Running Deep Learning Models on Smartphones as Real-Time Apps for Signal and Image Processing Applications 🗓

— how deep learning models can be turned into apps running in real-time on smartphones

Meeting
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IEEE Orange County Communications Society and Signal Processing Society (ComSig) Chapter
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Meeting Date: July 11, 2019
Time: 6:00 PM Networking; 6:30PM Food; 7:15 PM Presentation
Speaker: Prof. Nasser Kehtarnavaz, Department of Electrical and Computer Engineering, University of Texas
Location: Santa Ana,
Cost: First 10 early-birds (first-come-first-serve) are free! After that, $20 for non-members with dinner, $10 for IEEE members with dinner, $5 for student-members with dinner, free for presentation only
RSVP: requested, through website
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Summary: In many signal and image processing applications, deep learning models or deep neural networks have provided superior performance compared with conventional machine learning solutions. This talk covers how deep learning models can be turned into apps running in real-time on smartphones (both Android and iOS). One signal and one image processing application are presented. The image processing application involves real-time implementation of a deep learning model as a smartphone app to detect retinal abnormalities in an on-the-fly manner as retina images are captured by the smartphone camera through commercially available lenses. The motivation behind this application is to use smartphones as an alternative to fundus cameras providing a cost-effective and widely accessible approach to first-pass eye examination. The signal processing application involves real-time implementation of the speech processing pipeline of hearing aids as a smartphone app. The components of the implemented pipeline include a deep learning-based voice activity detection, noise reduction, noise classification, and compression. The motivation behind this application is to use smartphones as an open-source, programmable, and portable signal processing platform to conduct hearing enhancement studies in realistic audio environments.

Bio: Prof. Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor with the Department of Electrical and Computer Engineering at the University of Texas. His research interests include real-time signal and image processing, machine learning and deep learning, and biomedical signal and image analysis. He has authored or co-authored 10 books and over 380 journal papers, conference papers, patents, manuals, and editorials in these areas. He is a Fellow of IEEE, a Fellow of SPIE, a licensed Professional Engineer, and is serving as Editor-in-Chief of Springer Journal of Real-Time Image Processing.