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Adds design documentation for rvCSI, a Rust-first / TypeScript-accessible / hardware-abstracted edge RF sensing runtime that normalizes WiFi CSI from Nexmon, ESP32, Intel, Atheros, file and replay sources into one validated CsiFrame schema, runs reusable DSP, emits typed confidence-scored events, and bridges to RuVector RF memory, an MCP tool server and a TS SDK. - docs/prd/rvcsi-platform-prd.md — purpose, users, success criteria, FR1-FR10, NFRs (safety/perf/reliability/privacy/security/portability), system architecture, runtime components, reference layout, data model - docs/adr/ADR-095-rvcsi-edge-rf-sensing-platform.md — the 15 architectural decisions (Rust core, C-at-the-boundary, TS SDK via napi-rs, normalized schema, validate-before-FFI, CSI-as-temporal-delta, RuVector as RF memory, replayability, detection != decision, local-first, read-first/write-gated MCP, mandatory quality scoring, versioned calibration, plugin adapters) - docs/ddd/rvcsi-domain-model.md — 7 bounded contexts (Capture, Validation, Signal, Calibration, Event, Memory, Agent) with aggregates, invariants, context map, data model and domain services - indexed in docs/adr/README.md and docs/ddd/README.md; CHANGELOG entry Design-only; no code or crates added yet. https://claude.ai/code/session_01CdYAPvRTjcch6YrYf42n1z |
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| .. | ||
| chci-domain-model.md | ||
| deployment-platform-domain-model.md | ||
| hardware-platform-domain-model.md | ||
| README.md | ||
| ruvsense-domain-model.md | ||
| rvcsi-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 |
| rvCSI | Edge RF sensing runtime: multi-source CSI ingestion, validation, normalization, event extraction, RuVector RF memory, agent/MCP integration | 7 contexts: Capture, Validation, Signal, Calibration, Event, Memory, Agent |
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