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
rUv
078c5d8957
minor updates
2025-06-07 17:11:45 +00:00
rUv
7b5df5c077
updates
2025-06-07 13:55:28 +00:00
rUv
6dd89f2ada
docs: Revamp README and UI documentation; enhance CLI usage instructions and API configuration details
2025-06-07 13:40:52 +00:00
rUv
b15e2b7182
docs: Update installation instructions and enhance API documentation in README
2025-06-07 13:35:43 +00:00
rUv
94f0a60c10
fix: Update badge links in README for PyPI and Docker
2025-06-07 13:34:06 +00:00
rUv
6fe0d42f90
Add comprehensive CSS styles for UI components and dark mode support
2025-06-07 13:28:02 +00:00
rUv
f3c77b1750
Add WiFi DensePose implementation and results
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- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads.
- Added a comprehensive training utility for the model, including loss functions and training steps.
- Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
2025-06-07 05:23:07 +00:00
rUv
6cab230908
Initial commit
2025-06-07 00:32:31 -04:00
