Commit graph

105 commits

Author SHA1 Message Date
ruv
d09baa6a09 fix: remove hardcoded Tailscale IPs and usernames from public files
- ADR-079: strip SSH user/IP from optimization description
- mac-mini-train.sh: replace hardcoded IP with env var WINDOWS_HOST

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-06 14:39:21 -04:00
ruv
486392bb68 docs: update ADR-079 with validated hardware, ruvector optimizations, baseline
- Status: Proposed → Accepted
- Add O6-O10 optimizations (subcarrier selection, attention, Stoer-Wagner
  min-cut, multi-SPSA, Mac M4 Pro training via Tailscale)
- Add validated hardware table (Mac camera, MediaPipe, M4 Pro GPU, Tailscale)
- Add baseline benchmark results (PCK@20: 35.3%)
- Update implementation plan with completion status

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-06 14:38:40 -04:00
ruv
e3522ddcda feat: camera ground-truth training pipeline (ADR-079, #362)
Add 4 scripts for camera-supervised WiFlow pose training:

- collect-ground-truth.py: synchronized webcam + CSI capture via
  MediaPipe PoseLandmarker (17 COCO keypoints at 30fps)
- align-ground-truth.js: time-align camera keypoints with CSI windows
  using binary search, confidence-weighted averaging
- train-wiflow-supervised.js: 3-phase supervised training (contrastive
  pretrain → supervised keypoint regression → bone-constrained
  refinement) with curriculum learning and CSI augmentation
- eval-wiflow.js: PCK@10/20/50, MPJPE, per-joint breakdown, baseline
  proxy mode for benchmarking

Baseline benchmark (proxy poses, no camera supervision):
  PCK@10: 11.8% | PCK@20: 35.3% | PCK@50: 94.1% | MPJPE: 0.067

Camera pipeline validated over Tailscale to Mac Mini M4 Pro
(1920x1080, 14/17 keypoints visible, MediaPipe confidence 0.94-1.0).

Target after camera-supervised training: PCK@20 > 50%

Closes #362

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-06 14:07:25 -04:00
ruv
b3fd0e2951 docs: add HuggingFace models, 17 sensing apps, v0.6.0 to README + user guide
README:
- New "Pre-Trained Models" section with HuggingFace download link
- Model table (safetensors, q4, q2, presence head, LoRA adapters)
- Updated benchmarks (0.008ms, 164K emb/s, 51.6% contrastive)
- "17 Sensing Applications" section (health, environment, multi-freq)
- v0.6.0 in release table as Latest

User guide:
- "Pre-Trained Models" section with quick start + huggingface-cli
- What the models do (presence, fingerprinting, anomaly, activity)
- Retraining instructions
- "Health & Wellness Applications" section with all 4 health scripts
- Medical disclaimer

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-03 10:28:29 -04:00
ruv
4fc491dea5 feat: ADR-078 — 5 multi-frequency mesh applications
RF tomography (2D backprojection imaging), passive bistatic radar
(neighbor APs as illuminators), frequency-selective material
classification (metal/water/wood/glass), through-wall motion
detection (per-channel penetration weighting), device fingerprinting
(RF emission signatures per SSID)

All impossible with single-channel WiFi — require 6-channel hopping.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-03 08:52:50 -04:00
ruv
4f6780f884 feat: ADR-077 — 6 novel RF sensing applications
Sleep monitor (hypnogram + efficiency), apnea detector (AHI scoring),
stress monitor (HRV + LF/HF via FFT), gait analyzer (cadence + tremor),
material detector (null pattern classification), room fingerprint
(k-means clustering + anomaly scoring)

All validated on overnight data (113K frames). Pure Node.js, zero deps.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-03 08:50:48 -04:00
ruv
28368b2c70 feat: ADR-076 CNN spectrogram embeddings + graph transformer fusion
CSI-as-image: 64x20 subcarrier×time matrix → 224x224 → CNN → 128-dim
embedding. Same-node similarity 0.95+, cross-node 0.6-0.8.

- csi-spectrogram.js: WASM CNN embedding, ASCII visualization, Seed ingest
- mesh-graph-transformer.js: GATv2 multi-head attention over ESP32 mesh,
  fuses multi-node features, generalizes to 3+ nodes

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-03 00:36:38 -04:00
ruv
4bb8c3303f feat: ADR-075 min-cut person separation — fixes #348
Stoer-Wagner min-cut on subcarrier correlation graph replaces broken
threshold-based person counting (was always 4, now correct).

Validated: 24/24 windows correctly report 1 person on test data
where old firmware reported 4. Pure JS, <5ms per window.

- mincut-person-counter.js: live UDP + JSONL replay, overrides vitals
- csi-graph-visualizer.js: ASCII spectrum + correlation heatmap
- ADR-075: algorithm, comparison, migration path

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-03 00:34:57 -04:00
ruv
b9778c5ad2 feat: ADR-074 spiking neural network for real-time CSI sensing
128→64→8 SNN with STDP online learning — adapts to room in <30s
without labels. Event-driven: 16-160x less compute than FC encoder.

- snn-csi-processor.js: live UDP with ASCII visualization, EWMA
- ADR-073 updated with SNN integration for multi-channel fusion
- Fixed magic number parsing to use ADR-018 format (0xC5110001)

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-03 00:34:31 -04:00
ruv
b4c9e7743f feat: ADR-073 multi-frequency mesh RF scanning
Live RF room scanner with ASCII spectrum visualization:
- rf-scan.js: single-channel scanner with null/dynamic/reflector classification,
  cross-node correlation, phase coherence, Unicode spectrum display
- rf-scan-multifreq.js: wideband view merging 6 channels, null diversity,
  per-channel penetration quality, frequency-dependent scatterer detection
- benchmark-rf-scan.js: null diversity gain, spectrum flatness, resolution estimate

Validated: 228 frames in 5s, 23 fps/node, 19% nulls detected,
0.993 cross-node correlation, line-of-sight confirmed

ADR-073: interleaved channel hopping (Node 1: ch 1/6/11, Node 2: ch 3/5/9)
targets 6x subcarrier diversity, <5% null gap, ~15cm resolution

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-03 00:18:29 -04:00
ruv
8f2de7e9f2 feat: ADR-072 WiFlow SOTA architecture — TCN + axial attention + pose decoder
Pure JS implementation of WiFlow (arXiv:2602.08661) adapted for ESP32:
- TCN temporal encoder (dilated causal conv, k=7, dilation 1/2/4/8)
- Asymmetric spatial encoder (1x3 residual blocks, stride-2)
- Axial self-attention (width + height, 8 heads, 256 channels)
- Pose decoder (adaptive pooling → 17x2 COCO keypoints)
- SmoothL1 + bone constraint loss (14 skeleton connections)
- 1.8M params (1.6 MB at INT8), 198M FLOPs

Integrated with camera-free pipeline (pose proxy labels from
RSSI triangulation + subcarrier asymmetry + vibration)

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 23:40:23 -04:00
ruv
74c965f7ec docs: remove HuggingFace publishing section from user guide
Contains GCloud project ID and secret names — not appropriate for
a public repo. Publishing instructions kept in scripts/ only.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 23:14:20 -04:00
ruv
73d4cb9fc2 docs: update README + user guide with v0.5.4 capabilities
README:
- Test badge 1300+ → 1463
- Updated capability table (171K emb/s, 100% presence, 0.012ms)
- Added "What's New in v0.5.4" section with full benchmark table
- Training pipeline quick start commands

User guide:
- Camera-Free Pose Training section (10 sensor signals, 5-phase pipeline)
- ruvllm Training Pipeline section (5 phases, quantization options)
- Publishing to HuggingFace section
- Updated table of contents

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 23:11:30 -04:00
ruv
ba82fcfc37 feat: camera-free 17-keypoint pose training (10 sensor signals)
Multi-modal pipeline using PIR, BME280, reed switch, vibration,
RSSI triangulation, subcarrier asymmetry — no camera needed.

Phases: multi-modal collection → weak label generation → enhanced
contrastive → 5-keypoint pose proxy → 17-keypoint interpolation
→ self-refinement (3 rounds) → LoRA + TurboQuant + EWC

Validated: 2,360 frames, 100% presence, 0 skeleton violations,
82.8 KB model (8 KB at 4-bit), 114.8s training

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 23:05:07 -04:00
ruv
a73a17e264 feat: ADR-071 ruvllm training pipeline — contrastive + LoRA + TurboQuant
5-phase training pipeline using ruvllm (Rust-native, no PyTorch):
1. Contrastive pretraining (triplet + InfoNCE, 5 triplet strategies)
2. Task head training (presence, activity, vitals via SONA)
3. Per-node LoRA refinement (rank-4, room-specific adaptation)
4. TurboQuant quantization (2/4/8-bit, 6-8x compression)
5. EWC consolidation (prevent catastrophic forgetting)

Exports: SafeTensors, HuggingFace config, RVF, per-node LoRA, quantized
Validated: 249 triplets, 37,775 emb/s, 100% presence accuracy on test data
Target: <5 min training on M4 Pro, <10ms inference on Pi Zero

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 22:27:24 -04:00
ruv
9a2bc1839a feat: HuggingFace model publishing pipeline + model card
- publish-huggingface.sh: retrieves HF token from GCloud Secrets,
  uploads models to ruvnet/wifi-densepose-pretrained
- publish-huggingface.py: Python alternative with --dry-run support
- docs/huggingface/MODEL_CARD.md: beginner-friendly model card with
  WiFi sensing explanation, quick start code, hardware BOM, and citation

GCloud Secret: HUGGINGFACE_API_KEY in project cognitum-20260110

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 22:04:16 -04:00
ruv
77a2e7e4e9 docs: add Cognitum Seed pretraining tutorial (530 lines)
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Step-by-step guide covering hardware setup, Seed pairing, 2-node ESP32
provisioning, bridge operation, 6-scenario data collection protocol,
feature vector explanation, kNN queries, troubleshooting, and next steps.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 20:49:05 -04:00
ruv
b46b789e9e feat: ADR-070 self-supervised pretraining from live ESP32 CSI + Seed
4-phase pipeline: data collection (2 nodes), contrastive pretraining,
downstream heads (presence/count/activity/vitals), package & distribute.
Validated: 118 features from 2 nodes in 60s, witness chain intact.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 20:42:37 -04:00
ruv
27d17431c5 docs: update README and user guide with Cognitum Seed integration
- Add ESP32 + Cognitum Seed as recommended hardware option ($27 BOM)
- Add v0.5.4-esp32 to firmware release table
- Add Cognitum Seed setup section to user guide with bridge usage,
  feature vector dimensions, and architecture diagram
- Update table of contents

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 19:48:01 -04:00
ruv
a4bd2308b7 feat: ADR-069 ESP32 CSI → Cognitum Seed RVF pipeline (v0.5.4-esp32)
Hardware-validated pipeline connecting ESP32-S3 CSI sensing to Cognitum
Seed (Pi Zero 2 W) edge intelligence appliance via 8-dim feature vectors.

Firmware:
- New 48-byte feature vector packet (magic 0xC5110003) at 1 Hz with
  normalized presence, motion, breathing, heart rate, phase variance,
  person count, fall detection, and RSSI
- Compressed frame magic reassigned 0xC5110003 → 0xC5110005
- Guard against uninitialized s_top_k read when count=0

Bridge (scripts/seed_csi_bridge.py):
- UDP→HTTPS ingest with bearer token, hash-based vector IDs
- --validate (kNN), --stats, --compact, --allowed-sources modes
- NaN/inf rejection, retry logic, SEED_TOKEN env var support

Validated on live hardware:
- 941 vectors ingested, 100% kNN exact match
- Witness chain SHA-256 verified (1,325 entries)
- 1,463 Rust tests passed, Python proof VERDICT: PASS

Research: 26 docs covering Arena Physica, Maxwell's equations in WiFi
sensing, SOTA survey 2025-2026, GOAP implementation plan

Security: removed hardcoded credentials, added NVS patterns to
.gitignore, source IP filtering, NaN validation

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-02 19:32:18 -04:00
ruv
23dedecf0c docs(adr): ADR-068 per-node state pipeline for multi-node sensing (#249)
Documents the architectural change from single shared state to per-node
HashMap<u8, NodeState> in the sensing server. Includes scaling analysis
(256 nodes < 13 MB), QEMU validation plan, and aggregation strategy.

Also links README hero image to the explainer video.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-27 17:45:43 -04:00
Reuven
6c98c98920 docs(adr): ADR-067 RuVector v2.0.5 upgrade + new crate adoption plan
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4-phase plan to upgrade core ruvector dependencies and adopt new crates:
- Phase 1: Bump 5 core crates 2.0.4→2.0.5 (10-30% mincut perf, security fixes)
- Phase 2: Add ruvector-coherence for spectral multi-node CSI coherence
- Phase 3: Add SONA adaptive learning to replace manual logistic regression
- Phase 4: Evaluate ruvector-core ONNX embeddings for CSI pattern matching

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-23 21:51:43 -04:00
rUv
2b8a7cc458
feat: happiness scoring pipeline + ESP32 swarm with Cognitum Seed (#285)
* feat: happiness scoring pipeline with ESP32 swarm + Cognitum Seed coordinator

ADR-065: Hotel guest happiness scoring from WiFi CSI physiological proxies.
ADR-066: ESP32 swarm with Cognitum Seed as coordinator for multi-zone analytics.

Firmware:
- swarm_bridge.c/h: FreeRTOS task on Core 0, HTTP client with Bearer auth,
  registers with Seed, sends heartbeats (30s) and happiness vectors (5s)
- nvs_config: seed_url, seed_token, zone_name, swarm intervals
- provision.py: --seed-url, --seed-token, --zone CLI args
- esp32-hello-world: capability discovery firmware for 4MB ESP32-S3 variant

WASM edge modules:
- exo_happiness_score.rs: 8-dim happiness vector from gait speed, stride
  regularity, movement fluidity, breathing calm, posture, dwell time
  (events 690-694, 11 tests, ESP32-optimized buffers + event decimation)
- ghost_hunter.rs standalone binary: 5.7 KB WASM, feature-gated default pipeline

RuView Live:
- --mode happiness dashboard with bar visualization
- --seed flag for Cognitum Seed bridge (urllib, background POST)
- HappinessScorer + SeedBridge classes (stdlib only, no deps)

Examples:
- seed_query.py: CLI tool (status, search, witness, monitor, report)
- provision_swarm.sh: batch provisioning for multi-node deployment
- happiness_vector_schema.json: 8-dim vector format documentation

Verified live: ESP32 on COM5 (4MB flash) registered with Seed at 10.1.10.236,
vectors flowing, witness chain growing (epoch 455, chain 1108).

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

* ci: raise firmware binary size gate to 1100 KB for HTTP client stack

The swarm bridge (ADR-066) adds esp_http_client for Seed communication,
which pulls in the HTTP/TLS stack (~150 KB). Binary grew from ~978 KB to
~1077 KB. Raise the gate from 950 KB to 1100 KB. Still fits comfortably
in both 4MB (1856 KB OTA slot, 43% free) and 8MB flash variants.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-20 18:46:34 -04:00
ruv
92a6986b79 docs: update all docs for v0.5.0-esp32 release
- README: v0.5.0 in release table, binary size 990/773 KB
- CHANGELOG: v0.5.0 entry with mmWave fusion, ADR-063/064
- User guide: v0.5.0 as recommended, binary size updated
- CLAUDE.md: supported hardware table, firmware build/release
  process, real-hardware-first testing policy

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 16:17:40 -04:00
rUv
66e2fa0835
feat: ADR-063/064 mmWave sensor fusion + multimodal ambient intelligence (#269)
* docs: ADR-063 mmWave sensor fusion with WiFi CSI

60 GHz mmWave radar (Seeed MR60BHA2, HLK-LD2410/LD2450) fusion
with WiFi CSI for dual-confirm fall detection, clinical-grade
vitals, and self-calibrating CSI pipeline.

Covers auto-detection, 6 supported sensors, Kalman fusion,
extended 48-byte vitals packet, RuVector/RuvSense integration
points, and 6-phase implementation plan.

Based on live hardware capture from ESP32-C6 + MR60BHA2 on COM4.

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

* feat(firmware): ADR-063 mmWave sensor fusion — full implementation

Phase 1-2 of ADR-063:

mmwave_sensor.c/h:
- MR60BHA2 UART parser (60 GHz: HR, BR, presence, distance)
- LD2410 UART parser (24 GHz: presence, distance)
- Auto-detection: probes UART for known frame headers at boot
- Mock generator for QEMU testing (synthetic HR 72±2, BR 16±1)
- Capability flag registration per sensor type

edge_processing.c/h:
- 48-byte fused vitals packet (magic 0xC5110004)
- Kalman-style fusion: mmWave 80% + CSI 20% when both available
- Automatic fallback to CSI-only 32-byte packet when no mmWave
- Dual presence flag (Bit3 = mmwave_present)

main.c:
- mmwave_sensor_init() called at boot with auto-detect
- Status logged in startup banner

Fuzz stubs updated for mmwave_sensor API.
Build verified: QEMU mock build passes.

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

* fix(firmware): correct MR60BHA2 + LD2410 UART protocols (ADR-063)

MR60BHA2: SOF=0x01 (not 0x5359), XOR+NOT checksums on header and
data, frame types 0x0A14 (BR), 0x0A15 (HR), 0x0A16 (distance),
0x0F09 (presence). Based on Seeed Arduino library research.

LD2410: 256000 baud (not 115200), 0xAA report head marker,
target state byte at offset 2 (after data_type + head_marker).

Auto-detect: probes MR60 at 115200 first, then LD2410 at 256000.
Sets final baud rate after detection.

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

* feat: ADR-063 Phase 6 server-side mmWave + CSI fusion bridge

Python script reads both serial ports simultaneously:
- COM4 (ESP32-C6 + MR60BHA2): parses ESPHome debug output for HR, BR, presence, distance
- COM7 (ESP32-S3): reads CSI edge processing frames

Kalman-style fusion: mmWave 80% + CSI 20% for vitals, OR gate for presence.

Verified on real hardware: mmWave HR=75bpm, BR=25/min at 52cm range,
CSI frames flowing concurrently. Both sensors live for 30 seconds.

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

* docs: ADR-064 multimodal ambient intelligence roadmap

25+ applications across 4 tiers from practical to exotic:
- Tier 1 (build now): zero-FP fall detection, sleep monitoring,
  occupancy HVAC, baby breathing, bathroom safety
- Tier 2 (research): gait analysis, stress detection, gesture
  control, respiratory screening, multi-room activity
- Tier 3 (frontier): cardiac arrhythmia, RF tomography, sign
  language, cognitive load, swarm sensing
- Tier 4 (exotic): emotion contagion, lucid dreaming, plant
  monitoring, pet behavior

Priority matrix with effort estimates. All P0-P1 items work with
existing hardware (ESP32-S3 + MR60BHA2 + BH1750).

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

* fix(ci): add ESP_ERR_NOT_FOUND to fuzz stubs

mmwave_sensor stub returns ESP_ERR_NOT_FOUND which wasn't
defined in the minimal esp_stubs.h for host-based fuzz testing.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 16:10:10 -04:00
ruv
2b3c3e4b45 docs: update user guide for v0.4.3.1 (release table, fall threshold, binary size)
- Release table: v0.4.3.1 as recommended, importance note updated
- fall_thresh default: 500→15000 with unit explanation
- Binary size: updated to 978 KB / 755 KB (was 777 KB)

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 12:27:31 -04:00
rUv
5b2aacd923
fix(firmware): fall detection, 4MB flash, QEMU CI (#263, #265)
* fix(firmware): fall detection false positives + 4MB flash support (#263, #265)

Issue #263: Default fall_thresh raised from 2.0 to 15.0 rad/s² — normal
walking produces accelerations of 2.5-5.0 which triggered constant false
"Fall Detected" alerts. Added consecutive-frame requirement (3 frames)
and 5-second cooldown debounce to prevent alert storms.

Issue #265: Added partitions_4mb.csv and sdkconfig.defaults.4mb for
ESP32-S3 boards with 4MB flash (e.g. SuperMini). OTA slots are 1.856MB
each, fitting the ~978KB firmware binary with room to spare.

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

* fix(ci): repair all 3 QEMU workflow job failures

1. Fuzz Tests: add esp_timer_create_args_t, esp_timer_create(),
   esp_timer_start_periodic(), esp_timer_delete() stubs to
   esp_stubs.h — csi_collector.c uses these for channel hop timer.

2. QEMU Build: add libgcrypt20-dev to apt dependencies —
   Espressif QEMU's esp32_flash_enc.c includes <gcrypt.h>.
   Bump cache key v4→v5 to force rebuild with new dep.

3. NVS Matrix: switch to subprocess-first invocation of
   nvs_partition_gen to avoid 'str' has no attribute 'size' error
   from esp_idf_nvs_partition_gen API change. Falls back to
   direct import with both int and hex size args.

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

* fix(ci): pip3 in IDF container + fix swarm QEMU artifact path

QEMU Test jobs: espressif/idf:v5.4 container has pip3, not pip.
Swarm Test: use /opt/qemu-esp32 (fixed path) instead of
${{ github.workspace }}/qemu-build which resolves incorrectly
inside Docker containers.

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

* fix(ci): source IDF export.sh before pip install in container

espressif/idf:v5.4 container doesn't have pip/pip3 on PATH — it
lives inside the IDF Python venv which is only activated after
sourcing $IDF_PATH/export.sh.

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

* fix(ci): pad QEMU flash image to 8MB with --fill-flash-size

QEMU rejects flash images that aren't exactly 2/4/8/16 MB.
esptool merge_bin produces a sparse image (~1.1 MB) by default.
Add --fill-flash-size 8MB to pad with 0xFF to the full 8 MB.

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

* fix(ci): source IDF export before NVS matrix generation in QEMU tests

The generate_nvs_matrix.py script needs the IDF venv's python
(which has esp_idf_nvs_partition_gen installed) rather than the
system /usr/bin/python3 which doesn't have the package.

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

* fix(ci): QEMU validation treats WARNs as OK + swarm IDF export

1. validate_qemu_output.py: WARNs exit 0 by default (no real WiFi
   hardware in QEMU = no CSI data = expected WARNs for frame/vitals
   checks). Add --strict flag to fail on warnings when needed.

2. Swarm Test: source IDF export.sh before running qemu_swarm.py
   so pip-installed pyyaml is on the Python path.

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

* fix(ci): provision.py subprocess-first NVS gen + swarm IDF venv

provision.py had same 'str' has no attribute 'size' bug as the
NVS matrix generator — switch to subprocess-first approach.
Swarm test also needs IDF export for the swarm smoke test step.

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

* fix(ci): handle missing 'ip' command in QEMU swarm orchestrator

The IDF container doesn't have iproute2 installed, so 'ip' binary
is missing. Add shutil.which() check to can_tap guard and catch
FileNotFoundError in _run_ip() for robustness.

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

* fix(ci): skip Rust aggregator when cargo not available in swarm test

The IDF container doesn't have Rust installed. Check for cargo
with shutil.which() before attempting to spawn the aggregator,
falling back to aggregator-less mode (QEMU nodes still boot and
exercise the firmware pipeline).

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

* fix(ci): treat swarm test WARNs as acceptable in CI

The max_boot_time_s assertion WARNs because QEMU doesn't produce
parseable boot time data. Exit code 1 (WARN) is acceptable in CI
without real hardware; only exit code 2+ (FAIL/FATAL) should fail.

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

* fix(firmware): Kconfig EDGE_FALL_THRESH default 2000→15000

The nvs_config.c fallback (15.0f) was never reached because
Kconfig always defines CONFIG_EDGE_FALL_THRESH. The Kconfig
default was still 2000 (=2.0 rad/s²), causing false fall alerts
on real WiFi CSI data (7 alerts in 45s).

Fixed to 15000 (=15.0 rad/s²). Verified on real ESP32-S3 hardware
with live WiFi CSI: 0 false fall alerts in 60s / 1300+ frames.

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

* docs: update README, CHANGELOG, user guide for v0.4.3-esp32

- README: add v0.4.3 to release table, 4MB flash instructions,
  fix fall-thresh example (5000→15000)
- CHANGELOG: v0.4.3-esp32 entry with all fixes and additions
- User guide: 4MB flash section with esptool commands

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-15 11:49:29 -04:00
rUv
523be943b0
feat: QEMU ESP32-S3 testing platform + swarm configurator (ADR-061/062) (#260)
9-layer QEMU testing platform (ADR-061) and YAML-driven swarm
configurator (ADR-062) for ESP32-S3 firmware testing without hardware.

12 commits, 56 files, +9,500 lines. Tested on Windows with
Espressif QEMU 9.0.0 — firmware boots, mock CSI generates frames,
14/16 validation checks pass. 39 bugs found and fixed across
2 deep code reviews.

Closes #259

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-14 13:39:51 -04:00
ruv
a467dfed9f docs: ADR-061 QEMU ESP32-S3 firmware testing platform (9 layers)
Comprehensive QEMU emulation strategy for ESP32-S3 CSI node firmware:
- Layer 1: Mock CSI generator with 10 test scenarios
- Layer 2: QEMU runner + CI workflow with NVS matrix
- Layer 3: Multi-node mesh simulation (TAP networking)
- Layer 4: GDB remote debugging (zero-cost, no JTAG)
- Layer 5: Code coverage (gcov/lcov)
- Layer 6: Fuzz testing (libFuzzer for CSI parser, NVS, WASM)
- Layer 7: NVS provisioning matrix (14 configs)
- Layer 8: Snapshot & replay (<100ms restore)
- Layer 9: Chaos testing (9 fault injection scenarios)

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-13 09:02:09 -04:00
rUv
d793c1f49f
feat(firmware): --channel and --filter-mac provisioning (ADR-060)
- provision.py: add --channel (CSI channel override) and --filter-mac
  (AA:BB:CC:DD:EE:FF format) arguments with validation
- nvs_config: add csi_channel, filter_mac[6], filter_mac_set fields;
  read from NVS on boot
- csi_collector: auto-detect AP channel when no NVS override is set;
  filter CSI frames by source MAC when filter_mac is configured
- ADR-060 documents the design and rationale

Fixes #247, fixes #229
2026-03-13 08:27:08 -04:00
rUv
7c1351fd5d
feat(demo): wire all 6 RuVector WASM attention mechanisms into pose fusion
* feat: dual-modal WASM browser pose estimation demo (ADR-058)

Live webcam video + WiFi CSI fusion for real-time pose estimation.
Two parallel CNN pipelines (ruvector-cnn-wasm) with attention-weighted
fusion and dynamic confidence gating. Three modes: Dual, Video-only,
CSI-only. Includes pre-built WASM package (~52KB) for browser deployment.

- ADR-058: Dual-modal architecture design
- ui/pose-fusion.html: Main demo page with dark theme UI
- 7 JS modules: video-capture, csi-simulator, cnn-embedder, fusion-engine,
  pose-decoder, canvas-renderer, main orchestrator
- Pre-built ruvector-cnn-wasm WASM package for browser
- CSI heatmap, embedding space visualization, latency metrics
- WebSocket support for live ESP32 CSI data
- Navigation link added to main dashboard

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

* fix: motion-responsive skeleton + through-wall CSI tracking

- Pose decoder now uses per-cell motion grid to track actual arm/head
  positions — raising arms moves the skeleton's arms, head follows
  lateral movement
- Motion grid (10x8 cells) tracks intensity per body zone: head,
  left/right arm upper/mid, legs
- Through-wall mode: when person exits frame, CSI maintains presence
  with slow decay (~10s) and skeleton drifts in exit direction
- CSI simulator persists sensing after video loss, ghost pose renders
  with decreasing confidence
- Reduced temporal smoothing (0.45) for faster response to movement

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

* fix: video fills available space + correct WASM path resolution

- Remove fixed aspect-ratio and max-height from video panel so it
  fills the available viewport space without scrolling
- Grid uses 1fr row for content area, overflow:hidden on main grid
- Fix WASM path: resolve relative to JS module file using import.meta.url
  instead of hardcoded ./pkg/ which resolved incorrectly on gh-pages
- Responsive: mobile still gets aspect-ratio constraint

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

* feat: live ESP32 CSI pipeline + auto-connect WebSocket

- Add auto-connect to local sensing server WebSocket (ws://localhost:8765)
- Demo shows "Live ESP32" when connected to real CSI data
- Add build_firmware.ps1 for native Windows ESP-IDF builds (no Docker)
- Add read_serial.ps1 for ESP32 serial monitor

Pipeline: ESP32 → UDP:5005 → sensing-server → WS:8765 → browser demo

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

* docs: add ADR-059 live ESP32 CSI pipeline + update README with demo links

- ADR-059: Documents end-to-end ESP32 → sensing server → browser pipeline
- README: Add dual-modal pose fusion demo link, update ADR count to 49
- References issue #245

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

* feat: RSSI visualization, RuVector attention WASM, cache-bust fixes

- Add animated RSSI Signal Strength panel with sparkline history
- Fix RuVector WasmMultiHeadAttention retptr calling convention
- Wire up RuVector Multi-Head + Flash Attention in CNN embedder
- Add ambient temporal drift to CSI simulator for visible heatmap animation
- Fix embedding space projection (sparse projection replaces cancelling sum)
- Add auto-scaling to embedding space renderer
- Add cache busters (?v=4) to all ES module imports to prevent stale caches
- Add diagnostic logging for module version verification
- Add RSSI tracking with quality labels and color-coded dBm display
- Includes ruvector-attention-wasm v2.0.5 browser ESM wrapper

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

* feat: 26-keypoint dexterous pose + full RuVector attention pipeline

Pose Decoder (17 → 26 keypoints):
- Add finger approximations: thumb, index, pinky per hand (6 new)
- Add toe tips: left/right foot index (2 new)
- Add neck keypoint (1 new)
- Hand openness driven by arm motion intensity
- Finger positions computed from wrist-elbow axis angles

CNN Embedder (full RuVector WASM pipeline):
- Stage 1: Multi-Head Attention (global spatial reasoning)
- Stage 2: Hyperbolic Attention (hierarchical body-part tree)
- Stage 3: MoE Attention (3 experts: upper/lower/extremities, top-2)
- Blended 40/30/30 weighting → final embedding projection

Canvas Renderer:
- Magenta finger joints with distinct glow
- Cyan toe tips
- White neck keypoint
- Thinner limb lines for hand/foot connections
- Joint count shown in overlay label

CSI Simulator:
- Skip synthetic person state when live ESP32 connected
- Only simulate CSI data in demo mode (was already correct)

Embedding Space:
- Fixed projection: sparse 8-dim projection replaces cancelling sum
- Auto-scaling normalizes point spread to fill canvas

Cache busters bumped to v=5 on all imports.

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

* fix: centroid-based pose tracking for responsive limb movement

Rewrites pose decoder from intensity-based to position-based tracking:
- Arms now track toward motion centroid in each body zone
- Elbow/wrist positions computed along shoulder→centroid vector
- Legs track toward lower-body zone centroids
- Smoothing reduced from 0.45 to 0.25 for responsiveness
- Zone centroids blend 30% old / 70% new each frame

6 body zones with overlapping coverage:
- Head (top 20%, center cols)
- Left/Right Arm (rows 10-60%, outer cols)
- Torso (rows 15-55%, center cols)
- Left/Right Leg (rows 50-100%, half cols each)

Hand openness now driven by arm spread distance + raise amount.
Cache busters v=6.

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

* fix: remove duplicate lAnkleX/rAnkleX declarations in pose-decoder

Stale code block from old intensity-based tracking was left behind,
re-declaring variables already defined by centroid-based tracking.

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

* feat(demo): wire all 6 RuVector WASM attention mechanisms into pose fusion

- Add WasmLinearAttention and WasmLocalGlobalAttention to browser ESM wrapper
- Add 6 WASM utility functions (batch_normalize, pairwise_distances, etc.)
- Extend CnnEmbedder to 6-stage pipeline: Flash → MHA → Hyperbolic → Linear → MoE → L+G
- Use log-energy softmax blending across all 6 stages
- Wire WASM cosine_similarity and normalize into FusionEngine
- Add RuVector pipeline stats panel to UI (energy, refinement, pose impact)
- Compute embedding-to-joint mapping stats without modifying joint positions
- Center camera prompt with flexbox layout
- Add cache busters v=12

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-12 20:59:57 -04:00
ruv
6e03a47867 docs: update user guide with v0.4.1 firmware release and CSI troubleshooting
- Add v0.4.1 to firmware release table as recommended stable release
- Update flash command with correct partition offsets (8MB, OTA)
- Add "CSI not enabled" troubleshooting entry
- Add warning about pre-v0.4.1 firmware CSI bug

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-12 13:49:20 -04:00
ruv
952f27a1ce fix(firmware): enable CSI in sdkconfig and add build guard (ADR-057)
The committed sdkconfig had CONFIG_ESP_WIFI_CSI_ENABLED disabled, causing
all builds to crash at runtime with "CSI not enabled in menuconfig".
Root cause: sdkconfig.defaults.template existed but ESP-IDF only reads
sdkconfig.defaults (no .template suffix).

Fixes:
- Add sdkconfig.defaults with CONFIG_ESP_WIFI_CSI_ENABLED=y
- Add #error compile guard in csi_collector.c to prevent recurrence
- Fix NVS encryption default (requires eFuse, breaks clean builds)

Verified: Docker build + flash to ESP32-S3 + CSI callbacks confirmed.

Closes #241
Relates to #223, #238, #234, #210, #190

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-12 13:49:20 -04:00
Reuven
f7d043d727 docs: fix Docker commands to use CSI_SOURCE environment variable
The Docker image uses CSI_SOURCE env var to select the data source,
not command-line arguments appended after the image name.

Fixed:
- ESP32 mode examples now use -e CSI_SOURCE=esp32
- Training mode example now uses --entrypoint override
- Added CSI_SOURCE value table in Docker section

Fixes #226

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-10 12:16:06 -04:00
Reuven
e12749bf68 feat(desktop): v0.4.2 - Integrated sensing server with real WebSocket data
- Bundle sensing-server binary in app resources (bin/sensing-server)
- Add find_server_binary() for multi-path binary discovery
- Connect Sensing page to real WebSocket endpoint (ws://localhost:8765/ws/sensing)
- Add DataSource type and source config for data source selection
- Default to simulate mode when no ESP32 hardware present
- Add ADR-055: Integrated Sensing Server architecture
- Add ADR-056: Complete RuView Desktop Capabilities Reference

Closes integration of sensing server as single-package distribution.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-10 00:08:31 -04:00
rUv
0b98917dff
feat(desktop): RuView Desktop v0.4.0 - Full ADR-054 Implementation (#212)
* fix(desktop): implement save_settings and get_settings commands

Fixes #206 - Settings can now be saved and loaded in Desktop v0.3.0

- Add commands/settings.rs with get_settings and save_settings Tauri commands
- Settings persisted to app data directory as settings.json
- Supports all AppSettings fields: ports, bind address, OTA PSK, discovery, theme
- Add unit tests for serialization and defaults

Settings are stored at:
- macOS: ~/Library/Application Support/net.ruv.ruview/settings.json
- Windows: %APPDATA%/net.ruv.ruview/settings.json
- Linux: ~/.config/net.ruv.ruview/settings.json

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

* feat(desktop): RuView Desktop v0.4.0 - Full ADR-054 Implementation

This release completes all 14 Tauri commands specified in ADR-054,
making the desktop app fully production-ready for ESP32 node management.

## New Features

### Discovery Module
- Real mDNS discovery (_ruview._udp.local)
- UDP broadcast probe on port 5006
- Serial port enumeration with ESP32 chip detection

### Flash Module
- Full espflash CLI integration
- Real-time progress streaming via Tauri events
- SHA-256 firmware verification
- Support for ESP32, S2, S3, C3, C6 chips

### OTA Module
- HTTP multipart firmware upload
- HMAC-SHA256 signature with PSK authentication
- Sequential and parallel batch update strategies
- Reboot confirmation polling

### WASM Module
- 67 edge modules across 14 categories
- App-store style module library with ratings/downloads
- Full module lifecycle (upload/start/stop/unload)
- RVF format deployment paths

### Server Module
- Child process spawn with config
- Graceful SIGTERM + SIGKILL fallback
- Memory/CPU monitoring via sysinfo

### Provision Module
- NVS binary serial protocol
- Read/write/erase operations
- Mesh config generation for multi-node setup

## Security
- Input validation (IP, port, path)
- Binary validation (ESP/WASM magic bytes)
- PSK authentication for OTA

## Breaking Changes
None - backwards compatible with v0.3.0

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

---------

Co-authored-by: Reuven <cohen@ruv-mac-mini.local>
2026-03-09 21:58:06 -04:00
rUv
341d9e05a8
ruv-neural: publish 11 crates to crates.io — full implementation, no stubs
* Add temporal graph evolution & RuVector integration research

GOAP Agent 8 output: 1,528-line SOTA research document covering temporal
graph models (TGN, JODIE, DyRep), RuVector graph memory design, mincut
trajectory tracking with Kalman filtering, event detection pipelines,
compressed temporal storage, cross-room transition graphs, and a 5-phase
integration roadmap.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add transformer architectures for graph sensing research

GOAP Agent 4 output: 896-line SOTA document covering Graph Transformers
(Graphormer, SAN, GPS, TokenGT), Temporal Graph Transformers (TGN, TGAT,
DyRep), ViT for RF spectrograms, transformer-based mincut prediction,
positional encoding for RF graphs, foundation models for RF sensing, and
efficient edge deployment with INT8 quantization.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add attention mechanisms for RF sensing research

GOAP Agent 3 output: 1,110-line document covering GAT for RF graphs,
self-attention for CSI sequences, cross-attention multi-link fusion,
attention-weighted differentiable mincut, spatial node attention,
antenna-level subcarrier attention, and efficient attention variants
(linear, sparse, LSH, S4/Mamba). 8 ASCII architecture diagrams.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add sublinear mincut algorithms research

GOAP Agent 5 output: 698-line document covering classical mincut complexity,
sublinear approximation (sampling, sparsifiers), dynamic mincut with lazy
recomputation hybrid, streaming sketch algorithms, Benczur-Karger
sparsification, local partitioning (PageRank-guided cuts), randomized
methods reliability analysis, and Rust implementation with const-generic
RfGraph, zero-alloc Stoer-Wagner, SIMD batch updates.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add CSI edge weight computation research

GOAP Agent 2 output: ~700-line document covering CSI feature extraction,
coherence metrics (cross-correlation, mutual information, phasor coherence),
multipath stability scoring (MUSIC, ESPRIT, ISTA), temporal windowing
(EMA, Welford, Kalman), noise robustness (phase noise, AGC, clock drift),
edge weight normalization, and implementation architecture showing 32KB
memory for 120 edges within ESP32-S3 capability.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add contrastive learning for RF coherence research

GOAP Agent 7 output: 1,226-line document covering SimCLR/MoCo/BYOL for CSI,
AETHER-Topo dual-head extension, coherence boundary detection with multi-scale
analysis, delta-driven updates (2-12x efficiency), self-supervised pre-training
protocol, triplet networks for 5-state edge classification, and MERIDIAN
cross-environment transfer with EWC continual learning.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add resolution and spatial granularity analysis research

GOAP Agent 9 output: 1,383-line document covering Fresnel zone analysis,
node density vs resolution (16-node/5m room → 30-60cm), Cramer-Rao lower
bounds with Fisher Information Matrix, graph cut resolution theory,
multi-frequency enhancement (6cm coherent dual-band limit), RF tomography
comparison, experimental validation protocols, and resolution scaling laws
(8.8cm theoretical limit).

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add RF graph theory and minimum cut foundations research

GOAP Agent 1 output: Graph-theoretic foundations covering max-flow/min-cut
for RF (Ford-Fulkerson, Stoer-Wagner, Karger), RF as dynamic graph with
CSI coherence weights, topological change detection via Fiedler vector and
Cheeger inequality, dynamic graph algorithms, comparison to classical RF
sensing, formal mathematical framework, and 9 open research questions.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ESP32 mesh hardware constraints research

GOAP Agent 6 output: ESP32 CSI capabilities (52/114 subcarriers), 16-node
mesh topology with 120 edges, TDM synchronized sensing (3ms slots),
computational budget (Stoer-Wagner uses 0.07% of one core), channel hopping,
power analysis (0.44W/node), dual-core firmware architecture, and edge vs
server computing with 100x data reduction on-device.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add system architecture and prototype design research

GOAP Agent 10 output: End-to-end architecture with pipeline diagrams,
existing crate integration mapping, new rf_topology module design (DDD
aggregate roots), 100ms latency budget breakdown, 3-phase prototype plan
(4-node POC → 16-node room → 72-node multi-room), benchmark design with
8 metrics, ADR-044 draft, and Rust trait definitions (EdgeWeightComputer,
TopologyGraph, MinCutSolver, BoundaryInterpolator).

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add quantum sensing and quantum biomedical research documents

Agent 11: Quantum-level sensors (729 lines) — NV centers, SQUIDs, Rydberg
atoms, quantum illumination, quantum graph theory (walks, spectral, QAOA),
hybrid classical-quantum architecture, quantum ML (VQC, kernels, reservoir
computing), NISQ applications (D-Wave, VQE), hardware roadmap.

Agent 12: Quantum biomedical sensing (827 lines) — whole body biomagnetic
mapping, neural field imaging without electrodes, circulation sensing,
cellular EM signaling, non-contact diagnostics, coherence-based diagnostics
(disease as coherence breakdown), neural interfaces, multimodal observatory,
room-scale ambient health monitoring, graph-based biomedical analysis.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add research index synthesizing all 12 documents (14,322 lines)

Master index for RF Topological Sensing research compendium covering:
graph theory foundations, CSI edge weights, attention mechanisms,
transformers, sublinear algorithms, ESP32 hardware, contrastive learning,
temporal graphs, resolution analysis, system architecture, quantum sensors,
and quantum biomedical sensing. Includes key findings, proposed ADRs
(044, 045), and 5-phase implementation roadmap.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add SOTA neural decoding landscape and 10 application domains research

- Doc 21: Comprehensive SOTA map (2023-2026) of brain sensors, decoders,
  and visualization systems with RuVector/mincut positioning analysis
- Doc 22: Ten application domains for brain state observatory including
  disease detection, BCI, cognitive monitoring, mental health diagnostics,
  neurofeedback, dream reconstruction, cognitive research, HCI, wearables,
  and brain network digital twins with strategic roadmap

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add NV diamond neural magnetometry research document (13/22)

Comprehensive 600+ line document covering NV center physics, neural
magnetic field sources, sensor architecture, SQUID comparison, signal
processing pipeline, RuVector integration, and development roadmap.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ruv-neural workspace Cargo.toml with 12 crate definitions

Workspace structure for the rUv Neural brain topology analysis system.
12 mix-and-match crates with shared dependencies including RuVector
integration, petgraph, rustfft, and WASM/ESP32 support.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ruv-neural crate ecosystem — 12 mix-and-match crates (WIP)

Initial implementation of the rUv Neural brain topology analysis system:
- ruv-neural-core: Core types, traits, errors, RVF format (compiles)
- ruv-neural-sensor: NV diamond, OPM, EEG sensor interfaces (in progress)
- ruv-neural-signal: DSP, filtering, spectral, connectivity (in progress)
- ruv-neural-graph: Brain connectivity graph construction (in progress)
- ruv-neural-mincut: Dynamic minimum cut topology analysis (in progress)
- ruv-neural-embed: RuVector graph embeddings (in progress)
- ruv-neural-memory: Persistent neural state memory + HNSW (compiles)
- ruv-neural-decoder: Cognitive state classification + BCI (in progress)
- ruv-neural-esp32: ESP32 edge sensor integration (compiles)
- ruv-neural-wasm: WebAssembly browser bindings (in progress)
- ruv-neural-viz: Visualization + ASCII rendering (in progress)
- ruv-neural-cli: CLI tool (in progress)

Agents still writing remaining modules. Next: fix compilation, tests, push.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Fix ruv-neural crate compilation: all 12 crates build and 1200+ tests pass

- Fix node2vec.rs type inference error (Vec<_> → Vec<Vec<f64>>)
- Fix artifact.rs with full filter-based detection implementations
- Fix signal crate ConnectivityMetric re-export and trait method names
- Fix embed crate EmbeddingGenerator trait implementations
- Complete spectral, topology, and node2vec embedders with tests
- Complete preprocessing pipeline with sequential stage processing
- All workspace crates compile cleanly, 0 test failures

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ruv-neural-cli README

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* fix: convert desktop icons from RGB to RGBA for Tauri build

Tauri's generate_context!() macro requires RGBA PNG icons. All 5 icon
files (32x32.png, 128x128.png, 128x128@2x.png, icon.icns, icon.ico)
were RGB-only, causing a proc macro panic on Linux builds.

Fixes #200

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

* Add Subcarrier Manifold and Vitals Oracle modules for 3D visualizations

- Implemented Subcarrier Manifold to visualize amplitude data as a 3D surface with height and age attributes.
- Created Vitals Oracle to represent vital signs using toroidal rings and particle trails, incorporating breathing and heart rate dynamics.
- Both modules utilize Three.js for rendering and include custom shaders for visual effects.

* feat: complete ruv-neural implementation — physics models, security, witness verification

Replace all stubs/mocks with production physics-based signal models:
- NV Diamond: ODMR Lorentzian dip, 1/f pink noise (Voss-McCartney), brain oscillations
- OPM: SERF-mode, 50/60Hz powerline harmonics, full cross-talk compensation
  via Gaussian elimination with partial pivoting
- EEG: 5 frequency bands, eye blink artifacts (Fp1/Fp2), muscle artifacts,
  impedance-based thermal noise floor
- ESP32 ADC: ring-buffer reader with calibration signal generator, i16 clamp

Security hardening (SEC-001 through SEC-005):
- RVF bounded allocation (16MB metadata, 256MB payload)
- sample_rate validation (>0, finite)
- Signal NaN/Inf rejection
- ADC resolution_bits overflow clamp
- HNSW HashSet visited tracking + bounds checks

Performance optimizations (PERF-001 through PERF-005):
- 67x fewer FFTs via pre-computed analytic signals
- VecDeque O(1) eviction in memory store
- Thread-local FFT planner caching
- BrainGraph::validate() for edge/weight integrity
- Eigenvalue convergence early termination

Ed25519 witness verification system:
- 41 capability attestations across all 12 crates
- SHA-256 digest + Ed25519 signature
- CLI commands: `witness --output` and `witness --verify`

README: ethics warning, hardware parts list (AliExpress), assembly instructions

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

* docs: add crates.io badges and install instructions to ruv-neural README

Add version badges linking to each published crate on crates.io,
cargo add instructions, and crate search link in the Crate Map table.

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

---------

Co-authored-by: Claude <noreply@anthropic.com>
2026-03-09 10:52:24 -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
75d4685d25 feat: cross-platform WiFi collector factory with graceful degradation (ADR-049)
- Add create_collector() factory function that auto-detects platform and never raises
- Add LinuxWifiCollector.is_available() classmethod for probe-without-exception
- Refactor ws_server.py to use create_collector(), removing ~30 lines of duplicated platform detection
- Add 10 unit tests covering all platform paths and edge cases
- Add ADR-049 documenting the cross-platform detection and fallback chain

Docker, WSL, and headless users now get SimulatedCollector automatically
with a clear WARNING log instead of a RuntimeError crash.

Closes #148
Closes #155

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-06 15:09:32 -05:00
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
8b57a6f64c docs: update README with ADR-045–048, Observatory, adaptive classifier, AMOLED display
- Update ADR count from 44 to 48
- Add adaptive classifier (ADR-048) to Intelligence features
- Add Observatory visualization (ADR-047) and AMOLED display (ADR-045) to Deployment features
- Update screenshot to v2-screen.png
- Add ADR-045 (AMOLED), ADR-046 (Android TV), ADR-047 (Observatory), DDD deployment model
- Add AMOLED display firmware (display_hal, display_task, display_ui, LVGL config)
- Add Observatory UI (13 Three.js modules, CSS, HTML entry point)
- Add trained adaptive model JSON
- Update .gitignore for managed_components, recordings, .swarm

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-05 10:20:48 -05:00
rUv
5fa61ba7ea
feat: adaptive CSI classifier with signal smoothing pipeline (ADR-048) (#144)
Add environment-tuned activity classification that learns from labeled
ESP32 CSI recordings, replacing brittle static thresholds.

- Adaptive classifier: 15-feature logistic regression trained from JSONL
  recordings (variance, motion band, subcarrier stats: skew, kurtosis,
  entropy, IQR). Trains in <1s, persists as JSON, auto-loads on restart.
- Three-stage signal smoothing: adaptive baseline subtraction (α=0.003),
  EMA + trimmed-mean median filter (21-frame window), hysteresis debounce
  (4 frames). Motion classification now stable across seconds, not frames.
- Vital signs stabilization: outlier rejection (±8 BPM HR, ±2 BPM BR),
  trimmed mean, dead-band (±2 BPM HR), EMA α=0.02. HR holds steady for
  10+ seconds instead of jumping 50 BPM every frame.
- Observatory auto-detect: always probes /health on startup, connects
  WebSocket to live ESP32 data automatically.
- New API endpoints: POST /api/v1/adaptive/train, GET /adaptive/status,
  POST /adaptive/unload for runtime model management.
- Updated user guide with Observatory, adaptive classifier tutorial,
  signal smoothing docs, and new troubleshooting entries.
2026-03-05 10:15:18 -05:00
ruv
b995adea87 docs: update user guide for multi-arch Docker and RuView repo rename
- Update GitHub URLs from ruvnet/wifi-densepose to ruvnet/RuView
- Update git clone directory references to RuView
- Note multi-architecture support (amd64 + arm64) for Docker image
- Add troubleshooting entry for macOS arm64 manifest error

Fixes ruvnet/RuView#136

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-04 10:21:22 -05:00
ruv
2ad510782e docs: add 4 DDD domain models covering all major subsystems
Create complete Domain-Driven Design specifications for:
- Signal Processing (3 contexts: CSI Preprocessing, Feature Extraction, Motion Analysis)
- Training Pipeline (4 contexts: Dataset Management, Model Architecture, Training Orchestration, Embedding & Transfer)
- Hardware Platform (5 contexts: Sensor Node, Edge Processing, WASM Runtime, Aggregation, Provisioning)
- Sensing Server (5 contexts: CSI Ingestion, Model Management, CSI Recording, Training Pipeline, Visualization)

Update DDD index (3 → 7 models) and README docs table.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-03 17:39:57 -05:00
ruv
8658cc3de0 docs: improve RuvSense domain model and add DDD index
- Add intro explaining DDD purpose and bounded context overview table
- Add Edge Intelligence bounded context (#7) for on-device sensing
- Add ubiquitous language terms: Edge Tier, WASM Module
- Fix frame rate 20 Hz -> 28 Hz (measured on hardware)
- Link each context to its source files and ADRs
- Add NVS configuration table and invariants for edge processing
- Create docs/ddd/README.md introducing all 3 domain models
- Update main README docs table to link to DDD index

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-03 17:02:39 -05:00
ruv
7e43edf26a docs: add ADR index with intro on ADRs for AI-assisted development
Explains why ADRs matter for AI-generated code (prevents drift,
provides constraints and rationale), how they work with DDD domain
models, and indexes all 44 ADRs by category.

Also fixes ADR count 43 -> 44 in main README.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-03 16:35:17 -05:00
ruv
f995f69622 docs: update ADRs with ENOMEM crash fix proof (Issue #127)
- ADR-018: Document rate-limiting and ENOMEM backoff safeguards in firmware
- ADR-029: Add note about rate-limiting requirement for channel hopping, mark
  lwIP pbuf exhaustion risk as resolved
- ADR-039: Add finding #5 documenting the sendto ENOMEM crash and fix
  (947 KB binary, hardware-verified 200+ callbacks with zero errors)
- CHANGELOG: Add entries for Issue #127 fix and Issue #130 provisioning fix

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-03 16:14:54 -05:00
ruv
b544545cb0 docs: ADR-044 provisioning tool enhancements
5-phase plan to close remaining gaps in provision.py:
- Phase 1: 7 missing NVS keys (hop_count, chan_list, dwell_ms,
  power_duty, wasm_max, wasm_verify, wasm_pubkey)
- Phase 2: JSON config file for mesh provisioning
- Phase 3: Named presets (basic, vitals, mesh-3, mesh-6-vitals)
- Phase 4: --read (dump NVS) and --verify (boot check)
- Phase 5: Auto-detect serial port

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-03 15:57:34 -05:00
rUv
86f08303e6
docs: update changelog, user guide, and README for ADR-043 (#128)
- CHANGELOG: add ADR-043 entries (14 new API endpoints, WebSocket fix,
  mobile WS fix, 25 real mobile tests)
- README: update ADR count from 41 to 43
- CLAUDE.md: update ADR count from 32 to 43
- User guide: add 14 new REST endpoints to API reference table, note
  that /ws/sensing is available on the HTTP port, update ADR count
2026-03-03 15:21:52 -05:00
rUv
d4fb7d30d3
fix: complete sensing server API, WebSocket connectivity, and mobile tests (#125)
The web UI had persistent 404 errors on model, recording, and training
endpoints, and the sensing WebSocket never connected on Dashboard/Live
Demo tabs because sensingService.start() was only called lazily on
Sensing tab visit.

Server (main.rs):
- Add 14 fully-functional Axum handlers: model CRUD (7), recording
  lifecycle (4), training control (3)
- Scan data/models/ and data/recordings/ at startup
- Recording writes CSI frames to .jsonl via tokio background task
- Model load/unload lifecycle with state tracking

Web UI (app.js):
- Import and start sensingService early in initializeServices() so
  Dashboard and Live Demo tabs connect to /ws/sensing immediately

Mobile (ws.service.ts):
- Fix WebSocket URL builder to use same-origin port instead of
  hardcoded port 3001

Mobile (jest.config.js):
- Fix testPathIgnorePatterns that was ignoring the entire test directory

Mobile (25 test files):
- Replace all it.todo() placeholder tests with real implementations
  covering components, services, stores, hooks, screens, and utils

ADR-043 documents all changes.
2026-03-03 13:27:03 -05:00