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5a7f431b0e4f2d5864a522146b07c1702e7797e2
99 Commits
| Author | SHA1 | Message | Date | |
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 rUv
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5a7f431b0e |
ADR-081: Implement 5-layer adaptive CSI mesh firmware kernel (#404)
* ADR-081: adaptive CSI mesh firmware kernel + scaffolding
Introduces a 5-layer firmware kernel that reframes the existing ESP32
modules as components of a chipset-agnostic architecture and authorizes
adaptive control + a compact feature-state stream as the default upstream.
Layers:
L1 Radio Abstraction Layer — rv_radio_ops_t vtable + ESP32 binding
L2 Adaptive Controller — fast/medium/slow loops (200ms/1s/30s)
L3 Mesh Sensing Plane — anchor/observer/relay/coordinator (spec)
L4 On-device Feature Extr. — rv_feature_state_t (magic 0xC5110006)
L5 Rust handoff — feature_state default; debug raw gated
Files:
docs/adr/ADR-081-adaptive-csi-mesh-firmware-kernel.md (new)
firmware/esp32-csi-node/main/rv_radio_ops.h (new)
firmware/esp32-csi-node/main/rv_radio_ops_esp32.c (new)
firmware/esp32-csi-node/main/rv_feature_state.{h,c} (new)
firmware/esp32-csi-node/main/adaptive_controller.{h,c} (new)
firmware/esp32-csi-node/main/main.c (wire L1+L2)
firmware/esp32-csi-node/main/CMakeLists.txt (add 4 sources)
firmware/esp32-csi-node/main/Kconfig.projbuild (controller knobs)
CHANGELOG.md (Unreleased)
Default policy is conservative: enable_channel_switch and
enable_role_change are off, so behavior matches today's firmware
unless an operator opts in via menuconfig. The pure
adaptive_controller_decide() is exposed for offline unit tests.
Reuses (does not rewrite): csi_collector, edge_processing (ADR-039),
swarm_bridge (ADR-066), secure_tdm (ADR-032), wasm_runtime (ADR-040).
* ADR-081: implement Layers 1/2/4 end-to-end + host tests + QEMU hooks
Turns the ADR-081 scaffolding into a working adaptive CSI mesh kernel:
Layer 1 radio abstraction has an ESP32 binding and a mock binding; Layer 2
adaptive controller runs on FreeRTOS timers; Layer 4 feature-state packet
is emitted at 5 Hz by default, replacing raw ADR-018 CSI as the default
upstream.
New files:
firmware/esp32-csi-node/main/adaptive_controller_decide.c (pure policy)
firmware/esp32-csi-node/main/rv_radio_ops_mock.c (QEMU binding)
firmware/esp32-csi-node/tests/host/Makefile (host tests)
firmware/esp32-csi-node/tests/host/test_adaptive_controller.c
firmware/esp32-csi-node/tests/host/test_rv_feature_state.c
firmware/esp32-csi-node/tests/host/esp_err.h (shim)
firmware/esp32-csi-node/tests/host/.gitignore
Modified:
adaptive_controller.c — includes pure decide.c; emit_feature_state()
wired into fast loop (200 ms = 5 Hz)
rv_radio_ops_esp32.c — get_health() fills pkt_yield + send_fail
csi_collector.{c,h} — pkt_yield/send_fail accessors (ADR-081 L1)
rv_feature_state.h — packed size corrected to 60 bytes
(was incorrectly 80 in initial commit)
main.c — mock binding registered under mock CSI
CMakeLists.txt — rv_radio_ops_mock.c under CSI_MOCK_ENABLED
scripts/validate_qemu_output.py — 3 new ADR-081 checks (17/18/19)
docs/adr/ADR-081-*.md — status → Accepted (partial);
implementation-status matrix; measured
benchmarks (decide 3.2 ns, CRC32 614 ns);
bandwidth 300 B/s @ 5 Hz (99.7% vs raw);
verification section
CHANGELOG.md — artifact-level entries
Tests (host, gcc -O2 -std=c11):
test_adaptive_controller: 18/18 pass, decide() = 3.2 ns/call
test_rv_feature_state: 15/15 pass, CRC32(56 B) = 614 ns/pkt, 87 MB/s
sizeof(rv_feature_state_t) == 60 asserted
IEEE CRC32 known vectors verified
Deferred (tracked in ADR-081 roadmap Phase 3/4):
Layer 3 mesh-plane message types, role-assignment FSM, Rust-side mirror
trait in crates/wifi-densepose-hardware/src/radio_ops.rs.
* ADR-081: Layer 3 mesh plane + Rust mirror trait — all 5 layers landed
Fully implements the remaining deferred pieces of the adaptive CSI mesh
firmware kernel. All 5 layers (Radio Abstraction, Adaptive Controller,
Mesh Sensing Plane, On-device Feature Extraction, Rust handoff) are
now implemented and host-tested end-to-end.
Layer 3 — Mesh Sensing Plane (firmware/esp32-csi-node/main/rv_mesh.{h,c}):
* 4 node roles: Unassigned / Anchor / Observer / FusionRelay / Coordinator
* 7 message types: TIME_SYNC, ROLE_ASSIGN, CHANNEL_PLAN,
CALIBRATION_START, FEATURE_DELTA, HEALTH, ANOMALY_ALERT
* 3 auth classes: None / HMAC-SHA256-session / Ed25519-batch
* Payload types: rv_node_status_t (28 B), rv_anomaly_alert_t (28 B),
rv_time_sync_t (16 B), rv_role_assign_t (16 B),
rv_channel_plan_t (24 B), rv_calibration_start_t (20 B)
* 16-byte envelope + payload + IEEE CRC32 trailer
* Pure rv_mesh_encode()/rv_mesh_decode() plus typed convenience encoders
* rv_mesh_send_health() + rv_mesh_send_anomaly() helpers
Controller wiring (adaptive_controller.c):
* Slow loop (30 s default) now emits HEALTH
* apply_decision() emits ANOMALY_ALERT on transitions to ALERT /
DEGRADED
* Role + mesh epoch tracked in module state; epoch bumps on role
change
Layer 5 — Rust mirror (crates/wifi-densepose-hardware/src/radio_ops.rs):
* RadioOps trait mirrors rv_radio_ops_t vtable
* MockRadio backend for offline tests
* MeshHeader / NodeStatus / AnomalyAlert types mirror rv_mesh.h
* Byte-identical IEEE CRC32 (poly 0xEDB88320) verified against
firmware test vectors (0xCBF43926 for "123456789")
* decode_mesh / decode_node_status / decode_anomaly_alert / encode_health
* 8 unit tests, including mesh_constants_match_firmware which asserts
MESH_MAGIC/VERSION/HEADER_SIZE/MAX_PAYLOAD match rv_mesh.h
byte-for-byte
* Exported from lib.rs
* signal/ruvector/train/mat crates untouched — satisfies ADR-081
portability acceptance test
Tests (all passing):
test_adaptive_controller: 18/18 (C, decide() 3.2 ns/call)
test_rv_feature_state: 15/15 (C, CRC32 87 MB/s)
test_rv_mesh: 27/27 (C, roundtrip 1.0 µs)
radio_ops::tests (Rust): 8/8
--- total: 68/68 assertions green ---
Docs:
* ADR-081 status flipped to Accepted
* Implementation-status matrix updated; L3 + Rust mirror both
marked Implemented
* Benchmarks table extended with rv_mesh encode+decode roundtrip
* Verification section updated with cargo test invocation
* CHANGELOG: two new entries for L3 mesh plane + Rust mirror
Remaining follow-ups (Phase 3.5 polish, not blocking):
* Mesh RX path (UDP listener + dispatch) on the firmware
* Ed25519 signing for CHANNEL_PLAN / CALIBRATION_START
* Hardware validation on COM7
* Add test_rv_mesh to host-test .gitignore
Fixes an untracked-file warning from the repo stop-hook: the compiled
binary was built by make but the .gitignore update was missed in
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voidborne-d
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e38c0f4dcc |
fix: Docker entrypoint arg handling + configurable model directory
Fixes #384: docker run with --source/--tick-ms flags now works correctly. Fixes #399: model files in mounted volumes are now discoverable via MODELS_DIR env var. Root cause (issue #384): The Dockerfile used ENTRYPOINT ["/bin/sh", "-c"] with a shell-form CMD. When users passed flags like `--source wifi --tick-ms 500` as docker run arguments, Docker replaced CMD entirely, resulting in `/bin/sh -c "--source wifi --tick-ms 500"` which executes `--source` as a shell command → `--source: not found`. Root cause (issue #399): Model directory was hardcoded to the relative path `data/models`. When Docker users mounted models to `/app/models/`, the scan looked in the wrong place. Changes: 1. docker/docker-entrypoint.sh (new): - Proper entrypoint script that handles both env-var-based defaults and user-passed CLI flags - No arguments → starts server with CSI_SOURCE env var as --source - Flag arguments (start with -) → prepends /app/sensing-server + defaults, appends user flags (clap last-wins allows overrides) - Non-flag first arg → exec passthrough (e.g., /bin/sh for debugging) - Sets --bind-addr 0.0.0.0 (was 127.0.0.1 which blocks container access) 2. docker/Dockerfile.rust: - Switch from ENTRYPOINT ["/bin/sh", "-c"] to exec-form entrypoint - Add MODELS_DIR env var (default: data/models) - COPY the entrypoint script into the image 3. docker/docker-compose.yml: - Remove shell-form command (entrypoint handles defaults) - Add MODELS_DIR env var 4. model_manager.rs + main.rs: - Replace hardcoded `data/models` path with `effective_models_dir()` / `models_dir()` that reads MODELS_DIR env var at runtime - Docker users can now: docker run -v /host/models:/app/models -e MODELS_DIR=/app/models 5. tests/test_docker_entrypoint.sh (new, 17 tests): - Default CSI_SOURCE substitution (6 assertions) - Custom CSI_SOURCE propagation - User-passed flag arguments (--source, --tick-ms, --model) - Unset CSI_SOURCE defaults to auto - Explicit command passthrough - MODELS_DIR env var propagation |
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ruv
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5bd0d59aa6 |
feat: ADR-080 P1+P2 remediation — refactor, perf, tests, safety
P1 fixes (this sprint): - P1-6: Extract sensing-server modules (cli, types, csi, pose) from main.rs - P1-7: DDA ray march for tomography — O(max(n)) replaces O(n^3) voxel scan - P1-8: Batch neural inference — Tensor::stack/split for single GPU call - P1-10: Eliminate 112KB/frame alloc — islice replaces deque→list copy P2 fixes (this quarter): - P2-11: Python unit tests for 8 modules (rate_limit, auth, error_handler, pose_service, stream_service, hardware_service, health_check, metrics) - P2-13: MAT simulated data safety guard — blocking overlay + pulsing banner - P2-14: Wire token blacklist into auth verification + logout endpoint - P2-15: Frame budget benchmark — confirms pipeline well under 50ms budget Addresses 8 of 10 remaining issues from QE analysis (ADR-080). Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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b7650b5243 |
feat(server): accuracy sprint 001 — Kalman tracker, multi-node fusion, eigenvalue counting
Original work by @taylorjdawson (PR #341). Merged with v0.5.5 firmware preserved (ADR-069 feature vectors, ADR-073 channel hopping, batch-limited watchdog from #266 fix). New server features: - Kalman tracker bridge for temporal smoothing - Multi-node CSI fusion with field model - Eigenvalue-based person counting - Calibration endpoints (start/stop/status) - Node positions parsing - Adaptive classifier enhancements Co-Authored-By: taylorjdawson <taylor@users.noreply.github.com> Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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a23bd2ec01 |
fix(server): resolve adversarial review findings C1-C5, H1-H3, H5, M1-M2
Critical fixes: - C1: FieldModel created with n_links=1 (single_link_config) so feed_calibration/extract_perturbation no longer get DimensionMismatch - C2: variance_explained now uses centered covariance trace (E[x²]-E[x]²) matching mode_energies normalization - C3: MP ratio uses total_obs = frames * links for consistent threshold between calibration and runtime - C4: Noise estimator filters to positive eigenvalues only, preventing collapse to ~0 on rank-deficient matrices (p > n) - C5: ESP32 paths gate total_persons on presence — empty room reports 0 High fixes: - H1: Bounding box computed from observed keypoints only (confidence > 0), preventing collapse from centroid-filled unobserved slots - H2: fuse_or_fallback returns Option<usize> instead of sentinel 0, eliminating type ambiguity between "fusion succeeded" and "zero people" - H3: Monotonic epoch-relative timestamps replace wall-clock/Instant mixing, preventing spurious TimestampMismatch on NTP steps - H5: ndarray-linalg gated behind "eigenvalue" feature flag (default=on), diagonal fallback used with --no-default-features Moderate fixes: - M1: calibration_start guards against replacing Fresh calibration - M2: parse_node_positions logs warning for malformed entries Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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3733e54aef |
feat: cross-node fusion + DynamicMinCut + RSSI tracking (v0.5.3)
* feat(server): cross-node RSSI-weighted feature fusion + benchmarks Adds fuse_multi_node_features() that combines CSI features across all active ESP32 nodes using RSSI-based weighting (closer node = higher weight). Benchmark results (2 ESP32 nodes, 30s, ~1500 frames): Metric | Baseline | Fusion | Improvement ---------------------|----------|---------|------------ Variance mean | 109.4 | 77.6 | -29% noise Variance std | 154.1 | 105.4 | -32% stability Confidence | 0.643 | 0.686 | +7% Keypoint spread std | 4.5 | 1.3 | -72% jitter Presence ratio | 93.4% | 94.6% | +1.3pp Person count still fluctuates near threshold — tracked as known issue. Verified on real hardware: COM6 (node 1) + COM9 (node 2) on ruv.net. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(ui): add client-side lerp smoothing to pose renderer Keypoints now interpolate between frames (alpha=0.25) instead of jumping directly to new positions. This eliminates visual jitter that persists even with server-side EMA smoothing, because the renderer was drawing every WebSocket frame at full rate. Applied to skeleton, keypoints, and dense body rendering paths. Co-Authored-By: claude-flow <ruv@ruv.net> * feat: DynamicMinCut person separation + UI lerp smoothing - Added ruvector-mincut dependency to sensing server - Replaced variance-based person scoring with actual graph min-cut on subcarrier temporal correlation matrix (Pearson correlation edges, DynamicMinCut exact max-flow) - Recalibrated feature scaling for real ESP32 data ranges - UI: client-side lerp interpolation (alpha=0.25) on keypoint positions - Dampened procedural animation (noise, stride, extremity jitter) - Person count thresholds retuned for mincut ratio Co-Authored-By: claude-flow <ruv@ruv.net> * docs: update CHANGELOG with v0.5.1-v0.5.3 releases Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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cd84c35f8f |
feat: cross-node RSSI-weighted feature fusion (benchmarked)
Adds fuse_multi_node_features() that combines CSI features across all active ESP32 nodes using RSSI-based weighting (closer node = higher weight). Benchmark results (2 ESP32 nodes, 30s, ~1500 frames): Metric | Baseline | Fusion | Improvement ---------------------|----------|---------|------------ Variance mean | 109.4 | 77.6 | -29% noise Variance std | 154.1 | 105.4 | -32% stability Confidence | 0.643 | 0.686 | +7% Keypoint spread std | 4.5 | 1.3 | -72% jitter Presence ratio | 93.4% | 94.6% | +1.3pp Person count still fluctuates near threshold — tracked as known issue. Verified on real hardware: COM6 (node 1) + COM9 (node 2) on ruv.net. |
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rUv
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dd45160cc5 |
fix: skeleton jitter + person count stability (hardware-verified)
* 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> * fix(server): use max instead of sum for multi-node person aggregation With nodes in the same room, each node sees the same people. Summing per-node counts double-counted (2 nodes × 1 person = 2 persons). Now uses max() so 2 nodes × 1 person = 1 person. Verified on real hardware: COM6 (node 1) + COM9 (node 2) on ruv.net, estimated_persons=1 with 1 person in room. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(server): reduce skeleton jitter + raise person count thresholds - EMA alpha 0.3→0.15, low-coherence 0.1→0.05 - Remove tick-based noise (main jitter source) - Breathing 5x slower, extremity jitter 3x smaller, stride 2x smaller - Person count 1→2 threshold 0.65→0.80 - Aggregation sum→max for same-room nodes Verified on COM6+COM9: 1 person stable. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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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> |
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rUv
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6f23e89909 |
fix: deep review optimizations — firmware + server
* feat(signal): subcarrier importance weighting via mincut partition (Phase 1) Adds subcarrier_importance_weights() to ruvector signal crate — converts mincut partition into per-subcarrier float weights (>1.0 for sensitive, 0.5 for insensitive subcarriers). Sensing server now uses weighted mean/variance in extract_features_from_frame instead of treating all 56 subcarriers equally. This emphasizes body-motion- sensitive subcarriers and reduces noise from static multipath. Expected: ~26% reduction in keypoint jitter (±15cm → ±11cm RMS). 284 tests pass (191 trainer + 51 lib + 18 vital_signs + 16 dataset + 8 multi_node). Co-Authored-By: claude-flow <ruv@ruv.net> * fix(firmware): stack overflow risk + tick-rate independence (review findings) Critical fixes from deep review: 1. **Stack overflow prevention**: Moved BPM scratch buffers (br_buf, hr_buf) from stack to static storage in both process_frame() and update_multi_person_vitals(). Combined stack was ~6.5-7.5 KB of 8 KB limit — now reduced by ~4 KB to safe margins. 2. **Tick-rate independence**: Post-batch yield now uses pdMS_TO_TICKS(20) with min-1 guard instead of raw vTaskDelay(2). Previously assumed 100Hz tick rate. 3. **EDGE_BATCH_LIMIT to header**: Moved from local const to edge_processing.h #define for configurability. Firmware builds clean at 843 KB. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(server): stale node eviction, remove unsafe pointer (review findings) Critical fixes from deep review: 1. **Stale node eviction**: node_states HashMap now evicts nodes with no frame for >60 seconds, every 100 ticks. Prevents unbounded memory growth and stale smoothing data when nodes are replaced. 2. **Remove unsafe raw pointer**: Replaced the unsafe raw pointer to adaptive_model (used to break borrow checker deadlock with node_states) with a safe .clone() before the mutable borrow. AdaptiveModel derives Clone so this is a clean copy. 284 tests pass, zero failures. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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1dcf5d42eb |
feat(signal): subcarrier importance weighting — RuVector Phase 1
Adds subcarrier_importance_weights() to ruvector signal crate — converts mincut partition into per-subcarrier float weights (>1.0 for sensitive, 0.5 for insensitive subcarriers). Sensing server now uses weighted mean/variance in extract_features_from_frame instead of treating all 56 subcarriers equally. This emphasizes body-motion- sensitive subcarriers and reduces noise from static multipath. Expected: ~26% reduction in keypoint jitter (±15cm → ±11cm RMS). 284 tests pass (191 trainer + 51 lib + 18 vital_signs + 16 dataset + 8 multi_node). |
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rUv
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9814d2bc62 |
fix(server): correct RSSI byte offset in frame parser (#332)
The server parsed rssi from buf[14] and noise_floor from buf[15], but the firmware (csi_collector.c) packs them at buf[16] and buf[17]: Firmware: n_subcarriers=u16(6-7) freq=u32(8-11) seq=u32(12-15) rssi=i8(16) Server: n_subcarriers=u8(6) freq=u16(8-9) seq=u32(10-13) rssi=i8(14) ← WRONG This caused RSSI to read the high byte of the sequence counter instead of the actual signed RSSI value, producing positive values (e.g., +9) instead of the correct negative values (e.g., -46 dBm). Added inline documentation of the frame layout matching csi_collector.c. Closes #332 |
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rUv
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74e0ebbd41 |
feat(server): accuracy sprint 001 — Kalman tracker, multi-node fusion, eigenvalue counting
Wire three existing signal-crate components into the live sensing path: Step 1 — Kalman Tracker (tracker_bridge.rs): - PoseTracker from wifi-densepose-signal wired into all 5 mutable derive_pose_from_sensing call sites - Stable TrackId-based person IDs replace ephemeral 0-based indices - Greedy Mahalanobis assignment with proper lifecycle transitions (Tentative → Active → Lost → Terminated) - Kalman-smoothed keypoint positions reduce frame-to-frame jitter Step 2 — Multi-Node Fusion (multistatic_bridge.rs): - MultistaticFuser replaces naive .sum() aggregation at both ESP32 paths - Attention-weighted CSI fusion across nodes with cosine-similarity weights - Fallback uses max (not sum) to avoid double-counting overlapping coverage - Node positions configurable via --node-positions CLI arg - Single-node passthrough preserved (min_nodes=1) Step 3 — Eigenvalue Person Counting (field_model.rs upgrade): - Full covariance matrix accumulation (replaces diagonal variance approx) - True eigendecomposition via ndarray-linalg Eigh (Marcenko-Pastur threshold) - estimate_occupancy() for runtime eigenvalue-based counting - Calibration API: POST /calibration/start|stop, GET /calibration/status - Graceful fallback to score_to_person_count when uncalibrated New files: tracker_bridge.rs, multistatic_bridge.rs, field_bridge.rs Modified: sensing-server main.rs, Cargo.toml; signal field_model.rs, Cargo.toml Refs: .swarm/plans/accuracy-sprint-001.md Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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7f02c87c6f |
test(server): add multi-node mesh integration tests (ADR-068)
8 tests covering per-node state pipeline: - Frame builder validity (CSI + vitals packet formats) - Different nodes produce different I/Q patterns - Multi-node UDP send (1/3/5/7/11 nodes) - Mesh simulation with variable rates and node dropout - Large mesh: 100 nodes x 10 frames = 1,000 frames - Max scale: 255 unique node_ids All 26 server tests pass (8 new + 18 existing vital signs). Co-Authored-By: claude-flow <ruv@ruv.net> |
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Taylor Dawson
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d88994816f |
feat: dynamic classifier classes, per-node UI, XSS fix, RSSI fix
Complements #326 (per-node state pipeline) with additional features: - Dynamic adaptive classifier: discover activity classes from training data filenames instead of hardcoded array. Users add classes via filename convention (train_<class>_<desc>.jsonl), no code changes. - Per-node UI cards: SensingTab shows individual node status with color-coded markers, RSSI, variance, and classification per node. - Colored node markers in 3D gaussian splat view (8-color palette). - Per-node RSSI history tracking in sensing service. - XSS fix: UI uses createElement/textContent instead of innerHTML. - RSSI sign fix: ensure dBm values are always negative. - GET /api/v1/nodes endpoint for per-node health monitoring. - node_features field in WebSocket SensingUpdate messages. - Firmware watchdog fix: yield after every frame to prevent IDLE1 starvation. Addresses #237, #276, #282 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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rUv
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3c02f6cfb0 |
feat(server): per-node state pipeline for multi-node sensing (#249)
* 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> * feat(server): per-node state pipeline for multi-node sensing (ADR-068, #249) Replaces the single shared state pipeline with per-node HashMap<u8, NodeState>. Each ESP32 node now gets independent: - frame_history (temporal analysis) - smoothed_person_score / prev_person_count - smoothed_motion / baseline / debounce state - vital sign detector + smoothing buffers - RSSI history Multi-node aggregation: - Person count = sum of per-node counts for active nodes (seen <10s) - SensingUpdate.nodes includes all active nodes - estimated_persons reflects cross-node aggregate Single-node deployments behave identically (HashMap has one entry). Simulated data path unchanged for backward compatibility. Closes #249 Refs #237, #276, #282 Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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40f19622af |
fix(firmware,server): watchdog crash + no detection from edge vitals (#321, #323)
* fix(firmware,server): watchdog crash on busy LANs + no detection from edge vitals (#321, #323) **Firmware (#321):** edge_dsp task now batch-limits frame processing to 4 frames before a 10ms yield. On corporate LANs with high CSI frame rates, the previous 1-tick-per-frame yield wasn't enough to prevent IDLE1 starvation and task watchdog triggers. **Sensing server (#323):** When ESP32 runs the edge DSP pipeline (Tier 2+), it sends vitals packets (magic 0xC5110002) instead of raw CSI frames. Previously, the server broadcast these as raw edge_vitals but never generated a sensing_update, so the UI showed "connected" but "0 persons". Now synthesizes a full sensing_update from vitals data including classification, person count, and pose generation. Closes #321 Closes #323 Co-Authored-By: claude-flow <ruv@ruv.net> * fix(firmware): address review findings — idle busy-spin and observability - Fix pdMS_TO_TICKS(5)==0 at 100Hz causing busy-spin in idle path (use vTaskDelay(1) instead) - Post-batch yield now 2 ticks (20ms) for genuinely longer pause - Add s_ring_drops counter to ring_push for diagnosing frame drops - Expose drop count in periodic vitals log line Co-Authored-By: claude-flow <ruv@ruv.net> * fix(server): set breathing_band_power for skeleton animation from vitals When presence is detected via edge vitals, set breathing_band_power to 0.5 so the UI's torso breathing animation works. Previously hardcoded to 0.0 which made the skeleton appear static even when breathing rate was being reported. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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7a13877fa3 |
fix(sensing-server): detect ESP32 offline after 5s frame timeout (#300)
The source field was set to "esp32" on the first UDP frame but never reverted when frames stopped arriving. This caused the UI to show "Real hardware connected" indefinitely after powering off all nodes. Changes: - Add last_esp32_frame timestamp to AppStateInner - Add effective_source() method with 5-second timeout - Source becomes "esp32:offline" when no frames received within 5s - Health endpoint shows "degraded" instead of "healthy" when offline - All 6 status/health/info API endpoints use effective_source() Fixes #297 Co-authored-by: Reuven <cohen@ruv-mac-mini.local> |
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rUv
|
5f3c90bf1c |
fix(sensing-server): add real hysteresis to person count estimation (#295)
The person-count heuristic was causing widespread flickering (#237, #249, #280, #292) because: 1. Threshold 0.50 for 2-persons was too low — multipath reflections in small rooms easily exceeded it 2. No actual hysteresis despite the comment claiming asymmetric thresholds 3. EMA smoothing (α=0.15) was too responsive to transient spikes Changes: - Raise up-thresholds: 1→2 persons at 0.65 (was 0.50), 2→3 at 0.85 (was 0.80) - Add true hysteresis with asymmetric down-thresholds: 2→1 at 0.45, 3→2 at 0.70 - Track prev_person_count in SensingState for state-aware transitions - Increase EMA smoothing to α=0.10 (~2s time constant at 20 Hz) - Update all 4 call sites (ESP32, Windows WiFi, multi-BSSID, simulated) Fixes #292, #280, #237 Co-authored-by: Reuven <cohen@ruv-mac-mini.local> |
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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> |
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ruv
|
1d4af7c757 |
chore: add runtime artifacts to .gitignore and untrack them
Remove from index: daemon.pid, vectors.db, memory.db, pending-insights.jsonl, session state, node_modules. These are machine-specific runtime artifacts that should never have been committed. Co-Authored-By: claude-flow <ruv@ruv.net> |
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Reuven
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ff91d4e8cf |
fix(desktop): remove bundled sensing-server resource for CI build
The sensing-server binary was referenced in tauri.conf.json but doesn't exist in CI environment. Removed the resources section to fix the build. Co-Authored-By: claude-flow <ruv@ruv.net> |
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Reuven
|
fc92436f52 |
chore: add build artifacts and session state
- NVS config binaries for ESP32 WiFi provisioning - macOS Tauri schema - package-lock.json update - Claude Flow session state Co-Authored-By: claude-flow <ruv@ruv.net> |
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Reuven
|
285bb0ad37 |
feat(desktop): v0.4.4 - WiFi configuration via serial port
## New Features - WiFi Configuration Modal: Configure ESP32 WiFi credentials directly from the desktop app - Serial port WiFi commands: Sends wifi_config/wifi/set ssid commands via serial - Improved feedback UI with status indicators (Success/Commands Sent/Error) ## API Improvements - New Tauri command: configure_esp32_wifi(port, ssid, password) - 21 new integration tests covering all API functionality - ESP32 VID/PID detection for CP210x, CH340, FTDI, and native USB ## UI Enhancements - WiFi button in Serial Ports table for ESP32-compatible devices - Modal with SSID/password inputs and clear status feedback - "Done" button after configuration with "Try Again" option ## Testing - 18 unit tests + 21 integration tests = 39 total tests passing - Tests cover: discovery, settings, server, flash, OTA, provision, WASM, state, domain models Co-Authored-By: claude-flow <ruv@ruv.net> |
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Reuven
|
b5ec4ef043 |
chore: update Cargo.lock
Co-Authored-By: claude-flow <ruv@ruv.net> |
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Reuven
|
21aba2df8d |
feat(desktop): v0.4.3 - USB device discovery and data source toggle
## Changes - Auto-scan serial ports on Discovery page load (not just Serial tab) - Show USB device hint when no network nodes found but USB devices detected - Add "Flash →" button in Serial Ports table for quick navigation - Fix server stop: proper SIGTERM/SIGKILL with process group handling - Add data source selector on Sensing page (simulate/auto/wifi/esp32) - Fix log viewer scroll (use containerRef.scrollTop instead of scrollIntoView) - Add fallback serial port scanning for macOS when tokio_serial fails ## Fixes - ESP32 USB devices now visible immediately on Discovery page - Server processes properly terminated on stop - Log viewer no longer scrolls entire page Co-Authored-By: claude-flow <ruv@ruv.net> |
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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> |
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Reuven
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3b37aaf460 |
fix(desktop): v0.4.1 - Fix Dashboard Quick Actions and Scan Network
- Add navigation to Quick Actions (Flash, OTA, WASM buttons now work) - Add error feedback for Scan Network failures - Create version.ts as single source of truth for version - Switch reqwest from rustls-tls to native-tls for Windows compatibility - Version bump to 0.4.1 Co-Authored-By: claude-flow <ruv@ruv.net> |
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Reuven
|
d3c683cc7e |
fix(desktop): use native-tls for Windows compatibility
- Switch from rustls-tls to native-tls for better Windows support - Fix Cargo.toml formatting (remove duplicate sections) Co-Authored-By: claude-flow <ruv@ruv.net> |
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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> |
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Reuven
|
26a7d6775a |
feat(desktop): add GitHub Actions workflow for cross-platform releases
- Add desktop-release.yml workflow for automated Windows/macOS builds - Fix frontendDist path in tauri.conf.json for production builds - Builds macOS (arm64 + x64) and Windows (MSI + NSIS) on native runners - Creates GitHub Release with all artifacts on tag push or manual dispatch To trigger a release: git tag desktop-v0.3.0 && git push origin desktop-v0.3.0 Or use workflow_dispatch from GitHub Actions UI Co-Authored-By: claude-flow <ruv@ruv.net> |
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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> |
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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> |
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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> |
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ruv
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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> |
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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. |
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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. |
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ruv
|
e94c7056f2 |
feat: add ADR-042 CHCI protocol, 24 new edge modules, README restructure
- ADR-042: Coherent Human Channel Imaging (non-CSI sensing protocol) with DDD domain model (6 bounded contexts) - 24 new WASM edge modules: medical (5), retail (5), security (5), building (5), industrial (5), exotic (8) - README: plain-language rewrites, moved detail sections below TOC, added edge module links to use case tables, firmware release docs - User guide: firmware release table, edge intelligence documentation - .gitignore: added rules for wasm, esp32 temp files, NVS binaries - WASM edge crate: cargo config, integration tests, module registry Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
d63d4d95d1 |
feat: implement 24 vendor-integrated WASM edge modules (ADR-041)
Complete implementation of all 24 vendor-integrated sensing modules across 7 categories, compiled to wasm32-unknown-unknown for ESP32-S3 WASM3 runtime deployment. All 243 unit tests pass. Signal Intelligence (6): flash attention, coherence gate, temporal compress, sparse recovery, min-cut person match, optimal transport. Adaptive Learning (4): DTW gesture learn, anomaly attractor, meta adapt, EWC++ lifelong learning. Spatial Reasoning (3): PageRank influence, micro-HNSW, spiking tracker. Temporal Analysis (3): pattern sequence, temporal logic guard, GOAP. AI Security (2): prompt shield, behavioral profiler. Quantum-Inspired (2): quantum coherence, interference search. Autonomous Systems (2): psycho-symbolic engine, self-healing mesh. Exotic (2): time crystal detector, hyperbolic space embedding. Includes vendor_common.rs shared library, security audit with 5 fixes, and security audit report. Co-Authored-By: claude-flow <ruv@ruv.net> |
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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> |
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rUv
|
113011e704 |
fix: WebSocket race condition, data source indicators, auto-start pose detection (#96)
* feat: RVF training pipeline & UI integration (ADR-036) Implement full model training, management, and inference pipeline: Backend (Rust): - recording.rs: CSI recording API (start/stop/list/download/delete) - model_manager.rs: RVF model loading, LoRA profile switching, model library - training_api.rs: Training API with WebSocket progress streaming, simulated training mode with realistic loss curves, auto-RVF export on completion - main.rs: Wire new modules, recording hooks in all CSI paths, data dirs UI (new components): - ModelPanel.js: Dark-mode model library with load/unload, LoRA dropdown - TrainingPanel.js: Recording controls, training config, live Canvas charts - model.service.js: Model REST API client with events - training.service.js: Training + recording API client with WebSocket progress UI (enhancements): - LiveDemoTab: Model selector, LoRA profile switcher, A/B split view toggle, training quick-panel with 60s recording shortcut - SettingsPanel: Full dark mode conversion (issue #92), model configuration (device, threads, auto-load), training configuration (epochs, LR, patience) - PoseDetectionCanvas: 10-frame pose trail with ghost keypoints and motion trajectory lines, cyan trail toggle button - pose.service.js: Model-inference confidence thresholds UI (plumbing): - index.html: Training tab (8th tab) - app.js: Panel initialization and tab routing - style.css: ~250 lines of training/model panel dark-mode styles 191 Rust tests pass, 0 failures. Closes #92. Refs: ADR-036, #93 Co-Authored-By: claude-flow <ruv@ruv.net> * fix: real RuVector training pipeline + UI service fixes Training pipeline (training_api.rs): - Replace simulated training with real signal-based training loop - Load actual CSI data from .csi.jsonl recordings or live frame history - Extract 180 features per frame: subcarrier amplitudes, temporal variance, Goertzel frequency analysis (9 bands), motion gradients, global stats - Train calibrated linear CSI-to-pose mapping via mini-batch gradient descent with L2 regularization (ridge regression), Xavier init, cosine LR decay - Self-supervised: teacher targets from derive_pose_from_sensing() heuristics - Real validation metrics: MSE and PCK@0.2 on 80/20 train/val split - Export trained .rvf with real weights, feature normalization stats, witness - Add infer_pose_from_model() for live inference from trained model - 16 new tests covering features, training, inference, serialization UI fixes: - Fix double-URL bug in model.service.js and training.service.js (buildApiUrl was called twice — once in service, once in apiService) - Fix route paths to match Rust backend (/api/v1/train/*, /api/v1/recording/*) - Fix request body formats (session_name, nested config object) - Fix top-level await in LiveDemoTab.js blocking module graph - Dynamic imports for ModelPanel/TrainingPanel in app.js - Center nav tabs with flex-wrap for 8-tab layout Co-Authored-By: claude-flow <ruv@ruv.net> * fix: WebSocket onOpen race condition, data source indicators, auto-start pose detection - Fix WebSocket onOpen race condition in websocket.service.js where setupEventHandlers replaced onopen after socket was already open, preventing pose service from receiving connection signal - Add 4-state data source indicator (LIVE/SIMULATED/RECONNECTING/OFFLINE) across Dashboard, Sensing, and Live Demo tabs via sensing.service.js - Add hot-plug ESP32 auto-detection in sensing server (auto mode runs both UDP listener and simulation, switches on ESP32_TIMEOUT) - Auto-start pose detection when backend is reachable - Hide duplicate PoseDetectionCanvas controls when enableControls=false - Add standalone Demo button in LiveDemoTab for offline animated demo - Add data source banner and status styling Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
8166d8d822 |
fix: live demo static pose & inaccurate sensing data (issue #86)
- Docker default changed from --source simulated to --source auto (auto-detects ESP32 on UDP 5005, falls back to simulation) - Pose derivation now driven by real sensing features: motion_band_power, breathing_band_power, variance, dominant_freq_hz, change_points - Temporal feature extraction: 100-frame circular buffer, Goertzel breathing rate estimation (0.1-0.5 Hz), frame-to-frame L2 motion detection, SNR-based signal quality metric - Signal field driven by subcarrier variance spatial mapping instead of fixed animation circle - UI data source indicators: LIVE/RECONNECTING/SIMULATED banner on sensing tab, estimation mode badge on live demo tab - Setup guide panel explaining ESP32 count requirements for each capability level (1x: presence, 3x: localization, 4x+: full pose) - Tick rate improved from 500ms to 100ms (2fps to 10fps) - Fixed Option<f64> division bug from PR #83 - ADR-035 documents all decisions Closes #86 Co-Authored-By: claude-flow <ruv@ruv.net> |
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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> |
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ruv
|
0c01157e36 |
feat: ADR-032a midstreamer QUIC transport + secure TDM + temporal gesture + attractor drift
Integrate midstreamer ecosystem for QUIC-secured mesh transport and advanced signal analysis: QUIC Transport (hardware crate): - quic_transport.rs: SecurityMode (ManualCrypto/QuicTransport), FramedMessage wire format, connection management, fallback support (856 lines, 30 tests) - secure_tdm.rs: ReplayWindow, AuthenticatedBeacon (28-byte HMAC format), SecureTdmCoordinator with dual-mode security (994 lines, 20 tests) - transport_bench.rs: Criterion benchmarks (plain vs authenticated vs QUIC) Signal Analysis (signal crate): - temporal_gesture.rs: DTW/LCS/EditDistance gesture matching via midstreamer-temporal-compare, quantized feature comparison (517 lines, 13 tests) - attractor_drift.rs: Takens' theorem phase-space embedding, Lyapunov exponent classification (Stable/Periodic/Chaotic) via midstreamer-attractor (573 lines, 13 tests) ADR-032 updated with Section 6: QUIC Transport Layer (ADR-032a) README updated with CRV signal-line section, badge 1100+, ADR count 33 Dependencies: midstreamer-quic 0.1.0, midstreamer-scheduler 0.1.0, midstreamer-temporal-compare 0.1.0, midstreamer-attractor 0.1.0 Total: 3,136 new lines, 76 tests, 6 benchmarks Co-Authored-By: claude-flow <ruv@ruv.net> |
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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> |
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ruv
|
95c68139bc |
fix: correct failing ADR-030 tests in field_model, longitudinal, and tomography
Fix 4 test failures in the ADR-030 exotic sensing tier modules: - field_model::test_perturbation_extraction: Use 8 subcarriers with 2 modes and varied calibration data so perturbation on subcarrier 5 (not captured by any environmental mode) remains visible in residual. - longitudinal::test_drift_detected_after_sustained_deviation: Use 30 baseline days with tiny noise to anchor Welford stats, then inject deviation of 5.0 (vs 0.1 baseline) so z-score exceeds 2.0 even as drifted values are accumulated into the running statistics. - longitudinal::test_monitoring_level_escalation: Same strategy with 30 baseline days and deviation of 10.0 to sustain z > 2.0 for 7+ days, reaching RiskCorrelation monitoring level. - tomography::test_nonzero_attenuation_produces_density: Fix ISTA solver oscillation by replacing max-column-norm Lipschitz estimate with Frobenius norm squared upper bound, ensuring convergent step size. Also use stronger attenuations (5.0-16.0) and lower lambda (0.001). All 209 ruvsense tests now pass. Workspace compiles cleanly. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
ba9c88ee30 |
fix: correct noisy PCK test to use sufficient noise magnitude
The make_noisy_kpts test helper used noise=0.1 with GT coordinates spread across [0, 0.85], producing a large bbox diagonal that made even noisy predictions fall within PCK@0.2 threshold. Reduce GT coordinate range and increase noise to 0.5 so the test correctly verifies that noisy predictions produce PCK < 1.0. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
5541926e6a |
fix(security): harden RuvSense pipeline against overflow and numerical instability
- tomography.rs: use checked_mul for nx*ny*nz to prevent integer overflow on adversarial grid configurations - phase_align.rs: add defensive bounds check in mean_phase_on_indices to prevent panic on out-of-range subcarrier indices - multistatic.rs: stabilize softmax in attention_weighted_fusion with max-subtraction to prevent exp() overflow on extreme similarity values Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
37b54d649b |
feat: implement ADR-029/030/031 — RuvSense multistatic sensing + field model + RuView fusion
12,126 lines of new Rust code across 22 modules with 285 tests: ADR-029 RuvSense Core (signal crate, 10 modules): - multiband.rs: Multi-band CSI frame fusion from channel hopping - phase_align.rs: Cross-channel LO phase rotation correction - multistatic.rs: Attention-weighted cross-node viewpoint fusion - coherence.rs: Z-score per-subcarrier coherence scoring - coherence_gate.rs: Accept/PredictOnly/Reject/Recalibrate gating - pose_tracker.rs: 17-keypoint Kalman tracker with re-ID - mod.rs: Pipeline orchestrator ADR-030 Persistent Field Model (signal crate, 7 modules): - field_model.rs: SVD-based room eigenstructure, Welford stats - tomography.rs: Coarse RF tomography from link attenuations (ISTA) - longitudinal.rs: Personal baseline drift detection over days - intention.rs: Pre-movement prediction (200-500ms lead signals) - cross_room.rs: Cross-room identity continuity - gesture.rs: Gesture classification via DTW template matching - adversarial.rs: Physically impossible signal detection ADR-031 RuView (ruvector crate, 5 modules): - attention.rs: Scaled dot-product with geometric bias - geometry.rs: Geometric Diversity Index, Cramer-Rao bounds - coherence.rs: Phase phasor coherence gating - fusion.rs: MultistaticArray aggregate, fusion orchestrator - mod.rs: Module exports Training & Hardware: - ruview_metrics.rs: 3-metric acceptance test (PCK/OKS, MOTA, vitals) - esp32/tdm.rs: TDM sensing protocol, sync beacons, drift compensation - Firmware: channel hopping, NDP injection, NVS config extensions Security fixes: - field_model.rs: saturating_sub prevents timestamp underflow - longitudinal.rs: FIFO eviction note for bounded buffer README updated with RuvSense section, new feature badges, changelog v3.1.0. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
303871275b |
feat: ADR-029/031 TDM sensing protocol, channel hopping, and NVS config
Implement the hardware and firmware portions of RuvSense (ADR-029) and RuView (ADR-031) for multistatic WiFi sensing: Rust (wifi-densepose-hardware): - TdmSchedule: uniform slot assignments with configurable cycle period, guard intervals, and processing window (default 4-node 20 Hz) - TdmCoordinator: manages sensing cycles, tracks per-slot completion, cumulative clock drift compensation (±10 ppm over 50 ms = 0.5 us) - SyncBeacon: 16-byte wire format for cycle synchronization with drift correction offsets - TdmSlotCompleted event for aggregator notification - 18 unit tests + 4 doctests, all passing Firmware (C, ESP32): - Channel-hop table in csi_collector.c (s_hop_channels, configurable via csi_collector_set_hop_table) - Timer-driven channel hopping via esp_timer at dwell intervals - NDP frame injection stub via esp_wifi_80211_tx() - Backward-compatible: hop_count=1 disables hopping entirely - NVS config extension: hop_count, chan_list, dwell_ms, tdm_slot, tdm_node_count with bounds validation and Kconfig fallback defaults Co-Authored-By: claude-flow <ruv@ruv.net> |
