The Electrical and Computer Engineering (ECE) Department at Rutgers is proud to announce that five of our doctoral students have been awarded the prestigious Best AI Innovation prize in the TE AI Cup 2022-23. The team, consisting of 5 ECE doctoral students—Yung-Ting Hsieh, Chuanneng Sun, Zhuoran Qi, Khizar Anjum (all from Prof. Pompili’s CPS Lab), and Ke Xia from Prof. Wei's lab—made significant contributions to real-world Artificial Intelligence (AI) applications, impressing the TE jury and earning recognition among their international peers.

The Rutgers team proposed novel neural network architectures and designed signal pre-processing methods to predict Channel Operating Margin (COM) parameters based on IEEE standards, which was a time-consuming process when using traditional model-based MATLAB scripts. Their work demonstrated significant potential savings in tester time and capital expenditure in high-speed tests, estimated at $12.5 million.

The team's success in the TE AI Cup, an international event hosted by TE Connectivity—an American Swiss-domiciled technology company specializing in connectors and sensors—underscores the excellence of our students and their ability to apply advanced AI practices in a competitive environment. The competition engaged 40 teams from 25 universities globally.

Congratulations to Yung-Ting, Chuanneng, Zhouran, Khizar, and Ke as well as to their advisors!

ECE faculty member Waheed Bajwa, in collaboration with David Zald, Linden Parkes, and Avram Holmes from the Center for Advanced Human Brain Imaging Research (CAHBIR), has received the Rutgers Office for the Vice Provost for Research (OVPR) ML/AI Pilot Seed Grant. This seed grant, amounting to $50,000, will support the research team from July 1, 2023, to June 30, 2024. Their project, titled "Enabling Precision Medicine in Psychiatry Through Advances in Machine Learning for Multimodal Neuroimaging Data," has been selected by the OVPR selection committee for its potential to revolutionize precision psychiatry and significantly advance mental healthcare.

This project strives to transform the current practice in psychiatry by identifying reliable biomarkers through the use of multimodal magnetic resonance imaging (MRI) data. By developing and refining machine learning algorithms that can process complex spatiotemporal dependencies inherent in MRI data, the team aims to provide personalized and precise diagnoses and treatments for psychiatric disorders, thereby bringing us closer to a future where mental healthcare matches the precision seen in other areas of medicine.

Congratulations to Waheed and his team!
 

The 7x24 Exchange National Conference, The leading knowledge exchange for Data Center, IT and Mission Critical professionals was held in Orlando, FL, this year in June. For the first time, a team of Rutgers students attended this conference, where they presented their poster on "Implementation of Thermoelectric Generators Within Mission Critical Facilities". This was a Capstone project conducted by students Ashwin Gokhale, Ashwin Anand and Jonathan Golba under the supervision of Prof. Wade Trappe and Mr. Don Bachman in Spring 2023. The project was very well received by attendees at the conference. This provided great visibility of Rutgers ECE and largely motivated our undergraduate students. Congratulations to the students and Sasan Haghani who led the student team to attend the conference!

The ECE Department at Rutgers wishes to acknowledge the support of the 7x24 Exchange Metro NY Chapter with special thanks to Don Bachman and Heather Bacci for their continued support.

ECE PhD students Morriel Kasher and Rahul Aggarwal (under the supervision of Dr. Predrag Spasojevic) were selected as a finalist team for the 2023 Qualcomm Innovation Fellowship (QIF) for their proposal entitled Efficient Fixed-Point Design of Wideband MIMO Radar for Autonomous Driving.

The Qualcomm Innovation Fellowship is a highly nation-wide competitive award offered to teams of two PhD students. Applicants submit proposals for innovative research ideas in the fields of communications, electronics, computing, and machine learning. This year 182 proposals were submitted representing two dozen universities across North America, from which only a small handful of finalists were selected. Morriel and Rahul are only the 8th team from Rutgers to reach the finalist stage in the past 15 years.

Their proposal combines a cutting-edge multi-antenna radar architecture with state-of-the-art signal design and a novel adaptive processing algorithm to produce fast, accurate, and interference-robust radar measurements in real-time. This is all made feasible to implement using further innovations in low-resolution communication and signal processing. One immediate application is in autonomous driving, where range and angle measurements must be taken from a radar with extremely low latency and high accuracy to appropriately position the vehicle even in high-traffic areas.

Congratulations to Predrag, Morriel and Rahul.

Rahul Aggarwal, an ECE WINLAB Ph.D. candidate, has been selected as the recipient for the IEEE Communications Society Phoenix ISS Award 2023.  Prof. Predrag Spasojevic is Rahul's advisor.   Rahul's research lies at the intersection of signal processing, signal design and communication systems.  His paper titled “Low Latency Adaptive Receiver Processing using Nested Complete Complementary Codes” is about an efficient adaptive processing algorithm for MIMO Radars where the transmit signal is based on Golay complementary sequences. Since June 2021, Rahul has been collaborating with a team at DEVCOM Army Research Lab (Adelphi, MD) on Low Probability of Detection Communication and Network Protocols. Rahul received his bachelor’s degree in Electrical Engineering from Indian Institute of Technology, Patna in August 2019.

The abstract of Rahul's paper follows:

"We present a method to generate a paraunitary transmit matrix for a 2m x 2m MIMO radar system by recursively using the transmit matrix for a 2 x 2 MIMO radar system where the constituent waveforms in the transmit matrix are based on Golay complementary sequences. By applying this recursive method, the matrix that we obtain each time possesses a nested structure wherein the 2 x 2 MIMO radar system is the basic unit. We further present an adaptive receiver processing technique that exploits this very nested structure to provide a low latency approach to detecting the target location and speed. In this technique the transmit matrix used each time is the same. However, at the time of receiver processing, instead of processing the entire received matrix, the smaller submatrices of the received matrix are processed sequentially to detect the target location and speed. This results in significant savings in the computational processing that is required for detection. Moreover, it provides a low latency approach to obtain detection results. The particular example of a 16 x 16 paraunitary transmit matrix is used to illustrate these techniques."

Graduation and Capstone Day photos are in the Capstone Google Photo Album.

You can get there from Academics > Undergraduate  > ECE Capstone page

then click on 2023 Capstone Google Photo Album, 

or click this link:

ECE Google Photo Album

The graduation photos are at the very bottom, so scroll all the way to view them.

ECE Professor Michael Caggiano has been voted by the Rutgers SOE Undergraduate Student Body to receive the 2022-23 Engineering Governing Council (EGC) Professor of the Year Award from within the Department of ECE. This award is annually given to one faculty member from each department in Rutgers SOE who best exemplifies the SOE mission of “Education, Research, and Service.” Michael has been an outstanding teacher in ECE covering courses in the important area of sustainable energy. 

Congratulations to Michael!