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|[DDD Domain Model](docs/ddd/ruvsense-domain-model.md)| RuvSense bounded contexts, aggregates, domain events, and ubiquitous language |
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| 🎯 |**AI Signal Processing**| Attention networks, graph algorithms, and smart compression replace hand-tuned thresholds — adapts to each room automatically ([RuVector](https://github.com/ruvnet/ruvector)) |
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| 🌍 |**Works Everywhere**| Train once, deploy in any room — adversarial domain generalization strips environment bias so models transfer across rooms, buildings, and hardware ([ADR-027](docs/adr/ADR-027-cross-environment-domain-generalization.md)) |
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| 👁️ |**Cross-Viewpoint Fusion**| Learned attention fuses multiple viewpoints with geometric bias — reduces body occlusion and depth ambiguity that physics prevents any single sensor from solving ([ADR-031](docs/adr/ADR-031-ruview-sensing-first-rf-mode.md)) |
| I | Ideograms | Raw CSI gestalt (manmade/natural/movement/energy) | Poincare ball hyperbolic embeddings |
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| II | Sensory | Amplitude textures, phase patterns, frequency colors | Multi-head attention vectors |
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| III | Dimensional | AP mesh spatial topology, node geometry | GNN graph topology |
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| IV | Emotional/AOL | Coherence gating — signal vs noise separation | SNN temporal encoding |
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| V | Interrogation | Cross-stage probing — query pose against CSI history | Differentiable search |
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| VI | 3D Model | Composite person estimation, MinCut partitioning | Graph partitioning |
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**Cross-Session Convergence**: When multiple AP clusters observe the same person, CRV convergence analysis finds agreement in their signal embeddings — directly mapping to cross-room identity continuity.
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## 6. Related ADRs
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## 6. QUIC Transport Layer (ADR-032a Amendment)
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### 6.1 Motivation
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The original ADR-032 design (Sections 2.1--2.2) uses manual HMAC-SHA256 and SipHash-2-4 over plain UDP. While correct and efficient on constrained ESP32 hardware, this approach has operational drawbacks:
### 6.2 Decision: Adopt `midstreamer-quic` for Aggregator Uplinks
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For aggregator-class nodes (Raspberry Pi, x86 gateway) that have sufficient CPU and memory, replace the manual crypto layer with `midstreamer-quic` v0.1.0, which provides:
| 2 | Control plane | Bidirectional | Normal (config, key rotation, health) |
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### 6.4 Additional Midstreamer Integrations
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Beyond QUIC transport, three additional midstreamer crates enhance the sensing pipeline:
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1.**`midstreamer-scheduler` v0.1.0** -- Replaces manual timer-based TDM slot scheduling with an ultra-low-latency real-time task scheduler. Provides deterministic slot firing with sub-microsecond jitter.
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2.**`midstreamer-temporal-compare` v0.1.0** -- Enhances gesture DTW matching (ADR-030 Tier 6) with temporal sequence comparison primitives. Provides optimized Sakoe-Chiba band DTW, LCS, and edit-distance kernels.
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3.**`midstreamer-attractor` v0.1.0** -- Enhances longitudinal drift detection (ADR-030 Tier 4) with dynamical systems analysis. Detects phase-space attractor shifts that indicate biomechanical regime changes before they manifest as simple metric drift.
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### 6.5 Fallback Strategy
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The QUIC transport layer is additive, not a replacement:
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-**ESP32-S3 nodes**: Continue using manual HMAC/SipHash over UDP (Sections 2.1--2.2). These devices lack the memory for a full TLS 1.3 stack.
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-**Aggregator nodes**: Use `midstreamer-quic` by default. Fall back to manual crypto if QUIC handshake fails (e.g., network partitions).
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-**Mixed deployments**: The aggregator auto-detects whether an incoming connection is QUIC (by TLS ClientHello) or plain UDP (by magic byte) and routes accordingly.
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### 6.6 Acceptance Criteria (QUIC)
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| ID | Criterion | Test Method |
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|----|-----------|-------------|
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| Q-1 | QUIC connection established between two nodes within 100ms | Integration test: connect, measure handshake time |
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