Uncategorized

Friday, September 16, 2022
10:20 – 11:10 a.m. (CST)
Virtual via Zoom: https://tamu.zoom.us/j/93347193479 (password in emails or syllabus)

Dr. Joerg Widmer
Research Professor and Research Director of IMDEA Networks
Madrid, Spain

Title: “Millimeter-Wave Joint Communication and Sensing and Location-based Network Control”

Talking Points:

• Practical challenges of millimeter-wave communications
• Benefits of millimeter-wave frequencies for high-precision sensing and localization

Abstract

The high bandwidth available at millimeter-wave frequencies allows for very high data rates, and the latest wireless technologies already exploit this part of the radio spectrum to achieve rates of several GBit/s per user. Communication at these frequencies typically uses directional antennas which brings about interesting challenges to align antenna beams. Given the high penetration loss, most obstacles (e.g., a person) also completely block the signal. This results in a very dynamic radio environment and channels may appear and disappear over very short time intervals. This seminar highlights some approaches to deal with these networking challenges. The very large bandwidth available at mm-wave frequencies also allows to design highly accurate location systems, and such location information be used to facilitate beam training, optimize access point association, and predict future blockage. We discuss how to implement such millimeter-wave location systems and present network optimization mechanisms based on simultaneous localization and mapping of the environment.

Finally, the information about the wireless channel of millimeter-wave systems can also be used for highly accurate environment sensing. We discuss how to use communication hardware to perform zero-cost monitoring of human movement and activities in indoor spaces (rather than using dedicated radars). We show that access points can be retrofitted to perform radar-like extraction of the micro-Doppler effects caused by the motion of multiple human subjects. We then use this to achieve fine-grained sensing applications such as simultaneous activity recognition and person identification. We will specifically focus on the practical implementation aspects, testbed design and experimental results with such systems.

Biography

Dr. Joerg Widmer is a Research Professor and Research Director of IMDEA Networks in Madrid, Spain. Before, he held positions at DOCOMO Euro-Labs in Munich, Germany and EPFL, Switzerland. He was a visiting researcher at the International Computer Science Institute in Berkeley, USA, University College London, UK, and TU Darmstadt, Germany. His research focuses on wireless networks, ranging from extremely high frequency millimeter-wave communication and MAC layer design to mobile network architectures. Joerg Widmer authored more than 200 conference and journal papers and three IETF RFCs, and holds 14 patents. He was awarded an ERC consolidator grant, the Friedrich Wilhelm Bessel Research Award of the Alexander von Humboldt Foundation, a Mercator Fellowship of the German Research Foundation, a Spanish Ramon y Cajal grant, as well as nine best paper awards. He is an IEEE Fellow and Distinguished Member of the ACM.

More Info on Dr. Widmer at: https://networks.imdea.org/team/imdea-networks-team/people/joerg-widmer/

More info. on past and future CESG Seminars at CESG Seminars (tamu.edu)

Please join on Friday, 9/16/22 at 10:20 a.m. via Zoom.

Friday, September 9, 2022
10:20 – 11:10 a.m. (CST)
Virtual via Zoom: https://tamu.zoom.us/j/93347193479 (password in emails or syllabus)

Dr. Subhonmesh Bose
Assistant Professor, University of Illinois Urbana Champaign

Title: “Modeling Risk in Power System Operations and Planning”

Talking Points:

• How you model risk should depend on how you can optimize against it
• Primal-dual algorithms for risk-sensitive optimization

Abstract

Integration of variable renewable and distributed energy resources in the grid makes demand and supply conditions uncertain. In this talk, we will first explore electricity market design for daily operations that explicitly models power delivery risks and system constraint violation risks. Then, we will discuss the distributed solar hosting capacity problem that considers said risks in electricity infrastructure planning. Throughout the talk, we will encode risks via the conditional value at risk (CVaR) measure, properties of which allow efficient, often customized, algorithm design for optimization, and facilitate the definition of economically meaningful prices in market design contexts.

Biography

Dr. Subhonmesh Bose is an Assistant Professor and Stanley Helm Fellow in the Department of Electrical and Computer Engineering at UIUC. His research focuses on facilitating the integration of renewable and distributed energy resources into the grid, leveraging tools from optimization, control and game theory. Before joining UIUC, he was a postdoctoral fellow at the Atkinson Center for Sustainability at Cornell University. Prior to that, he received his MS and Ph.D. degrees from Caltech in 2012 and 2014, respectively. He received the NSF CAREER Award in 2021. His research projects have been supported by grants from NSF, PSERC, Siebel Energy Institute and C3.ai, among others.

More Info: http://boses.ece.illinois.edu/

Please join on Friday, 9/9/22 at 10:20 a.m. via Zoom.

Thursday, June 2, 2022
10:00 – 10:50 a.m. (CST)
ETB 1035 – **In-person**

Dr. Muhammad Awais Bin Altaf
Assistant Professor, Electrical Engineering
Lahore University of Management Sciences (LUMS), Pakistan

Title: “On-Chip Energy-Efficient Neural Diagnostics: Advancing Neuroscience through Wearable Devices”

Talking Points:

• Wearable Healthcare
• On-Chip Energy Efficient Digital Processing Techniques for ML algorithms
• Early Detection of Negative Emotion Outburst

Abstract
Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. Today, neurology faces multiple challenges in the field of diagnostic and management modalities. This ranges from simple issues like identification of healthy sleep patterns to more complicated issues like early detection and reduction in the duration of rehabilitation of acute ischemic stroke diagnosis of rare subtypes of epilepsy. The increasing availability and progress of analytical techniques are opening new doors in health care. Machine learning, neural networks and other AI tools are used to classify the patient’s electroencephalogram (EEG) data to help neurologists in making an early diagnosis and improving care. Hence, the development of ultra-low-power System-on-Chip (SoC) for the next generation of the neuro-wearables, in the realms of detecting, diagnosing, and even preventing irreversible outcomes due to neurological disorders is essential. The uptake in the use of neuro-wearable technology by both patients and clinicians will have a huge impact on the future of healthcare.

This talk will cover the design strategies of energy-efficient patient-specific SoC biomedical devices. I will first explore the challenges, limitations and potential pitfalls in wearable interface circuit design, and strategies to overcome such issues. Moreover, I will describe on-chip energy-efficient digital processing techniques for the implementation of machine-learning algorithms for disease detection focusing on the negative emotion outburst early detection in Autistic patients. The talk will conclude with interesting aspects and opportunities that lie ahead.

Biography
Muhammad Awais Bin Altaf  (S’11–M’16) received a B.S. degree from the University of Engineering and Technology, Lahore, Pakistan, in 2008, and the M.Sc. and Ph.D. degrees in microsystems engineering and interdisciplinary engineering from the Masdar Institute of Science and Technology (MIST), Abu Dhabi, United Arab Emirates, in 2012 and 2016, respectively. From 2012 to 2013, he was a Digital Design Engineer Intern at Design Solutions, Global Foundries, Dresden, Germany, where he was involved in the implementation of digital test chips in support of 20 and 14 nm technologies. In 2015, he was an exchange-Ph.D. a student with the Massachusetts Institute of Technology, Cambridge, MA, USA.

During his stay at MIST, he developed an energy-efficient machine-learning-based feature extraction and classification processor as well as a SoC for epileptic seizure detection. He is the recipient of the IEEE Solid-State Circuits Society Predoctoral award for his work on efficient machine learning hardware implementation for wearable healthcare in 2016. Since 2016, he has been with the Electrical Engineering Department, Lahore University of Management Sciences (LUMS), Lahore, Pakistan where he is currently an Assistant Professor. His current research interests include analog and digital IC design, energy-efficient applied AI and the development of ultra-low-power circuits and systems for wearable bio-medical applications.

Google Scholar: https://scholar.google.ae/citations?user=XVyrDmgAAAAJ&hl=en

In-Person @ ETB 1035 @ 10:00 a.m. on Thursday, 6/2/22

CESG is proud to list our Spring 2022 graduating students. We celebrate these Masters students who are off to conquer their next goals in academia and industry!

Inimfon Idongesit Akpabio
Georges Alsankary
Suprith Balasubramanian
Ruilian Gao
Chia-Hang Lee
Hao Li
Kyler Ray Scott
Christopher P. Weber
Fangqing Xia
Chaoyang Zhu

May y’all have success and happiness in your futures and live by the Aggie Core Values!

Friday, April 29, 2022
4:10 – 5:00 p.m.
Zoom: https://tamu.zoom.us/j/96343481647
 
Dr. Vijay Subramanian
Associate Professor in the EECS at the University of Michigan, Ann Arbor, MI

Talking Points

• Multi-agent dynamic games with asymmetric information
• Information states
• Compression based equilibria
• Sequential decomposition

Title
“Games in Multi-Agent Dynamic Systems: Decision-Making with Compressed Information”

Abstract
The model of multi-agent dynamic systems has a wide range of applications in numerous socioeconomic and engineering settings: spectrum markets, e-commerce, transportation networks, power systems, etc. In this model, each agent takes  actions over time to interact with the underlying system as well as each other to achieve their respective objectives. In many applications of this model, agents have access to a huge amount of information that increases over time. Determining solutions of such multi-agent dynamic games can be complicated due to the huge domains of strategies. Meanwhile, agents have restrictions on their computational power and communication capability as well as latency limitations, which prevent them from implementing complicated strategies. Therefore, it is important to identify suitable compression schemes so that at equilibrium each agent can make decisions based on a compressed version of their information instead of the full information. However, compression of information could result in loss of some or all equilibrium outcomes. This talk  presents results on this issue for a general class of multi-agent dynamic games, and designs and analyzes appropriate  information compression schemes. Our results highlight the tension among information compression, preservation of  equilibrium outcomes, and applicability of sequential decomposition algorithms to find compression-based equilibria. This is joint work with Dengwang Tang (University of California, Berkeley) and Demos Teneketzis (University of Michigan).

Biography
Vijay Subramanian received the Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign,  Champaign, IL, USA, in 1999. He worked at Motorola Inc., at the Hamilton Institute, Maynooth, Ireland, for many years, and in the EECS Department, Northwestern University, Evanston, IL, USA. In Fall 2014, he started in his current position as Associate Professor with the  EECS Department at the University of Michigan, Ann Arbor. His research interests are in stochastic analysis, random  graphs, multi-agent systems, and game theory (mechanism and information design) with applications to social, economic and technological networks.

Dr. Subramanian: https://subramanian.engin.umich.edu

On Zoom @ 4:10 p.m. on Friday, 4/29/22

Join Zoom Meeting
https://tamu.zoom.us/j/96343481647
Meeting ID: 963 4348 1647

Friday, April 22, 2022
4:10 – 5:00 p.m.
 
ETB 1020 – **In-person**
 
Takashi Tanaka
Assistant Professor, University of Texas at Austin
Department of Aerospace Engineering and Engineering Mechanics
 
Title: “Minimum-Information Kalman-Bucy Filtering and Fundamental Limitation of Continuous-Time Data Compression”

Talking Points:

• Event-based vs. frame-based cameras
• Information theory for networked control systems
• Causal source coding

Abstract
Motivated by a practical scenario where a continuous-time source signal is encoded, compressed, and transmitted to a remote user where the signal is reproduced in a real-time manner (e.g., streaming of neuromorphic camera data), we study the fundamental trade-off between the encoding data rate and the best achievable data quality (distortion). After briefly reviewing the “causal” rate-distortion theory in discrete-time, in this talk, we consider the problem of estimating a continuous-time Gauss-Markov source process observed through a vector Gaussian channel with an adjustable channel gain matrix. For a given (generally time-varying) channel gain matrix, we provide formulas to compute (i) the mean-square estimation error attainable by the classical Kalman-Bucy filter, and (ii) the mutual information between the source process and its Kalman-Bucy estimate. We then formulate a novel “optimal channel gain control problem” where the objective is to control the channel gain matrix strategically to minimize the weighted sum of these two performance metrics. To develop insights into the optimal solution, we first consider the problem of controlling a time-varying channel gain over a finite time interval. A necessary optimality condition is derived based on Pontryagin’s minimum principle. For a scalar system, we show that the optimal channel gain is a piece-wise constant signal with at most two discontinuities. We also consider the problem of designing the optimal time-invariant gain to minimize the average cost over an infinite time horizon. A novel semidefinite programming (SDP) heuristic is proposed to compute the optimal solution.

Biography
Takashi Tanaka is an Assistant Professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin since 2017. He received his B.S. degree from the University of Tokyo in 2006, M.S. and Ph.D. degrees from UIUC in 2009 and 2012, all in Aerospace Engineering. Prior to joining UT Austin, he held postdoctoral researcher positions at MIT and KTH Royal Institute of Technology. His research interest is broad in control, optimization, games, and information theory; most recently their applications to networked control systems, real-time data sharing, and strategic perception. He is the recipient of the DARPA Young Faculty Award, the AFOSR Young Investigator Program award, and the NSF Career award.

Personal Website: http://sites.utexas.edu/tanaka/

In-Person @ ETB 1020 @ 4:10 p.m. on Friday, 4/22/22