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The expertise areas of our ECE faculty include embedded systems and software, electric power, electro-hydrodynamics, control, signal and image processing and communications. The following list provides more specific areas of expertise of individual faculty members.
AC/DC microgrids, renewable energy systems, energy storage systems, flexible AC transmission systems (FACTS)
Power system design and analysis
Embedded systems, Cyber-Physical Systems
Wireless communications and networks, stochastic signal processing, Embedded Systems
Electric drives modeling and control, real-time systems; IoT security and implementation, web application and security
Computing architectures and algorithms for signal and image processing, neural networks, and Internet-of-Things (IoT)
Modeling and design of electric machines, and control of electric drives
For more information and/or other projects, please feel free to contact individual faculty members or send inquiries to ecesupport@gannon.edu.
This research involving Dr. Yong-Kyu Jung explores information security & integrity in cyberspace, cyber-physical systems, rapid prototyping with FPGA, and real-time simulator with various approaches to heterogeneous modeling and prototyping.
This research project involving Dr. Wookwon Lee is to develop a complete near-space ballooning system in preparation for the spectacular natural event of total solar eclipse on April 8, 2024 (more specifically, full beginning at 3:16:22 pm EDT, maximum occurring at 3:18:13 pm, and full ending 3:20:05 pm). The passage of the 2024 total solar eclipse includes the skies right above the downtown of Erie, PA and the shoreline of Lake Erie (right at the home ground of 成人禁区!).
This research involving Dr. Wookwon Lee is to develop a science payload to detect cosmic rays, high-energy particles of astrophysical origin, in the energy range ~1–100 GeV, and also create an analytical model that relates the energy of cosmic rays to the output of an electronic integrator. The payload will be carried to an altitude of 120,000 ft for a flight duration of ~6 hours on a Small Balloon System operating in close collaboration with NASA’s Balloon Program Office.
This project involving our Laboratory Engineer, Mr. Jack Little is for blended engineering product development and for participation in professional competition of autonomous systems. IGVs are aimed to be able to recognize, avoid, and navigate through dynamic obstacles. Artificial intelligence, machine learning, and control techniques are explored.
This project involving Dr. Fong Mak is to implement a Person Identification and Health (Temperature) Scanned System that replaces the existing manual attendance and daily thermal recording requirement for K-12 schools or institutions to improve the current manual or COVID-monitoring processes.
K12 schools have been manually tracked their students' daily attendance records by taking counts of the students when they are either in a classroom or entrance to a school. In addition, some schools' policy requires schools to take students' daily temperature for their health-tracking requirements. The existing systems either have these two attendance and temperature scans, processes conducted separately, or lack a coordinated database system for these two processes. Coupled with the COVID-19 situation, the need to have a better system becomes urgent. This project is supported by MAKTEAM Software.
The significant challenges for the project are (1) integrating embedded hardware, web technology, and cybersecurity knowledge into this product development, (2) security authentication of each endpoint, (3) constant health check of each endpoint, (4) secure communication between endpoint and command center, (5) integration of collected data with the existing SIS, (6) capable of performing Q.R. code or facial identification, (7) temperature scanned is accurate in all in-door environmental conditions, and (8) dashboard design that appeals to customers.
This project involves Dr. Ramakrishnan Sundaram. Radio frequency signals can be used to perform non-invasive and device-free target localization of objects or entities in space. Radio tomographic imaging uses wireless sensor networks to form images from the attenuation of the radio frequency signals. Radio tomographic imaging is useful to locate security breaches, to perform rescue operations, and to design “smart” buildings. The integrated radio tomographic imaging system comprises subsystems identified as the wireless sensor network, the command and data collection platform, and the user interface. The project will set up the space hardware laboratory to assemble and test the subsystems, to design and document the project activities on the integrated radio tomographic imaging system, and to deliver STEM outreach with pK-12 schools using the integrated system.
The project comprises the development of the platform to model and implement self-directed and reinforcement learning. Self-directed and reinforcement learning models can effectively extract information from patterns observed in nature. The model uses agents trained to understand the functional characteristics of organisms. The agents comprise neurons which learn and retain genetic characteristics through the connections between the neurons and the properties of each neuron. The neurons are arranged in layers from input to output. The neurons are trained to produce genomic sequences which must meet the specified “Win” condition. The platform is expected to isolate messenger-RNA sequences from coded-DNA sequences.
This research involving Dr. Ramakrishnan Sundaram is to enable the general public to have better communication with hearing-impaired people through the American Sign Language by using an automated translation system. The design of the translation system is based on the current state-of-the-art machine-learning technology, specifically the Deep Convolutional Neural Networks, which is one of the sub-fields of Artificial Intelligence. The models designed and tested as part of this research, performed significantly better and learned faster when mask videos were used instead of raw videos, and fusion nets performed the best, with the best per-class top-1 accuracy of 96.6% over the 500-class dataset. More importantly, it confirms that keeping a constant temporal dimension in a deep network is a viable approach to sign language recognition, especially when the temporal information from all the available snapshots can be fully exploited by a time-sensitive construct such as ConvLSTM.
