2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献_第5页

Hyperspectral Image Classification With Online Structured Dictionary Learning 基于在线结构化字典学习的高光谱图像分类

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964900

Saeideh Ghanbari Azar, S. Meshgini, T. Y. Rezaii, A. Farzamnia

引用次数: 0

Online QoS Multicast Routing in Multi-Channel Multi-Radio Wireless Mesh Networks using Network Coding 基于网络编码的多通道多无线电无线网状网络在线QoS组播路由

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964980

A. Rezaei, Leili Farzinvash

引用次数: 2

Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization 基于k均值聚类和布谷鸟搜索优化的乳腺肿瘤分割

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964794

A. Arjmand, S. Meshgini, R. Afrouzian, A. Farzamnia

引用次数: 14

A Neutrosophic based Non-Local Means Filter for Despeckling of Medical Ultrasound Images 基于中性粒细胞的医学超声图像去斑非局部均值滤波器

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8965128

Niloofar Rahimizadeh, Reza P. R. Hasanzadeh, M. Ghahramani, F. Janabi-Sharifi

引用次数: 4

Robust Real-time Magnetic-based Object Localization to Sensor’s Fault using Recurrent Neural Networks 基于递归神经网络的传感器故障鲁棒实时磁目标定位

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964748

Sara Naseri-Golestani, Hamed Rafei, M. Akbarzadeh-T., A. Akbarzadeh, Amirmohammad Naddafshargh, Sadra Naddaf-sh

{"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}

引用次数: 1

Acoustic Scene Classification using Binaural Representation and Classifier Combination 基于双耳表示和分类器组合的声学场景分类

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964809

Fatemeh Arabnezhad, B. Nasersharif

{"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}

引用次数: 1

Design Space Exploration of the AES Encryption Algorithm Implementation for Securing CAN Protocol CAN协议安全AES加密算法实现的设计空间探索

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8965120

Mohamad Sadegh Monfared, Hamid Noori, M. Abazari

{"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}

引用次数: 0

Joint Subchannel Allocation and Power Control in OFDMA Femtocell Networks OFDMA飞蜂窝网络中的联合子信道分配与功率控制

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964733

Zahra Habibinejad, M. Rasti

引用次数: 0

MCILS: Monte-Carlo Interpolation Least-Square Algorithm for Approximation of Edge-Reliability Polynomial 边缘可靠性多项式逼近的蒙特卡罗插值最小二乘算法

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8965173

A. H. Hadian-Rasanan, D. Rahmati, S. Gorgin, J. Rad

引用次数: 0

Generalized Sequential Forward Selection Method for Channel Selection in EEG Signals for Classification of Left or Right Hand Movement in BCI 脑机接口左、右手运动分类中脑电信号通道选择的广义顺序正向选择方法

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8965159

Moein Radman, Ali Chaibakhsh, N. Nariman-zadeh, Huiguang He

{"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}

引用次数: 10