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2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)最新文献

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Clustering of arrhythmic ECG beats using morphological properties and windowed raw ECG data 利用形态学特征和带窗的原始心电数据聚类心律失常心电搏动
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-06-23 DOI: 10.1109/SIU.2011.5929756
Berat Levent Gezer, D. Kuntalp, M. Kuntalp
{"title":"Clustering of arrhythmic ECG beats using morphological properties and windowed raw ECG data","authors":"Berat Levent Gezer, D. Kuntalp, M. Kuntalp","doi":"10.1109/SIU.2011.5929756","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929756","url":null,"abstract":"In this study, six types of arrhythmia beats observed in ECG signals have been analysed by using clustering methods. A set of morphological properties and windowed raw ECG data are used as feature vectors in clustering algorithms. Purpose of the analysis is to see if the examined arrhytmia types form natural groups in the feature spaces. The performances of the clustering algorithms are tested by different distance metrics and algorithms. The results are examined based on the average sensitivity, specificity, selectivity and accuracy of the classifier. The results show that k-means clustering technique with the distance parameter set at cosine values by using the windowed raw data features give better results. Results also show that analyzed arrythmia types do not form distinct clusters in examined feature spaces. On the other hand, in some cases very high specificity results are observed for some arrythmia types. That means suggested features could be quite useful in elimination processes in hierarchic classifiers.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123271665","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
An advanced ABR measurement system design based on C6713 digital signal processor 基于C6713数字信号处理器的先进ABR测量系统设计
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-04-20 DOI: 10.1109/SIU.2011.5929585
Mehmet Gundogan, M. Ozden, I. Karagoz
{"title":"An advanced ABR measurement system design based on C6713 digital signal processor","authors":"Mehmet Gundogan, M. Ozden, I. Karagoz","doi":"10.1109/SIU.2011.5929585","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929585","url":null,"abstract":"In this study, an ABR measurement system, which is used for determining auditory functional loss, threshold of hearing and brainstem neurologic disorders is designed and implemented. The system consists of four main stages. In the first stage, bioelectrical signals which occur after the transmission of stimulus signals are accumulated from skull skin by using electrodes. In the second stage, the obtained signals are transferred to analog module of the system and in this module, amplification and filtering processes are performed the analog signals. In the third stage, the analog signals are digitized by using the TMS320C6713 module and passed through several digital signal processing functions. In the last stage, demonstration and evaluation of the measurement result is carried out by the user interface software which is used for control of the system. Finally, an advanced ABR measurement system which is user-friendly and accessible to all phases of hardware and software and open to development for academic purpose, is designed and implemented.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115129877","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
Comparison of iterative sparse recovery algorithms 迭代稀疏恢复算法的比较
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-04-20 DOI: 10.1109/SIU.2011.5929786
Celalettin Karakus, A. Gurbuz
{"title":"Comparison of iterative sparse recovery algorithms","authors":"Celalettin Karakus, A. Gurbuz","doi":"10.1109/SIU.2011.5929786","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929786","url":null,"abstract":"Most signals can be represented sparsely in a basis. Recently, Compressive Sensing Theorem which offers convex optimization algorithms based on ℓ1-minimization for sparse signal recovery is often being used. In this paper, some of the iterative signal recovery algorithms alternative to ℓ1-minimization solution which are Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP), Iterative Hard Thresholding (IHT) and Lipschitz Iterative Hard Theresholding (LIHT) are compared in noisy and noiseless conditions with various tests. Iterative algorithms alternative to the ℓ1 optimization method with similar performance are verified. OMP algorithm that works at higher true reconstruction rates in noisy and noiseless conditions can be preferred instead of convex optimization methods.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115151219","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: 11
Compressive Sensing of Linear Frequency Modulated Signals in Fractional Fourier Domains 分数阶傅立叶域中线性调频信号的压缩感知
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-04-20 DOI: 10.1109/SIU.2011.5929757
S. Aldirmaz, L. Durak-Ata
{"title":"Compressive Sensing of Linear Frequency Modulated Signals in Fractional Fourier Domains","authors":"S. Aldirmaz, L. Durak-Ata","doi":"10.1109/SIU.2011.5929757","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929757","url":null,"abstract":"Compressive sensing is a new technique that allows sampling at very low rates compared to the Nyquist sampling rate, if the signal is sparse. Thus the signal should either be sparse in time domain or we should be able to determine any domain in which the signal is represented sparsely. In the reconstruction process, the signal is reconstructed by using linear projections of itself in an iterative way rather than using all samples of the signal. In this paper, multi-component linear frequency modulated (LFM) signals that are highly dense in time and frequency domains, are transformed into fractional Fourier domains in order to form sparse representations. Then, it is shown that by using compressive sensing in fractional Fourier domains, LFM signals can be represented almost by half of their lengths with high accuracy.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115498087","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: 4
Bullet matching using SIFT feature 使用SIFT特征进行子弹匹配
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-04-20 DOI: 10.1109/SIU.2011.5929662
Ahmet Sayar, Fatih Tetiker, Erman Acar, Banu Oskay Acar, U. Sakarya
{"title":"Bullet matching using SIFT feature","authors":"Ahmet Sayar, Fatih Tetiker, Erman Acar, Banu Oskay Acar, U. Sakarya","doi":"10.1109/SIU.2011.5929662","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929662","url":null,"abstract":"Firearms leave special marks on the bullet while the bullet travels through the barrel. In this work, visual word codes obtained from interest points were used in bullet matching. Visual codebook was constructed by clustering Scale Invariant Feature Transform (SIFT) features using interest point orientation information as semi-supervised clustering constraint. The ratio of the number of visual words in common to the total number of visual words was used as a similarity metric in the comparison of images. Visual words are weighted by inverse document frequency which is frequently used in text document comparisons. Experiment results show that the proposed method presents promising results in bullet matching.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116785428","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
Performance based pruning and weighted voting with classification ensembles 基于性能的分类集成剪枝和加权投票
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-04-20 DOI: 10.1109/SIU.2011.5929620
M. Amasyali, O. Ersoy
{"title":"Performance based pruning and weighted voting with classification ensembles","authors":"M. Amasyali, O. Ersoy","doi":"10.1109/SIU.2011.5929620","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929620","url":null,"abstract":"Ensemble algorithms have been a very popular research topic because of their high performances. In this work, performance based ensemble pruning and decision weighting methods are investigated on 3 ensemble algorithms (Bagging, Random Subspaces, Random Forest) over 26 classification datasets. According to our experiments; the algorithm including most diversity among its base learners is Random Subspaces. The best performed ensemble algorithm is Random Subspaces with decision weighting.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"34 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121000727","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
Evaluation of a target recognition algorithm using the sensor model 利用传感器模型评价一种目标识别算法
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-04-20 DOI: 10.1109/SIU.2011.5929748
Naci Saldi, M. Serkan Tokay, Mert Küçük, Z. Gürkan Figen, S. G. Tanyer, E. Bu, Ir G Is¸görüntüleme
{"title":"Evaluation of a target recognition algorithm using the sensor model","authors":"Naci Saldi, M. Serkan Tokay, Mert Küçük, Z. Gürkan Figen, S. G. Tanyer, E. Bu, Ir G Is¸görüntüleme","doi":"10.1109/SIU.2011.5929748","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929748","url":null,"abstract":"In this work, the performance of a target recognition algorithm is evaluated on images distorted by blur and noise. We simulate such image distortions using our Sensor Model Software. The software includes a blur model based on the modulation transfer function and a 3-D noise model. The main objective of the work is to show the importance of the sensor model for evaluating the performance of target recognition algorithms. An example algorithm, which is based on the Scale Invariant Feature Transform has been selected for this evaluation. Under conditions for which atmospheric factors (turbulence and aerosol) are dominant, image degradations have been simulated and their effect on the algorithm has been evaluated using relevant measurement criteria. This work is believed to shed light to the efforts for increasing the efficiency of target detection, tracking and recognition algorithms in the infrared band by employing synthetic image generators.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"27 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125947585","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 target detection algorithm based on peripheral anomaly 一种基于周边异常的目标检测算法
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-04-20 DOI: 10.1109/SIU.2011.5929606
A. O. Karali, T. Aytaç, A. Yildirim, O. Gerek
{"title":"A target detection algorithm based on peripheral anomaly","authors":"A. O. Karali, T. Aytaç, A. Yildirim, O. Gerek","doi":"10.1109/SIU.2011.5929606","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929606","url":null,"abstract":"Automatic target detection algorithms enable in military and civilian applications the detection of low-contrast and spatially small targets that can not be detected by a human operator and remove the requirement of permanent operator control for surveillance systems. Considering many different background patterns and target models, it is not possible to define a single target detection algorithm that succeeds in all scenarios. In this paper, a novel target detection algorithm is proposed for targets above sea-level and sky targets. Peripheral anomaly values are calculated for image blocks depending on the difference of the statistical data between target and background patterns and target and background regions are differentiated by comparing these values with scenario dependent threshold values. Experimental results show that the proposed method has high success rate in the detection of targets above sea-level and sky targets in visual band images.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125353743","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
Relevance feedback for semantic classification: A comparative study 语义分类相关反馈的比较研究
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-04-20 DOI: 10.1109/SIU.2011.5929823
Tugrul K. Ates, Savas Ozkan, M. Soysal, Aydin Alatan
{"title":"Relevance feedback for semantic classification: A comparative study","authors":"Tugrul K. Ates, Savas Ozkan, M. Soysal, Aydin Alatan","doi":"10.1109/SIU.2011.5929823","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929823","url":null,"abstract":"Immense increase in the number of multimedia content accessible from television and internet with the help developing technologies reveals efficient supervision and classification of such content as a problem. Relevance feedback is a technique which relies on evaluation of retrieval results by humans and enables reduce the semantic gap between ideas and low level representations. Content based high level classification system may employ relevance feedback for improved retrieval performance. In this paper, different relevance feedback algorithms, which can be utilized to increase generalized semantic classification performance, are discussed and compared inside an experimental framework. Some improvements are also proposed over obtained results.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115997435","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
Adaptive enhancement of platforms above sea-level in infrared images based on clustering of wavelet coefficients 基于小波系数聚类的红外图像海平面以上平台自适应增强
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2011-04-20 DOI: 10.1109/SIU.2011.5929784
A. O. Karali, O. E. Okman, T. Aytaç
{"title":"Adaptive enhancement of platforms above sea-level in infrared images based on clustering of wavelet coefficients","authors":"A. O. Karali, O. E. Okman, T. Aytaç","doi":"10.1109/SIU.2011.5929784","DOIUrl":"https://doi.org/10.1109/SIU.2011.5929784","url":null,"abstract":"This study proposes an adaptive infrared image enhancement technique for platforms above sea-level based on clustering of wavelet coefficients. Feature vectors constructed from subband images are computed using discrete wavelet transform and similar feature vectors are grouped using clustering operation. Depending on the feature vectors, a weight is assigned to each cluster and these weights are used to compute gain matrices used to multiply wavelet coefficients for the enhancement of the original image. In the paper, enhancement results are presented and a comparison of the performance of the proposed algorithm is given through subjective tests with other well known frequency and histogram based enhancement techniques. The proposed algorithm outperforms previous ones in the truthfulness, detail visibility of the target, artificiality, and total quality criteria, while providing an acceptable computational load.","PeriodicalId":114797,"journal":{"name":"2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122388518","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
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