Pub Date : 2019-10-01DOI: 10.1109/ICCKE48569.2019.8964900
Saeideh Ghanbari Azar, S. Meshgini, T. Y. Rezaii, A. Farzamnia
In this study, the spectral and spatial redundancies of hyperspectral images are used for designing a sparse representation-based classification approach. The spectral redundancy is used to define spectral blocks and they are used to adaptively recognize the distinctive bands. The most distinctive blocks are identified as active blocks in a block sparse representation approach. Then the sparse coefficients within each spatial group are imposed to share a common subspace. To achieve this hierarchical sparsity pattern a sparse coding algorithm is proposed. This sparse coding is done over a block-structured dictionary, which is learned from the image data using the online dictionary learning algorithm. The obtained sparse coefficients are then classified using a support vector machine classifier. This structured sparsity pattern alleviates the instability of the sparse coefficients. Experiments on two standard datasets namely, Indian Pines and Pavia University, verify the effectiveness of the proposed approach for the classification of hyperspectral images.
{"title":"Hyperspectral Image Classification With Online Structured Dictionary Learning","authors":"Saeideh Ghanbari Azar, S. Meshgini, T. Y. Rezaii, A. Farzamnia","doi":"10.1109/ICCKE48569.2019.8964900","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964900","url":null,"abstract":"In this study, the spectral and spatial redundancies of hyperspectral images are used for designing a sparse representation-based classification approach. The spectral redundancy is used to define spectral blocks and they are used to adaptively recognize the distinctive bands. The most distinctive blocks are identified as active blocks in a block sparse representation approach. Then the sparse coefficients within each spatial group are imposed to share a common subspace. To achieve this hierarchical sparsity pattern a sparse coding algorithm is proposed. This sparse coding is done over a block-structured dictionary, which is learned from the image data using the online dictionary learning algorithm. The obtained sparse coefficients are then classified using a support vector machine classifier. This structured sparsity pattern alleviates the instability of the sparse coefficients. Experiments on two standard datasets namely, Indian Pines and Pavia University, verify the effectiveness of the proposed approach for the classification of hyperspectral images.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"19 1","pages":"276-281"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81580712","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/ICCKE48569.2019.8964980
A. Rezaei, Leili Farzinvash
This paper investigates online QoS multicast routing in multi-channel multi-radio (MC-MR) wireless mesh networks (WMNs). In the proposed scheme, we assume that multicast sessions arrive dynamically, and each session has bandwidth and delay requirements. We investigate the acceptance of an arrived session in two steps. The first step devotes to establishing some paths from the source node to the receivers, where the selected paths satisfy delay constraint. In the next step, the multicast data is transmitted over the determined paths. In the proposed algorithm, multicast routing is performed using network coding to exploit its capacity boosting. The wireless broadcast advantage (WBA) is also exploited to diminish the amount of utilized bandwidth. Our simulation results confirm that the proposed algorithm improves the multicast acceptance rate compared to existing approaches.
{"title":"Online QoS Multicast Routing in Multi-Channel Multi-Radio Wireless Mesh Networks using Network Coding","authors":"A. Rezaei, Leili Farzinvash","doi":"10.1109/ICCKE48569.2019.8964980","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964980","url":null,"abstract":"This paper investigates online QoS multicast routing in multi-channel multi-radio (MC-MR) wireless mesh networks (WMNs). In the proposed scheme, we assume that multicast sessions arrive dynamically, and each session has bandwidth and delay requirements. We investigate the acceptance of an arrived session in two steps. The first step devotes to establishing some paths from the source node to the receivers, where the selected paths satisfy delay constraint. In the next step, the multicast data is transmitted over the determined paths. In the proposed algorithm, multicast routing is performed using network coding to exploit its capacity boosting. The wireless broadcast advantage (WBA) is also exploited to diminish the amount of utilized bandwidth. Our simulation results confirm that the proposed algorithm improves the multicast acceptance rate compared to existing approaches.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"226 1","pages":"53-59"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72826661","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/ICCKE48569.2019.8964794
A. Arjmand, S. Meshgini, R. Afrouzian, A. Farzamnia
Today, there are various methods for detecting tumors in breasts. But researchers are still trying to find an exact automatic way to segment the tumors from breast images. In this paper we propose a clustering-based algorithm for automatic tumor segmentation in the MRI samples. In the proposed method, we use k-means clustering algorithm for segmentation and also we use cuckoo search optimization (CSO) algorithm to initialize centroids in the k-means algorithm. We have used RIDER breast dataset to evaluate the proposed method and results clearly show that our algorithm outperforms similar methods such as simple k-means clustering algorithm and Fuzzy C-Means (FCM).
{"title":"Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization","authors":"A. Arjmand, S. Meshgini, R. Afrouzian, A. Farzamnia","doi":"10.1109/ICCKE48569.2019.8964794","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964794","url":null,"abstract":"Today, there are various methods for detecting tumors in breasts. But researchers are still trying to find an exact automatic way to segment the tumors from breast images. In this paper we propose a clustering-based algorithm for automatic tumor segmentation in the MRI samples. In the proposed method, we use k-means clustering algorithm for segmentation and also we use cuckoo search optimization (CSO) algorithm to initialize centroids in the k-means algorithm. We have used RIDER breast dataset to evaluate the proposed method and results clearly show that our algorithm outperforms similar methods such as simple k-means clustering algorithm and Fuzzy C-Means (FCM).","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"35 1","pages":"305-308"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76551955","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/ICCKE48569.2019.8965128
Niloofar Rahimizadeh, Reza P. R. Hasanzadeh, M. Ghahramani, F. Janabi-Sharifi
In this paper, a new weight function based on neutrosophic logic is presented for improving the performance of non-local means (NLM) filter to deal with speckle noise in ultrasound (US) images. In neutrosophic domain, each pixel is characterized by three components including truth membership T, indeterminacy membership I and falsity membership F. In our proposed method, according to the nature of noise in US images, modified functions are introduced for obtaining neutrosophic components. Then, we apply these components for measuring the similarity between pixels and designing a proper weight function to improve despeckling performance of NLM filter. The evaluations on synthetic and real US data show superiority of our proposed method compared to other state-of-the-art techniques.
{"title":"A Neutrosophic based Non-Local Means Filter for Despeckling of Medical Ultrasound Images","authors":"Niloofar Rahimizadeh, Reza P. R. Hasanzadeh, M. Ghahramani, F. Janabi-Sharifi","doi":"10.1109/ICCKE48569.2019.8965128","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965128","url":null,"abstract":"In this paper, a new weight function based on neutrosophic logic is presented for improving the performance of non-local means (NLM) filter to deal with speckle noise in ultrasound (US) images. In neutrosophic domain, each pixel is characterized by three components including truth membership T, indeterminacy membership I and falsity membership F. In our proposed method, according to the nature of noise in US images, modified functions are introduced for obtaining neutrosophic components. Then, we apply these components for measuring the similarity between pixels and designing a proper weight function to improve despeckling performance of NLM filter. The evaluations on synthetic and real US data show superiority of our proposed method compared to other state-of-the-art techniques.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"4 1","pages":"249-254"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79199701","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/ICCKE48569.2019.8964748
Sara Naseri-Golestani, Hamed Rafei, M. Akbarzadeh-T., A. Akbarzadeh, Amirmohammad Naddafshargh, Sadra Naddaf-sh
Magnetic sensors often experience faults such as no-response, noisy signal, and saturation. Yet, they have considerable object localization applications that require high precision, such as in medical operations. Conventionally, Dipole Magnetic (DM) position tracking is used for magnetic localization, even while a sensory fault occurs. But DM position tracking is not sufficiently accurate, and its computational cost is a matter of concern. Accordingly, the proposed approach here is in three folds. First, we propose to use a heuristic to detect faulty sensors and to stop the propagation of faulty reading by setting their readings to zero. Second is using a nonlinear modeling platform, Recurrent Neural Network (RNN) for the actual nonlinear mapping of the magnet sensory readings and placement due to its’ accurate outputs. And third is to prepare a sufficiently rich data set for training the network that is prepared under no sensory fault. The experimental study here confirms that the faulty sensory reading is successfully identified and set to zero by the proposed heuristic, and the nonlinear mapping of the neural network provides a good assessment of magnet localization even when the corresponding inputs from faulty sensors are set to zero. The experimental setup here consists of a network of eight magnetic sensors, one of which becomes faulty during the experimentation process. More specifically, results show that the accuracy of our method has improved up to 444.3% to DM method and its robustness enhanced to 105.3% to an RNN which is trained without our rich data set.
{"title":"Robust Real-time Magnetic-based Object Localization to Sensor’s Fault using Recurrent Neural Networks","authors":"Sara Naseri-Golestani, Hamed Rafei, M. Akbarzadeh-T., A. Akbarzadeh, Amirmohammad Naddafshargh, Sadra Naddaf-sh","doi":"10.1109/ICCKE48569.2019.8964748","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964748","url":null,"abstract":"Magnetic sensors often experience faults such as no-response, noisy signal, and saturation. Yet, they have considerable object localization applications that require high precision, such as in medical operations. Conventionally, Dipole Magnetic (DM) position tracking is used for magnetic localization, even while a sensory fault occurs. But DM position tracking is not sufficiently accurate, and its computational cost is a matter of concern. Accordingly, the proposed approach here is in three folds. First, we propose to use a heuristic to detect faulty sensors and to stop the propagation of faulty reading by setting their readings to zero. Second is using a nonlinear modeling platform, Recurrent Neural Network (RNN) for the actual nonlinear mapping of the magnet sensory readings and placement due to its’ accurate outputs. And third is to prepare a sufficiently rich data set for training the network that is prepared under no sensory fault. The experimental study here confirms that the faulty sensory reading is successfully identified and set to zero by the proposed heuristic, and the nonlinear mapping of the neural network provides a good assessment of magnet localization even when the corresponding inputs from faulty sensors are set to zero. The experimental setup here consists of a network of eight magnetic sensors, one of which becomes faulty during the experimentation process. More specifically, results show that the accuracy of our method has improved up to 444.3% to DM method and its robustness enhanced to 105.3% to an RNN which is trained without our rich data set.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"2015 1","pages":"426-430"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73302147","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/ICCKE48569.2019.8964809
Fatemeh Arabnezhad, B. Nasersharif
Detection and Classification of Acoustic scene is a subtask of DCASE 2017 challenge which is trying to classify noisy structured sounds to predefinedclasses. This is a challenging task due to the content of audio signals and the lack of enough data. Thus most of the recent works used different classifier ensemble methods for acoustic scene classification. In this paper, we use Harmonic-Percussive Source Separation (HPSS) to decompose audio spectrogram to its constructing components and then use its harmonic component. After that, we propose to use different audio forms based on a binaural representation of sound recordings. We also use multilayer perceptron (MLP) neural networks as our classifier and propose two weighing techniques for classifier combination: inverse of entropy at softmax layer output and binary weights for the classifiers. The proposed methods outperform the baseline system of DCASE 2017. The entropy based weighing and binary weighing methods achieved 70.55% and 72.09% accuracy on evaluation dataset of DCASE 2017 challenge in comparison to 61% accuracy of DCASE 2017 baseline system.
{"title":"Acoustic Scene Classification using Binaural Representation and Classifier Combination","authors":"Fatemeh Arabnezhad, B. Nasersharif","doi":"10.1109/ICCKE48569.2019.8964809","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964809","url":null,"abstract":"Detection and Classification of Acoustic scene is a subtask of DCASE 2017 challenge which is trying to classify noisy structured sounds to predefinedclasses. This is a challenging task due to the content of audio signals and the lack of enough data. Thus most of the recent works used different classifier ensemble methods for acoustic scene classification. In this paper, we use Harmonic-Percussive Source Separation (HPSS) to decompose audio spectrogram to its constructing components and then use its harmonic component. After that, we propose to use different audio forms based on a binaural representation of sound recordings. We also use multilayer perceptron (MLP) neural networks as our classifier and propose two weighing techniques for classifier combination: inverse of entropy at softmax layer output and binary weights for the classifiers. The proposed methods outperform the baseline system of DCASE 2017. The entropy based weighing and binary weighing methods achieved 70.55% and 72.09% accuracy on evaluation dataset of DCASE 2017 challenge in comparison to 61% accuracy of DCASE 2017 baseline system.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"35 1","pages":"351-355"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82050451","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/ICCKE48569.2019.8965120
Mohamad Sadegh Monfared, Hamid Noori, M. Abazari
IoT technology is growing very fast and one of the requirements of this technology is integrating different communications protocols and networks. In an IoT network, security of such a heterogeneous and large network is very important. Transport systems are part of this super network and in-vehicle protocols are used in such systems. Unfortunately, the Controller Area Network (CAN) protocol, the most popular protocol in the systems, designed without security in mind. In this paper, the Advanced Encryption Standard (AES), an encryption algorithm, is used to prevent masquerade and replay attacks in order to secure CAN protocol to an appropriate level. The paper has a plan to explore for an efficient implementation of AES encryption algorithm for the communication protocol. These implementations have been evaluated on an FPGA ML605 development board. The best implementation of the AES among 8-, 16-, 32- and 64-bit data paths has been investigated. The most important criteria for the protocol in these AES designs are such as consumed power, area, and cost in addition to providing better throughput. The 64-bit structure of the designed AES is selected which has the frequency of 21.4 MHz, significant throughput of 412.39 Mbps, reasonable area of 784 slice on Spartan III FPGA.
物联网技术发展非常迅速，该技术的要求之一是集成不同的通信协议和网络。在物联网网络中，这样一个异构的大型网络的安全性是非常重要的。传输系统是这个超级网络的一部分，车载协议在这些系统中使用。不幸的是，控制器区域网络(CAN)协议是系统中最流行的协议，在设计时没有考虑到安全性。本文采用高级加密标准AES (Advanced Encryption Standard)加密算法来防止伪装攻击和重放攻击，从而使CAN协议达到适当的安全级别。本文计划探索一种有效实现AES加密算法的通信协议。这些实现已经在FPGA ML605开发板上进行了评估。研究了8位、16位、32位和64位数据路径下AES的最佳实现。在这些AES设计中，最重要的协议标准除了提供更好的吞吐量外，还包括功耗、面积和成本。所设计的AES选择64位结构，频率为21.4 MHz，显著吞吐量为412.39 Mbps，在Spartan III FPGA上合理面积为784片。
{"title":"Design Space Exploration of the AES Encryption Algorithm Implementation for Securing CAN Protocol","authors":"Mohamad Sadegh Monfared, Hamid Noori, M. Abazari","doi":"10.1109/ICCKE48569.2019.8965120","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965120","url":null,"abstract":"IoT technology is growing very fast and one of the requirements of this technology is integrating different communications protocols and networks. In an IoT network, security of such a heterogeneous and large network is very important. Transport systems are part of this super network and in-vehicle protocols are used in such systems. Unfortunately, the Controller Area Network (CAN) protocol, the most popular protocol in the systems, designed without security in mind. In this paper, the Advanced Encryption Standard (AES), an encryption algorithm, is used to prevent masquerade and replay attacks in order to secure CAN protocol to an appropriate level. The paper has a plan to explore for an efficient implementation of AES encryption algorithm for the communication protocol. These implementations have been evaluated on an FPGA ML605 development board. The best implementation of the AES among 8-, 16-, 32- and 64-bit data paths has been investigated. The most important criteria for the protocol in these AES designs are such as consumed power, area, and cost in addition to providing better throughput. The 64-bit structure of the designed AES is selected which has the frequency of 21.4 MHz, significant throughput of 412.39 Mbps, reasonable area of 784 slice on Spartan III FPGA.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"46 1","pages":"380-385"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84135906","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/ICCKE48569.2019.8964733
Zahra Habibinejad, M. Rasti
In this paper, we investigate the problems of joint sub-channel allocation and power control in Orthogonal Frequency Division Multiple Access (OFDMA) based on two-tier networks. First, we propose the power control problem to minimize the transmission power of macrocell in half-duplex (HD) mode. Then we investigate the joint sub-channel allocation and power control problem to maximize the total throughput of femtocells in full-duplex (FD) mode, subject to constraint of quality-of-service of delay-sensitive users (DS) and constraint of co-tier, cross-tier and other interferences caused by full-duplex transmissions. Also, femtocells are able to switch between HD and FD modes. Finally, to solve these problems, we propose suboptimal and distributed algorithms for macrocell and femtocells. The simulation results demonstrate that the proposed algorithms improve the total throughput compared to existing algorithms.
{"title":"Joint Subchannel Allocation and Power Control in OFDMA Femtocell Networks","authors":"Zahra Habibinejad, M. Rasti","doi":"10.1109/ICCKE48569.2019.8964733","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964733","url":null,"abstract":"In this paper, we investigate the problems of joint sub-channel allocation and power control in Orthogonal Frequency Division Multiple Access (OFDMA) based on two-tier networks. First, we propose the power control problem to minimize the transmission power of macrocell in half-duplex (HD) mode. Then we investigate the joint sub-channel allocation and power control problem to maximize the total throughput of femtocells in full-duplex (FD) mode, subject to constraint of quality-of-service of delay-sensitive users (DS) and constraint of co-tier, cross-tier and other interferences caused by full-duplex transmissions. Also, femtocells are able to switch between HD and FD modes. Finally, to solve these problems, we propose suboptimal and distributed algorithms for macrocell and femtocells. The simulation results demonstrate that the proposed algorithms improve the total throughput compared to existing algorithms.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"88 1","pages":"219-224"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86512686","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/ICCKE48569.2019.8965173
A. H. Hadian-Rasanan, D. Rahmati, S. Gorgin, J. Rad
Edge reliability problem has many applications in different field of science and engineering such as: cognitive science, neuroscience, electrical engineering, network science and so on. The major challenge in this problem is time complexity of the exact algorithm. Computing the reliability of a network is NP-hard problem. So, computing the reliability of a large scale network is a challenging problem. In this paper, we present a novel algorithm based on a hybrid Monte-Carlo, interpolation and least-square methods to approximate the reliability of a network. The presented algorithm is applied on some networks that the exact reliability polynomial is available for them. the experiments show that the presented algorithm is accurate and robust.
{"title":"MCILS: Monte-Carlo Interpolation Least-Square Algorithm for Approximation of Edge-Reliability Polynomial","authors":"A. H. Hadian-Rasanan, D. Rahmati, S. Gorgin, J. Rad","doi":"10.1109/ICCKE48569.2019.8965173","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965173","url":null,"abstract":"Edge reliability problem has many applications in different field of science and engineering such as: cognitive science, neuroscience, electrical engineering, network science and so on. The major challenge in this problem is time complexity of the exact algorithm. Computing the reliability of a network is NP-hard problem. So, computing the reliability of a large scale network is a challenging problem. In this paper, we present a novel algorithm based on a hybrid Monte-Carlo, interpolation and least-square methods to approximate the reliability of a network. The presented algorithm is applied on some networks that the exact reliability polynomial is available for them. the experiments show that the presented algorithm is accurate and robust.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"27 1","pages":"295-299"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85828841","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/ICCKE48569.2019.8965159
Moein Radman, Ali Chaibakhsh, N. Nariman-zadeh, Huiguang He
Most of the BCI systems need EEG data with several channels to reach good accuracy. However, exceedingly increasing the channel need will increase the amount of calculation, and in some cases, decrease the accuracy and will also make the implementation of a BCI system difficult. Therefore, identifying the most effective channels in BCI systems is crucial because it will decrease the complexity and increase system accuracy. The Generalized Sequential forward selection (GSFS) method is used in this paper to choose the channel in a motor imagery BCI system for classification of right and left hand. Firstly, data is filtered to be in the frequency range of 4-30 Hz because the results of previous research revealed that the highest effect of motor imagery is exerted inside this frequency range. The Common Spatial Pattern (CSP) features and frequency domain features are simultaneously used in order to improve the system performance. Moreover, a PCVM classifier is used to enhance the classification performance. Employing the GSFS method and also simultaneously extracting the CSP and frequency domain features have increased the system output accuracy. The computation cost of this method is low compared to that of the genetic algorithm method for channel selection. The classification precision in the method used in this research is higher with respect to that of the SVM-RFE method which shows the advantage of this method over other methods for channel selection in an MI-BCI system.
{"title":"Generalized Sequential Forward Selection Method for Channel Selection in EEG Signals for Classification of Left or Right Hand Movement in BCI","authors":"Moein Radman, Ali Chaibakhsh, N. Nariman-zadeh, Huiguang He","doi":"10.1109/ICCKE48569.2019.8965159","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965159","url":null,"abstract":"Most of the BCI systems need EEG data with several channels to reach good accuracy. However, exceedingly increasing the channel need will increase the amount of calculation, and in some cases, decrease the accuracy and will also make the implementation of a BCI system difficult. Therefore, identifying the most effective channels in BCI systems is crucial because it will decrease the complexity and increase system accuracy. The Generalized Sequential forward selection (GSFS) method is used in this paper to choose the channel in a motor imagery BCI system for classification of right and left hand. Firstly, data is filtered to be in the frequency range of 4-30 Hz because the results of previous research revealed that the highest effect of motor imagery is exerted inside this frequency range. The Common Spatial Pattern (CSP) features and frequency domain features are simultaneously used in order to improve the system performance. Moreover, a PCVM classifier is used to enhance the classification performance. Employing the GSFS method and also simultaneously extracting the CSP and frequency domain features have increased the system output accuracy. The computation cost of this method is low compared to that of the genetic algorithm method for channel selection. The classification precision in the method used in this research is higher with respect to that of the SVM-RFE method which shows the advantage of this method over other methods for channel selection in an MI-BCI system.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"77 1","pages":"137-142"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76618920","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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