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
This commit was merged in pull request #52.
This commit is contained in:
rUv
2026-03-01 01:44:38 -05:00
committed by GitHub
parent 44b9c30dbc
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# wifi-densepose-core
[![Crates.io](https://img.shields.io/crates/v/wifi-densepose-core.svg)](https://crates.io/crates/wifi-densepose-core)
[![Documentation](https://docs.rs/wifi-densepose-core/badge.svg)](https://docs.rs/wifi-densepose-core)
[![License](https://img.shields.io/crates/l/wifi-densepose-core.svg)](LICENSE)
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
```rust
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:
```rust
use wifi_densepose_core::prelude::*;
```
## Architecture
```text
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
```
## Related Crates
| Crate | Role |
|-------|------|
| [`wifi-densepose-signal`](../wifi-densepose-signal) | CSI signal processing algorithms |
| [`wifi-densepose-nn`](../wifi-densepose-nn) | Neural network inference backends |
| [`wifi-densepose-train`](../wifi-densepose-train) | Training pipeline with ruvector |
| [`wifi-densepose-mat`](../wifi-densepose-mat) | Disaster detection (MAT) |
| [`wifi-densepose-hardware`](../wifi-densepose-hardware) | Hardware sensor interfaces |
| [`wifi-densepose-vitals`](../wifi-densepose-vitals) | Vital sign extraction |
| [`wifi-densepose-wifiscan`](../wifi-densepose-wifiscan) | Multi-BSSID WiFi scanning |
## License
MIT OR Apache-2.0