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bsj-4033
Improved throughput of Elliptic Curve Digital Signature Algorithm (ECDSA) processor implementation over Koblitz curve k-163 on Field Programmable Gate Array (FPGA)
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            The widespread use of the Internet of things (IoT) in different aspects of an individual’s life like banking, wireless intelligent devices and smartphones has led to new security and performance challenges under restricted resources. The Elliptic Curve Digital Signature Algorithm (ECDSA) is the most suitable choice for the environments due to the smaller size of the encryption key and changeable security related parameters. However, major performance metrics such as area, power, latency and throughput are still customisable and based on the design requirements of the device.

The present paper puts forward an enhancement for the throughput performance metric by proposing a more efficient design for the hardware implementation of ECDSA. The design raised the throughput to 0.08207 Mbit/s, leading to an increase of 6.95% from the existing design. It also includes the design and implementation of the Universal Asynchronous Receiver Transmitter (UART) module. The present work is based on a 163-bit key-size over Koblitz curve k-163 and secure hash function SHA-1. A serial module for the underlying modular layer, high-speed architecture of Koblitz point addition and Koblitz point multiplication have been considered in this work, in addition to utilising the carry-save-multiplier, modular adder-subtractor and Extended Euclidean module for ECDSA protocols. All modules are designed using VHDL and implemented on the platform Virtex5 xc5vlx155t-3ff1738. Signature generation requires 0.55360ms, while its validation consumes 1.10947288ms. Thus, the total time required to complete both processes is equal to 1.66ms and the maximum frequency is approximately 83.477MHZ, consuming a power of 99mW with the efficiency approaching 3.39 * 10-6.

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Publication Date
Sat Nov 30 2019
Journal Name
Journal Of Engineering And Applied Sciences
Using Particle Swarm Optimization Algorithm to Address the Multicollinearity Problem
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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
A Parallel Adaptive Genetic Algorithm for Job Shop Scheduling Problem
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Publication Date
Sun Dec 01 2013
Journal Name
2013 Ieee International Rf And Microwave Conference (rfm)
Differential Evolution algorithm for linear frequency modulation radar signal denoising
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Signal denoising is directly related to sample estimation of received signals, either by estimating the equation parameters for the target reflections or the surrounding noise and clutter accompanying the data of interest. Radar signals recorded using analogue or digital devices are not immune to noise. Random or white noise with no coherency is mainly produced in the form of random electrons, and caused by heat, environment, and stray circuitry loses. These factors influence the output signal voltage, thus creating detectable noise. Differential Evolution (DE) is an effectual, competent, and robust optimisation method used to solve different problems in the engineering and scientific domains, such as in signal processing. This paper looks

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Publication Date
Thu Jan 01 2015
Journal Name
Journal Of Al-mansoor College
An Improvement to Face Detection Algorithm for Non-Frontal Faces
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Publication Date
Fri Apr 01 2011
Journal Name
Al-mustansiriyah Journal Of Science
A Genetic Algorithm Based Approach For Generating Unit Maintenance Scheduling
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Publication Date
Thu May 01 2008
Journal Name
2008 International Conference On Computer And Communication Engineering
A binary Particle Swarm Optimization for attacking knapsacks Cipher Algorithm
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Publication Date
Mon Jul 01 2019
Journal Name
2019 International Joint Conference On Neural Networks (ijcnn)
A Fast Feature Extraction Algorithm for Image and Video Processing
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Publication Date
Wed Jan 01 2025
Journal Name
Fusion: Practice And Applications
Enhanced EEG Signal Classification Using Machine Learning and Optimization Algorithm
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This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

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Publication Date
Mon May 01 2017
Journal Name
Australian Journal Of Basic And Applied Sciences
Sprite Region Allocation Using Fast Static Sprite Area Detection Algorithm
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Background: Sprite coding is a very effective technique for clarifying the background video object. The sprite generation is an open issue because of the foreground objects which prevent the precision of camera motion estimation and blurs the created sprite. Objective: In this paper, a quick and basic static method for sprite area detection in video data is presented. Two statistical methods are applied; the mean and standard deviation of every pixel (over all group of video frame) to determine whether the pixel is a piece of the selected static sprite range or not. A binary map array is built for demonstrating the allocated sprite (as 1) while the non-sprite (as 0) pixels valued. Likewise, holes and gaps filling strategy was utilized to re

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Publication Date
Thu May 05 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Classification SINGLE-LEAD ECG by using conventional neural network algorithm
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