Files

Claude 6ed69a3d48 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

2026-01-13 03:11:16 +00:00

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WiFi-DensePose Rust Port - 15-Agent Swarm Configuration

Mission Statement

Port the WiFi-DensePose Python system to Rust using ruvnet/ruvector patterns, with modular crates, WASM support, and comprehensive documentation following ADR/DDD principles.

Agent Swarm Architecture

Tier 1: Orchestration (1 Agent)

Orchestrator Agent - Coordinates all agents, manages dependencies, tracks progress

Tier 2: Architecture & Documentation (3 Agents)

ADR Agent - Creates Architecture Decision Records for all major decisions
DDD Agent - Designs Domain-Driven Design models and bounded contexts
Documentation Agent - Maintains comprehensive documentation, README, API docs

Tier 3: Core Implementation (5 Agents)

Signal Processing Agent - Ports CSI processing, phase sanitization, FFT algorithms
Neural Network Agent - Ports DensePose head, modality translation using tch-rs/onnx
API Agent - Implements Axum/Actix REST API and WebSocket handlers
Database Agent - Implements SQLx PostgreSQL/SQLite with migrations
Config Agent - Implements configuration management, environment handling

Tier 4: Platform & Integration (3 Agents)

WASM Agent - Implements wasm-bindgen, browser compatibility, wasm-pack builds
Hardware Agent - Ports CSI extraction, router interfaces, hardware abstraction
Integration Agent - Integrates ruvector crates, vector search, GNN layers

Tier 5: Quality Assurance (3 Agents)

Test Agent - Writes unit, integration, and benchmark tests
Validation Agent - Validates against Python implementation, accuracy checks
Optimization Agent - Profiles, benchmarks, and optimizes hot paths

Crate Workspace Structure

wifi-densepose-rs/
├── Cargo.toml                    # Workspace root
├── crates/
│   ├── wifi-densepose-core/      # Core types, traits, errors
│   ├── wifi-densepose-signal/    # Signal processing (CSI, phase, FFT)
│   ├── wifi-densepose-nn/        # Neural networks (DensePose, translation)
│   ├── wifi-densepose-api/       # REST/WebSocket API (Axum)
│   ├── wifi-densepose-db/        # Database layer (SQLx)
│   ├── wifi-densepose-config/    # Configuration management
│   ├── wifi-densepose-hardware/  # Hardware abstraction
│   ├── wifi-densepose-wasm/      # WASM bindings
│   └── wifi-densepose-cli/       # CLI application
├── docs/
│   ├── adr/                      # Architecture Decision Records
│   ├── ddd/                      # Domain-Driven Design docs
│   └── api/                      # API documentation
├── benches/                      # Benchmarks
└── tests/                        # Integration tests

Domain Model (DDD)

Bounded Contexts

Signal Domain - CSI data, phase processing, feature extraction
Pose Domain - DensePose inference, keypoints, segmentation
Streaming Domain - WebSocket, real-time updates, connection management
Storage Domain - Persistence, caching, retrieval
Hardware Domain - Router interfaces, device management

Core Aggregates

CsiFrame - Raw CSI data aggregate
ProcessedSignal - Cleaned and extracted features
PoseEstimate - DensePose inference result
Session - Client session with history
Device - Hardware device state

ADR Topics to Document

ADR-001: Rust Workspace Structure
ADR-002: Signal Processing Library Selection
ADR-003: Neural Network Inference Strategy
ADR-004: API Framework Selection (Axum vs Actix)
ADR-005: Database Layer Strategy (SQLx)
ADR-006: WASM Compilation Strategy
ADR-007: Error Handling Approach
ADR-008: Async Runtime Selection (Tokio)
ADR-009: ruvector Integration Strategy
ADR-010: Configuration Management

Phase Execution Plan

Phase 1: Foundation

Set up Cargo workspace
Create all crate scaffolding
Write ADR-001 through ADR-005
Define core traits and types

Phase 2: Core Implementation

Port signal processing algorithms
Implement neural network inference
Build API layer
Database integration

Phase 3: Platform

WASM compilation
Hardware abstraction
ruvector integration

Phase 4: Quality

Comprehensive testing
Python validation
Benchmarking
Optimization

Success Metrics

Feature parity with Python implementation
< 10ms latency improvement over Python
WASM bundle < 5MB
100% test coverage
All ADRs documented

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