Spring Seminar Series 2024 – Dr. Yimin D. Zhang

Sparse Sensor Array Processing for High-Resolution Sensing

Yimin D. Zhang, Ph.D.
Associate Professor
Department of Electrical and Computer Engineering
Temple University, Philadelphia, PA
IEEE Fellow, SPIE Fellow, IEEE SPS Distinguished Lecturer
http://yiminzhang.com

2/2/2024
11am – 12pm CST
Location: San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204

Organizers and Sponsors:
UTSA MATRIX AI Consortium; UTSA Department of ECE; IEEE Signal Processing Society (Distinguished Lecturer Program); IEEE Lone Star Section, Joint Communications and Signal Processing Chapter

Abstract:
Array signal processing plays a crucial role in various applications, including radar sensing, wireless communications, and medical imaging. It serves as a vital technology for tasks like directional beamforming, interference suppression, multiuser access, direction finding, signal localization, and computational imaging. The evolving needs of modern radar sensing and wireless communications demand higher spatial resolution and improved estimation accuracy. However, these requirements often translate into a significant hardware cost due to the large array aperture and the high number of array sensors.To address these challenges, sparse sensor array design and processing offer low-complexity solutions that enhance sensing capability and improve performance with a reduced number of array sensors. This presentation explores recent advancements in concepts, design strategies, and processing methods for sparse array-based sensing, aiming to achieve high spatial resolution and an increased number of degrees of freedom. These advancements leverage recent progress in compressive sensing, optimization, and machine learning. Various sparse array processing techniques will be discussed and array designs that reduce difference lag redundancies and exploit frequency diversity will be presented, providing insights into efficient and cost-effective solutions for array signal processing in diverse applications.

Spring Seminar Series 2024 – Dr. Anna Scaglione

Artificial Intelligence Architectures for Grid Data Analysis and Safe Control of Electric Power Systems

Anna Scaglione, Ph.D.
Professor, IEEE Fellow
Department of Electrical and Computer Engineering
Cornell Tech, New York, NY

Date:
02/09/2024

Time:
11:00 am – 12:00 pm CST

Zoom: https://utsa.zoom.us/j/94807623288

Abstract:
There is a flurry of research activities recently focusing on applications of Artificial Intelligence in electric power systems analysis and operation. It is often motivated by emerging trends that are tied to decarbonizing the grid through the adoption of distributed renewable energy sources and demand response programs that call for methods account for the stochasticity of the net-demand. The vast majority of the research takes AI tools as a black box that internalizes every aspect of the observations, including the physics of the system (namely Ohm’s law) and learns to control the system by being rewarded in choosing good actions. In this talk we discuss how to go a step further. First we introduce Complex Graph Neural Network and show that they are most effective in both inference and reinforcement learning tasks, thanks to their physics inspired feature extraction capabilities. Then we move to introduce the problem of “safe” reinforcement learning, which allows to enforce the physical and safety constraints in the optimal policy.

Spring Seminar Series 2024 – Dr. George Sklivanitis

Toward Secure, Resilient, and AI-Driven Machine-to-Machine Wireless Communications

George Sklivanitis, Ph.D.
Research Associate Professor
Florida Atlantic University
Boca Raton, FL

Date:
02/16/2024

Time:
11:00 am – 12:00 pm CST

Location:
San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204

Zoom: https://utsa.zoom.us/j/94807623288

Abstract:
Next-generation (NextG) network systems such as 6G cellular, future versions of WiFi, satellite networks are expected to connect billions of heterogeneous Internet of Thing (IoT) devices along with billions of people, enable machine-to-machine communications, and provide low-latency computational and storage resources on-demand at the edge and in the cloud. Any failure, tampering or degradation in network service can have highly disruptive, if not potentially catastrophic, effects. NextG network systems should be secure, intelligent and support autonomous decision-making. In this talk, I will present our work on model-based algorithms for waveform design for NextG wireless systems that autonomously optimize how to exchange information with any desirable level of interference avoidance allowed by the physics of the medium and hardware limitations. I will explore challenges and opportunities associated with AI-driven spectrum sensing architectures that provide the support for NextG waveform design and optimization. I will also cover data-driven frameworks enabling NextG security at the physical layer with functionalities such as radio fingerprinting for device authentication and dynamic link encryption using wireless channel state information. I will present our implementation work and experiments with software-defined radio platforms and discuss the way forward.

Spring Seminar Series 2024 – Dr. Gonzalo Mateos

Learning with Graphs

Gonzalo Mateos, Ph.D.
Associate Professor
University of Rochester, Rochester, NY

Date:
02/23/2024

Time:
11:00 am – 12:00 pm CST

Location:
San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204

Zoom: https://utsa.zoom.us/j/94807623288

Abstract:
This talk is broadly about learning from network data, which arises for instance with applications involving online social media, recommendation systems, transportation, and network neuroscience. By fruitfully exploiting the inductive biases in relational data, graph neural networks (GNNs) have attained unprecedented performance in various machine learning tasks, including node/graph classification, link prediction, and graph generation. To provide additional motivation, I will start with a user-friendly and didactic introduction to graph signal processing. The goal is to establish the foundations and basic concepts that will be useful to introduce graph GNNs in an intuitive way. After discussing architectures and key properties that make GNNs the model of choice when it comes to learning from relational data, I will highlight several success stories of GNN-based learning for Amazon’s recommendation system, Google Maps navigation, antibiotic discovery, and our own work on explainable brain age prediction.

Spring Seminar Series 2024 – Dr. Carol Espy-Wilson

Speech Inversion based on Articulatory Phonology

Carol Espy-Wilson, Ph.D.
Professor
University of Maryland
College Park, MD

Date:
03/01/2024

Time:
11:00 am – 12:00 pm CST

Zoom: https://utsa.zoom.us/j/94807623288

Abstract:
Speech articulation is a complex activity requiring finely timed coordination across the articulators: lips, jaw, tongue, glottis and soft palate. This systematic coordination of the articulators differs between individuals, and across languages and dialects of the same language, accounting for many aspects of foreign accent, speech disorders and speaking style. Visualization of articulatory movements has required expensive and specialized equipment. However, we have developed a Speech Inversion (SI) system based on Articulatory Phonology that can accurately recover articulatory movements directly from the acoustic speech signal using machine learning techniques. In this talk, we will discuss the development of the SI system and how we are using the generated articulatory information for mental health assessment. I will also discuss an autoencoder architecture (a Mirror network) that we developed to explore sensorimotor learning. We show that the Mirror network can be used to control an articulatory synthesizer that takes as input articulatory parameters to generate continuous, natural sounding speech for unseen speakers. Further, we show that the Mirror network can learn in an unsupervised way meaningful articulatory representations with comparable accuracy to the SI system that is trained in a completely supervised fashion.