60e0e6d3c4
Implement full CRV (Coordinate Remote Viewing) signal-line protocol mapping to WiFi CSI sensing via ruvector-crv: - Stage I: CsiGestaltClassifier (6 gestalt types from amplitude/phase) - Stage II: CsiSensoryEncoder (texture/color/temperature/sound/luminosity/dimension) - Stage III: Mesh topology encoding (AP nodes/links → GNN graph) - Stage IV: Coherence gate → AOL detection (signal vs noise separation) - Stage V: Pose interrogation via differentiable search - Stage VI: Person partitioning via MinCut clustering - Cross-session convergence for cross-room identity New files: - crv/mod.rs: 1,430 lines, 43 tests - crv_bench.rs: 8 criterion benchmarks (gestalt, sensory, pipeline, convergence) - ADR-033: 740-line architecture decision with 30+ acceptance criteria - patches/ruvector-crv: Fix ruvector-gnn 2.0.5 API mismatch Dependencies: ruvector-crv 0.1.1, ruvector-gnn 2.0.5 Co-Authored-By: claude-flow <ruv@ruv.net>
32 lines
938 B
TOML
32 lines
938 B
TOML
[package]
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name = "wifi-densepose-ruvector"
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version.workspace = true
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edition.workspace = true
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authors.workspace = true
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license.workspace = true
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description = "RuVector v2.0.4 integration layer — ADR-017 signal processing and MAT ruvector integrations"
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repository.workspace = true
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keywords = ["wifi", "csi", "ruvector", "signal-processing", "disaster-detection"]
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categories = ["science", "computer-vision"]
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readme = "README.md"
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[dependencies]
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ruvector-mincut = { workspace = true }
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ruvector-attn-mincut = { workspace = true }
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ruvector-temporal-tensor = { workspace = true }
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ruvector-solver = { workspace = true }
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ruvector-attention = { workspace = true }
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ruvector-crv = { workspace = true }
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ruvector-gnn = { workspace = true }
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thiserror = { workspace = true }
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serde = { workspace = true }
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serde_json = { workspace = true }
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[dev-dependencies]
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approx = "0.5"
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criterion = { workspace = true }
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[[bench]]
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name = "crv_bench"
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harness = false
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