From aaec69922305ad0b2d842de5a59133e511450c09 Mon Sep 17 00:00:00 2001 From: ruv Date: Sun, 1 Mar 2026 11:23:15 -0500 Subject: [PATCH] docs: move AI Backbone into collapsed section under Models & Training - Remove RuVector AI section from Rust Crates details block - Add as own collapsed
in Models & Training with anchor link - Add cross-reference from crates table to new section - Link to issue #67 for deep dive with code examples Co-Authored-By: claude-flow --- README.md | 63 +++++++++++++++++++++++++++++++------------------------ 1 file changed, 36 insertions(+), 27 deletions(-) diff --git a/README.md b/README.md index cb27051..453c226 100644 --- a/README.md +++ b/README.md @@ -364,33 +364,7 @@ cargo add wifi-densepose-ruvector # RuVector v2.0.4 integration layer (ADR-017 | [`wifi-densepose-config`](https://crates.io/crates/wifi-densepose-config) | Configuration management | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-config.svg)](https://crates.io/crates/wifi-densepose-config) | | [`wifi-densepose-db`](https://crates.io/crates/wifi-densepose-db) | Database persistence (PostgreSQL, SQLite, Redis) | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-db.svg)](https://crates.io/crates/wifi-densepose-db) | -#### AI Backbone: [RuVector v2.0.4](https://github.com/ruvnet/ruvector) - -Raw WiFi signals are noisy, redundant, and environment-dependent. [RuVector](https://github.com/ruvnet/ruvector) is the AI intelligence layer that transforms them into clean, structured input for the DensePose neural network. It uses **attention mechanisms** to learn which signals to trust, **graph algorithms** that automatically discover which WiFi channels are sensitive to body motion, and **compressed representations** that make edge inference possible on an $8 microcontroller. - -``` -Raw WiFi CSI (56 subcarriers, noisy) - | - +-- ruvector-mincut ---------- Which channels carry body-motion signal? (learned graph partitioning) - +-- ruvector-attn-mincut ----- Which time frames are signal vs noise? (attention-gated filtering) - +-- ruvector-attention ------- How to fuse multi-antenna data? (learned weighted aggregation) - | - v -Clean, structured signal --> DensePose Neural Network --> 17-keypoint body pose - --> FFT Vital Signs -----------> breathing rate, heart rate - --> ruvector-solver ------------> physics-based localization -``` - -The [`wifi-densepose-ruvector`](https://crates.io/crates/wifi-densepose-ruvector) crate ([ADR-017](docs/adr/ADR-017-ruvector-signal-mat-integration.md)) connects all 7 integration points: - -| AI Capability | What It Replaces | RuVector Crate | Result | -|--------------|-----------------|----------------|--------| -| **Self-optimizing channel selection** | Hand-tuned thresholds that break when rooms change | `ruvector-mincut` | Graph min-cut adapts to any environment automatically | -| **Attention-based signal cleaning** | Fixed energy cutoffs that miss subtle breathing | `ruvector-attn-mincut` | Learned gating amplifies body signals, suppresses noise | -| **Learned signal fusion** | Simple averaging where one bad channel corrupts all | `ruvector-attention` | Transformer-style attention downweights corrupted channels | -| **Physics-informed localization** | Expensive nonlinear solvers | `ruvector-solver` | Sparse least-squares Fresnel geometry in real-time | -| **O(1) survivor triangulation** | O(N^3) matrix inversion | `ruvector-solver` | Neumann series linearization for instant position updates | -| **75% memory compression** | 13.4 MB breathing buffers that overflow edge devices | `ruvector-temporal-tensor` | Tiered 3-8 bit quantization fits 60s of vitals in 3.4 MB | +All crates integrate with [RuVector v2.0.4](https://github.com/ruvnet/ruvector) — see [AI Backbone](#ai-backbone-ruvector) below.
@@ -724,6 +698,41 @@ See [ADR-014](docs/adr/ADR-014-sota-signal-processing.md) for full mathematical ## 🧠 Models & Training +
+🤖 AI Backbone: RuVector — Attention, graph algorithms, and edge-AI compression powering the sensing pipeline + +Raw WiFi signals are noisy, redundant, and environment-dependent. [RuVector](https://github.com/ruvnet/ruvector) is the AI intelligence layer that transforms them into clean, structured input for the DensePose neural network. It uses **attention mechanisms** to learn which signals to trust, **graph algorithms** that automatically discover which WiFi channels are sensitive to body motion, and **compressed representations** that make edge inference possible on an $8 microcontroller. + +Without RuVector, WiFi DensePose would need hand-tuned thresholds, brute-force matrix math, and 4x more memory — making real-time edge inference impossible. + +``` +Raw WiFi CSI (56 subcarriers, noisy) + | + +-- ruvector-mincut ---------- Which channels carry body-motion signal? (learned graph partitioning) + +-- ruvector-attn-mincut ----- Which time frames are signal vs noise? (attention-gated filtering) + +-- ruvector-attention ------- How to fuse multi-antenna data? (learned weighted aggregation) + | + v +Clean, structured signal --> DensePose Neural Network --> 17-keypoint body pose + --> FFT Vital Signs -----------> breathing rate, heart rate + --> ruvector-solver ------------> physics-based localization +``` + +The [`wifi-densepose-ruvector`](https://crates.io/crates/wifi-densepose-ruvector) crate ([ADR-017](docs/adr/ADR-017-ruvector-signal-mat-integration.md)) connects all 7 integration points: + +| AI Capability | What It Replaces | RuVector Crate | Result | +|--------------|-----------------|----------------|--------| +| **Self-optimizing channel selection** | Hand-tuned thresholds that break when rooms change | `ruvector-mincut` | Graph min-cut adapts to any environment automatically | +| **Attention-based signal cleaning** | Fixed energy cutoffs that miss subtle breathing | `ruvector-attn-mincut` | Learned gating amplifies body signals, suppresses noise | +| **Learned signal fusion** | Simple averaging where one bad channel corrupts all | `ruvector-attention` | Transformer-style attention downweights corrupted channels | +| **Physics-informed localization** | Expensive nonlinear solvers | `ruvector-solver` | Sparse least-squares Fresnel geometry in real-time | +| **O(1) survivor triangulation** | O(N^3) matrix inversion | `ruvector-solver` | Neumann series linearization for instant position updates | +| **75% memory compression** | 13.4 MB breathing buffers that overflow edge devices | `ruvector-temporal-tensor` | Tiered 3-8 bit quantization fits 60s of vitals in 3.4 MB | + +See [issue #67](https://github.com/ruvnet/wifi-densepose/issues/67) for a deep dive with code examples, or [`cargo add wifi-densepose-ruvector`](https://crates.io/crates/wifi-densepose-ruvector) to use it directly. + +
+
📦 RVF Model Container — Single-file deployment with progressive loading