Uploaded February 2025 | Updated September 2026, 14 hours ago
Slide Deck can be downloaded from github.com/OpenResearchInstitute/documents/blob/master/Papers_Articles_Presentations/Slide_Presentations/IEEE-SD-TALK.pdf
Summary:
Small Unmanned Aerial Systems (sUAS) must be monitored closely to identify, diagnose, and potentially mitigate flight problems as they arise. During the flight, the vast amounts of multivariate time series data typically generated by sUAS flight controllers can be complex to understand and analyze. While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, critical thresholds, and complex data interactions can be overwhelming to non-experts who subsequently seek help from discussion forums to interpret their data logs. Solutions based on deep learning or heuristics can be used to detect anomalies in different time-series data attributes. However, understanding and mitigating the root cause of flight problems based upon the combination of multiple detected data anomalies requires significant domain expertise. To address these challenges, this talk will present approaches that leverage deep learning and heuristic methods to detect anomalies in flight data, both for real-time and post-flight analysis. Additionally, it will explore how the combination of multiple detected anomalies can be utilized to diagnose the root cause of the flight issue. The solutions proposed aim to simplify the anomaly detection process while providing a more systematic approach for diagnosing complex flight behaviors.
Speaker Biography: Dr. Md Nafee Al Islam is an Assistant Professor of Computer Science at the University of San Diego. He earned his PhD and MS in Computer Science and Engineering from the University of Notre Dame and his Bachelor’s degree in Electrical and Electronics Engineering from the Islamic University of Technology, Bangladesh. His research centers on Cyber-Physical Systems, Data Analytics, and Artificial Intelligence, with a particular focus on applying these technologies to Small Unmanned Aerial Systems (sUAS) to address real-world challenges in their operation.
Slide Deck can be downloaded from github.com/OpenResearchInstitute/documents/blob/master/Papers_Articles_Presentations/Slide_Presentations/IEEE-SD-TALK.pdf
Summary:
Small Unmanned Aerial Systems (sUAS) must be monitored closely to identify, diagnose, and potentially mitigate flight problems as they arise. During the flight, the vast amounts of multivariate time series data typically generated by sUAS flight controllers can be complex to understand and analyze. While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, critical thresholds, and complex data interactions can be overwhelming to non-experts who subsequently seek help from discussion forums to interpret their data logs. Solutions based on deep learning or heuristics can be used to detect anomalies in different time-series data attributes. However, understanding and mitigating the root cause of flight problems based upon the combination of multiple detected data anomalies requires significant domain expertise. To address these challenges, this talk will present approaches that leverage deep learning and heuristic methods to detect anomalies in flight data, both for real-time and post-flight analysis. Additionally, it will explore how the combination of multiple detected anomalies can be utilized to diagnose the root cause of the flight issue. The solutions proposed aim to simplify the anomaly detection process while providing a more systematic approach for diagnosing complex flight behaviors.
Speaker Biography: Dr. Md Nafee Al Islam is an Assistant Professor of Computer Science at the University of San Diego. He earned his PhD and MS in Computer Science and Engineering from the University of Notre Dame and his Bachelor’s degree in Electrical and Electronics Engineering from the Islamic University of Technology, Bangladesh. His research centers on Cyber-Physical Systems, Data Analytics, and Artificial Intelligence, with a particular focus on applying these technologies to Small Unmanned Aerial Systems (sUAS) to address real-world challenges in their operation.
