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-003-neural-network-inference.md

@@ -0,0 +1,57 @@
+# ADR-003: Neural Network Inference Strategy
+
+## Status
+Accepted
+
+## Context
+The WiFi-DensePose system requires neural network inference for:
+1. Modality translation (CSI → visual features)
+2. DensePose estimation (body part segmentation + UV mapping)
+
+We need to select an inference strategy that supports pre-trained models and multiple backends.
+
+## Decision
+We will implement a multi-backend inference engine:
+
+### Primary Backend: ONNX Runtime (`ort` crate)
+- Load pre-trained PyTorch models exported to ONNX
+- GPU acceleration via CUDA/TensorRT
+- Cross-platform support
+
+### Alternative Backends (Feature-gated)
+- `tch-rs`: PyTorch C++ bindings
+- `candle`: Pure Rust ML framework
+
+### Architecture
+```rust
+pub trait Backend: Send + Sync {
+    fn load_model(&mut self, path: &Path) -> NnResult<()>;
+    fn run(&self, inputs: HashMap<String, Tensor>) -> NnResult<HashMap<String, Tensor>>;
+    fn input_specs(&self) -> Vec<TensorSpec>;
+    fn output_specs(&self) -> Vec<TensorSpec>;
+}
+```
+
+### Feature Flags
+```toml
+[features]
+default = ["onnx"]
+onnx = ["ort"]
+tch-backend = ["tch"]
+candle-backend = ["candle-core", "candle-nn"]
+cuda = ["ort/cuda"]
+tensorrt = ["ort/tensorrt"]
+```
+
+## Consequences
+
+### Positive
+- Use existing trained models (no retraining)
+- Multiple backend options for different deployments
+- GPU acceleration when available
+- Feature flags minimize binary size
+
+### Negative
+- ONNX model conversion required
+- ort crate pulls in C++ dependencies
+- tch requires libtorch installation