2018 8th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献_第5页

DR: Overhead Efficient RLC Crosstalk Avoidance Code DR：超前高效 RLC 避免串扰代码

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566456

Z. Shirmohammadi, H. Sabzi

{"title":"DR: Overhead Efficient RLC Crosstalk Avoidance Code","authors":"Z. Shirmohammadi, H. Sabzi","doi":"10.1109/ICCKE.2018.8566456","DOIUrl":"https://doi.org/10.1109/ICCKE.2018.8566456","url":null,"abstract":"Recently proposed crosstalk avoidance coding mechanisms (CACs) are not able to prevent inductance effects. For solving this problem, an efficient numerical-based CAC mechanism, so called Double Rounded (DR) is proposed in this paper that considers inductance effects. The DR CAC reduces crosstalk faults by deleting bit patterns ‘11111’ and ‘00000’ completely. These patterns are the main sources of crosstalk faults considering inductance effects. The DR coding mechanism increases the reliability of chip channels and offers invariant delay for on-chip channels. The proposed coding mechanism uses a novel numerical system in generating code words that minimizes overheads of codec and is applicable for any arbitrary width of wires. To evaluate DR coding mechanism, VHDL simulations is used. Results of codec power consumption, area occupation, and critical path is calculated. These results show that worst crosstalk-induced transition patterns are completely avoided in wires using DR coding mechanism. Moreover DR coding mechanism provides improvements of 22 % in power consumption, 12.1 % in area occupation and 8% in critical path compared to the-state-of-the-art mechanism.","PeriodicalId":283700,"journal":{"name":"2018 8th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129401089","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: 5

One Dimensional Second Order Derivative Local Binary Pattern for Hand Gestures Classification Using sEMG Signals 基于表面肌电信号的一维二阶导数局部二值模式手势分类

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566385

S. M. Tabatabaei, A. Chalechale

引用次数: 3

Single Sample Face Recognition: Discriminant Scaled Space vs Sparse Representation-Based Classification 单样本人脸识别:判别尺度空间与稀疏表示分类

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566424

R. Serajeh, A. Mousavinia

引用次数: 1

Optimal Camera Placement Using Sine-Cosine Algorithm 利用正弦余弦算法优化摄像机位置

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566344

Ahmad Fatlawi, Abedin Vahedian, Naseer K. Bachache

引用次数: 6

Enhancement of CT Brain Images Classification Based on Deep Learning Network with Adaptive Activation Functions 基于自适应激活函数的深度学习网络增强CT脑图像分类

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566362

Roxana ZahediNasab, H. Mohseni

{"title":"Enhancement of CT Brain Images Classification Based on Deep Learning Network with Adaptive Activation Functions","authors":"Roxana ZahediNasab, H. Mohseni","doi":"10.1109/ICCKE.2018.8566362","DOIUrl":"https://doi.org/10.1109/ICCKE.2018.8566362","url":null,"abstract":"Deep neural networks are one of the most important branches of machine learning that have been recently used in many fields of pattern recognition and machine vision applications successfully. One of the most famous networks in this area is convolutional neural networks which are biologically inspired variants of multi-layer perceptions. In these networks, activation function plays a significant role especially when the data come in different scales. Recently, there is an interest to adaptive activation functions which adapts their parameters to the input data during network training process. Therefore, in this paper, inspired from a successful convolutional neural network tuned for medical image classification, we have investigated the effect of applying adaptive activation functions in a modified convolutional network by combining basic activation functions in linear (mixed) and nonlinear (gated) ways. The effectiveness of using these adaptive functions is shown on a CT brain images dataset (as a complex medical dataset) and the well-known MNIST hand-written digits dataset. The done experiments show that the classification accuracy of the proposed network with adaptive activation functions is higher compared to the ones using basic activation functions.","PeriodicalId":283700,"journal":{"name":"2018 8th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122057934","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: 2

Fast Motif Discovery Using a New Motif Extension Algorithm 基于新Motif扩展算法的快速Motif发现

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566603

Raheleh Mohammadi, Morteza Moradi, Mahmoud Naghibzadeh, Abdorreza Savadi

{"title":"Fast Motif Discovery Using a New Motif Extension Algorithm","authors":"Raheleh Mohammadi, Morteza Moradi, Mahmoud Naghibzadeh, Abdorreza Savadi","doi":"10.1109/ICCKE.2018.8566603","DOIUrl":"https://doi.org/10.1109/ICCKE.2018.8566603","url":null,"abstract":"In biology, proteins are modeled as a long chain of amino acids in primary structure. Generally, each protein is composed of 20 types of amino acids and the number and the arrangement of amino acids vary among different proteins. A sequence motif is a repeated pattern of consecutive amino acids in the primary structure of proteins which can provide information about some important biological features such as transcription factor binding and protein-protein interaction sites. In this paper, we proposed a new motif extension algorithm to enhance the performance of de Bruijn which is one of the recent motif discovery algorithms. The proposed algorithm receives an initial set of candidate motifs and tries to extend them to a desired length using a two-sided approach. In the proposed algorithm, the problem state is limited by a similarity threshold which is given by the user as a constraint. The algorithm for the development of candidate motifs always selects a characters whose appearance are greater than that of the specified similarity threshold. We conducted some experiments on real hardware and real inputs to evaluate our algorithm. The results showed that the proposed algorithm is at least 20 times faster than the original de Bruijn algorithm. Furthermore, the average similarity of identified motifs to the input protein family was 28% higher than the counterpart.","PeriodicalId":283700,"journal":{"name":"2018 8th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133886299","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

Obstacle Detection Using GoogleNet 使用GoogleNet进行障碍物检测

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566315

Pouyan Salavati, H. Mohammadi

引用次数: 18

Presenting a Computing Method for Finding the Central Verse of Quranic Surahs 提出一种寻找古兰经中心经文的计算方法

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566366

Meisam Ahmadi, Ehsan Khadangi, S. P. Shariatpanahi, Mohammad-Hadi Foroughmand-Araabi

{"title":"Presenting a Computing Method for Finding the Central Verse of Quranic Surahs","authors":"Meisam Ahmadi, Ehsan Khadangi, S. P. Shariatpanahi, Mohammad-Hadi Foroughmand-Araabi","doi":"10.1109/ICCKE.2018.8566366","DOIUrl":"https://doi.org/10.1109/ICCKE.2018.8566366","url":null,"abstract":"In recent years, many efforts have been made to present the structure of surahs in Quran and consequently to present interpretative content from these structures. Despite the considerable improvements in this field and spread of novel knowledge from the appearance of Quranic verses, there is still no clear and firm method for finding structure of different surahs and the selection of different structures by scientists is in most cases carried out based on their personal knowledge. The present study deals with the introduction of a computing model to find the central verse of Quranic surahs. In this method, the amount of similarity of Quranic verses is defined through quantification of the similarity between Quranic roots. Based on the results, verses' similarity graph is modeled. Then, a tree structure is gained for the surah by finding the maximum spanning tree of the similarity network. Finally, the central verse is detected using different centrality measures. For studying the benefits and limitations of our method, it is applied to the surah Ya-Seen particularly and the tree structure of this surah's verses is modeled. Based on applying betweenness and closeness to the tree structure, the central verse of this surah is obtained. Accordingly, some interpretational contents are presented with regard to the meaning of the central verse which are innovatively clear and coherent.","PeriodicalId":283700,"journal":{"name":"2018 8th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124637237","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

A Stacked Autoencoders Approach for a P300 Speller BCI P300拼写器BCI的堆叠自编码器方法

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566534

Hamed Ghazikhani, M. Rouhani

{"title":"A Stacked Autoencoders Approach for a P300 Speller BCI","authors":"Hamed Ghazikhani, M. Rouhani","doi":"10.1109/ICCKE.2018.8566534","DOIUrl":"https://doi.org/10.1109/ICCKE.2018.8566534","url":null,"abstract":"This paper addresses a new approach through detecting the P300 and its application to the BCI speller systems. This research employed stacked autoencoders which is based on many autoencoders and a classifier that is regularly a Softmax. This deep structure, decrease the dimension of the data and eventually, the reduced features of the last autoencoder are passed to the Softmax classifier. Subsequently, the parameters of the network would be ameliorated through a fine-tuning phase. Chebyshev Type I, is employed for filtering the EEG signals and using them as an input to the deep neural network. Hyperparameters such as the number of neurons and layers are attained empirically. Therefore, the final structure of the proposed network is 420-210-100-50-20-10-2. To analyze the suggested structure, the second dataset of the third BCI Competition is employed. According to the results, this approach can willingly enhance the character recognition in the BCI speller systems. Thus, the best accuracy percentage according to this research, in an average manner, is 91.5% of both A and B subjects. Consequently, according to the achievements, this method can be comparable to the other state-of-the-art algorithms and, therefore, can improve the recognition rate in the BCI industry.","PeriodicalId":283700,"journal":{"name":"2018 8th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115319201","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

Effects of Pre-Processing on the ECG Signal Sparsity and Compression Quality 预处理对心电信号稀疏度和压缩质量的影响

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2018-10-01 DOI: 10.1109/ICCKE.2018.8566610

Sara Monem Khorasani, G. Hodtani, M. M. Kakhki

引用次数: 1