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Ibrahim Sadek
Researcher at Université de Sherbrooke
Publications - 150
Citations - 1670
Ibrahim Sadek is an academic researcher from Université de Sherbrooke. The author has contributed to research in topics: Optimal control & Maximum principle. The author has an hindex of 20, co-authored 147 publications receiving 1431 citations. Previous affiliations of Ibrahim Sadek include University of North Carolina at Wilmington & Helwan University.
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Ballistocardiogram signal processing: a review.
TL;DR: The sensors that are being used for obtaining ballistocardiogram signals are introduced and an in-depth review of the signal processing methods as applied to the various sensors are presented, to analyze the BCG signal and extract physiological parameters such heart rate and breathing rate, as well as determining sleep stages.
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Optimal piezo-actuator locations/lengths and applied voltage for shape control of beams
TL;DR: In this paper, a shape control of a beam under general loading conditions is implemented using piezoceramic actuators to provide the control forces, and the objective of the shape control is to minimize the maximum deflection of the beam to obtain a min-max deflection configuration with respect to loading and piezo-actuators.
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Sunitinib: the antiangiogenic effects and beyond.
Zhonglin Hao,Ibrahim Sadek +1 more
TL;DR: Emerging evidence points to an immunomodulatory role for sunitinib, and it is likely to contribute to the overall outcomes, especially those seen in metastatic renal cell carcinoma, and such effects are thought to be mediated by the proto-oncogene cKIT receptor.
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Nonintrusive Vital Signs Monitoring for Sleep Apnea Patients: A Preliminary Study
TL;DR: The capacity of the microbend fiber optic sensor to monitor heart rate and respiration in a nonintrusive manner is evaluated and is expected that this preliminary research will pave the way toward unobtrusive detection of vital signs in real time.
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Discrimination of retinal images containing bright lesions using sparse coded features and SVM
TL;DR: This paper proposes to use sparse coding techniques for retinal images classification using a linear SVM with the obtained sparse coded features and achieves superior performance as compared with the popular Bag-of-Visual-Word approach for image classification.