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
This commit is contained in:
Claude
2026-01-13 03:11:16 +00:00
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# Optimization Commands
Commands for optimization operations in Claude Flow.
## Available Commands
- [topology-optimize](./topology-optimize.md)
- [parallel-execute](./parallel-execute.md)
- [cache-manage](./cache-manage.md)

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# Automatic Topology Selection
## Purpose
Automatically select the optimal swarm topology based on task complexity analysis.
## How It Works
### 1. Task Analysis
The system analyzes your task description to determine:
- Complexity level (simple/medium/complex)
- Required agent types
- Estimated duration
- Resource requirements
### 2. Topology Selection
Based on analysis, it selects:
- **Star**: For simple, centralized tasks
- **Mesh**: For medium complexity with flexibility needs
- **Hierarchical**: For complex tasks requiring structure
- **Ring**: For sequential processing workflows
### 3. Example Usage
**Simple Task:**
```
Tool: mcp__claude-flow__task_orchestrate
Parameters: {"task": "Fix typo in README.md"}
Result: Automatically uses star topology with single agent
```
**Complex Task:**
```
Tool: mcp__claude-flow__task_orchestrate
Parameters: {"task": "Refactor authentication system with JWT, add tests, update documentation"}
Result: Automatically uses hierarchical topology with architect, coder, and tester agents
```
## Benefits
- 🎯 Optimal performance for each task type
- 🤖 Automatic agent assignment
- ⚡ Reduced setup time
- 📊 Better resource utilization
## Hook Configuration
The pre-task hook automatically handles topology selection:
```json
{
"command": "npx claude-flow hook pre-task --optimize-topology"
}
```
## Direct Optimization
```
Tool: mcp__claude-flow__topology_optimize
Parameters: {"swarmId": "current"}
```
## CLI Usage
```bash
# Auto-optimize topology via CLI
npx claude-flow optimize topology
```

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# cache-manage
Manage operation cache for performance.
## Usage
```bash
npx claude-flow optimization cache-manage [options]
```
## Options
- `--action <type>` - Action (view, clear, optimize)
- `--max-size <mb>` - Maximum cache size
- `--ttl <seconds>` - Time to live
## Examples
```bash
# View cache stats
npx claude-flow optimization cache-manage --action view
# Clear cache
npx claude-flow optimization cache-manage --action clear
# Set limits
npx claude-flow optimization cache-manage --max-size 100 --ttl 3600
```

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# parallel-execute
Execute tasks in parallel for maximum efficiency.
## Usage
```bash
npx claude-flow optimization parallel-execute [options]
```
## Options
- `--tasks <file>` - Task list file
- `--max-parallel <n>` - Maximum parallel tasks
- `--strategy <type>` - Execution strategy
## Examples
```bash
# Execute task list
npx claude-flow optimization parallel-execute --tasks tasks.json
# Limit parallelism
npx claude-flow optimization parallel-execute --tasks tasks.json --max-parallel 5
# Custom strategy
npx claude-flow optimization parallel-execute --strategy adaptive
```

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# Parallel Task Execution
## Purpose
Execute independent subtasks in parallel for maximum efficiency.
## Coordination Strategy
### 1. Task Decomposition
```
Tool: mcp__claude-flow__task_orchestrate
Parameters: {
"task": "Build complete REST API with auth, CRUD operations, and tests",
"strategy": "parallel",
"maxAgents": 8
}
```
### 2. Parallel Workflows
The system automatically:
- Identifies independent components
- Assigns specialized agents
- Executes in parallel where possible
- Synchronizes at dependency points
### 3. Example Breakdown
For the REST API task:
- **Agent 1 (Architect)**: Design API structure
- **Agent 2-3 (Coders)**: Implement auth & CRUD in parallel
- **Agent 4 (Tester)**: Write tests as features complete
- **Agent 5 (Documenter)**: Update docs continuously
## CLI Usage
```bash
# Execute parallel tasks via CLI
npx claude-flow parallel "Build REST API" --max-agents 8
```
## Performance Gains
- 🚀 2.8-4.4x faster execution
- 💪 Optimal CPU utilization
- 🔄 Automatic load balancing
- 📈 Linear scalability with agents
## Monitoring
```
Tool: mcp__claude-flow__swarm_monitor
Parameters: {"interval": 1000, "swarmId": "current"}
```
Watch real-time parallel execution progress!

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# topology-optimize
Optimize swarm topology for current workload.
## Usage
```bash
npx claude-flow optimization topology-optimize [options]
```
## Options
- `--analyze-first` - Analyze before optimizing
- `--target <metric>` - Optimization target
- `--apply` - Apply optimizations
## Examples
```bash
# Analyze and suggest
npx claude-flow optimization topology-optimize --analyze-first
# Optimize for speed
npx claude-flow optimization topology-optimize --target speed
# Apply changes
npx claude-flow optimization topology-optimize --target efficiency --apply
```