Scholars

TEES Researcher at TAMU-San Antonio

Positions:

TEES Researcher at TAMU-San Antonio, TEES Regional Divisions, Texas A&M Engineering Experiment Station (TEES)
Instructional Associate Professor, Computation, Engineering and Math Sciences, Texas A&M University – San Antonio - (San Antonio, Txas, United States) Texas A&M University – San Antonio - (San Antonio, Texas, United States)

overview

Wael Deabes has made significant contributions to the field of electrical tomography systems and the ?application of artificial intelligence (AI) in enhancing these systems. His work also extends to the ?development of smart cities, integrating advanced technologies to create more efficient and ?sustainable urban environments. Below is an overview of his research in these areas:?

Electrical Tomography (ET) is a non-invasive imaging technique used to visualize the interior ?conductivity distribution of an object. Wael Deabes' research in this field includes:?

?1. Developing and optimizing ET systems to improve image resolution and accuracy. His work often ?involves the design of sensor arrays and the enhancement of data acquisition systems.?

?2. Addressing the inverse problem in ET, which involves reconstructing an image from boundary ?measurements. Deabes has contributed to the development of robust algorithms that improve the ?quality and reliability of these reconstructions.?

?3. Applying ET to various industrial processes, such as monitoring the integrity of pipelines.?

?4. Utilizing machine learning algorithms to enhance the reconstruction process in ET. ?

?5. Data Analysis and Pattern Recognition

?6. Implementing AI-driven methods for real-time processing of ET data, enabling faster and more ?accurate monitoring and diagnostics in industrial applications.?

Wael Deabes' research extends to the development of smart cities, focusing on the integration of ?advanced technologies to create more efficient, sustainable, and livable urban environments. His ?contributions in this area include:?

?1. Designing and implementing smart infrastructure systems that utilize sensors, IoT devices, and AI to ?monitor and manage urban resources such as electricity, water, and transportation.?

?2. Energy Management**: Optimize energy consumption, and enhance grid reliability. His work in this ?area often involves the use of AI and machine learning to predict energy demand and manage supply.?

?3. Applying data analytics and AI to improve urban planning and mobility. This includes the ?development of intelligent transportation systems that reduce traffic congestion, enhance public ?transportation, and promote sustainable mobility solutions.?

education and training

Ph.D. in Electrical and Computer Engineering, Tennessee Technological University - (Cookeville, Tennessee, United States) 2010
M.C.S. in Electrical Engineering, Mansoura University - (Al Mansurah, Egypt) 2003

selected publications

Academic Articles19

Deabes, W., & Abdel-Hakim, A. E. (2024). CGAN-ECT: Reconstruction of Electrical Capacitance Tomography images from capacitance measurements using Conditional Generative Adversarial Networks. Flow Measurement and Instrumentation. 96, 102566-102566.
Deabes, W., Bouazza, K. E., & Algthami, W. (2023). Smart Fuzzy Petri Net-Based Temperature Control Framework for Reducing Building Energy Consumption.. Sensors. 23(13), 5985-5985.
Deabes, W., Abdel-Hakim, A. E., Bouazza, K. E., & Althobaiti, H. (2022). Adversarial Resolution Enhancement for Electrical Capacitance Tomography Image Reconstruction.. Sensors. 22(9), 3142-3142.
Hedar, A., Deabes, W., Amin, H. H., Almaraashi, M., & Fukushima, M. (2022). Global sensing search for nonlinear global optimization. Journal of Global Optimization. 82(4), 753-802.
Deabes, W., & Bouazza, K. E. (2022). Residual Autoencoder Deep Neural Network for Electrical Capacitance Tomography. Computers, Materials and Continua. 73(3), 6307-6326.

Books1

Deabes, W., & Abdelrahman, M. (2011). Electrical Capacitance Tomography for Conductive Materials. VDM Verlag.

Conference Papers4

Deabes, W., & Abdel-Hakim, A. E. (2018). Teaming Up Pre-Trained Deep Neural Networks. 2018 International Conference on Signal Processing and Information Security (ICSPIS). 1-4.
Amin, H. H., Deabes, W., & Bouazza, K. (2017). Hybrid Spiking Neural Model for Clustering Smart Environment Activities. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). 206-211.
Hakim, A., & Deabes, W. A. (2017). Impact of Sensor Data Glut on Activity Recognition in Smart Environments. 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB). 1-5.
Deabes, W. A., & Abdelrahman, M. (2016). Shape Reconstruction Method for Imaging Conductive Materials in Electrical Capacitance Tomography. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. 1055-1060.