UT Arlington Department of Electrical Engineering

10/01/2020

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The University of Texas at Arlington Department of Electrical Engineering

09/28/2020

Exploiting Compressive Sensing for MIMO Radar Image Resolution Enhancement

Neda Rojhani, Ph.D.
Research Fellow
Department of Information Engineering
University of Florence, Italy

ABSTRACT: In radar systems technology one of the main constraints is represented by the cross-range resolution, which is the radar capability to tracking targets at the same range but placed at different angles. This ability is dominated by the radar angular resolution. For co-located radar systems, increasing the antenna aperture is a straightforward approach to improve the corresponding system performance, however, increasing the number of elements in the antenna array raises the overall dimensions of the device and the complexity of the radar front-end. Recently, the adoption of specific transmitted signal waveforms has been considered as a viable approach to improve angular resolution, while limiting the number of antenna elements. This approach leads to the development of Multiple-Input Multiple-Output (MIMO) radars. MIMO radars are basic technology in improving spatial resolution due to their antennas and waveform diversity. Although the higher angular resolution is essential to achieve the desired target detection, the hardware cost of several transmitters and receivers and high energy consumption curb the use of MIMO radars in a wide-ranging network.
Compressive sensing (CS) is a recent technique that addressed to improve this limitation. CS theory states that a signal sparse in range-angle space can be recovered using far fewer samples than those needed by the Nyquist sampling criterion. Applying CS to the MIMO radar allows a considerable reduction in the number of antennas respects to a dense array based on the Nyquist criterion, while, achieving performance similar to the filled array based on Nyquist theory.
In this presentation, a new design of 2x2 CS-MIMO radar is reported that exploits the CS technique to provide a sparse linear framework on a MIMO radar in which transmitter and receiver antennas are positioned randomly, lastly, it is compared to standard MIMO radar as a benchmark. The comparison signifies that the angular resolution can be increased through a random array CS-MIMO by a factor of at least 2.9° regarding conventional MIMO.

BIOGRAPHY: Neda Rojhani received her Ph.D. degree in Electronic and Electromagnetism Engineering from the University of Florence, Florence, Italy, in 2019. She has currently collaborated with the Department of Information Engineering, University of Pisa. Her research interests include Ground-Based Radar (GB-SAR), Ground-Penetrating Radar (GPR), Multi-Input Multi-Output Radar (MIMO), Antenna theory, and Antenna design.

11:00 am, Friday, October 2

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09/21/2020

Theoretical and Experimental Outcomes of Closed-Loop Neuromuscular Control Methods to Yield Human Limb Motion

Warren Dixon, Ph.D.
Newton C. Ebaugh Professor
Mechanical and Aerospace Engineering Department
University of Florida

ABSTRACT: Neuromuscular Electrical Stimulation (NMES) is prescribed by clinicians to aid in the recovery of strength, size, and function of human skeletal muscles to obtain physiological and functional benefits for impaired individuals. The two primary applications of NMES include: 1) rehabilitation of skeletal muscle size and function via plastic changes in the neuromuscular system, and 2) activation of muscle to elicit movements that result in functional performance (i.e., standing, stepping, reaching, etc.) termed functional electrical stimulation (FES). In both applications, stimulation protocols of appropriate duration and intensity are critical for preferential results. Automated NMES methods hold the potential to maximize the treatment by self-adjusting to the particular individual (facilitating potential in-home use and enabling positive therapeutic outcomes from less experienced clinicians). Yet, the development of automated NMES devices is complicated by the uncertain nonlinear musculoskeletal response to stimulation, including difficult to model disturbances such as fatigue. Unfortunately, NMES dosage (i.e., number of contractions, intensity of contractions) is limited by the onset of fatigue and poor muscle response during fatigue. This talk describes recent advances and experimental outcomes of control methods that seek to compensate for the uncertain nonlinear muscle response to electrical stimulation due to physiological variations, fatigue, and delays.

BIOGRAPHY: Prof. Warren Dixon received his Ph.D. in 2000 from the Department of Electrical and Computer Engineering from Clemson University. After completing his doctoral studies he was selected as an Eugene P. Wigner Fellow at Oak Ridge National Laboratory (ORNL). In 2004, Dr. Dixon joined the University of Florida in the Mechanical and Aerospace Engineering Department, where he currently holds the Newton C. Ebaugh Professorship. Dr. Dixon’s main research interest has been the development and application of Lyapunov-based control techniques for uncertain nonlinear systems. The results from this work have won a number of career contribution and best-paper awards, including being elected as an ASME and IEEE Fellow. Particular to closed-loop muscle control, his team was awarded the 2019 IEEE Control Systems Technology Award, the 2009 and 2015 American Automatic Control Council (AACC) O. Hugo Schuck Best Paper Award, and best student paper awards.

11:00 am, Friday, September 25
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09/09/2020

Free from CAPS for International students at UTA:

09/08/2020

EpICMavs Networking Event: Personal & Professional Goal Setting

Welcome Mavericks and local entrepreneurs to the fall semester of EpICMavs! EpICMavs is a weekly, interactive workshop series focusing on topics related to entrepreneurship. Although we normally meet in person, for the 2020 Fall semester EpICMavs will be held every Thursday from 4:30-5:30 pm on Microsoft Teams. Topics include how to network, branding your business, financial planning and much more!
The Fall 2020 kickoff networking event will be held Thursday, September 10, from 4:30 pm – 6:00 pm. The networking event will include a discussion about setting personal and professions goals, led by the Assistant Director of TechFW.
To join the networking event please download Microsoft Teams. The link below will allow you access to the meeting on Sept. 10th. For all other meetings, entrepreneurial resources, and more please email [email protected] and ask to be added to the Microsoft Team, EpICMavs.

Thursday, September 10, 4:30pm-6 p.m.
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09/04/2020

09/01/2020

Here is an opportunity to buy some interview attire.

Are you in need of some new professional attire? Take an extra 30% off on career-related apparel during the JCPenney Suit Up Event happening Friday, September 4 - Sunday, September 6 both in store and online. Discount code details coming soon! Participate in our Suit Up Competition for a chance to w...

08/31/2020

UTA researcher working to make data more usable and accessible

08/28/2020

UTA Computing Study Space Map and Places to Plug In

08/25/2020

Better performance of autonomous vehicles
UTA researchers optimize performance of networks of human-operated and autonomous vehicles
Friday, May 29, 2020 • Herb Booth : Contact

A trio of electrical engineering faculty members from The University of Texas at Arlington has received a three-year, $750,000 grant to improve control of networked convoys that include autonomous vehicles and those operated by humans.
The Army Research Office recently awarded the funding to Professor Frank Lewis and Associate Professors Yan Wan and Ali Davoudi.
Autonomous vehicles often operate in complex dynamic networked environments that feature spatiotemporal diverse threats generated by malicious attacks, functional failures and human errors. The team will develop bio-inspired learning methods that can learn optimal control solutions by observing the behaviors of neighbors in real time.
When aircraft fly in formation as part of a networked convoy, for example, each aircraft has an objective that will allow the group to accomplish certain goals, such as saving fuel, maintaining specific distances from other aircraft or maintaining specific airspeeds. At the same time, they may also be subject to attacks from malicious aircraft.
New reinforcement learning constructs will allow observation of the actions taken by malicious agents, predictions of worst-case scenarios and development of optimal defensive responses.
“The desired result is to get the entire group to perform as a single entity that improves the behavior of each individual agent,” Lewis said. “This will also help individual agents and the group to reject the negative effects of adversaries, because each agent can adjust and learn from its neighbors if there is suspicious behavior.”
The group will use fundamental science and mathematics theories to investigate interactions of humans, communication topologies, autonomous agents, adversarial agents and uncertain environments to develop adaptive and optimal solutions.
“Our work will make robots more intelligent,” Wan said. “They can improve their decisions through interactions with their peers and the environments in which they are operating.”
The group has three other contracts for related research in controls and reinforcement learning—two from the National Science Foundation and the Office of Naval Research totaling $1.48 million and one from the Ford Motor Co. for $150,000.
“Drs. Lewis, Wan and Davoudi continue to apply the knowledge they have gained from previous research to expand knowledge around autonomous behavior and controls,” said Peter Crouch, dean of the College of Engineering. “This latest grant is an example of the importance of fundamental research to our understanding of autonomous controls and reinforcement learning.”
- Written by Jeremy Agor, College of Engineering

08/10/2020

Diana Huffaker Takes Over as Electrical Engineering Department Chair
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05/07/2020

Alfred R. and Janet H. Potvin
Outstanding EE Student Scholarship

Requirements:

§ Undergraduate students majoring in EE
§ Junior or Senior
§ High academic achievement
§ Graduate of a Texas high school

Apply: Log in to Mav ScholarShop with your Net ID and password to apply
Deadline for application: May 31, 2020

The Bernard and Ann Svihel Memorial Scholarships
for EE Majors

Requirements:

§ Undergraduate EE student currently enrolled or accepted for enrollment in the coming semester
§ History of academic achievement
§ Demonstrated financial need

Apply: Log in to Mav ScholarShop with your Net ID and password to apply
Deadline for application: May 31, 2020

The Ron L. Cates Endowed Scholarship
for Electrical Engineering

Requirements:

§ Must be Electrical Engineering - Intended

§ Preferred: Undergraduate student who has earned at least 30 Hours (sophomore status at UT Arlington)
§ UTA GPA at least 3.0

Apply: Log in to Mav ScholarShop with your Net ID and password to apply
Deadline for application: May 31, 2020