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Dan Alec
8e846a9a36
Three independent fixes that each resolve a red job on main. Security floors (v2/Cargo.lock, v2/Cargo.toml) cargo audit reported 10 advisories across three transitive crates: quinn-proto 0.11.15, rustls 0.23.45 and wasmtime 36.0.16. Bump to 0.11.18 / 0.23.46 / 36.0.18. All three arrive through midstreamer-quic = 0.3, so only the lockfile enforces them; v2/Cargo.toml now records the floors so a plain `cargo update` cannot walk them back without explanation. Dashboard accessibility 18 axe failures (3 browsers x 6 views): 58 role=switch toggles with no accessible name, two unlabelled range inputs, low-contrast timestamp / chip / badge text, and two scrollable regions with no keyboard access. The welcome modal also left the accessibility tree only by fading to opacity 0. A closed state at opacity 0 still gets scanned, and axe composes the semi-transparent text over the darkened backdrop, which produced a false color-contrast failure (ratio 1.55 reported where the real ratio was ~17:1). It now leaves via visibility, and connectedCallback exposes a `ready` attribute once its async storage read settles so the smoke test stops sampling the modal mid-transition. README visual asset validation The workflow regenerates the 20 SVGs and requires byte-identical output. scripts/render-readme-visuals.py stripped unused CSS rules but left the trailing newline behind, so 14 removed rules collapsed into 16 blank lines. The producer now consumes the orphan newline and the assets are regenerated (94,576 -> 94,027 bytes). Verified locally: cargo audit 0.22.2 exits 0, cargo check --workspace --no-default-features --all-targets is clean, the README renderer is idempotent with verify passing, and the axe suite is 18/18 across chromium, firefox and webkit on six consecutive runs.
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