Generative Adversarial Network (GAN) is a powerful idea to train generative models and has recently shown amazing results in computer vision. Broadly, the framework estimates generative models via an adversarial process in which the generator and discriminator play a two-player game. Although there are only a few successful cases, the GAN framework is useful for NLP tasks and overcomes the inherent limitations in conventional methods.

In this talk, I will talk on recent project outcomes of my research group, Sabanci University -Microelectronics Research Group.

This presentation will discuss the challenges designing and testing highly integrated low cost SOC products for the 60GHz band. Products discussed include a high data rate SOC with phased array front-end and a high resolution short range radar SOC. Topics will include radio architecture overview, self- calibration, designing for reliability and testing challenges.

Increasingly, objects—previously unidentified as requiring networked communications—are becoming a part of what is known as the “Internet of Things (IoT)”.  On the battlefield, future warfighter operations and missions will rely more heavily on networked devices designed with autonomous cognitive decision-making capabilities to perform a broad range of tasks, including cognitive sensing, communicating with human warfighters, conducting operations in congested environments, and securely processing and communicating data to other autonomous agents.