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a4bd2308b7
Hardware-validated pipeline connecting ESP32-S3 CSI sensing to Cognitum Seed (Pi Zero 2 W) edge intelligence appliance via 8-dim feature vectors. Firmware: - New 48-byte feature vector packet (magic 0xC5110003) at 1 Hz with normalized presence, motion, breathing, heart rate, phase variance, person count, fall detection, and RSSI - Compressed frame magic reassigned 0xC5110003 → 0xC5110005 - Guard against uninitialized s_top_k read when count=0 Bridge (scripts/seed_csi_bridge.py): - UDP→HTTPS ingest with bearer token, hash-based vector IDs - --validate (kNN), --stats, --compact, --allowed-sources modes - NaN/inf rejection, retry logic, SEED_TOKEN env var support Validated on live hardware: - 941 vectors ingested, 100% kNN exact match - Witness chain SHA-256 verified (1,325 entries) - 1,463 Rust tests passed, Python proof VERDICT: PASS Research: 26 docs covering Arena Physica, Maxwell's equations in WiFi sensing, SOTA survey 2025-2026, GOAP implementation plan Security: removed hardcoded credentials, added NVS patterns to .gitignore, source IP filtering, NaN validation Co-Authored-By: claude-flow <ruv@ruv.net> |
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| .. | ||
| chci-domain-model.md | ||
| deployment-platform-domain-model.md | ||
| hardware-platform-domain-model.md | ||
| README.md | ||
| ruvsense-domain-model.md | ||
| sensing-server-domain-model.md | ||
| signal-processing-domain-model.md | ||
| training-pipeline-domain-model.md | ||
| wifi-mat-domain-model.md | ||
Domain Models
This folder contains Domain-Driven Design (DDD) specifications for each major subsystem in RuView.
DDD organizes the codebase around the problem being solved — not around technical layers. Each bounded context owns its own data, rules, and language. Contexts communicate through domain events, not by sharing mutable state. This makes the system easier to reason about, test, and extend — whether you're a person or an AI agent.
Models
| Model | What it covers | Bounded Contexts |
|---|---|---|
| RuvSense | Multistatic WiFi sensing, pose tracking, vital signs, edge intelligence | 7 contexts: Sensing, Coherence, Tracking, Field Model, Longitudinal, Spatial Identity, Edge Intelligence |
| Signal Processing | SOTA signal processing: phase cleaning, feature extraction, motion analysis | 3 contexts: CSI Preprocessing, Feature Extraction, Motion Analysis |
| Training Pipeline | ML training: datasets, model architecture, embeddings, domain generalization | 4 contexts: Dataset Management, Model Architecture, Training Orchestration, Embedding & Transfer |
| Hardware Platform | ESP32 firmware, edge intelligence, WASM runtime, aggregation, provisioning | 5 contexts: Sensor Node, Edge Processing, WASM Runtime, Aggregation, Provisioning |
| Sensing Server | Single-binary Axum server: CSI ingestion, model management, recording, training, visualization | 5 contexts: CSI Ingestion, Model Management, CSI Recording, Training Pipeline, Visualization |
| WiFi-Mat | Disaster response: survivor detection, START triage, mass casualty assessment | 3 contexts: Detection, Localization, Alerting |
| CHCI | Coherent Human Channel Imaging: sub-millimeter body surface reconstruction | 3 contexts: Sounding, Channel Estimation, Imaging |
How to read these
Each model defines:
- Ubiquitous Language — Terms with precise meanings used in both code and conversation
- Bounded Contexts — Independent subsystems with clear responsibilities and boundaries
- Aggregates — Clusters of objects that enforce business rules (e.g., a PoseTrack owns its keypoints)
- Value Objects — Immutable data with meaning (e.g., a CoherenceScore is not just a float)
- Domain Events — Things that happened that other contexts may care about
- Invariants — Rules that must always be true (e.g., "drift alert requires >2sigma for >3 days")
- Anti-Corruption Layers — Adapters that translate between contexts without leaking internals
Related
- Architecture Decision Records — Why each technical choice was made
- User Guide — Setup and API reference