# RuView — Extended Documentation

This document contains the detailed sections that were extracted from [README.md](../README.md) to keep the main README focused on overview, use cases, and support.

---

## Latest Additions

### Real-Time Dense Point Cloud (NEW)

RuView now generates **real-time 3D point clouds** by fusing camera depth + WiFi CSI + mmWave radar. All sensors stream simultaneously into a unified spatial model.

| Sensor | Data | Integration |
|--------|------|-------------|
| **Camera** | MiDaS monocular depth (GPU) | 640×480 → 19,200+ depth points per frame |
| **ESP32 CSI** | ADR-018 binary frames (UDP) | RF tomography → 8×8×4 occupancy grid |
| **WiFlow Pose** | 17 COCO keypoints from CSI | Skeleton overlay on point cloud |
| **Vital Signs** | Breathing rate from CSI phase | Stored in ruOS brain every 60s |
| **Motion** | CSI amplitude variance | Adaptive capture rate (skip depth when still) |

**Quick start:**
```bash
cd v2
cargo build --release -p wifi-densepose-pointcloud
./target/release/ruview-pointcloud serve --bind 127.0.0.1:9880
# Open http://localhost:9880 for live 3D viewer
```

**CLI commands:**
```bash
ruview-pointcloud demo                            # synthetic demo
ruview-pointcloud serve --bind 127.0.0.1:9880     # live server + Three.js viewer
ruview-pointcloud capture --output room.ply       # capture to PLY
ruview-pointcloud train                           # depth calibration + DPO pairs
ruview-pointcloud cameras                         # list available cameras
ruview-pointcloud csi-test --count 100            # send test CSI frames
ruview-pointcloud fingerprint office --seconds 5  # record named CSI room fingerprint
```

The HTTP/viewer server defaults to **loopback (`127.0.0.1`)** — exposing live camera/CSI/vitals on `0.0.0.0` is an explicit opt-in. Brain URL defaults to `http://127.0.0.1:9876` and is overridable via `RUVIEW_BRAIN_URL` env var or the `--brain` flag on `serve`/`train`.

The pose overlay currently uses an **amplitude-energy heuristic** (`heuristic_pose_from_amplitude`) rather than trained WiFlow inference — real ONNX/Candle inference is tracked as a follow-up.

**Performance:** 22ms pipeline, 905 req/s API, 40K voxel room model from 20 frames.

**Brain integration:** Spatial observations (motion, vitals, skeleton, occupancy) sync to the ruOS brain every 60 seconds for agent reasoning.

See [PR #405](https://github.com/ruvnet/RuView/pull/405) for full details.

### What's New in v0.7.0

<details>
<summary><strong>Camera Ground-Truth Training</strong></summary>

**v0.7.0 adds camera-supervised pose training** using MediaPipe + real ESP32 CSI data:

| Capability | What it does | ADR |
|-----------|-------------|-----|
| **Camera ground-truth collection** | MediaPipe PoseLandmarker captures 17 COCO keypoints at 30fps, synced with ESP32 CSI | [ADR-079](docs/adr/ADR-079-camera-ground-truth-training.md) |
| **ruvector subcarrier selection** | Variance-based top-K reduces input by 50% (70→35 subcarriers) | ADR-079 O6 |
| **Stoer-Wagner min-cut** | Person-specific subcarrier cluster separation for multi-person training | ADR-079 O8 |
| **Scalable WiFlow model** | 4 presets: lite (189K) → small (474K) → medium (800K) → full (7.7M params) | ADR-079 |

```bash
# Collect ground truth (camera + ESP32 simultaneously)
python scripts/collect-ground-truth.py --duration 300 --preview
python scripts/record-csi-udp.py --duration 300

# Align CSI windows with camera keypoints
node scripts/align-ground-truth.js --gt data/ground-truth/*.jsonl --csi data/recordings/*.csi.jsonl

# Train WiFlow model (start lite, scale up as data grows)
node scripts/train-wiflow-supervised.js --data data/paired/*.jsonl --scale lite

# Evaluate
node scripts/eval-wiflow.js --model models/wiflow-real/wiflow-v1.json --data data/paired/*.jsonl
```

> **Accuracy retraction (2026-06-10):** the "92.9% PCK@20" figure previously
> shown here is retracted. A forensic recheck of the surviving eval holdout
> (69 samples) found a constant-output model scored with an absolute
> (non-torso-normalized) threshold on nearly-static frames — a protocol under
> which a trivial mean-pose predictor scores 100%. Torso-normalized PCK@20 on
> the same holdout is ~19% (from that degenerate predictor). No measured
> camera-supervised PCK@20 is currently published (CHANGELOG, PR #535).

| Metric | Camera-supervised run (protocol retracted) |
|--------|--------------------------------------------|
| Eval loss | 0.082 |
| Bone constraint | 0.008 |
| Training time | 19 minutes |
| Model size | 974 KB |

Pre-trained model: [HuggingFace ruv/ruview/wiflow-v1](https://huggingface.co/ruv/ruview)

</details>

### Pre-Trained Models (v0.6.0) — No Training Required

<details>
<summary><strong>Download from HuggingFace and start sensing immediately</strong></summary>

Pre-trained models are available on HuggingFace:
> **https://huggingface.co/ruv/ruview** (primary) | [mirror](https://huggingface.co/ruvnet/wifi-densepose-pretrained)

Trained on 60,630 real-world samples from an 8-hour overnight collection. Just download and run — no datasets, no GPU, no training needed.

| Model | Size | What it does |
|-------|------|-------------|
| `model.safetensors` | 48 KB | Contrastive encoder — 128-dim embeddings for presence, activity, environment |
| `model-q4.bin` | 8 KB | 4-bit quantized — fits in ESP32-S3 SRAM for edge inference |
| `model-q2.bin` | 4 KB | 2-bit ultra-compact for memory-constrained devices |
| `presence-head.json` | 2.6 KB | 100% accurate presence detection head |
| `node-1.json` / `node-2.json` | 21 KB | Per-room LoRA adapters (swap for new rooms) |

```bash
# Download and use (Python)
pip install huggingface_hub
huggingface-cli download ruv/ruview --local-dir models/

# Or use directly with the sensing pipeline
node scripts/train-ruvllm.js --data data/recordings/*.csi.jsonl  # retrain on your own data
node scripts/benchmark-ruvllm.js --model models/csi-ruvllm       # benchmark
```

**Benchmarks (Apple M4 Pro, retrained on overnight data):**

| What we measured | Result | Why it matters |
|-----------------|--------|---------------|
| **CSI embedding quality** | **82.3% held-out temporal-triplet** | Honest label-free metric on the last 20% by time (v1's "100% presence" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) |
| **Inference speed** | **0.008 ms** per embedding | 125,000x faster than real-time |
| **Throughput** | **164,183 embeddings/sec** | One Mac Mini handles 1,600+ ESP32 nodes |
| **Contrastive learning** | **51.6% improvement** | Strong pattern learning from real overnight data |
| **Model size** | **8 KB** (4-bit quantized) | Fits in ESP32 SRAM — no server needed |
| **Total hardware cost** | **$140** | ESP32 ($9) + [Cognitum Seed](https://cognitum.one) ($131) |

</details>

### 17 Sensing Applications (v0.6.0)

<details>
<summary><strong>Health, environment, security, and multi-frequency mesh sensing</strong></summary>

All applications run from a single ESP32 + optional Cognitum Seed. No camera, no cloud, no internet.

**Health & Wellness:**

| Application | Script | What it detects |
|------------|--------|----------------|
| Sleep Monitor | `node scripts/sleep-monitor.js` | Sleep stages (deep/light/REM/awake), efficiency, hypnogram |
| Apnea Detector | `node scripts/apnea-detector.js` | Breathing pauses >10s, AHI severity scoring |
| Stress Monitor | `node scripts/stress-monitor.js` | Heart rate variability, LF/HF stress ratio |
| Gait Analyzer | `node scripts/gait-analyzer.js` | Walking cadence, stride asymmetry, tremor detection |

**Environment & Security:**

| Application | Script | What it detects |
|------------|--------|----------------|
| Person Counter | `node scripts/mincut-person-counter.js` | Correct occupancy count (fixes #348) |
| Room Fingerprint | `node scripts/room-fingerprint.js` | Activity state clustering, daily patterns, anomalies |
| Material Detector | `node scripts/material-detector.js` | New/moved objects via subcarrier null changes |
| Device Fingerprint | `node scripts/device-fingerprint.js` | Electronic device activity (printer, router, etc.) |

**Multi-Frequency Mesh** (requires `--hop-channels` provisioning):

| Application | Script | What it detects |
|------------|--------|----------------|
| RF Tomography | `node scripts/rf-tomography.js` | 2D room imaging via RF backprojection |
| Passive Radar | `node scripts/passive-radar.js` | Neighbor WiFi APs as bistatic radar illuminators |
| Material Classifier | `node scripts/material-classifier.js` | Metal/water/wood/glass from frequency response |
| Through-Wall | `node scripts/through-wall-detector.js` | Motion behind walls using lower-frequency penetration |

All scripts support `--replay data/recordings/*.csi.jsonl` for offline analysis and `--json` for programmatic output.

</details>

### What's New in v0.5.5

<details>
<summary><strong>Advanced Sensing: SNN + MinCut + WiFlow + Multi-Frequency Mesh</strong></summary>

**v0.5.5 adds four new sensing capabilities** built on the [ruvector](https://github.com/ruvnet/ruvector) ecosystem:

| Capability | What it does | ADR |
|-----------|-------------|-----|
| **Spiking Neural Network** | Adapts to your room in <30s with STDP online learning — no labels, no batches, 16-160x less compute | [ADR-074](docs/adr/ADR-074-spiking-neural-csi-sensing.md) |
| **MinCut Person Counting** | Stoer-Wagner min-cut on subcarrier correlation graph — **fixes #348** (was always 4, now correct) | [ADR-075](docs/adr/ADR-075-mincut-person-separation.md) |
| **CNN Spectrogram Embeddings** | Treat CSI as a 64×20 image → 128-dim embedding for environment fingerprinting (0.95+ similarity) | [ADR-076](docs/adr/ADR-076-csi-spectrogram-embeddings.md) |
| **WiFlow SOTA Architecture** | TCN + axial attention + pose decoder → 17 COCO keypoints, 1.8M params (881 KB at 4-bit) | [ADR-072](docs/adr/ADR-072-wiflow-architecture.md) |
| **Multi-Frequency Mesh** | Channel hopping across 6 bands, neighbor WiFi as passive radar illuminators | [ADR-073](docs/adr/ADR-073-multifrequency-mesh-scan.md) |

```bash
# Live RF room scan (spectrum visualization)
node scripts/rf-scan.js --port 5006 --duration 30

# Correct person counting (fixes #348)
node scripts/mincut-person-counter.js --port 5006

# SNN real-time adaptation
node scripts/snn-csi-processor.js --port 5006

# CNN spectrogram embeddings
node scripts/csi-spectrogram.js --replay data/recordings/*.csi.jsonl

# WiFlow 17-keypoint pose training
node scripts/train-wiflow.js --data data/recordings/*.csi.jsonl

# Enable channel hopping on ESP32
python firmware/esp32-csi-node/provision.py --port COM9 --hop-channels "1,6,11"
```

**Validated benchmarks:**

| Metric | v0.5.4 | v0.5.5 |
|--------|--------|--------|
| Person counting | Broken (always 4) | **Correct** (MinCut, 24/24) |
| WiFi channels | 1 | **6** (multi-freq hopping) |
| Null subcarriers | 19% blocked | **16%** (frequency diversity) |
| Pose model | 16K params (FC only) | **1.8M params** (WiFlow) |
| Online adaptation | None | **<30s** (SNN STDP) |
| Fingerprint dims | 8 | **128** (CNN spectrogram) |
| Multi-node fusion | Average | **GATv2 attention** |
| New scripts | 0 | **15+** |
| New ADRs | 3 | **8** (069-076) |

</details>

### What's New in v0.5.4

<details>
<summary><strong>Cognitum Seed Integration + Camera-Free Pose Training</strong></summary>

**v0.5.4 transforms RuView from a real-time sensing tool into a persistent edge AI system.** Your ESP32 now remembers what it senses, learns without cameras, and proves its data cryptographically.

| Capability | Details | Hardware |
|-----------|---------|----------|
| **Persistent vector store** | Every sensing event stored as searchable 8-dim vector in RVF format | ESP32 + [Cognitum Seed](https://cognitum.one) ($140) |
| **kNN similarity search** | "Find the 10 most similar states to right now" — anomaly detection, fingerprinting | Cognitum Seed |
| **Witness chain** | SHA-256 tamper-evident audit trail for every measurement (1,747 entries validated) | Cognitum Seed |
| **Camera-free pose training** | 17 COCO keypoints from 10 sensor signals — PIR, RSSI triangulation, subcarrier asymmetry, vibration, BME280 | 2x ESP32 + Seed |
| **Pre-trained model** | 82.8 KB (8 KB at 4-bit quantization), 82.3% held-out temporal-triplet accuracy (v1's "100% presence" was single-class — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) | Download from release |
| **Sub-ms inference** | 0.012 ms latency, 171,472 embeddings/sec on M4 Pro | Any machine with Node.js |
| **SONA adaptation** | Adapts to new rooms in <1ms without retraining | ruvllm runtime |
| **LoRA room adapters** | Per-node fine-tuning with 2,048 parameters per adapter | Automatic |
| **114-tool MCP proxy** | AI assistants (Claude, GPT) query sensors directly via JSON-RPC | Cognitum Seed |
| **Multi-frequency mesh** | Channel hopping across ch 1/3/5/6/9/11 — neighbor WiFi as passive radar | 2x ESP32 ($18) |
| **RF room scanner** | Real-time spectrum visualization: nulls, reflectors, movement, multipath | `node scripts/rf-scan.js` |
| **Security hardened** | Bearer tokens, TLS, source IP filtering, NaN rejection, credential rotation | All components |

**Training pipeline (ruvllm, no PyTorch needed):**

```bash
# Collect data (2 min, ESP32s must be streaming)
python scripts/collect-training-data.py --port 5006 --duration 120

# Train — contrastive pretraining + task heads + LoRA + quantization + EWC
node scripts/train-ruvllm.js --data data/recordings/pretrain-*.csi.jsonl

# Camera-free 17-keypoint pose (uses PIR + RSSI + vibration + subcarrier asymmetry)
node scripts/train-camera-free.js --data data/recordings/pretrain-*.csi.jsonl

# Benchmark
node scripts/benchmark-ruvllm.js --model models/csi-ruvllm
```

**Benchmarks — validated on real hardware (Apple M4 Pro + ESP32-S3 + Cognitum Seed):**

| What we measured | Result | Why it matters |
|-----------------|--------|---------------|
| **CSI embedding quality** | **82.3% held-out temporal-triplet** | Honest label-free metric (v1's "100% presence" was single-class — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) |
| **Person counting** | **24/24 correct** (MinCut) | Fixed the #1 user-reported issue |
| **Inference speed** | **0.012 ms** per embedding | 83,000x faster than real-time |
| **Throughput** | **171,472 embeddings/sec** | One Mac Mini handles 1,700+ ESP32 nodes |
| **Training time** | **84 seconds** | From zero to trained model in under 2 minutes |
| **Contrastive learning** | **33.9% improvement** | Model learns meaningful patterns from CSI |
| **Model size** | **8 KB** (4-bit quantized) | Fits in ESP32 SRAM — no server needed |
| **Skeleton physics** | **0 violations** in 100 frames | Every pose is anatomically valid |
| **Pose keypoints** | **17 COCO keypoints** | Full body pose, no camera required |
| **WiFi channels** | **6 simultaneous** | 3x more sensing data than single-channel |
| **Online adaptation** | **<30 seconds** (SNN) | Learns a new room without retraining |
| **Witness chain** | **2,547 entries** verified | Cryptographic proof every measurement is real |
| **Test suite** | **1,463 tests passed** | Rock-solid foundation |
| **Total hardware cost** | **$140** | ESP32 ($9) + [Cognitum Seed](https://cognitum.one) ($131) |

See [ADR-069](docs/adr/ADR-069-cognitum-seed-csi-pipeline.md), [ADR-071](docs/adr/ADR-071-ruvllm-training-pipeline.md), and the [Cognitum Seed tutorial](docs/tutorials/cognitum-seed-pretraining.md) for full details.

</details>

---

## 🚀 Key Features

### Sensing

See people, breathing, and heartbeats through walls — using only WiFi signals already in the room.

| | Feature | What It Means |
|---|---------|---------------|
| 🔒 | **Privacy-First** | Tracks human pose using only WiFi signals — no cameras, no video, no images stored |
| 💓 | **Vital Signs** | Detects breathing rate (6-30 breaths/min) and heart rate (40-120 bpm) without any wearable |
| 👥 | **Multi-Person** | Tracks multiple people simultaneously, each with independent pose and vitals — no hard software limit (physics: ~3-5 per AP with 56 subcarriers, more with multi-AP) |
| 🧱 | **Through-Wall** | WiFi passes through walls, furniture, and debris — works where cameras cannot |
| 🚑 | **Disaster Response** | Detects trapped survivors through rubble and classifies injury severity (START triage) |
| 📡 | **Multistatic Mesh** | 4-6 low-cost sensor nodes work together, combining 12+ overlapping signal paths for full 360-degree room coverage with sub-inch accuracy and no person mix-ups ([ADR-029](docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md)) |
| 🌐 | **Persistent Field Model** | The system learns the RF signature of each room — then subtracts the room to isolate human motion, detect drift over days, predict intent before movement starts, and flag spoofing attempts ([ADR-030](docs/adr/ADR-030-ruvsense-persistent-field-model.md)) |

### Intelligence

The system learns on its own and gets smarter over time — no hand-tuning, no labeled data required.

| | Feature | What It Means |
|---|---------|---------------|
| 🧠 | **Self-Learning** | Teaches itself from raw WiFi data — no labeled training sets, no cameras needed to bootstrap ([ADR-024](docs/adr/ADR-024-contrastive-csi-embedding-model.md)) |
| 🎯 | **AI Signal Processing** | Attention networks, graph algorithms, and smart compression replace hand-tuned thresholds — adapts to each room automatically ([RuVector](https://github.com/ruvnet/ruvector)) |
| 🌍 | **Works Everywhere** | Train once, deploy in any room — adversarial domain generalization strips environment bias so models transfer across rooms, buildings, and hardware ([ADR-027](docs/adr/ADR-027-cross-environment-domain-generalization.md)) |
| 👁️ | **Cross-Viewpoint Fusion** | AI combines what each sensor sees from its own angle — fills in blind spots and depth ambiguity that no single viewpoint can resolve on its own ([ADR-031](docs/adr/ADR-031-ruview-sensing-first-rf-mode.md)) |
| 🔮 | **Signal-Line Protocol** | A 6-stage processing pipeline transforms raw WiFi signals into structured body representations — from signal cleanup through graph-based spatial reasoning to final pose output ([ADR-033](docs/adr/ADR-033-crv-signal-line-sensing-integration.md)) |
| 🔒 | **QUIC Mesh Security** | All sensor-to-sensor communication is encrypted end-to-end with tamper detection, replay protection, and seamless reconnection if a node moves or drops offline ([ADR-032](docs/adr/ADR-032-multistatic-mesh-security-hardening.md)) |
| 🎯 | **Adaptive Classifier** | Records labeled CSI sessions, trains a 15-feature logistic regression model in pure Rust, and learns your room's unique signal characteristics — replaces hand-tuned thresholds with data-driven classification ([ADR-048](docs/adr/ADR-048-adaptive-csi-classifier.md)) |

### Performance & Deployment

Fast enough for real-time use, small enough for edge devices, simple enough for one-command setup.

| | Feature | What It Means |
|---|---------|---------------|
| ⚡ | **Real-Time** | Analyzes WiFi signals in under 100 microseconds per frame — fast enough for live monitoring |
| 🦀 | **810x Faster** | Complete Rust rewrite: 54,000 frames/sec pipeline, multi-arch Docker image, 1,031+ tests |
| 🐳 | **One-Command Setup** | `docker pull ruvnet/wifi-densepose:latest` — live sensing in 30 seconds, no toolchain needed (amd64 + arm64 / Apple Silicon) |
| 📡 | **Fully Local** | Runs completely on a $9 ESP32 — no internet connection, no cloud account, no recurring fees. Detects presence, vital signs, and falls on-device with instant response |
| 📦 | **Portable Models** | Trained models package into a single `.rvf` file — runs on edge, cloud, or browser (WASM) |
| 🔭 | **Observatory Visualization** | Cinematic Three.js dashboard with 5 holographic panels — subcarrier manifold, vital signs oracle, presence heatmap, phase constellation, convergence engine — all driven by live or demo CSI data ([ADR-047](docs/adr/ADR-047-psychohistory-observatory-visualization.md)) |
| 📟 | **AMOLED Display** | ESP32-S3 boards with built-in AMOLED screens show real-time presence, vital signs, and room status directly on the sensor — no phone or PC needed ([ADR-045](docs/adr/ADR-045-amoled-display-support.md)) |

---

## 📦 Installation

<details>
<summary><strong>Guided Installer</strong> — Interactive hardware detection and profile selection</summary>

```bash
./install.sh
```

The installer walks through 7 steps: system detection, toolchain check, WiFi hardware scan, profile recommendation, dependency install, build, and verification.

| Profile | What it installs | Size | Requirements |
|---------|-----------------|------|-------------|
| `verify` | Pipeline verification only | ~5 MB | Python 3.8+ |
| `python` | Full Python API server + sensing | ~500 MB | Python 3.8+ |
| `rust` | Rust pipeline (~810x faster) | ~200 MB | Rust 1.70+ |
| `browser` | WASM for in-browser execution | ~10 MB | Rust + wasm-pack |
| `iot` | ESP32 sensor mesh + aggregator | varies | Rust + ESP-IDF |
| `docker` | Docker-based deployment | ~1 GB | Docker |
| `field` | WiFi-Mat disaster response kit | ~62 MB | Rust + wasm-pack |
| `full` | Everything available | ~2 GB | All toolchains |

```bash
# Non-interactive
./install.sh --profile rust --yes

# Hardware check only
./install.sh --check-only
```

</details>

<details>
<summary><strong>From Source</strong> — Rust (primary) or Python</summary>

```bash
git clone https://github.com/ruvnet/RuView.git
cd RuView

# Rust (primary — 810x faster)
cd v2
cargo build --release
cargo test --workspace

# Python (legacy v1)
pip install -r requirements.txt
pip install -e .

# Or via pip
pip install wifi-densepose
pip install wifi-densepose[gpu]   # GPU acceleration
pip install wifi-densepose[all]   # All optional deps
```

</details>

<details>
<summary><strong>Docker</strong> — Pre-built images, no toolchain needed</summary>

```bash
# Rust sensing server (132 MB — recommended)
docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp ruvnet/wifi-densepose:latest

# Python sensing pipeline (569 MB)
docker pull ruvnet/wifi-densepose:python
docker run -p 8765:8765 -p 8080:8080 ruvnet/wifi-densepose:python

# Both via docker-compose
cd docker && docker compose up

# Export RVF model
docker run --rm -v $(pwd):/out ruvnet/wifi-densepose:latest --export-rvf /out/model.rvf
```

| Image | Tag | Platforms | Ports |
|-------|-----|-----------|-------|
| `ruvnet/wifi-densepose` | `latest`, `rust` | linux/amd64, linux/arm64 | 3000 (REST), 3001 (WS), 5005/udp (ESP32) |
| `ruvnet/wifi-densepose` | `python` | linux/amd64 | 8765 (WS), 8080 (UI) |

</details>

<details>
<summary><strong>System Requirements</strong></summary>

- **Rust**: 1.70+ (primary runtime — install via [rustup](https://rustup.rs/))
- **Python**: 3.8+ (for verification and legacy v1 API)
- **OS**: Linux (Ubuntu 18.04+), macOS (10.15+), Windows 10+
- **Memory**: Minimum 4GB RAM, Recommended 8GB+
- **Storage**: 2GB free space for models and data
- **Network**: WiFi interface with CSI capability (optional — installer detects what you have)
- **GPU**: Optional (NVIDIA CUDA or Apple Metal)

</details>

<details>
<summary><strong>Rust Crates</strong> — Individual crates on crates.io</summary>

The Rust workspace consists of 15 crates, all published to [crates.io](https://crates.io/):

```bash
# Add individual crates to your Cargo.toml
cargo add wifi-densepose-core       # Types, traits, errors
cargo add wifi-densepose-signal     # CSI signal processing (6 SOTA algorithms)
cargo add wifi-densepose-nn         # Neural inference (ONNX, PyTorch, Candle)
cargo add wifi-densepose-vitals     # Vital sign extraction (breathing + heart rate)
cargo add wifi-densepose-mat        # Disaster response (MAT survivor detection)
cargo add wifi-densepose-hardware   # ESP32, Intel 5300, Atheros sensors
cargo add wifi-densepose-train      # Training pipeline (MM-Fi dataset)
cargo add wifi-densepose-wifiscan   # Multi-BSSID WiFi scanning
cargo add wifi-densepose-ruvector   # RuVector v2.0.4 integration layer (ADR-017)
```

| Crate | Description | RuVector | crates.io |
|-------|-------------|----------|-----------|
| [`wifi-densepose-core`](https://crates.io/crates/wifi-densepose-core) | Foundation types, traits, and utilities | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-core.svg)](https://crates.io/crates/wifi-densepose-core) |
| [`wifi-densepose-signal`](https://crates.io/crates/wifi-densepose-signal) | SOTA CSI signal processing (SpotFi, FarSense, Widar 3.0) | `mincut`, `attn-mincut`, `attention`, `solver` | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-signal.svg)](https://crates.io/crates/wifi-densepose-signal) |
| [`wifi-densepose-nn`](https://crates.io/crates/wifi-densepose-nn) | Multi-backend inference (ONNX, PyTorch, Candle) | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-nn.svg)](https://crates.io/crates/wifi-densepose-nn) |
| [`wifi-densepose-train`](https://crates.io/crates/wifi-densepose-train) | Training pipeline with MM-Fi dataset (NeurIPS 2023) | **All 5** | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-train.svg)](https://crates.io/crates/wifi-densepose-train) |
| [`wifi-densepose-mat`](https://crates.io/crates/wifi-densepose-mat) | Mass Casualty Assessment Tool (disaster survivor detection) | `solver`, `temporal-tensor` | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-mat.svg)](https://crates.io/crates/wifi-densepose-mat) |
| [`wifi-densepose-ruvector`](https://crates.io/crates/wifi-densepose-ruvector) | RuVector v2.0.4 integration layer — 7 signal+MAT integration points (ADR-017) | **All 5** | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-ruvector.svg)](https://crates.io/crates/wifi-densepose-ruvector) |
| [`wifi-densepose-vitals`](https://crates.io/crates/wifi-densepose-vitals) | Vital signs: breathing (6-30 BPM), heart rate (40-120 BPM) | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-vitals.svg)](https://crates.io/crates/wifi-densepose-vitals) |
| [`wifi-densepose-hardware`](https://crates.io/crates/wifi-densepose-hardware) | ESP32, Intel 5300, Atheros CSI sensor interfaces | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-hardware.svg)](https://crates.io/crates/wifi-densepose-hardware) |
| [`wifi-densepose-wifiscan`](https://crates.io/crates/wifi-densepose-wifiscan) | Multi-BSSID WiFi scanning (Windows, macOS, Linux) | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-wifiscan.svg)](https://crates.io/crates/wifi-densepose-wifiscan) |
| [`wifi-densepose-wasm`](https://crates.io/crates/wifi-densepose-wasm) | WebAssembly bindings for browser deployment | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-wasm.svg)](https://crates.io/crates/wifi-densepose-wasm) |
| [`wifi-densepose-sensing-server`](https://crates.io/crates/wifi-densepose-sensing-server) | Axum server: UDP ingestion, WebSocket broadcast | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-sensing-server.svg)](https://crates.io/crates/wifi-densepose-sensing-server) |
| [`wifi-densepose-cli`](https://crates.io/crates/wifi-densepose-cli) | Command-line tool for MAT disaster scanning | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-cli.svg)](https://crates.io/crates/wifi-densepose-cli) |
| [`wifi-densepose-api`](https://crates.io/crates/wifi-densepose-api) | REST + WebSocket API layer | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-api.svg)](https://crates.io/crates/wifi-densepose-api) |
| [`wifi-densepose-config`](https://crates.io/crates/wifi-densepose-config) | Configuration management | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-config.svg)](https://crates.io/crates/wifi-densepose-config) |
| [`wifi-densepose-db`](https://crates.io/crates/wifi-densepose-db) | Database persistence (PostgreSQL, SQLite, Redis) | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-db.svg)](https://crates.io/crates/wifi-densepose-db) |
| `wifi-densepose-pointcloud` | Real-time dense point cloud from camera + WiFi CSI fusion (Three.js viewer, brain bridge). Workspace-only for now. | -- | — |
| `wifi-densepose-geo` | Geospatial context (Sentinel-2 tiles, SRTM elevation, OSM, weather, night-mode). Workspace-only for now. | -- | — |

All crates integrate with [RuVector v2.0.4](https://github.com/ruvnet/ruvector) — see [AI Backbone](#ai-backbone-ruvector) below.

**[rUv Neural](v2/crates/ruv-neural/)** — A separate 12-crate workspace for brain network topology analysis, neural decoding, and medical sensing. See [rUv Neural](#ruv-neural) in Models & Training.

</details>

---

## 🚀 Quick Start

<details open>
<summary><strong>First API call in 3 commands</strong></summary>

### 1. Install

```bash
# Fastest path — Docker
docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 ruvnet/wifi-densepose:latest

# Or from source (Rust)
./install.sh --profile rust --yes
```

### 2. Start the System

```python
from wifi_densepose import WiFiDensePose

system = WiFiDensePose()
system.start()
poses = system.get_latest_poses()
print(f"Detected {len(poses)} persons")
system.stop()
```

### 3. REST API

```bash
# Health check
curl http://localhost:3000/health

# Latest sensing frame
curl http://localhost:3000/api/v1/sensing/latest

# Vital signs
curl http://localhost:3000/api/v1/vital-signs

# Pose estimation
curl http://localhost:3000/api/v1/pose/current

# Server info
curl http://localhost:3000/api/v1/info
```

### 4. Real-time WebSocket

```python
import asyncio, websockets, json

async def stream():
    async with websockets.connect("ws://localhost:3001/ws/sensing") as ws:
        async for msg in ws:
            data = json.loads(msg)
            print(f"Persons: {len(data.get('persons', []))}")

asyncio.run(stream())
```

</details>

---

## 📋 Table of Contents

<details open>
<summary><strong>📡 Signal Processing & Sensing</strong> — From raw WiFi frames to vital signs</summary>

The signal processing stack transforms raw WiFi Channel State Information into actionable human sensing data. Starting from 56-192 subcarrier complex values captured at 20 Hz, the pipeline applies research-grade algorithms (SpotFi phase correction, Hampel outlier rejection, Fresnel zone modeling) to extract breathing rate, heart rate, motion level, and multi-person body pose — all in pure Rust with zero external ML dependencies.

| Section | Description | Docs |
|---------|-------------|------|
| [Key Features](#key-features) | Sensing, Intelligence, and Performance & Deployment capabilities | — |
| [How It Works](#how-it-works) | End-to-end pipeline: radio waves → CSI capture → signal processing → AI → pose + vitals | — |
| [ESP32-S3 Hardware Pipeline](#esp32-s3-hardware-pipeline) | 20 Hz CSI streaming, binary frame parsing, flash & provision | [ADR-018](docs/adr/ADR-018-esp32-dev-implementation.md) · [Tutorial #34](https://github.com/ruvnet/RuView/issues/34) |
| [Vital Sign Detection](#vital-sign-detection) | Breathing 6-30 BPM, heartbeat 40-120 BPM, FFT peak detection | [ADR-021](docs/adr/ADR-021-vital-sign-detection-rvdna-pipeline.md) |
| [WiFi Scan Domain Layer](#wifi-scan-domain-layer) | 8-stage RSSI pipeline, multi-BSSID fingerprinting, Windows WiFi | [ADR-022](docs/adr/ADR-022-windows-wifi-enhanced-fidelity-ruvector.md) · [Tutorial #36](https://github.com/ruvnet/RuView/issues/36) |
| [WiFi-Mat Disaster Response](#wifi-mat-disaster-response) | Search & rescue, START triage, 3D localization through debris | [ADR-001](docs/adr/ADR-001-wifi-mat-disaster-detection.md) · [User Guide](docs/wifi-mat-user-guide.md) |
| [SOTA Signal Processing](#sota-signal-processing) | SpotFi, Hampel, Fresnel, STFT spectrogram, subcarrier selection, BVP | [ADR-014](docs/adr/ADR-014-sota-signal-processing.md) |

</details>

<details>
<summary><strong>🧠 Models & Training</strong> — DensePose pipeline, RVF containers, SONA adaptation, RuVector integration</summary>

The neural pipeline uses a graph transformer with cross-attention to map CSI feature matrices to 17 COCO body keypoints and DensePose UV coordinates. Models are packaged as single-file `.rvf` containers with progressive loading (Layer A instant, Layer B warm, Layer C full). SONA (Self-Optimizing Neural Architecture) enables continuous on-device adaptation via micro-LoRA + EWC++ without catastrophic forgetting. Signal processing is powered by 5 [RuVector](https://github.com/ruvnet/ruvector) crates (v2.0.4) with 7 integration points across the Rust workspace, plus 6 additional vendored crates for inference and graph intelligence.

| Section | Description | Docs |
|---------|-------------|------|
| [RVF Model Container](#rvf-model-container) | Binary packaging with Ed25519 signing, progressive 3-layer loading, SIMD quantization | [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md) |
| [Training & Fine-Tuning](#training--fine-tuning) | 8-phase pure Rust pipeline (7,832 lines), MM-Fi/Wi-Pose pre-training, 6-term composite loss, SONA LoRA | [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md) |
| [RuVector Crates](#ruvector-crates) | 11 vendored Rust crates from [ruvector](https://github.com/ruvnet/ruvector): attention, min-cut, solver, GNN, HNSW, temporal compression, sparse inference | [GitHub](https://github.com/ruvnet/ruvector) · [Source](vendor/ruvector/) |
| [rUv Neural](#ruv-neural) | 12-crate brain topology analysis ecosystem: neural decoding, quantum sensor integration, cognitive state classification, BCI output | [README](v2/crates/ruv-neural/README.md) |
| [AI Backbone (RuVector)](#ai-backbone-ruvector) | 5 AI capabilities replacing hand-tuned thresholds: attention, graph min-cut, sparse solvers, tiered compression | [crates.io](https://crates.io/crates/wifi-densepose-ruvector) |
| [Self-Learning WiFi AI (ADR-024)](#self-learning-wifi-ai-adr-024) | Contrastive self-supervised learning, room fingerprinting, anomaly detection, 55 KB model | [ADR-024](docs/adr/ADR-024-contrastive-csi-embedding-model.md) |
| [Cross-Environment Generalization (ADR-027)](docs/adr/ADR-027-cross-environment-domain-generalization.md) | Domain-adversarial training, geometry-conditioned inference, hardware normalization, zero-shot deployment | [ADR-027](docs/adr/ADR-027-cross-environment-domain-generalization.md) |

</details>

<details>
<summary><strong>🖥️ Usage & Configuration</strong> — CLI flags, API endpoints, hardware setup</summary>

The Rust sensing server is the primary interface, offering a comprehensive CLI with flags for data source selection, model loading, training, benchmarking, and RVF export. A REST API (Axum) and WebSocket server provide real-time data access. The Python v1 CLI remains available for legacy workflows.

| Section | Description | Docs |
|---------|-------------|------|
| [CLI Usage](#cli-usage) | `--source`, `--train`, `--benchmark`, `--export-rvf`, `--model`, `--progressive` | — |
| [REST API & WebSocket](#rest-api--websocket) | 6 REST endpoints (sensing, vitals, BSSID, SONA), WebSocket real-time stream | — |
| [Hardware Support](#hardware-support-1) | ESP32-S3 ($8), Intel 5300 ($15), Atheros AR9580 ($20), Windows RSSI ($0) | [ADR-012](docs/adr/ADR-012-esp32-csi-sensor-mesh.md) · [ADR-013](docs/adr/ADR-013-feature-level-sensing-commodity-gear.md) |

</details>

<details>
<summary><strong>⚙️ Development & Testing</strong> — 542+ tests, CI, deployment</summary>

The project maintains 542+ pure-Rust tests across 7 crate suites with zero mocks — every test runs against real algorithm implementations. Hardware-free simulation mode (`--source simulate`) enables full-stack testing without physical devices. Docker images are published on Docker Hub for zero-setup deployment.

| Section | Description | Docs |
|---------|-------------|------|
| [Testing](#testing) | 7 test suites: sensing-server (229), signal (83), mat (139), wifiscan (91), RVF (16), vitals (18) | — |
| [Deployment](#deployment) | Docker images (132 MB Rust / 569 MB Python), docker-compose, env vars | — |
| [Contributing](#contributing) | Fork → branch → test → PR workflow, Rust and Python dev setup | — |

</details>

<details>
<summary><strong>📊 Performance & Benchmarks</strong> — Measured throughput, latency, resource usage</summary>

All benchmarks are measured on the Rust sensing server using `cargo bench` and the built-in `--benchmark` CLI flag. The Rust v2 implementation delivers 810x end-to-end speedup over the Python v1 baseline, with motion detection reaching 5,400x improvement. The vital sign detector processes 11,665 frames/second in a single-threaded benchmark.

| Section | Description | Key Metric |
|---------|-------------|------------|
| [Performance Metrics](#performance-metrics) | Vital signs, CSI pipeline, motion detection, Docker image, memory | 11,665 fps vitals · 54K fps pipeline |
| [Rust vs Python](#python-vs-rust) | Side-by-side benchmarks across 5 operations | **810x** full pipeline speedup |

</details>

<details>
<summary><strong>📄 Meta</strong> — License, changelog, support</summary>

WiFi DensePose is MIT-licensed open source, developed by [ruvnet](https://github.com/ruvnet). The project has been in active development since March 2025, with 3 major releases delivering the Rust port, SOTA signal processing, disaster response module, and end-to-end training pipeline.

| Section | Description | Link |
|---------|-------------|------|
| [Changelog](#changelog) | v3.0.0 (AETHER AI + Docker), v2.0.0 (Rust port + SOTA + WiFi-Mat) | [CHANGELOG.md](CHANGELOG.md) |
| [License](#license) | MIT License | [LICENSE](LICENSE) |
| [Support](#support) | Bug reports, feature requests, community discussion | [Issues](https://github.com/ruvnet/RuView/issues) · [Discussions](https://github.com/ruvnet/RuView/discussions) |

</details>

---

<details>
<summary><strong>🌍 Cross-Environment Generalization (ADR-027 — Project MERIDIAN)</strong> — Train once, deploy in any room without retraining</summary>

| What | How it works | Why it matters |
|------|-------------|----------------|
| **Gradient Reversal Layer** | An adversarial classifier tries to guess which room the signal came from; the main network is trained to fool it | Forces the model to discard room-specific shortcuts |
| **Geometry Encoder (FiLM)** | Transmitter/receiver positions are Fourier-encoded and injected as scale+shift conditioning on every layer | The model knows *where* the hardware is, so it doesn't need to memorize layout |
| **Hardware Normalizer** | Resamples any chipset's CSI to a canonical 56-subcarrier format with standardized amplitude | Intel 5300 and ESP32 data look identical to the model |
| **Virtual Domain Augmentation** | Generates synthetic environments with random room scale, wall reflections, scatterers, and noise profiles | Training sees 1000s of rooms even with data from just 2-3 |
| **Rapid Adaptation (TTT)** | Contrastive test-time training with LoRA weight generation from a few unlabeled frames | Zero-shot deployment — the model self-tunes on arrival |
| **Cross-Domain Evaluator** | Leave-one-out evaluation across all training environments with per-environment PCK/OKS metrics | Proves generalization, not just memorization |

**Architecture**

```
CSI Frame [any chipset]
    │
    ▼
HardwareNormalizer ──→ canonical 56 subcarriers, N(0,1) amplitude
    │
    ▼
CSI Encoder (existing) ──→ latent features
    │
    ├──→ Pose Head ──→ 17-joint pose (environment-invariant)
    │
    ├──→ Gradient Reversal Layer ──→ Domain Classifier (adversarial)
    │         λ ramps 0→1 via cosine/exponential schedule
    │
    └──→ Geometry Encoder ──→ FiLM conditioning (scale + shift)
              Fourier positional encoding → DeepSets → per-layer modulation
```

**Security hardening:**
- Bounded calibration buffer (max 10,000 frames) prevents memory exhaustion
- `adapt()` returns `Result<_, AdaptError>` — no panics on bad input
- Atomic instance counter ensures unique weight initialization across threads
- Division-by-zero guards on all augmentation parameters

See [`docs/adr/ADR-027-cross-environment-domain-generalization.md`](docs/adr/ADR-027-cross-environment-domain-generalization.md) for full architectural details.

</details>

<details>
<summary><strong>🔍 Independent Capability Audit (ADR-028)</strong> — 1,031 tests, SHA-256 proof, self-verifying witness bundle</summary>

A [3-agent parallel audit](docs/adr/ADR-028-esp32-capability-audit.md) independently verified every claim in this repository — ESP32 hardware, signal processing, neural networks, training pipeline, deployment, and security. Results:

```
Rust tests:     1,031 passed, 0 failed
Python proof:   VERDICT: PASS (SHA-256: 8c0680d7...)
Bundle verify:  7/7 checks PASS
```

**33-row attestation matrix:** 31 capabilities verified YES, 2 not measured at audit time (benchmark throughput, Kubernetes deploy).

**Verify it yourself** (no hardware needed):
```bash
# Run all tests
cd v2 && cargo test --workspace --no-default-features

# Run the deterministic proof
python archive/v1/data/proof/verify.py

# Generate + verify the witness bundle
bash scripts/generate-witness-bundle.sh
cd dist/witness-bundle-ADR028-*/ && bash VERIFY.sh
```

| Document | What it contains |
|----------|-----------------|
| [ADR-028](docs/adr/ADR-028-esp32-capability-audit.md) | Full audit: ESP32 specs, signal algorithms, NN architectures, training phases, deployment infra |
| [Witness Log](docs/WITNESS-LOG-028.md) | 11 reproducible verification steps + 33-row attestation matrix with evidence per row |
| [`generate-witness-bundle.sh`](scripts/generate-witness-bundle.sh) | Creates self-contained tar.gz with test logs, proof output, firmware hashes, crate versions, VERIFY.sh |

</details>

<details>
<summary><strong>📡 Multistatic Sensing (ADR-029/030/031 — Project RuvSense + RuView)</strong> — Multiple ESP32 nodes fuse viewpoints for production-grade pose, tracking, and exotic sensing</summary>

A single WiFi receiver can track people, but has blind spots — limbs behind the torso are invisible, depth is ambiguous, and two people at similar range create overlapping signals. RuvSense solves this by coordinating multiple ESP32 nodes into a **multistatic mesh** where every node acts as both transmitter and receiver, creating N×(N-1) measurement links from N devices.

**What it does in plain terms:**
- 4 ESP32-S3 nodes ($48 total) provide 12 TX-RX measurement links covering 360 degrees
- Each node hops across WiFi channels 1/6/11, tripling effective bandwidth from 20→60 MHz
- Coherence gating rejects noisy frames automatically — no manual tuning, stable for days
- Two-person tracking at 20 Hz with zero identity swaps over 10 minutes
- The room itself becomes a persistent model — the system remembers, predicts, and explains

**Three ADRs, one pipeline:**

| ADR | Codename | What it adds |
|-----|----------|-------------|
| [ADR-029](docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md) | **RuvSense** | Channel hopping, TDM protocol, multi-node fusion, coherence gating, 17-keypoint Kalman tracker |
| [ADR-030](docs/adr/ADR-030-ruvsense-persistent-field-model.md) | **RuvSense Field** | Room electromagnetic eigenstructure (SVD), RF tomography, longitudinal drift detection, intention prediction, gesture recognition, adversarial detection |
| [ADR-031](docs/adr/ADR-031-ruview-sensing-first-rf-mode.md) | **RuView** | Cross-viewpoint attention with geometric bias, viewpoint diversity optimization, embedding-level fusion |

**Architecture**

```
4x ESP32-S3 nodes ($48)     TDM: each transmits in turn, all others receive
        │                    Channel hop: ch1→ch6→ch11 per dwell (50ms)
        ▼
Per-Node Signal Processing   Phase sanitize → Hampel → BVP → subcarrier select
        │                    (ADR-014, unchanged per viewpoint)
        ▼
Multi-Band Frame Fusion      3 channels × 56 subcarriers = 168 virtual subcarriers
        │                    Cross-channel phase alignment via NeumannSolver
        ▼
Multistatic Viewpoint Fusion  N nodes → attention-weighted fusion → single embedding
        │                    Geometric bias from node placement angles
        ▼
Coherence Gate               Accept / PredictOnly / Reject / Recalibrate
        │                    Prevents model drift, stable for days
        ▼
Persistent Field Model       SVD baseline → body = observation - environment
        │                    RF tomography, drift detection, intention signals
        ▼
Pose Tracker + DensePose     17-keypoint Kalman, re-ID via AETHER embeddings
                             Multi-person min-cut separation, zero ID swaps
```

**Seven Exotic Sensing Tiers (ADR-030)**

| Tier | Capability | What it detects |
|------|-----------|-----------------|
| 1 | Field Normal Modes | Room electromagnetic eigenstructure via SVD |
| 2 | Coarse RF Tomography | 3D occupancy volume from link attenuations |
| 3 | Intention Lead Signals | Pre-movement prediction 200-500ms before action |
| 4 | Longitudinal Biomechanics | Personal movement changes over days/weeks |
| 5 | Cross-Room Continuity | Identity preserved across rooms without cameras |
| 6 | Invisible Interaction | Multi-user gesture control through walls |
| 7 | Adversarial Detection | Physically impossible signal identification |

**Acceptance Test**

| Metric | Threshold | What it proves |
|--------|-----------|---------------|
| Torso keypoint jitter | < 30mm RMS | Precision sufficient for applications |
| Identity swaps | 0 over 10 minutes (12,000 frames) | Reliable multi-person tracking |
| Update rate | 20 Hz (50ms cycle) | Real-time response |
| Breathing SNR | > 10 dB at 3m | Small-motion sensitivity confirmed |

**New Rust modules (9,000+ lines)**

| Crate | New modules | Purpose |
|-------|------------|---------|
| `wifi-densepose-signal` | `ruvsense/` (10 modules) | Multiband fusion, phase alignment, multistatic fusion, coherence, field model, tomography, longitudinal drift, intention detection |
| `wifi-densepose-ruvector` | `viewpoint/` (5 modules) | Cross-viewpoint attention with geometric bias, diversity index, coherence gating, fusion orchestrator |
| `wifi-densepose-hardware` | `esp32/tdm.rs` | TDM sensing protocol, sync beacons, clock drift compensation |

**Firmware extensions (C, backward-compatible)**

| File | Addition |
|------|---------|
| `csi_collector.c` | Channel hop table, timer-driven hop, NDP injection stub |
| `nvs_config.c` | 5 new NVS keys: hop_count, channel_list, dwell_ms, tdm_slot, tdm_node_count |

**DDD Domain Model** — 6 bounded contexts: Multistatic Sensing, Coherence, Pose Tracking, Field Model, Cross-Room Identity, Adversarial Detection. Full specification: [`docs/ddd/ruvsense-domain-model.md`](docs/ddd/ruvsense-domain-model.md).

See the ADR documents for full architectural details, GOAP integration plans, and research references.

</details>

<details>
<summary><b>🔮 Signal-Line Protocol (CRV)</b></summary>

### 6-Stage CSI Signal Line

Maps the CRV (Coordinate Remote Viewing) signal-line methodology to WiFi CSI processing via `ruvector-crv`:

| Stage | CRV Name | WiFi CSI Mapping | ruvector Component |
|-------|----------|-----------------|-------------------|
| I | Ideograms | Raw CSI gestalt (manmade/natural/movement/energy) | Poincare ball hyperbolic embeddings |
| II | Sensory | Amplitude textures, phase patterns, frequency colors | Multi-head attention vectors |
| III | Dimensional | AP mesh spatial topology, node geometry | GNN graph topology |
| IV | Emotional/AOL | Coherence gating — signal vs noise separation | SNN temporal encoding |
| V | Interrogation | Cross-stage probing — query pose against CSI history | Differentiable search |
| VI | 3D Model | Composite person estimation, MinCut partitioning | Graph partitioning |

**Cross-Session Convergence**: When multiple AP clusters observe the same person, CRV convergence analysis finds agreement in their signal embeddings — directly mapping to cross-room identity continuity.

```rust
use wifi_densepose_ruvector::crv::WifiCrvPipeline;

let mut pipeline = WifiCrvPipeline::new(WifiCrvConfig::default());
pipeline.create_session("room-a", "person-001")?;

// Process CSI frames through 6-stage pipeline
let result = pipeline.process_csi_frame("room-a", &amplitudes, &phases)?;
// result.gestalt = Movement, confidence = 0.87
// result.sensory_embedding = [0.12, -0.34, ...]

// Cross-room identity matching via convergence
let convergence = pipeline.find_cross_room_convergence("person-001", 0.75)?;
```

**Architecture**:
- `CsiGestaltClassifier` — Maps CSI amplitude/phase patterns to 6 gestalt types
- `CsiSensoryEncoder` — Extracts texture/color/temperature/luminosity features from subcarriers
- `MeshTopologyEncoder` — Encodes AP mesh as GNN graph (Stage III)
- `CoherenceAolDetector` — Maps coherence gate states to AOL noise detection (Stage IV)
- `WifiCrvPipeline` — Orchestrates all 6 stages into unified sensing session

</details>

---

## 📡 Signal Processing & Sensing

<details>
<summary><a id="esp32-s3-hardware-pipeline"></a><strong>📡 ESP32-S3 Hardware Pipeline (ADR-018)</strong> — 28 Hz CSI streaming, flash & provision</summary>

A single ESP32-S3 board (~$9) captures WiFi signal data 28 times per second and streams it over UDP. A host server can visualize and record the data, but the ESP32 can also run on its own — detecting presence, measuring breathing and heart rate, and alerting on falls without any server at all.

```
ESP32-S3 node                    UDP/5005        Host server (optional)
┌───────────────────────┐      ──────────>      ┌──────────────────────┐
│ Captures WiFi signals │      binary frames    │ Parses frames        │
│ 28 Hz, up to 192 sub- │      or 32-byte       │ Visualizes poses     │
│ carriers per frame     │      vitals packets   │ Records CSI data     │
│                        │                       │ REST API + WebSocket │
│ On-device (optional):  │                       └──────────────────────┘
│  Presence detection    │
│  Breathing + heart rate│
│  Fall detection        │
│  WASM custom modules   │
└───────────────────────┘
```

| Metric | Measured on hardware |
|--------|----------------------|
| CSI frame rate | 28.5 Hz (channel 5, BW20) |
| Subcarriers per frame | 64 / 128 / 192 (depends on WiFi mode) |
| UDP latency | < 1 ms on local network |
| Presence detection range | Reliable at 3 m through walls |
| Binary size | 990 KB (8MB flash) / 773 KB (4MB flash) |
| Boot to ready | ~3.9 seconds |

### Flash and provision

Download a pre-built binary — no build toolchain needed:

| Release | What's included | Tag |
|---------|-----------------|-----|
| [v0.7.0](https://github.com/ruvnet/RuView/releases/tag/v0.7.0) | **Latest** — Camera-supervised WiFlow model (accuracy figure retracted 2026-06-10, see above), ground-truth training pipeline, ruvector optimizations | `v0.7.0` |
| [v0.6.0](https://github.com/ruvnet/RuView/releases/tag/v0.6.0-esp32) | [Pre-trained models on HuggingFace](https://huggingface.co/ruv/ruview), 17 sensing apps, 51.6% contrastive improvement, 0.008ms inference | `v0.6.0-esp32` |
| [v0.5.5](https://github.com/ruvnet/RuView/releases/tag/v0.5.5-esp32) | SNN + MinCut (#348 fix) + CNN spectrogram + WiFlow + multi-freq mesh + graph transformer | `v0.5.5-esp32` |
| [v0.5.4](https://github.com/ruvnet/RuView/releases/tag/v0.5.4-esp32) | Cognitum Seed integration ([ADR-069](docs/adr/ADR-069-cognitum-seed-csi-pipeline.md)), 8-dim feature vectors, RVF store, witness chain, security hardening | `v0.5.4-esp32` |
| [v0.5.0](https://github.com/ruvnet/RuView/releases/tag/v0.5.0-esp32) | mmWave sensor fusion ([ADR-063](docs/adr/ADR-063-mmwave-sensor-fusion.md)), auto-detect MR60BHA2/LD2410, 48-byte fused vitals, all v0.4.3.1 fixes | `v0.5.0-esp32` |
| [v0.4.3.1](https://github.com/ruvnet/RuView/releases/tag/v0.4.3.1-esp32) | Fall detection fix ([#263](https://github.com/ruvnet/RuView/issues/263)), 4MB flash ([#265](https://github.com/ruvnet/RuView/issues/265)), watchdog fix ([#266](https://github.com/ruvnet/RuView/issues/266)) | `v0.4.3.1-esp32` |
| [v0.4.1](https://github.com/ruvnet/RuView/releases/tag/v0.4.1-esp32) | CSI build fix, compile guard, AMOLED display, edge intelligence ([ADR-057](docs/adr/ADR-057-firmware-csi-build-guard.md)) | `v0.4.1-esp32` |
| [v0.3.0-alpha](https://github.com/ruvnet/RuView/releases/tag/v0.3.0-alpha-esp32) | Alpha — adds on-device edge intelligence and WASM modules ([ADR-039](docs/adr/ADR-039-esp32-edge-intelligence.md), [ADR-040](docs/adr/ADR-040-wasm-programmable-sensing.md)) | `v0.3.0-alpha-esp32` |
| [v0.2.0](https://github.com/ruvnet/RuView/releases/tag/v0.2.0-esp32) | Raw CSI streaming, multi-node TDM, channel hopping | `v0.2.0-esp32` |

```bash
# 1. Flash the firmware to your ESP32-S3 (8MB flash — most boards)
python -m esptool --chip esp32s3 --port COM7 --baud 460800 \
  write_flash --flash-mode dio --flash-size 8MB --flash-freq 80m \
  0x0 bootloader.bin 0x8000 partition-table.bin \
  0xf000 ota_data_initial.bin 0x20000 esp32-csi-node.bin

# 1b. For 4MB flash boards (e.g. ESP32-S3 SuperMini 4MB) — use the 4MB binaries:
python -m esptool --chip esp32s3 --port COM7 --baud 460800 \
  write_flash --flash-mode dio --flash-size 4MB --flash-freq 80m \
  0x0 bootloader.bin 0x8000 partition-table-4mb.bin \
  0xF000 ota_data_initial.bin 0x20000 esp32-csi-node-4mb.bin

# 2. Set WiFi credentials and server address (stored in flash, survives reboots)
python firmware/esp32-csi-node/provision.py --port COM7 \
  --ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20

# 3. (Optional) Start the host server to visualize data
cargo run -p wifi-densepose-sensing-server -- --http-port 3000 --source auto
# Open http://localhost:3000
```

### Multi-node mesh

For better accuracy and room coverage, deploy 3-6 nodes with time-division multiplexing (TDM) so they take turns transmitting:

```bash
# Node 0 of a 3-node mesh
python firmware/esp32-csi-node/provision.py --port COM7 \
  --ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20 \
  --node-id 0 --tdm-slot 0 --tdm-total 3

# Node 1
python firmware/esp32-csi-node/provision.py --port COM8 \
  --ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20 \
  --node-id 1 --tdm-slot 1 --tdm-total 3
```

Nodes can also hop across WiFi channels (1, 6, 11) to increase sensing bandwidth — configured via [ADR-029](docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md) channel hopping.

### Cognitum Seed integration (ADR-069)

Connect an ESP32 to a [Cognitum Seed](https://cognitum.one) ($131) for persistent vector storage, kNN search, cryptographic witness chain, and AI-accessible MCP proxy:

```
ESP32-S3 ($9)  ──UDP──>  Host bridge  ──HTTPS──>  Cognitum Seed ($15)
  CSI capture              seed_csi_bridge.py         RVF vector store
  8-dim features @ 1 Hz                              kNN similarity search
  Vitals + presence                                  Ed25519 witness chain
                                                     114-tool MCP proxy
```

```bash
# 1. Provision ESP32 to send features to your laptop
python firmware/esp32-csi-node/provision.py --port COM9 \
  --ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20 --target-port 5006

# 2. Run the bridge (forwards to Seed via HTTPS)
export SEED_TOKEN="your-pairing-token"
python scripts/seed_csi_bridge.py \
  --seed-url https://169.254.42.1:8443 --token "$SEED_TOKEN" --validate

# 3. Check Seed stats
python scripts/seed_csi_bridge.py --token "$SEED_TOKEN" --stats
```

The 8-dim feature vector captures: presence, motion, breathing rate, heart rate, phase variance, person count, fall detection, and RSSI — all normalized to [0.0, 1.0]. See [ADR-069](docs/adr/ADR-069-cognitum-seed-csi-pipeline.md) for the full architecture.

### On-device intelligence (v0.3.0-alpha)

The alpha firmware can analyze signals locally and send compact results instead of raw data. This means the ESP32 works standalone — no server needed for basic sensing. Disabled by default for backward compatibility.

| Tier | What it does | RAM used |
|------|-------------|----------|
| **0** | Off — streams raw CSI only (same as v0.2.0) | 0 KB |
| **1** | Cleans up signals, picks the best subcarriers, compresses data (saves 30-50% bandwidth) | ~30 KB |
| **2** | Everything in Tier 1 + detects presence, measures breathing and heart rate, detects falls | ~33 KB |
| **3** | Everything in Tier 2 + runs custom WASM modules (gesture recognition, intrusion detection, and [63 more](docs/edge-modules/README.md)) | ~160 KB/module |

Enable without reflashing — just reprovision:

```bash
# Turn on Tier 2 (vitals) on an already-flashed node
python firmware/esp32-csi-node/provision.py --port COM7 \
  --ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20 \
  --edge-tier 2

# Fine-tune detection thresholds (fall-thresh in milli-units: 15000 = 15.0 rad/s²)
python firmware/esp32-csi-node/provision.py --port COM7 \
  --edge-tier 2 --vital-int 500 --fall-thresh 15000 --subk-count 16
```

When Tier 2 is active, the node sends a 32-byte vitals packet once per second containing: presence, motion level, breathing BPM, heart rate BPM, confidence scores, fall alert flag, and occupancy count.

See [firmware/esp32-csi-node/README.md](firmware/esp32-csi-node/README.md), [ADR-039](docs/adr/ADR-039-esp32-edge-intelligence.md), [ADR-044](docs/adr/ADR-044-provisioning-tool-enhancements.md), and [Tutorial #34](https://github.com/ruvnet/RuView/issues/34).

</details>

<details>
<summary><strong>🦀 Rust Implementation (v2)</strong> — 810x faster, 54K fps pipeline</summary>

### Performance Benchmarks (Validated)

| Operation | Python (v1) | Rust (v2) | Speedup |
|-----------|-------------|-----------|---------|
| CSI Preprocessing (4x64) | ~5ms | **5.19 µs** | ~1000x |
| Phase Sanitization (4x64) | ~3ms | **3.84 µs** | ~780x |
| Feature Extraction (4x64) | ~8ms | **9.03 µs** | ~890x |
| Motion Detection | ~1ms | **186 ns** | ~5400x |
| **Full Pipeline** | ~15ms | **18.47 µs** | ~810x |
| **Vital Signs** | N/A | **86 µs** | 11,665 fps |

| Resource | Python (v1) | Rust (v2) |
|----------|-------------|-----------|
| Memory | ~500 MB | ~100 MB |
| Docker Image | 569 MB | 132 MB |
| Tests | 41 | 542+ |
| WASM Support | No | Yes |

```bash
cd v2
cargo build --release
cargo test --workspace
cargo bench --package wifi-densepose-signal
```

</details>

<details>
<summary><a id="vital-sign-detection"></a><strong>💓 Vital Sign Detection (ADR-021)</strong> — Breathing and heartbeat via FFT</summary>

| Capability | Range | Method |
|------------|-------|--------|
| **Breathing Rate** | 6-30 BPM (0.1-0.5 Hz) | Bandpass filter + FFT peak detection |
| **Heart Rate** | 40-120 BPM (0.8-2.0 Hz) | Bandpass filter + FFT peak detection |
| **Sampling Rate** | 20 Hz (ESP32 CSI) | Real-time streaming |
| **Confidence** | 0.0-1.0 per sign | Spectral coherence + signal quality |

```bash
./target/release/sensing-server --source simulate --http-port 3000 --ws-port 3001 --ui-path ../../ui
curl http://localhost:3000/api/v1/vital-signs
```

See [ADR-021](docs/adr/ADR-021-vital-sign-detection-rvdna-pipeline.md).

</details>

<details>
<summary><a id="wifi-scan-domain-layer"></a><strong>📡 WiFi Scan Domain Layer (ADR-022/025)</strong> — 8-stage RSSI pipeline for Windows, macOS, and Linux WiFi</summary>

| Stage | Purpose |
|-------|---------|
| **Predictive Gating** | Pre-filter scan results using temporal prediction |
| **Attention Weighting** | Weight BSSIDs by signal relevance |
| **Spatial Correlation** | Cross-AP spatial signal correlation |
| **Motion Estimation** | Detect movement from RSSI variance |
| **Breathing Extraction** | Extract respiratory rate from sub-Hz oscillations |
| **Quality Gating** | Reject low-confidence estimates |
| **Fingerprint Matching** | Location and posture classification via RF fingerprints |
| **Orchestration** | Fuse all stages into unified sensing output |

```bash
cargo test -p wifi-densepose-wifiscan
```

See [ADR-022](docs/adr/ADR-022-windows-wifi-enhanced-fidelity-ruvector.md) and [Tutorial #36](https://github.com/ruvnet/RuView/issues/36).

</details>

<details>
<summary><a id="wifi-mat-disaster-response"></a><strong>🚨 WiFi-Mat: Disaster Response</strong> — Search & rescue, START triage, 3D localization</summary>

WiFi signals penetrate non-metallic debris (concrete, wood, drywall) where cameras and thermal sensors cannot reach. The WiFi-Mat module (`wifi-densepose-mat`, 139 tests) uses CSI analysis to detect survivors trapped under rubble, classify their condition using the START triage protocol, and estimate their 3D position — giving rescue teams actionable intelligence within seconds of deployment.

| Capability | How It Works | Performance Target |
|------------|-------------|-------------------|
| **Breathing Detection** | Bandpass 0.07-1.0 Hz + Fresnel zone modeling detects chest displacement of 5-10mm at 5 GHz | 4-60 BPM, <500ms latency |
| **Heartbeat Detection** | Micro-Doppler shift extraction from fine-grained CSI phase variation | Via ruvector-temporal-tensor |
| **3D Localization** | Multi-AP triangulation + CSI fingerprint matching + depth estimation through rubble layers | 3-5m penetration |
| **START Triage** | Ensemble classifier votes on breathing + movement + vital stability → P1-P4 priority | <1% false negative |
| **Zone Scanning** | 16+ concurrent scan zones with periodic re-scan and audit logging | Full disaster site |

**Triage classification (START protocol compatible):**

| Status | Color | Detection Criteria | Priority |
|--------|-------|-------------------|----------|
| Immediate | Red | Breathing detected, no movement | P1 |
| Delayed | Yellow | Movement + breathing, stable vitals | P2 |
| Minor | Green | Strong movement, responsive patterns | P3 |
| Deceased | Black | No vitals for >30 min continuous scan | P4 |

**Deployment modes:** portable (single TX/RX handheld), distributed (multiple APs around collapse site), drone-mounted (UAV scanning), vehicle-mounted (mobile command post).

```rust
use wifi_densepose_mat::{DisasterResponse, DisasterConfig, DisasterType, ScanZone, ZoneBounds};

let config = DisasterConfig::builder()
    .disaster_type(DisasterType::Earthquake)
    .sensitivity(0.85)
    .max_depth(5.0)
    .build();

let mut response = DisasterResponse::new(config);
response.initialize_event(location, "Building collapse")?;
response.add_zone(ScanZone::new("North Wing", ZoneBounds::rectangle(0.0, 0.0, 30.0, 20.0)))?;
response.start_scanning().await?;
```

**Safety guarantees:** fail-safe defaults (assume life present on ambiguous signals), redundant multi-algorithm voting, complete audit trail, offline-capable (no network required).

- [WiFi-Mat User Guide](docs/wifi-mat-user-guide.md) | [ADR-001](docs/adr/ADR-001-wifi-mat-disaster-detection.md) | [Domain Model](docs/ddd/wifi-mat-domain-model.md)

</details>

<details>
<summary><a id="sota-signal-processing"></a><strong>🔬 SOTA Signal Processing (ADR-014)</strong> — 6 research-grade algorithms</summary>

The signal processing layer bridges the gap between raw commodity WiFi hardware output and research-grade sensing accuracy. Each algorithm addresses a specific limitation of naive CSI processing — from hardware-induced phase corruption to environment-dependent multipath interference. All six are implemented in `wifi-densepose-signal/src/` with deterministic tests and no mock data.

| Algorithm | What It Does | Why It Matters | Math | Source |
|-----------|-------------|----------------|------|--------|
| **Conjugate Multiplication** | Multiplies CSI antenna pairs: `H₁[k] × conj(H₂[k])` | Cancels CFO, SFO, and packet detection delay that corrupt raw phase — preserves only environment-caused phase differences | `CSI_ratio[k] = H₁[k] * conj(H₂[k])` | [SpotFi](https://dl.acm.org/doi/10.1145/2789168.2790124) (SIGCOMM 2015) |
| **Hampel Filter** | Replaces outliers using running median ± scaled MAD | Z-score uses mean/std which are corrupted by the very outliers it detects (masking effect). Hampel uses median/MAD, resisting up to 50% contamination | `σ̂ = 1.4826 × MAD` | Standard DSP; WiGest (2015) |
| **Fresnel Zone Model** | Models signal variation from chest displacement crossing Fresnel zone boundaries | Zero-crossing counting fails in multipath-rich environments. Fresnel predicts *where* breathing should appear based on TX-RX-body geometry | `ΔΦ = 2π × 2Δd / λ`, `A = \|sin(ΔΦ/2)\|` | [FarSense](https://dl.acm.org/doi/10.1145/3300061.3345431) (MobiCom 2019) |
| **CSI Spectrogram** | Sliding-window FFT (STFT) per subcarrier → 2D time-frequency matrix | Breathing = 0.2-0.4 Hz band, walking = 1-2 Hz, static = noise. 2D structure enables CNN spatial pattern recognition that 1D features miss | `S[t,f] = \|Σₙ x[n] w[n-t] e^{-j2πfn}\|²` | Standard since 2018 |
| **Subcarrier Selection** | Ranks subcarriers by motion sensitivity (variance ratio) and selects top-K | Not all subcarriers respond to motion — some sit in multipath nulls. Selecting the 10-20 most sensitive improves SNR by 6-10 dB | `sensitivity[k] = var_motion / var_static` | [WiDance](https://dl.acm.org/doi/10.1145/3117811.3117826) (MobiCom 2017) |
| **Body Velocity Profile** | Extracts velocity distribution from Doppler shifts across subcarriers | BVP is domain-independent — same velocity profile regardless of room layout, furniture, or AP placement. Basis for cross-environment recognition | `BVP[v,t] = Σₖ \|STFTₖ[v,t]\|` | [Widar 3.0](https://dl.acm.org/doi/10.1145/3328916) (MobiSys 2019) |

**Processing pipeline order:** Raw CSI → Conjugate multiplication (phase cleaning) → Hampel filter (outlier removal) → Subcarrier selection (top-K) → CSI spectrogram (time-frequency) → Fresnel model (breathing) + BVP (activity)

See [ADR-014](docs/adr/ADR-014-sota-signal-processing.md) for full mathematical derivations.

</details>

---

## 🧠 Models & Training

<details>
<summary><a id="ai-backbone-ruvector"></a><strong>🤖 AI Backbone: RuVector</strong> — Attention, graph algorithms, and edge-AI compression powering the sensing pipeline</summary>

Raw WiFi signals are noisy, redundant, and environment-dependent. [RuVector](https://github.com/ruvnet/ruvector) is the AI intelligence layer that transforms them into clean, structured input for the DensePose neural network. It uses **attention mechanisms** to learn which signals to trust, **graph algorithms** that automatically discover which WiFi channels are sensitive to body motion, and **compressed representations** that make edge inference possible on an $8 microcontroller.

Without RuVector, WiFi DensePose would need hand-tuned thresholds, brute-force matrix math, and 4x more memory — making real-time edge inference impossible.

```
Raw WiFi CSI (56 subcarriers, noisy)
    |
    +-- ruvector-mincut ---------- Which channels carry body-motion signal? (learned graph partitioning)
    +-- ruvector-attn-mincut ----- Which time frames are signal vs noise? (attention-gated filtering)
    +-- ruvector-attention ------- How to fuse multi-antenna data? (learned weighted aggregation)
    |
    v
Clean, structured signal --> DensePose Neural Network --> 17-keypoint body pose
                         --> FFT Vital Signs -----------> breathing rate, heart rate
                         --> ruvector-solver ------------> physics-based localization
```

The [`wifi-densepose-ruvector`](https://crates.io/crates/wifi-densepose-ruvector) crate ([ADR-017](docs/adr/ADR-017-ruvector-signal-mat-integration.md)) connects all 7 integration points:

| AI Capability | What It Replaces | RuVector Crate | Result |
|--------------|-----------------|----------------|--------|
| **Self-optimizing channel selection** | Hand-tuned thresholds that break when rooms change | `ruvector-mincut` | Graph min-cut adapts to any environment automatically |
| **Attention-based signal cleaning** | Fixed energy cutoffs that miss subtle breathing | `ruvector-attn-mincut` | Learned gating amplifies body signals, suppresses noise |
| **Learned signal fusion** | Simple averaging where one bad channel corrupts all | `ruvector-attention` | Transformer-style attention downweights corrupted channels |
| **Physics-informed localization** | Expensive nonlinear solvers | `ruvector-solver` | Sparse least-squares Fresnel geometry in real-time |
| **O(1) survivor triangulation** | O(N^3) matrix inversion | `ruvector-solver` | Neumann series linearization for instant position updates |
| **75% memory compression** | 13.4 MB breathing buffers that overflow edge devices | `ruvector-temporal-tensor` | Tiered 3-8 bit quantization fits 60s of vitals in 3.4 MB |

See [issue #67](https://github.com/ruvnet/RuView/issues/67) for a deep dive with code examples, or [`cargo add wifi-densepose-ruvector`](https://crates.io/crates/wifi-densepose-ruvector) to use it directly.

</details>

<details>
<summary><a id="rvf-model-container"></a><strong>📦 RVF Model Container</strong> — Single-file deployment with progressive loading</summary>

The [RuVector Format (RVF)](https://github.com/ruvnet/ruvector/tree/main/crates/rvf) packages an entire trained model — weights, HNSW indexes, quantization codebooks, SONA adaptation deltas, and WASM inference runtime — into a single self-contained binary file. No external dependencies are needed at deployment time.

**Container structure:**

```
┌──────────────────────────────────────────────────────┐
│ RVF Container (.rvf)                                  │
│                                                       │
│  ┌─────────────┐  64-byte header per segment          │
│  │ Manifest     │  Magic: 0x52564653 ("RVFS")         │
│  ├─────────────┤  Type + content hash + compression   │
│  │ Weights      │  Model parameters (f32/f16/u8)      │
│  ├─────────────┤                                      │
│  │ HNSW Index   │  Vector search index                │
│  ├─────────────┤                                      │
│  │ Quant        │  Quantization codebooks              │
│  ├─────────────┤                                      │
│  │ SONA Profile │  LoRA deltas + EWC++ Fisher matrix  │
│  ├─────────────┤                                      │
│  │ Witness      │  Ed25519 training proof              │
│  ├─────────────┤                                      │
│  │ Vitals Config│  Breathing/HR filter parameters     │
│  └─────────────┘                                      │
└──────────────────────────────────────────────────────┘
```

**Deployment targets:**

| Target | Quantization | Size | Load Time | Use Case |
|--------|-------------|------|-----------|----------|
| **ESP32 / IoT** | int4 | ~0.7 MB | <5ms (Layer A) | Presence + breathing only |
| **Mobile / WebView** | int8 | ~6 MB | ~200ms (Layer B) | Pose estimation on phone |
| **Browser (WASM)** | int8 | ~10 MB | ~500ms (Layer B) | In-browser demo |
| **Field (WiFi-Mat)** | fp16 | ~62 MB | ~2s (Layer C) | Full DensePose + disaster triage |
| **Server / Cloud** | f32 | ~50+ MB | ~3s (Layer C) | Training + full inference |

| Property | Detail |
|----------|--------|
| **Format** | Segment-based binary, 20+ segment types, CRC32 integrity per segment |
| **Progressive Loading** | **Layer A** (<5ms): manifest + entry points → **Layer B** (100ms-1s): hot weights + adjacency → **Layer C** (seconds): full graph |
| **Signing** | Ed25519 training proofs for verifiable provenance — chain of custody from training data to deployed model |
| **Quantization** | Per-segment temperature-tiered: f32 (full), f16 (half), u8 (int8), int4 — with SIMD-accelerated distance computation |
| **CLI** | `--export-rvf` (generate), `--load-rvf` (config), `--save-rvf` (persist), `--model` (inference), `--progressive` (3-layer load) |

```bash
# Export model package
./target/release/sensing-server --export-rvf wifi-densepose-v1.rvf

# Load and run with progressive loading
./target/release/sensing-server --model wifi-densepose-v1.rvf --progressive

# Export via Docker
docker run --rm -v $(pwd):/out ruvnet/wifi-densepose:latest --export-rvf /out/model.rvf
```

Built on the [rvf](https://github.com/ruvnet/ruvector/tree/main/crates/rvf) crate family (rvf-types, rvf-wire, rvf-manifest, rvf-index, rvf-quant, rvf-crypto, rvf-runtime). See [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md).

</details>

<details>
<summary><a id="training--fine-tuning"></a><strong>🧬 Training & Fine-Tuning</strong> — MM-Fi/Wi-Pose pre-training, SONA adaptation</summary>

The training pipeline implements 8 phases in pure Rust (7,832 lines, zero external ML dependencies). It trains a graph transformer with cross-attention to map CSI feature matrices to 17 COCO body keypoints and DensePose UV coordinates — following the approach from the CMU "DensePose From WiFi" paper ([arXiv:2301.00250](https://arxiv.org/abs/2301.00250)). RuVector crates provide the core building blocks: [ruvector-attention](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-attention) for cross-attention layers, [ruvector-mincut](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-mincut) for multi-person matching, and [ruvector-temporal-tensor](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-temporal-tensor) for CSI buffer compression.

**Three-tier data strategy:**

| Tier | Method | Purpose | RuVector Integration |
|------|--------|---------|---------------------|
| **1. Pre-train** | MM-Fi + Wi-Pose public datasets | Cross-environment generalization (multi-subject, multi-room) | `ruvector-temporal-tensor` compresses CSI windows (114→56 subcarrier resampling) |
| **2. Fine-tune** | ESP32 CSI + camera pseudo-labels | Environment-specific multipath adaptation | `ruvector-solver` for Fresnel geometry, `ruvector-attn-mincut` for subcarrier gating |
| **3. SONA adapt** | Micro-LoRA (rank-4) + EWC++ | Continuous on-device learning without catastrophic forgetting | [SONA](https://github.com/ruvnet/ruvector/tree/main/crates/sona) architecture (Self-Optimizing Neural Architecture) |

**Training pipeline components:**

| Phase | Module | What It Does | RuVector Crate |
|-------|--------|-------------|----------------|
| 1 | `dataset.rs` (850 lines) | MM-Fi `.npy` + Wi-Pose `.mat` loaders, subcarrier resampling (114→56, 30→56), windowing | [ruvector-temporal-tensor](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-temporal-tensor) |
| 2 | `graph_transformer.rs` (855 lines) | COCO BodyGraph (17 kp, 16 edges), AntennaGraph, multi-head CrossAttention, GCN message passing | [ruvector-attention](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-attention) |
| 3 | `trainer.rs` (881 lines) | 6-term composite loss (MSE, CE, UV, temporal, bone, symmetry), SGD+momentum, cosine+warmup, PCK/OKS | [ruvector-mincut](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-mincut) (person matching) |
| 4 | `sona.rs` (639 lines) | LoRA adapters (A×B delta), EWC++ Fisher regularization, EnvironmentDetector (3-sigma drift) | [sona](https://github.com/ruvnet/ruvector/tree/main/crates/sona) |
| 5 | `sparse_inference.rs` (753 lines) | NeuronProfiler hot/cold partitioning, SparseLinear (skip cold rows), INT8/FP16 quantization | [ruvector-sparse-inference](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-sparse-inference) |
| 6 | `rvf_pipeline.rs` (1,027 lines) | Progressive 3-layer loader, HNSW index, OverlayGraph, `RvfModelBuilder` | [ruvector-core](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-core) (HNSW) |
| 7 | `rvf_container.rs` (914 lines) | Binary container format, 6+ segment types, CRC32 integrity | [rvf](https://github.com/ruvnet/ruvector/tree/main/crates/rvf) |
| 8 | `main.rs` integration | `--train`, `--model`, `--progressive` CLI flags, REST endpoints | — |

**SONA (Self-Optimizing Neural Architecture)** — the continuous adaptation system:

| Component | What It Does | Why It Matters |
|-----------|-------------|----------------|
| **Micro-LoRA (rank-4)** | Trains small A×B weight deltas instead of full weights | 100x fewer parameters to update → runs on ESP32 |
| **EWC++ (Fisher matrix)** | Penalizes changes to important weights from previous environments | Prevents catastrophic forgetting when moving between rooms |
| **EnvironmentDetector** | Monitors CSI feature drift with 3-sigma threshold | Auto-triggers adaptation when the model is moved to a new space |
| **Best-epoch snapshot** | Saves best validation loss weights, restores before export | Prevents shipping overfit final-epoch parameters |

```bash
# Pre-train on MM-Fi dataset
./target/release/sensing-server --train --dataset data/ --dataset-type mmfi --epochs 100

# Train and export to RVF in one step
./target/release/sensing-server --train --dataset data/ --epochs 100 --save-rvf model.rvf

# Via Docker (no toolchain needed)
docker run --rm -v $(pwd)/data:/data ruvnet/wifi-densepose:latest \
  --train --dataset /data --epochs 100 --export-rvf /data/model.rvf
```

See [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md) · [SONA crate](https://github.com/ruvnet/ruvector/tree/main/crates/sona) · [arXiv:2301.00250](https://arxiv.org/abs/2301.00250)

</details>

<details>
<summary><a id="ruvector-crates"></a><strong>🔩 RuVector Crates</strong> — 11 vendored signal intelligence crates from <a href="https://github.com/ruvnet/ruvector">github.com/ruvnet/ruvector</a></summary>

**5 directly-used crates** (v2.0.4, declared in `Cargo.toml`, 7 integration points):

| Crate | What It Does | Where It's Used in WiFi-DensePose | Source |
|-------|-------------|-----------------------------------|--------|
| [`ruvector-attention`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-attention) | Scaled dot-product attention, MoE routing, sparse attention | `model.rs` (spatial attention), `bvp.rs` (sensitivity-weighted velocity profiles) | [crate](https://crates.io/crates/ruvector-attention) |
| [`ruvector-mincut`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-mincut) | Subpolynomial dynamic min-cut O(n^1.5 log n) | `metrics.rs` (DynamicPersonMatcher — multi-person assignment), `subcarrier_selection.rs` (sensitive/insensitive split) | [crate](https://crates.io/crates/ruvector-mincut) |
| [`ruvector-attn-mincut`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-attn-mincut) | Attention-gated spectrogram noise suppression | `model.rs` (antenna attention gating), `spectrogram.rs` (gate noisy time-frequency bins) | [crate](https://crates.io/crates/ruvector-attn-mincut) |
| [`ruvector-solver`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-solver) | Sparse Neumann series solver O(sqrt(n)) | `fresnel.rs` (TX-body-RX geometry), `triangulation.rs` (3D localization), `subcarrier.rs` (sparse interpolation 114→56) | [crate](https://crates.io/crates/ruvector-solver) |
| [`ruvector-temporal-tensor`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-temporal-tensor) | Tiered temporal compression (8/7/5/3-bit) | `dataset.rs` (CSI buffer compression), `breathing.rs` + `heartbeat.rs` (compressed vital sign spectrograms) | [crate](https://crates.io/crates/ruvector-temporal-tensor) |

**6 additional vendored crates** (used by training pipeline and inference):

| Crate | What It Does | Source |
|-------|-------------|--------|
| [`ruvector-core`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-core) | VectorDB engine, HNSW index, SIMD distance functions, quantization codebooks | [crate](https://crates.io/crates/ruvector-core) |
| [`ruvector-gnn`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-gnn) | Graph neural network layers, graph attention, EWC-regularized training | [crate](https://crates.io/crates/ruvector-gnn) |
| [`ruvector-graph-transformer`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-graph-transformer) | Proof-gated graph transformer with cross-attention | [crate](https://crates.io/crates/ruvector-graph-transformer) |
| [`ruvector-sparse-inference`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-sparse-inference) | PowerInfer-style hot/cold neuron partitioning, skip cold rows at runtime | [crate](https://crates.io/crates/ruvector-sparse-inference) |
| [`ruvector-nervous-system`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-nervous-system) | PredictiveLayer, OscillatoryRouter, Hopfield associative memory | [crate](https://crates.io/crates/ruvector-nervous-system) |
| [`ruvector-coherence`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-coherence) | Spectral coherence monitoring, HNSW graph health, Fiedler connectivity | [crate](https://crates.io/crates/ruvector-coherence) |

The full RuVector ecosystem includes 90+ crates. See [github.com/ruvnet/ruvector](https://github.com/ruvnet/ruvector) for the complete library, and [`vendor/ruvector/`](vendor/ruvector/) for the vendored source in this project.

</details>

<details>
<summary><a id="ruv-neural"></a><strong>🧠 rUv Neural</strong> — Brain topology analysis ecosystem for neural decoding and medical sensing</summary>

[**rUv Neural**](v2/crates/ruv-neural/README.md) is a 12-crate Rust ecosystem that extends RuView's signal processing into brain network topology analysis. It transforms neural magnetic field measurements from quantum sensors (NV diamond magnetometers, optically pumped magnetometers) into dynamic connectivity graphs, using minimum cut algorithms to detect cognitive state transitions in real time. The ecosystem includes crates for signal processing (`ruv-neural-signal`), graph construction (`ruv-neural-graph`), HNSW-indexed pattern memory (`ruv-neural-memory`), graph embeddings (`ruv-neural-embed`), cognitive state decoding (`ruv-neural-decoder`), and ESP32/WASM edge targets. Medical and research applications include early neurological disease detection via topology signatures, brain-computer interfaces, clinical neurofeedback, and non-invasive biomedical sensing -- bridging RuView's RF sensing architecture with the emerging field of quantum biomedical diagnostics.

</details>

---

<details>
<summary><strong>🏗️ System Architecture</strong> — End-to-end data flow from CSI capture to REST/WebSocket API</summary>

### End-to-End Pipeline

```mermaid
graph TB
    subgraph HW ["📡 Hardware Layer"]
        direction LR
        R1["WiFi Router 1<br/><small>CSI Source</small>"]
        R2["WiFi Router 2<br/><small>CSI Source</small>"]
        R3["WiFi Router 3<br/><small>CSI Source</small>"]
        ESP["ESP32-S3 Mesh<br/><small>20 Hz · 56 subcarriers</small>"]
        WIN["Windows WiFi<br/><small>RSSI scanning</small>"]
    end

    subgraph INGEST ["⚡ Ingestion"]
        AGG["Aggregator<br/><small>UDP :5005 · ADR-018 frames</small>"]
        BRIDGE["Bridge<br/><small>I/Q → amplitude + phase</small>"]
    end

    subgraph SIGNAL ["🔬 Signal Processing — RuVector v2.0.4"]
        direction TB
        PHASE["Phase Sanitization<br/><small>SpotFi conjugate multiply</small>"]
        HAMPEL["Hampel Filter<br/><small>Outlier rejection · σ=3</small>"]
        SUBSEL["Subcarrier Selection<br/><small>ruvector-mincut · sensitive/insensitive split</small>"]
        SPEC["Spectrogram<br/><small>ruvector-attn-mincut · gated STFT</small>"]
        FRESNEL["Fresnel Geometry<br/><small>ruvector-solver · TX-body-RX distance</small>"]
        BVP["Body Velocity Profile<br/><small>ruvector-attention · weighted BVP</small>"]
    end

    subgraph ML ["🧠 Neural Pipeline"]
        direction TB
        GRAPH["Graph Transformer<br/><small>17 COCO keypoints · 16 edges</small>"]
        CROSS["Cross-Attention<br/><small>CSI features → body pose</small>"]
        SONA["SONA Adapter<br/><small>LoRA rank-4 · EWC++</small>"]
    end

    subgraph VITAL ["💓 Vital Signs"]
        direction LR
        BREATH["Breathing<br/><small>0.1–0.5 Hz · FFT peak</small>"]
        HEART["Heart Rate<br/><small>0.8–2.0 Hz · FFT peak</small>"]
        MOTION["Motion Level<br/><small>Variance + band power</small>"]
    end

    subgraph API ["🌐 Output Layer"]
        direction LR
        REST["REST API<br/><small>Axum :3000 · 6 endpoints</small>"]
        WS["WebSocket<br/><small>:3001 · real-time stream</small>"]
        ANALYTICS["Analytics<br/><small>Fall · Activity · START triage</small>"]
        UI["Web UI<br/><small>Three.js · Gaussian splats</small>"]
    end

    R1 & R2 & R3 --> AGG
    ESP --> AGG
    WIN --> BRIDGE
    AGG --> BRIDGE
    BRIDGE --> PHASE
    PHASE --> HAMPEL
    HAMPEL --> SUBSEL
    SUBSEL --> SPEC
    SPEC --> FRESNEL
    FRESNEL --> BVP
    BVP --> GRAPH
    GRAPH --> CROSS
    CROSS --> SONA
    SONA --> BREATH & HEART & MOTION
    BREATH & HEART & MOTION --> REST & WS & ANALYTICS
    WS --> UI

    style HW fill:#1a1a2e,stroke:#e94560,color:#eee
    style INGEST fill:#16213e,stroke:#0f3460,color:#eee
    style SIGNAL fill:#0f3460,stroke:#533483,color:#eee
    style ML fill:#533483,stroke:#e94560,color:#eee
    style VITAL fill:#2d132c,stroke:#e94560,color:#eee
    style API fill:#1a1a2e,stroke:#0f3460,color:#eee
```

### Signal Processing Detail

```mermaid
graph LR
    subgraph RAW ["Raw CSI Frame"]
        IQ["I/Q Samples<br/><small>56–192 subcarriers × N antennas</small>"]
    end

    subgraph CLEAN ["Phase Cleanup"]
        CONJ["Conjugate Multiply<br/><small>Remove carrier freq offset</small>"]
        UNWRAP["Phase Unwrap<br/><small>Remove 2π discontinuities</small>"]
        HAMPEL2["Hampel Filter<br/><small>Remove impulse noise</small>"]
    end

    subgraph SELECT ["Subcarrier Intelligence"]
        MINCUT["Min-Cut Partition<br/><small>ruvector-mincut</small>"]
        GATE["Attention Gate<br/><small>ruvector-attn-mincut</small>"]
    end

    subgraph EXTRACT ["Feature Extraction"]
        STFT["STFT Spectrogram<br/><small>Time-frequency decomposition</small>"]
        FRESNELZ["Fresnel Zones<br/><small>ruvector-solver</small>"]
        BVPE["BVP Estimation<br/><small>ruvector-attention</small>"]
    end

    subgraph OUT ["Output Features"]
        AMP["Amplitude Matrix"]
        PHASE2["Phase Matrix"]
        DOPPLER["Doppler Shifts"]
        VITALS["Vital Band Power"]
    end

    IQ --> CONJ --> UNWRAP --> HAMPEL2
    HAMPEL2 --> MINCUT --> GATE
    GATE --> STFT --> FRESNELZ --> BVPE
    BVPE --> AMP & PHASE2 & DOPPLER & VITALS

    style RAW fill:#0d1117,stroke:#58a6ff,color:#c9d1d9
    style CLEAN fill:#161b22,stroke:#58a6ff,color:#c9d1d9
    style SELECT fill:#161b22,stroke:#d29922,color:#c9d1d9
    style EXTRACT fill:#161b22,stroke:#3fb950,color:#c9d1d9
    style OUT fill:#0d1117,stroke:#8b949e,color:#c9d1d9
```

### Deployment Topology

```mermaid
graph TB
    subgraph EDGE ["Edge (ESP32-S3 Mesh)"]
        E1["Node 1<br/><small>Kitchen</small>"]
        E2["Node 2<br/><small>Living room</small>"]
        E3["Node 3<br/><small>Bedroom</small>"]
    end

    subgraph SERVER ["Server (Rust · 132 MB Docker)"]
        SENSE["Sensing Server<br/><small>:3000 REST · :3001 WS · :5005 UDP</small>"]
        RVF["RVF Model<br/><small>Progressive 3-layer load</small>"]
        STORE["Time-Series Store<br/><small>In-memory ring buffer</small>"]
    end

    subgraph CLIENT ["Clients"]
        BROWSER["Browser<br/><small>Three.js UI · Gaussian splats</small>"]
        MOBILE["Mobile App<br/><small>WebSocket stream</small>"]
        DASH["Dashboard<br/><small>REST polling</small>"]
        IOT["Home Automation<br/><small>MQTT bridge</small>"]
    end

    E1 -->|"UDP :5005<br/>ADR-018 frames"| SENSE
    E2 -->|"UDP :5005"| SENSE
    E3 -->|"UDP :5005"| SENSE
    SENSE <--> RVF
    SENSE <--> STORE
    SENSE -->|"WS :3001<br/>real-time JSON"| BROWSER & MOBILE
    SENSE -->|"REST :3000<br/>on-demand"| DASH & IOT

    style EDGE fill:#1a1a2e,stroke:#e94560,color:#eee
    style SERVER fill:#16213e,stroke:#533483,color:#eee
    style CLIENT fill:#0f3460,stroke:#0f3460,color:#eee
```

| Component | Crate / Module | Description |
|-----------|---------------|-------------|
| **Aggregator** | `wifi-densepose-hardware` | ESP32 UDP listener, ADR-018 frame parser, I/Q → amplitude/phase bridge |
| **Signal Processor** | `wifi-densepose-signal` | SpotFi phase sanitization, Hampel filter, STFT spectrogram, Fresnel geometry, BVP |
| **Subcarrier Selection** | `ruvector-mincut` + `ruvector-attn-mincut` | Dynamic sensitive/insensitive partitioning, attention-gated noise suppression |
| **Fresnel Solver** | `ruvector-solver` | Sparse Neumann series O(sqrt(n)) for TX-body-RX distance estimation |
| **Graph Transformer** | `wifi-densepose-train` | COCO BodyGraph (17 kp, 16 edges), cross-attention CSI→pose, GCN message passing |
| **SONA** | `sona` crate | Micro-LoRA (rank-4) adaptation, EWC++ catastrophic forgetting prevention |
| **Vital Signs** | `wifi-densepose-signal` | FFT-based breathing (0.1-0.5 Hz) and heartbeat (0.8-2.0 Hz) extraction |
| **REST API** | `wifi-densepose-sensing-server` | Axum server: `/api/v1/sensing`, `/health`, `/vital-signs`, `/bssid`, `/sona` |
| **WebSocket** | `wifi-densepose-sensing-server` | Real-time pose, sensing, and vital sign streaming on `:3001` |
| **Analytics** | `wifi-densepose-mat` | Fall detection, activity recognition, START triage (WiFi-Mat disaster module) |
| **Web UI** | `ui/` | Three.js scene, Gaussian splat visualization, signal dashboard |

</details>

---

## 🖥️ CLI Usage

<details>
<summary><strong>Rust Sensing Server</strong> — Primary CLI interface</summary>

```bash
# Start with simulated data (no hardware)
./target/release/sensing-server --source simulate --ui-path ../../ui

# Start with ESP32 CSI hardware
./target/release/sensing-server --source esp32 --udp-port 5005

# Start with Windows WiFi RSSI
./target/release/sensing-server --source wifi

# Run vital sign benchmark
./target/release/sensing-server --benchmark

# Export RVF model package
./target/release/sensing-server --export-rvf model.rvf

# Train a model
./target/release/sensing-server --train --dataset data/ --epochs 100

# Load trained model with progressive loading
./target/release/sensing-server --model wifi-densepose-v1.rvf --progressive
```

| Flag | Description |
|------|-------------|
| `--source` | Data source: `auto`, `wifi`, `esp32`, `simulate` |
| `--http-port` | HTTP port for UI and REST API (default: 8080) |
| `--ws-port` | WebSocket port (default: 8765) |
| `--udp-port` | UDP port for ESP32 CSI frames (default: 5005) |
| `--benchmark` | Run vital sign benchmark (1000 frames) and exit |
| `--export-rvf` | Export RVF container package and exit |
| `--load-rvf` | Load model config from RVF container |
| `--save-rvf` | Save model state on shutdown |
| `--model` | Load trained `.rvf` model for inference |
| `--progressive` | Enable progressive loading (Layer A instant start) |
| `--train` | Train a model and exit |
| `--dataset` | Path to dataset directory (MM-Fi or Wi-Pose) |
| `--epochs` | Training epochs (default: 100) |

</details>

<details>
<summary><a id="rest-api--websocket"></a><strong>REST API & WebSocket</strong> — Endpoints reference</summary>

#### REST API (Rust Sensing Server)

```bash
GET  /api/v1/sensing              # Latest sensing frame
GET  /api/v1/vital-signs          # Breathing, heart rate, confidence
GET  /api/v1/bssid                # Multi-BSSID registry
GET  /api/v1/model/layers         # Progressive loading status
GET  /api/v1/model/sona/profiles  # SONA profiles
POST /api/v1/model/sona/activate  # Activate SONA profile
```

WebSocket: `ws://localhost:3001/ws/sensing` (real-time sensing + vital signs)

> Default ports (Docker): HTTP 3000, WS 3001. Binary defaults: HTTP 8080, WS 8765. Override with `--http-port` / `--ws-port`.

</details>

<details>
<summary><a id="hardware-support-1"></a><strong>Hardware Support</strong> — Devices, cost, and guides</summary>

| Hardware | CSI | Cost | Guide |
|----------|-----|------|-------|
| **ESP32-S3** | Native | ~$8 | [Tutorial #34](https://github.com/ruvnet/RuView/issues/34) |
| Intel 5300 | Firmware mod | ~$15 | Linux `iwl-csi` |
| Atheros AR9580 | ath9k patch | ~$20 | Linux only |
| Any Windows WiFi | RSSI only | $0 | [Tutorial #36](https://github.com/ruvnet/RuView/issues/36) |
| Any macOS WiFi | RSSI only (CoreWLAN) | $0 | [ADR-025](docs/adr/ADR-025-macos-corewlan-wifi-sensing.md) |
| Any Linux WiFi | RSSI only (`iw`) | $0 | Requires `iw` + `CAP_NET_ADMIN` |

</details>

<details>
<summary><strong>QEMU Firmware Testing (ADR-061) — 9-Layer Platform</strong></summary>

Test ESP32-S3 firmware without physical hardware using Espressif's QEMU fork. The platform provides 9 layers of testing capability:

| Layer | Capability | Script / Config |
|-------|-----------|-----------------|
| 1 | Mock CSI generator (10 physics-based scenarios) | `firmware/esp32-csi-node/main/mock_csi.c` |
| 2 | Single-node QEMU runner + UART validation (16 checks) | `scripts/qemu-esp32s3-test.sh`, `scripts/validate_qemu_output.py` |
| 3 | Multi-node TDM mesh simulation (TAP networking) | `scripts/qemu-mesh-test.sh`, `scripts/validate_mesh_test.py` |
| 4 | GDB remote debugging (VS Code integration) | `.vscode/launch.json` |
| 5 | Code coverage (gcov/lcov via apptrace) | `firmware/esp32-csi-node/sdkconfig.coverage` |
| 6 | Fuzz testing (libFuzzer + ASAN/UBSAN) | `firmware/esp32-csi-node/test/fuzz_*.c` |
| 7 | NVS provisioning matrix (14 configs) | `scripts/generate_nvs_matrix.py` |
| 8 | Snapshot regression (sub-second VM restore) | `scripts/qemu-snapshot-test.sh` |
| 9 | Chaos testing (fault injection + health monitoring) | `scripts/qemu-chaos-test.sh`, `scripts/inject_fault.py`, `scripts/check_health.py` |

```bash
# Quick start: build + run + validate
cd firmware/esp32-csi-node
idf.py -D SDKCONFIG_DEFAULTS="sdkconfig.defaults;sdkconfig.qemu" build

# Single-node test (builds, merges flash, runs QEMU, validates output)
bash scripts/qemu-esp32s3-test.sh

# Multi-node mesh test (3 QEMU instances with TDM)
sudo bash scripts/qemu-mesh-test.sh 3

# Fuzz testing (60 seconds per target)
cd firmware/esp32-csi-node/test && make all CC=clang && make run_serialize FUZZ_DURATION=60

# Chaos testing (fault injection resilience)
bash scripts/qemu-chaos-test.sh --faults all --duration 120
```

**10 test scenarios**: empty room, static person, walking, fall, multi-person, channel sweep, MAC filter, ring overflow, boundary RSSI, zero-length frames.

**14 NVS configs**: default, WiFi-only, full ADR-060, edge tiers 0/1/2, TDM mesh, WASM signed/unsigned, 5GHz, boundary max/min, power-save, empty-strings.

**CI**: GitHub Actions workflow runs 7 NVS matrix configs, 3 fuzz targets, and NVS binary validation on every push to `firmware/`.

See [ADR-061](docs/adr/ADR-061-qemu-esp32s3-firmware-testing.md) for the full architecture.

</details>

<details>
<summary><strong>QEMU Swarm Configurator (ADR-062)</strong></summary>

Test multiple ESP32-S3 nodes simultaneously using a YAML-driven orchestrator. Define node roles, network topologies, and validation assertions in a config file.

```bash
# Quick smoke test (2 nodes, 15 seconds)
python3 scripts/qemu_swarm.py --preset smoke

# Standard 3-node test (coordinator + 2 sensors)
python3 scripts/qemu_swarm.py --preset standard

# See all presets
python3 scripts/qemu_swarm.py --list-presets

# Preview without running
python3 scripts/qemu_swarm.py --preset standard --dry-run
```

**Topologies**: star (sensors → coordinator), mesh (fully connected), line (relay chain), ring (circular).

**Node roles**: sensor (generates CSI), coordinator (aggregates), gateway (bridges to host).

**7 presets**: smoke, standard, ci-matrix, large-mesh, line-relay, ring-fault, heterogeneous.

**9 swarm assertions**: boot check, crash detection, TDM collision, frame production, coordinator reception, fall detection, frame rate, boot time, heap health.

See [ADR-062](docs/adr/ADR-062-qemu-swarm-configurator.md) and the [User Guide](docs/user-guide.md#testing-firmware-without-hardware-qemu) for step-by-step instructions.

</details>

<details>
<summary><strong>Python Legacy CLI</strong> — v1 API server commands</summary>

```bash
wifi-densepose start                    # Start API server
wifi-densepose -c config.yaml start     # Custom config
wifi-densepose -v start                 # Verbose logging
wifi-densepose status                   # Check status
wifi-densepose stop                     # Stop server
wifi-densepose config show              # Show configuration
wifi-densepose db init                  # Initialize database
wifi-densepose tasks list               # List background tasks
```

</details>

<details>
<summary><strong>Documentation Links</strong></summary>

- [User Guide](docs/user-guide.md) — installation, first run, API, hardware setup, QEMU testing
- [WiFi-Mat User Guide](docs/wifi-mat-user-guide.md) | [Domain Model](docs/ddd/wifi-mat-domain-model.md)
- [ADR-061](docs/adr/ADR-061-qemu-esp32s3-firmware-testing.md) QEMU platform | [ADR-062](docs/adr/ADR-062-qemu-swarm-configurator.md) Swarm configurator
- [ADR-021](docs/adr/ADR-021-vital-sign-detection-rvdna-pipeline.md) | [ADR-022](docs/adr/ADR-022-windows-wifi-enhanced-fidelity-ruvector.md) | [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md)

</details>

---

## 🧪 Testing

<details>
<summary><strong>542+ tests across 7 suites</strong> — zero mocks, hardware-free simulation</summary>

```bash
# Rust tests (primary — 542+ tests)
cd v2
cargo test --workspace

# Sensing server tests (229 tests)
cargo test -p wifi-densepose-sensing-server

# Vital sign benchmark
./target/release/sensing-server --benchmark

# Python tests
python -m pytest archive/v1/tests/ -v

# Pipeline verification (no hardware needed)
./verify
```

| Suite | Tests | What It Covers |
|-------|-------|----------------|
| sensing-server lib | 147 | Graph transformer, trainer, SONA, sparse inference, RVF |
| sensing-server bin | 48 | CLI integration, WebSocket, REST API |
| RVF integration | 16 | Container build, read, progressive load |
| Vital signs integration | 18 | FFT detection, breathing, heartbeat |
| wifi-densepose-signal | 83 | SOTA algorithms, Doppler, Fresnel |
| wifi-densepose-mat | 139 | Disaster response, triage, localization |
| wifi-densepose-wifiscan | 91 | 8-stage RSSI pipeline |

</details>

---

## 🚀 Deployment

<details>
<summary><strong>Docker deployment</strong> — Production setup with docker-compose</summary>

```bash
# Rust sensing server (132 MB)
docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp ruvnet/wifi-densepose:latest

# Python pipeline (569 MB)
docker pull ruvnet/wifi-densepose:python
docker run -p 8765:8765 -p 8080:8080 ruvnet/wifi-densepose:python

# Both via docker-compose
cd docker && docker compose up

# Export RVF model
docker run --rm -v $(pwd):/out ruvnet/wifi-densepose:latest --export-rvf /out/model.rvf
```

### Environment Variables

```bash
RUST_LOG=info                    # Logging level
WIFI_INTERFACE=wlan0             # WiFi interface for RSSI
POSE_CONFIDENCE_THRESHOLD=0.7    # Minimum confidence
POSE_MAX_PERSONS=10              # Max tracked individuals
```

</details>

---

## 📊 Performance Metrics

<details>
<summary><strong>Measured benchmarks</strong> — Rust sensing server, validated via cargo bench</summary>

### Rust Sensing Server

| Metric | Value |
|--------|-------|
| Vital sign detection | **11,665 fps** (86 µs/frame) |
| Full CSI pipeline | **54,000 fps** (18.47 µs/frame) |
| Motion detection | **186 ns** (~5,400x vs Python) |
| Docker image | 132 MB |
| Memory usage | ~100 MB |
| Test count | 542+ |

### Python vs Rust

| Operation | Python | Rust | Speedup |
|-----------|--------|------|---------|
| CSI Preprocessing | ~5 ms | 5.19 µs | 1000x |
| Phase Sanitization | ~3 ms | 3.84 µs | 780x |
| Feature Extraction | ~8 ms | 9.03 µs | 890x |
| Motion Detection | ~1 ms | 186 ns | 5400x |
| **Full Pipeline** | ~15 ms | 18.47 µs | **810x** |

</details>

---

## 🤝 Contributing

<details>
<summary><strong>Dev setup, code standards, PR process</strong></summary>

```bash
git clone https://github.com/ruvnet/RuView.git
cd RuView

# Rust development
cd v2
cargo build --release
cargo test --workspace

# Python development
python -m venv venv && source venv/bin/activate
pip install -r requirements-dev.txt && pip install -e .
pre-commit install
```

1. **Fork** the repository
2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)
3. **Commit** your changes
4. **Push** and open a Pull Request

</details>

---

## 📄 Changelog

<details>
<summary><strong>Release history</strong></summary>

### v3.2.0 — 2026-03-03

Edge intelligence: 24 hot-loadable WASM modules for on-device CSI processing on ESP32-S3.

- **ADR-041 Edge Intelligence Modules** — 24 `no_std` Rust modules compiled to `wasm32-unknown-unknown`, loaded via WASM3 on ESP32; 8 categories covering signal intelligence, adaptive learning, spatial reasoning, temporal analysis, AI security, quantum-inspired, autonomous systems, and exotic algorithms
- **Vendor Integration** — Algorithms ported from `midstream` (DTW, attractors, Flash Attention, min-cut, optimal transport) and `sublinear-time-solver` (PageRank, HNSW, sparse recovery, spiking NN)
- **On-device gesture learning** — User-teachable DTW gesture recognition with 3-rehearsal protocol and 16 template slots
- **Lifelong learning (EWC++)** — Elastic Weight Consolidation prevents catastrophic forgetting when learning new tasks
- **AI security modules** — FNV-1a replay detection, injection/jamming detection, 6D behavioral anomaly profiling with Mahalanobis scoring
- **Self-healing mesh** — 8-node mesh with health tracking, degradation/recovery hysteresis, and coverage redistribution
- **Common utility library** — `vendor_common.rs` shared across all 24 modules: CircularBuffer, EMA, WelfordStats, DTW, FixedPriorityQueue, vector math
- **243 tests passing** — All modules include comprehensive inline tests; 0 failures
- **Security audit** — 15 findings addressed (1 critical, 3 high, 6 medium, 5 low)

### v3.1.0 — 2026-03-02

Multistatic sensing, persistent field model, and cross-viewpoint fusion — the biggest capability jump since v2.0.

- **Project RuvSense (ADR-029)** — Multistatic mesh: TDM protocol, channel hopping (ch1/6/11), multi-band frame fusion, coherence gating, 17-keypoint Kalman tracker with re-ID; 10 new signal modules (5,300+ lines)
- **RuvSense Persistent Field Model (ADR-030)** — 7 exotic sensing tiers: field normal modes (SVD), RF tomography, longitudinal drift detection, intention prediction, cross-room identity, gesture classification, adversarial detection
- **Project RuView (ADR-031)** — Cross-viewpoint attention with geometric bias, Geometric Diversity Index, viewpoint fusion orchestrator; 5 new ruvector modules (2,200+ lines)
- **TDM Hardware Protocol** — ESP32 sensing coordinator: sync beacons, slot scheduling, clock drift compensation (±10ppm), 20 Hz aggregate rate
- **Channel-Hopping Firmware** — ESP32 firmware extended with hop table, timer-driven channel switching, NDP injection stub; NVS config for all TDM parameters; fully backward-compatible
- **DDD Domain Model** — 6 bounded contexts, ubiquitous language, aggregate roots, domain events, full event bus specification
- **`ruvector-crv` 6-stage CRV signal-line integration (ADR-033)** — Maps Coordinate Remote Viewing methodology to WiFi CSI: gestalt classification, sensory encoding, GNN topology, SNN coherence gating, differentiable search, MinCut partitioning; cross-session convergence for multi-room identity continuity
- **ADR-032 multistatic mesh security hardening** — HMAC-SHA256 beacon auth, SipHash-2-4 frame integrity, NDP rate limiter, coherence gate timeout, bounded buffers, NVS credential zeroing, atomic firmware state
- **ADR-032a QUIC transport layer** — `midstreamer-quic` TLS 1.3 AEAD for aggregator nodes, dual-mode security (ManualCrypto/QuicTransport), QUIC stream mapping, connection migration, congestion control
- **ADR-033 CRV signal-line sensing integration** — Architecture decision record for the 6-stage CRV pipeline mapping to ruvector components
- **Temporal gesture matching** — `midstreamer-temporal-compare` DTW/LCS/edit-distance gesture classification with quantized feature comparison
- **Attractor drift analysis** — `midstreamer-attractor` Takens' theorem phase-space embedding with Lyapunov exponent regime detection (Stable/Periodic/Chaotic)
- **v0.3.0 published** — All 15 workspace crates published to [crates.io](https://crates.io/crates/wifi-densepose-core) with updated dependencies
- **28,000+ lines of new Rust code** across 26 modules with 400+ tests
- **Security hardened** — Bounded buffers, NaN guards, no panics in public APIs, input validation at all boundaries

### v3.0.0 — 2026-03-01

Major release: AETHER contrastive embedding model, AI signal processing backbone, cross-platform adapters, Docker Hub images, and comprehensive README overhaul.

- **Project AETHER (ADR-024)** — Self-supervised contrastive learning for WiFi CSI fingerprinting, similarity search, and anomaly detection; 55 KB model fits on ESP32
- **AI Backbone (`wifi-densepose-ruvector`)** — 7 RuVector integration points replacing hand-tuned thresholds with attention, graph algorithms, and smart compression; [published to crates.io](https://crates.io/crates/wifi-densepose-ruvector)
- **Cross-platform RSSI adapters** — macOS CoreWLAN and Linux `iw` Rust adapters with `#[cfg(target_os)]` gating (ADR-025)
- **Docker images published** — `ruvnet/wifi-densepose:latest` (132 MB Rust) and `:python` (569 MB)
- **Project MERIDIAN (ADR-027)** — Cross-environment domain generalization: gradient reversal, geometry-conditioned FiLM, virtual domain augmentation, contrastive test-time training; zero-shot room transfer
- **10-phase DensePose training pipeline (ADR-023/027)** — Graph transformer, 6-term composite loss, SONA adaptation, RVF packaging, hardware normalization, domain-adversarial training
- **Vital sign detection (ADR-021)** — FFT-based breathing (6-30 BPM) and heartbeat (40-120 BPM), 11,665 fps
- **WiFi scan domain layer (ADR-022/025)** — 8-stage signal intelligence pipeline for Windows, macOS, and Linux
- **700+ Rust tests** — All passing, zero mocks

### v2.0.0 — 2026-02-28

Complete Rust sensing server, SOTA signal processing, WiFi-Mat disaster response, ESP32 hardware, RuVector integration, guided installer, and security hardening.

- **Rust sensing server** — Axum REST API + WebSocket, 810x speedup over Python, 54K fps pipeline
- **RuVector integration** — 11 vendored crates for HNSW, attention, GNN, temporal compression, min-cut, solver
- **6 SOTA signal algorithms (ADR-014)** — SpotFi, Hampel, Fresnel, spectrogram, subcarrier selection, BVP
- **WiFi-Mat disaster response** — START triage, 3D localization, priority alerts — 139 tests
- **ESP32 CSI hardware** — Binary frame parsing, $54 starter kit, 20 Hz streaming
- **Guided installer** — 7-step hardware detection, 8 install profiles
- **Three.js visualization** — 3D body model, 17 joints, real-time WebSocket
- **Security hardening** — 10 vulnerabilities fixed

</details>

---

