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Squashed merge of feat/nvsim-pipeline-simulator (29 commits). ## Shipped - ADR-089 nvsim crate (Accepted) — 50/50 tests, ~4.5 M samples/s, pinned witness cc8de9b01b0ff5bd… - ADR-092 dashboard implementation (Implemented) — 8/12 §11 gates ✅, 4/12 ⚠ (external infra) - ADR-093 dashboard gap analysis (Implemented) — 21/21 catalogued gaps closed - Plus ADR-090 (proposed conditional) and ADR-091 (proposed research-only) ## Live deploy https://ruvnet.github.io/RuView/nvsim/ ## Infra - nvsim-server Dockerfile + GHCR publish workflow (.github/workflows/nvsim-server-docker.yml) - axe-core + Playwright cross-browser CI (.github/workflows/dashboard-a11y.yml) - gh-pages auto-deploy workflow already in place (preserves observatory + pose-fusion siblings) Co-Authored-By: claude-flow <ruv@ruv.net>
WiFi-Mat v3.2 - AI Thermal Monitor + WiFi CSI Sensing ====================================================== Embedded AI system combining thermal monitoring with WiFi-based presence detection, inspired by WiFi-DensePose technology. For Heltec ESP32-S3 with OLED Display CORE CAPABILITIES: ------------------ * Thermal Pattern Learning - Spiking Neural Network (LIF neurons) * WiFi CSI Sensing - Through-wall motion/presence detection * Breathing Detection - Respiratory rate from WiFi phase * Anomaly Detection - Ruvector-inspired attention weights * HNSW Indexing - Fast O(log n) pattern matching * Power Optimization - Adaptive sleep modes VISUAL INDICATORS: ------------------ * Animated motion figure when movement detected * Radar sweep with detection blips * Breathing wave visualization with BPM * Status bar: WiFi/Motion/Alert icons * Screen flash on anomaly or motion alerts * Dynamic confidence bars DISPLAY MODES (cycle with double-tap): -------------------------------------- 1. STATS - Temperature, zone, patterns, attention level 2. GRAPH - Temperature history graph (40 samples) 3. PTRNS - Learned pattern list with scores 4. ANOM - Anomaly detection with trajectory view 5. AI - Power optimization metrics 6. CSI - WiFi CSI motion sensing with radar 7. RF - RF device presence detection 8. INFO - Device info, uptime, memory AI POWER OPTIMIZATION (AI mode): -------------------------------- * Mode: ACTIVE/LIGHT/DEEP sleep states * Energy: Estimated power savings (0-95%) * Neurons: Active vs idle neuron ratio * HNSW: Hierarchical search efficiency * Spikes: Neural spike efficiency * Attn: Pattern attention weights WIFI CSI SENSING (CSI mode): ---------------------------- Uses WiFi Channel State Information for through-wall sensing: * MOTION/STILL - Real-time motion detection * Radar Animation - Sweep with confidence blips * Breathing Wave - Sine wave + BPM when detected * Confidence % - Detection confidence level * Detection Count - Cumulative motion events * Variance Metrics - Signal variance analysis Technology based on WiFi-DensePose concepts: - Phase unwrapping for movement detection - Amplitude variance for presence sensing - Frequency analysis for breathing rate - No cameras needed - works through walls BUTTON CONTROLS: ---------------- * TAP (quick) - Learn current thermal pattern * DOUBLE-TAP - Cycle display mode * HOLD 1 second - Pause/Resume monitoring * HOLD 2 seconds - Reset all learned patterns * HOLD 3+ seconds - Show device info INSTALLATION: ------------- 1. Connect Heltec ESP32-S3 via USB 2. Run flash.bat (Windows) or flash.ps1 (PowerShell) 3. Enter COM port when prompted (e.g., COM7) 4. Wait for flash to complete (~60 seconds) 5. Device auto-connects to configured WiFi REQUIREMENTS: ------------- * espflash tool: cargo install espflash * Heltec WiFi LoRa 32 V3 (ESP32-S3) * USB-C cable * Windows 10/11 WIFI CONFIGURATION: ------------------- Default network: ruv.net To change WiFi credentials, edit source and rebuild: C:\esp\src\main.rs (lines 43-44) HARDWARE PINOUT: ---------------- * OLED SDA: GPIO17 * OLED SCL: GPIO18 * OLED RST: GPIO21 * OLED PWR: GPIO36 (Vext) * Button: GPIO0 (PRG) * Thermal: MLX90614 on I2C TECHNICAL SPECS: ---------------- * MCU: ESP32-S3 dual-core 240MHz * Flash: 8MB * RAM: 512KB SRAM + 8MB PSRAM * Display: 128x64 OLED (SSD1306) * WiFi: 802.11 b/g/n (2.4GHz) * Bluetooth: BLE 5.0 NEURAL NETWORK: --------------- * Architecture: Leaky Integrate-and-Fire (LIF) * Neurons: 16 configurable * Patterns: Up to 32 learned * Features: 6 sparse dimensions * Indexing: 3-layer HNSW hierarchy SOURCE CODE: ------------ Full Rust source: C:\esp\src\main.rs WiFi CSI module: C:\esp\src\wifi_csi.rs Build script: C:\esp\build.ps1 BASED ON: --------- * Ruvector - Vector database with HNSW indexing * WiFi-DensePose - WiFi CSI for pose estimation * esp-rs - Rust on ESP32 LICENSE: -------- Created with Claude Code https://github.com/ruvnet/wifi-densepose