Towards Next-Generation Intelligent Radar Sensors: Adopting a Hardware-Software-Data Framework for System Design
#radar
#sensor
#mttsevent
#technicaltalk
#AIforhardware
The rising popularity of radar sensors in advanced applications like automotive navigation and security has brought forth a re-imagining of radar functionality. A radar of the future will not only be responsible for its primary task of target detection, tracking, and/or imaging but will also require coexisting with other sensors. This must occur both in the informational domain, through multi-sensor fusion, as well as in the electromagnetic spectral domain with other wireless systems – all of which require processing vast amounts of sensor data. While recent advancements in artificial intelligence (AI) may be leveraged to address these challenges, a truly useful solution from a practical standpoint must have low latency and high energy efficiency. This talk proposes a co-designed hardware-software-data framework as a potential research avenue for next-generation radar systems by posing two questions (i) ‘Can emerging AI-in-hardware technologies help create intelligent microwave systems with low latency?’ and (ii) ‘How can we generate high-quality experimental datasets with maximum reusability potential?’. This tutorial-style talk will cover emerging topics on hardware for AI such as microwave components for machine learning and system design using analog in-compute technologies, and the advantages and limitations of the same. For any AI-based solution, the quality of training data is as important as the algorithms themselves. Therefore, the talk will conclude by examining efforts made by the research community to generate open-source datasets of wireless signals.
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- Starts
14 July 2025 07:00 AM UTC
- Ends
16 July 2025 12:00 PM UTC
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Speakers
Dr. Arya Menon
Topic:
Towards Next-Generation Intelligent Radar Sensors: Adopting a Hardware-Software-Data Framework for System Design
Abstract: The rising popularity of radar sensors in advanced applications like automotive navigation and security has brought forth a re-imagining of radar functionality. A radar of the future will not only be responsible for its primary task of target detection, tracking, and/or imaging but will also require coexisting with other sensors. This must occur both in the informational domain, through multi-sensor fusion, as well as in the electromagnetic spectral domain with other wireless systems – all of which require processing vast amounts of sensor data. While recent advancements in artificial intelligence (AI) may be leveraged to address these challenges, a truly useful solution from a practical standpoint must have low latency and high energy efficiency. This talk proposes a co-designed hardware-software-data framework as a potential research avenue for next-generation radar systems by posing two questions (i) ‘Can emerging AI-in-hardware technologies help create intelligent microwave systems with low latency?’ and (ii) ‘How can we generate high-quality experimental datasets with maximum reusability potential?’. This tutorial-style talk will cover emerging topics on hardware for AI such as microwave components for machine learning and system design using analog in-compute technologies, and the advantages and limitations of the same. For any AI-based solution, the quality of training data is as important as the algorithms themselves. Therefore, the talk will conclude by examining efforts made by the research community to generate open-source datasets of wireless signals.
Biography:
Dr. Arya Menon is an Assistant Professor in the Department of Electrical and Computer Engineering at Texas A&M University (TAMU). Dr. Menon received the Bachelor of Engineering degree in electronics and communication from Manipal Institute of Technology, India in 2014, M.S. in electrical engineering from the University of South Florida in 2016, and Ph.D. in electrical and computer engineering from Oregon State University in 2021. She completed her postdoctoral fellowship at Texas A&M University in 2023. Her current research focuses on investigating bio-inspired intelligence in radar design. Her group focuses on three thrusts - hardware-software co-design for cognitive radars, creation of sharable wireless datasets, and sensor fusion with radar. Her other research interests include design of radiometers, sensor calibration techniques, and dielectric characterization methods. Her research has earned multiple awards and recognition including selection as a 2025 MTT-S Outstanding YP Lecturer, 2022 DARPA Riser for ‘Bioinspired Hardware-Software Co-Design for Intelligent High- Frequency Active Sensors’, the 2020 ARTFG Roger Pollard Student Fellowship (Silver), and the 2019 IEEE Microwave Theory and Techniques Society Graduate Fellowship, among others. Dr Menon is also a passionate educator and was the recipient of the University of South Florida’s Provost’s Award for Outstanding Teaching in STEM by a Graduate Teaching Assistant in 2018 for her contributions towards developing laboratory experiments for a new undergraduate course in electromagnetics.
