Work on advanced RuView sensing with signals from multiple WiFi nodes. Explore position estimation, three-dimensional occupancy reconstruction, changes in movement patterns over time, and defenses against suspicious signals, with guidance on implementation modules and validation.
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Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.
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The deep end: multistatic mesh, tomography, persistent field models, and the security model that protects them. Most of this lives in wifi-densepose-signal/src/ruvsense/ (14 modules) and wifi-densepose-ruvector/src/viewpoint/ (5 modules).
Treat every WiFi link in range — including neighbours' APs — as a bistatic radar pair, then fuse them.
Module (signal/src/ruvsense/) | Purpose |
|---|---|
multiband.rs | Multi-band CSI frame fusion, cross-channel coherence |
phase_align.rs | Iterative LO phase-offset estimation, circular mean |
multistatic.rs | Attention-weighted fusion, geometric diversity |
coherence.rs / coherence_gate.rs | Z-score coherence scoring; Accept / PredictOnly / Reject / Recalibrate gate decisions |
pose_tracker.rs | 17-keypoint Kalman tracker with AETHER re-ID embeddings |
field_model.rs | SVD room eigenstructure, perturbation extraction |
tomography.rs | RF tomography, ISTA L1 solver, voxel grid |
longitudinal.rs | Welford stats, biomechanics drift detection |
intention.rs | Pre-movement lead signals (200–500 ms ahead) |
cross_room.rs | Environment fingerprinting, transition graph |
gesture.rs | DTW template-matching gesture classifier |
adversarial.rs | Physically-impossible-signal detection, multi-link consistency |
Combine 2+ nodes geometrically — more nodes, more independent looks, tighter localization.
Module (ruvector/src/viewpoint/) | Purpose |
|---|---|
attention.rs | CrossViewpointAttention, GeometricBias, softmax with G_bias |
geometry.rs | GeometricDiversityIndex, Cramér–Rao bounds, Fisher Information |
coherence.rs | Phase-phasor coherence, hysteresis gate |
fusion.rs | MultistaticArray aggregate root, domain events |
Host-side helpers to explore the geometry before deploying: node scripts/mesh-graph-transformer.js, node scripts/passive-radar.js, node scripts/deep-scan.js.
field_model.rs builds an SVD eigenstructure of the room and stores it (RVF, ideally on a Cognitum Seed). New CSI frames are projected against it; the residual is the perturbation. Lets you ask "what's different from the empty-room baseline?" and survive restarts.
tomography.rs reconstructs a voxel occupancy grid from the multistatic link set via an ISTA L1 solver (sparse — most voxels are empty). Use with cross-viewpoint geometry for through-wall volumetric imaging. RuVector solver crates back the sparse interpolation (114→56 subcarriers).
Using neighbours' APs as illuminators and pooling links across a mesh expands the attack surface. Mitigations:
adversarial.rs rejects physically impossible signals and cross-checks multi-link consistency.coherence_gate.rs quarantines low-coherence / suspicious links (Reject / Recalibrate).ruview-verify and docs/security-audit-wasm-edge-vendor.md).cd v2 && cargo test --workspace --no-default-features # incl. ruvsense + viewpoint tests
cargo test -p wifi-densepose-signal --no-default-features
cargo test -p wifi-densepose-ruvector --no-default-features
cd .. && python archive/v1/data/proof/verify.py
v2/crates/wifi-densepose-signal/src/ruvsense/ · v2/crates/wifi-densepose-ruvector/src/viewpoint/docs/research/, docs/security-audit-wasm-edge-vendor.md아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
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Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.
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Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.
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