Generative Adversarial Networks (GANs) implement Machine Learning (ML) algorithms that can address competitive resource allocation problems, together with detection and mitigation of anomalous behavior. In this talk, the speaker will discuss their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii) mitigating security attacks. GANs have the following advantages. First, they can learn and synthesize field data, which can be costly, time-consuming, and non-repeatable. Second, they enable pre-training classifiers by using semisupervised data. Third, they facilitate increased resolution. Fourth, they enable recovering corrupted bits in the spectrum. The talk will provide basics of GANs, a comparative discussion on different kinds of GANs, performance measures for GANs in computer vision and image processing as well as wireless applications, a number of datasets for wireless applications, performance measures for general classifiers, a survey of the literature on GANs for i)–iii) above, some simulation results, and future research directions. In the spectrum sharing problem, connections to cognitive wireless networks are established. Simulation results show that a particular GAN implementation is better than a convolutional autoencoder for an outlier detection problem in spectrum sensing. Co-sponsored by: Vishnu S. Pendyala, SJSU Speaker(s): Dr. Vishnu S. Pendyala, Prof. Ender Ayanoglu Virtual: https://events.vtools.ieee.org/m/493301

EXCOM Meeting for IEEE PES Seattle Officers Virtual: https://events.vtools.ieee.org/m/492311

Come to enjoy the magnificent Sunset Dinner at the Columbia Tower Club, on the 76th floor of the Columbia Tower. Let us welcome Thomas Coughlin, the IEEE past President, 2024 join us. Agenda: 6:00 - 6:30 pm Networking 6:30- 7:30 pm Dinner with your fellow chapter members, discuss the chapter plan, or nominate officers. ( You are encouraged to inform S Wen of your intention to run for the open seat of the officers before the meeting) 7:30 - 8:30 pm Technical meeting and discuss IEEE New Era AI exciting programs and watch the Sunset. 8:30 pm end Room: 76 floor, Bldg: columbia tower, 701 5th Ave 75th floor, , Seattle , Washington, United States, 98104

Free Registration (with a Zoom account; you can get one for free if you don't already have it. This requirement is to avoid Zoom bombing. Please sign in using the email address tied to your Zoom account — not necessarily the one you used to register for the event.): https://sjsu.zoom.us/webinar/register/WN_DIDfdbkgRFeEd3JbzURIUQ Synopsis: How are machine learning algorithms able to answer questions from any nook and corner of the World Wide Web? How are trending hashtags from the near infinite microblog posts, unique visitors and other distinct counts in the near infinite website traffic determined? How do blogging websites avoid recommending articles a user has previously read? In general, how can we answer complex queries about enormous data streams without storing them entirely, in real-time? The answer often lies in clever approximation algorithms and data "sketches" that capture essential properties using vastly reduced space. The relentless flow of data in modern systems indeed presents significant challenges. These data streams are often too large to store and too fast to process exhaustively with traditional methods. This talk introduces key sketching and approximation techniques that help generate real-time data insights by processing data streams. --------------------------------------------------------------- By registering for this event, you agree that IEEE and the organizers are not liable to you for any loss, damage, injury, or any incidental, indirect, special, consequential, or economic loss or damage (including loss of opportunity, exemplary or punitive damages). The event will be recorded and will be made available for public viewing. Speaker(s): Dr. Vishnu S. Pendyala Virtual: https://events.vtools.ieee.org/m/482936