ruview-advanced-sensing
RuView Advanced Sensing
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).
RuvSense multistatic mode (ADR-029)
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) |
More from ruvnet/ruview
browser
Web browser automation with AI-optimized snapshots for claude-flow agents
24skill builder
Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.
22v3 deep integration
Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
21agentdb memory patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
20v3 performance optimization
Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.
20swarm orchestration
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
20