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

rust-port/wifi-densepose-rs/docs/adr/ADR-002-signal-processing.md

@@ -0,0 +1,40 @@
+# ADR-002: Signal Processing Library Selection
+
+## Status
+Accepted
+
+## Context
+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.
+
+## Decision
+We will use the following libraries:
+
+| Library | Purpose | Python Equivalent |
+|---------|---------|-------------------|
+| `ndarray` | N-dimensional arrays | NumPy |
+| `rustfft` | FFT operations | numpy.fft |
+| `num-complex` | Complex numbers | complex |
+| `num-traits` | Numeric traits | - |
+
+### Key Implementations
+
+1. **Phase Sanitization**: Multiple unwrapping methods (Standard, Custom, Itoh, Quality-Guided)
+2. **CSI Processing**: Amplitude/phase extraction, temporal smoothing, Hamming windowing
+3. **Feature Extraction**: Doppler, PSD, amplitude, phase, correlation features
+4. **Motion Detection**: Variance-based with adaptive thresholds
+
+## Consequences
+
+### Positive
+- Pure Rust implementation (no FFI overhead)
+- WASM compatible (rustfft is pure Rust)
+- NumPy-like API with ndarray
+- High performance with SIMD optimizations
+
+### Negative
+- ndarray-linalg requires BLAS backend for advanced operations
+- Learning curve for ndarray patterns
+
+## References
+- [ndarray documentation](https://docs.rs/ndarray)
+- [rustfft documentation](https://docs.rs/rustfft)