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788d685b9f
Closes the two findings from the adversarial review that were verified but left unfixed. Both now have proper tests, each proven to fail against the old code. 1. PyO3 bindings panicked / over-allocated on caller input (aether.rs). `EmbeddingExtractor(n_heads=0)` reached `d_model % n_heads` in the transformer and panicked (surfacing to Python as an opaque PanicException); a non-divisor head count tripped the native assert; and `AetherConfig(d_model=100_000)` allocated multi-gigabyte weight matrices that abort the interpreter. The binding did no validation. Now both constructors return `PyResult` and validate at the boundary — positive dims, `d_model % n_heads == 0`, and a generous MAX_DIM/MAX_LAYERS cap — raising `ValueError`. Proven: `n_heads=0` -> "n_heads must be positive", `d_model=100_000` -> a clean ValueError, both previously a PanicException / abort. +10 pytest cases (test_aether.py); a valid config still constructs and embeds. 2. The single-training-job guard was a TOCTOU race (training_api.rs). `spawn_training_job` checked `is_active()` under a `state` READ lock, released it, then set `active` later. A tokio RwLock read lock is SHARED, so two concurrent `POST /train/start` could both hold it, both see the slot free, and both spawn jobs — sharing/overwriting one status+cancel and orphaning a task handle. Extracted `claim_training_slot`, which does the check-and-set in ONE `status` mutex scope — the atomicity lives on the status mutex, not the coarse state lock — so concurrent starts serialise and exactly one wins. This also makes it unit-testable without a full AppState. Test: 32 threads hit a barrier and race to claim; asserts EXACTLY ONE wins. Mutation-proven — reverting to the split check-then-set makes it fail (`left: 3, right: 1`), and it returns to 1 with the fix. Verified on aarch64/macOS: training_api 28 pass (26 existing + 2 new), full python/tests suite 237 pass (227 + 10). The native module keeps its internal assert as a defence-in-depth invariant; the binding now enforces it at the edge. Co-Authored-By: Ruflo & AQE
wifi-densepose-sensing-server
Lightweight Axum server for real-time WiFi sensing with RuVector signal processing.
Overview
wifi-densepose-sensing-server is the operational backend for WiFi-DensePose. It receives raw CSI
frames from ESP32 hardware over UDP, runs them through the RuVector-powered signal processing
pipeline, and broadcasts processed sensing updates to browser clients via WebSocket. A built-in
static file server hosts the sensing UI on the same port.
The crate ships both a library (wifi_densepose_sensing_server) exposing the training and inference
modules, and a binary (sensing-server) that starts the full server stack.
Integrates wifi-densepose-wifiscan for multi-BSSID WiFi scanning per ADR-022 Phase 3.
Features
- UDP CSI ingestion -- Receives ESP32 CSI frames on port 5005 and parses them into the internal
CsiFramerepresentation. - Vital sign detection -- Pure-Rust FFT-based breathing rate (0.1--0.5 Hz) and heart rate (0.67--2.0 Hz) estimation from CSI amplitude time series (ADR-021).
- RVF container -- Standalone binary container format for packaging model weights, metadata, and
configuration into a single
.rvffile with 64-byte aligned segments. - RVF pipeline -- Progressive model loading with streaming segment decoding.
- Graph Transformer -- Cross-attention bottleneck between antenna-space CSI features and the
COCO 17-keypoint body graph, followed by GCN message passing (ADR-023 Phase 2). Pure
std, no ML dependencies. - SONA adaptation -- LoRA + EWC++ online adaptation for environment drift without catastrophic forgetting (ADR-023 Phase 5).
- Contrastive CSI embeddings -- Self-supervised SimCLR-style pretraining with InfoNCE loss, projection head, fingerprint indexing, and cross-modal pose alignment (ADR-024).
- Sparse inference -- Activation profiling, sparse matrix-vector multiply, INT8/FP16 quantization, and a full sparse inference engine for edge deployment (ADR-023 Phase 6).
- Dataset pipeline -- Training dataset loading and batching.
- Multi-BSSID scanning -- Windows
netshintegration for BSSID discovery viawifi-densepose-wifiscan(ADR-022). - WebSocket broadcast -- Real-time sensing updates pushed to all connected clients at
ws://localhost:8765/ws/sensing. - Static file serving -- Hosts the sensing UI on port 8080 with CORS headers.
Modules
| Module | Description |
|---|---|
vital_signs |
Breathing and heart rate extraction via FFT spectral analysis |
rvf_container |
RVF binary format builder and reader |
rvf_pipeline |
Progressive model loading from RVF containers |
graph_transformer |
Graph Transformer + GCN for CSI-to-pose estimation |
trainer |
Training loop orchestration |
dataset |
Training data loading and batching |
sona |
LoRA adapters and EWC++ continual learning |
sparse_inference |
Neuron profiling, sparse matmul, INT8/FP16 quantization |
embedding |
Contrastive CSI embedding model and fingerprint index |
Quick Start
# Build the server
cargo build -p wifi-densepose-sensing-server
# Run with default settings (HTTP :8080, UDP :5005, WS :8765)
cargo run -p wifi-densepose-sensing-server
# Run with custom ports
cargo run -p wifi-densepose-sensing-server -- \
--http-port 9000 \
--udp-port 5005 \
--static-dir ./ui
Using as a library
use wifi_densepose_sensing_server::vital_signs::VitalSignDetector;
// Create a detector with 20 Hz sample rate
let mut detector = VitalSignDetector::new(20.0);
// Feed CSI amplitude samples
for amplitude in csi_amplitudes.iter() {
detector.push_sample(*amplitude);
}
// Extract vital signs
if let Some(vitals) = detector.detect() {
println!("Breathing: {:.1} BPM", vitals.breathing_rate_bpm);
println!("Heart rate: {:.0} BPM", vitals.heart_rate_bpm);
}
Architecture
ESP32 ──UDP:5005──> [ CSI Receiver ]
|
[ Signal Pipeline ]
(vital_signs, graph_transformer, sona)
|
[ WebSocket Broadcast ]
|
Browser <──WS:8765── [ Axum Server :8080 ] ──> Static UI files
Related Crates
| Crate | Role |
|---|---|
wifi-densepose-wifiscan |
Multi-BSSID WiFi scanning (ADR-022) |
wifi-densepose-core |
Shared types and traits |
wifi-densepose-signal |
CSI signal processing algorithms |
wifi-densepose-hardware |
ESP32 hardware interfaces |
wifi-densepose-wasm |
Browser WASM bindings for the sensing UI |
wifi-densepose-train |
Full training pipeline with ruvector |
wifi-densepose-mat |
Disaster detection module |
License
MIT OR Apache-2.0