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Author SHA1 Message Date
ruv
303871275b feat: ADR-029/031 TDM sensing protocol, channel hopping, and NVS config 
Implement the hardware and firmware portions of RuvSense (ADR-029) and
RuView (ADR-031) for multistatic WiFi sensing:

Rust (wifi-densepose-hardware):
- TdmSchedule: uniform slot assignments with configurable cycle period,
  guard intervals, and processing window (default 4-node 20 Hz)
- TdmCoordinator: manages sensing cycles, tracks per-slot completion,
  cumulative clock drift compensation (±10 ppm over 50 ms = 0.5 us)
- SyncBeacon: 16-byte wire format for cycle synchronization with
  drift correction offsets
- TdmSlotCompleted event for aggregator notification
- 18 unit tests + 4 doctests, all passing

Firmware (C, ESP32):
- Channel-hop table in csi_collector.c (s_hop_channels, configurable
  via csi_collector_set_hop_table)
- Timer-driven channel hopping via esp_timer at dwell intervals
- NDP frame injection stub via esp_wifi_80211_tx()
- Backward-compatible: hop_count=1 disables hopping entirely
- NVS config extension: hop_count, chan_list, dwell_ms, tdm_slot,
  tdm_node_count with bounds validation and Kconfig fallback defaults

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 21:33:48 -05:00
ruv
b4f1e55546 feat: combine ADR-029/030/031 + DDD domain model into implementation branch 
Merges two feature branches into ruvsense-full-implementation:
- ADR-029: RuvSense multistatic sensing mode
- ADR-030: RuvSense persistent field model (7 exotic tiers)
- ADR-031: RuView sensing-first RF mode (renumbered from ADR-028-ruview)
- DDD domain model (6 bounded contexts, event bus)
- Research docs (multistatic fidelity architecture, SOTA 2026)

Renames ADR-028-ruview → ADR-031 to avoid conflict with ADR-028 (ESP32 audit).
Updates CLAUDE.md with all 31 ADRs.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 21:25:14 -05:00
ruv
d4dc5cb0bc Merge remote-tracking branch 'origin/claude/use-cases-implementation-plan-tT4s9' into ruvsense-full-implementation 2026-03-01 21:21:24 -05:00
Claude
374b0fdcef docs: add RuView (ADR-028) sensing-first RF mode for multistatic fidelity 
Introduce Project RuView — RuVector Viewpoint-Integrated Enhancement — a
sensing-first RF mode that improves WiFi DensePose fidelity through
cross-viewpoint embedding fusion on commodity ESP32 hardware.

Research document (docs/research/ruview-multistatic-fidelity-sota-2026.md):
- SOTA analysis of three fidelity levers: bandwidth, carrier frequency, viewpoints
- Multistatic array theory with virtual aperture and TDM sensing protocol
- ESP32 multistatic path ($84 BOM) and Cognitum v1 + RF front end path
- IEEE 802.11bf alignment and forward-compatibility mapping
- RuVector pipeline: all 5 crates mapped to cross-viewpoint operations
- Three-metric acceptance suite: joint error (PCK/OKS), multi-person
  separation (MOTA), vital sign sensitivity with Bronze/Silver/Gold tiers

ADR-028 (docs/adr/ADR-028-ruview-sensing-first-rf-mode.md):
- DDD bounded context: ViewpointFusion with MultistaticArray aggregate,
  ViewpointEmbedding entity, GeometricDiversityIndex value object
- Cross-viewpoint attention fusion via ruvector-attention with geometric bias
- TDM sensing protocol: 6 nodes, 119 Hz aggregate, 20 Hz per viewpoint
- Coherence-gated environment updates for multi-day stability
- File-level implementation plan across 4 phases (8 new source files)
- ADR interaction map: ADR-012, 014, 016/017, 021, 024, 027

https://claude.ai/code/session_01JBad1xig7AbGdbNiYJALZc
 2026-03-02 02:07:31 +00:00
Claude
c707b636bd docs: add RuvSense persistent field model, exotic tiers, and appliance categories 
Expands the RuvSense architecture from pose estimation to spatial
intelligence platform with persistent electromagnetic world model.

Research (Part II added):
- 7 exotic capability tiers: field normal modes, RF tomography,
  intention lead signals, longitudinal biomechanics drift,
  cross-room continuity, invisible interaction layer, adversarial detection
- Signals-not-diagnoses framework with 3 monitoring levels
- 5 appliance product categories: Invisible Guardian, Spatial Digital Twin,
  Collective Behavior Engine, RF Interaction Surface, Pre-Incident Drift Monitor
- Regulatory classification (consumer wellness → clinical decision support)
- Extended acceptance tests: 7-day autonomous, 30-day appliance validation

ADR-030 (new):
- Persistent field model architecture with room eigenstructure
- Longitudinal drift detection via Welford statistics + HNSW memory
- All 5 ruvector crates mapped across 7 exotic tiers
- GOAP implementation priority: field modes → drift → tomography → intent
- Invisible Guardian recommended as first hardware SKU vertical

DDD model (extended):
- 3 new bounded contexts: Field Model, Longitudinal Monitoring, Spatial Identity
- Full aggregate roots, value objects, domain events for each context
- Extended context map showing all 6 bounded contexts
- Repository interfaces for field baselines, personal baselines, transitions
- Invariants enforcing signals-not-diagnoses boundary

https://claude.ai/code/session_01QTX772SDsGVSPnaphoNgNY
 2026-03-02 01:59:21 +00:00
Claude
25b005a0d6 docs: add RuvSense sensing-first RF mode architecture 
Research, ADR, and DDD specification for multistatic WiFi DensePose
with coherence-gated tracking and complete ruvector integration.

- docs/research/ruvsense-multistatic-fidelity-architecture.md:
  SOTA research covering bandwidth/frequency/viewpoint fidelity levers,
  ESP32 multistatic mesh design, coherence gating, AETHER embedding
  integration, and full ruvector crate mapping

- docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md:
  Architecture decision for sensing-first RF mode on existing ESP32
  silicon. GOAP integration plan (9 actions, 4 phases, 36 cost units).
  TDMA schedule for 20 Hz update rate from 4-node mesh.
  IEEE 802.11bf forward-compatible design.

- docs/ddd/ruvsense-domain-model.md:
  Domain-Driven Design with 3 bounded contexts (Multistatic Sensing,
  Coherence, Pose Tracking), aggregate roots, domain events, context
  map, anti-corruption layers, and repository interfaces.

Acceptance test: 2 people, 20 Hz, 10 min stable tracks, zero ID swaps,
<30mm torso keypoint jitter.

https://claude.ai/code/session_01QTX772SDsGVSPnaphoNgNY
 2026-03-02 00:17:30 +00:00
ruv
08a6d5a7f1 docs: add validation and witness verification instructions to CLAUDE.md 
- Add Validation & Witness Verification section with 4-step procedure
- Document proof hash regeneration workflow
- List witness bundle contents and key proof artifacts
- Update ADR list (now 28 ADRs including ADR-024, ADR-027, ADR-028)
- Update Pre-Merge Checklist: add proof verification and witness bundle steps
- Update test commands to full workspace (1,031+ tests)
- Set default branch to main

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 16:18:44 -05:00
rUv
322eddbcc3 Merge pull request #71 from ruvnet/adr-028-esp32-capability-audit 
ADR-028 capability audit: 1,031 tests, proof PASS, witness bundle 7/7
 2026-03-01 15:54:26 -05:00
ruv
9c759f26db docs: add ADR-028 audit overview to README + collapsed section 
- New collapsed section before Installation linking to witness log,
  ADR-028, and bundle generator
- Shows test counts, proof hash, and 3-command verification steps

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 15:54:14 -05:00
ruv
093be1f4b9 feat: 100% validated witness bundle with proof hash + generator script 
- Regenerate Python proof hash for numpy 2.4.2 + scipy 1.17.1 (PASS)
- Update ADR-028 and WITNESS-LOG-028 with passing proof status
- Add scripts/generate-witness-bundle.sh — creates self-contained
  tar.gz with witness log, test results, proof verification,
  firmware hashes, crate manifest, and VERIFY.sh for recipients
- Bundle self-verifies: 7/7 checks PASS
- Attestation: 1,031 Rust tests passing, 0 failures

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 15:51:38 -05:00
ruv
05430b6a0f docs: ADR-028 ESP32 capability audit + witness verification log 
- ADR-028: Full 3-agent parallel audit of ESP32 hardware, signal processing,
  neural networks, training pipeline, deployment, and security
- WITNESS-LOG-028: Reproducible 11-step verification procedure with
  33-row attestation matrix (30 YES, 1 PARTIAL, 2 NOT MEASURED)
- 1,031 Rust tests passing at audit time (0 failures)
- Documents honest gaps: no on-device ML, no real CSI dataset bundled,
  proof hash needs numpy version pin

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 15:47:58 -05:00
ruv
96b01008f7 docs: fix broken README links and add MERIDIAN details section 
- 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>
 2026-03-01 12:54:41 -05:00
rUv
38eb93e326 Merge pull request #69 from ruvnet/adr-027-cross-environment-domain-generalization 
feat: ADR-027 MERIDIAN — Cross-Environment Domain Generalization
 2026-03-01 12:49:28 -05:00
ruv
eab364bc51 docs: update user guide with MERIDIAN cross-environment adaptation 
- Training pipeline: 8 phases → 10 phases (hardware norm + MERIDIAN)
- New section: Cross-Environment Adaptation explaining 10-second calibration
- Updated FAQ: accuracy answer mentions MERIDIAN
- Updated test count: 542+ → 700+
- Updated ADR count: 24 → 27

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 12:16:25 -05:00
ruv
3febf72674 chore: bump all crates to v0.2.0 for MERIDIAN release 
Workspace version 0.1.0 → 0.2.0. All internal cross-crate
dependencies updated to match.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 12:14:39 -05:00
ruv
8da6767273 fix: harden MERIDIAN modules from code review + security audit 
- domain.rs: atomic instance counter for unique Linear weight seeds (C3)
- rapid_adapt.rs: adapt() returns Result instead of panicking (C5),
  bounded calibration buffer with max_buffer_frames cap (F1-HIGH),
  validate lora_rank >= 1 (F10)
- geometry.rs: 24-bit PRNG precision matching f32 mantissa (C2)
- virtual_aug.rs: guard against room_scale=0 division-by-zero (F6)
- signal/lib.rs: re-export AmplitudeStats from hardware_norm (W1)
- train/lib.rs: crate-root re-exports for all MERIDIAN types (W2)

All 201 tests pass (96 unit + 24 integration + 18 subcarrier +
10 metrics + 7 doctests + 105 signal + 10 validation + 1 signal doctest).

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 12:11:56 -05:00
ruv
2d6dc66f7c docs: update README, CHANGELOG, and associated ADRs for MERIDIAN 
- CHANGELOG: add MERIDIAN (ADR-027) to Unreleased section
- README: add "Works Everywhere" to Intelligence features, update How It Works
- ADR-002: status → Superseded by ADR-016/017
- ADR-004: status → Partially realized by ADR-024, extended by ADR-027
- ADR-005: status → Partially realized by ADR-023, extended by ADR-027
- ADR-006: status → Partially realized by ADR-023, extended by ADR-027

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 12:06:09 -05:00
ruv
0a30f7904d feat: ADR-027 MERIDIAN — all 6 phases implemented (1,858 lines, 72 tests) 
Phase 1: HardwareNormalizer (hardware_norm.rs, 399 lines, 14 tests)
  - Catmull-Rom cubic interpolation: any subcarrier count → canonical 56
  - Z-score normalization, phase unwrap + linear detrend
  - Hardware detection: ESP32-S3, Intel 5300, Atheros, Generic

Phase 2: DomainFactorizer + GRL (domain.rs, 392 lines, 20 tests)
  - PoseEncoder: Linear→LayerNorm→GELU→Linear (environment-invariant)
  - EnvEncoder: GlobalMeanPool→Linear (environment-specific, discarded)
  - GradientReversalLayer: identity forward, -lambda*grad backward
  - AdversarialSchedule: sigmoidal lambda annealing 0→1

Phase 3: GeometryEncoder + FiLM (geometry.rs, 364 lines, 14 tests)
  - FourierPositionalEncoding: 3D coords → 64-dim
  - DeepSets: permutation-invariant AP position aggregation
  - FilmLayer: Feature-wise Linear Modulation for zero-shot deployment

Phase 4: VirtualDomainAugmentor (virtual_aug.rs, 297 lines, 10 tests)
  - Room scale, reflection coeff, virtual scatterers, noise injection
  - Deterministic Xorshift64 RNG, 4x effective training diversity

Phase 5: RapidAdaptation (rapid_adapt.rs, 255 lines, 7 tests)
  - 10-second unsupervised calibration via contrastive TTT + entropy min
  - LoRA weight generation without pose labels

Phase 6: CrossDomainEvaluator (eval.rs, 151 lines, 7 tests)
  - 6 metrics: in-domain/cross-domain/few-shot/cross-hw MPJPE,
    domain gap ratio, adaptation speedup

All 72 MERIDIAN tests pass. Full workspace compiles clean.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 12:03:40 -05:00
ruv
b078190632 docs: add gap closure mapping for all proposed ADRs (002-011) to ADR-027 
Maps every proposed-but-unimplemented ADR to MERIDIAN:
- Directly addressed: ADR-004 (HNSW fingerprinting), ADR-005 (SONA),
  ADR-006 (GNN patterns)
- Superseded: ADR-002 (by ADR-016/017)
- Enabled: ADR-003 (cognitive containers), ADR-008 (consensus),
  ADR-009 (WASM runtime)
- Independent: ADR-007 (PQC), ADR-010 (witness chains),
  ADR-011 (proof-of-reality)

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:51:32 -05:00
ruv
fdd2b2a486 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 <ruv@ruv.net>
 2026-03-01 11:49:16 -05:00
ruv
d8fd5f4eba docs: add How It Works section, fix ToC, update changelog to v3.0.0, add crates.io badge 
- Add "How It Works" explainer between Key Features and Use Cases
- Add Self-Learning WiFi AI and AI Backbone to Table of Contents
- Update Key Features entry in ToC to match new sub-sections
- Fix changelog: v2.3.0/v2.2.0/v2.1.0 → v3.0.0/v2.0.0 (matches CHANGELOG.md)
- Add crates.io badge for wifi-densepose-ruvector

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:37:25 -05:00
ruv
9e483e2c0f docs: break Key Features into three titled tables with descriptions 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:34:44 -05:00
ruv
f89b81cdfa docs: organize Key Features into Sensing, Intelligence, and Performance groups 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:33:26 -05:00
ruv
86e8ccd3d7 docs: add Self-Learning and AI Signal Processing to Key Features table 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:31:48 -05:00
ruv
1f9dc60da4 docs: add Pre-Merge Checklist to CLAUDE.md 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:30:03 -05:00
ruv
342e5cf3f1 docs: add pre-merge checklist and remove SWARM_CONFIG.md 2026-03-01 11:27:47 -05:00
ruv
4f7ad6d2e6 docs: fix model size inconsistency and add AI Backbone cross-reference in ADR-024 section 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:25:35 -05:00
ruv
aaec699223 docs: move AI Backbone into collapsed section under Models & Training 
- Remove RuVector AI section from Rust Crates details block
- Add as own collapsed <details> in Models & Training with anchor link
- Add cross-reference from crates table to new section
- Link to issue #67 for deep dive with code examples

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:23:15 -05:00
ruv
72f031ae80 docs: rewrite RuVector section with AI-focused framing 
Replace dry API reference table with AI pipeline diagram, plain-language
capability descriptions, and "what it replaces" comparisons. Reframes
graph algorithms and sparse solvers as learned, self-optimizing AI
components that feed the DensePose neural network.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:21:02 -05:00
rUv
1c815bbfd5 Merge pull request #66 from ruvnet/claude/analyze-repo-structure-aOtgs 
Add survivor tracking and RuVector integration (ADR-026, ADR-017)
 2026-03-01 11:02:53 -05:00
ruv
00530aee3a merge: resolve README conflict (26 ADRs includes ADR-025 + ADR-026) 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:02:18 -05:00
ruv
6a2ef11035 docs: cross-platform support in README, changelog, user guide 
- README: update hardware table, crate description, scan layer heading
  for macOS + Linux support, bump ADR count to 25
- CHANGELOG: add cross-platform adapters and byte counter fix
- User guide: add macOS CoreWLAN and Linux iw data source sections
- CLAUDE.md: add pre-merge checklist (8 items)

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 11:00:46 -05:00
rUv
e446966340 Merge pull request #64 from zqyhimself/feature/macos-corewlan 
Thank you for the contribution! 🎉
 2026-03-01 10:59:11 -05:00
ruv
e2320e8e4b feat(wifiscan): add Rust macOS + Linux adapters, fix Python byte counters 
- Add MacosCoreWlanScanner (macOS): CoreWLAN Swift helper adapter with
  synthetic BSSID generation via FNV-1a hash for redacted MACs (ADR-025)
- Add LinuxIwScanner (Linux): parses `iw dev <iface> scan` output with
  freq-to-channel conversion and BSS stanza parsing
- Both adapters produce Vec<BssidObservation> compatible with the
  existing WindowsWifiPipeline 8-stage processing
- Platform-gate modules with #[cfg(target_os)] so each adapter only
  compiles on its target OS
- Fix Python MacosWifiCollector: remove synthetic byte counters that
  produced misleading tx_bytes/rx_bytes data (set to 0)
- Add compiled Swift binary (mac_wifi) to .gitignore

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 10:51:45 -05:00
Claude
ed3261fbcb feat(ruvector): implement ADR-017 as wifi-densepose-ruvector crate + fix MAT warnings 
New crate `wifi-densepose-ruvector` implements all 7 ruvector v2.0.4
integration points from ADR-017 (signal processing + MAT disaster detection):

signal::subcarrier   — mincut_subcarrier_partition (ruvector-mincut)
signal::spectrogram  — gate_spectrogram (ruvector-attn-mincut)
signal::bvp          — attention_weighted_bvp (ruvector-attention)
signal::fresnel      — solve_fresnel_geometry (ruvector-solver)
mat::triangulation   — solve_triangulation TDoA (ruvector-solver)
mat::breathing       — CompressedBreathingBuffer 50-75% mem reduction (ruvector-temporal-tensor)
mat::heartbeat       — CompressedHeartbeatSpectrogram tiered compression (ruvector-temporal-tensor)

16 tests, 0 compilation errors. Workspace grows from 14 → 15 crates.

MAT crate: fix all 54 warnings (0 remaining in wifi-densepose-mat):
- Remove unused imports (Arc, HashMap, RwLock, mpsc, Mutex, ConfidenceScore, etc.)
- Prefix unused variables with _ (timestamp_low, agc, perm)
- Add #![allow(unexpected_cfgs)] for onnx feature gates in ML files
- Move onnx-conditional imports under #[cfg(feature = "onnx")] guards

README: update crate count 14→15, ADR count 24→26, add ruvector crate
table with 7-row integration summary.

Total tests: 939 → 955 (16 new). All passing, 0 regressions.

https://claude.ai/code/session_0164UZu6rG6gA15HmVyLZAmU
 2026-03-01 15:50:05 +00:00
zqyhimself
09f01d5ca6 feat(sensing): native macOS CoreWLAN WiFi sensing adapter 
Add native macOS LiDAR / WiFi sensing support via CoreWLAN:
- mac_wifi.swift: Swift helper to poll RSSI/Noise at 10Hz
- MacosWifiCollector: Python adapter for the sensing pipeline
- Auto-detect Darwin platform in ws_server.py
 2026-03-01 21:06:17 +08:00
Claude
838451e014 feat(mat/tracking): complete SurvivorTracker aggregate root — all tests green 
Completes ADR-026 implementation. Full survivor track lifecycle management
for wifi-densepose-mat with Kalman filter, CSI fingerprint re-ID, and
state machine. 162 tests pass, 0 failures.

tracking/tracker.rs — SurvivorTracker aggregate root (~815 lines):
- TrackId: UUID-backed stable identifier (survives re-ID)
- DetectionObservation: position (optional) + vital signs + confidence
- AssociationResult: matched/born/lost/reidentified/terminated/rescued
- TrackedSurvivor: Survivor + KalmanState + CsiFingerprint + TrackLifecycle
- SurvivorTracker::update() — 8-step algorithm per tick:
  1. Kalman predict for all non-terminal tracks
  2. Mahalanobis-gated cost matrix
  3. Hungarian assignment (n ≤ 10) with greedy fallback
  4. Fingerprint re-ID against Lost tracks
  5. Birth new Tentative tracks from unmatched observations
  6. Kalman update + vitals + fingerprint EMA for matched tracks
  7. Lifecycle hit/miss + expiry with transition recording
  8. Cleanup Terminated tracks older than 60s

Fix: birth observation counts as first hit so birth_hits_required=2
confirms after exactly one additional matching tick.

18 tracking tests green: kalman, fingerprint, lifecycle, tracker (birth,
miss→lost, re-ID).

https://claude.ai/code/session_0164UZu6rG6gA15HmVyLZAmU
 2026-03-01 08:03:30 +00:00
Claude
fa4927ddbc feat(mat/tracking): add fingerprint re-ID + lib.rs integration (WIP) 
- tracking/fingerprint.rs: CsiFingerprint for CSI-based survivor re-ID
  across signal gaps. Weighted normalized Euclidean distance on breathing
  rate, breathing amplitude, heartbeat rate, and location hint.
  EMA update (α=0.3) blends new observations into the fingerprint.

- lib.rs: fully integrated tracking bounded context
  - pub mod tracking added
  - TrackingEvent added to domain::events re-exports
  - pub use tracking::{SurvivorTracker, TrackerConfig, TrackId, ...}
  - DisasterResponse.tracker field + with_defaults() init
  - tracker()/tracker_mut() public accessors
  - prelude updated with tracking types

Remaining: tracking/tracker.rs (SurvivorTracker aggregate root)

https://claude.ai/code/session_0164UZu6rG6gA15HmVyLZAmU
 2026-03-01 07:54:28 +00:00
Claude
01d42ad73f feat(mat): add ADR-026 + survivor track lifecycle module (WIP) 
ADR-026 documents the design decision to add a tracking bounded context
to wifi-densepose-mat to address three gaps: no Kalman filter, no CSI
fingerprint re-ID across temporal gaps, and no explicit track lifecycle
state machine.

Changes:
- docs/adr/ADR-026-survivor-track-lifecycle.md — full design record
- domain/events.rs — TrackingEvent enum (Born/Lost/Reidentified/Terminated/Rescued)
  with DomainEvent::Tracking variant and timestamp/event_type impls
- tracking/mod.rs — module root with re-exports
- tracking/kalman.rs — constant-velocity 3-D Kalman filter (predict/update/gate)
- tracking/lifecycle.rs — TrackState, TrackLifecycle, TrackerConfig

Remaining (in progress): fingerprint.rs, tracker.rs, lib.rs integration

https://claude.ai/code/session_0164UZu6rG6gA15HmVyLZAmU
 2026-03-01 07:53:28 +00:00
rUv
5124a07965 refactor(rust-port): remove unused once-cell crate (#58) 
refactor(rust-port): remove unused `once-cell` crate
v0.1.0-esp32 		2026-03-01 02:36:51 -05:00
Tuan Tran
0723af8f8a update cargo.lock 2026-03-01 14:30:12 +07:00
Tuan Tran
504875e608 remove unused once-cell package 2026-03-01 14:26:29 +07:00
ruv
ab76925864 docs: Comprehensive CHANGELOG update covering v1.0.0 through v3.0.0 
Rewrites CHANGELOG.md with detailed entries for every significant
feature, fix, and security patch across all three major versions:

- v3.0.0: AETHER contrastive embedding model (ADR-024), Docker Hub
  images, UI port auto-detection fix, Mermaid architecture diagrams,
  33 use cases across 4 verticals
- v2.0.0: Rust sensing server, DensePose training pipeline (ADR-023),
  RuVector v2.0.4 integration (ADR-016/017), ESP32-S3 firmware
  (ADR-018), SOTA signal processing (ADR-014), vital sign detection
  (ADR-021), WiFi-Mat disaster module, 7 security patches, Python
  sensing pipeline, Three.js visualization
- v1.1.0: Python CSI system, API services, UI dark mode
- v1.0.0: Initial release with core pose estimation

All entries reference specific commit hashes for traceability.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 02:20:52 -05:00
ruv
a6382fb026 feat: Add macOS CoreWLAN WiFi sensing adapter and user guide 
- Introduced ADR-025 documenting the implementation of a macOS CoreWLAN sensing adapter using a Swift helper binary and Rust integration.
- Added a new user guide detailing installation, usage, and hardware setup for WiFi DensePose, including Docker and source build instructions.
- Included sections on data sources, REST API reference, WebSocket streaming, and vital sign detection.
- Documented hardware requirements and troubleshooting steps for various setups.
 2026-03-01 02:15:44 -05:00
ruv
3b72f35306 fix: UI auto-detects server port from page origin (#55) 
The UI had hardcoded localhost:8080 for HTTP and localhost:8765 for
WebSocket, causing "Backend unavailable" when served from Docker
(port 3000) or any non-default port.

Changes:
- api.config.js: BASE_URL now uses window.location.origin instead
  of hardcoded localhost:8080
- api.config.js: buildWsUrl() uses window.location.host instead of
  hardcoded localhost:8080
- sensing.service.js: WebSocket URL derived from page origin instead
  of hardcoded localhost:8765
- main.rs: Added /ws/sensing route to the HTTP server so WebSocket
  and REST are reachable on a single port

Fixes #55

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 02:09:23 -05:00
ruv
a0b5506b8c docs: rename embedding section to Self-Learning WiFi AI 
Reframe the ADR-024 section header to emphasize AI self-learning and
adaptive optimization rather than technical CSI embedding terminology.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 01:47:21 -05:00
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
ruv
44b9c30dbc fix: Docker port mismatch — server now binds 3000/3001 as documented 
The sensing server defaults to HTTP :8080 and WS :8765, but Docker
exposes :3000/:3001. Added --http-port 3000 --ws-port 3001 to CMD
in both Dockerfile.rust and docker-compose.yml.

Verified both images build and run:
- Rust: 133 MB, all endpoints responding (health, sensing/latest,
  vital-signs, pose/current, info, model/info, UI)
- Python: 569 MB, all packages importable (websockets, fastapi)
- RVF file: 13 KB, valid RVFS magic bytes

Also fixed README Quick Start endpoints to match actual routes:
- /api/v1/health → /health
- /api/v1/sensing → /api/v1/sensing/latest
- Added /api/v1/pose/current and /api/v1/info examples
- Added port mapping note for Docker vs local dev

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 00:56:41 -05:00
ruv
50f0fc955b docs: Replace ASCII architecture with Mermaid diagrams 
Replace the single ASCII box diagram with 3 styled Mermaid diagrams:

1. End-to-End Pipeline — full data flow from WiFi routers through
   signal processing (6 stages with ruvector crate labels), neural
   pipeline (graph transformer + SONA), vital signs, to output layer
   (REST, WebSocket, Analytics, UI). Dark theme with color-coded
   subsystem groups.

2. Signal Processing Detail — zoomed-in CSI cleanup pipeline showing
   conjugate multiply, phase unwrap, Hampel filter, min-cut partition,
   attention gate, STFT, Fresnel, and BVP stages.

3. Deployment Topology — ESP32 mesh (edge) → Rust sensing server
   (3 ports) → clients (browser, mobile, dashboard, IoT).

Component table expanded from 7 to 11 entries with crate/module
column linking each component to its source.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 00:48:57 -05:00
ruv
0afd9c5434 docs: Expand Use Cases into visible intro + 4 collapsed verticals 
Restructure Use Cases & Applications as a visible section with:
- Intro paragraph + scaling note (always visible)
- "Why WiFi wins" comparison table vs cameras/PIR (always visible)
- 4 collapsed tiers: Everyday (8 use cases), Specialized (9),
  Robotics & Industrial (8, new), Extreme (8)
- Each row now includes a Key Metric column
- New robotics section: cobots, AMR navigation, android spatial
  awareness, manufacturing, construction, agricultural, drones,
  clean rooms

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 00:45:21 -05:00