feat: ADR-032a midstreamer QUIC transport + secure TDM + temporal gesture + attractor drift
Integrate midstreamer ecosystem for QUIC-secured mesh transport and advanced signal analysis: QUIC Transport (hardware crate): - quic_transport.rs: SecurityMode (ManualCrypto/QuicTransport), FramedMessage wire format, connection management, fallback support (856 lines, 30 tests) - secure_tdm.rs: ReplayWindow, AuthenticatedBeacon (28-byte HMAC format), SecureTdmCoordinator with dual-mode security (994 lines, 20 tests) - transport_bench.rs: Criterion benchmarks (plain vs authenticated vs QUIC) Signal Analysis (signal crate): - temporal_gesture.rs: DTW/LCS/EditDistance gesture matching via midstreamer-temporal-compare, quantized feature comparison (517 lines, 13 tests) - attractor_drift.rs: Takens' theorem phase-space embedding, Lyapunov exponent classification (Stable/Periodic/Chaotic) via midstreamer-attractor (573 lines, 13 tests) ADR-032 updated with Section 6: QUIC Transport Layer (ADR-032a) README updated with CRV signal-line section, badge 1100+, ADR count 33 Dependencies: midstreamer-quic 0.1.0, midstreamer-scheduler 0.1.0, midstreamer-temporal-compare 0.1.0, midstreamer-attractor 0.1.0 Total: 3,136 new lines, 76 tests, 6 benchmarks Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
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README.md
52
README.md
@@ -6,7 +6,7 @@ WiFi DensePose turns commodity WiFi signals into real-time human pose estimation
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[](https://www.rust-lang.org/)
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[](https://opensource.org/licenses/MIT)
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[](https://github.com/ruvnet/wifi-densepose)
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[](https://github.com/ruvnet/wifi-densepose)
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[](https://hub.docker.com/r/ruvnet/wifi-densepose)
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[](#vital-sign-detection)
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[](#esp32-s3-hardware-pipeline)
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@@ -49,7 +49,7 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
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| [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training |
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| [WiFi-Mat User Guide](docs/wifi-mat-user-guide.md) | Disaster response module: search & rescue, START triage |
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| [Build Guide](docs/build-guide.md) | Building from source (Rust and Python) |
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| [Architecture Decisions](docs/adr/) | 31 ADRs covering signal processing, training, hardware, security, domain generalization, multistatic sensing |
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| [Architecture Decisions](docs/adr/) | 33 ADRs covering signal processing, training, hardware, security, domain generalization, multistatic sensing, CRV signal-line integration |
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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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---
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@@ -80,6 +80,7 @@ The system learns on its own and gets smarter over time — no hand-tuning, no l
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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)) |
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| 🔮 | **Signal-Line Protocol** | `ruvector-crv` 6-stage CRV pipeline maps CSI sensing to Poincare ball embeddings, GNN topology, SNN temporal encoding, and MinCut partitioning | -- |
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### Performance & Deployment
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@@ -113,6 +114,8 @@ Signal Processing: Hampel, SpotFi, Fresnel, BVP, spectrogram → clean features
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↓
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AI Backbone (RuVector): attention, graph algorithms, compression, field model
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↓
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Signal-Line Protocol (CRV): 6-stage gestalt → sensory → topology → coherence → search → model
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↓
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Neural Network: processed signals → 17 body keypoints + vital signs + room model
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↓
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Output: real-time pose, breathing, heart rate, room fingerprint, drift alerts
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@@ -463,6 +466,48 @@ See the ADR documents for full architectural details, GOAP integration plans, an
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</details>
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<details>
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<summary><b>🔮 Signal-Line Protocol (CRV)</b></summary>
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### 6-Stage CSI Signal Line
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Maps the CRV (Coordinate Remote Viewing) signal-line methodology to WiFi CSI processing via `ruvector-crv`:
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| Stage | CRV Name | WiFi CSI Mapping | ruvector Component |
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|-------|----------|-----------------|-------------------|
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| 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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```rust
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use wifi_densepose_ruvector::crv::WifiCrvPipeline;
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let mut pipeline = WifiCrvPipeline::new(WifiCrvConfig::default());
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pipeline.create_session("room-a", "person-001")?;
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// Process CSI frames through 6-stage pipeline
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let result = pipeline.process_csi_frame("room-a", &litudes, &phases)?;
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// result.gestalt = Movement, confidence = 0.87
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// result.sensory_embedding = [0.12, -0.34, ...]
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// Cross-room identity matching via convergence
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let convergence = pipeline.find_cross_room_convergence("person-001", 0.75)?;
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```
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**Architecture**:
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- `CsiGestaltClassifier` — Maps CSI amplitude/phase patterns to 6 gestalt types
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- `CsiSensoryEncoder` — Extracts texture/color/temperature/luminosity features from subcarriers
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- `MeshTopologyEncoder` — Encodes AP mesh as GNN graph (Stage III)
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- `CoherenceAolDetector` — Maps coherence gate states to AOL noise detection (Stage IV)
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- `WifiCrvPipeline` — Orchestrates all 6 stages into unified sensing session
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</details>
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---
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## 📦 Installation
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@@ -1539,6 +1584,9 @@ Multistatic sensing, persistent field model, and cross-viewpoint fusion — the
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- **TDM Hardware Protocol** — ESP32 sensing coordinator: sync beacons, slot scheduling, clock drift compensation (±10ppm), 20 Hz aggregate rate
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- **Channel-Hopping Firmware** — ESP32 firmware extended with hop table, timer-driven channel switching, NDP injection stub; NVS config for all TDM parameters; fully backward-compatible
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- **DDD Domain Model** — 6 bounded contexts, ubiquitous language, aggregate roots, domain events, full event bus specification
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- **`ruvector-crv` 6-stage CRV signal-line integration (ADR-033)** — Maps Coordinate Remote Viewing methodology to WiFi CSI: gestalt classification, sensory encoding, GNN topology, SNN coherence gating, differentiable search, MinCut partitioning; cross-session convergence for multi-room identity continuity
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- **ADR-032 multistatic mesh security hardening** — Bounded calibration buffers, atomic counters, division-by-zero guards, NaN-safe normalization across all multistatic modules
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- **ADR-033 CRV signal-line sensing integration** — Architecture decision record for the 6-stage CRV pipeline mapping to ruvector components
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- **9,000+ lines of new Rust code** across 17 modules with 300+ tests
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- **Security hardened** — Bounded buffers, NaN guards, no panics in public APIs, input validation at all boundaries
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