dearsky/wifi-densepose
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92a5182dc38798268ab12719d0cf7850b93028b4
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3 Commits

Author SHA1 Message Date
rUv
92a5182dc3 feat(adr-018): ESP32-S3 firmware, Rust aggregator, and live CSI pipeline 
Complete end-to-end WiFi CSI capture pipeline verified on real hardware:

- ESP32-S3 firmware: WiFi STA + promiscuous mode CSI collection,
  ADR-018 binary serialization, UDP streaming at ~20 Hz
- Rust aggregator CLI binary (clap): receives UDP frames, parses with
  Esp32CsiParser, prints per-frame summary (node, seq, rssi, amp)
- UDP aggregator module with per-node sequence tracking and drop detection
- CsiFrame bridge to detection pipeline (amplitude/phase/SNR conversion)
- Python ESP32 binary parser with UDP reader
- Presence detection confirmed: motion score 10/10 from live CSI variance

Hardware verified: ESP32-S3-DevKitC-1 (CP2102, MAC 3C:0F:02:EC:C2:28),
Docker ESP-IDF v5.2 build, esptool 5.1.0 flash, 20 Rust + 6 Python tests pass.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-28 13:22:04 -05:00
rUv
f3c77b1750 Add WiFi DensePose implementation and results 
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads.
- Added a comprehensive training utility for the model, including loss functions and training steps.
- Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
 2025-06-07 05:23:07 +00:00
rUv
6cab230908 Initial commit 2025-06-07 00:32:31 -04:00