Sedighe Mirbolouk | Engineering | Editorial Board Member

Published on by Research Awards

Dr. Sedighe Mirbolouk | Engineering | Editorial Board Member 

Iran National Science Foundation | Iran

Dr. Sedighe Mirbolouk is a dedicated postdoctoral researcher and advanced machine learning specialist with strong expertise in communication systems, data science, and artificial intelligence. She is affiliated with the Iran National Science Foundation and has built a diverse research portfolio spanning deep learning, wireless communication optimization, image processing, and intelligent sensing systems. Her technical proficiency covers a wide spectrum of tools and programming environments, including Python, MATLAB, LATEX, and advanced libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, SciPy, Pandas, and Matplotlib. With a strong theoretical foundation in data telecommunication networks, convex optimization, communication theory, and signal and image processing, she integrates computational intelligence with modern communication challenges. In her role as a Postdoctoral Researcher (2024–2025) at the Iran National Science Foundation, Dr. Mirbolouk focuses on cutting-edge topics in graph learning and federated learning, particularly designing machine learning approaches for predictive beamforming in Reconfigurable Intelligent Surface (RIS)-aided Integrated Sensing and Communication (ISAC) systems. Her work aims to improve efficiency, adaptability, and intelligence in next-generation wireless communication networks. Previously, she served as a Visiting Researcher (2022) at the University of Oulu in Finland, where she explored advanced deep reinforcement learning methods to enhance ISAC designs. These research experiences have positioned her at the frontier of combining AI with communication technologies. During her doctoral studies at the University of Urmia (2018–2021), Dr. Mirbolouk contributed significantly to satellite–UAV cooperative network optimization. She developed innovative solutions involving UAV selection and power allocation for CoMP-NOMA transmissions, introducing both Lagrangian and heuristic algorithms that advanced energy-efficient communication frameworks. Alongside communications research, she proposed image processing solutions such as fuzzy histogram weighting methods and contrast enhancement techniques. Her academic involvement includes teaching core engineering subjects—Digital Communication, Probability and Statistics, and Signals and Systems—and assisting courses on Stochastic Processes and Digital Signal Processing. Her work at the National Elite Foundation (2020–2022) expanded her portfolio into biomedical machine learning applications, where she designed systems for automatic breast cancer detection using histopathology images and cardiac arrhythmia recognition using ECG signals through deep learning approaches. Dr. Mirbolouk holds a Ph.D. in Electrical Engineering, with earlier B.Sc. and M.Sc. degrees from the University of Guilan, where she studied SAR radar Doppler ambiguity for moving targets. Her scholarly contributions include high-impact publications in journals such as IEEE Transactions on Vehicular Technology, Physical Communication, and Multimedia Tools and Applications. Collectively, her research reflects an outstanding integration of machine learning, optimization, sensing, and communication technologies.

Profile: Google Scholar

Featured Publications

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Ali, S. (2022). Relay selection and power allocation for energy efficiency maximization in hybrid satellite-UAV networks with CoMP-NOMA transmission. IEEE Transactions on Vehicular Technology, 71(5), 5087–5100.

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Choukali, M. A. (2021). A fuzzy histogram weighting method for efficient image contrast enhancement. Multimedia Tools and Applications, 80(2), 2221–2241.

Mirbolouk, S., Choukali, M. A., Valizadeh, M., & Amirani, M. C. (2020). Relay selection for CoMP-NOMA transmission in satellite and UAV cooperative networks. 2020 28th Iranian Conference on Electrical Engineering (ICEE), 1–5.

Choukali, M. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2023). A desired histogram estimation accompanied with an exact histogram matching method for image contrast enhancement. Multimedia Tools and Applications, 82(18), 28345–28365.

Hussein, A. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2025). Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework. Scientific Reports, 15(1), 25071.

Choukali, M. A., Mirbolouk, S., Valizadeh, M., & Amirani, M. C. (2024). Deep contextual bandits-based energy-efficient beamforming for integrated sensing and communication. Physical Communication, 68, 102576.

William Gardner | Engineering | Best Researcher Award

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Prof. William Gardner | Engineering | Best Researcher Award 

University of California, Davis | United States

Dr. William A. Gardner is an esteemed scholar and pioneer in statistical signal processing, particularly renowned for his foundational contributions to cyclostationary signal processing theory and methods. His postsecondary education began with a Certificate in Aircraft Radio Repair (1961) at Keesler Air Force Base, followed by coursework in electronics and electrical engineering at Foothill College and Stanford University, where he earned his M.S. in Electrical Engineering (1967). He pursued further graduate studies at MIT and Bell Labs, and earned his Ph.D. in Electrical Engineering from the University of Massachusetts Amherst (1972). Dr. Gardner joined the University of California, Davis in 1972, where he advanced to Professor VII before becoming Professor Emeritus in 2001. Over his career, he supervised numerous M.S. and Ph.D. theses focused on statistical signal processing, especially the exploitation of cyclostationarity in communications and signals intelligence. In 1986, Dr. Gardner founded Statistical Signal Processing, Inc. (SSPI), a private research firm dedicated to advanced algorithm development for radio reconnaissance, signals intelligence, and cellular communications. The firm, which operated for 25 years, licensed its technologies to major corporations including Apple Inc. and Lockheed Martin. Post-retirement, he continued research collaborations—most notably with Prof. Antonio Napolitano—on advanced statistical cyclicity and nonstationary signal behavior. His recent work has expanded into electromagnetic modeling of cosmic plasma and laboratory-confined plasma, supporting paradigm-challenging efforts such as the Plasma Universe, Thunderbolts Project, and the SAFIRE Project, all aimed at redefining astrophysical theory and clean energy generation. Dr. Gardner is the author of four influential books, including Introduction to Random Processes and Statistical Spectral Analysis, and editor of Cyclostationarity in Communications and Signal Processing. He has contributed chapters to five other books, authored or co-authored over 110 peer-reviewed journal papers, and holds 15 U.S. patents. His academic impact is reflected in a citation count exceeding 7489, an h-index of 33, and continued recognition for shaping the theoretical underpinnings of modern signal processing. He has delivered invited lectures globally and remains a thought leader across academia, industry, and emerging scientific paradigms.

Profiles:  Scopus | Orcid | Google Scholar

Featured Publications

Gardner, W. A. (2002). Exploitation of spectral redundancy in cyclostationary signals. IEEE Signal Processing Magazine, 8(2), 14–36.

Gardner, W. A. (1990). Introduction to random processes: With applications to signals and systems. McGraw-Hill.

Gardner, W. A., Napolitano, A., & Paura, L. (2006). Cyclostationarity: Half a century of research. Signal Processing, 86(4), 639–697.

Gardner, W. A., & Robinson, E. A. (1989). Statistical spectral analysis—A nonprobabilistic theory. Prentice-Hall.

Gardner, W. A. (1994). Cyclostationarity in communications and signal processing. IEEE Press.

Gardner, W. A. (2002). Signal interception: A unifying theoretical framework for feature detection. IEEE Transactions on Communications, 36(8), 897–906.

Gardner, W. A., Brown, W., & Chen, C. K. (1987). Spectral correlation of modulated signals: Part II—Digital modulation. IEEE Transactions on Communications, 35(6), 595–601.

Gardner, W. A., & Franks, L. E. (1975). Characterization of cyclostationary random signal processes. IEEE Transactions on Information Theory, 21(1), 4–14.

Gardner, W. A., & Spooner, C. M. (1992). Signal interception: Performance advantages of cyclic-feature detectors. IEEE Transactions on Communications, 40(1), 149–159.