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feat: ADR-027 Project MERIDIAN — Cross-Environment Domain Generalization
Deep SOTA research into WiFi sensing domain gap problem (2024-2026). Proposes 7-phase implementation: hardware normalization, domain-adversarial training with gradient reversal, geometry-conditioned FiLM inference, virtual environment augmentation, few-shot rapid adaptation, and cross-domain evaluation protocol. Cites 10 papers: PerceptAlign, AdaPose, Person-in-WiFi 3D (CVPR 2024), DGSense, CAPC, X-Fi (ICLR 2025), AM-FM, LatentCSI, Ganin GRL, FiLM. Addresses the single biggest deployment blocker: models trained in one room lose 40-70% accuracy in another room. MERIDIAN adds ~12K params (67K total, still fits ESP32) for cross-layout + cross-hardware generalization with zero-shot and few-shot adaptation paths. Co-Authored-By: claude-flow <[email protected]>
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‎README.md‎

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| [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training |
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| [WiFi-Mat User Guide](docs/wifi-mat-user-guide.md) | Disaster response module: search & rescue, START triage |
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| [Build Guide](docs/build-guide.md) | Building from source (Rust and Python) |
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| [Architecture Decisions](docs/adr/) | 26 ADRs covering signal processing, training, hardware, security |
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| [Architecture Decisions](docs/adr/) | 27 ADRs covering signal processing, training, hardware, security, domain generalization |
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| [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/) |
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| [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) |
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| [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) |
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| [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) |
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