Innovations in VLSI, Signal Processing and Computational Technologies

Gayatri Mehta is a Professor in the department of electrical engineering at the University of North Texas (UNT), USA. She received her Ph.D. in electrical and computer engineering from the University of Pittsburgh in 2009. Her research interests are broadly in electronic design automation, reconfigurable computing, low-power VLSI design, system-on-a-chip design, embedded systems, and portable/wearable computing. Dr. Mehta is the director of the Reconfigurable Computing Lab at UNT. She has received the UNT College of Engineering Faculty Service Award in 2023, the UNT TAMS and Honors College Excellence in Undergraduate Mentoring Award in 2021 and the UNT College of Engineering Research Award in 2017. She has also received the IEEE-HKN C. Holmes MacDonald Outstanding Teaching Award in 2013. She has designed an interactive mapping game UNTANGLED to uncover human mapping strategies. UNTANGLED received the People's Choice Award in the Games & Apps category of the 2012 International Science & Engineering Visualization Challenge conducted by the Science and National Science Foundation.Deepti Kakkar pursued her Bachelor of Technology in Electronics and Communication Engineering from Himachal Pradesh University, India in 2003 and her Masters of Engineering in Electronics Product Design and Technology from Punjab University, India. Dr. Kakkar obtained her Ph.D. in spectrum sensing in cognitive radios from Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India. She has a total academic experience of 18 years and is currently an Associate professor in the Electronics and Communication department with Dr. B. R. Ambedkar National Institute of Technology. She has guided more than 50 postgraduate engineering dissertations and several projects. She is currently guiding 3 Ph.D. scholars and 1 Ph.D. has submitted under her guidance. She has more than 50 papers in various international journals and conferences. Nilmini Wickramasinghe, as of August 1 2023, Professor Wickramasinghe is the Professor and Optus chair of Digital Health at La Trobe University within the School of Engineering. She also holds honorary research professor positions at the Peter MacCallum Cancer Centre, MCRI, Epworth HealthCare and Northern Health.  After completing 5 degrees at the University of Melbourne, she completed PhD studies at Case Western Reserve University, and later executive education at Harvard Business School, USA in Value-based HealthCare. For over 20 years, she has been actively, researching and teaching within the health informatics/digital health domain with over 350 scholarly publications, a patent, 25 books, numerous posters and book chapters and a very successful grant funding portfolio.  In 2020, she was awarded the prestigious Alexander von Humboldt award for her outstanding contribution to digital health.

• A Comprehensive Review to Investigate the Effect of Read Port Topology on the Performance of Different 7T SRAM Cells.- Metrics Evaluation of Bell pepper disease classification using Deep Convolutional Neural Network (DCNN).- Self-Attention based Deep learning approach for Machine Translation of Low Resource Languages: A case of Sanskrit-Hindi.- Rotor Unbalance Severity Detection using Maximum Overlap Discrete Wavelet Transform.- SOT-MRAM Memories for energy efficient Embedded and AI applications.- Reinforcement Learning Method for Identifying Health Issues for People with Chronic Diseases.- NG-PON3 based bidirectional high-speed PON with 1:32/64/128 split ratio.- Effect of lithium dopant on Stanene Nanotube’s properties.- All-p-type Digital Circuits using Single Gate and Double Gate Organic Field effect Transistors.- VLSI Floorplan Area Optimization Technique.- Analysis of Agricultural Commodities Prices using BART A Machine learning technique.- Deep learning model-based approach for agricultural crop price prediction in Indian Market.- Internet of Healthcare Things Enabled Open-Source Non-Invasive Wearable Sensor Architecture for Incessant Real-Time Pneumonia Patient Monitoring.- Data Pre Processing Techniques for Brain Tumor Classification.- A systematic approach for Effective Apgar Score assessment in 1 and 5 minutes using manifold machine learning algorithms.- A Novel Approach for Detection of Lumpy Virus.- Deep – Learning based Multi-Label Image Classification for Chest X-Rays.- Detection and Classification of Blood Cancer Using Deep Learning Framework.- Enhanced Intracranial tumor strain prediction and detection using Transfer and Multilevel Ensemble Learning.- RF-MEMS SPDT Capacitive Switch: Accelerating the Performance in B5G Applications.- Fine-Tuning The Deep Learning Models Using Transfer Learning For Lung Diseases Classification From Chest Radiographs.- Entity Perception Using Remotely Piloted Aerial Vehicle.