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Towards Efficient FPGA Implementation of Elliptic Curve Crypto-Processor for Security in IoT and Embedded Devices 面向物联网和嵌入式设备安全的椭圆曲线加密处理器的高效FPGA实现
Pub Date : 2020-07-01 DOI: 10.21608/mjeer.2020.103280
Shaimaa Abu Khadra, S. E. S. E. Abdulrahman, N. A. Ismail
An Elliptic Curve Crypto-Processor (ECCP) is a favorite public-key cryptosystem due to its small key size and its high security arithmetic unit. It is applied in constrained devices which often run on batteries and have limited processing, storage capabilities and low power. This research work presents an effective ECCP architecture for security in IoT and embedded devices. A finite field polynomial multiplier takes the most implementation effort of an ECCP because it is the most consuming operation for time and area. So, the objective is to implement the main operation of Point Multiplication (PM) 𝑄=𝑘𝑃 using FPGA. The aim is to obtain the optimal registers number for an area optimization of ECCP architecture. Moreover, it proposes a time optimization of ECCP based on the liveness analysis and exploiting forward paths. Also, a comparison between sequential and parallel hardware design of PM based on Montgomery ladder algorithm is provided.The developed ECCP design is implemented over Galois Fields GF (2163) and GF (2409) on Xilinx Integrated Synthesizes Environment (ISE) Virtex 6 FPGA. In case of GF (2163), this work achieved an area saving that uses 2083 Flip Flops (FFs), 40876 Lookup Tables (LUTs) and 19824 occupied slices. The execution time is 1.963 s runs at a frequency of 369.529 MHz and consumes 5237.00 mW. In case of GF (2409), this work achieved an area saving that uses 8129 Flip Flops (FFs), 42300 Lookup Tables (LUTs) and 18807 occupied slices. The execution time is 29 s runs at a frequency of 253.770 MHz and consumes 2 W. The obtained results are highly comparable with other state-of-the-art crypto-processor designs. The developed ECCP is applied as a case study of a cryptography protocol in ATMs.
椭圆曲线加密处理器(ECCP)是一种最受欢迎的公钥密码系统,因为它的密钥大小小,算术单元安全性高。它应用于受限设备,这些设备通常依靠电池运行,处理、存储能力有限,功耗低。本研究工作提出了一种有效的ECCP架构,用于物联网和嵌入式设备的安全。有限域多项式乘法器在ECCP的实现中花费的精力最多,因为它是最耗费时间和面积的操作。因此,目标是使用FPGA实现点乘法(PM)𝑄=𝑘< 0.05的主要操作。目的是为ECCP结构的面积优化获得最优寄存器数。并提出了一种基于活度分析和开发正向路径的ECCP时间优化方法。同时,对基于Montgomery梯形算法的PM的顺序和并行硬件设计进行了比较。所开发的ECCP设计在Galois Fields GF(2163)和GF(2409)上在Xilinx Integrated synthesis Environment (ISE) Virtex 6 FPGA上实现。在GF(2163)的情况下,这项工作实现了使用2083个触发器(ff)、40876个查找表(lut)和19824个占用片的面积节省。执行时间为1.963s,运行频率为369.529 MHz,功耗为527.00 mW。在GF(2409)的情况下,这项工作实现了使用8129个触发器(ff)、42300个查找表(lut)和18807个占用片的面积节省。执行时间为29s,运行频率为253.770 MHz,功耗为2w。所获得的结果与其他最先进的加密处理器设计高度可比。本文将所开发的ECCP作为一种加密协议在atm机中的应用实例。
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引用次数: 0
Machine Learning Model for Cancer Diagnosis based on RNAseq Microarray 基于RNAseq芯片的癌症诊断机器学习模型
Pub Date : 2020-03-18 DOI: 10.21608/mjeer.2020.20533.1000
Hanaa Torkey, Mostafa Atlam, N. El-Fishawy, Hanaa Salem
Microarray technology is one of the most important recent breakthroughs in experimental molecular biology. This novel technology for thousands of genes concurrently allows the supervising of expression levels in cells and has been increasingly used in cancer research to understand more of the molecular variations among tumors so that a more reliable classification becomes attainable. Machine learning techniques are loosely used to create substantial and precise classification models. In this paper, a function called Feature Reduction Classification Optimization (FeRCO) is proposed. FeRCO function uses machine learning techniques applied upon RNAseq microarray data for predicting whether the patient is diseased or not. The main purpose of FeRCO function is to define the minimum number of features using the most fitting reduction technique along with classification technique that give the highest classification accuracy. These techniques include Support Vector Machine (SVM) both linear and kernel, Decision Trees (DT), Random Forest (RF), K-Nearest Neighbours (KNN) and Naïve Bayes (NB). Principle Component Analysis (PCA) both linear and kernel, Linear Discriminant Analysis (LDA) and Factor Analysis (FA) along with different machine learning techniques were used to find a lower-dimensional subspace with better discriminatory features for better classification. The major outcomes of this research can be considered as a roadmap for interesting researchers in this field to be able to choose the most suitable machine learning algorithm whatever classification or reduction. The results show that FA and LPCA are the best reduction techniques to be used with the three datasets providing an accuracy up to 100% with TCGA and simulation datasets and accuracy up to 97.86% with WDBC datasets. LSVM is the best classification technique to be used with Linear PCA (LPCA), FA and LDA. RF is the best classification technique to be used with Kernel PCA (KPCA). Keywords— Cancer Classification, Diagnosis, Gene Expression, Gene Reduction, Machine learning.
微阵列技术是近年来实验分子生物学领域最重要的突破之一。这项新技术可以同时监测数千个基因在细胞中的表达水平,并越来越多地用于癌症研究,以了解更多的肿瘤分子变异,从而实现更可靠的分类。机器学习技术被松散地用于创建大量和精确的分类模型。本文提出了一种特征约简分类优化(FeRCO)函数。FeRCO函数使用应用于RNAseq微阵列数据的机器学习技术来预测患者是否患病。FeRCO函数的主要目的是使用最拟合的约简技术和分类技术来定义最小数量的特征,从而获得最高的分类精度。这些技术包括线性和核支持向量机(SVM)、决策树(DT)、随机森林(RF)、k近邻(KNN)和Naïve贝叶斯(NB)。采用线性和核主成分分析(PCA)、线性判别分析(LDA)和因子分析(FA)以及不同的机器学习技术,寻找具有更好判别特征的低维子空间,以进行更好的分类。本研究的主要成果可以被视为该领域有趣的研究人员能够选择最适合的机器学习算法的路线图,无论是分类还是约简。结果表明,FA和LPCA是三种数据集的最佳约简技术,对TCGA和模拟数据集的准确率可达100%,对WDBC数据集的准确率可达97.86%。LSVM是与线性主成分分析(LPCA)、FA和LDA结合使用的最佳分类技术。RF是核主成分分析(KPCA)的最佳分类技术。关键词:癌症分类,诊断,基因表达,基因还原,机器学习。
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引用次数: 6
Hierarchal Clusters Based Traffic Control System 基于层次集群的交通控制系统
Pub Date : 2020-01-01 DOI: 10.21608/mjeer.2020.68928
Fady Taher, A. El-Sayed, A. Shouman, A. El-Mahalawy
Traffic jam is a crucial issue affecting cities around the world. They are only getting worse as the population and number of vehicles continues to increase significantly. Traffic signal controllers are considered as the most important mechanism to control the traffic, specifically at intersections, the field of Machine Learning offers more advanced techniques which can be applied to provide more flexibility and make the controllers more adaptive to the traffic state. Efficient and adaptive traffic controllers can be designed using a multi-agent reinforcement learning approach, in which, each controller is considered as an agent and is responsible for controlling traffic lights around a single junction. A major problem of reinforcement learning approach is the need for coordination between agents and exponential growth in the state-action space. This paper proposes using machine learning clustering algorithm, namely, hierarchal clustering, in order to divide the targeted network into smaller sub-networks, using real traffic data of 65 intersection of the city of Ottawa to build our simulations, the paper shows that applying the proposed methodology helped solving the curse of dimensionality problem and improved the overall network performance.
交通堵塞是影响世界各地城市的一个重要问题。随着人口和车辆数量的大幅增加,情况只会越来越糟。交通信号控制器被认为是控制交通的最重要的机制,特别是在十字路口,机器学习领域提供了更先进的技术,可以提供更大的灵活性,使控制器更能适应交通状态。使用多智能体强化学习方法可以设计高效的自适应交通控制器,其中每个控制器被视为一个智能体,负责控制单个路口周围的交通灯。强化学习方法的一个主要问题是需要智能体之间的协调和状态-行为空间中的指数增长。本文提出使用机器学习聚类算法,即层次聚类,将目标网络划分为更小的子网络,并使用渥太华市65个十字路口的真实交通数据构建我们的仿真,本文表明,应用所提出的方法有助于解决维数问题,提高整体网络性能。
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引用次数: 1
Classification of Corneal Pattern Based on Convolutional LSTM Neural Network 基于卷积LSTM神经网络的角膜模式分类
Pub Date : 2019-12-01 DOI: 10.21608/mjeer.2019.76998
N. Haggag, Ahmed Sedik, Gh. M. ElBanby, A. El-Fishawy, M. Dessouky, A. Khalaf
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引用次数: 4
Simulative Study of Wavelength Division Multiplexing Fiber Bragg Grating in Nuclear Reactors Monitoring 波分复用光纤布拉格光栅在核反应堆监测中的仿真研究
Pub Date : 2019-10-01 DOI: 10.21608/mjeer.2019.62729
A. Mohamed, A. Rashed, M. Zaky, Ahmed I. Elsaket, M. A. Gaheen
The technologies of wavelength division multiplexing (WDM) have been theoretically studied and analyzed for multiplexing fiber Bragg grating (FBG) in a single optical fiber. This method allows a single fiber to carry many of identical FBGs, making this sensor more appropriate in the nuclear reactors. The analysis demonstrates that the multiplexing capacity can be incredibly enhance small data rates and high channel spacing. The interference effect among FBGs multi-reflections channels must be taken into account. This paper simulate WDM based FBG for a channel spacing of 0.1, 0.3, 0.5, 0.8, 1 nm Gaussian apodized FBGs at data rates of 2.5, 10, 40,100,160, 250 Gb/s respectively for nuclear applications. All simulations were performed in Optisystem software.
对波分复用(WDM)技术进行了理论研究,并对单根光纤中复用光纤布拉格光栅(FBG)技术进行了分析。这种方法允许一根光纤携带许多相同的fbg,使这种传感器更适合于核反应堆。分析表明,多路复用容量可以极大地提高小数据速率和高信道间隔。光纤光栅多反射通道间的干扰效应必须加以考虑。本文模拟了基于波分复用的光纤光栅,通道间距分别为0.1、0.3、0.5、0.8和1 nm,数据速率分别为2.5、10、40、100、160和250 Gb/s。所有模拟均在Optisystem软件中进行。
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引用次数: 1
Quality Assessment of Images Transmitted over Optical Fiber Communications Systems based on Statistical Metrics 基于统计度量的光纤通信系统图像传输质量评价
Pub Date : 2019-07-01 DOI: 10.21608/mjeer.2019.62791
Hanan S. Ghanem, W. El-shafai, El-Sayed M. El-Rabaie, A. Mohamed, A. Rashed, F. E. El-Samie, M. Tabbour
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引用次数: 0
Efficient Epileptic Seizure Prediction Approach Based on Hilbert Transform 基于希尔伯特变换的癫痫发作预测方法
Pub Date : 2019-07-01 DOI: 10.21608/mjeer.2019.62744
Heba M. Emara, Mohamed Elwekeil, T. Taha, A. El-Fishawy, S. El-Rabaie, T. Alotaiby, S. Alshebeili, F. El-Samie
This paper introduces a patient-specific method for seizure prediction applied to scalp Electroencephalography (sEEG) signals. The proposed method depends on computing the instantaneous amplitude of the analytic signal by applying Hilbert transform on EEG signals. Then, the Probability Density Functions (PDFs) are estimated for amplitude, local mean, local variance, derivative and median as major features. This is followed by a threshold-based classifier which discriminates between pre-ictal and inter-ictal periods. The proposed approach utilizes an adaptive algorithm for channel selection to identify the optimum number of needed channels which is useful for real-time applications. It is applied to all patients from the CHB-MIT database, achieving an average prediction rate of 96.46%, an average false alarm rate of 0.028077/h and an average prediction time of 60.1595 minutes using a 90-minute prediction horizon. Experimental results prove that Hilbert transform is more efficient for prediction than other existing approaches.
本文介绍了一种应用于头皮脑电图(sEEG)信号的患者特异性癫痫发作预测方法。该方法通过对脑电信号进行希尔伯特变换,计算分析信号的瞬时幅值。然后,估计概率密度函数(pdf)的振幅,局部均值,局部方差,导数和中位数作为主要特征。接下来是一个基于阈值的分类器,它可以区分临界前和间隔时期。该方法利用自适应信道选择算法来确定所需信道的最佳数量,这对实时应用非常有用。应用于CHB-MIT数据库的所有患者,在90分钟的预测范围内,平均预测率为96.46%,平均虚警率为0.028077/h,平均预测时间为60.1595分钟。实验结果证明,希尔伯特变换的预测效率高于现有的其他方法。
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引用次数: 0
An Efficient Method Of ECG Beats Feature Extraction/Classification With Multiclass SVM Error Correcting Output Codes 基于多类支持向量机纠错输出码的心电心跳特征提取/分类方法
Pub Date : 2019-07-01 DOI: 10.21608/mjeer.2019.62765
Salma El-Soudy, A. El-Sayed, A. Khalil, Irshad Khalil, T. Taha, F. A. Abd El-Samie
This paper presents an efficient algorithm for classifying the ECG beats to the main four types. These types are normal beat (normal), Left Bundle Branch Block beats (LBBB), Right Bundle Branch Block beats (RBBB), Atrial Premature Contraction (APC). Feature extraction is performed from each type using Legendre moments as a tool for characterizing the signal beats. A Multiclass Support Vector Machine (multiclass SVM) is used for the classification on process with Legendre polynomial coefficients as inputs. A comparison study is presented between the proposed and some existing approaches. Simulation results reveal that the proposed approach gives 97.7% accuracy levels compared to 95.7447%, 95.88%, 95.03% , 93.40%, 96.02%, 95.95%, 96.24% achieved with Discrete wavelet (DWT), Haar wavelet and principle component analysis (PCA) as feature extractors and ANN, Simple Logic Random Forest, LibSVM and J48 as classifiers.
本文提出了一种将心电拍分为四种主要类型的有效算法。这些类型是正常心跳(normal)、左束支传导阻滞心跳(LBBB)、右束支传导阻滞心跳(RBBB)、心房早搏(APC)。使用勒让德矩作为表征信号拍的工具,从每种类型中进行特征提取。采用多类支持向量机(Multiclass Support Vector Machine,简称Multiclass SVM)对以勒让德多项式系数为输入的过程进行分类。并将所提出的方法与现有的方法进行了比较研究。仿真结果表明,与离散小波(DWT)、Haar小波和主成分分析(PCA)作为特征提取器和人工神经网络(ANN)、简单逻辑随机森林(Simple Logic Random Forest)、LibSVM和J48作为分类器的准确率分别为95.7447%、95.88%、95.03%、93.40%、96.02%、95.95%、96.24%相比,该方法的准确率为97.7%。
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引用次数: 2
Sensitivity of Seizure Pattern Prediction to EEG Signal Compression 癫痫发作模式预测对脑电图信号压缩的敏感性
Pub Date : 2019-07-01 DOI: 10.21608/mjeer.2019.62768
Sally El-Gindy, S. El-Dolil, A. El-Fishawy, El-Sayed M. El-Rabaie, M. Dessouky, F. El-Samie, Turky Elotaiby, Saleh Elshebeily
This paper presents a framework for Electroencephalography (EEG) seizure prediction in time domain. Moreover, it studies an efficient lossy EEG signal compression technique and its effect on further processing for seizure prediction in a realistic signal acquisition and compression scenario. Compression of EEG signals are one of the most important solutions in saving speed up signals transfer, reduction of energy transmission and the required memory for storage in addition to reduction costs for storage hardware and network bandwidth. The main objective of this research is to use trigonometric compression techniques including; Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) algorithms on EEG signals and study the impact of the reconstructed EEG signals on its seizure prediction ability. Simulation results show that the DCT achieves the best prediction results compared with DST technique achieving sensitivity of 95.238% and 85.714% respectively. The proposed approach gives longer prediction times compared to traditional EEG seizure prediction approaches. Therefore, it will help specialists for the prediction of epileptic seizure as earlier as possible.
提出了一种脑电图癫痫发作的时域预测框架。此外,研究了一种有效的有损脑电图信号压缩技术及其在实际信号采集和压缩场景下对癫痫发作预测进一步处理的影响。脑电图信号的压缩是提高信号传输速度、减少能量传输、节省存储空间、降低存储硬件和网络带宽成本的重要解决方案之一。本研究的主要目的是使用三角压缩技术,包括;对脑电信号进行离散余弦变换(DCT)和离散正弦变换(DST)算法,研究重构后的脑电信号对其癫痫发作预测能力的影响。仿真结果表明,与DST技术相比,DCT预测效果最好,灵敏度分别为95.238%和85.714%。与传统的脑电图癫痫发作预测方法相比,该方法的预测时间更长。因此,它将有助于专家尽早预测癫痫发作。
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引用次数: 0
Image Forgery Detection Based on Trigonometric Transforms 基于三角变换的图像伪造检测
Pub Date : 2019-07-01 DOI: 10.21608/mjeer.2019.62776
F. Al_azrak, M. Dessouky, F. El-Samie, A. Elkorany, Z. Elsharkawy
Image forgery detection is the basic key to solve many problems, especially with regard to the social problems such as those in Facebook, and court cases. Copy-move forgery is the type of forgery where a part of the image is copied to other location of the same image to hide important information or duplicate certain objects in the original image which makes the viewer suffer from difficulties to detect the forged region. In this type of image forgery, it is easy to perform forgery, but more difficult to detect it, because the features on the copied parts are similar to those of other parts of the image. This paper presents a comparison study between different trigonometric transforms in 1D and 2D for detecting the forgery parts in the image. This comparison study is based on the completeness rate and the time of processing for the detection. This comparison concludes that the DFT in 1D or 2D implementation is the best choice to detect copy-move forgery compared to other trigonometric transforms. The proposed algorithm can also be used for active forgery detection because of its robustness to detect the manipulation of digital watermarked images or images with signatures.
图像伪造检测是解决许多问题的基本关键,特别是在Facebook等社会问题和法庭案件中。复制-移动伪造是将图像的一部分复制到同一图像的其他位置,以隐藏重要信息或复制原图像中的某些对象,使观看者难以检测伪造区域的伪造类型。在这种类型的图像伪造中,容易进行伪造,但更难检测,因为被复制部分的特征与图像的其他部分相似。本文对三维和二维三角变换检测图像中伪造部分的方法进行了比较研究。这项比较研究是基于检测的完成率和处理时间。这个比较得出结论,与其他三角变换相比,DFT在1D或2D实现中是检测复制-移动伪造的最佳选择。由于该算法对数字水印图像或带有签名的图像的检测具有鲁棒性,因此也可用于主动伪造检测。
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引用次数: 0
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Menoufia Journal of Electronic Engineering Research
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