liquidinstruments/moku-examples
Jul 2025 – Jul 2025
1 Month active
Languages Used
Python
Technical Skills
AutoencodersData AcquisitionDeep LearningKerasMachine LearningMoku API
MICHELA-BENEDETTI
PROFILE
Michela-benedetti
Michela developed a real-time anomaly detection feature for the liquidinstruments/moku-examples repository, targeting the Moku FPGA platform. She designed and implemented an end-to-end pipeline using Python and TensorFlow, leveraging autoencoders for low-latency, onboard anomaly monitoring. Her work encompassed data acquisition from the Moku device, dataset preparation, model training, and on-device evaluation, enabling the system to detect anomalies without offloading data for external processing. By integrating Moku API-based data ingestion and signal processing, Michela streamlined the workflow and improved performance. She also updated documentation and usage examples, ensuring the feature’s deployment and evaluation steps were clear and reproducible for users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,055
Activity Months1
Your Network
23 peopleSame Organization
@LIQUIDINSTRUMENTS.COM10
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025Month: 2025-07. Focused on delivering a real-time anomaly detection capability on the Moku FPGA within the liquidinstruments/moku-examples repository. Implemented an autoencoder-based detector, establishing a complete end-to-end pipeline from data acquisition on the Moku device through dataset preparation, model training, and on-device evaluation to support onboard, low-latency anomaly monitoring.
1 Commits • 1 Features
Jul 1, 2025Month: 2025-07. Focused on delivering a real-time anomaly detection capability on the Moku FPGA within the liquidinstruments/moku-examples repository. Implemented an autoencoder-based detector, establishing a complete end-to-end pipeline from data acquisition on the Moku device through dataset preparation, model training, and on-device evaluation to support onboard, low-latency anomaly monitoring.
July 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AutoencodersData AcquisitionDeep LearningKerasMachine LearningMoku APIPythonSignal ProcessingTensorFlow
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