ECE faculty awarded a NOAA grant for Detection, Monitoring, and Removal of Microplastics

• September 4th, 2024

ECE Professor Mark Cheng was recently awarded a grant from the National Oceanic and Atmospheric Administration titled “Holistic Detection, Monitoring, and Removal of Microplastics Using Integrated Sensing and Filtration Systems”

The project proposes (1) to create a low-fidelity product of the portable, cost-effective integrated system of microplastic filters and sensors that real-time monitor, capture, and remove micro and nano plastics in water streams, coastal rivers, and other waterbodies, classifying them by size, shape, morphology, and chemical composition. (2) to refine filtration and sampling methods based on user-centered design in partnership with potential users, including federal agencies, academic institutions and private sectors, (3) and to establish a user community promoting public awareness of microplastic challenges and responsible behaviors that can help reduce microplastic inputs and accumulation in the ocean, and mitigate the environmental impact due to such a micro and nano plastic pollution. Combining these novel sensor and filter devices will enable the holistic operation of capture, removal, and monitoring of targeted micro and nanoplastics from stream water and water resources recovery facilities to the Gulf River and oceanic environment, positively impacting many local and coastal communities.

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UA Undergraduates Present Summer Research at ICFDA 2024

• August 14th, 2024

A group of 9 students from the College of Engineering  (from both the ECE and CS departments) continued their international research experiences this summer by presenting posters of their research at the 2024 International Conference on Fractional Differentiation and Applications (ICFDA) in Bordeaux, France.

Students shared the results of their projects, which were focused on fractional-order circuits and systems, to audiences of mathematicians, engineers, and scientists across a range of disciplines.  While at the conference students were able to participate in workshops and network with leading professionals in the field, including Dr. Igor Podluby and Dr. Roberto Garrappa (researcher’s whose work they have extensively used in their projects).

These students are part of an NSF-funded International Research Experiences for Students (IRES) program coordinated by Dr. Todd Freeborn (Department of Electrical Engineering) in collaboration with Dr. Jaroslav Koton (Vice-Dean for Research and PhD Study in the Faculty of Electrical Engineering and Communication) at the Brno University of Technology in the Czech Republic.  Students spent 12 weeks researching with faculty at the Brno University of Technology from May to July and are returning to UA to continue their studies for the Fall semester.

Alabama Teachers Join Engineering Research Labs

• July 25th, 2024

This summer Dr. Todd Freeborn (Department of Electrical and Computer Engineering) and Dr. Chris Crawford (Department of Computer Science) launched an NSF-supported Research Experiences for Teachers (RET) site at the University of Alabama.

This site, Engaging  and Training Alabama STEM Teachers in Sensing Technologies, has brought 11 Alabama middle and high school teachers to campus for June and July to engage in 7-weeks of research under the mentorship of faculty from the College of Engineering.  This years group of teachers are from schools in the Tuscaloosa City, Tuscaloosa County, Greene County, and Hale County districts.

The goal of the program is to get teachers hands-on experience with engineering research that they can take back into their classrooms to use in their lessons and teaching.  All research projects that the teachers will support use sensing and measurement technologies to explore physiological and environmental monitoring. This common theme on sensing is based on the fact that knowing how to measure and analyze physical features of materials, devices, tissues, and/or the environment underlies many engineering research questions.

Beyond their research, teachers are participating in weekly workshops to learn more about engineering careers, sensors/programming, and receive support to translate these experiences into curriculum for their classrooms.  The program will wrap up on July 25, 2024 when all participants present their summer research results at a Summer Research Symposium hosted in Shelby Hall.

ECE Faculty receive a grant dor development of Rare-Earth-Free magnets

• July 12th, 2024

A group of ECE faculty with Dr. Shuhui Li as the PI and Drs. Yang-Ki Hong and Qianzhi Zhang as co-PIs received a National Science Foundation award titled “RAISE: CET: Rare-Earth-Free Magnet and AI-Driven Control for Power Generation, transmission, and Grid Integration of Offshore Wind”.

The main objectives of this project are to advance offshore wind technologies from the level of individual wind turbines to their integration into the system level of an offshore wind farm. The first goal is to develop magnets that do not rely on rare-earth materials. By doing so, the project seeks to reduce the U.S.’s reliance on foreign suppliers for rare-earth materials in offshore wind turbines. The second goal is to design an AI-driven control system at the MW scale that can overcome the unstable operations of individual permanent magnet synchronous generator (PMSG) wind turbines that have been reported in the literature and by the industry in offshore applications. The third goal aims to develop temporal and spatial models that can be used to manage highly distributed offshore wind turbines and wind farms over a large geographic area. These models will improve offshore wind transmission planning and integration of the offshore grid with the onshore main grid. Lastly, the project aims to establish a new testing mechanism using a unique per-unit and time-angular domain transformation. This will enable experimental research and evaluation of high-voltage, high-power hybrid AC-DC networks of the integrated electric power system with offshore wind, based on low-voltage, low-power hybrid AC/DC equivalent systems.

Dr. Shunqiao Sun receives a CAREER award

• April 11th, 2024

Dr. Shunqiao Sun received the 2024 National Science Foundation (NSF) CAREER Award, for the project entitled, “CAREER: Towards Fundamentals of Adaptive, Collaborative and Intelligent Radar Sensing and Perception”.

Automotive radar imaging represents a pressing technological need in perception for automotive active safety and autonomous driving. Automotive radar operating at millimeter-wave frequencies (typically around 77 GHz) is indispensable due to its superior capability in measuring range, velocity, and offering better perception performance in occlusion situations under all weather conditions and much lower cost than Lidar. The state-of-the-art automotive radar is prone to mutual interference and multipath issues, and its limited angular resolution – approximately 13 degrees – is inadequate for facilitating perception tasks for fully autonomous driving. This CAREER project aims to innovate the automotive radar perception in service of well-being of individuals in society and reduction of fatal accidents on U.S. highways by exploring adaptive, collaborative sensing and radar imaging physics principles. It deepens our understanding of how such sensing can enhance the dynamic range and address the ill-posed nature of the radar imaging inverse problem. Addressing the challenges in automotive radar imaging necessitates collaboration between academia and industry. Through collaboration with automotive industry sector, this project will make the research more impactful and pertinent, hastening the transition of research findings into automotive industry applications. Autonomous vehicle research helps attract underrepresented researchers. Through K-12 outreach activities and recruiting underrepresented groups and women in engineering, this project will promote diversity in the STEM research, inspiring them to pursue advanced research in radar field.

This CAREER project addresses the ill-posed inverse problem inherent in automotive radar imaging by investigation of learning based adaptive and collaborative methodologies. The project tackles the robust radar perception for autonomous vehicles problem through incorporation of the radar imaging physics to drive the design of innovative machine learning algorithms. Intelligent signal processing and machine learning techniques will be developed at multiple layers, including 1) learning-based adaptive radar transmit parameter adjustment, 2) iterative optimization algorithms to enhance the dynamic range of automotive radar sensing by exploiting constructive interference, 3) model-based learning framework for collaborative high-resolution radar imaging in an automotive radar network, 4) physics-aware radar machine learning algorithms for robust environment perception. These new techniques will demonstrate how the science insights advance high resolution radar imaging, and robustly detect and classify objects in the highly dynamic autonomous driving environment.

Dr. Ajun Song is featured in an article about benefits of undergraduate research

• February 5th, 2024

Serving as a mentor for undergraduate students can have a profound impact on their undergraduate experience. This UA News article features Dr. Aijun Song the Department of Electrical and Computer Engineering and one of his undergraduate researchers to learn more about the importance of mentoring students and including them in research.

Link to the article

UA Leading Project to Give EV Batteries Second Life

• October 24th, 2023

ME and ECE faculty receive an NSF grant for development of Affordable Robotic Lower-Limb Prosthesis

• September 26th, 2023

NSF has awarded a grant titled “PFI-RP: Developing Market-Ready Affordable Robotic Lower-Limb Prostheses through Unified Joint Actuator Design and AI-Enhanced Multi-Modal Interactive Control” to Professors Xiangrong Shen (PI, Mechanical Engineering), Fei Hu and Edward Sazonov (co-PIs, Electrical and Computer Engineering)

The broader impact/commercial potential of this Partnerships for Innovation – Research Partnerships (PFI-RP) project is the availability of a highly affordable powered lower-limb prosthesis that provides versatile (multi-modal) operation in amputee users’ daily life. Currently there are approximately 2 million people living with limb losses in the U.S., and half of them are lower-limb amputees. Currently available prosthetic devices are mostly passive, unable to generate joint power in locomotion. As such, the passive prosthesis users typically experience substantial difficulty and discomfort in daily life, for example, being unable to climb stairs, walking in an obviously asymmetric way, and expending more energy in walking. There are two powered prosthetic devices for lower-limb amputees, but limited success has been reported due to issues such as high cost and difficulties in control and user intent recognition. The research and development efforts in the proposed project aim at overcoming these problems by creating an innovative lower-limb prosthesis that combines low cost, light weight, high reliability, and task versatility. With this innovative prosthetic device, the PFI team anticipates accelerating the lower-limb amputees’ acceptance of advanced robotic prostheses and significantly improve their mobility and quality of life in daily living.

The proposed project will explore multiple innovative technologies towards the development of the new powered lower-limb prosthesis. Firstly, it includes the development of a novel Common-Core-Components Knee-Ankle Prosthesis (C3KAP) design technology providing simple, low-cost, and highly reliable prosthetic joints that can be easily configured as prosthetic knee or ankle, supplemented by a novel variable stiffness force-moment load cell providing sensitive and reliable load measurement for prosthesis control. Secondly, it aims to develop a new bi-modal prosthesis motion control technology that provides natural leg swing motion, interactive stance-phase control, as well as simple and standardizable controller tuning for individual users. Finally, the PFI team will create an advanced AI-based intent recognizer to recognize the prosthesis users’ locomotive mode and desired mode transition, enabling amputees to enjoy the freedom of true multi-modal locomotion (walking and stair ascent/descent), significantly improved mobility and quality of life.

Prof. Sevgi Gurbuz receives an NSF award

• August 18th, 2023

Assistant Professor Sevgi Gurbuz received an NSF award for the project titled “Collaborative Research: ECCS: Small: Personalized RF Sensing: Learning Optimal Representations of Human Activities and Ethogram on the Fly”.

Radio Frequency (RF) sensing can be a game changer to reduce healthcare costs and disparities, improve quality of care, and facilitate aging-in-place because they are non-contact, low-power devices that are effective in the dark, do not limit or alter freedom of movement, and do not acquire private visual or audio recordings. However, a significant impediment to the advancement of RF technologies for recognition and health assessment of human gait is the continuous and sequential nature of human movement, which can be characterized by periods of activity and transitions between activities that depend upon a person’s mobility. Gait is a person-specific trait that embodies important health information for many disorders as well as aging related impacts. Thus, a second important challenge is the development of personalized machine learning (ML) models that continually learn from person-specific RF data to improve health-driven gait analysis.

The goal of this project is the design of a personalized RF-sensing framework for the monitoring of activities of daily living (ADL), detection and characterization of pathological, gaits round-the-clock 24/7 in a natural setting. The proposal?s two main objectives are: 1) Create a human ethogram via the formulation of a new and general framework for interpretation, segmentation, and categorization of a broad swath of human activities based on modeling the structure and dynamics of individual mobility; 2) Design a new approach for personalization of deep neural networks, where consecutive and contiguous observations are used to increase the classification accuracy of ADL for the monitored person. The ethogram is a quantitative, structured approach to describing daily human behavior in terms of ?body states? while activities are then modeled as transitions between states. By introducing the concept of personalization to RF sensing, this project will broaden the realm of personalized devices beyond its current scope of wearable and implantable devices to now include RF sensors. The proposal integrates sensor and kinematic knowledge into DNN design, resulting in novel architectures with greater accuracy that will advance the state-of-the-art in RF signal classification more generally. Moreover, the central ideas in this proposal are independent of device specifications and can be generalized to other sensing modalities, paving the way for non-contact, ubiquitous, fine-grained personalized gait classification and analysis. The outcomes of this project will pave the way for timely interventions and more effective treatments in both home and clinical settings, reducing the costs and improving the accessibility of health care.