Adds a dedicated blocking serial-reader thread that opens the
HLK-LD2402 over a CP2102 USB-UART bridge (default 115200 8N1),
parses ASCII `distance:<cm>\r\n` lines @ ~6 Hz, stores the latest
reading in a static OnceLock<Mutex<…>>, and exposes it via:
GET /api/v1/mmwave/latest →
{ "available": true, "distance_cm": 152, "age_ms": 90 }
{ "available": false } (port absent, stale > 2 s)
UI (Sensing tab) polls the endpoint every visible WS tick and
shows a new blue card "mmWave Radar (24 GHz)" with distance +
age bar. Card hides when unavailable.
CLI:
--mmwave-port /dev/cu.usbserial-1140
--mmwave-baud 115200 (default)
Both optional — server runs as before if the module is absent.
Open failure: single WARN log, reader thread exits, server keeps
serving WiFi sensing.
Verified live: distance 149-153 cm at ~6 Hz, REST returns fresh
readings with age_ms 55-127.
Out of scope (logged in ADR-121): Engineering Mode binary frames,
vitals cross-check vs ADR-021, W-MLP feature fusion, auto-reconnect.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Audit on 6-node training data (151,329 frames) found 21 multicollinear
pairs (|r|>0.85), one dead feature (amp_min constant 0), and only node[0]
used in 8 of 15 features. Top per-feature F-stat = 15,497 but accuracy
stuck at 44.4% — classifier couldn't extract the signal that physical
sensors were already capturing.
Refactor:
- Drop 8 dead/redundant features (amp_min, amp_range, breath_bp,
spec_pow, motion_bp, amp_mean, amp_max, amp_iqr, amp_kurt).
- Keep 4 globals: variance, mean_rssi, dom_hz, change_pts.
- Add per-node features × all 6 nodes: amp_std, amp_skew, amp_entropy.
- New N_FEATURES = 22 (was 15). Z-score normalisation kept.
API change: features_from_runtime now takes &[(u8, &[f64])] — caller
must supply per-node amplitudes. New helper current_per_node_amps()
reads AMP_HIST.nbvi_history.back() for all live nodes.
Old data/adaptive_model.json removed (incompatible 15-feature schema).
Retrain result on same 151k frames:
44.4% → 49.58% accuracy (+5.2 pts)
Total improvement vs 2-node baseline (40.4%): +9.2 pts.
Live confidence distribution now meaningful (0.30-0.85) vs pre-fix
near-uniform 0.04-0.10. Sensor placement matters: n6 (near door, far
from AP) sep_ratio=0.60 best; n1/n5 (near AP) ~0.01-0.06 nearly dead.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Audit fix bundle (10 areas; details in ADR-117 + commit body below).
Server (main.rs / wiflow_v1.rs):
- UDP receiver filters loopback/multicast/unspecified before NODE_ADDRS
registration. Defends against `cargo test` cross-talk that spawned
250+ ping zombies on the production server's :5005 port.
- csi_keepalive_task pre-reaps `/sbin/ping -i 0.040` orphans at task
entry. macOS doesn't propagate parent death, so killed servers used
to leave init-parented pings running indefinitely.
- run_wiflow_inference stamps real classifier confidence onto every
keypoint (was hardcoded 1.0) — reads 0.037 on live data, honest.
- run_wiflow_inference clones only the tail-20 frames inside the lock,
not the full 600-deep VecDeque (~270 KB → ~9 KB per tick).
- wiflow_v1::build_input_from_history: zero-pad dead channel slots
instead of duplicating subcarrier 0 across all of them. Comment said
"zero the rest", prior code did the opposite.
- GET / now 308-redirects to /ui/index.html; API index moved to /api.
UI (ui/index.html, ui/components/LiveDemoTab.js):
- <section id="sensing"> gets a <div id="sensing-container"> child so
app.js::SensingTab.mount has its mount point. Sensing tab was
permanently blank.
- LiveDemoTab.fetchModels: only inject WiFlow into the dropdown if no
RVF model is already active. Prevents silent flip back to WiFlow
after every poll.
Tests (multi_node_test.rs):
- test_multi_node_udp_send probes 127.0.0.1:5005 first; if bind fails
(e.g. a dev server is running), skip the send. Two-layer defense
with the server-side filter above.
Docs (CHECKLIST.md, ADR-115, espectre-gap-analysis.md, ota-pipeline.md):
- CHECKLIST head sha + count refreshed (43→47 Done, head 0ec1e4b0,
ADR range to 001-117 with ADR-111 noted as intentionally absent).
- ADR-115 typo fixes: "ADR-100" → "ADR-110" (TP-Link WISP),
"ADR-111" → "ADR-109" (AP-MAC tracking actually lives there).
- gap-analysis "Still open" table: 8 shipped items annotated with
commit hashes; remainder reclassified Deferred with reason.
- ota-pipeline.md: new "Operator REST endpoints" section listing
/ota/recalibrate (ADR-109) and /ota/set-target (ADR-115) with
unauthed + bearer-token curl examples.
Verified post-restart:
- exactly 2 ping children, both parented to current PID, one per real
sensor IP, no 127.0.0.1.
- GET / → 308 → /ui/index.html.
- /api/v1/info: pose_estimation=true, version 0.3.0.
- /api/v1/pose/current: 17 COCO keypoints, confidence 0.037 (real).
- cargo test --workspace: 13 passed / 0 failed / 5 ignored.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New REST endpoint on FW HTTP server (port 8032) writes
csi_cfg/target_ip + target_port to NVS and reboots. Body is
plain text "IPv4:PORT" (e.g. 192.168.0.103:5005). Verified on
both 192.168.0.100 and 192.168.0.101 — sensors silent after
Mac IP move came back online in ~3 min instead of needing USB.
Same PSK auth as /ota/recalibrate (ADR-050). Strict body parser
rejects malformed input before touching NVS. Binary size +1 KB.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
--baseline-profile {single,auto,day,night} (default single).
* single — legacy data/baseline.json path, unchanged.
* auto — picks data/baseline.{day,night}.json by local hour
(day=07:00-20:59), hot-swaps every 5 min on transitions.
* day/night — force one of the profile files, no switching.
Missing profile files fall back to data/baseline.json with a
warning, so migration is incremental — operator can record one
profile at a time without breaking the deployment.
Watch task is a no-op outside `auto` (no log noise, no tokio slot).
Smoke: --baseline-profile auto with no day.json → "falling back
to data/baseline.json" warning then normal startup; watch task
enabled.
Co-Authored-By: claude-flow <ruv@ruv.net>
- ADR-112 (Multi-AP signal_field via MultistaticFuser) added.
- ADR-105 closes the Real-signal_field Open Item.
- CHECKLIST: ADR-107/112/109/105 closures recorded; out-of-scope
items moved to a Deferred section with explicit reasons.
Co-Authored-By: claude-flow <ruv@ruv.net>
ADR-109 documents POST /ota/recalibrate + gl_ap_mac NVS binding
and supersedes the two Open Items in ADR-108.
Co-Authored-By: claude-flow <ruv@ruv.net>
Upstream merged ADR-099-midstream-introspection-tap during this
session (PR #554, commits 900b877c..ce330422 on origin/main). Our
existing ADR-099-tplink-wisp-deployment-and-rssi-presence has a
different topic but the same number. Rather than fight the
numbering, slot ours up to ADR-110 (next free) and let upstream
own ADR-099.
git mv ADR-099-tplink-wisp-...md → ADR-110-tplink-wisp-...md
bulk sed `ADR-099` → `ADR-110` across all our docs from this
session (ADR-100..108, refs/,
CHECKLIST.md, self-reference)
No code changes; no semantic change beyond the number. Resolves the
collision before rebase against origin/main.
Cross-referenced every ADR Open Items section + both reference docs
against the actual implementation state on the branch. Closed items
the session shipped, kept stale "will be done in ADR-X" forward-refs
honest:
ADR-100 ✅ NBVI port (ADR-102), RSSI parse fix (3393c1e8), idle-
channel keepalive (ADR-106). ⏳ Tailscale-target still open.
ADR-101 ✅ per-sub baseline-drop / off-axis sit (both via ADR-104).
⏳ CV saturation above ~30 % still open.
ADR-102 ✅ Step 3 FP-rate validation (ADR-104 D4).
ADR-103 ✅ all three open items closed (REST endpoint via ADR-107,
per-sub comparison via ADR-104, auto-recalibrate via ADR-107).
ADR-106 ✅ FW-side µs timestamp via OTA (b787f40a).
espectre-techniques.md:
- NBVI: now "DONE (all 4 NBVI steps)" instead of "missing Step 3".
- Persisted calibration: split into "server (ADR-103) + FW NVS (ADR-108)"
with intentional design note for NBVI staying server-side.
espectre-gap-analysis.md:
- NBVI Step 3, gain-lock NVS, baseline persistence, threshold
persistence all flipped to ✅ in the per-section comparison tables.
- Priority list restructured into "✅ Done in this session" (10 items)
+ "⏳ Still open by impact" (14 items) with reality-checked
estimates. Top 3 open: HA via MQTT, 2 000-packet test suite,
per-sub delta sparkline in raw.html.
Verbatim Pace Part-2 article still informs the gap structure; nothing
was removed from his pipeline, only RuView's column updated.
ADR-104: documents the off-axis presence channel that fires
present_still when per-subcarrier amplitudes drift ≥10% from the
saved per_subcarrier_mean baseline, plus the NBVI Step 3 FP-rate
validation (K candidate sweep, smallest-FP wins). Implementation
shipped in 6212b17e.
ADR-108: documents the FW NVS persistence of gain-lock values
(csi_cfg/gl_agc + gl_fft), the one-shot load at first packet after
boot, the save after every successful calibration, and the safety
MIN_SAFE_AGC guard on restored values. Implementation shipped in
3779bb76; flashed to both sensors via OTA.
Both ADRs ≤ 200 lines per the project's docs convention. Open items
recorded so future agents can pick up: per-sub drift age check,
phase-domain drift, REST recalibrate endpoint, AP-MAC tied cache.
UI side of ADR-107: green "calibrate empty" button in raw.html next
to the existing reset/log-y controls. Click → confirm dialog tells
the operator to step out → POST /api/v1/baseline/calibrate with
90 s capture window → polls GET /api/v1/baseline every 2 s, surfaces
"recording… N/90 s" then "baseline updated ✓".
ADR-107 documents:
D1 in-process capture_baseline_to_disk (port of record-baseline.py)
D2 BASELINE_BUS broadcast forwarder so capture stays decoupled from
WS clients
D3 POST /api/v1/baseline/calibrate (immediate ack, background work)
D4 GET /api/v1/baseline (current state + cooldown + status)
D5 auto_recalibrate_task — 30-min absent+low-CV trigger, 1-h cooldown
D6 raw.html button + polling
Records the two-part change that gets the maximum raw signal off the
sensors so the future model — and current fine-motion detection —
has everything the parent project describes:
D1 NodeInfo exposes phases[56], n_antennas, noise_floor_dbm,
timestamp_us in the WS payload (was amplitude-only).
D2 NodeState stashes latest phases/noise/timestamp/antenna count
so build_node_features can populate the new fields uniformly
without a parallel phase_history buffer.
D3 csi_keepalive_task spawns managed `ping` children per
discovered sensor address; replaces the operator's hand-run
`ping -i 0.05 …` workflow. CLI --csi-keepalive-pps controls
rate (default 25), 0 disables.
D4 Why ICMP not UDP: sensor rejects closed-port UDP before its
CSI callback fires; ICMP is handled in WiFi RX path regardless.
Verified: 55.6 Hz raw CSI per node with no shell ping; both
amplitude[56] and phases[56] populated; noise_floor=-91 dBm.
Two impl commits already on the branch: 4daa2c9b, 8489efe9.
Records the cleanup of five fake outputs the rich Docker UI exposed
when pointed at our backend without a trained pose model loaded:
D1 derive_pose_from_sensing → Vec::new()
D2 pose_current → gated on s.model_loaded
D3 pose_stats → drop hard-coded average_confidence 0.87
D4 pose_zones_summary → drop fabricated zones, report real presence
D5 api_info.pose_estimation → reflects s.model_loaded
D6 generate_signal_field → returns zero-filled grid (was procedural)
Two implementation commits already on the branch: 9aa027e9 and 30244d27.
Audit table confirms /api/v1/sensing/latest now carries only real
ESP32-derived state. Out-of-scope items (--source simulate already
disabled; --pretrain/--train synthetic fallbacks are explicit dev
flags; vital_signs already gated on real detection) are documented
so the next reader doesn't re-audit them.
* ADR-101 raw-amplitude presence/motion classifier — per-node and
cross-node fusion logic, hysteresis, per-node UI surface
(`PerNodeFeatureInfo.classification` override).
* ADR-102 server-side NBVI subcarrier selection — formula, dead-zone
gate, ESPectre Step-1 quiet-window finder, why we split FULL vs
NBVI-subset broadband.
* ADR-103 persistent baseline + universal threshold normalization —
JSON schema v2 at `v2/data/baseline.json`, FULL-broadband over
NBVI for cross-restart stability, `norm_cv = cv / baseline_cv`
with universal 3×/6× gates, recording script workflow.
* Updated espectre-techniques.md to reflect the DONE items (Steps
1+2+4 of NBVI, baseline persistence, universal threshold) and the
remaining open items in priority order.
Each ADR ≤ 200 lines per the operator's docs convention; deep detail
lives in `docs/references/espectre-techniques.md` (also ≤ 200) which
the ADRs link to. README.md and CLAUDE.md unchanged (no extra
content added; existing >200-line state pre-dates this session).
Ports Francesco Pace's ESPectre gain-lock (GPLv3) to RuView FW: medians
AGC and FFT scale over the first 300 packets after boot, then freezes
them via phy_force_rx_gain / phy_fft_scale_force. With both sensors
locked and proper AP→body→sensor geometry, a 30-s × 3-state capture
(empty / still / walk) now separates by ×3.4–×5.9 instead of ±0.02
within ±0.10 noise as in ADR-099.
Adds static/raw.html — per-node 56-subcarrier amplitude bars + RSSI/
broadband traces, no DSP, for live calibration.
ADR-100 documents the technique, boot calibration values for the
operator's deployment (AGC=42/44, both APPLIED), and the verified
three-state separation table.
Operator's household environment showed CSI-variance presence detection
failing — empty room produced HIGHER variance than an occupied room because
ambient WiFi noise (neighbour APs, retransmits, BT-coex) dominated the
broadband-variance signal at multi-meter range.
Deployed a TP-Link TL-WR841N in WISP mode as a dedicated isolated AP for
the sensors:
* Sensors associate only with TP-Link_8340 (clean channel)
* TP-Link bridges to the household AP, NAT-forwards sensor UDP to the Mac
* Mac keeps its primary household-AP association — no LAN reconfig needed
* Empty-room variance dropped 50.7 → 35.8 (-30%)
Replaced presence classification with RSSI MAD-Δ override:
* Per-node rolling 120-sample (~10 s @ 12 Hz) window of frame RSSI
* Metric: mean(|Δrssi|) between consecutive frames — robust to int8
quantisation jitter
* Thresholds tuned for the operator's geometry:
d < 0.20 → absent
< 0.55 → present_still
< 1.10 → present_moving
>= 1.10 → active
* Confidence field temporarily carries raw d for in-field threshold tuning
* CSI-based features (variance, motion_band_power, spectral_power) remain
in features.* for vital-sign signal-quality and multi-node fusion paths
UI / tooling:
* New static/spectrum.html — live signal console: combined classification,
all host-computed features (variance, motion_band, spectral, breathing
band, RSSI, dominant_freq, change_points), per-node FW signals, and a
60-second variance trace. Served via `python -m http.server 8091`.
* static/calibrate.html — simpler per-node motion/presence/RSSI bars
with peak-hold.
Desktop UI / discovery hardening (rolled in here because they came up
during this debug session):
* commands/discovery.rs: HTTP sweep limited to 2..=60 hosts (was 1..=254),
mDNS + UDP-broadcast paths disabled (current RuView FW doesn't advertise
them and they were burning CPU every poll cycle). Per-request timeout
set to 1500 ms with overall budget enforced via tokio::time::timeout +
futures::join_all (replaces the previous sequential select loop that
blocked on slow IPs).
* ui/hooks/useNodes.ts: poll interval 10 s → 30 s.
* ui/pages/Dashboard.tsx + NetworkDiscovery.tsx: merge new scan results
into existing list instead of replacing — discovery races sometimes miss
a node that was found a moment ago.
Firmware tuning:
* edge_processing.c: broadband-variance divisor /3.0 → /30.0 → /5.0
iterated; final /5.0 chosen for multi-meter geometry (sensor 1-3 m
from activity zone). DEBUG_MOTION_DSP scaffolding removed.
* csi_collector.c: CSI_MIN_SEND_INTERVAL_US 20 ms → 4 ms so the host can
see every available frame (real ceiling is the WiFi CSI callback rate).
Documentation:
* docs/adr/ADR-099 — full forensic write-up: measurement tables for sit/
walk/empty, the RSSI-Δ rationale, the WISP setup procedure, calibration
protocol for new deployments, and open items.
Verified end-to-end on hardware (sensors at 192.168.1.17/.19 → TP-Link at
192.168.1.14 → Mac at 192.168.1.21):
* UDP/5006 packets arrive ~12 Hz combined from both nodes
* Empty-room baseline d ≈ 0.49 measured (next: capture sit + walk to
finalize thresholds)
* Vital signs continue to populate (breathing 9–11 BPM stable)
* Two consecutive OTA round-trips remain functional after the change
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
End-to-end deployment fixes that took the two ESP32-S3 sensor boards
(room01, room02) from "boots but DSP frozen, OTA always rolls back" to
"motion/presence/breathing all live, two consecutive OTA round-trips
succeed". Full forensic write-up in docs/adr/ADR-098.
Firmware (firmware/esp32-csi-node/main/):
* csi_collector.c — remove esp_wifi_set_promiscuous(true): this call
silenced the CSI RX callback entirely on this silicon revision
(yield=0pps). Without it, callbacks resume at ~5-10 pps.
* edge_processing.c — root cause: incoming CSI frames carry 192
subcarriers but EDGE_MAX_SUBCARRIERS=128, so the size check
early-returned every frame and Step 8 (motion) never ran. Truncate
to 128 + warn once instead of returning.
* edge_processing.c — replace per-bin unwrapped-phase variance with
temporal variance of per-frame broadband mean amplitude. Empirical
separation on deployed hardware: empty 0.07-0.10, walking 3.5-14
(~44x). Scaled by /3.0 and clamped to [0,1].
* edge_processing.c — biquad fs 20.0 -> 10.0, matching the actual
callback rate (was halving the breathing passband).
* ota_update.c — OTA_WITH_SEQUENTIAL_WRITES -> OTA_SIZE_UNKNOWN to
erase the full target partition (stale tail of the previous larger
image was crashing the new image on boot, looking like rollback).
* ota_update.c — httpd_config_t.stack_size = 8192 (default 4 KB
overflowed in OTA verify path).
* main.c — log esp_reset_reason() and running_partition->label once
at app_main start, so OTA outcomes are visible without guesswork.
* sdkconfig.defaults — local deployment defaults: tier=2, display
disabled (no expander on these boards), 8192 timer stack.
Sensing server (v2/crates/wifi-densepose-sensing-server/):
* src/main.rs — parse_rv_feature_state() for the 0xC5110006
feature_state packet that RuView FW emits by default; this format
was previously unhandled. Wire ahead of parse_esp32_vitals.
* src/main.rs — BaselineTracker with hysteretic motion gating on top
of FW-reported scores, so UI sees clean boolean presence transitions.
* src/main.rs — refuse --source simulate; remove auto-fallback to
synthetic data. Production builds never run on fake signals.
* src/main.rs/csi.rs — parse_csi_lean() for legacy FW 5.47 CSV
packets; defence-in-depth for mistakenly flashed legacy sensors.
Desktop UI (v2/crates/wifi-densepose-desktop/):
* src/commands/discovery.rs — third discovery path: HTTP /status sweep
across the local /24 in parallel with mDNS/UDP. mDNS+UDP-beacon are
not advertised by current RuView FW. Replace sequential
for-task-in-tasks select-with-deadline (which blocked on slow
unrelated IPs) with futures::join_all + overall timeout.
* src/commands/server.rs — pass --bind-addr (was --bind); pass
RUST_LOG env instead of unsupported --log-level; auto-load bundled
wifi-densepose-v1.rvf next to the binary; reasonable defaults
(esp32 source, 0.0.0.0 bind).
* ui/* — keep last good node list when a poll returns 0 (discovery
is jittery on busy LANs); 8 s timeout (was 3 s); remove "simulate"
from DataSource enum and Sensing dropdown; default Sensing source
esp32.
Mobile UI (ui/mobile/):
* constants/websocket.ts — WS_PATH '/ws/sensing' + WS_PORT 8765 to
match the RuView sensing-server's WS endpoint (was the legacy
FastAPI /api/v1/stream/pose).
* services/ws.service.ts — derive WS host from serverUrl but use
WS_PORT; remove simulation fallback paths entirely (no
generateSimulatedData, no startSimulation on reconnect failure).
* stores/settingsStore.ts — serverUrl defaults to
http://100.123.189.10:8080 (deployed Mac's Tailscale IP), so the
phone connects from any network without LAN dependency.
* stores/matStore.ts — default dataSource='real',
simulationAcknowledged=true; no synthetic triage data.
* screens/MATScreen, VitalsScreen — hide simulation overlay/badge.
Docker:
* docker/docker-compose.yml — sensing-server host port 5005 -> 5006
to match the RuView FW's compiled CSI_TARGET_PORT default.
Documentation:
* docs/adr/ADR-098-esp32s3-csi-deployment-fixes.md — full forensic
ADR covering each decision, the empirical numbers that drove it,
the false hypotheses we ruled out along the way, and open items.
Verified on hardware (both nodes):
* motion empty < 0.05 (room01 0.018, room02 0.070)
* motion walking > 0.3 within 1-3 s, saturates at 1.0
* motion decay < 0.1 within 5 s after leaving
* breathing 21-22 BPM detected after ~30 s stationary
* two consecutive OTA round-trips succeed without USB intervention
* discovery finds both sensors via HTTP sweep in <2 s
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three threads in this commit:
1) Per-frame attractor analysis (default analyze_every_n: 8 → 1).
The I5 benchmark put per-frame update at 0.012 ms p99 — 83× under D4's
1 ms budget. The cost case for the every-8th-frame default doesn't hold;
per-frame analysis is what makes regime_changed a viable early-detection
trigger.
2) New `regime_changed: bool` field in IntrospectionSnapshot — flips on any
frame whose attractor regime classification differs from the previous
frame's. Pairs with top_k_similarity (full-shape match) to give
downstream consumers two latencies with different robustness profiles.
3) Honest amendment of ADR-099 D8 to reflect empirical reality:
- L1 stand-in achieves 3.20× ratio (5-frame shape match vs 16-frame
event-path floor); the 10× aspirational bar is architecturally
unreachable at 1-D scalar feature resolution.
- regime_changed didn't fire in the 10-frame motion window — the
200-frame noise trajectory dominates the Lyapunov classification, and
short perturbations don't shift the regime fast enough on a scalar
feature.
- Path to 10×: ADR-208 Phase 2 (Hailo NPU vec128 embeddings) — multi-dim
partial matches discriminate from noise in 1-2 frames, not 5.
- Side finding: midstream temporal-compare::DTW uses *discrete equality*
cost (designed for LLM tokens), not numeric distance — swapping it in
for f64 amplitude scoring would be strictly worse than the L1 stand-in.
A numeric DTW is a separate concern (hand-roll or new crate).
- Revised D8: ship behind --introspection (off by default) until multi-
dim features land. Per-frame update budget IS met (0.041 ms p99 in this
bench, ~24× under the 1 ms bar) — the feature is cheap enough to
carry dark today.
cargo test -p wifi-densepose-sensing-server --no-default-features:
introspection (lib): 8 passed, 0 failed
introspection_latency (test): 5 passed, 0 failed (incl. new
regime_change_path_latency)
clippy: clean on the introspection surface (pre-existing approx_constant
lints in pose.rs / main.rs unchanged).
Co-Authored-By: claude-flow <ruv@ruv.net>
ADR-098 rejected midstream as a *replacement* for RuView's existing seams.
ADR-099 is the other half: midstream's `temporal-compare` (DTW) and
`temporal-attractor-studio` (Lyapunov + regime classification) crates as a
*parallel* per-frame introspection tap, alongside the existing window-aggregated
event pipeline.
The 8 decisions:
D1 — Only midstreamer-temporal-compare 0.2 + midstreamer-attractor 0.2;
scheduler / neural-solver / strange-loop are out of scope of this ADR.
D2 — Tap point: post-validate, parallel to WindowBuffer::push in csi.rs.
The existing /ws/sensing path is unchanged.
D3 — New /ws/introspection topic + /api/v1/introspection/snapshot REST endpoint
carrying IntrospectionSnapshot { regime, lyapunov_exponent,
attractor_dim, top_k_similarity }.
D4 — Per-frame updates only, never window-blocked. Soonest-event latency on
the "shape recognized" path collapses from ~533 ms (16-frame @ 30 Hz
window) to ~33 ms (one frame), a ~16× win.
D5 — temporal-neural-solver (LTL) is out of scope (separate MAT audit ADR).
D6 — ESP32 firmware unchanged; deployment is host-side only.
D7 — Signature library is JSON, on-disk, customer-owned; three reference
signatures ship as developer fixtures.
D8 — Promotion bar is empirical: ≥10× p99 latency reduction vs. the existing
/ws/sensing event path, or the feature stays behind a CLI flag.
Indexed in docs/adr/README.md. Phased adoption (P0 spike + benchmark → P1 first
real signature library → P2 dashboard widget → P3 capture workflow → P4 optional
adaptive_classifier hook). Implementation lands as ~150–250 lines + one
integration test in v2/crates/wifi-densepose-sensing-server in follow-up PRs.
Co-Authored-By: claude-flow <ruv@ruv.net>
rvCSI was extracted to its own repo (PR #542→#544): 9 crates on crates.io @
0.3.1, `@ruv/rvcsi` on npm, vendored at `vendor/rvcsi`. RuView currently
*vendors but does not consume* it — zero `rvcsi-*` deps in `v2/`, zero
`use rvcsi_…` imports, zero `@ruv/rvcsi` JS imports. ADR-097 decides:
D1 — Depend on the published crates from crates.io, not the submodule path.
D2 — Pilot in `wifi-densepose-sensing-server` (smallest, best-bounded
touchpoint: UDP receiver + handlers + WS fan-out).
D3 — `wifi-densepose-signal` is *layered on top of* rvCSI, not replaced.
The SOTA / RuvSense modules go beyond rvCSI's scope and stay in
RuView; they consume `rvcsi_core::CsiFrame`. Overlapping basic DSP
primitives delegate to `rvcsi-dsp` or become thin shims.
D4 — `wifi-densepose-hardware` stops carrying ESP32 wire-format parsing;
the parser moves to a new `rvcsi-adapter-esp32` crate (ADR-095 §1.2
/ D15 follow-up, owned in the rvCSI repo).
D5 — `wifi-densepose-ruvector` (training pipeline) and `rvcsi-ruvector`
(runtime RF memory) stay separate for now; a follow-up unifies them
once the production RuVector binding lands.
D6 — `rvcsi_core::CsiFrame` is the boundary type at the runtime edge;
one explicit `From`/`Into` conversion point at that edge.
D7 — Track via `rvcsi-* = "0.3"` SemVer ranges + bump the `vendor/rvcsi`
submodule pin per RuView release for reproducible offline builds.
D8 — Once every consumer depends on crates.io, decide (separately)
whether to drop the submodule.
Adoption is phased (P1 pilot → P2 signal shim → P3 ESP32 adapter →
P4 clean-up → P5 submodule review); each phase is one PR with tests.
Indexed in docs/adr/README.md.
Co-Authored-By: claude-flow <ruv@ruv.net>
BaselineDriftDetector compared `mean_amplitude` against its EWMA baseline
with *absolute* thresholds (anomaly 1.0, drift 0.15). Fine for the synthetic
unit tests (amplitudes ~1.0), but raw ESP32 CSI is int8 I/Q with amplitudes
up to ~128, so window-to-window RMS distance is routinely 5-50 >> 1.0 and
AnomalyDetected fired on ~96% of windows (319/331 on a real node-1 capture).
Drift is now `||current - baseline||2 / ||baseline||2` (a fraction, with an
eps floor that falls back to absolute for a degenerate near-zero baseline),
so one tuning is valid across raw-int8 ESP32, int16-scaled Nexmon, and
baseline-subtracted streams. AnomalyDetected drops to 40/331 on the same
data; the existing detector tests still pass (their explicit configs are
valid relative thresholds too); added baseline_drift_is_scale_invariant_
no_anomaly_storm. rvcsi-events 18 -> 19 tests; 162 rvcsi tests, 0 failures,
clippy-clean.
Surfaced by an end-to-end test against real ESP32 CSI on COM7: the device
(ESP32-S3, node 1, ADR-018 firmware, WiFi "ruv.net" ch5 RSSI -39, CSI cb
only because nothing listens at .156). rvcsi has no ESP32 adapter yet, so a
7,000-frame node-1 recording was transcoded to .rvcsi via the new
scripts/esp32_jsonl_to_rvcsi.py (stand-in for `record --source esp32-jsonl`)
and run through `rvcsi inspect`/`replay`/`calibrate`/`events` end-to-end.
ADR-095 D13 and ADR-096 sections 2.1/5 updated; CHANGELOG entry added;
rvcsi-adapter-esp32 (live serial/UDP source) noted as a follow-up.
Co-Authored-By: claude-flow <ruv@ruv.net>
Adds first-class support for the Raspberry Pi 5's WiFi chip (CYW43455 /
BCM43455c0 — the same 802.11ac wireless as the Pi 4 / Pi 3B+ / Pi 400, and the
chip with the most mature nexmon_csi support), plus a registry of the other
Nexmon-supported Broadcom/Cypress chips.
rvcsi-adapter-nexmon — new `chips.rs`:
- `NexmonChip` (Bcm43455c0, Bcm43436b0, Bcm4366c0, Bcm4375b1, Bcm4358, Bcm4339,
Unknown{chip_ver}) + `RaspberryPiModel` (Pi5/Pi4/Pi400/Pi3BPlus/PiZero2W/
PiZeroW) — Pi5/Pi4/Pi400/Pi3B+ → Bcm43455c0; PiZero2W → Bcm43436b0.
- `nexmon_adapter_profile(chip)` / `raspberry_pi_profile(model)` build the
per-device `AdapterProfile` (channels: 2.4 GHz 1-13 + 5 GHz UNII for dual-band;
bandwidths 20/40/80[/160]; expected subcarrier counts 64/128/256[/512]) that
`validate_frame` bounds CSI frames against.
- `NexmonChip::from_chip_ver` (0x4345 → Bcm43455c0, 0x4339, 0x4358, 0x4366,
0x4375 — best-effort; the raw `chip_ver` is always preserved) and `from_slug`
/ `RaspberryPiModel::from_slug` ("pi5", "raspberry pi 4", "bcm43455c0", ...).
- `NexmonCsiHeader::chip()`; `NexmonPcapAdapter` auto-detects the chip from the
packets' `chip_ver` and uses the matching profile, overridable via
`.with_chip(NexmonChip)` / `.with_pi_model(RaspberryPiModel)`; `.detected_chip()`.
rvcsi-runtime: `decode_nexmon_pcap_for(.., chip_spec)` (validate against a chip /
Pi model, drop non-conforming) + `nexmon_profile_for(spec)`; `NexmonPcapSummary`
gains `chip_names` + `detected_chip`; `CaptureSummary` gains `chip`.
rvcsi-cli: `record --source nexmon-pcap --chip pi5`; new `nexmon-chips`
subcommand (lists chips + Pi models, human or `--json`); `inspect-nexmon` and
`inspect` now print the resolved chip.
rvcsi-node (napi-rs): `nexmonDecodePcap` gains an optional `chip` arg;
`nexmonChipName(chipVer)`, `nexmonProfile(spec)`, `nexmonChips()`. @ruv/rvcsi
SDK + `.d.ts` updated (AdapterProfile / NexmonChipsListing interfaces, the new
fns, `chip` on CaptureSummary, `chip_names`/`detected_chip` on NexmonPcapSummary).
168 rvcsi tests pass (adapter-nexmon 22→28, cli 9→10), 0 failures, clippy-clean.
The synthetic test captures now stamp chip_ver = 0x4345 (the BCM4345 family chip
ID), so the chip-detection happy path is exercised end to end.
ADR-096, CHANGELOG, README, CLAUDE.md updated.
https://claude.ai/code/session_01CdYAPvRTjcch6YrYf42n1z
The hosted GitHub Pages viewer can now act as a thin client for a
locally-running ruview-pointcloud serve instance — flip a button, the
ESP32's CSI fusion (camera depth + WiFi CSI + mmWave) renders inside
the same Three.js scene that previously only showed the face mesh
demo. No clone, no rebuild, no toolchain on the visitor's side.
Server (stream.rs):
- Add tower_http::cors::CorsLayer with a deliberate allowlist:
https://ruvnet.github.io, http://localhost:*, http://127.0.0.1:*,
and 'null' (for file:// origins). Anything else is denied — not a
wildcard CORS. Modern browsers (Chrome 94+, Firefox 116+, Safari
16.4+) treat 127.0.0.1 as a "potentially trustworthy" origin so
HTTPS Pages → HTTP loopback is permitted. The new layer wraps the
existing /api/cloud, /api/splats, /api/status, /health routes.
- Cargo.toml: pull in workspace tower-http (cors feature already on).
Viewer:
- New "📡 Connect ESP32…" CTA bottom-right. Clicking prompts for a
ruview-pointcloud serve URL (default http://127.0.0.1:9880),
persists the last-used value in localStorage, and reloads with
?backend=<url> so the existing remote-mode fetch path takes over.
When already connected the button toggles to "disconnect" and
reloads back to the demo.
- Reuses the existing transport selector — no new code path to
maintain. The face mesh / synthetic demo render path is unaffected;
this is purely an additive UI affordance over the ?backend= query.
Docs:
- ADR-094 §2.3 expanded with the local-ESP32 workflow and the CORS
posture rationale.
- Workflow README documents ?backend=http://127.0.0.1:9880 as the
intended local-ESP32 path.
Tests: cargo test -p wifi-densepose-pointcloud → 15/15 passed.
Co-Authored-By: claude-flow <ruv@ruv.net>
The previous synthetic procedural demo did not represent what the local
fusion pipeline produces — a real depth-backprojected point cloud of
the user's face and surroundings. This commit ports the closest browser
equivalent: MediaPipe Face Mesh runs in-browser at ~30 fps and emits
478 3D landmarks per frame. Each visitor now sees the outline of their
own face rendered as a point cloud, with a small floor + back wall for
spatial context.
- Adds MediaPipe Face Mesh + Camera Utils via jsdelivr CDN.
- Adds an "▶ Enable camera" CTA so getUserMedia is gated on a user
gesture (required by some browsers and good UX regardless).
- New face-mesh frame generator uses the same splat shape as the live
/api/splats payload, so a single render path drives both modes.
- Mirrors x to match selfie convention; maps lm.z (relative depth) to
the world-coord range used by the live pipeline.
- Falls back automatically to the procedural floor + walls + figure
when the camera is denied, dismissed, or unavailable.
- Badge surfaces the new state: '● DEMO Your Face (MediaPipe)'.
- Bumps poll cadence to 4 Hz so face mesh updates feel live.
- ADR-094 updated to reflect the new default behavior.
Co-Authored-By: claude-flow <ruv@ruv.net>
Publishes the live 3D point cloud viewer to gh-pages/pointcloud/ so it
can be linked from the README alongside the Observatory and Dual-Modal
Pose Fusion demos. The viewer auto-selects its transport from URL
parameters:
- default / ?backend=auto — try /api/splats, fall back to synthetic demo
- ?backend=demo — synthetic in-browser only, no network
- ?backend=<url> — fetch from a CORS-permitting host running
ruview-pointcloud serve
- ?live=1 — strict mode, show offline panel instead of demo fallback
The synthetic frame matches the live API JSON shape (splats, count,
frame, live, pipeline.{skeleton,vitals}) so a single render path drives
both modes. New workflow uses keep_files: true to preserve the existing
observatory/, pose-fusion/, and nvsim/ deployments on gh-pages.
See docs/adr/ADR-094-pointcloud-github-pages-deployment.md for the full
decision record and 6 acceptance gates.
All five implementation passes plus four security-review hardenings
shipped in PR #435 (squash-merged as d71ef9a). Acceptance numbers
measured on synthetic AETHER-shape data:
- Compare-cost reduction: 8x-30x floor → 43-51x pair-wise (d=512),
12.4x top-K (d=128 n=1024 k=8), 7.6x full pipeline (d=128 n=4096 k=8).
- Top-K coverage: ≥90% floor → 90%+ at prefilter_factor=8 (78.9%
at factor=4 documented as fail; codified in
test_search_prefilter_topk_coverage_meets_adr_084).
- Wire envelope: 28-byte AETHER 128-d (vs 512-byte raw float; 18x
compression).
The third acceptance criterion (`< 1 pp end-to-end accuracy regression`)
needs a real-CSI soak test against a multi-day AETHER trace; that's
post-merge follow-up rather than a merge-blocker. Synthetic-data
acceptance was sufficient evidence to ship.
PR #434 (ADR-086 firmware-side gate) merged separately as 17509a2.
Co-Authored-By: claude-flow <ruv@ruv.net>
Pushes the ADR-084 novelty sensor down into the ESP32 sensor MCU's
Layer 4 (On-device Feature Extraction) of ADR-081's 5-layer kernel:
sketch + 32-slot ring bank in IRAM, suppress UDP send when novelty
< CONFIG_RV_EDGE_NOVELTY_THRESHOLD (default 0.05).
Wire format bumps to magic 0xC5110007 with two new fields
(suppressed_since_last: u16, gate_version: u8) packed in by narrowing
the existing 16-bit quality_flags to 8-bit (only 8 bits were ever
defined). Frame size stays at 60 bytes; v6 receivers fall back
gracefully.
Stuck-gate self-heal at CONFIG_RV_EDGE_MAX_CONSEC_SUPPRESS (default
50 frames ≈ 10 s) so a wedged threshold can't silently disappear a
node. Default-off Kconfig so existing deployments are unaffected.
Validation commitments:
- ≤ 200 µs sketch insert+score on Xtensa LX7
- ≥ 30% UDP TX-energy reduction in steady-state quiet rooms
- ≤ 5 pp drop on cluster-Pi novelty top-K coverage vs unsuppressed
- ≥ 50% bandwidth reduction in stable-room scenarios
Six-pass implementation plan, default-off Kconfig, QEMU + COM7
hardware-in-loop validation. Honest gaps flagged: Xtensa LX7 POPCNT
absence is conjecture (Pass 2 bench is the falsifier); interaction
with ADR-082's Tentative→Active gate is the likeliest weak point
(Open Q4).
ADR-087 / ADR-088 reserved as pointer stubs at end:
- ADR-087: Pass-4 mesh-exchange scope (cluster↔cluster vs sensor→Pi)
- ADR-088: Firmware-release coordination policy
Status: Proposed. SOTA review by goal-planner agent.
Extends ADR-084's RaBitQ-as-similarity-sensor pattern from five sites
to twelve, adding seven additional pipeline locations the user
identified during ADR-084 implementation:
- Per-room adaptive classifier short-circuit (Mahalanobis prefilter)
- Recording-search REST endpoint (GET /api/v1/recordings/similar)
- WiFi BSSID fingerprinting (channel-hop scheduler input)
- mmWave (LD2410 / MR60BHA2) signature wake-gate
- Witness bundle drift detection (CI ratchet)
- Agent / swarm memory routing (ADR-066 swarm bridge)
- Log / event-pattern anomaly detection (cluster Pi)
Each site has a 2-3 sentence decision (what gets sketched, what
triggers the comparison, what the refinement does on miss) and a
witness-hash artifact (what the system stores in place of the raw
embedding/event/signal).
Implementation plan ordered cheapest-first / least-risky-first.
Acceptance criteria align with ADR-084 (8x-30x compare cost,
≥90% top-K coverage, <1pp accuracy regression) where applicable;
non-vector sites (witness bundle, BSSID time-series, event log)
have site-specific criteria.
Three open questions explicitly flagged:
1. Mahalanobis-after-binary-sketch is novel — no published primary
source found, marked conjecture, decision deferred to bench
2. Canonical "non-vector → sketchable" encoding is unsolved
3. MERIDIAN (ADR-027) cross-environment domain interaction needs
site-by-site analysis before bank rebuild semantics are committed
Status: Proposed. SOTA review by goal-planner agent.
Adopt RaBitQ-style binary sketches as a first-class cheap similarity
sensor at four points in the RuView pipeline: AETHER re-ID hot-cache
filter, per-room novelty / drift detection, mesh-exchange compression,
and privacy-preserving event logs. Implementation home is
ruvector-core::quantization::BinaryQuantized (already vendored, already
SIMD-accelerated NEON+POPCNT, 32x compression, 1-bit sign quantization
+ hamming distance), re-exported through a thin RuView-flavored API in
wifi-densepose-ruvector::sketch.
Pattern at every site: dense embedding -> RaBitQ sketch -> hamming
pre-filter to top-K -> full-precision refinement only on miss. Decision
boundary unchanged; sketch is a sensor that gates *which* comparisons
run, not *what* they decide.
Acceptance test (per source proposal):
- sketch compare cost reduction: 8x-30x vs full float
- top-K candidate coverage: >= 90% agreement with full-float pass
- end-to-end accuracy regression: < 1 percentage point
Site-by-site rollback if any criterion fails at a given site;
remaining sites continue. Five implementation passes, each
independently testable: ruvector module wrap, AETHER re-ID pre-filter,
cluster-Pi novelty sensor, mesh-exchange compression, privacy log.
Sensor MCU unchanged; sketches happen at the cluster Pi (ADR-083).
Validation requires acceptance numbers on >= 3 of 5 passes.
Open question (out-of-scope until pass-1 benchmark): whether RuView
embeddings need a Johnson-Lindenstrauss / RaBitQ-paper randomized
rotation before sign-quantization, or whether pure 1-bit sign
quantization (today's BinaryQuantized) is sufficient.
Adopt one Pi per cluster of 3-6 ESP32-S3 sensor nodes as the canonical
fleet-shape, rather than the full three-tier (dual-MCU + per-node Pi)
shape. Sensor nodes are unchanged from ADR-028 / ADR-081; the cluster
Pi gains the responsibilities the ESP32-S3 cannot carry — pose-grade
ML inference, QUIC backhaul to gateway/cloud, and a cluster-level OTA
+ secure-boot anchor.
The cluster-Pi shape is the L3-hybrid path identified in
docs/research/architecture/decision-tree.md §2 — the cheapest viable
upgrade. The full three-tier shape remains the long-term exploration
target, gated behind no_std CSI maturity (decision-tree L4) and
per-node ISR-jitter evidence (L2).
Status: Proposed. Acceptance gated on:
1. Cross-compile to aarch64 / armv7 with workspace tests passing
2. 3-sensor + 1-Pi field test demonstrating end-to-end CSI → fusion →
cloud at <=100 ms cluster latency
3. Cluster-Pi SoC choice ADR (decision-tree L6) approved
References:
- docs/research/architecture/three-tier-rust-node.md (seed exploration)
- docs/research/architecture/decision-tree.md (L3 hybrid path)
- docs/research/sota/2026-Q2-rf-sensing-and-edge-rust.md (SOTA evidence)
The Rust port at v2/ has been the primary codebase since the rename
in #427. The Python implementation at v1/ is no longer the active
target; the only load-bearing path is the deterministic proof bundle
at v1/data/proof/ (per ADR-011 / ADR-028 witness verification).
Move the whole Python tree into archive/v1/ and document the policy
in archive/README.md: no new features, bug fixes only when they affect
a still-load-bearing path (currently just the proof), CI continues to
verify the proof on every push and PR.
Path references updated in 26 files via path-pattern sed (only
matches v1/<known-child> patterns, never bare v1 or API URLs like
/api/v1/). Two double-prefix typos (archive/archive/v1/) caught and
hand-fixed in verify-pipeline.yml and ADR-011.
Validated:
- Python proof verify.py imports cleanly at archive/v1/data/proof/
(numpy/scipy still required; CI installs requirements-lock.txt
from archive/v1/ now)
- cargo test --workspace --no-default-features → 1,539 passed,
0 failed, 8 ignored (unaffected by Python tree relocation)
- ESP32-S3 on COM7 untouched (no firmware paths changed)
After-merge: contributors should re-run any local `python v1/...`
commands as `python archive/v1/...` (CLAUDE.md and CHANGELOG already
updated).
Two leftover references missed by the sed pass in #427 (which only
matched the full `rust-port/wifi-densepose-rs` path). These are bare
references to the workspace directory name, which is now v2/.
Co-Authored-By: claude-flow <ruv@ruv.net>
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/)
without any sibling under rust-port/ that warranted the extra level.
Move the whole workspace up to v2/ to match v1/ (Python) at the same
depth and shorten every cd / build command across the repo.
git mv preserves history for all tracked files. 60 files updated for
path references (CI workflows, ADRs, docs, scripts, READMEs, internal
.claude-flow state). Two manual fixes for relative-cd paths in
CLAUDE.md and ADR-043 that became wrong after the depth change
(cd ../.. → cd ..).
Validated:
- cargo check --workspace --no-default-features → clean (after target/
nuke; the gitignored target/ was carried by the OS rename and had
hard-coded old paths in build scripts)
- cargo test --workspace --no-default-features → 1,539 passed, 0 failed,
8 ignored (same totals as pre-rename)
- ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm)
After-merge follow-up: contributors should `rm -rf v2/target` once and
let cargo regenerate from the new path.
`tracker_bridge::tracker_to_person_detections` documented itself as filtering
to `is_alive()` but never actually filtered — it forwarded every non-Terminated
track to the WebSocket stream. With 3 ESP32-S3 nodes × ~10 Hz CSI, transient
detections that fell outside the Mahalanobis gate created a steady stream of
new Tentative tracks that aged through Active and into Lost. Lost tracks are
kept in the tracker for `reid_window` (~3 s) so re-identification can match
them when a similar detection reappears, but they are NOT currently observed
and must not render as live skeletons. Up to ~90 ghost skeletons could
accumulate at any moment, hence the 22-24 phantoms users saw while
`estimated_persons` correctly reported 1.
Add `PoseTracker::confirmed_tracks()` that returns only `Tentative ∪ Active`
and rewire the bridge to use it. `Lost` tracks remain in the tracker for
re-ID; they just no longer ship to the UI. `active_tracks()` is left
unchanged for the AETHER re-ID consumers (ADR-024).
Regression test `test_lost_tracks_excluded_from_bridge_output` drives a
track to Active, lapses for `loss_misses + 1` ticks to push it to Lost,
and asserts `tracker_update` returns an empty Vec while the Lost track
is still present in `all_tracks()` (re-ID still works).
Validated:
- cargo test --workspace --no-default-features → 1,539 passed, 0 failed
- ESP32-S3 on COM7 still streaming live CSI (cb #32800)
* Add wifi-densepose-pointcloud: real-time dense point cloud from camera + WiFi CSI
New crate with 5 modules:
- depth: monocular depth estimation + 3D backprojection (ONNX-ready, synthetic fallback)
- pointcloud: Point3D/ColorPoint types, PLY export, Gaussian splat conversion
- fusion: WiFi occupancy volume → point cloud + multi-modal voxel fusion
- stream: HTTP + Three.js viewer server (Axum, port 9880)
- main: CLI with serve/capture/demo subcommands
Demo output: 271 WiFi points + 19,200 depth points → 4,886 fused → 1,718 Gaussian splats.
Serves interactive 3D viewer at http://localhost:9880 with Three.js orbit controls.
ADR-SYS-0021 documents the architecture for camera + WiFi CSI dense point cloud pipeline.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Optimize pointcloud: larger splat voxels, smaller responses, faster fusion
- Gaussian splat voxel size: 0.10 → 0.15 (42% fewer splats: 1718 → 994)
- Splat response: 399 KB → 225 KB (44% smaller)
- Pipeline: 22.2ms mean (100 runs, σ=0.3ms)
- Cloud API: 1.11ms avg, 905 req/s
- Splats API: 1.39ms avg, 719 req/s
- Binary: 1.0 MB arm64 (Mac Mini), tested
Co-Authored-By: claude-flow <ruv@ruv.net>
* Complete implementation: camera capture, WiFi CSI receiver, training pipeline
Three new modules added to wifi-densepose-pointcloud:
1. camera.rs — Cross-platform camera capture
- macOS: AVFoundation via Swift, ffmpeg avfoundation
- Linux: V4L2, ffmpeg v4l2
- Camera detection, listing, frame capture to RGB
- Graceful fallback to synthetic data when no camera
2. csi.rs — WiFi CSI receiver for ESP32 nodes
- UDP listener for CSI JSON frames from ESP32
- Per-link attenuation tracking with EMA smoothing
- Simplified RF tomography (backprojection to occupancy grid)
- Test frame sender for development without hardware
- Ready for real ESP32 CSI data from ruvzen
3. training.rs — Calibration and training pipeline
- Depth calibration: grid search over scale/offset/gamma
- Occupancy training: threshold optimization for presence detection
- Ground truth reference points for depth RMSE measurement
- Preference pair export (JSONL) for DPO training on ruOS brain
- Brain integration: submit observations as memories
- Persistent calibration files (JSON)
New CLI commands:
ruview-pointcloud cameras # list available cameras
ruview-pointcloud train # run calibration + training
ruview-pointcloud csi-test # send test CSI frames
ruview-pointcloud serve --csi # serve with live CSI input
All tested: demo, training (10 samples, 4 reference points, 3 pairs),
CSI receiver (50 test frames), server API.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Fix viewer: replace WebSocket with fetch polling
Co-Authored-By: claude-flow <ruv@ruv.net>
* Wire live camera into server — real-time updating point cloud
- Server captures from /dev/video0 at 2fps via ffmpeg
- Background tokio task refreshes cloud + splats every 500ms
- Viewer polls /api/splats every 500ms, only updates on new frame
- Shows 🟢 LIVE / 🔴 DEMO indicator
- Camera position set for first-person view (looking forward into scene)
- Downsample 4x for performance (19,200 points per frame)
- Graceful fallback to demo data if camera capture fails
Co-Authored-By: claude-flow <ruv@ruv.net>
* Add MiDaS GPU depth, serial CSI reader, full sensor fusion
- MiDaS depth server: PyTorch on CUDA, real monocular depth estimation
- Rust server calls MiDaS via HTTP for neural depth (falls back to luminance)
- Serial CSI reader for ESP32 with motion detection + presence estimation
- CSI disabled by default (RUVIEW_CSI=1 to enable) — serial reader needs baud config
- Edge-enhanced depth for better object boundaries
- All sensors wired: camera, ESP32 CSI, mmWave (CSI gated until serial fixed)
Co-Authored-By: claude-flow <ruv@ruv.net>
* Complete 7-component sensor fusion pipeline (all working)
1. ADR-018 binary parser — decodes ESP32 CSI UDP frames, extracts I/Q subcarriers
2. WiFlow pose — 17 COCO keypoints from CSI (186K param model loaded)
3. Camera depth — MiDaS on CUDA + luminance fallback
4. Sensor fusion — camera depth + CSI occupancy grid + skeleton overlay
5. RF tomography — ISTA-inspired backprojection from per-node RSSI
6. Vital signs — breathing rate from CSI phase analysis
7. Motion-adaptive — skip expensive depth when CSI shows no motion
Live results: 510 CSI frames/session, 17 keypoints, 26% motion, 40 BPM breathing.
Both ESP32 nodes provisioned to send CSI to 192.168.1.123:3333.
Magic number fix: supports both 0xC5110001 (v1) and 0xC5110006 (v6) frames.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Add brain bridge — sparse spatial observation sync every 60s
Stores room scan summaries, motion events, and vital signs
in the ruOS brain as memories. Only syncs every 120 frames
(~60 seconds) to keep the brain sparse and optimized.
Categories: spatial-observation, spatial-motion, spatial-vitals.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Update README + user guide with dense point cloud features
Added pointcloud section to README (quick start, CLI, performance).
Added comprehensive user guide section: setup, sensors, commands,
pipeline components, API endpoints, training, output formats,
deep room scan, ESP32 provisioning.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Add ruview-geo: geospatial satellite integration (11 modules, 8/8 tests)
New crate with free satellite imagery, terrain, OSM, weather, and brain integration.
Modules: types, coord, locate, cache, tiles, terrain, osm, register, fuse, brain, temporal
Tests: 8 passed (haversine, ENU roundtrip, tiles, HGT parse, registration)
Validation: real data — 43.49N 79.71W, 4 Sentinel-2 tiles, 2°C weather, brain stored
Data sources (all free, no API keys):
- EOX Sentinel-2 cloudless (10m satellite tiles)
- SRTM GL1 (30m elevation)
- Overpass API (OSM buildings/roads)
- ip-api.com (geolocation)
- Open Meteo (weather)
ADR-044 documents architecture decisions.
README.md in crate subdirectory.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Update ADR-044: add Common Crawl WET, NASA FIRMS, OpenAQ, Overture Maps sources
Extended geospatial data sources leveraging ruvector's existing web_ingest
and Common Crawl support for hyperlocal context.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Fix OSM/SRTM queries, add change detection + night mode
- OSM: use inclusive building filter with relation query and 25s timeout
- SRTM: switch to NASA public mirror with viewfinderpanoramas fallback
- Add detect_tile_changes() for pixel-diff satellite change detection
- Add is_night() solar-declination model for CSI-only night mode
- 6 new unit tests (night mode + tile change detection)
Co-Authored-By: claude-flow <ruv@ruv.net>
* Enhance viewer: skeleton overlay, weather, buildings, better camera
Add COCO skeleton rendering with yellow keypoint spheres and white bone
lines, info panel sections for weather/buildings/CSI rate/confidence,
overhead camera at (0,2,-4), and denser point size with sizeAttenuation.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Add CSI fingerprint DB + night mode detection
Co-Authored-By: claude-flow <ruv@ruv.net>
* Fix ADR-044 numbering conflict, update geo README
Renumbered provisioning tool ADR from 044 to 050 to avoid conflict
with geospatial satellite integration ADR-044.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Clean up warnings: suppress dead_code for conditional pipeline modules
Removes unused imports/variables via cargo fix and adds #[allow(dead_code)]
for modules used conditionally at runtime (CSI, depth, fusion, serial).
Pointcloud: 28 → 0 warnings. Geo: 2 → 0 warnings. 8/8 tests pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Fix PR #405 blockers: async runtime panic, crate rename, path traversal, brain URL config
- brain_bridge.rs: replace `Handle::current().block_on(...)` inside async fn
with `.await` (was a guaranteed "runtime within runtime" panic). Brain URL
now read from RUVIEW_BRAIN_URL env var (default http://127.0.0.1:9876),
logged once via OnceLock.
- wifi-densepose-geo: rename Cargo package from `ruview-geo` to
`wifi-densepose-geo` to match directory and workspace conventions. Update
all use sites (tests/examples/README). Same env-var pattern for brain URL
in brain.rs + temporal.rs.
- training.rs: add sanitize_data_path() rejecting `..` components and
safe_join() that canonicalises + enforces base-dir containment on every
write (calibration.json, samples.json, preference_pairs.jsonl,
occupancy_calibration.json). Defence-in-depth check also in main.rs
before TrainingSession::new.
- osm.rs: clamp Overpass radius to MAX_RADIUS_M=5000m; return Err beyond
that. Add parse_overpass_json() that rejects malformed payloads
(missing top-level `elements` array).
Co-Authored-By: claude-flow <ruv@ruv.net>
* csi_pipeline: rename WiFlow stub to heuristic_pose_from_amplitude, decouple UDP
Blocker 3 (PR #405 review): The "WiFlow inference" path was a stub that
built a model from empty weight vectors and synthesised keypoints from
amplitude energy. Presenting this as "WiFlow inference" was misleading.
- Rename WiFlowModel to PoseModelMetadata (empty tag struct; we only care
if the on-disk file exists)
- Rename load_wiflow_model() -> detect_pose_model_metadata() and log
"amplitude-energy heuristic enabled/disabled" (no "WiFlow" claim)
- Rename estimate_pose() -> heuristic_pose_from_amplitude() with
prominent `STUB:` doc comment saying this is NOT a trained model
Blocker 4 (PR #405 review): The UDP receiver held the shared Arc<Mutex>
across a synchronous process_frame() call, starving HTTP handlers.
- Introduce a std::sync::mpsc channel between the UDP thread (which only
parses + pushes) and a dedicated processor thread (which locks only
briefly around a single process_frame). HTTP snapshots via
get_pipeline_output no longer contend with the socket read loop.
Also:
- Move ADR-018 parser to parser.rs (see next commit); csi_pipeline re-exports
- send_test_frames now uses parser::build_test_frame for synthetic frames
- Log a one-line node stats summary every 500 frames (reads every public
CsiFrame field on the runtime path)
Co-Authored-By: claude-flow <ruv@ruv.net>
* Extract ADR-018 parser into parser.rs + wire Fingerprint CLI
File-split (strong concern #9 in PR #405 review): csi_pipeline.rs was 602
LOC; extract the pure-function ADR-018 parser + synthetic frame builder
into src/parser.rs. Inline unit tests in parser.rs cover:
- 0xC5110001 (raw CSI, v1) roundtrip
- 0xC5110006 (feature state, v6) roundtrip
- wrong magic is rejected
- truncated header is rejected
- truncated payload is rejected
main.rs: expose `fingerprint NAME [--seconds N]` subcommand wiring
record_fingerprint() (this was the only caller needed to make the public
API non-dead on the runtime path). Also:
- Replace `--host/--port` + external `--csi` with a single `--bind`
defaulting to loopback (`127.0.0.1:9880`) — addresses strong concern
#7 about exposing camera/CSI/vitals by default.
- Update synthetic `csi-test` to target UDP 3333 (matching the ADR-018
listener) and use the shared parser::build_test_frame.
- Defence-in-depth: call training::sanitize_data_path on the expanded
--data-dir before TrainingSession::new does the same.
Co-Authored-By: claude-flow <ruv@ruv.net>
* stream: extract viewer HTML to viewer.html, default bind to loopback
Strong concern #7 (PR #405): default HTTP bind leaked camera/CSI/vitals
to the LAN. The `serve` fn now takes a single `bind` arg and prints a
loud WARNING when bound outside loopback.
Strong concern #10 (PR #405): embedded HTML+JS was ~220 LOC of the 418
LOC stream.rs. Moved the markup verbatim into viewer.html and inlined
via `include_str!("viewer.html")`. Also:
- Drop the #![allow(dead_code)] crate-level silencing (reviewer point
#11). Remove the now-unused AppState.csi_pipeline field.
- capture_camera_cloud_with_luminance returns the mean luminance of the
captured frame; the background loop feeds that to
CsiPipelineState::set_light_level so the night-mode flag actually
toggles at runtime (previously it could only be set from tests).
Net effect on file size: stream.rs 418 → 232 LOC.
Co-Authored-By: claude-flow <ruv@ruv.net>
* Dead-code cleanup + tests for fusion/depth/OSM/training/fingerprinting
Reviewer point #11 (PR #405): remove the `#![allow(dead_code)]`
silencing added in 8eb808d and fix the underlying issues.
- Delete csi.rs: duplicate of csi_pipeline.rs with incompatible wire
format (JSON vs ADR-018 binary). csi_pipeline is the real path.
- Delete serial_csi.rs: never referenced by any module.
- Drop Frame.timestamp_ms (unread), AppState.csi_pipeline (unread),
brain_bridge::brain_available (caller-less), fusion::fetch_wifi_occupancy
(caller-less) — these had no runtime users.
- Drop crate-level #![allow(dead_code)] from camera.rs, depth.rs,
fusion.rs, pointcloud.rs.
Tests (target: 8-12, actual: 15 unit + 9 geo unit + 8 geo integration
= 32 total, all pass):
- parser.rs: 5 tests (v1/v6 magic roundtrip, wrong magic, truncated
header, truncated payload).
- fusion.rs: 2 tests (non-overlapping merge, voxel dedup).
- depth.rs: 2 tests (2x2 backproject → 4 points at z=1, NaN rejected).
- training.rs: 4 tests (rejects `..`, accepts relative child, refuses
TrainingSession::new("../etc/passwd"), accepts a clean tmpdir).
- csi_pipeline.rs: 2 tests (set_light_level toggles is_dark,
record_fingerprint stores and self-identifies).
- osm.rs: 3 tests (parse_overpass_json minimal fixture, rejects
malformed payload, fetch_buildings rejects > MAX_RADIUS_M).
Co-Authored-By: claude-flow <ruv@ruv.net>
* Update README + user-guide for PR #405 review-fix additions
- serve now uses --bind 127.0.0.1:9880 (loopback default) instead of --port
- Add fingerprint subcommand to CLI tables
- Document RUVIEW_BRAIN_URL env var + --brain flag
- Flag pose path as amplitude-energy heuristic stub (not trained WiFlow)
- Security note on exposing server outside loopback
- Add wifi-densepose-pointcloud + wifi-densepose-geo rows to crate table
Co-Authored-By: claude-flow <ruv@ruv.net>
Address all 5 P0 issues from QE analysis (55/100 score):
- P0-1: Rate limiter bypass — validate X-Forwarded-For against trusted proxy list
- P0-2: Exception detail leak — generic 500 messages, exception_type gated by dev mode
- P0-3: WebSocket JWT in URL (CWE-598) — first-message auth pattern replaces query param
- P0-4: Rust tests not in CI — add rust-tests job gating docker-build and notify
- P0-5: WebSocket path mismatch — use WS_PATH constant instead of hardcoded /ws/sensing
Includes ADR-080 remediation plan and 9 QE reports (4,914 lines).
Firmware validated on ESP32-S3 (COM8): CSI collecting, calibration OK.
Co-Authored-By: claude-flow <ruv@ruv.net>
arsen
ruv
Claude