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docs: fix broken README links and add MERIDIAN details section

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- Fix 5 broken anchor links → direct ADR doc paths (ADR-024, ADR-027, RuVector)
- Add full <details> section for Cross-Environment Generalization (ADR-027)
  matching the existing ADR-024 section pattern
- Add Project MERIDIAN to v3.0.0 changelog
- Update training pipeline 8-phase → 10-phase in changelog
- Update test count 542+ → 700+ in changelog

Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
2026-03-01 12:54:41 -05:00
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@@ -73,9 +73,9 @@ The system learns on its own and gets smarter over time — no hand-tuning, no l
| | Feature | What It Means |
|---|---------|---------------|
| 🧠 | **Self-Learning** | Teaches itself from raw WiFi data — no labeled training sets, no cameras needed to bootstrap ([ADR-024](#self-learning-wifi-ai-adr-024)) |
| 🎯 | **AI Signal Processing** | Attention networks, graph algorithms, and smart compression replace hand-tuned thresholds — adapts to each room automatically ([RuVector](#ai-backbone-ruvector)) |
| 🌍 | **Works Everywhere** | Train once, deploy in any room — adversarial domain generalization strips environment bias so models transfer across rooms, buildings, and hardware ([ADR-027](#cross-environment-generalization-adr-027)) |
| 🧠 | **Self-Learning** | Teaches itself from raw WiFi data — no labeled training sets, no cameras needed to bootstrap ([ADR-024](docs/adr/ADR-024-contrastive-csi-embedding-model.md)) |
| 🎯 | **AI Signal Processing** | Attention networks, graph algorithms, and smart compression replace hand-tuned thresholds — adapts to each room automatically ([RuVector](https://github.com/ruvnet/ruvector)) |
| 🌍 | **Works Everywhere** | Train once, deploy in any room — adversarial domain generalization strips environment bias so models transfer across rooms, buildings, and hardware ([ADR-027](docs/adr/ADR-027-cross-environment-domain-generalization.md)) |
### Performance & Deployment
@@ -108,7 +108,7 @@ Neural Network maps processed signals → 17 body keypoints + vital signs
Output: real-time pose, breathing rate, heart rate, presence, room fingerprint
```
No training cameras required — the [Self-Learning system (ADR-024)](#self-learning-wifi-ai-adr-024) bootstraps from raw WiFi data alone. [MERIDIAN (ADR-027)](#cross-environment-generalization-adr-027) ensures the model works in any room, not just the one it trained in.
No training cameras required — the [Self-Learning system (ADR-024)](docs/adr/ADR-024-contrastive-csi-embedding-model.md) bootstraps from raw WiFi data alone. [MERIDIAN (ADR-027)](docs/adr/ADR-027-cross-environment-domain-generalization.md) ensures the model works in any room, not just the one it trained in.
---
@@ -277,6 +277,59 @@ See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](docs/adr/ADR-024-con
</details>
<details>
<summary><a id="cross-environment-generalization-adr-027"></a><strong>🌍 Cross-Environment Generalization (ADR-027 — Project MERIDIAN)</strong> — Train once, deploy in any room without retraining</summary>
WiFi pose models trained in one room lose 40-70% accuracy when moved to another — even in the same building. The model memorizes room-specific multipath patterns instead of learning human motion. MERIDIAN forces the network to forget which room it's in while retaining everything about how people move.
**What it does in plain terms:**
- Models trained in Room A work in Room B, C, D — without any retraining or calibration data
- Handles different WiFi hardware (ESP32, Intel 5300, Atheros) with automatic chipset normalization
- Knows where the WiFi transmitters are positioned and compensates for layout differences
- Generates synthetic "virtual rooms" during training so the model sees thousands of environments
- At deployment, adapts to a new room in seconds using a handful of unlabeled WiFi frames
**Key Components**
| What | How it works | Why it matters |
|------|-------------|----------------|
| **Gradient Reversal Layer** | An adversarial classifier tries to guess which room the signal came from; the main network is trained to fool it | Forces the model to discard room-specific shortcuts |
| **Geometry Encoder (FiLM)** | Transmitter/receiver positions are Fourier-encoded and injected as scale+shift conditioning on every layer | The model knows *where* the hardware is, so it doesn't need to memorize layout |
| **Hardware Normalizer** | Resamples any chipset's CSI to a canonical 56-subcarrier format with standardized amplitude | Intel 5300 and ESP32 data look identical to the model |
| **Virtual Domain Augmentation** | Generates synthetic environments with random room scale, wall reflections, scatterers, and noise profiles | Training sees 1000s of rooms even with data from just 2-3 |
| **Rapid Adaptation (TTT)** | Contrastive test-time training with LoRA weight generation from a few unlabeled frames | Zero-shot deployment — the model self-tunes on arrival |
| **Cross-Domain Evaluator** | Leave-one-out evaluation across all training environments with per-environment PCK/OKS metrics | Proves generalization, not just memorization |
**Architecture**
```
CSI Frame [any chipset]
│
▼
HardwareNormalizer ──→ canonical 56 subcarriers, N(0,1) amplitude
│
▼
CSI Encoder (existing) ──→ latent features
│
├──→ Pose Head ──→ 17-joint pose (environment-invariant)
│
├──→ Gradient Reversal Layer ──→ Domain Classifier (adversarial)
│ λ ramps 0→1 via cosine/exponential schedule
│
└──→ Geometry Encoder ──→ FiLM conditioning (scale + shift)
Fourier positional encoding → DeepSets → per-layer modulation
```
**Security hardening:**
- Bounded calibration buffer (max 10,000 frames) prevents memory exhaustion
- `adapt()` returns `Result<_, AdaptError>` — no panics on bad input
- Atomic instance counter ensures unique weight initialization across threads
- Division-by-zero guards on all augmentation parameters
See [`docs/adr/ADR-027-cross-environment-domain-generalization.md`](docs/adr/ADR-027-cross-environment-domain-generalization.md) for full architectural details.
</details>
---
## 📦 Installation
@@ -512,7 +565,7 @@ The neural pipeline uses a graph transformer with cross-attention to map CSI fea
| [RuVector Crates](#ruvector-crates) | 11 vendored Rust crates from [ruvector](https://github.com/ruvnet/ruvector): attention, min-cut, solver, GNN, HNSW, temporal compression, sparse inference | [GitHub](https://github.com/ruvnet/ruvector) · [Source](vendor/ruvector/) |
| [AI Backbone (RuVector)](#ai-backbone-ruvector) | 5 AI capabilities replacing hand-tuned thresholds: attention, graph min-cut, sparse solvers, tiered compression | [crates.io](https://crates.io/crates/wifi-densepose-ruvector) |
| [Self-Learning WiFi AI (ADR-024)](#self-learning-wifi-ai-adr-024) | Contrastive self-supervised learning, room fingerprinting, anomaly detection, 55 KB model | [ADR-024](docs/adr/ADR-024-contrastive-csi-embedding-model.md) |
| [Cross-Environment Generalization (ADR-027)](#cross-environment-generalization-adr-027) | Domain-adversarial training, geometry-conditioned inference, hardware normalization, zero-shot deployment | [ADR-027](docs/adr/ADR-027-cross-environment-domain-generalization.md) |
| [Cross-Environment Generalization (ADR-027)](docs/adr/ADR-027-cross-environment-domain-generalization.md) | Domain-adversarial training, geometry-conditioned inference, hardware normalization, zero-shot deployment | [ADR-027](docs/adr/ADR-027-cross-environment-domain-generalization.md) |
</details>
@@ -1351,10 +1404,11 @@ Major release: AETHER contrastive embedding model, AI signal processing backbone
- **AI Backbone (`wifi-densepose-ruvector`)** — 7 RuVector integration points replacing hand-tuned thresholds with attention, graph algorithms, and smart compression; [published to crates.io](https://crates.io/crates/wifi-densepose-ruvector)
- **Cross-platform RSSI adapters** — macOS CoreWLAN and Linux `iw` Rust adapters with `#[cfg(target_os)]` gating (ADR-025)
- **Docker images published** — `ruvnet/wifi-densepose:latest` (132 MB Rust) and `:python` (569 MB)
- **8-phase DensePose training pipeline (ADR-023)** — Graph transformer, 6-term composite loss, SONA adaptation, RVF packaging
- **Project MERIDIAN (ADR-027)** — Cross-environment domain generalization: gradient reversal, geometry-conditioned FiLM, virtual domain augmentation, contrastive test-time training; zero-shot room transfer
- **10-phase DensePose training pipeline (ADR-023/027)** — Graph transformer, 6-term composite loss, SONA adaptation, RVF packaging, hardware normalization, domain-adversarial training
- **Vital sign detection (ADR-021)** — FFT-based breathing (6-30 BPM) and heartbeat (40-120 BPM), 11,665 fps
- **WiFi scan domain layer (ADR-022/025)** — 8-stage signal intelligence pipeline for Windows, macOS, and Linux
- **542+ Rust tests** — All passing, zero mocks
- **700+ Rust tests** — All passing, zero mocks
### v2.0.0 — 2026-02-28