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2007 IEEE 15th Signal Processing and Communications Applications最新文献

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Cooperative Balanced Space-Time Block Codes for Relay Networks 中继网络协同均衡空时分组码
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298836
A. Eksim, M. Çelebi
{"title":"Cooperative Balanced Space-Time Block Codes for Relay Networks","authors":"A. Eksim, M. Çelebi","doi":"10.1109/SIU.2007.4298836","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298836","url":null,"abstract":"In this paper, a novel coding method for cooperative relay networks is proposed, which guarantees full diversity for any number of mobile relays with minimal delay, provided that few bits of feedback from the destination to the source and relays are available. As in the case of orthogonal space-time block codes, all transmitted symbols are separately decoded both in the relays and the destination. So, decoding complexity grows linearly. Many of the proposed solutions in the literature are distributed space-time codes which are designed limited number of relays. The proposed scheme has better signal-to-noise ratio improvement with respect to relay selection scheme for N relays present in the wireless system. Moreover, it requires minimal amount of delay. Lastly, the new method satisfies power consumption fairness among relay terminals and requires no knowledge of topology information.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126322918","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
Musical Genre Classification Using Higher-Order Statistics 使用高阶统计的音乐类型分类
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298681
N. Avcu, D. Kuntalp, v.A. Alpkocak
{"title":"Musical Genre Classification Using Higher-Order Statistics","authors":"N. Avcu, D. Kuntalp, v.A. Alpkocak","doi":"10.1109/SIU.2007.4298681","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298681","url":null,"abstract":"In this study, we examine the effects of higher order statistics of timbral features to improve performance of genre classification. It was seen that the first and second order statistics of the features extracted, in this research, is not as discriminative as the third and forth order statistics of the features. For the purpose of designing a classifier, which could be used for real time applications in future studies, randomly taken 3 second-long segments are used for classification. Out of 225 songs from 3 genres, ISO of them are used for training and 45 of them are used for testing. Five different lists that are created using different train and test sets are used to reduce the dependency of the results to the test set while increasing the number of validation data. Average values of validation test results are compared with the results of the similar works, which are based on MIDI format, using the same data set.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127675490","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
Endpoint Detection of Isolated Words Using Center of Gravity Method 用重心法检测孤立词的端点
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298665
M. B. Gulmezoglu, R. Edizkan, S. Ergin, A. Barkana
{"title":"Endpoint Detection of Isolated Words Using Center of Gravity Method","authors":"M. B. Gulmezoglu, R. Edizkan, S. Ergin, A. Barkana","doi":"10.1109/SIU.2007.4298665","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298665","url":null,"abstract":"In this study, center of gravity (COG) method is proposed to detect endpoints of isolated words. Common vector approach (CVA) is employed to evaluate the effect of the proposed method in the isolated word recognition. Since the CVA method is sensitive to shifts through the time axis, endpoint detection of words is extremely important. For the comparison purpose, one of the well-known endpoint detection methods is also used together with CVA. The recognition rates obtained by using COG and CVA methods for Tl-digit database are greater than those obtained by using other endpoint detection and CVA methods.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126733307","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
HMM Based Method for Dynamic Texture Detection 基于HMM的动态纹理检测方法
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298714
B. U. Toreyin, A. Cetin
{"title":"HMM Based Method for Dynamic Texture Detection","authors":"B. U. Toreyin, A. Cetin","doi":"10.1109/SIU.2007.4298714","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298714","url":null,"abstract":"A method for detection of dynamic textures in video is proposed. It is observed that the motion vectors of most of the dynamic textures (e.g. sea waves, swaying tree leaves and branches in the wind, etc.) exhibit random motion. On the other hand, regular motion of ordinary video objects has well-defined directions. In this paper, motion vectors of moving objects are estimated and tracked based on a minimum distance based metric. The direction of the motion vectors are then quantized to define two three-state Markov models corresponding to dynamic textures and ordinary moving objects with consistent directions. Hidden Markov models (HMMs) are used to classify the moving objects in the final step of the algorithm.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114177821","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
Impact of Pitch Frequency on Speaker Identification 基音频率对说话人识别的影响
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298591
O. Eskidere, F. Ertas
{"title":"Impact of Pitch Frequency on Speaker Identification","authors":"O. Eskidere, F. Ertas","doi":"10.1109/SIU.2007.4298591","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298591","url":null,"abstract":"In this paper, the impact of pitch frequency on speaker identification using Gaussian mixture model has been investigated employing clean speech (TIMIT) and telephone speech (NTIMIT) databases. Pitch frequency, as directly related to human vocal tract, may also be used as a speaker discriminating feature in noisy environments, such as telephone lines. Although the performance of pitch frequency alone is poor on telephone speech, it provides %8.34 enhancement in identification performance when used in combination with mel-frequency cepstral coefficients (mfee).","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122711262","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
Detection of Partially Occluded Upper Body Pose Using Hidden Markov Model 基于隐马尔可夫模型的部分遮挡上半身姿态检测
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298743
N. Adar, Nazmi Alper Kale, S. Canbek, E. Seke
{"title":"Detection of Partially Occluded Upper Body Pose Using Hidden Markov Model","authors":"N. Adar, Nazmi Alper Kale, S. Canbek, E. Seke","doi":"10.1109/SIU.2007.4298743","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298743","url":null,"abstract":"This paper presents a method that assembles detected human body parts into a upper body human configuration. In the proposed method, six body parts (face, torso, legs and hands) are found by dedicated detectors using support vector machines (SVMs). Next, body configurations are assembled from the detected parts using hidden Markov model (HMM). Utilizing three different HMM with a decision mechanism, partially occluded human upper body poses are successfully assembled. The detection method shows promising results when tested on images from MIT pedestrian database and additional human body pictures.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126846158","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
Silhouette Based Gait Recognition 基于轮廓的步态识别
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298633
E. Gedi̇kli̇, M. Ekinci
{"title":"Silhouette Based Gait Recognition","authors":"E. Gedi̇kli̇, M. Ekinci","doi":"10.1109/SIU.2007.4298633","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298633","url":null,"abstract":"This paper presents a approach for gait recognition based on binarized silhouette of a motion object which is represented by distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. First, gait cycle estimation is performed based on normalized correlation on the distance vectors. Gait patterns are then extracted by using distance vectors for each projection independently. Then gait patterns are normalized according to dimensions of bounding box and gait cycle. Second, PCA based eigenspace transform is applied to gait patterns and Euclidean distances based supervised pattern classification is finally performed in the lower-dimensional eigenspace for human identification. Experimental results on four databases (CMU MoBo, SOTON, USF, NLPR) show that the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127883137","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
LPI Radar Sinyallerinin Ozimge Yaklasimi ile Siniflandirilmasi Classification of LPI Radar Signals via Eigenimage Approach
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298644
Engin Kocaadam, Yacup Ozkcazanc
{"title":"LPI Radar Sinyallerinin Ozimge Yaklasimi ile Siniflandirilmasi Classification of LPI Radar Signals via Eigenimage Approach","authors":"Engin Kocaadam, Yacup Ozkcazanc","doi":"10.1109/SIU.2007.4298644","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298644","url":null,"abstract":"Low Probability of Intercept (LPI) Radar signals have the property of wide bandwidth, low peak power to make them difflcult to be detected by Electronic Support Measures (ESM) receivers. In this study, the detection performances of LPI Radar signals with Wigner-Ville Distributions are examined by treating each distribution as an image. The classification can be performed with an image processing algorithm, eigenimage approach over Wigner - Ville Distributions.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127845374","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
A comparative Study of MIMO Channel Models MIMO信道模型的比较研究
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298863
Müge Karaman Çolakoglu, M. Safak
{"title":"A comparative Study of MIMO Channel Models","authors":"Müge Karaman Çolakoglu, M. Safak","doi":"10.1109/SIU.2007.4298863","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298863","url":null,"abstract":"This paper presents a comparison between the outage capacity of multiple-input multiple-output (MIMO) channels predicted by Kronecker and Muller models as a function of the number of scatterers, transmit-and receive antennas. The results show that the channel capacity predictions by the Muller model are higher than those by the Kronecker model. This is because the Muller model is based on single scattering between transmit and receive antenna arrays, while the Kronecker model considers only the double-scattering.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124060047","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
Wavelet Denoising Before Support Vector Classification of Hyperspectral Images 高光谱图像支持向量分类前的小波去噪
2007 IEEE 15th Signal Processing and Communications Applications Pub Date : 2007-06-11 DOI: 10.1109/SIU.2007.4298728
B. Demir, S. Erturk
{"title":"Wavelet Denoising Before Support Vector Classification of Hyperspectral Images","authors":"B. Demir, S. Erturk","doi":"10.1109/SIU.2007.4298728","DOIUrl":"https://doi.org/10.1109/SIU.2007.4298728","url":null,"abstract":"Hyperspectral image classification using support vector machines (SVM) after wavelet domain denoising is proposed in this paper. In the proposed approach, hyperspectral images are classified using SVM after noise reduction is carried out in each band independent of other bands using spatially adaptive Bayesian shrinkage. It is shown that support vector machine classification of denoised hyperspectral images gives significantly better classification accuracy and furthermore improves sparsity. Therefore this approach has faster testing time, compared with direct SVM based classification. This feature makes the denoised SVM based hyperspectral classification approach more suitable for applications that require low-complexity, and possibly real-time classification.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124435080","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
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