9bbe95648c
Full implementation of Project AETHER — Contrastive CSI Embedding Model. ## Phases Delivered 1. ProjectionHead (64→128→128) + L2 normalization 2. CsiAugmenter (5 physically-motivated augmentations) 3. InfoNCE contrastive loss + SimCLR pretraining 4. FingerprintIndex (4 index types: env, activity, temporal, person) 5. RVF SEG_EMBED (0x0C) + CLI integration 6. Cross-modal alignment (PoseEncoder + InfoNCE) 7. Deep RuVector: MicroLoRA, EWC++, drift detection, hard-negative mining, SEG_LORA ## Stats - 276 tests passing (191 lib + 51 bin + 16 rvf + 18 vitals) - 3,342 additions across 8 files - Zero unsafe/unwrap/panic/todo stubs - ~55KB INT8 model for ESP32 edge deployment Also fixes deprecated GitHub Actions (v3→v4) and adds feat/* branch CI triggers. Closes #50
115 lines
4.9 KiB
Markdown
115 lines
4.9 KiB
Markdown
# wifi-densepose-mat
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[](https://crates.io/crates/wifi-densepose-mat)
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[](https://docs.rs/wifi-densepose-mat)
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[](LICENSE)
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Mass Casualty Assessment Tool for WiFi-based disaster survivor detection and localization.
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## Overview
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`wifi-densepose-mat` uses WiFi Channel State Information (CSI) to detect and locate survivors
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trapped in rubble, debris, or collapsed structures. The crate follows Domain-Driven Design (DDD)
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with event sourcing, organized into three bounded contexts -- detection, localization, and
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alerting -- plus a machine learning layer for debris penetration modeling and vital signs
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classification.
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Use cases include earthquake search and rescue, building collapse response, avalanche victim
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location, flood rescue operations, and mine collapse detection.
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## Features
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- **Vital signs detection** -- Breathing patterns, heartbeat signatures, and movement
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classification with ensemble classifier combining all three modalities.
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- **Survivor localization** -- 3D position estimation through debris via triangulation, depth
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estimation, and position fusion.
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- **Triage classification** -- Automatic START protocol-compatible triage with priority-based
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alert generation and dispatch.
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- **Event sourcing** -- All state changes emitted as domain events (`DetectionEvent`,
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`AlertEvent`, `ZoneEvent`) stored in a pluggable `EventStore`.
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- **ML debris model** -- Debris material classification, signal attenuation prediction, and
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uncertainty-aware vital signs classification.
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- **REST + WebSocket API** -- `axum`-based HTTP API for real-time monitoring dashboards.
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- **ruvector integration** -- `ruvector-solver` for triangulation math, `ruvector-temporal-tensor`
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for compressed CSI buffering.
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### Feature flags
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| Flag | Default | Description |
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|---------------|---------|----------------------------------------------------|
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| `std` | yes | Standard library support |
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| `api` | yes | REST + WebSocket API (enables serde for all types) |
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| `ruvector` | yes | ruvector-solver and ruvector-temporal-tensor |
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| `serde` | no | Serialization (also enabled by `api`) |
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| `portable` | no | Low-power mode for field-deployable devices |
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| `distributed` | no | Multi-node distributed scanning |
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| `drone` | no | Drone-mounted scanning (implies `distributed`) |
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## Quick Start
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```rust
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use wifi_densepose_mat::{
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DisasterResponse, DisasterConfig, DisasterType,
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ScanZone, ZoneBounds,
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};
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#[tokio::main]
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async fn main() -> anyhow::Result<()> {
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let config = DisasterConfig::builder()
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.disaster_type(DisasterType::Earthquake)
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.sensitivity(0.8)
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.build();
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let mut response = DisasterResponse::new(config);
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// Define scan zone
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let zone = ScanZone::new(
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"Building A - North Wing",
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ZoneBounds::rectangle(0.0, 0.0, 50.0, 30.0),
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);
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response.add_zone(zone)?;
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// Start scanning
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response.start_scanning().await?;
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Ok(())
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}
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```
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## Architecture
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```text
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wifi-densepose-mat/src/
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lib.rs -- DisasterResponse coordinator, config builder, MatError
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domain/
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survivor.rs -- Survivor aggregate root
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disaster_event.rs -- DisasterEvent, DisasterType
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scan_zone.rs -- ScanZone, ZoneBounds
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alert.rs -- Alert, Priority
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vital_signs.rs -- VitalSignsReading, BreathingPattern, HeartbeatSignature
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triage.rs -- TriageStatus, TriageCalculator (START protocol)
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coordinates.rs -- Coordinates3D, LocationUncertainty
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events.rs -- DomainEvent, EventStore, InMemoryEventStore
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detection/ -- BreathingDetector, HeartbeatDetector, MovementClassifier, EnsembleClassifier
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localization/ -- Triangulator, DepthEstimator, PositionFuser
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alerting/ -- AlertGenerator, AlertDispatcher, TriageService
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ml/ -- DebrisPenetrationModel, VitalSignsClassifier, UncertaintyEstimate
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api/ -- axum REST + WebSocket router
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integration/ -- SignalAdapter, NeuralAdapter, HardwareAdapter
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```
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## Related Crates
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| Crate | Role |
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|-------|------|
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| [`wifi-densepose-core`](../wifi-densepose-core) | Foundation types and traits |
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| [`wifi-densepose-signal`](../wifi-densepose-signal) | CSI preprocessing for detection pipeline |
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| [`wifi-densepose-nn`](../wifi-densepose-nn) | Neural inference for ML models |
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| [`wifi-densepose-hardware`](../wifi-densepose-hardware) | Hardware sensor data ingestion |
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| [`ruvector-solver`](https://crates.io/crates/ruvector-solver) | Triangulation and position math |
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| [`ruvector-temporal-tensor`](https://crates.io/crates/ruvector-temporal-tensor) | Compressed CSI buffering |
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## License
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MIT OR Apache-2.0
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