News Highlights

The latest updates and briefs from the Department of Electrical and Computing Engineering.




    Tensor Decomposition for Signal Processing and Machine Learning Is a Go-To Primer for Students and Practitioners

    September 02, 2020

    Each year, hundreds if not thousands of papers are published on tensor-related topics, easily overwhelming students and practitioners alike. Nikolaos Sidiropoulos, professor and chair of the Charles L. Brown department of electrical and computer engineering at the University of Virginia, led an effort to create an easy on-ramp for those eager to learn about and work with tensors. Sidiropoulos, a fellow of the Institute of Electrical and Electronics Engineers, collaborated with a small group of IEEE co-authors and other colleagues to publish Tensor Decomposition for Signal Processing and Machine Learning. The paper is ranked #1 in Google Scholar metrics for Transactions on Signal Processing, the flagship journal of the IEEE Signal Processing Society, for 2015-2020, and is the journal's most popular and frequently accessed paper in IEEE Xplore. It is recommended reading for anyone who needs to incorporate tensors in their research and algorithm development.


    Making the Pitch: Rapid-Relay Sensor System for Wildfire Warning

    August 12, 2020

    It’s said a picture is worth a thousand words. Jay Sheth’s vivid account of the Australian wildfires’ path of destruction made the case for rapid detection and response. Sheth described a dense network of reliable, inexpensive and self-powered detectors able to autonomously relay a fire warning in real time across thousands of square miles. His pitch earned first place at the IEEE Microwave Week three-minute thesis competition. Microwave Week celebrates the technical accomplishments of the top three conferences convened under the umbrella of the IEEE Microwave Theory and Techniques Society. The competition helps renew public interest in microwaves as a transformative—and potentially life-saving—technology. Sheth designed the power amplifiers for the ultra-low power transmitters as part of his dissertation research at the Integrated Electromagnetics, Circuits, and Systems lab led by Steven Bowers, assistant professor of electrical and computer engineering at the University of Virginia. Read the full story:  https://at.virginia.edu/2QLXRce.


    New Research Partnership Explores Wireless Spectrum Sharing

    August 11, 2020

    The University of Virginia’s Charles L. Brown Department of Electrical and Computer Engineering is charting a path toward a vibrant future wireless environment compatible with both commercial applications and scientific research. A multi-institution research team is working to establish a center for Wireless Hardware Innovations and Signal Processing for Enhanced Radio-Astronomy and Scientific Spectrum Sharing, or WHISPERS. A planning grant from the National Science Foundation Spectrum Innovation Initiative supports their effort to address policy and technology challenges created by the worldwide growth of wireless systems and applications.

    WHISPERS leverages researchers’ expertise in design, metrology, detectors, devices and system hardware solutions that have advanced international scientific research operating in microwave to terahertz frequencies and beyond. The National Radio Astronomy Observatory, Northwestern University's McCormick School of Engineering and Virginia Diodes, Inc., have joined the WHISPERS partnership led by principal investigator Robert M. Weikle, II, University of Virginia professor of electrical and computer engineering. Read more.   

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    UVA Engineering Researchers Tackle Critical Challenge in Threat Detection for National and Homeland Defense

    July 21, 2020

    Two researchers in the University of Virginia’s Charles L. Brown Department of Electrical and Computer Engineering are applying machine learning and control theory to meet this need. Professors Scott Acton and Zongli Lin have earned a grant from the U.S. Army Research Office to tackle a long-standing challenge in intelligence, to keep eyes on an object that may change in appearance or may be temporarily hidden from view. Acton and Lin are developing a visual object tracking system that switches between automated and manual analysis to deliver the best results. READ MORE.


    New Bulk Data Transfer Method Achieves High Performance with Less Complexity

    July 21, 2020

    A team of computer engineers and computer scientists at Fuzhou University in China, Shanghai Jiao Tong University and @University of Virginia are helping organizations access networked data centers for fast decision-making and smooth operations. Their store-and-forward scheduling method temporarily stores bulk data that is not time-sensitive at intermediate sites and forwards the data when the network is less congested. In place of conventional scheduling that requires knowledge of the entire network, the team’s method reduces complexity and computing resources by relying on the status of a few pre-selected routes. They earned a best paper award for this research, presented at the Institute of Electrical and Electronics Engineers 2020 International Conference on High Performance Switching and Routing and published in the Journal of Optical and Communications Networking, Optical Switching and Networking. READ MORE.


    NASA Honors Ph.D. Graduate Harold Haldren

    June 26, 2020

    NASA honored Ph.D. graduate Harold Haldren (ECE ’20) and his adviser Mool Gupta, Langley Distinguished Professor of electrical and computer engineering at the University of Virginia, who earned second place in the 2020 H.J.E. Reid award competition. The Reid award is the highest recognition for a scientific research publication authored by those who support space mission projects at the NASA Langley Research Center. Daniel Perey, William Yost and Elliott Cramer, who lead research projects and programs for NASA Langley’s Nondestructive Evaluation Services Branch, join Haldren and Gupta as co-authors. The team’s paper, “Swept-Frequency Ultrasonic Phase Evaluation of Adhesive Bonding in Tri-layer Structures,” was published in the May 2019 issue of the Journal of the Acoustical Society of America. Read more.


    Research Team Wins NSF Grant to Meet Need for Large Data Capacity in Future Wireless Communication Systems

    June 26, 2020

    A research team in UVA’s Department of Electrical and Computer Engineering has won a four-year, $1.3 million National Science Foundation grant to address the need for large data capacity in future wireless communication systems. Associate Professor Andreas Beling and Assistant Professors Xu Yi and Steven Bowers are combining their expertise in integrated photonics, nonlinear optics and wideband integrated antennas to enable a platform that promises extremely fast data and video downloads for a large number of smartphone users in urban environments. They envision a chip-sized system that can provide a vast array of wireless channels in the millimeter-wave spectrum with over one tera-bit-per-second data capacity. READ MORE.


    Cong Shen Earns Grant to Autonomously Configure 5G and Beyond Wireless Networks Using Machine Learning

    June 26, 2020

    Cong Shen, assistant professor of electrical and computer engineering at the University of Virginia, and Jing Yang, assistant professor of electrical engineering and computer science at Penn State, have earned a $550,000 three-year grant to pursue machine learning for wireless networking systems.The National Science Foundation and Intel have partnered to fund the multi-university research program to accelerate fundamental, broad-based research on wireless-specific machine learning techniques applied to new wireless system and architecture design. Intel announced the grant award winners on June 25. Shen and Yang combine reinforcement learning algorithms with domain knowledge to help network managers reliably meet user demands for virtual reality and high-resolution video applications, embedded and wearable tech and large-scale infrastructure for smart homes and autonomous cars. Read More.