Pub Date : 2019-10-01DOI: 10.1109/ICCKE48569.2019.8964911
Masoumeh Siar, M. Teshnehlab
Estimating the age of the brains of individuals from brain images can be very useful in many applications. The brain’s age has greatly contributed to predicting and preventing early deaths in the medical community. It can also be very useful for diagnosing diseases, such as Alzheimer’s. According to the authors knowledge, this paper is one of the first researches that have been done in age detection by brain images using Deep Learning (DL). In this paper, the convolution neural network (CNN), used for age detection from brain magnetic resonance images (MRI). The images used in this paper are from the imaging centers and collected by the author of the paper. In this paper 1290 images have been collected, 941 images for train data and 349 images for test images. Images collected at the centers were labeled age. In this paper, the Alexnet model is used in CNN architecture. The used architecture of the architecture has 5 Convolutional layers and 3 Sub-sampling layers that the last layer has been used to categorize the image. The CNN that the last layer has been used to categorize the images into five age classes.The accuracy of the CNN is obtained by the Softmax classifier 79%, Support Vector Machine (SVM) classifier 75% and the Decision Tree (DT) classifier, 49%. In addition to the accuracy criterion, we use the benchmarks of Recall, Precision and F1-Score to evaluate network performance.
{"title":"Age Detection from Brain MRI Images Using the Deep Learning","authors":"Masoumeh Siar, M. Teshnehlab","doi":"10.1109/ICCKE48569.2019.8964911","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964911","url":null,"abstract":"Estimating the age of the brains of individuals from brain images can be very useful in many applications. The brain’s age has greatly contributed to predicting and preventing early deaths in the medical community. It can also be very useful for diagnosing diseases, such as Alzheimer’s. According to the authors knowledge, this paper is one of the first researches that have been done in age detection by brain images using Deep Learning (DL). In this paper, the convolution neural network (CNN), used for age detection from brain magnetic resonance images (MRI). The images used in this paper are from the imaging centers and collected by the author of the paper. In this paper 1290 images have been collected, 941 images for train data and 349 images for test images. Images collected at the centers were labeled age. In this paper, the Alexnet model is used in CNN architecture. The used architecture of the architecture has 5 Convolutional layers and 3 Sub-sampling layers that the last layer has been used to categorize the image. The CNN that the last layer has been used to categorize the images into five age classes.The accuracy of the CNN is obtained by the Softmax classifier 79%, Support Vector Machine (SVM) classifier 75% and the Decision Tree (DT) classifier, 49%. In addition to the accuracy criterion, we use the benchmarks of Recall, Precision and F1-Score to evaluate network performance.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"34 1","pages":"369-374"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89204174","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: 10.1109/ICCKE48569.2019.8965163
Mohammad Kamyar Arbab, Mahmoud Naghibzadeh, S. R. Kamel Tabbakh
scientific workflows are suitable models in many applications for recognition and execution of tasks in parallel, especially in the Cloud. Different aspects of resource provisioning and the resource pricing model in the cloud environment cause the scheduling problem of workflow very complex. One of the main aims of the scheduling algorithms is to satisfy the users’ different quality of service requirements. Communication delay between tasks is an important affecting factor in optimal scheduling of workflows. It can also highly increase the cost of workflow scheduling if proper actions are not taken. To solve this problem, we propose a tasks duplication-based list scheduling algorithm called Communication-Critical Task Duplication (CCTD). We first define the concept of communication critical task (CCT) for a workflow. Then, by presenting a ranking-based approach, we identify communication critical tasks in a workflow as well as duplicating candidates. Task duplication in idle time slots of leased virtual machines which their children tasks are mapped to. This idea, while eliminating the cost and time of data transfer between parent-child tasks, reduces the time of execution of tasks and effectively uses leased time intervals of resources. According to the proposed scheduling algorithm, a new heuristic method has been proposed for the budget distribution. This method distribute overall budget to tasks in proportional to the workload and duplication rank of each task. The proposed algorithm was evaluated and verified using four well-known scientific workflows. The simulation results show that the CCTD algorithm, while respecting the user budget constraint, reduces the workflow overall completion time.
{"title":"Communication-Critical Task Duplication for Cloud Workflow Scheduling with Time and Budget Concerns","authors":"Mohammad Kamyar Arbab, Mahmoud Naghibzadeh, S. R. Kamel Tabbakh","doi":"10.1109/ICCKE48569.2019.8965163","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965163","url":null,"abstract":"scientific workflows are suitable models in many applications for recognition and execution of tasks in parallel, especially in the Cloud. Different aspects of resource provisioning and the resource pricing model in the cloud environment cause the scheduling problem of workflow very complex. One of the main aims of the scheduling algorithms is to satisfy the users’ different quality of service requirements. Communication delay between tasks is an important affecting factor in optimal scheduling of workflows. It can also highly increase the cost of workflow scheduling if proper actions are not taken. To solve this problem, we propose a tasks duplication-based list scheduling algorithm called Communication-Critical Task Duplication (CCTD). We first define the concept of communication critical task (CCT) for a workflow. Then, by presenting a ranking-based approach, we identify communication critical tasks in a workflow as well as duplicating candidates. Task duplication in idle time slots of leased virtual machines which their children tasks are mapped to. This idea, while eliminating the cost and time of data transfer between parent-child tasks, reduces the time of execution of tasks and effectively uses leased time intervals of resources. According to the proposed scheduling algorithm, a new heuristic method has been proposed for the budget distribution. This method distribute overall budget to tasks in proportional to the workload and duplication rank of each task. The proposed algorithm was evaluated and verified using four well-known scientific workflows. The simulation results show that the CCTD algorithm, while respecting the user budget constraint, reduces the workflow overall completion time.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"24 1","pages":"255-262"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87529148","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: 10.1109/ICCKE48569.2019.8965051
Amir Hossein Fakhteh, Vahid Sattari-Naeini, H. Naji
Software-Defined Network is a new networking generation to increase network flexibility and performance improvement. However, budget constraints, technical limitations and the risk of architectural changes that organizations are encountered with, are barriers to the full implementation of the SDN. For these reasons, Internet Service Providers (ISPs) should replace only a limited number of conventional switches with SDN switches at one time. Put both switches together in a topology, create hybrid SDN. In this paper, we first cluster the switches into two clusters, based on a graph concept which is called closeness-centrality. Afterward, we have one cluster with low closeness centrality and another with high; then, we propose a heuristic migration scheme for deploying SDN switches in hybrid SDNs. We first allocate all existing budget to a cluster at low closeness-centrality and then we upgrade the switches according to the proposed scheme. There are four main issues in this paper: (1) improving the network control ability with a given upgrading budget constraint, (2) minimizing the upgrading cost to achieve the best network control ability, (3) increasing hybrid SDN flexibility and (4) improving network fault tolerance by deploying a link fault recovery algorithm. Also, in order to perform controller placement, we propose a simple solution to provide an acceptable delay between controller and SDN switches. We evaluate our scheme in three real topologies and one synthetic topology. The results show that our scheme can achieve better network control ability and flexibility with the help of graph theory parameters compared to other related papers according to budget constraint.
{"title":"Increasing The Network Control Ability and Flexibility In Incremental Switch Deployment for Hybrid Software-Defined Networks","authors":"Amir Hossein Fakhteh, Vahid Sattari-Naeini, H. Naji","doi":"10.1109/ICCKE48569.2019.8965051","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965051","url":null,"abstract":"Software-Defined Network is a new networking generation to increase network flexibility and performance improvement. However, budget constraints, technical limitations and the risk of architectural changes that organizations are encountered with, are barriers to the full implementation of the SDN. For these reasons, Internet Service Providers (ISPs) should replace only a limited number of conventional switches with SDN switches at one time. Put both switches together in a topology, create hybrid SDN. In this paper, we first cluster the switches into two clusters, based on a graph concept which is called closeness-centrality. Afterward, we have one cluster with low closeness centrality and another with high; then, we propose a heuristic migration scheme for deploying SDN switches in hybrid SDNs. We first allocate all existing budget to a cluster at low closeness-centrality and then we upgrade the switches according to the proposed scheme. There are four main issues in this paper: (1) improving the network control ability with a given upgrading budget constraint, (2) minimizing the upgrading cost to achieve the best network control ability, (3) increasing hybrid SDN flexibility and (4) improving network fault tolerance by deploying a link fault recovery algorithm. Also, in order to perform controller placement, we propose a simple solution to provide an acceptable delay between controller and SDN switches. We evaluate our scheme in three real topologies and one synthetic topology. The results show that our scheme can achieve better network control ability and flexibility with the help of graph theory parameters compared to other related papers according to budget constraint.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"87 1","pages":"263-268"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83773948","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: 10.1109/iccke48569.2019.8964957
{"title":"[ICCKE 2019 Authors]","authors":"","doi":"10.1109/iccke48569.2019.8964957","DOIUrl":"https://doi.org/10.1109/iccke48569.2019.8964957","url":null,"abstract":"","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82489942","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: 10.1109/ICCKE48569.2019.8964826
Navid Ghassemi, A. Shoeibi, M. Rouhani, Hossein Hosseini-Nejad
In this paper, a new scheme for diagnosis of epileptic seizures in EEG signals using Tunable-Q wavelet transform (TQWT) framework is proposed and benchmarked with Bonn dataset. First, a segmentation of the EEG signals into smaller windows is performed, then a high-pass Butterworth filter with a cutoff frequency of 0.5 Hz is applied to eliminate possible noise. After that, a TQWT with parameters J=8, r=3, Q=1 has been utilized for decomposing the segmented EEG Signals into nine sub-bands. Next, a combination of statistical, entropy-based, and fractal dimension features are extracted from each sub-band. Finally, different ensemble learning-based classifiers, specifically, Adaboost, Gradient Boosting (GB), Hist Gradient Boosting (HistGB), and Random Forest (RF) are employed to classify signals. Also, a feature ranking is driven from classifiers to further analyze the importance of each feature in this particular task. Comparing our method to previous ones, introduced scheme outperforms most of the state-of-the-art works in this field, indicating the effectiveness of the proposed epileptic seizures detection method.
{"title":"Epileptic seizures detection in EEG signals using TQWT and ensemble learning","authors":"Navid Ghassemi, A. Shoeibi, M. Rouhani, Hossein Hosseini-Nejad","doi":"10.1109/ICCKE48569.2019.8964826","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964826","url":null,"abstract":"In this paper, a new scheme for diagnosis of epileptic seizures in EEG signals using Tunable-Q wavelet transform (TQWT) framework is proposed and benchmarked with Bonn dataset. First, a segmentation of the EEG signals into smaller windows is performed, then a high-pass Butterworth filter with a cutoff frequency of 0.5 Hz is applied to eliminate possible noise. After that, a TQWT with parameters J=8, r=3, Q=1 has been utilized for decomposing the segmented EEG Signals into nine sub-bands. Next, a combination of statistical, entropy-based, and fractal dimension features are extracted from each sub-band. Finally, different ensemble learning-based classifiers, specifically, Adaboost, Gradient Boosting (GB), Hist Gradient Boosting (HistGB), and Random Forest (RF) are employed to classify signals. Also, a feature ranking is driven from classifiers to further analyze the importance of each feature in this particular task. Comparing our method to previous ones, introduced scheme outperforms most of the state-of-the-art works in this field, indicating the effectiveness of the proposed epileptic seizures detection method.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"48 1","pages":"403-408"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91352687","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: 10.1109/ICCKE48569.2019.8965093
J. Bahri, Hamid Reza Shayegh Borojeni
Electronic voting systems are one of the most important issues in today's world. At the present time, most systems are traditional and centralized, and the counting of votes is done by a central party, and security depends on cryptographic techniques. Therefore, the need to create decentralized voting systems is increasing day by day, so in this way, preservation and counting of votes are done directly with the help of a consensus algorithm. Having the difficulties mentioned above, this paper presents an unbiased electronic voting model in which the votes from ballot boxes are sent to the candidates, and the candidates should confirm the received votes, after candidates' confirmation, they agree on the received votes by running an DE-PBFT consensus, and then add votes in their DAG data structure . In this way, all candidates are responsible for maintaining, validating and counting votes, and the trust through consensus protocols is provided in a decentralized system. The voter's anonymity has also been preserved through ring signature.
{"title":"Electronic voting through DE-PBFT consensus and DAG data structure","authors":"J. Bahri, Hamid Reza Shayegh Borojeni","doi":"10.1109/ICCKE48569.2019.8965093","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965093","url":null,"abstract":"Electronic voting systems are one of the most important issues in today's world. At the present time, most systems are traditional and centralized, and the counting of votes is done by a central party, and security depends on cryptographic techniques. Therefore, the need to create decentralized voting systems is increasing day by day, so in this way, preservation and counting of votes are done directly with the help of a consensus algorithm. Having the difficulties mentioned above, this paper presents an unbiased electronic voting model in which the votes from ballot boxes are sent to the candidates, and the candidates should confirm the received votes, after candidates' confirmation, they agree on the received votes by running an DE-PBFT consensus, and then add votes in their DAG data structure . In this way, all candidates are responsible for maintaining, validating and counting votes, and the trust through consensus protocols is provided in a decentralized system. The voter's anonymity has also been preserved through ring signature.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"11 1","pages":"391-396"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78084993","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: 10.1109/ICCKE48569.2019.8964824
F. Z. Boroujeni, Simindokht Jahangard, R. Rahmat
Extracting an accurate skeletal representation of coronary arteries is an important step for subsequent analysis of angiography images such as image registration and 3D reconstruction of the arterial tree. This step is usually performed by enhancing vessel-like objects in the image, in order to differentiate between blood vessels and background, followed by applying the thinning algorithm to obtain the final output. Another approach is direct extraction of centerline points using exploratory tracing algorithm preceded by a seed point detection schema to provide a set of reliable starting points for the tracing algorithm. A large number of methods fall in these two approaches and this paper aims to contrast them through a brief review of their innate characteristics, associated limitations and current challenges and issues.
{"title":"A Brief Review on Vessel Extraction and Tracking Methods","authors":"F. Z. Boroujeni, Simindokht Jahangard, R. Rahmat","doi":"10.1109/ICCKE48569.2019.8964824","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964824","url":null,"abstract":"Extracting an accurate skeletal representation of coronary arteries is an important step for subsequent analysis of angiography images such as image registration and 3D reconstruction of the arterial tree. This step is usually performed by enhancing vessel-like objects in the image, in order to differentiate between blood vessels and background, followed by applying the thinning algorithm to obtain the final output. Another approach is direct extraction of centerline points using exploratory tracing algorithm preceded by a seed point detection schema to provide a set of reliable starting points for the tracing algorithm. A large number of methods fall in these two approaches and this paper aims to contrast them through a brief review of their innate characteristics, associated limitations and current challenges and issues.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"8 1","pages":"46-52"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85360074","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: 10.1109/ICCKE48569.2019.8964846
Masoumeh Siar, M. Teshnehlab
Brain tumor can be classified into two types: benign and malignant. Timely and prompt disease detection and treatment plan leads to improved quality of life and increased life expectancy in these patients. One of the most practical and important methods is to use Deep Neural Network (DNN). In this paper, a Convolutional Neural Network (CNN) has been used to detect a tumor through brain Magnetic Resonance Imaging (MRI) images. Images were first applied to the CNN. The accuracy of Softmax Fully Connected layer used to classify images obtained 98.67%. Also, the accuracy of the CNN is obtained with the Radial Basis Function (RBF) classifier 97.34% and the Decision Tree (DT) classifier, is 94.24%. In addition to the accuracy criterion, we use the benchmarks of Sensitivity, Specificity and Precision evaluate network performance. According to the results obtained from the categorizers, the Softmax classifier has the best accuracy in the CNN according to the results obtained from network accuracy on the image testing. This is a new method based on the combination of feature extraction techniques with the CNN for tumor detection from brain images. The method proposed accuracy 99.12% on the test data. Due to the importance of the diagnosis given by the physician, the accuracy of the doctors help in diagnosing the tumor and treating the patient increased.
脑肿瘤可分为良性和恶性两种。及时和迅速的疾病检测和治疗计划可以改善这些患者的生活质量和延长预期寿命。其中最实用和重要的方法之一是使用深度神经网络(DNN)。在本文中，卷积神经网络(CNN)已被用于通过脑磁共振成像(MRI)图像检测肿瘤。图像首先应用于CNN。Softmax full Connected layer用于图像分类的准确率达到了98.67%。采用径向基函数(RBF)分类器和决策树(DT)分类器分别获得了97.34%和94.24%的准确率。除了准确性标准外，我们还使用灵敏度，特异性和精度的基准来评估网络性能。从分类器得到的结果来看，在图像测试上从网络精度得到的结果来看，在CNN中Softmax分类器的准确率是最好的。这是一种基于特征提取技术与CNN相结合的脑图像肿瘤检测新方法。该方法在测试数据上的准确率为99.12%。由于医生诊断的重要性，医生帮助诊断肿瘤和治疗病人的准确性提高了。
{"title":"Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm","authors":"Masoumeh Siar, M. Teshnehlab","doi":"10.1109/ICCKE48569.2019.8964846","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964846","url":null,"abstract":"Brain tumor can be classified into two types: benign and malignant. Timely and prompt disease detection and treatment plan leads to improved quality of life and increased life expectancy in these patients. One of the most practical and important methods is to use Deep Neural Network (DNN). In this paper, a Convolutional Neural Network (CNN) has been used to detect a tumor through brain Magnetic Resonance Imaging (MRI) images. Images were first applied to the CNN. The accuracy of Softmax Fully Connected layer used to classify images obtained 98.67%. Also, the accuracy of the CNN is obtained with the Radial Basis Function (RBF) classifier 97.34% and the Decision Tree (DT) classifier, is 94.24%. In addition to the accuracy criterion, we use the benchmarks of Sensitivity, Specificity and Precision evaluate network performance. According to the results obtained from the categorizers, the Softmax classifier has the best accuracy in the CNN according to the results obtained from network accuracy on the image testing. This is a new method based on the combination of feature extraction techniques with the CNN for tumor detection from brain images. The method proposed accuracy 99.12% on the test data. Due to the importance of the diagnosis given by the physician, the accuracy of the doctors help in diagnosing the tumor and treating the patient increased.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"363-368"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89761090","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: 10.1109/ICCKE48569.2019.8964942
Mohammad Javad Parvardeh, Shahriar Baradaran Shokouhi
Physically Unclonable Functions (PUFs) are new hardware solution for low cost and secure communication primitives such as hardware authentication and secure key generation. PUFs use the random properties of a device to produce unique data. Ring Oscillator PUFs (RO PUF) are one of the most applicable PUFs because of its good reliability and easy implementation on FPGAs. One major problem of Ro PUFs is limited Challenge-Response pairs number. We consider this problem and introduce a new architecture of RO PUF that increases Challenge-Response pairs of a typical PUF with using minimum resources. In our architecture, we use an XOR gate to prevent direct response disclosure and increase our response unpredictability. In this manner, we increase Challenge-Response pairs up to $frac{{nleft( {n - 1} right)}}{2}$. We also show the result of the unity and uniqueness of our proposed PUF by implementing 110 PUFs on five XC3S400 FPGAs.
{"title":"A Ring Oscillator PUF Architecture With Enhanced Challenge-Response Set","authors":"Mohammad Javad Parvardeh, Shahriar Baradaran Shokouhi","doi":"10.1109/ICCKE48569.2019.8964942","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964942","url":null,"abstract":"Physically Unclonable Functions (PUFs) are new hardware solution for low cost and secure communication primitives such as hardware authentication and secure key generation. PUFs use the random properties of a device to produce unique data. Ring Oscillator PUFs (RO PUF) are one of the most applicable PUFs because of its good reliability and easy implementation on FPGAs. One major problem of Ro PUFs is limited Challenge-Response pairs number. We consider this problem and introduce a new architecture of RO PUF that increases Challenge-Response pairs of a typical PUF with using minimum resources. In our architecture, we use an XOR gate to prevent direct response disclosure and increase our response unpredictability. In this manner, we increase Challenge-Response pairs up to $frac{{nleft( {n - 1} right)}}{2}$. We also show the result of the unity and uniqueness of our proposed PUF by implementing 110 PUFs on five XC3S400 FPGAs.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"13 1","pages":"444-449"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88507333","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: 10.1109/ICCKE48569.2019.8964968
Maytham Allahi Roodposhti, M. Valinataj
In this paper, a new carry select adder (CSLA) architecture is proposed with a lower area and a higher speed compared to previous CSLAs. The proposed CSLA is a low-area and high-speed design utilizing a new add-one circuit that is used instead of the second ripple carry adder (RCA) with the input carry equal to one (Cin=1) inside each group of the basic CSLA. Moreover, to expedite the add operation, a new grouping structure is proposed instead of the basic square-root (SQRT) grouping as well as utilizing a faster RCA in each group. Despite the fact that the proposed CSLA has not attained the lowest power consumption among all existing designs based on the PDK 45nm standard cell library, but it has achieved the lowest area and delay compared to previous CSLAs. Implementation results show that 8 to 33% area reduction and 12 to 44% speed improvement are achieved in the proposed CSLA compared to previous designs.
{"title":"A Novel Area-Delay Efficient Carry Select Adder Based on New Add-one Circuit","authors":"Maytham Allahi Roodposhti, M. Valinataj","doi":"10.1109/ICCKE48569.2019.8964968","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964968","url":null,"abstract":"In this paper, a new carry select adder (CSLA) architecture is proposed with a lower area and a higher speed compared to previous CSLAs. The proposed CSLA is a low-area and high-speed design utilizing a new add-one circuit that is used instead of the second ripple carry adder (RCA) with the input carry equal to one (Cin=1) inside each group of the basic CSLA. Moreover, to expedite the add operation, a new grouping structure is proposed instead of the basic square-root (SQRT) grouping as well as utilizing a faster RCA in each group. Despite the fact that the proposed CSLA has not attained the lowest power consumption among all existing designs based on the PDK 45nm standard cell library, but it has achieved the lowest area and delay compared to previous CSLAs. Implementation results show that 8 to 33% area reduction and 12 to 44% speed improvement are achieved in the proposed CSLA compared to previous designs.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"94 1","pages":"225-230"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91348687","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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