2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE)最新文献_第2页

An evolvable fuzzy logic system for handoff managementin heterogeneous wireless networks 异构无线网络切换管理的可进化模糊逻辑系统

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395359

H. Fayyazi, M. Sabokrou

引用次数: 8

Semantic role based sentence compression 基于语义角色的句子压缩

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395380

F. Pourgholamali, M. Kahani

引用次数: 3

Lying human activity recognition based on shape characteristics 基于形状特征的说谎人体活动识别

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395378

A. Rad, N. M. Charkari, J. Nasiri, H. R. S. Broojeni

引用次数: 4

Data conflict resolution among same entities in Web of Data Web of Data中相同实体之间的数据冲突解决

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395392

Mojgan Askarizade, M. Nematbakhsh, Ensieh Davoodi Jam

引用次数: 3

A multi-criteria hybrid citation recommendation system based on linked data 基于关联数据的多标准混合引文推荐系统

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395393

F. Zarrinkalam, M. Kahani

引用次数: 31

Optimum routing and scheduling for estimation in wireless sensor networks 无线传感器网络中最优路由和调度估计

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395386

A. Mohajerzadeh, V. Fakoor, M. Yaghmaee, E. Mirfarah

引用次数: 4

An extension to IEEE 1609.4 for dynamic multichannel interval adjustment in WAVE architecture 对IEEE 1609.4的扩展，用于在WAVE架构中动态调整多通道间隔

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395391

A. Akbarifar, J. Bagherzadeh, S. Yousefi

引用次数: 6

A new Fourier shape descriptor using smallest rectangle distance 利用最小矩形距离的傅里叶形状描述子

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395349

S. Eini, A. Chalechale, E. Akbari

引用次数: 3

Gait recognition using dynamic texture descriptors 基于动态纹理描述符的步态识别

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395343

B. Abdolahi, N. Gheissari

{"title":"Gait recognition using dynamic texture descriptors","authors":"B. Abdolahi, N. Gheissari","doi":"10.1109/ICCKE.2012.6395343","DOIUrl":"https://doi.org/10.1109/ICCKE.2012.6395343","url":null,"abstract":"The human motion analysis is an attractive topic in biometric research. Common biometrics is usually time-consuming, limited and collaborative. These drawbacks pose major challenges to recognition process. Recent researches indicate people have considerable ability to recognize others by their natural walking. Therefore, gait recognition has obtained great tendency in biometric systems. Gait analysis is inconspicuous, needs no contact, cannot be hidden and is evaluated at distance. This paper presents a bag of word method for gait recognition based on dynamic textures. Dynamic textures combine appearance and motion information. Since human walking has statistical variations in both spatial and temporal space, it can be described with dynamic texture features. To obtain these features, we extract spatiotemporal interest points and describe them by a dynamic texture descriptor. To get more suitable results, we extend LBP-TOP as a rotation invariant dynamic texture descriptor. Afterwards, hierarchical K-means algorithm is employed to map features into visual words. At result, human walking represent as a histogram of video-words occurrences. We evaluate the performance of our method on two dataset: the KTH dataset and IXMAS multiview dataset.","PeriodicalId":154379,"journal":{"name":"2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE)","volume":"35 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121156113","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: 3

Automatic signal segmentation based on singular spectrum analysis and imperialist competitive algorithm 基于奇异频谱分析和帝国竞争算法的信号自动分割

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-12-31 DOI: 10.1109/ICCKE.2012.6395351

H. Azami, S. Sanei

{"title":"Automatic signal segmentation based on singular spectrum analysis and imperialist competitive algorithm","authors":"H. Azami, S. Sanei","doi":"10.1109/ICCKE.2012.6395351","DOIUrl":"https://doi.org/10.1109/ICCKE.2012.6395351","url":null,"abstract":"Electroencephalogram (EEG) is generally known as a non-stationary signal. Dividing a signal into the epochs within which the signals can be considered stationary, segmentation, is very important in many signal processing applications. Noise often influences the performance of an automatic signal segmentation system. In this article, a new approach for segmentation of the EEG signals based on singular spectrum analysis (SSA) and imperialist competitive algorithm (ICA) is proposed. As the first step, SSA is employed to reduce the effect of various noise sources. Then, fractal dimension (FD) of the signal is estimated and used as a feature extraction for automatic segmentation of the EEG. In order to select two acceptable parameters related to the FD, ICA that is a more powerful evolutionary algorithm than traditional ones is applied. By using synthetic and real EEG signals, the proposed method is compared with original approach (i.e. without using SSA and ICA). The simulation results show that the speed of SSA is much better than that of the discrete wavelet transform (DWT) which has been one of the most popular preprocessing filters for signal segmentation. Also, the simulation results indicate the performance superiority of the proposed method.","PeriodicalId":154379,"journal":{"name":"2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114737417","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: 14