
  - Export CsiDataBuffer from detection module
  - Add missing imports in test modules

- All 83 tests now passing
 2026-01-13 17:55:50 +00:00
Claude
6ed69a3d48 feat: Complete Rust port of WiFi-DensePose with modular crates 
Major changes:
- Organized Python v1 implementation into v1/ subdirectory
- Created Rust workspace with 9 modular crates:
  - wifi-densepose-core: Core types, traits, errors
  - wifi-densepose-signal: CSI processing, phase sanitization, FFT
  - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch)
  - wifi-densepose-api: Axum-based REST/WebSocket API
  - wifi-densepose-db: SQLx database layer
  - wifi-densepose-config: Configuration management
  - wifi-densepose-hardware: Hardware abstraction
  - wifi-densepose-wasm: WebAssembly bindings
  - wifi-densepose-cli: Command-line interface

Documentation:
- ADR-001: Workspace structure
- ADR-002: Signal processing library selection
- ADR-003: Neural network inference strategy
- DDD domain model with bounded contexts

Testing:
- 69 tests passing across all crates
- Signal processing: 45 tests
- Neural networks: 21 tests
- Core: 3 doc tests

Performance targets:
- 10x faster CSI processing (~0.5ms vs ~5ms)
- 5x lower memory usage (~100MB vs ~500MB)
- WASM support for browser deployment
 2026-01-13 03:11:16 +00:00