Panel Moderators:
Lynne Grewe, California State Univ. (United States)
Ivan Kadar, Interlink Systems Sciences, Inc. (United States)

Panelists:
Andreas Savakis, Rochester Institute of Technology (United States)
Yu Chen, Binghamton University (SUNY) (United States)
Genshe Chen, Intelligent Fusion, Inc (United States)
Yufeng Zheng, University of Mississippi Medical Center (United States)

Traditionally, sensor fusion methods included close-in multimodal measurements associated with ground robotics but evolved to incorporate many types of data from different sources. As the explosion of computational processing was available, the terms sensor, data, and information fusion enabled “big data” methods such as large-area imaging from electro-optical sensors and hundreds of sensors monitoring a wide geographical area. To process such large quantities of data, powerful computers and systems resulted in cloud computing approaches as centralized sensor fusion. However, there was still a need to develop methods for decentralized and distributed sensor fusion from sensors at the edge. To balance these approaches of centralized-cloud and decentralized-edge techniques, notions of fog-enabled distributed sensor fusion methods were developed to orchestrate the data flow, processing, and analysis. The panel focuses on the trends that seek to utilize edge-computing platforms amongst a large corpus of sensor, contextual, and social sources. For example, the advent of recent concepts of consideration includes digital twin technology, large language models, deep learning, and neuromorphic/neurosymbolic, among others. The panel will highlight not just the small-scale edge computing of the past, but how edge-computing sensor fusion is coordinated in terms of information and source fusion domains available from cloud and fog computing.

 

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Event Details

FORMAT: Panel discussion followed by audience Q&A.
MENU: Coffee, decaf, and tea will be available outside the presentation room.
SETUP: Classroom and theater style seating.

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