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
Developed a real-time anomaly detection feature for the liquidinstruments/moku-examples repository, enabling onboard monitoring on the Moku FPGA. The solution leveraged autoencoders and deep learning techniques, utilizing Python and TensorFlow to construct an end-to-end pipeline from data acquisition on the Moku device through dataset preparation, model training, and on-device evaluation. Integrated Moku API-based data ingestion and preprocessing to support low-latency inference directly on the hardware, reducing reliance on off-device processing. Updated documentation and usage examples to clarify the anomaly detection workflow and deployment steps, ensuring reproducibility and ease of adoption for future contributors and users of the repository.
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
27 peopleSame Organization
@LIQUIDINSTRUMENTS.COM12
Shared Repositories
15
Ben NizetteMember
Brian NeffMember
Dylan PerezMember
EC2 Default UserMember
EC2 Default UserMember
Heyang LongMember
Indira Shinn ReesMember
jasonliquidinstMember
jobinMember
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
Repositories Contributed To
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