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NEXT MEETUP | NVIDIA presents a demo exploring the RF Spectrum with Deep Learning

“Exploring the RF Spectrum with Deep Learning” — Adam Thompson

Deep Learning has revolutionized the computer vision domain with many networks now outperforming traditional machine learning and image processing techniques for scene classification and understanding. Can these same ideas, however, be applied to non-imagery data? In this talk, we will highlight the developments and implications of deep learning on RF data – speaking specifically about signal classification, identification, anomaly detection, and scheduling.

Adam Thompson is a Senior Solutions Architect at NVIDIA. With a background in signal processing, he has spent his career participating in and leading programs focused on deep learning for RF classification, data compression, high-performance computing, and managing and designing applications targeting large collection frameworks. His research interests include deep learning, high-performance computing, systems engineering, cloud architecture/integration, and statistical signal processing. He holds a Masters degree in Electrical & Computer Engineering from Georgia Tech and a Bachelors from Clemson University.

Tuesday, June 4, 2019, | 6:00 PM to 8:00 PM


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To boil down an extremely complicated topic into a few short words, Symmetric encryption uses the same key to encrypt and decrypt, while Asymmetric uses different keys for encryption and decryption. Symmetric is usually much faster, but is difficult to implement most times due to the fact that you would have to transfer the key over an unencrypted channel. Therefore many times an Asymmetric connection will be established first, then send creates the Symmetric connection. This leads us into the next topic…

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