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docs: add multi-frequency mesh + RF scanner to README 

New capabilities: 6-channel hopping, neighbor APs as passive radar,
real-time RF spectrum visualization with null/reflector/movement detection

Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv 2026-04-03 00:26:48 -04:00
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@ -56,6 +56,7 @@ In practice this means ordinary environments gain a new kind of spatial awarenes
> | **Through-wall** | Fresnel zone geometry + multipath modeling | Up to 5m depth |
> | **Edge intelligence** | 8-dim feature vectors + RVF store on Cognitum Seed | $27 total BOM |
> | **Camera-free training** | 10 sensor signals, no labels needed | 84s on M4 Pro |
> | **Multi-frequency mesh** | Channel hopping across 6 bands, neighbor APs as illuminators | 3x sensing bandwidth |
```bash
# 30 seconds to live sensing — no toolchain required
@ -98,6 +99,8 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
| **SONA adaptation** | Adapts to new rooms in <1ms without retraining | ruvllm runtime |
| **LoRA room adapters** | Per-node fine-tuning with 2,048 parameters per adapter | Automatic |
| **114-tool MCP proxy** | AI assistants (Claude, GPT) query sensors directly via JSON-RPC | Cognitum Seed |
| **Multi-frequency mesh** | Channel hopping across ch 1/3/5/6/9/11 — neighbor WiFi as passive radar | 2x ESP32 ($18) |
| **RF room scanner** | Real-time spectrum visualization: nulls, reflectors, movement, multipath | `node scripts/rf-scan.js` |
| **Security hardened** | Bearer tokens, TLS, source IP filtering, NaN rejection, credential rotation | All components |
**Training pipeline (ruvllm, no PyTorch needed):**