Publications

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2011
Rushdi, M., and J. Ho, "Large-scale-invariant Texture Recognition", {VISAPP} 2011 - Proceedings of the Sixth International Conference on Computer Vision Theory and Applications, Vilamoura, Algarve, Portugal, 5-7 March, 2011, pp. 442–445, 2011. Abstract
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Rushdi, M., and J. Ho, "Texture Classification using Sparse {K-SVD} Texton Dictionaries", {VISAPP} 2011 - Proceedings of the Sixth International Conference on Computer Vision Theory and Applications, Vilamoura, Algarve, Portugal, 5-7 March, 2011, pp. 187–193, 2011. Abstract
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2012
Rushdi, M., and J. Ho, "Augmented Coupled Dictionary Learning for Image Super-Resolution", 11th International Conference on Machine Learning and Applications, ICMLA, Boca Raton, FL, USA, December 12-15, 2012. Volume 1, pp. 262–267, 2012. Abstract
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2013
-, Y., M. Ali, M. Rushdi, and J. Ho, "Affine-Constrained Group Sparse Coding and Its Application to Image-Based Classifications", {IEEE} International Conference on Computer Vision, {ICCV} 2013, Sydney, Australia, December 1-8, 2013, pp. 681–688, 2013. Abstract
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Rushdi, M., M. Ali, and J. Ho, "Color de-rendering using coupled dictionary learning", {IEEE} International Conference on Image Processing, {ICIP} 2013, Melbourne, Australia, September 15-18, 2013, pp. 315–319, 2013. Abstract
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Shahed, N. S. M., M. Rushdi, and J. Ho, "Visual Tracking Using Superpixel-Based Appearance Model", Computer Vision Systems - 9th International Conference, {ICVS} 2013, St. Petersburg, Russia, July 16-18, 2013. Proceedings, pp. 213–222, 2013. Abstract
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Nejhum, S. S. M., M. Rushdi, and J. Ho, "Visual Tracking using Structured Superpixels", 9th International Conference on Vision Systems (ICVS), Saint Petersburg, Russia, 12-15 July, 2013.
Rushdi, M., M. Ali, and J. Ho, "Color De-Rendering using Coupled Dictionary Learning", 20th International Conference on Image Processing (ICIP), Melbourne, Autralia, 15-18 September, 2013.
2014
Ali, M., M. Rushdi, and J. Ho, "Deconstructing Kernel Machines", Machine Learning and Knowledge Discovery in Databases - European Conference, {ECML} {PKDD} 2014, Nancy, France, September 15-19, 2014. Proceedings, Part {I}, pp. 34–49, 2014. Abstract
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2015
Taher, H., M. Rushdi, M. Islam, and A. M. Badawi, "Adaptive Saliency-Weighted 2D-to-3D Video Conversion", Computer Analysis of Images and Patterns - 16th International Conference, {CAIP} 2015, Valletta, Malta, September 2-4, 2015, Proceedings, Part {II}, pp. 737–748, 2015. Abstract
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Makram, A. W., M. A. Rushdi, A. M. Khalifa, and M. E. T. -, "Tag removal in cardiac tagged {MRI} images using coupled dictionary learning", 37th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society, {EMBC} 2015, Milan, Italy, August 25-29, 2015, pp. 7921–7924, 2015. Abstract
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2016
Anam, A. M., M. A. Rushdi, and A. S. Fahmy, "Enhancement of low-resolution HEp-2 cell image classification using partial least-square regression", 2016 {IEEE} International Conference on Image Processing, {ICIP} 2016, Phoenix, AZ, USA, September 25-28, 2016, pp. 1245–1249, 2016. Abstract
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Annaby, M. H., M. A. Rushdi, and E. A. Nehary, "Image encryption via discrete fractional Fourier-type transforms generated by random matrices", Sig. Proc.: Image Comm., vol. 49, pp. 25–46, 2016. AbstractWebsite
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2018
Abdelsamad, Y., M. Rushdi, and B. Tawfik, "Functional and Spatial Design of Emergency Departments Using Quality Function Deployment", Journal of Healthcare Engineering, vol. 2018, pp. 1-8, 2018.
Rushdi, A. M., and M. A. Rushdi, "Mathematics and Examples of the Modern Syllogistic Method of Propositional Logic", Frontiers in Information Systems: Bentham Books, 2018.
Rushdi, M. A. M., A. M. A. Rushdi, M. Zarouan, and W. Ahmad, "Satisfiability in intuitionistic fuzzy logic with realistic tautology", Kuwait Journal of Science, vol. 45, issue 2, pp. 15-21, 2018.
2019
Anam, A. M., and M. A. Rushdi, "Classification of scaled texture patterns with transfer learning", Expert Systems with Applications, vol. 120, issue 15 April 2019, pp. 448-460, 2019.
Annaby, M. H., Y. M. Fouda, and M. A. Rushdi, "Improved Normalized Cross-Correlation for Defect Detection in Printed-Circuit Boards", IEEE Transactions on Semiconductor Manufacturing, vol. 32, issue 2, pp. 199-211, 2019.
Amin, M. N., M. A. Rushdi, R. N. Marzaban, A. Yosry, K. Kim, and A. M. Mahmoud, "Wavelet-based computationally-efficient computer-aided characterization of liver steatosis using conventional B-mode ultrasound images", Biomedical Signal Processing and Control, vol. 52, issue 2019, pp. 84-96, 2019.
2020
Elkhouly, H. I., M. A. Rushdi, and R. K. Abdel-Magied, "Eco-friendly date-seed nanofillers for polyethylene terephthalate composite reinforcement", Materials Research Express, vol. 7, issue 2, pp. 025101, 2020.
Atwine, D., Y. W. Karanja, A. Ahluwalia, C. D. Maria, D. Assefa, V. Konde, E. Khundi, P. N. Makobore, M. Moshi, M. Nzomo, et al., "Nurturing next-generation biomedical engineers in Africa: The impact of Innovators’ Summer Schools", Global Health Innovation, vol. 3, issue 2, 2020.
Altalabi, W. M., M. A. Rushdi, and B. M. Tawfik, "Optimisation of medical equipment replacement using stochastic dynamic programming", Journal of Medical Engineering & Technology, vol. 44, issue 7, pp. 411-422, 2020.
Altalabi, W., M. A. Rushdi, and B. Tawfik, "OPTIMIZATION OF MEDICAL EQUIPMENT REPLACEMENT USING DETERMINISTIC DYNAMIC PROGRAMMING", JOURNAL OF ENGINEERING AND APPLIED SCIENCE, vol. 67, issue 2, pp. 467-486, 2020.
2021
Annaby, M. H., M. H. Said, A. M. Eldeib, and M. A. Rushdi, "EEG-based motor imagery classification using digraph Fourier transforms and extreme learning machines", Biomedical Signal Processing and Control, vol. 69, pp. 1-14, 2021.
Elbeshbeshy, A. M., M. A. Rushdi, and S. M. El-Metwally, "Electromyography Signal Analysis and Classification using Time-Frequency Representations and Deep Learning.", Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2021, pp. 661-664, 2021. Abstract

Analysis and classification of electromyography (EMG) signals are crucial for rehabilitation and motor control. This study investigates electromyogram (EMG) time-frequency representations and then creates conventional and deep learning models for EMG signal classification. Firstly, a dataset of single-channel surface EMG signals has been recorded for four subjects to differentiate between forearm flexion and extension. Then, different time-frequency EMG representations have been used to build conventional and deep learning models for EMG classification. We compared the performance of pre-trained convolutional neural network models, namely GoogLeNet, SqueezeNet and AlexNet, and achieved accuracies of 92.71%, 90.63% and 87.5%, respectively. Also, data augmentation techniques on the levels of raw EMG signals and their time- frequency representations helped improve the accuracy of GoogLeNet to 96.88%. Furthermore, our approach demonstrated superior performance on another publicly available 10-class EMG dataset, and also using traditional classifiers trained on hand-crafted features.

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