Dr. Braham Barkat
Dr. braham barkat Associate Professor Electrical Engineering And Computer Science

Contact Information
braham.barkat@ku.ac.ae 02-312-3274


Braham Barkat received the M.Sc. degree in 1988 from the University of Colorado (Boulder, USA). From 1989 to 1995, he had been a lecturer at the University of Blida, Algeria. In 1996, he joined the Signal Processing Research Centre at Queensland University of Technology (QUT, Brisbane, Australia), as a Senior Research Assistant and, then, as a PhD candidate in Signal Processing.

He spent the years 1999 and 2000, as a Postdoctoral Research Fellow, first at QUT and then at Curtin University of Technology (WA, Australia). From 2000 to 2005, he had been an Assistant Professor at the School of Electrical & Electronic Engineering at Nanyang Technological University, Singapore. In 2005, he became a faculty member of The Petroleum Institute (Abu Dhabi, UAE) as an Assistant Professor and later as an Associate Professor. Since 2017, he has been an Associate Professor at Khalifa University of Science & Technology (Abu Dhabi, U.A.E). He spent the academic year of 2016-2017 as a sabbatical guest at the Signal Processing Group at the Technische Universitat Darmstadt (Germany),

His research interests include Statistical Signal Processing, Statistical Array Processing, Seismic Data Analysis, and Partial Discharge Analysis. Dr. Barkat is a Senior Member of the IEEE.

  • M.Sc. in Control (University of Colorado, Boulder, USA)
  • PhD in Signal Processing (QUT, Brisbane, Australia)

  • Advanced Concepts in Stochastic ProcessesDetectionand Estimation Theory (ECCE735)
  • Digital Signal Processing (ECCE402)
  • Digital Signal Processing (ECCE402)
  • Electric Circuits II (ECCE222)
  • Signals and Systems (ECCE302)

Affiliated Research Institutes/Centers
  • Advanced Power and Energy Center
  • Petroleum Institute

Research Interests
  • Time-Frequency Analysis, Statistical Signal Processing, Statistical Array Processing, Seismic Data Analysis, Partial Discharge Analysis

Research Projects

According to the World Health Organization (WHO), 30% of all deaths in the UAE are due to cardiovascular diseases, including Coronary Artery Disease (CAD). Atherosclerosis is the most common cause of CAD, where plaques occlude the medium and large arteries of the heart. If left untreated, it hardens and narrows the arteries over a period of years, thus reducing the flow of oxygen-rich blood to the heart and other organs, and potentially leading to serious problems, such as myocardial/cerebral infarction, or even death.

The gold standard for coronary artery imaging is Computed Tomography Angiography (CTA). Clinical diagnosis by means of coronary CT imaging is a time-consuming task, due to the large amount of data produced in the scanning process (on average, 300 CT slices per patient). Interpretation of a CTA study is a labor-intensive and subjective task, as it requires examination of each major branch of the arteries, segment by segment. Moreover, recent studies imply that not all of the visible abnormalities on the image can be detected or correctly characterized through the retrospective review process. For instance, this is the case in soft (fat-based) atherosclerotic plaques, as opposed to contrast-enhanced, hard (calcified) plaques.

This research proposes the development of an automated image processing and deep learning framework for coronary tree segmentation, quantitative shape analysis of coronaries and stenosis characterization, based on Cardiac Computed Tomography Angiography (CCTA) images. The primary aim is to produce accurate and reproducible evaluation of the effects of cardiovascular disease, which will support the development of patient-specific 3D vascular models for treatment planning and diagnostic assessments, and ultimately, computer-aided diagnosis of cardiovascular pathologies.

To manage CCS operation in an efficient manner and reduce the leakage risk in Abu Dhabi, this project proposes an innovative approach by using a combination of cosmic-ray muons, satellite InSAR, and seismic. First, we propose a coupled analysis of cosmic ray muons and seismic waves, and develop a joint inversion analysis method. By these developments, seismic wave velocities, can be separated into elastic constants and densities. This separation allows us to estimate the geomechanical properties of the formation and the CO2 saturation degree in pore space of the formation, which have been difficult to evaluate so far. Second, we develop a method to identify permeable fractures which contribute to leakage pathways of CO2 by using seismic attenuation monitoring. Finally, we achieve a new multidisciplinary approach which connects the surface displacement caused by CO2 injection from InSAR with the subsurface geomechanical parameters identified by coupled analysis of cosmic-ray and seismic.