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rUv
0f64d23516
Sub-deliverable 8.2 of the benchmark/optimization milestone. Quantizes the 843,834-param "half" WiFlow-STD pose model (half_best.pth) to int8 two ways and MEASURES the accuracy/size trade-off vs fp32 under ONE locked normalization (ADR-173 torso-diameter PCK, upstream calculate_pck use_torso_norm=True), on the same seed-42 file-level 70/15/15 test split that produced the fp32 sweep numbers. MEASURED on ruvultra (RTX 5080, torch 2.11.0+cu128, fbgemm; clean test, torso-PCK): fp32 96.62% pck@20 99.47% pck@50 0.008981 mpjpe 3.351 MB int8 PTQ static 40.98% pck@20 94.98% pck@50 0.038262 mpjpe 1.046 MB (-55.64pp) int8 QAT (3 ep) 67.48% pck@20 98.69% pck@50 0.026548 mpjpe 1.043 MB (-29.15pp) Verdict (honest no): int8 is NOT a win at the strict PCK@20 edge target. Static PTQ collapses; QAT recovers a large share but still loses 29 pp @20 for a 3.2x size win — keep fp32/fp16 on the edge. Disclosed: QAT fake-quant val pck@20 was 83.45% but converted int8 scores 67.48% (~16pp convert_fx gap, reported honestly). Deliverables: - v2/crates/wifi-densepose-train/scripts/quantize_half_int8.py (reproducible: header carries the exact ssh command + run date; QAT primary, static PTQ fallback) - docs/adr/ADR-175-int8-quantization-half-pose-model-measured.md (MEASURED table, locked normalization, QAT-vs-PTQ labeling, verdict, reproduction, limitations) - CHANGELOG [Unreleased] ### Added entry No production Rust or signal-pipeline change. Python deterministic proof unchanged (f8e76f21a0f9852b70b6d9dd5318239f6b20cbcb4cdd995863263cecdc446f7a, bit-exact).
Architecture Decision Records
This folder contains 45 Architecture Decision Records (ADRs) that document every significant technical choice in the RuView / WiFi-DensePose project.
Why ADRs?
Building a system that turns WiFi signals into human pose estimation involves hundreds of non-obvious decisions: which signal processing algorithms to use, how to bridge ESP32 firmware to a Rust pipeline, whether to run inference on-device or on a server, how to handle multi-person separation with limited subcarriers.
ADRs capture the context, options considered, decision made, and consequences for each of these choices. They serve three purposes:
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Institutional memory — Six months from now, anyone (human or AI) can read why we chose IIR bandpass filters over FIR for vital sign extraction, not just see the code.
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AI-assisted development — When an AI agent works on this codebase, ADRs give it the constraints and rationale it needs to make changes that align with the existing architecture. Without them, AI-generated code tends to drift — reinventing patterns that already exist, contradicting earlier decisions, or optimizing for the wrong tradeoffs.
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Review checkpoints — Each ADR is a reviewable artifact. When a proposed change touches the architecture, the ADR forces the author to articulate tradeoffs before writing code, not after.
ADRs and Domain-Driven Design
The project uses Domain-Driven Design (DDD) to organize code into bounded contexts — each with its own language, types, and responsibilities. ADRs and DDD work together:
- ADRs define boundaries: ADR-029 (RuvSense) established multistatic sensing as a separate bounded context from single-node CSI. ADR-042 (CHCI) defined a new aggregate root for coherent channel imaging.
- DDD models define the language: The RuvSense domain model defines terms like "coherence gate", "dwell time", and "TDM slot" that ADRs reference precisely.
- Together they prevent drift: An AI agent reading ADR-039 knows that edge processing tiers are configured via NVS keys, not compile-time flags — because the ADR says so. The DDD model tells it which aggregate owns that configuration.
How ADRs are structured
Each ADR follows a consistent format:
- Context — What problem or gap prompted this decision
- Decision — What we chose to do and how
- Consequences — What improved, what got harder, and what risks remain
- References — Related ADRs, papers, and code paths
Statuses: Proposed (under discussion), Accepted (approved and/or implemented), Superseded (replaced by a later ADR).
ADR Index
Hardware and firmware
| ADR | Title | Status |
|---|---|---|
| ADR-012 | ESP32 CSI Sensor Mesh for Distributed Sensing | Accepted (partial) |
| ADR-018 | ESP32 Development Implementation Path | Proposed |
| ADR-028 | ESP32 Capability Audit and Witness Record | Accepted |
| ADR-029 | RuvSense Multistatic Sensing Mode (TDM, channel hopping) | Proposed |
| ADR-032 | Multistatic Mesh Security Hardening | Accepted |
| ADR-039 | ESP32-S3 Edge Intelligence Pipeline (on-device vitals) | Accepted (hardware-validated) |
| ADR-040 | WASM Programmable Sensing (Tier 3) | Accepted |
| ADR-041 | WASM Module Collection (65 edge modules) | Accepted (hardware-validated) |
| ADR-044 | Provisioning Tool Enhancements | Proposed |
| ADR-110 | ESP32-C6 firmware extension — Wi-Fi 6 / 802.15.4 / TWT / LP-core | Accepted, P1-P10 complete, firmware-side substrate closed at v0.7.0-esp32. Companion docs: WITNESS-LOG-110 (13 §A0.x entries · 99.56 % cross-board RX · 104.1 µs smoothed sync stdev · ≤100 µs target met), ADR-110-REVIEW-GUIDE (one-page reviewer tour), ADR-110-BRANCH-STATE (coordination map vs feat/adr-115-ha-mqtt-matter). Host decoders + tests: Python SyncPacketParser (10) + Rust wifi_densepose_hardware::SyncPacket (15), cross-language hex pin gates drift. |
Signal processing and sensing
| ADR | Title | Status |
|---|---|---|
| ADR-013 | Feature-Level Sensing on Commodity Gear | Accepted |
| ADR-014 | SOTA Signal Processing Algorithms | Accepted |
| ADR-021 | Vital Sign Detection (breathing, heart rate) | Partial |
| ADR-030 | Persistent Field Model and Drift Detection | Proposed |
| ADR-033 | CRV Signal Line Sensing Integration | Proposed |
| ADR-037 | Multi-Person Pose Detection from Single ESP32 | Proposed |
| ADR-042 | Coherent Human Channel Imaging (beyond CSI) | Proposed |
| ADR-134 | First-Class Channel Impulse Response (CIR) Support | Proposed |
| ADR-135 | Empty-Room Baseline Calibration (per-subcarrier Welford statistics) | Proposed |
Machine learning and training
| ADR | Title | Status |
|---|---|---|
| ADR-005 | SONA Self-Learning for Pose Estimation | Partial |
| ADR-006 | GNN-Enhanced CSI Pattern Recognition | Partial |
| ADR-015 | Public Dataset Strategy (MM-Fi, Wi-Pose) | Accepted |
| ADR-016 | RuVector Training Pipeline Integration | Accepted |
| ADR-017 | RuVector Signal + MAT Integration | Proposed |
| ADR-020 | Migrate AI Inference to Rust (ONNX Runtime) | Accepted |
| ADR-023 | Trained DensePose Model with RuVector Pipeline | Proposed |
| ADR-024 | Project AETHER: Contrastive CSI Embeddings | Required |
| ADR-027 | Project MERIDIAN: Cross-Environment Generalization | Proposed |
| ADR-149 | AetherArena: public spatial-intelligence benchmark on Hugging Face | Proposed |
| ADR-150 | RF Foundation Encoder: pose-preserving, subject/room/device-invariant CSI embedding | Proposed |
| ADR-151 | Per-Room Calibration & Specialized Model Training (room-first → bank of small ruVector specialists) | Proposed |
| ADR-152 | WiFi-Pose SOTA 2026 Intake: geometry-conditioned calibration, external benchmarks, foundation-encoder recipe | Proposed |
Platform and UI
| ADR | Title | Status |
|---|---|---|
| ADR-019 | Sensing-Only UI with Gaussian Splats | Accepted |
| ADR-022 | Windows WiFi Enhanced Fidelity (multi-BSSID) | Partial |
| ADR-025 | macOS CoreWLAN WiFi Sensing | Proposed |
| ADR-031 | RuView Sensing-First RF Mode | Proposed |
| ADR-034 | Expo React Native Mobile App | Accepted |
| ADR-035 | Live Sensing UI Accuracy and Data Transparency | Accepted |
| ADR-036 | Training Pipeline UI Integration | Proposed |
| ADR-043 | Sensing Server UI API Completion (14 endpoints) | Accepted |
| ADR-115 | Home Assistant integration via MQTT auto-discovery + Matter bridge (HA-DISCO + HA-FABRIC + HA-MIND) | Accepted (MQTT track) / Proposed (Matter SDK P8b) |
| ADR-169 | adam-mode — light theme toggle for the three.js realtime demo | Proposed |
| ADR-170 | yoga-mode — yoga pose detection, classification, and scoring for the three.js realtime demo | Proposed |
Architecture and infrastructure
| ADR | Title | Status |
|---|---|---|
| ADR-001 | WiFi-Mat Disaster Detection Architecture | Accepted |
| ADR-002 | RuVector RVF Integration Strategy | Superseded |
| ADR-003 | RVF Cognitive Containers for CSI | Proposed |
| ADR-004 | HNSW Vector Search for Fingerprinting | Partial |
| ADR-007 | Post-Quantum Cryptography for Sensing | Proposed |
| ADR-008 | Distributed Consensus for Multi-AP | Proposed |
| ADR-009 | RVF WASM Runtime for Edge Deployment | Proposed |
| ADR-010 | Witness Chains for Audit Trail Integrity | Proposed |
| ADR-011 | Proof-of-Reality and Mock Elimination | Proposed |
| ADR-026 | Survivor Track Lifecycle (MAT crate) | Accepted |
| ADR-038 | Sublinear GOAP for Roadmap Optimization | Proposed |
| ADR-095 | rvCSI — Edge RF Sensing Runtime Platform | Proposed |
| ADR-096 | rvCSI — Crate Topology, the napi-c Shim, and the napi-rs Node Surface | Proposed |
| ADR-097 | Adopt rvCSI as RuView's primary CSI runtime (phased adoption) | Proposed |
| ADR-098 | Evaluate ruvnet/midstream for RuView's CSI / WebSocket / mesh pipeline |
Rejected |
| ADR-099 | Adopt midstream as RuView's real-time introspection + low-latency tap | Proposed |
Related
- DDD Domain Models — Bounded context definitions, aggregate roots, and ubiquitous language
- User Guide — Setup, API reference, and hardware instructions
- Build Guide — Building from source