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Rruvdocs: update README with ADR-045–048, Observatory, adaptive classifier, AMOLED display
8b57a6f6创建于 3月5日历史提交
文件最后提交记录最后更新时间
README.md
docs: add 4 DDD domain models covering all major subsystems Create complete Domain-Driven Design specifications for: - Signal Processing (3 contexts: CSI Preprocessing, Feature Extraction, Motion Analysis) - Training Pipeline (4 contexts: Dataset Management, Model Architecture, Training Orchestration, Embedding & Transfer) - Hardware Platform (5 contexts: Sensor Node, Edge Processing, WASM Runtime, Aggregation, Provisioning) - Sensing Server (5 contexts: CSI Ingestion, Model Management, CSI Recording, Training Pipeline, Visualization) Update DDD index (3 → 7 models) and README docs table. Co-Authored-By: claude-flow <ruv@ruv.net> 2 个月前
chci-domain-model.md
feat: add ADR-042 CHCI protocol, 24 new edge modules, README restructure - ADR-042: Coherent Human Channel Imaging (non-CSI sensing protocol) with DDD domain model (6 bounded contexts) - 24 new WASM edge modules: medical (5), retail (5), security (5), building (5), industrial (5), exotic (8) - README: plain-language rewrites, moved detail sections below TOC, added edge module links to use case tables, firmware release docs - User guide: firmware release table, edge intelligence documentation - .gitignore: added rules for wasm, esp32 temp files, NVS binaries - WASM edge crate: cargo config, integration tests, module registry Co-Authored-By: claude-flow <ruv@ruv.net> 2 个月前
deployment-platform-domain-model.md
docs: update README with ADR-045–048, Observatory, adaptive classifier, AMOLED display - Update ADR count from 44 to 48 - Add adaptive classifier (ADR-048) to Intelligence features - Add Observatory visualization (ADR-047) and AMOLED display (ADR-045) to Deployment features - Update screenshot to v2-screen.png - Add ADR-045 (AMOLED), ADR-046 (Android TV), ADR-047 (Observatory), DDD deployment model - Add AMOLED display firmware (display_hal, display_task, display_ui, LVGL config) - Add Observatory UI (13 Three.js modules, CSS, HTML entry point) - Add trained adaptive model JSON - Update .gitignore for managed_components, recordings, .swarm Co-Authored-By: claude-flow <ruv@ruv.net> 2 个月前
hardware-platform-domain-model.md
docs: add 4 DDD domain models covering all major subsystems Create complete Domain-Driven Design specifications for: - Signal Processing (3 contexts: CSI Preprocessing, Feature Extraction, Motion Analysis) - Training Pipeline (4 contexts: Dataset Management, Model Architecture, Training Orchestration, Embedding & Transfer) - Hardware Platform (5 contexts: Sensor Node, Edge Processing, WASM Runtime, Aggregation, Provisioning) - Sensing Server (5 contexts: CSI Ingestion, Model Management, CSI Recording, Training Pipeline, Visualization) Update DDD index (3 → 7 models) and README docs table. Co-Authored-By: claude-flow <ruv@ruv.net> 2 个月前
ruvsense-domain-model.md
docs: improve RuvSense domain model and add DDD index - Add intro explaining DDD purpose and bounded context overview table - Add Edge Intelligence bounded context (#7) for on-device sensing - Add ubiquitous language terms: Edge Tier, WASM Module - Fix frame rate 20 Hz -> 28 Hz (measured on hardware) - Link each context to its source files and ADRs - Add NVS configuration table and invariants for edge processing - Create docs/ddd/README.md introducing all 3 domain models - Update main README docs table to link to DDD index Co-Authored-By: claude-flow <ruv@ruv.net> 2 个月前
sensing-server-domain-model.md
docs: add 4 DDD domain models covering all major subsystems Create complete Domain-Driven Design specifications for: - Signal Processing (3 contexts: CSI Preprocessing, Feature Extraction, Motion Analysis) - Training Pipeline (4 contexts: Dataset Management, Model Architecture, Training Orchestration, Embedding & Transfer) - Hardware Platform (5 contexts: Sensor Node, Edge Processing, WASM Runtime, Aggregation, Provisioning) - Sensing Server (5 contexts: CSI Ingestion, Model Management, CSI Recording, Training Pipeline, Visualization) Update DDD index (3 → 7 models) and README docs table. Co-Authored-By: claude-flow <ruv@ruv.net> 2 个月前
signal-processing-domain-model.md
docs: add 4 DDD domain models covering all major subsystems Create complete Domain-Driven Design specifications for: - Signal Processing (3 contexts: CSI Preprocessing, Feature Extraction, Motion Analysis) - Training Pipeline (4 contexts: Dataset Management, Model Architecture, Training Orchestration, Embedding & Transfer) - Hardware Platform (5 contexts: Sensor Node, Edge Processing, WASM Runtime, Aggregation, Provisioning) - Sensing Server (5 contexts: CSI Ingestion, Model Management, CSI Recording, Training Pipeline, Visualization) Update DDD index (3 → 7 models) and README docs table. Co-Authored-By: claude-flow <ruv@ruv.net> 2 个月前
training-pipeline-domain-model.md
docs: add 4 DDD domain models covering all major subsystems Create complete Domain-Driven Design specifications for: - Signal Processing (3 contexts: CSI Preprocessing, Feature Extraction, Motion Analysis) - Training Pipeline (4 contexts: Dataset Management, Model Architecture, Training Orchestration, Embedding & Transfer) - Hardware Platform (5 contexts: Sensor Node, Edge Processing, WASM Runtime, Aggregation, Provisioning) - Sensing Server (5 contexts: CSI Ingestion, Model Management, CSI Recording, Training Pipeline, Visualization) Update DDD index (3 → 7 models) and README docs table. Co-Authored-By: claude-flow <ruv@ruv.net> 2 个月前
wifi-mat-domain-model.md
feat: Add wifi-densepose-mat disaster detection module Implements WiFi-Mat (Mass Casualty Assessment Tool) for detecting and localizing survivors trapped in rubble, earthquakes, and natural disasters. Architecture: - Domain-Driven Design with bounded contexts (Detection, Localization, Alerting) - Modular Rust crate integrating with existing wifi-densepose-* crates - Event-driven architecture for audit trails and distributed deployments Features: - Breathing pattern detection from CSI amplitude variations - Heartbeat detection using micro-Doppler analysis - Movement classification (gross, fine, tremor, periodic) - START protocol-compatible triage classification - 3D position estimation via triangulation and depth estimation - Real-time alert generation with priority escalation Documentation: - ADR-001: Architecture Decision Record for wifi-Mat - DDD domain model specification 4 个月前
README.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