dearsky/wifi-densepose
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e99a41434dd32deb30d7e5e1f65ff6de4b0e4ea6
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12 Commits

Author SHA1 Message Date
ruv
e99a41434d chore: bump workspace to v0.3.0 and publish 15 crates to crates.io 
- Workspace version: 0.2.0 → 0.3.0
- All internal path dependency versions updated
- ruvector-crv/gnn gated behind optional `crv` feature (removed [patch.crates-io])
- All 15 crates published to crates.io at v0.3.0

Published crates (in order):
  1. wifi-densepose-core
  2. wifi-densepose-vitals
  3. wifi-densepose-wifiscan
  4. wifi-densepose-hardware
  5. wifi-densepose-config
  6. wifi-densepose-db
  7. wifi-densepose-signal
  8. wifi-densepose-nn
  9. wifi-densepose-ruvector
  10. wifi-densepose-api
  11. wifi-densepose-train
  12. wifi-densepose-mat
  13. wifi-densepose-wasm
  14. wifi-densepose-sensing-server
  15. wifi-densepose-cli

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-02 08:39:23 -05:00
ruv
60e0e6d3c4 feat: ADR-033 CRV signal-line integration + ruvector-crv 6-stage pipeline 
Implement full CRV (Coordinate Remote Viewing) signal-line protocol
mapping to WiFi CSI sensing via ruvector-crv:

- Stage I: CsiGestaltClassifier (6 gestalt types from amplitude/phase)
- Stage II: CsiSensoryEncoder (texture/color/temperature/sound/luminosity/dimension)
- Stage III: Mesh topology encoding (AP nodes/links → GNN graph)
- Stage IV: Coherence gate → AOL detection (signal vs noise separation)
- Stage V: Pose interrogation via differentiable search
- Stage VI: Person partitioning via MinCut clustering
- Cross-session convergence for cross-room identity

New files:
- crv/mod.rs: 1,430 lines, 43 tests
- crv_bench.rs: 8 criterion benchmarks (gestalt, sensory, pipeline, convergence)
- ADR-033: 740-line architecture decision with 30+ acceptance criteria
- patches/ruvector-crv: Fix ruvector-gnn 2.0.5 API mismatch

Dependencies: ruvector-crv 0.1.1, ruvector-gnn 2.0.5

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 22:21:59 -05:00
ruv
3febf72674 chore: bump all crates to v0.2.0 for MERIDIAN release 
Workspace version 0.1.0 → 0.2.0. All internal cross-crate
dependencies updated to match.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 12:14:39 -05:00
Claude
ed3261fbcb feat(ruvector): implement ADR-017 as wifi-densepose-ruvector crate + fix MAT warnings 
New crate `wifi-densepose-ruvector` implements all 7 ruvector v2.0.4
integration points from ADR-017 (signal processing + MAT disaster detection):

signal::subcarrier   — mincut_subcarrier_partition (ruvector-mincut)
signal::spectrogram  — gate_spectrogram (ruvector-attn-mincut)
signal::bvp          — attention_weighted_bvp (ruvector-attention)
signal::fresnel      — solve_fresnel_geometry (ruvector-solver)
mat::triangulation   — solve_triangulation TDoA (ruvector-solver)
mat::breathing       — CompressedBreathingBuffer 50-75% mem reduction (ruvector-temporal-tensor)
mat::heartbeat       — CompressedHeartbeatSpectrogram tiered compression (ruvector-temporal-tensor)

16 tests, 0 compilation errors. Workspace grows from 14 → 15 crates.

MAT crate: fix all 54 warnings (0 remaining in wifi-densepose-mat):
- Remove unused imports (Arc, HashMap, RwLock, mpsc, Mutex, ConfidenceScore, etc.)
- Prefix unused variables with _ (timestamp_low, agc, perm)
- Add #![allow(unexpected_cfgs)] for onnx feature gates in ML files
- Move onnx-conditional imports under #[cfg(feature = "onnx")] guards

README: update crate count 14→15, ADR count 24→26, add ruvector crate
table with 7-row integration summary.

Total tests: 939 → 955 (16 new). All passing, 0 regressions.

https://claude.ai/code/session_0164UZu6rG6gA15HmVyLZAmU
 2026-03-01 15:50:05 +00:00
rUv
9bbe95648c feat: ADR-024 Contrastive CSI Embedding Model — all 7 phases (#52) 
Full implementation of Project AETHER — Contrastive CSI Embedding Model.

## Phases Delivered
1. ProjectionHead (64→128→128) + L2 normalization
2. CsiAugmenter (5 physically-motivated augmentations)
3. InfoNCE contrastive loss + SimCLR pretraining
4. FingerprintIndex (4 index types: env, activity, temporal, person)
5. RVF SEG_EMBED (0x0C) + CLI integration
6. Cross-modal alignment (PoseEncoder + InfoNCE)
7. Deep RuVector: MicroLoRA, EWC++, drift detection, hard-negative mining, SEG_LORA

## Stats
- 276 tests passing (191 lib + 51 bin + 16 rvf + 18 vitals)
- 3,342 additions across 8 files
- Zero unsafe/unwrap/panic/todo stubs
- ~55KB INT8 model for ESP32 edge deployment

Also fixes deprecated GitHub Actions (v3→v4) and adds feat/* branch CI triggers.

Closes #50
 2026-03-01 01:44:38 -05:00
ruv
fc409dfd6a feat: ADR-023 full DensePose training pipeline (Phases 1-8) 
Implement complete WiFi CSI-to-DensePose neural network pipeline:

Phase 1 - Dataset loaders: .npy/.mat v5 parsers, MM-Fi + Wi-Pose
  loaders, subcarrier resampling (114->56, 30->56), DataPipeline
Phase 2 - Graph transformer: COCO BodyGraph (17 kp, 16 edges),
  AntennaGraph, multi-head CrossAttention, GCN message passing,
  CsiToPoseTransformer full pipeline
Phase 4 - Training loop: 6-term composite loss (MSE, cross-entropy,
  UV regression, temporal consistency, bone length, symmetry),
  SGD+momentum, cosine+warmup scheduler, PCK/OKS metrics, checkpoints
Phase 5 - SONA adaptation: LoRA (rank-4, A*B delta), EWC++ Fisher
  regularization, EnvironmentDetector (3-sigma drift), temporal
  consistency loss
Phase 6 - Sparse inference: NeuronProfiler hot/cold partitioning,
  SparseLinear (skip cold rows), INT8/FP16 quantization with <0.01
  MSE, SparseModel engine, BenchmarkRunner
Phase 7 - RVF pipeline: 6 new segment types (Index, Overlay, Crypto,
  WASM, Dashboard, AggregateWeights), HNSW index, OverlayGraph,
  RvfModelBuilder, ProgressiveLoader (3-layer: A=instant, B=hot, C=full)
Phase 8 - Server integration: --model, --progressive CLI flags,
  4 new REST endpoints, WebSocket pose_keypoints + model_status

229 tests passing (147 unit + 48 bin + 34 integration)
Benchmark: 9,520 frames/sec (105μs/frame), 476x real-time at 20 Hz
7,832 lines of pure Rust, zero external ML dependencies

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 23:22:15 -05:00
ruv
1192de951a feat: ADR-021 vital sign detection + RVF container format (closes #45) 
Implement WiFi CSI-based vital sign detection and RVF model container:

- Pure-Rust radix-2 DIT FFT with Hann windowing and parabolic interpolation
- FIR bandpass filter (windowed-sinc, Hamming) for breathing (0.1-0.5 Hz)
  and heartbeat (0.8-2.0 Hz) band isolation
- VitalSignDetector with rolling buffers (30s breathing, 15s heartbeat)
- RVF binary container with 64-byte SegmentHeader, CRC32 integrity,
  6 segment types (Vec, Manifest, Quant, Meta, Witness, Profile)
- RvfBuilder/RvfReader with file I/O and VitalSignConfig support
- Server integration: --benchmark, --load-rvf, --save-rvf CLI flags
- REST endpoint /api/v1/vital-signs and WebSocket vital_signs field
- 98 tests (32 unit + 16 RVF integration + 18 vital signs integration)
- Benchmark: 7,313 frames/sec (136μs/frame), 365x real-time at 20 Hz

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 22:52:19 -05:00
ruv
d956c30f9e feat: Rust sensing server with full DensePose-compatible API 
Replace Python FastAPI + WebSocket servers with a single 2.1MB Rust binary
(wifi-densepose-sensing-server) that serves all UI endpoints:

- REST: /health/*, /api/v1/info, /api/v1/pose/current, /api/v1/pose/stats,
  /api/v1/pose/zones/summary, /api/v1/stream/status
- WebSocket: /api/v1/stream/pose (pose_data with 17 COCO keypoints),
  /ws/sensing (raw sensing_update stream on port 8765)
- Static: /ui/* with no-cache headers

WiFi-derived pose estimation: derive_pose_from_sensing() generates 17 COCO
keypoints from CSI/WiFi signal data with motion-driven animation.

Data sources: ESP32 CSI via UDP :5005, Windows WiFi via netsh, simulation
fallback. Auto-detection probes each in order.

UI changes:
- Point all endpoints to Rust server on :8080 (was Python :8000)
- Fix WebSocket sensing URL to include /ws/sensing path
- Remove sensingOnlyMode gating — all tabs init normally
- Remove api.service.js sensing-only short-circuit
- Fix clearPingInterval bug in websocket.service.js

Also removes obsolete k8s/ template manifests.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 21:29:45 -05:00
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
2c5ca308a4 feat(rust): Add workspace deps, tests, and refine training modules 
- Cargo.toml: Add wifi-densepose-train to workspace members; add
  petgraph, ndarray-npy, walkdir, sha2, csv, indicatif, clap to
  workspace dependencies
- error.rs: Slim down to focused error types (TrainError, DatasetError)
- lib.rs: Wire up all module re-exports correctly
- losses.rs: Add generate_gaussian_heatmaps implementation
- tests/test_config.rs: Deterministic config roundtrip and validation tests

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
 2026-02-28 15:17:17 +00:00
Claude
a17b630c02 feat: Add wifi-densepose-mat disaster detection module 
Implements WiFi-Mat (Mass Casualty Assessment Tool) for detecting and
localizing survivors trapped in rubble, earthquakes, and natural disasters.

Architecture:
- Domain-Driven Design with bounded contexts (Detection, Localization, Alerting)
- Modular Rust crate integrating with existing wifi-densepose-* crates
- Event-driven architecture for audit trails and distributed deployments

Features:
- Breathing pattern detection from CSI amplitude variations
- Heartbeat detection using micro-Doppler analysis
- Movement classification (gross, fine, tremor, periodic)
- START protocol-compatible triage classification
- 3D position estimation via triangulation and depth estimation
- Real-time alert generation with priority escalation

Documentation:
- ADR-001: Architecture Decision Record for wifi-Mat
- DDD domain model specification
 2026-01-13 17:24:50 +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