IEEE Region 5

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, we 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 semi-supervised 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, several 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 auto encoder for an outlier detection problem in spectrum sensing. Speaker(s): Ender Ayanoglu, Virtual: https://events.vtools.ieee.org/m/466461

IEEE Central Texas Section Monthly ExCom Meeting Agenda: Officer Reports Committee Reports Virtual: https://events.vtools.ieee.org/m/469218

Monthly Lone Star Section Executive Committee Meeting Agenda: Room: CR 1.102, Bldg: 168, Southwest Research Institute, 6220 Culebra Road, San Antonio, Texas, United States, 78238, Virtual: https://events.vtools.ieee.org/m/455833

See agenda below. Agenda: Agenda Virtual: https://events.vtools.ieee.org/m/463090

Reinforcement learning has gained traction in research as a machine learning method for the control of autonomous vehicles. Its paradigm of learning by traversing through a series of states has shown to work well with the dynamic nature of driving. However, for safety reasons, much of the research effort is performed in simulators. This work sought to utilize a driving simulator to train an autonomous vehicle using various reinforcement learning methods, with the goal of the vehicle to reach a destination while staying within the road and avoiding collisions with other obstacles. Three methods were explored. The first utilized a Deep Q-Network (DQN) based algorithm with the state being defined by direct values and reward shaping. The second method extended upon the first, utilizing a Proximal Policy Optimization (PPO) based algorithm. It included additions to the reward function based on lessons learned and experimentation with a CNN based state representation. It was shown to learn to solve a driving scenario with less needed steps, as compared to the first method. The final method utilized behavioral cloning with images as its state representation. It was shown to be a minor improvement over its baseline. Talk is limited to US citizens. Registration is required for non-SwRI employees. Cookies and drinks will be provided. Speaker(s): Joseph Clemmons Bldg: Building 51, 6220 Culebra Rd, San Antonio, Texas, United States, 78238

IEEE Central Texas Section Monthly ExCom Meeting Agenda: Officer Reports Committee Reports Virtual: https://events.vtools.ieee.org/m/469219

Monthly Lone Star Section Executive Committee Meeting Agenda: Room: CR 1.102, Bldg: 168, Southwest Research Institute, 6220 Culebra Road, San Antonio, Texas, United States, 78238, Virtual: https://events.vtools.ieee.org/m/455834

Come join us on Saturday, 5Jul2025, for a tour of the Arvest Ballpark and stay for the game and fireworks show! We secured a group rate for tickets which cost $10 per person, Kids 3 and under are free. You should be able to pay for the ticket using the IEEE vTools system. We will need to give them a final ticket count by 5Jun2025. We will plan to meet at the Main Gate at 5pm for the tour, at which time they will scan our tickets, then meet with creative services coordinator. They will tour the controls for the Score Boards and Broadcast areas, along with other interesting communications and automation systems. [] Room: Main Gate, 3000 Gene George Blvd, Springdale, Arkansas, United States, 72762