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rUv 9bbe95648c feat: ADR-024 Contrastive CSI Embedding Model — all 7 phases (#52 )

Full implementation of Project AETHER — Contrastive CSI Embedding Model.

## Phases Delivered
1. ProjectionHead (64→128→128) + L2 normalization
2. CsiAugmenter (5 physically-motivated augmentations)
3. InfoNCE contrastive loss + SimCLR pretraining
4. FingerprintIndex (4 index types: env, activity, temporal, person)
5. RVF SEG_EMBED (0x0C) + CLI integration
6. Cross-modal alignment (PoseEncoder + InfoNCE)
7. Deep RuVector: MicroLoRA, EWC++, drift detection, hard-negative mining, SEG_LORA

## Stats
- 276 tests passing (191 lib + 51 bin + 16 rvf + 18 vitals)
- 3,342 additions across 8 files
- Zero unsafe/unwrap/panic/todo stubs
- ~55KB INT8 model for ESP32 edge deployment

Also fixes deprecated GitHub Actions (v3→v4) and adds feat/* branch CI triggers.

Closes #50

2026-03-01 01:44:38 -05:00

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wifi-densepose-signal

State-of-the-art WiFi CSI signal processing for human pose estimation.

Overview

wifi-densepose-signal implements six peer-reviewed signal processing algorithms that extract human motion features from raw WiFi Channel State Information (CSI). Each algorithm is traced back to its original publication and integrated with the ruvector family of crates for high-performance graph and attention operations.

Algorithms

Algorithm	Module	Reference
Conjugate Multiplication	`csi_ratio`	SpotFi, SIGCOMM 2015
Hampel Filter	`hampel`	WiGest, 2015
Fresnel Zone Model	`fresnel`	FarSense, MobiCom 2019
CSI Spectrogram	`spectrogram`	Common in WiFi sensing literature since 2018
Subcarrier Selection	`subcarrier_selection`	WiDance, MobiCom 2017
Body Velocity Profile (BVP)	`bvp`	Widar 3.0, MobiSys 2019

Features

CSI preprocessing -- Noise removal, windowing, normalization via CsiProcessor.
Phase sanitization -- Unwrapping, outlier removal, and smoothing via PhaseSanitizer.
Feature extraction -- Amplitude, phase, correlation, Doppler, and PSD features.
Motion detection -- Human presence detection with confidence scoring via MotionDetector.
ruvector integration -- Graph min-cut (person matching), attention mechanisms (antenna and spatial attention), and sparse solvers (subcarrier interpolation).

Quick Start

use wifi_densepose_signal::{
    CsiProcessor, CsiProcessorConfig,
    PhaseSanitizer, PhaseSanitizerConfig,
    MotionDetector,
};

// Configure and create a CSI processor
let config = CsiProcessorConfig::builder()
    .sampling_rate(1000.0)
    .window_size(256)
    .overlap(0.5)
    .noise_threshold(-30.0)
    .build();

let processor = CsiProcessor::new(config);

Architecture

wifi-densepose-signal/src/
  lib.rs                 -- Re-exports, SignalError, prelude
  bvp.rs                 -- Body Velocity Profile (Widar 3.0)
  csi_processor.rs       -- Core preprocessing pipeline
  csi_ratio.rs           -- Conjugate multiplication (SpotFi)
  features.rs            -- Amplitude/phase/Doppler/PSD feature extraction
  fresnel.rs             -- Fresnel zone diffraction model
  hampel.rs              -- Hampel outlier filter
  motion.rs              -- Motion and human presence detection
  phase_sanitizer.rs     -- Phase unwrapping and sanitization
  spectrogram.rs         -- Time-frequency CSI spectrograms
  subcarrier_selection.rs -- Variance-based subcarrier selection

Crate	Role
`wifi-densepose-core`	Foundation types and traits
`ruvector-mincut`	Graph min-cut for person matching
`ruvector-attn-mincut`	Attention-weighted min-cut
`ruvector-attention`	Spatial attention for CSI
`ruvector-solver`	Sparse interpolation solver

License

MIT OR Apache-2.0

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