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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

3.1 KiB

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wifi-densepose-core

Core types, traits, and utilities for the WiFi-DensePose pose estimation system.

Overview

wifi-densepose-core is the foundation crate for the WiFi-DensePose workspace. It defines the shared data structures, error types, and trait contracts used by every other crate in the ecosystem. The crate is no_std-compatible (with the std feature disabled) and forbids all unsafe code.

Features

Core data types -- CsiFrame, ProcessedSignal, PoseEstimate, PersonPose, Keypoint, KeypointType, BoundingBox, Confidence, Timestamp, and more.
Trait abstractions -- SignalProcessor, NeuralInference, and DataStore define the contracts for signal processing, neural network inference, and data persistence respectively.
Error hierarchy -- CoreError, SignalError, InferenceError, and StorageError provide typed error handling across subsystem boundaries.
no_std support -- Disable the default std feature for embedded or WASM targets.
Constants -- MAX_KEYPOINTS (17, COCO format), MAX_SUBCARRIERS (256), DEFAULT_CONFIDENCE_THRESHOLD (0.5).

Feature flags

Flag	Default	Description
`std`	yes	Enable standard library support
`serde`	no	Serialization via serde (+ ndarray serde)
`async`	no	Async trait definitions via `async-trait`

Quick Start

use wifi_densepose_core::{CsiFrame, Keypoint, KeypointType, Confidence};

// Create a keypoint with high confidence
let keypoint = Keypoint::new(
    KeypointType::Nose,
    0.5,
    0.3,
    Confidence::new(0.95).unwrap(),
);

assert!(keypoint.is_visible());

Or use the prelude for convenient bulk imports:

use wifi_densepose_core::prelude::*;

Architecture

wifi-densepose-core/src/
  lib.rs          -- Re-exports, constants, prelude
  types.rs        -- CsiFrame, PoseEstimate, Keypoint, etc.
  traits.rs       -- SignalProcessor, NeuralInference, DataStore
  error.rs        -- CoreError, SignalError, InferenceError, StorageError
  utils.rs        -- Shared helper functions

Crate	Role
`wifi-densepose-signal`	CSI signal processing algorithms
`wifi-densepose-nn`	Neural network inference backends
`wifi-densepose-train`	Training pipeline with ruvector
`wifi-densepose-mat`	Disaster detection (MAT)
`wifi-densepose-hardware`	Hardware sensor interfaces
`wifi-densepose-vitals`	Vital sign extraction
`wifi-densepose-wifiscan`	Multi-BSSID WiFi scanning

License

MIT OR Apache-2.0

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