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.
Saves the comprehensive OTA pipeline reference written by another
agent so future sessions don't lose the diagnostic flowchart or the
"three FW prerequisites" causal chain.
Tested live against current FW (v0.6.4): port 8032 reachable on both
sensors, scripts/ota-deploy.sh round-trip works, both nodes
successfully switched partitions (ota_0 ↔ ota_1) without USB+BOOT
dance. OTA is the supported path for future FW changes from this
session — sensor µs timestamp (ADR-106 open item), NVS persistence
of gain-lock (gap-analysis #5), and any larger FW work.
Kept whole (329 lines, over the usual 200 line cap for docs) because
the flowchart and pitfall table lose meaning if split. The cap is a
guideline for new project ADRs; a verbatim recipe is justified by
diagnostic value.
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.
Catalogues, section-by-section against Pace's Part-2 article, every
ESPectre technique RuView has and does not have, plus a prioritized
roadmap (9 items, NVS persistence and FP-rate validation top of list).
Replaces the 8-item inline "open items" stub in espectre-techniques.md
with a 1-line forward link. Both files stay ≤ 200 lines per the docs
convention.
* 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).
* docs/references/espectre-techniques.md — catalogues every Pace
technique from Part-2 against what RuView has implemented, doesn't
have, or has differently. Includes ranked open-items list.
* sensing-server: revert feature_state path to vec![] amplitudes.
The previous fix made bars LOOK live by reissuing the last raw-CSI
vector on every feature_state tick — operator reported this made
the bars misleading (visually busy but unresponsive to movement).
raw.html already skips empty-amp updates so bars now refresh only
on actual fresh CSI, which is honest.
* raw.html: comment on the skip-empty branch for future-me.
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
Addresses three findings from the 2026-05-11 training-pipeline audit:
#1/#2 — `wifi-densepose-signal` was a phantom dependency of `wifi-densepose-train`
(listed in Cargo.toml, never imported), and vitals/CSI signal features were
absent from the pipeline. New module `wifi_densepose_train::signal_features`:
`extract_signal_features(&Array4<f32>, &Array4<f32>) -> Array1<f32>` (and the
convenience method `CsiSample::signal_features()`) runs a windowed observation's
centre frame through `wifi_densepose_signal::features::FeatureExtractor`,
producing a fixed-length (FEATURE_LEN=12) amplitude / phase-coherence / PSD
feature vector — the hook for a future vitals / multi-task supervision head
(breathing- and heart-rate-band power are read off the PSD summary). The vector
is produced on demand and is not yet fed back into the loss; wiring it as a
training target is the documented follow-up. `wifi-densepose-signal` is now an
actually-used dependency. 5 new tests (2 unit in signal_features.rs, 3
integration in tests/test_dataset.rs); existing wifi-densepose-train tests
unchanged and green.
#3 — `docs/huggingface/MODEL_CARD.md` presented PIR/BME280 environmental-sensor
weak-label fine-tuning as a current capability; there is no env-sensor
ingestion in the training pipeline. Marked that path as planned/not-implemented
in the training-steps list and the data-provenance section.
(#5 — README's "92.9% PCK@20" overclaim — fixed separately in PR #535.)
CHANGELOG updated.
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.
- Move Latest Additions, Key Features, and everything from Installation
through Changelog (1855 lines) into docs/readme-details.md.
- Keep README focused on overview, capability table, How It Works,
Use Cases, Documentation, License, and Support.
- Add per-row emojis to the top capability table.
- Add 3D point cloud row noting optional camera + WiFi CSI + mmWave
fusion with link to the live viewer demo.
- Move Documentation table closer to the bottom (just above License).
- Collapse Edge Intelligence (ADR-041) into a <details> block matching
the sibling Use Case sections.
Co-Authored-By: claude-flow <ruv@ruv.net>
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.
Three exploratory research documents under docs/research/:
- architecture/three-tier-rust-node.md (3,382 words) — exploration of a
dual-ESP32-S3 + Pi Zero 2W node architecture with BQ24074 power-path,
ESP-WIFI-MESH + LoRa fallback + QUIC backhaul, and an esp-hal/Embassy
vs esp-idf-svc Rust toolchain split. Status: Exploratory — not adopted.
- sota/2026-Q2-rf-sensing-and-edge-rust.md (3,757 words) — twelve-section
state-of-the-art survey covering WiFi CSI through-wall pose, IEEE 802.11bf
(ratified 2025-09-26), edge ML on ESP32-class hardware, embedded Rust
ecosystem maturity (esp-hal 1.x, esp-radio rename, embassy-executor
ISR-safety on esp-idf-svc), LoRa for sensor mesh fallback, QUIC for IoT
backhaul, solar power-path management beyond BQ24074, mesh routing
alternatives, and Pi Zero 2W secure-boot reality.
- architecture/decision-tree.md (1,461 words) — Mermaid decision tree
mapping each load-bearing decision in the three-tier proposal to its
dependencies, evidence-for-yes/no, and prospective ADR slot.
No production code, firmware, or ADRs touched. Research-only.
Co-Authored-By: claude-flow <ruv@ruv.net>
`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>
arsen
ruv
Claude