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Your search keyword '"Radwan, Ahmed"' showing total 198 results
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198 results on '"Radwan, Ahmed"'

2. Smart irrigation system using IoT and machine learning methods

Author

Esmail, Ahmed A., primary, Ibrahim, Manar A., additional, Abdallah, Sayed M., additional, Radwan, Ahmed E., additional, Elsonbaty, Haya k., additional, Elsayed, Mohamed A., additional, Elnakeib, Noure A., additional, Dawoud, Merit Samir, additional, El-Ghamry, Amir, additional, Fouad, Khaled M., additional, and Moawad, Ibrahim F., additional

Published
2023
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19. Cross-Modal Distillation to Improve MRI-Based Brain Tumor Segmentation With Missing MRI Sequences.

Author

Rahimpour, Masoomeh, Bertels, Jeroen, Radwan, Ahmed, Vandermeulen, Henri, Sunaert, Stefan, Vandermeulen, Dirk, Maes, Frederik, Goffin, Karolien, and Koole, Michel

Subjects
BRAIN tumors, MAGNETIC resonance imaging, DISTILLATION, CONVOLUTIONAL neural networks, DIFFUSION magnetic resonance imaging
Abstract

Convolutional neural networks (CNNs) for brain tumor segmentation are generally developed using complete sets of magnetic resonance imaging (MRI) sequences for both training and inference. As such, these algorithms are not trained for realistic, clinical scenarios where parts of the MRI sequences which were used for training, are missing during inference. To increase clinical applicability, we proposed a cross-modal distillation approach to leverage the availability of multi-sequence MRI data for training and generate an enriched CNN model which uses only single-sequence MRI data for inference but outperforms a single-sequence CNN model. We assessed the performance of the proposed method for whole tumor and tumor core segmentation with multi-sequence MRI data available for training but only $T_{1}$ -weighted ($T_{\text{1}w}$) sequence data available for inference, using BraTS 2018, and in-house datasets. Results showed that cross-modal distillation significantly improved the Dice score for both whole tumor and tumor core segmentation when only $T_{\text{1}w}$ sequence data were available for inference. For the evaluation using the in-house dataset, cross-modal distillation achieved an average Dice score of 79.04% and 69.39% for whole tumor and tumor core segmentation, respectively, while a single-sequence U-Net model using $T_{\text{1}w}$ sequence data for both training and inference achieved an average Dice score of 73.60% and 62.62%, respectively. These findings confirmed cross-modal distillation as an effective method to increase the potential of single-sequence CNN models such that segmentation performance is less compromised by missing MRI sequences or having only one MRI sequence available for segmentation. [ABSTRACT FROM AUTHOR]

Published
2022
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30. Generic FPGA Design of Spiking Neuron Model

Author

Roshdy, Merna, primary, Mohsen, Menna, additional, Saeed, Ahmed, additional, Tolba, Mohammed F., additional, Saleh, Hassan I., additional, Radwan, Ahmed G., additional, and Madian, Ahmed H., additional

Published
2020
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35. Design and FPGA Verification of Custom-Shaped Chaotic Attractors Using Rotation, Offset Boosting and Amplitude Control.

Author

Sayed, Wafaa S., Roshdy, Merna, Said, Lobna A., and Radwan, Ahmed G.

Abstract

This brief proposes a method of generating custom-shaped attractors, which depends on a planarly rotating V-shape multi-scroll chaotic system with offset boosting and amplitude control, and its FPGA verification. The proposed planarly rotating, translational (offset boostable), and scalable (amplitude controllable) system exhibits a wide basin of attraction and can cover the whole space. Consequently, non-autonomous parameters enable generating attractors of custom-shapes, e.g., letters of the English alphabet as a demonstrating example. Furthermore, non-autonomous parameters are employed to generate attractors with the shape of complete words and sentences. This takes place by generating letter shapes successively and controlling the position and size of each letter. The chaotic behavior is quantified and indicated using spectral entropy. Experimental verification on FPGA shows good hardware resources utilization and throughput. [ABSTRACT FROM AUTHOR]

Published
2021
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36. FPGA Implementation of Reconfigurable CORDIC Algorithm and a Memristive Chaotic System With Transcendental Nonlinearities.

Author

Mohamed, Sara M., Sayed, Wafaa S., Radwan, Ahmed G., and Said, Lobna A.

Subjects
TRANSCENDENTAL functions, MATHEMATICAL functions, FIELD programmable gate arrays, ALGORITHMS
Abstract

Coordinate Rotation Digital Computer (CORDIC) is a robust iterative algorithm that computes many transcendental mathematical functions. This paper proposes a reconfigurable CORDIC hardware design and FPGA realization that includes all possible configurations of the CORDIC algorithm. The proposed architecture is introduced in two approaches: multiplier-less and single multiplier approaches, each with its advantages. Compared to recent related works, the proposed implementation overpasses them in the included number of configurations. Additionally, it demonstrates efficient hardware utilization and suitability for potential applications. Furthermore, the proposed design is applied to a memristive chaotic system with different transcendental functions computed using the proposed reconfigurable block. The memristive system design is realized on the Artix-7 FPGA board, yielding throughputs of 0.4483 and 0.3972 Gbit/s for the two approaches of reconfigurable CORDIC. [ABSTRACT FROM AUTHOR]

Published
2022
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