ruv
3febf72674
chore: bump all crates to v0.2.0 for MERIDIAN release
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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
rUv
9bbe95648c
feat: ADR-024 Contrastive CSI Embedding Model — all 7 phases ( #52 )
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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
Claude
cca91bd875
feat(adr-017): Implement ruvector integrations in signal crate (partial)
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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
3ccb301737
feat: Add comprehensive benchmarks and validation tests for Rust signal processing
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- Add signal_bench.rs with Criterion benchmarks for all signal components
- Add validation_test.rs proving mathematical correctness of algorithms
- Update README.md with validated benchmark results (810x-5400x speedup)
- Fix benchmark API usage (sanitize_phase, extract methods)
Benchmark Results (4x64 CSI data):
- CSI Preprocessing: 5.19 µs (~49 Melem/s)
- Phase Sanitization: 3.84 µs (~67 Melem/s)
- Feature Extraction: 9.03 µs (~7 Melem/s)
- Motion Detection: 186 ns (~5.4 Melem/s)
- Full Pipeline: 18.47 µs (~54K fps)
Validation Tests (all passing):
- Phase unwrapping: 0.0 radians max error
- Doppler estimation: 33.33 Hz exact match
- Correlation: 1.0 for identical signals
- Phase coherence: 1.0 for coherent signals
2026-01-13 03:38:38 +00:00
Claude
6ed69a3d48
feat: Complete Rust port of WiFi-DensePose with modular crates
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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
