Pub Date : 2020-07-01DOI: 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.
{"title":"Towards Efficient FPGA Implementation of Elliptic Curve Crypto-Processor for Security in IoT and Embedded Devices","authors":"Shaimaa Abu Khadra, S. E. S. E. Abdulrahman, N. A. Ismail","doi":"10.21608/mjeer.2020.103280","DOIUrl":"https://doi.org/10.21608/mjeer.2020.103280","url":null,"abstract":"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.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116969603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-18DOI: 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.
{"title":"Machine Learning Model for Cancer Diagnosis based on RNAseq Microarray","authors":"Hanaa Torkey, Mostafa Atlam, N. El-Fishawy, Hanaa Salem","doi":"10.21608/mjeer.2020.20533.1000","DOIUrl":"https://doi.org/10.21608/mjeer.2020.20533.1000","url":null,"abstract":"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.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121574361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 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.
{"title":"Hierarchal Clusters Based Traffic Control System","authors":"Fady Taher, A. El-Sayed, A. Shouman, A. El-Mahalawy","doi":"10.21608/mjeer.2020.68928","DOIUrl":"https://doi.org/10.21608/mjeer.2020.68928","url":null,"abstract":"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.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"58 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120973857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.21608/mjeer.2019.76998
N. Haggag, Ahmed Sedik, Gh. M. ElBanby, A. El-Fishawy, M. Dessouky, A. Khalaf
{"title":"Classification of Corneal Pattern Based on Convolutional LSTM Neural Network","authors":"N. Haggag, Ahmed Sedik, Gh. M. ElBanby, A. El-Fishawy, M. Dessouky, A. Khalaf","doi":"10.21608/mjeer.2019.76998","DOIUrl":"https://doi.org/10.21608/mjeer.2019.76998","url":null,"abstract":"","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127088236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 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.
{"title":"Simulative Study of Wavelength Division Multiplexing Fiber Bragg Grating in Nuclear Reactors Monitoring","authors":"A. Mohamed, A. Rashed, M. Zaky, Ahmed I. Elsaket, M. A. Gaheen","doi":"10.21608/mjeer.2019.62729","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62729","url":null,"abstract":"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.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125025220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 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
{"title":"Quality Assessment of Images Transmitted over Optical Fiber Communications Systems based on Statistical Metrics","authors":"Hanan S. Ghanem, W. El-shafai, El-Sayed M. El-Rabaie, A. Mohamed, A. Rashed, F. E. El-Samie, M. Tabbour","doi":"10.21608/mjeer.2019.62791","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62791","url":null,"abstract":"","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"15 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113938726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 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.
{"title":"Efficient Epileptic Seizure Prediction Approach Based on Hilbert Transform","authors":"Heba M. Emara, Mohamed Elwekeil, T. Taha, A. El-Fishawy, S. El-Rabaie, T. Alotaiby, S. Alshebeili, F. El-Samie","doi":"10.21608/mjeer.2019.62744","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62744","url":null,"abstract":"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.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122406877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 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%。
{"title":"An Efficient Method Of ECG Beats Feature Extraction/Classification With Multiclass SVM Error Correcting Output Codes","authors":"Salma El-Soudy, A. El-Sayed, A. Khalil, Irshad Khalil, T. Taha, F. A. Abd El-Samie","doi":"10.21608/mjeer.2019.62765","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62765","url":null,"abstract":"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.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131828103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 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.
{"title":"Sensitivity of Seizure Pattern Prediction to EEG Signal Compression","authors":"Sally El-Gindy, S. El-Dolil, A. El-Fishawy, El-Sayed M. El-Rabaie, M. Dessouky, F. El-Samie, Turky Elotaiby, Saleh Elshebeily","doi":"10.21608/mjeer.2019.62768","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62768","url":null,"abstract":"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.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124969657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 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.
{"title":"Image Forgery Detection Based on Trigonometric Transforms","authors":"F. Al_azrak, M. Dessouky, F. El-Samie, A. Elkorany, Z. Elsharkawy","doi":"10.21608/mjeer.2019.62776","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62776","url":null,"abstract":"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.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124992933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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