6ed69a3d48
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
41 lines
1.3 KiB
Markdown
41 lines
1.3 KiB
Markdown
# ADR-002: Signal Processing Library Selection
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## Status
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Accepted
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## Context
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CSI signal processing requires FFT operations, complex number handling, and matrix operations. We need to select appropriate Rust libraries that provide Python/NumPy equivalent functionality.
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## Decision
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We will use the following libraries:
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| Library | Purpose | Python Equivalent |
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|---------|---------|-------------------|
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| `ndarray` | N-dimensional arrays | NumPy |
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| `rustfft` | FFT operations | numpy.fft |
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| `num-complex` | Complex numbers | complex |
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| `num-traits` | Numeric traits | - |
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### Key Implementations
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1. **Phase Sanitization**: Multiple unwrapping methods (Standard, Custom, Itoh, Quality-Guided)
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2. **CSI Processing**: Amplitude/phase extraction, temporal smoothing, Hamming windowing
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3. **Feature Extraction**: Doppler, PSD, amplitude, phase, correlation features
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4. **Motion Detection**: Variance-based with adaptive thresholds
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## Consequences
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### Positive
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- Pure Rust implementation (no FFI overhead)
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- WASM compatible (rustfft is pure Rust)
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- NumPy-like API with ndarray
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- High performance with SIMD optimizations
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### Negative
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- ndarray-linalg requires BLAS backend for advanced operations
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- Learning curve for ndarray patterns
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## References
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- [ndarray documentation](https://docs.rs/ndarray)
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- [rustfft documentation](https://docs.rs/rustfft)
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