Omar’s Status Report 02/14/2026 – Team A7: Focus Field

Omar’s Status Report – 02/14/2026

This week I focused on transitioning FocusField from a local prototype into a deployable, reproducible system on the Raspberry Pi. The primary emphasis was observability, hardware bring-up, and eliminating ambiguity during debugging.

Run Artifacts & Deterministic Debugging

Every run now produces a fully self-contained artifact bundle under:

artifacts/<run_id>/

Each run includes:

run_meta.json (platform, versions, git commit, resolved devices)
config_effective.yaml (exact merged config used)
logs/events.jsonl (structured logs)
logs/perf.jsonl (latency + performance snapshots)
traces/*.jsonl (VAD, DOA peaks, beamformer debug, lock state)
audio/enhanced.wav (+ raw multichannel when enabled)
thumbs/*.jpg (1 FPS camera thumbnails)
crash/crash.json (traceback + last-known state on failure)

This allows us to zip a run folder and fully reconstruct what happened without attaching a debugger or guessing.

Raspberry Pi Bring-Up

I wrote a complete Pi runbook (docs/30_pi_runbook.md) covering:

OS package installation
Python virtual environment setup
Editable project installation
Stable /dev/v4l/by-id camera mapping
UMA-8 calibration workflow
Smoke testing procedures
Common failure modes (USB bandwidth, MJPEG, underruns)

Supporting automation scripts were added:

pi_setup.sh – installs dependencies and environment
list_cameras.py – verifies stable camera paths
calibrate_uma8.py – channel order + yaw alignment calibration
pi_smoke.py – full pipeline validation test
install_systemd_service.sh – deploys FocusField as a boot service

The smoke test validates 8-channel audio capture, enhanced output generation, camera frame ingestion, and artifact creation.

UMA-8 Calibration

I implemented a structured calibration helper for the miniDSP UMA-8:

Tap-based channel detection (clockwise ring)
Automatic channel order reconstruction
SRP-PHAT-based orientation test for yaw_offset_deg
Auto-generated YAML snippet for device_profiles.yaml

This ensures DOA and beamforming geometry align with physical orientation and camera reference.

Audio Pipeline Hardening

Major upgrades to the audio stack:

Dynamic device resolution (substring matching + required channels)
SRP-PHAT DOA with configurable bins and smoothing
MVDR beamformer with diagonal loading and condition number checks
Delay-and-sum fallback logic
Wiener frequency-domain denoiser
Channel drift detection (GCC-PHAT offset estimation)
Beamformer debug telemetry (target, fallback, gains, conditioning)

The UI now includes:

Beamformer status panel
Gain visualization bars
Health monitoring display
Runtime latency metrics

Crash Handling & Fail-Fast

Thread-level and system-level exception hooks were installed:

Automatic crash reports in crash/crash.json
Traceback + last known system state
Configurable fail_fast behavior

This makes Pi debugging viable even without live introspection.

Observability & Monitoring

Health monitor with topic staleness thresholds
Bus drop counters
Runtime performance monitor (latency estimation)
Structured JSONL logging
Live telemetry aggregation

The system now explicitly surfaces degradation rather than silently failing.

Current Pi Setup Status

Stabilizing 3-camera bring-up via /dev/v4l/by-id
Validating UMA-8 7+1 geometry alignment
Testing USB bandwidth under full load
Repeated smoke tests for deterministic startup
Preparing systemd deployment for continuous runtime

The objective is a configuration that boots cleanly, auto-starts the pipeline, generates valid artifact bundles every run, and remains stable under sustained load.

Next Steps

Validate MVDR stability in real room conditions
Tune DOA bin count vs CPU load on Pi
Run extended stress tests (30+ minutes)
Confirm thermal stability
Finalize systemd deployment
Begin quantitative FocusBench replay evaluation

FocusField has now transitioned from prototype to deployable system. The Raspberry Pi environment is structured, reproducible, and debuggable. The remaining work is stability tuning and performance validation.

— Omar