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All Journal International Journal of Electrical and Computer Engineering
Samir El Adib
University of Abdelmalek Essaadi
Computer Science & IT Electrical & Electronics Engineering

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Improvements in space radiation-tolerant FPGA implementation of land surface temperature-split window algorithm Assaad El Makhloufi; Nisrine Chekroun; Noha Tagmouti; Samir El Adib; Naoufal Raissouni
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3844-3854

Abstract

The trend in satellite remote sensing assignments has continuously been concerning using hardware devices with more flexibility, smaller size, and higher computational power. Therefore, field programmable gate arrays (FPGA) technology is often used by the developers of the scientific community and equipment for carrying out different satellite remote sensing algorithms. This article explains hardware implementation of land surface temperature split window (LST-SW) algorithm based on the FPGA. To get a high-speed process and real-time application, VHSIC hardware description language (VHDL) was employed to design the LST-SW algorithm. The paper presents the benefits of the used Virtex-4QV of radiation tolerant series FPGA. The experimental results revealed that the suggested implementation of the algorithm using Virtex4QV achieved higher throughput of 435.392 Mbps, and faster processing time with value of 2.95 ms. Furthermore, a comparison between the proposed implementation and existing work demonstrated that the proposed implementation has better performance in terms of area utilization; 1.17% reduction in number of Slice used and 1.06% reduction in of LUTs. Moreover, the significant advantage of area utilization would be the none use of block RAMs comparing to existing work using three blocks RAMs. Finally, comparison results show improvements using the proposed implementation with rates of 2.28% higher frequency, 3.66 x higher throughput, and 1.19% faster processing time.
Convolutional neural network based key generation for security of data through encryption with advanced encryption standard Ismail Negabi; Smail Ait El Asri; Samir El Adib; Naoufal Raissouni
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2589-2599

Abstract

Machine learning techniques, especially deep learning, are playing an increasingly important role in our lives. Deep learning uses different models to extract information from the data. They have already had a huge impact in areas such as health (i.e., cancer diagnosis), self-driving cars, speech recognition, and data encryption. Recently, deep learning models, including convolutional neural networks (CNN), have been proven to be more effective in the security field. Moreover, the National Institute of Standards and Technology (NIST) recommends the advanced encryption standard (AES) algorithm as the most often utilized encryption method in several security applications. In this paper, a crypt-intelligent system (CIS) capable of securing data is proposed. It is based on the combination of the performance of CNN with the AES, by substituting the key expansion unit of AES with a CNN architecture that performs the key generation. Our CIS is described using very high-speed integrated circuit (VHSIC) hardware description language (VHDL), simulated by ModelSim, synthesized, and implemented with Xilinx ISE 14.7. Finally, the Airtex-7 series XC7A100T device has achieved an encryption throughput of 965.88 Mbps. In addition, the CIS offers a high degree of flexibility and is supported by reconfigurability, based on the experimental results, if sufficient resources are available, the architecture can provide performance that can satisfy cryptographic applications.
Co-Authors Assaad El Makhloufi Ismail Negabi Naoufal Raissouni Nisrine Chekroun Noha Tagmouti Smail Ait El Asri