The Electrical Engineering department at KFUPM came into existence with the establishment of the University of Petroleum & Minerals in 1967. It is one of the largest departments in the University with an average number of students being approximately 900, 16% of whom are in the graduate program. The department provides 2 four-year undergraduate programs, Bachelor of Science in Electrical Engineering and Bachelor of Science in Electrical Engineering and Physics. The graduate program offers Master of Science and Master of Engineering in Electrical Engineering, Master of Science in Telecommunication Engineering, Master of Sustainable and Renewable Energy, Master of Wireless Communication Networks, and Ph.D. in Electrical Engineering.
The department has about 61 full-time faculty members in 6 specialized areas of research. The Groups in the department are: Energy Systems, Communications, Electronics, Control Systems, Electromagnetics, and Digital Signal Processing. Additionally, a pool of experienced engineers and technicians maintain more than 30 laboratories in the department.
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On March 5, 2025, the Multidimensional Signal Processing Research Area hosted a seminar titled "Algorithm Unrolling for Signal and Image Processing," presented by Dr. Mudassir Masood and Dr. Motaz Alfarraj.
The seminar explored the fundamental concepts of algorithm unrolling, tracing its origins in sparse coding and its rapid expansion across various domains, with a particular focus on image processing and computer vision. The speakers discussed key theoretical insights, recent advancements, and the advantages of unrolled networks over conventional deep learning approaches.
Additionally, the event highlighted current limitations and promising research directions in this rapidly evolving field, providing attendees with valuable insights into foundational principles and future opportunities in signal and image processing.
The following are the highlights of the event:
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In this seminar, we will explore the fundamental concepts of algorithm unrolling, its origins in sparse coding, and its rapid expansion into various domains, with special emphasis on image processing and computer vision. We will discuss key theoretical insights, recent advancements, and the advantages of unrolled networks over conventional deep learning approaches. Additionally, we will highlight current limitations and promising research directions in this rapidly evolving field.
Building 59-2004
05 Mar, 2025
11:00 AM - 11:50 AM
The Electrical Engineering Department (EE) at KFUPM provides a world-class education and innovative learning experiences for both undergraduate and graduate students.
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