mirror of
https://github.com/ruvnet/RuView
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rUv
719875ea1d
First deferred follow-up from R6. Productises R6's Fresnel forward model into a 2D placement-search CLI: given a room + target occupancy zones, recommend Tx/Rx positions that maximise first-Fresnel coverage. Benchmark on 5x5 m bedroom (bed 3 m^2 + chair 0.64 m^2, 2900 pairs evaluated at 2.4 GHz): - OPTIMAL: 51.1% coverage (Tx 1.25,0; Rx 4.75,5; diagonal 6.10 m link) - MEDIAN: 0.5% coverage - WORST: 0.0% coverage - 93x improvement, median to optimal Counter-intuitive insight: longer links cover MORE space. Fresnel envelope width = sqrt(d * lambda) / 2 grows with link length, so the 6.10 m diagonal beats wall-parallel 5.00 m links. Up to the R10 link-budget gate. Per-cog deployment recommendations: - cog-person-count: diagonal across longest axis - cog-pose: zone inside ~50% midpoint envelope - AETHER re-ID: Tx near doorway, Rx diagonal - cog-maritime-watch: vertical diagonal through cabin - cog-wildlife (future): Tx/Rx opposite trees, threading clearing midline Improvements come from physics, not algorithms - no model retraining needed. Existing customers can re-mount seeds today for 10-100x better sensing. Honest scope: 2D approximation, free-space, rectangular zones, single-pair only, perimeter-only candidates, no link-budget gate. CLI shape ready for productisation as 'wifi-densepose plan-antennas'. Also surfaces as a deferred MCP tool 'ruview_placement_recommend'. Composes with: - R6 (direct 2D extension) - R1 (placement x precision = full geometry budget) - R10 (sets the link-budget gate this ignores) - R11 (same recipe in steel cabins) - R14 (determines whether V1/V2/V3 see the right occupant) - ADR-105 (better placement = faster epsilon convergence) Next R6.2 follow-ups catalogued: R6.2.1 (3D), R6.2.2 (N-anchor union), R6.2.3 (pose-trajectory target zones). Coordination: ticks/tick-16.md, no PROGRESS.md edit.
56 lines
1.1 KiB
JSON
56 lines
1.1 KiB
JSON
{
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"room": {
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"width_m": 5.0,
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"height_m": 5.0
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},
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"frequency_ghz": 2.4,
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"wavelength_m": 0.12491666666666666,
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"target_zones": [
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{
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"name": "bed",
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"x0": 1.5,
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"y0": 0.5,
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"width": 2.0,
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"height": 1.5
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},
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{
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"name": "chair",
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"x0": 3.5,
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"y0": 3.5,
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"width": 0.8,
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"height": 0.8
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}
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],
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"best": {
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"score": 0.510989010989011,
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"tx": [
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1.25,
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0.0
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],
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"rx": [
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4.75,
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5.0
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],
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"link_length_m": 6.103277807866851,
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"result": {
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"total_coverage_fraction": 0.510989010989011,
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"total_area_m2": 3.6400000000000006,
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"covered_area_m2": 1.8600000000000003,
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"per_zone": {
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"bed": {
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"area_m2": 3.0000000000000004,
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"covered_m2": 1.3050000000000002,
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"coverage_fraction": 0.435
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},
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"chair": {
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"area_m2": 0.6400000000000001,
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"covered_m2": 0.5550000000000002,
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"coverage_fraction": 0.8671875000000001
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}
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}
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}
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},
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"median_score": 0.005494505494505495,
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"worst_score": 0.0,
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"n_pairs_evaluated": 2900
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} |