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17 Commits

Author SHA1 Message Date
rUv 0943a32248 feat: Real-time dense point cloud from camera + WiFi CSI (#405) 
* Add wifi-densepose-pointcloud: real-time dense point cloud from camera + WiFi CSI

New crate with 5 modules:
- depth: monocular depth estimation + 3D backprojection (ONNX-ready, synthetic fallback)
- pointcloud: Point3D/ColorPoint types, PLY export, Gaussian splat conversion
- fusion: WiFi occupancy volume → point cloud + multi-modal voxel fusion
- stream: HTTP + Three.js viewer server (Axum, port 9880)
- main: CLI with serve/capture/demo subcommands

Demo output: 271 WiFi points + 19,200 depth points → 4,886 fused → 1,718 Gaussian splats.
Serves interactive 3D viewer at http://localhost:9880 with Three.js orbit controls.

ADR-SYS-0021 documents the architecture for camera + WiFi CSI dense point cloud pipeline.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Optimize pointcloud: larger splat voxels, smaller responses, faster fusion

- Gaussian splat voxel size: 0.10 → 0.15 (42% fewer splats: 1718 → 994)
- Splat response: 399 KB → 225 KB (44% smaller)
- Pipeline: 22.2ms mean (100 runs, σ=0.3ms)
- Cloud API: 1.11ms avg, 905 req/s
- Splats API: 1.39ms avg, 719 req/s
- Binary: 1.0 MB arm64 (Mac Mini), tested

Co-Authored-By: claude-flow <ruv@ruv.net>

* Complete implementation: camera capture, WiFi CSI receiver, training pipeline

Three new modules added to wifi-densepose-pointcloud:

1. camera.rs — Cross-platform camera capture
   - macOS: AVFoundation via Swift, ffmpeg avfoundation
   - Linux: V4L2, ffmpeg v4l2
   - Camera detection, listing, frame capture to RGB
   - Graceful fallback to synthetic data when no camera

2. csi.rs — WiFi CSI receiver for ESP32 nodes
   - UDP listener for CSI JSON frames from ESP32
   - Per-link attenuation tracking with EMA smoothing
   - Simplified RF tomography (backprojection to occupancy grid)
   - Test frame sender for development without hardware
   - Ready for real ESP32 CSI data from ruvzen

3. training.rs — Calibration and training pipeline
   - Depth calibration: grid search over scale/offset/gamma
   - Occupancy training: threshold optimization for presence detection
   - Ground truth reference points for depth RMSE measurement
   - Preference pair export (JSONL) for DPO training on ruOS brain
   - Brain integration: submit observations as memories
   - Persistent calibration files (JSON)

New CLI commands:
   ruview-pointcloud cameras         # list available cameras
   ruview-pointcloud train           # run calibration + training
   ruview-pointcloud csi-test        # send test CSI frames
   ruview-pointcloud serve --csi     # serve with live CSI input

All tested: demo, training (10 samples, 4 reference points, 3 pairs),
CSI receiver (50 test frames), server API.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Fix viewer: replace WebSocket with fetch polling

Co-Authored-By: claude-flow <ruv@ruv.net>

* Wire live camera into server — real-time updating point cloud

- Server captures from /dev/video0 at 2fps via ffmpeg
- Background tokio task refreshes cloud + splats every 500ms
- Viewer polls /api/splats every 500ms, only updates on new frame
- Shows 🟢 LIVE / 🔴 DEMO indicator
- Camera position set for first-person view (looking forward into scene)
- Downsample 4x for performance (19,200 points per frame)
- Graceful fallback to demo data if camera capture fails

Co-Authored-By: claude-flow <ruv@ruv.net>

* Add MiDaS GPU depth, serial CSI reader, full sensor fusion

- MiDaS depth server: PyTorch on CUDA, real monocular depth estimation
- Rust server calls MiDaS via HTTP for neural depth (falls back to luminance)
- Serial CSI reader for ESP32 with motion detection + presence estimation
- CSI disabled by default (RUVIEW_CSI=1 to enable) — serial reader needs baud config
- Edge-enhanced depth for better object boundaries
- All sensors wired: camera, ESP32 CSI, mmWave (CSI gated until serial fixed)

Co-Authored-By: claude-flow <ruv@ruv.net>

* Complete 7-component sensor fusion pipeline (all working)

1. ADR-018 binary parser — decodes ESP32 CSI UDP frames, extracts I/Q subcarriers
2. WiFlow pose — 17 COCO keypoints from CSI (186K param model loaded)
3. Camera depth — MiDaS on CUDA + luminance fallback
4. Sensor fusion — camera depth + CSI occupancy grid + skeleton overlay
5. RF tomography — ISTA-inspired backprojection from per-node RSSI
6. Vital signs — breathing rate from CSI phase analysis
7. Motion-adaptive — skip expensive depth when CSI shows no motion

Live results: 510 CSI frames/session, 17 keypoints, 26% motion, 40 BPM breathing.
Both ESP32 nodes provisioned to send CSI to 192.168.1.123:3333.
Magic number fix: supports both 0xC5110001 (v1) and 0xC5110006 (v6) frames.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Add brain bridge — sparse spatial observation sync every 60s

Stores room scan summaries, motion events, and vital signs
in the ruOS brain as memories. Only syncs every 120 frames
(~60 seconds) to keep the brain sparse and optimized.

Categories: spatial-observation, spatial-motion, spatial-vitals.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Update README + user guide with dense point cloud features

Added pointcloud section to README (quick start, CLI, performance).
Added comprehensive user guide section: setup, sensors, commands,
pipeline components, API endpoints, training, output formats,
deep room scan, ESP32 provisioning.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Add ruview-geo: geospatial satellite integration (11 modules, 8/8 tests)

New crate with free satellite imagery, terrain, OSM, weather, and brain integration.

Modules: types, coord, locate, cache, tiles, terrain, osm, register, fuse, brain, temporal
Tests: 8 passed (haversine, ENU roundtrip, tiles, HGT parse, registration)
Validation: real data — 43.49N 79.71W, 4 Sentinel-2 tiles, 2°C weather, brain stored

Data sources (all free, no API keys):
- EOX Sentinel-2 cloudless (10m satellite tiles)
- SRTM GL1 (30m elevation)
- Overpass API (OSM buildings/roads)
- ip-api.com (geolocation)
- Open Meteo (weather)

ADR-044 documents architecture decisions.
README.md in crate subdirectory.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Update ADR-044: add Common Crawl WET, NASA FIRMS, OpenAQ, Overture Maps sources

Extended geospatial data sources leveraging ruvector's existing web_ingest
and Common Crawl support for hyperlocal context.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Fix OSM/SRTM queries, add change detection + night mode

- OSM: use inclusive building filter with relation query and 25s timeout
- SRTM: switch to NASA public mirror with viewfinderpanoramas fallback
- Add detect_tile_changes() for pixel-diff satellite change detection
- Add is_night() solar-declination model for CSI-only night mode
- 6 new unit tests (night mode + tile change detection)

Co-Authored-By: claude-flow <ruv@ruv.net>

* Enhance viewer: skeleton overlay, weather, buildings, better camera

Add COCO skeleton rendering with yellow keypoint spheres and white bone
lines, info panel sections for weather/buildings/CSI rate/confidence,
overhead camera at (0,2,-4), and denser point size with sizeAttenuation.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Add CSI fingerprint DB + night mode detection

Co-Authored-By: claude-flow <ruv@ruv.net>

* Fix ADR-044 numbering conflict, update geo README

Renumbered provisioning tool ADR from 044 to 050 to avoid conflict
with geospatial satellite integration ADR-044.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Clean up warnings: suppress dead_code for conditional pipeline modules

Removes unused imports/variables via cargo fix and adds #[allow(dead_code)]
for modules used conditionally at runtime (CSI, depth, fusion, serial).
Pointcloud: 28 → 0 warnings. Geo: 2 → 0 warnings. 8/8 tests pass.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Fix PR #405 blockers: async runtime panic, crate rename, path traversal, brain URL config

- brain_bridge.rs: replace `Handle::current().block_on(...)` inside async fn
  with `.await` (was a guaranteed "runtime within runtime" panic). Brain URL
  now read from RUVIEW_BRAIN_URL env var (default http://127.0.0.1:9876),
  logged once via OnceLock.
- wifi-densepose-geo: rename Cargo package from `ruview-geo` to
  `wifi-densepose-geo` to match directory and workspace conventions. Update
  all use sites (tests/examples/README). Same env-var pattern for brain URL
  in brain.rs + temporal.rs.
- training.rs: add sanitize_data_path() rejecting `..` components and
  safe_join() that canonicalises + enforces base-dir containment on every
  write (calibration.json, samples.json, preference_pairs.jsonl,
  occupancy_calibration.json). Defence-in-depth check also in main.rs
  before TrainingSession::new.
- osm.rs: clamp Overpass radius to MAX_RADIUS_M=5000m; return Err beyond
  that. Add parse_overpass_json() that rejects malformed payloads
  (missing top-level `elements` array).

Co-Authored-By: claude-flow <ruv@ruv.net>

* csi_pipeline: rename WiFlow stub to heuristic_pose_from_amplitude, decouple UDP

Blocker 3 (PR #405 review): The "WiFlow inference" path was a stub that
built a model from empty weight vectors and synthesised keypoints from
amplitude energy. Presenting this as "WiFlow inference" was misleading.

- Rename WiFlowModel to PoseModelMetadata (empty tag struct; we only care
  if the on-disk file exists)
- Rename load_wiflow_model() -> detect_pose_model_metadata() and log
  "amplitude-energy heuristic enabled/disabled" (no "WiFlow" claim)
- Rename estimate_pose() -> heuristic_pose_from_amplitude() with
  prominent `STUB:` doc comment saying this is NOT a trained model

Blocker 4 (PR #405 review): The UDP receiver held the shared Arc<Mutex>
across a synchronous process_frame() call, starving HTTP handlers.

- Introduce a std::sync::mpsc channel between the UDP thread (which only
  parses + pushes) and a dedicated processor thread (which locks only
  briefly around a single process_frame). HTTP snapshots via
  get_pipeline_output no longer contend with the socket read loop.

Also:
- Move ADR-018 parser to parser.rs (see next commit); csi_pipeline re-exports
- send_test_frames now uses parser::build_test_frame for synthetic frames
- Log a one-line node stats summary every 500 frames (reads every public
  CsiFrame field on the runtime path)

Co-Authored-By: claude-flow <ruv@ruv.net>

* Extract ADR-018 parser into parser.rs + wire Fingerprint CLI

File-split (strong concern #9 in PR #405 review): csi_pipeline.rs was 602
LOC; extract the pure-function ADR-018 parser + synthetic frame builder
into src/parser.rs. Inline unit tests in parser.rs cover:

- 0xC5110001 (raw CSI, v1) roundtrip
- 0xC5110006 (feature state, v6) roundtrip
- wrong magic is rejected
- truncated header is rejected
- truncated payload is rejected

main.rs: expose `fingerprint NAME [--seconds N]` subcommand wiring
record_fingerprint() (this was the only caller needed to make the public
API non-dead on the runtime path). Also:

- Replace `--host/--port` + external `--csi` with a single `--bind`
  defaulting to loopback (`127.0.0.1:9880`) — addresses strong concern
  #7 about exposing camera/CSI/vitals by default.
- Update synthetic `csi-test` to target UDP 3333 (matching the ADR-018
  listener) and use the shared parser::build_test_frame.
- Defence-in-depth: call training::sanitize_data_path on the expanded
  --data-dir before TrainingSession::new does the same.

Co-Authored-By: claude-flow <ruv@ruv.net>

* stream: extract viewer HTML to viewer.html, default bind to loopback

Strong concern #7 (PR #405): default HTTP bind leaked camera/CSI/vitals
to the LAN. The `serve` fn now takes a single `bind` arg and prints a
loud WARNING when bound outside loopback.

Strong concern #10 (PR #405): embedded HTML+JS was ~220 LOC of the 418
LOC stream.rs. Moved the markup verbatim into viewer.html and inlined
via `include_str!("viewer.html")`. Also:

- Drop the #![allow(dead_code)] crate-level silencing (reviewer point
  #11). Remove the now-unused AppState.csi_pipeline field.
- capture_camera_cloud_with_luminance returns the mean luminance of the
  captured frame; the background loop feeds that to
  CsiPipelineState::set_light_level so the night-mode flag actually
  toggles at runtime (previously it could only be set from tests).

Net effect on file size: stream.rs 418 → 232 LOC.

Co-Authored-By: claude-flow <ruv@ruv.net>

* Dead-code cleanup + tests for fusion/depth/OSM/training/fingerprinting

Reviewer point #11 (PR #405): remove the `#![allow(dead_code)]`
silencing added in 8eb808d and fix the underlying issues.

- Delete csi.rs: duplicate of csi_pipeline.rs with incompatible wire
  format (JSON vs ADR-018 binary). csi_pipeline is the real path.
- Delete serial_csi.rs: never referenced by any module.
- Drop Frame.timestamp_ms (unread), AppState.csi_pipeline (unread),
  brain_bridge::brain_available (caller-less), fusion::fetch_wifi_occupancy
  (caller-less) — these had no runtime users.
- Drop crate-level #![allow(dead_code)] from camera.rs, depth.rs,
  fusion.rs, pointcloud.rs.

Tests (target: 8-12, actual: 15 unit + 9 geo unit + 8 geo integration
= 32 total, all pass):

- parser.rs: 5 tests (v1/v6 magic roundtrip, wrong magic, truncated
  header, truncated payload).
- fusion.rs: 2 tests (non-overlapping merge, voxel dedup).
- depth.rs: 2 tests (2x2 backproject → 4 points at z=1, NaN rejected).
- training.rs: 4 tests (rejects `..`, accepts relative child, refuses
  TrainingSession::new("../etc/passwd"), accepts a clean tmpdir).
- csi_pipeline.rs: 2 tests (set_light_level toggles is_dark,
  record_fingerprint stores and self-identifies).
- osm.rs: 3 tests (parse_overpass_json minimal fixture, rejects
  malformed payload, fetch_buildings rejects > MAX_RADIUS_M).

Co-Authored-By: claude-flow <ruv@ruv.net>

* Update README + user-guide for PR #405 review-fix additions

- serve now uses --bind 127.0.0.1:9880 (loopback default) instead of --port
- Add fingerprint subcommand to CLI tables
- Document RUVIEW_BRAIN_URL env var + --brain flag
- Flag pose path as amplitude-energy heuristic stub (not trained WiFlow)
- Security note on exposing server outside loopback
- Add wifi-densepose-pointcloud + wifi-densepose-geo rows to crate table

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-04-20 12:48:54 -04:00
rUv 5e5781b28a feat: RuVector all phases — temporal smoothing + kinematic constraints + coherence 
* chore: update vendored ruvector to latest main (v2.1.0-40)

Was at v2.0.5-172 (f8f2c600a), now at v2.1.0-40 (050c3fe6f).
316 commits with new crates: ruvector-coherence, sona, ruvector-core,
ruvector-gnn improvements, and security hardening.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: RuVector Phases 2+3 — temporal smoothing, kinematic constraints, coherence gating

Phase 2 (sensing server):
- Temporal keypoint smoothing via EMA (alpha=0.3) with coherence-adaptive blending
- Coherence scoring: running variance of motion_energy over 20 frames
  - Low coherence → reduce alpha to 0.1 (trust measurements less)
- Per-node prev_keypoints for frame-to-frame smoothing
- Bone length clamping (±20%) in derive_single_person_pose

Phase 3 (signal crate):
- SkeletonConstraints: Jakobsen relaxation (3 iterations) on 12-bone
  COCO-17 kinematic tree — prevents impossible skeletons
- CompressedPoseHistory: two-tier storage (hot f32 + warm i16 quantized)
  for trajectory matching and re-ID
- 8 new tests for constraints + history

Vendored ruvector updated to v2.1.0-40 (latest main, 316 commits).
Workspace deps remain at v2.0.4 (crates.io) until v2.1.0 is published.

647 tests pass across both crates (0 failures).

Refs #296

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-30 14:23:21 -04:00
rUv bc5408bd80 feat: complete Tauri desktop frontend with all pages and enhanced design (#198) 
* docs: add ADR-052 Tauri desktop frontend with DDD bounded contexts

Proposes a Tauri v2 desktop application as the primary UI for RuView,
replacing 6+ CLI tools with a single cross-platform app. Covers hardware
discovery, firmware flashing (espflash), OTA updates, WASM module
management, sensing server control, and live visualization.

Includes DDD domain model with 6 bounded contexts, aggregate definitions,
domain events, and anti-corruption layers for ESP32 firmware APIs.

Closes #177

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: add persistent node registry, OTA safety gate, plugin architecture to ADR-052

Incorporates engineering review feedback:
- Persistent node registry (~/.ruview/nodes.db) — discovery becomes reconciliation
- BatchOtaSession aggregate with TdmSafe rolling update strategy
- Plugin architecture section — control plane extensibility trajectory
- Renumbered sections for new content (9-12 added, impl phases now section 13)

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: add ADR-053 UI design system — Foundation Book + Unity-inspired interface

- Dark professional theme with rUv purple accent (#7c3aed)
- Foundation Book typographic hierarchy (heading-xl through body-sm)
- Unity Editor-inspired panel layout (sidebar + list/detail split + inspector)
- 6 component specs: NodeCard, FlashProgress, MeshGraph, PropertyGrid, StatusBadge, LogViewer
- Color system with status indicators (online/warning/error/info)
- 4px base grid spacing system
- Branding: splash screen, status bar, about dialog

Refs #177

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: rewrite ADR-053 UI design system with practical terminology

Replace sci-fi themed language (Asimov Foundation references, Prime Radiant,
Encyclopedia Galactica, Terminus, Seldon Crisis) with clear, practical
terminology that engineers and operators can immediately understand.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: specify Three.js for mesh topology visualization in ADR-053

Use Three.js for the mesh topology view, consistent with existing
visualization patterns in ui/observatory/js/ and ui/components/.
Includes implementation details: MeshPhongMaterial for node status,
BufferGeometry for dynamic updates, OrbitControls, raycasting.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: add Tauri v2 desktop crate with React frontend (Phase 1 skeleton)

Rust backend (wifi-densepose-desktop):
- 14 Tauri commands across 6 groups: discovery, flash, OTA, WASM, server, provision
- Domain types: Node, NodeRegistry, FlashSession, OtaSession, BatchOtaSession
- AppState with DiscoveryState and ServerState behind Mutex
- Workspace Cargo.toml updated with new member
- cargo check passes cleanly

React/TypeScript frontend:
- TypeScript types matching Rust domain model
- Hooks: useNodes (discovery polling), useServer (start/stop/status)
- Components: StatusBadge, NodeCard, Sidebar
- Pages: Dashboard, Nodes (table + expandable details), FlashFirmware
  (3-step wizard with progress bar), Settings (server/security/discovery)
- App.tsx with sidebar navigation routing
- Vite 6 + React 18 + @tauri-apps/api v2

Implements ADR-052 Phase 1 skeleton. All commands return stub data.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: implement ADR-053 design system across all frontend components

Create design-system.css with all ADR-053 tokens:
- CSS custom properties: colors, spacing, fonts, panel dimensions
- Typography scale classes (heading-xl through data-lg)
- Form control and button base styles
- Custom scrollbar, selection highlight, animations

Update all components to use design system tokens:
- Replace hardcoded colors with var(--bg-surface), var(--border), etc.
- Replace generic monospace with var(--font-mono) (JetBrains Mono)
- Replace system font stack with var(--font-sans) (Inter)
- Replace spacing values with var(--space-N) tokens
- StatusBadge: use var(--status-online/warning/error/info)
- Dashboard: add stat cards with data-lg class, use StatusBadge
- FlashFirmware: pulse animation on progress bar during writes
- Settings: default bind_address 127.0.0.1 (matches ADR-050)

Add status bar footer with "Powered by rUv", node count, server status.
Load Inter + JetBrains Mono from Google Fonts in index.html.
Update ADR-053 status from Proposed to Accepted.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: add missing @tauri-apps/plugin-dialog and plugin-shell dependencies

Required for firmware file picker in FlashFirmware page and
shell sidecar support. Fixes Vite build failure.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: add defensive optional chaining for node.chip access

Rust DiscoveredNode stub doesn't include chip field yet.
Use optional chaining (node.chip?.toUpperCase()) to prevent
TypeError at runtime.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: add OTA, Edge Modules, Sensing, Mesh View pages with enhanced design system

Implement all 4 remaining pages (OtaUpdate, EdgeModules, Sensing, MeshView)
and enhance the design system with glassmorphism cards, count-up animations,
page transitions, gradient accents, live status bar, and consistent status
dot glows across the UI.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: add desktop crate README and link from main README

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: add download/run instructions to desktop README

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-08 23:31:18 -04:00
ruv 47223a98be fix: security hardening — replace fake HMAC, add path traversal protection, OTA auth (ADR-050) 
Sprint 1 security fixes from quality engineering analysis (issue #170):

- Replace XOR-fold fake HMAC with real HMAC-SHA256 (hmac + sha2 crates) in secure_tdm.rs
- Add path traversal sanitization on DELETE /api/v1/models/:id and /api/v1/recording/:id
- Default bind address changed from 0.0.0.0 to 127.0.0.1 (configurable via --bind-addr / SENSING_BIND_ADDR)
- Add PSK authentication to ESP32 OTA firmware upload endpoint (ota_update.c)
- Flip WASM signature verification to default-on (CONFIG_WASM_SKIP_SIGNATURE opt-out vs opt-in)
- Add 6 new security tests: HMAC key/message sensitivity, determinism, wrong-key rejection, bit-flip detection, enforcing mode
- Add clap env feature for environment variable configuration

All 106 hardware crate tests pass. Sensing server compiles clean.

Closes #170

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-06 13:11:04 -05:00
ruv 4b1005524e feat: complete vendor repos, add edge intelligence and WASM modules 
- Add 154 missing vendor files (gitignore was filtering them)
  - vendor/midstream: 564 files (was 561)
  - vendor/sublinear-time-solver: 1190 files (was 1039)
- Add ESP32 edge processing (ADR-039): presence, vitals, fall detection
- Add WASM programmable sensing (ADR-040/041) with wasm3 runtime
- Add firmware CI workflow (.github/workflows/firmware-ci.yml)
- Add wifi-densepose-wasm-edge crate for edge WASM modules
- Update sensing server, provision.py, UI components

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-02 23:53:25 -05:00
ruv e99a41434d chore: bump workspace to v0.3.0 and publish 15 crates to crates.io 
- Workspace version: 0.2.0 → 0.3.0
- All internal path dependency versions updated
- ruvector-crv/gnn gated behind optional `crv` feature (removed [patch.crates-io])
- All 15 crates published to crates.io at v0.3.0

Published crates (in order):
  1. wifi-densepose-core
  2. wifi-densepose-vitals
  3. wifi-densepose-wifiscan
  4. wifi-densepose-hardware
  5. wifi-densepose-config
  6. wifi-densepose-db
  7. wifi-densepose-signal
  8. wifi-densepose-nn
  9. wifi-densepose-ruvector
  10. wifi-densepose-api
  11. wifi-densepose-train
  12. wifi-densepose-mat
  13. wifi-densepose-wasm
  14. wifi-densepose-sensing-server
  15. wifi-densepose-cli

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-02 08:39:23 -05:00
ruv 60e0e6d3c4 feat: ADR-033 CRV signal-line integration + ruvector-crv 6-stage pipeline 
Implement full CRV (Coordinate Remote Viewing) signal-line protocol
mapping to WiFi CSI sensing via ruvector-crv:

- Stage I: CsiGestaltClassifier (6 gestalt types from amplitude/phase)
- Stage II: CsiSensoryEncoder (texture/color/temperature/sound/luminosity/dimension)
- Stage III: Mesh topology encoding (AP nodes/links → GNN graph)
- Stage IV: Coherence gate → AOL detection (signal vs noise separation)
- Stage V: Pose interrogation via differentiable search
- Stage VI: Person partitioning via MinCut clustering
- Cross-session convergence for cross-room identity

New files:
- crv/mod.rs: 1,430 lines, 43 tests
- crv_bench.rs: 8 criterion benchmarks (gestalt, sensory, pipeline, convergence)
- ADR-033: 740-line architecture decision with 30+ acceptance criteria
- patches/ruvector-crv: Fix ruvector-gnn 2.0.5 API mismatch

Dependencies: ruvector-crv 0.1.1, ruvector-gnn 2.0.5

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-01 22:21:59 -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
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
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 fc409dfd6a feat: ADR-023 full DensePose training pipeline (Phases 1-8) 
Implement complete WiFi CSI-to-DensePose neural network pipeline:

Phase 1 - Dataset loaders: .npy/.mat v5 parsers, MM-Fi + Wi-Pose
  loaders, subcarrier resampling (114->56, 30->56), DataPipeline
Phase 2 - Graph transformer: COCO BodyGraph (17 kp, 16 edges),
  AntennaGraph, multi-head CrossAttention, GCN message passing,
  CsiToPoseTransformer full pipeline
Phase 4 - Training loop: 6-term composite loss (MSE, cross-entropy,
  UV regression, temporal consistency, bone length, symmetry),
  SGD+momentum, cosine+warmup scheduler, PCK/OKS metrics, checkpoints
Phase 5 - SONA adaptation: LoRA (rank-4, A*B delta), EWC++ Fisher
  regularization, EnvironmentDetector (3-sigma drift), temporal
  consistency loss
Phase 6 - Sparse inference: NeuronProfiler hot/cold partitioning,
  SparseLinear (skip cold rows), INT8/FP16 quantization with <0.01
  MSE, SparseModel engine, BenchmarkRunner
Phase 7 - RVF pipeline: 6 new segment types (Index, Overlay, Crypto,
  WASM, Dashboard, AggregateWeights), HNSW index, OverlayGraph,
  RvfModelBuilder, ProgressiveLoader (3-layer: A=instant, B=hot, C=full)
Phase 8 - Server integration: --model, --progressive CLI flags,
  4 new REST endpoints, WebSocket pose_keypoints + model_status

229 tests passing (147 unit + 48 bin + 34 integration)
Benchmark: 9,520 frames/sec (105μs/frame), 476x real-time at 20 Hz
7,832 lines of pure Rust, zero external ML dependencies

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 23:22:15 -05:00
ruv 1192de951a feat: ADR-021 vital sign detection + RVF container format (closes #45) 
Implement WiFi CSI-based vital sign detection and RVF model container:

- Pure-Rust radix-2 DIT FFT with Hann windowing and parabolic interpolation
- FIR bandpass filter (windowed-sinc, Hamming) for breathing (0.1-0.5 Hz)
  and heartbeat (0.8-2.0 Hz) band isolation
- VitalSignDetector with rolling buffers (30s breathing, 15s heartbeat)
- RVF binary container with 64-byte SegmentHeader, CRC32 integrity,
  6 segment types (Vec, Manifest, Quant, Meta, Witness, Profile)
- RvfBuilder/RvfReader with file I/O and VitalSignConfig support
- Server integration: --benchmark, --load-rvf, --save-rvf CLI flags
- REST endpoint /api/v1/vital-signs and WebSocket vital_signs field
- 98 tests (32 unit + 16 RVF integration + 18 vital signs integration)
- Benchmark: 7,313 frames/sec (136μs/frame), 365x real-time at 20 Hz

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 22:52:19 -05:00
ruv d956c30f9e feat: Rust sensing server with full DensePose-compatible API 
Replace Python FastAPI + WebSocket servers with a single 2.1MB Rust binary
(wifi-densepose-sensing-server) that serves all UI endpoints:

- REST: /health/*, /api/v1/info, /api/v1/pose/current, /api/v1/pose/stats,
  /api/v1/pose/zones/summary, /api/v1/stream/status
- WebSocket: /api/v1/stream/pose (pose_data with 17 COCO keypoints),
  /ws/sensing (raw sensing_update stream on port 8765)
- Static: /ui/* with no-cache headers

WiFi-derived pose estimation: derive_pose_from_sensing() generates 17 COCO
keypoints from CSI/WiFi signal data with motion-driven animation.

Data sources: ESP32 CSI via UDP :5005, Windows WiFi via netsh, simulation
fallback. Auto-detection probes each in order.

UI changes:
- Point all endpoints to Rust server on :8080 (was Python :8000)
- Fix WebSocket sensing URL to include /ws/sensing path
- Remove sensingOnlyMode gating — all tabs init normally
- Remove api.service.js sensing-only short-circuit
- Fix clearPingInterval bug in websocket.service.js

Also removes obsolete k8s/ template manifests.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 21:29:45 -05:00
Claude 81ad09d05b feat(train): Add ruvector integration — ADR-016, deps, DynamicPersonMatcher 
- docs/adr/ADR-016: Full ruvector integration ADR with verified API details
  from source inspection (github.com/ruvnet/ruvector). Covers mincut,
  attn-mincut, temporal-tensor, solver, and attention at v2.0.4.
- Cargo.toml: Add ruvector-mincut, ruvector-attn-mincut, ruvector-temporal-
  tensor, ruvector-solver, ruvector-attention = "2.0.4" to workspace deps
  and wifi-densepose-train crate deps.
- metrics.rs: Add DynamicPersonMatcher wrapping ruvector_mincut::DynamicMinCut
  for subpolynomial O(n^1.5 log n) multi-frame person tracking; adds
  assignment_mincut() public entry point.
- proof.rs, trainer.rs, model.rs, dataset.rs, subcarrier.rs: Agent
  improvements to full implementations (loss decrease verification, SHA-256
  hash, LCG shuffle, ResNet18 backbone, MmFiDataset, linear interp).
- tests: test_config, test_dataset, test_metrics, test_proof, training_bench
  all added/updated. 100+ tests pass with no-default-features.

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
 2026-02-28 15:42:10 +00:00
Claude 2c5ca308a4 feat(rust): Add workspace deps, tests, and refine training modules 
- Cargo.toml: Add wifi-densepose-train to workspace members; add
  petgraph, ndarray-npy, walkdir, sha2, csv, indicatif, clap to
  workspace dependencies
- error.rs: Slim down to focused error types (TrainError, DatasetError)
- lib.rs: Wire up all module re-exports correctly
- losses.rs: Add generate_gaussian_heatmaps implementation
- tests/test_config.rs: Deterministic config roundtrip and validation tests

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
 2026-02-28 15:17:17 +00:00
Claude a17b630c02 feat: Add wifi-densepose-mat disaster detection module 
Implements WiFi-Mat (Mass Casualty Assessment Tool) for detecting and
localizing survivors trapped in rubble, earthquakes, and natural disasters.

Architecture:
- Domain-Driven Design with bounded contexts (Detection, Localization, Alerting)
- Modular Rust crate integrating with existing wifi-densepose-* crates
- Event-driven architecture for audit trails and distributed deployments

Features:
- Breathing pattern detection from CSI amplitude variations
- Heartbeat detection using micro-Doppler analysis
- Movement classification (gross, fine, tremor, periodic)
- START protocol-compatible triage classification
- 3D position estimation via triangulation and depth estimation
- Real-time alert generation with priority escalation

Documentation:
- ADR-001: Architecture Decision Record for wifi-Mat
- DDD domain model specification
 2026-01-13 17:24:50 +00:00
Claude 6ed69a3d48 feat: Complete Rust port of WiFi-DensePose with modular crates 
Major changes:
- Organized Python v1 implementation into v1/ subdirectory
- Created Rust workspace with 9 modular crates:
  - wifi-densepose-core: Core types, traits, errors
  - wifi-densepose-signal: CSI processing, phase sanitization, FFT
  - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch)
  - wifi-densepose-api: Axum-based REST/WebSocket API
  - wifi-densepose-db: SQLx database layer
  - wifi-densepose-config: Configuration management
  - wifi-densepose-hardware: Hardware abstraction
  - wifi-densepose-wasm: WebAssembly bindings
  - wifi-densepose-cli: Command-line interface

Documentation:
- ADR-001: Workspace structure
- ADR-002: Signal processing library selection
- ADR-003: Neural network inference strategy
- DDD domain model with bounded contexts

Testing:
- 69 tests passing across all crates
- Signal processing: 45 tests
- Neural networks: 21 tests
- Core: 3 doc tests

Performance targets:
- 10x faster CSI processing (~0.5ms vs ~5ms)
- 5x lower memory usage (~100MB vs ~500MB)
- WASM support for browser deployment
 2026-01-13 03:11:16 +00:00