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2013 21st Signal Processing and Communications Applications Conference (SIU)最新文献

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Investigation the effect of the distribution of receivers on the positioning error for emitter location finding 研究了接收机分布对辐射源定位误差的影响
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531491
O. Çakir, I. Kaya
{"title":"Investigation the effect of the distribution of receivers on the positioning error for emitter location finding","authors":"O. Çakir, I. Kaya","doi":"10.1109/SIU.2013.6531491","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531491","url":null,"abstract":"Emitter location can be found using three or more synchronized receivers. In order to determine the emitter location, different type of methods may be utilized such as received signal strength, direction/time of arrival, and time/frequency difference of arrival. In this study, the time differential of arrivals (TDOA) is selected as the basic method with particle swarm optimization (PSO) algorithm. In addition, the effect on positioning error of the various receiver distributions is examined.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123419347","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
Deciding the appropriate Mother Wavelet for extract features from brain computer interface signals 确定合适的母小波提取脑机接口信号特征
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531216
O. Aydemir, T. Kayikçioglu
{"title":"Deciding the appropriate Mother Wavelet for extract features from brain computer interface signals","authors":"O. Aydemir, T. Kayikçioglu","doi":"10.1109/SIU.2013.6531216","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531216","url":null,"abstract":"Feature extraction is a very challenging task because the choice of discriminative features directly affects the classification performance of brain computer interface system. The objective of this paper is to investigate the Mother Wavelets' affects on classification results. In order to execute this, we extracted features from three different data sets by using twelve Mother Wavelets. Then we classified the brain computer interface signals with three classification algorithms, including k-nearest neighbor, support vector machine and linear discriminant analysis. The experiments proved that Daubechies and Shannon are the most suitable wavelet families in order to extract more discriminative features from brain computer interface signals.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123688961","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
Reconfigurable hardware-based genome aligner using quality scores 使用质量分数的可重构硬件基因组比对器
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531567
Mehmet Yagmur Gök, M. Sagiroglu, C. Ünsalan, Sezer Gören
{"title":"Reconfigurable hardware-based genome aligner using quality scores","authors":"Mehmet Yagmur Gök, M. Sagiroglu, C. Ünsalan, Sezer Gören","doi":"10.1109/SIU.2013.6531567","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531567","url":null,"abstract":"Smith Waterman Algorithm is a widely used tool in bioinformatics to align reads from aligning to a reference in whole genome sequencing. Mapping millions of sequences read from sequencing is a computationally expensive operation. Accuracy and performance are two important aspects of this process. FPGA based solutions are widely studied. In this paper we tried to achieve a better mapping accuracy for optimum alignment using quality scores of the bases of read sequences while keeping the performance high. We are offering a new Smith Waterman processing unit and systolic array based on quality scores.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121610485","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
Scene classification with random forests and object and color distributions 场景分类与随机森林和对象和颜色分布
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531220
Ahmet Iscen, Eren Golge, Anil Armagan, P. D. Sahin
{"title":"Scene classification with random forests and object and color distributions","authors":"Ahmet Iscen, Eren Golge, Anil Armagan, P. D. Sahin","doi":"10.1109/SIU.2013.6531220","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531220","url":null,"abstract":"We propose a method to recognize the scene of an image by finding the objects and the colors it contains. We approach this problem by creating a binary vector of detected objects and a histogram of the colors that the image contains. We then use these features to train a random forest classifier in order to determine the scene of each image. For class-based classifiers, our method gives comparable results with the state of art methods, such as Object Bank method, for the indoor scene dataset that we used. Additionally, while well-known methods are computationally expensive, our method has a low computational cost.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121428977","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
Video poster creation 视频海报创作
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531379
Savas Özkan, Ilkay Atil, E. Esen, M. Soysal
{"title":"Video poster creation","authors":"Savas Özkan, Ilkay Atil, E. Esen, M. Soysal","doi":"10.1109/SIU.2013.6531379","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531379","url":null,"abstract":"We propose a novel method for summarization of videos as representative frames and visualization of these frames as a video poster with respect to each frame's importance and timing in the video. Effectiveness of the proposed method on summarization and visualization is observed on approximately 20 hours of video data in news, serial tv shows and debate genres and some example results are discussed in detail.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122557674","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
Estimating the missing Kinect depth information by polynomial fitting 用多项式拟合估计Kinect缺失深度信息
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531177
Gorkem Saygili, Caner Balim, H. Kalkan, E. Hendriks
{"title":"Estimating the missing Kinect depth information by polynomial fitting","authors":"Gorkem Saygili, Caner Balim, H. Kalkan, E. Hendriks","doi":"10.1109/SIU.2013.6531177","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531177","url":null,"abstract":"The Microsoft Kinect enables real time 3D reconstruction of an indoor environment with low computational power by producing depth measurements with high resolution. Yet, the measured depth images are not in perfect quality because of the reflectance properties of the objects inside the scene. In this paper, we propose a novel depth prediction algorithm that is based on fitting a polynomial on the unknown depth locations. The parameters of the polynomials are found using the features and the depth measures of the pixels with known depth value. We conducted several experiments on different images and achieved predicting the depth of unknown locations with low error.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122780768","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
Facial expression recognition using Local Zernike Moments 使用本地泽尼克时刻进行面部表情识别
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531264
Bilge Suheyla Akkoca, M. Gokmen
{"title":"Facial expression recognition using Local Zernike Moments","authors":"Bilge Suheyla Akkoca, M. Gokmen","doi":"10.1109/SIU.2013.6531264","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531264","url":null,"abstract":"This study shows that Local Zernike Moments which is a novel study and works in face recognition efficiently can be used for facial expression recognition. Local Zernike Moments (LZM) method based on the calculation of moments at every pixel of the image unlike traditional Zernike Moments produces one moment value over whole image. Promising results are obtained by classifying the feature vectors with KNN (K Nearest Neighbor), belongs to the facial images.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123965858","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
Cloud detection in high-resolution satellite images 高分辨率卫星图像中的云检测
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531467
E. Baseski, Çaglar Senaras
{"title":"Cloud detection in high-resolution satellite images","authors":"E. Baseski, Çaglar Senaras","doi":"10.1109/SIU.2013.6531467","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531467","url":null,"abstract":"Detecting the cloud covarage of a satellite image is crucial for determining the image quality as well as preventing a perfromance loose of automatic detection algorithms because of the clouds. In this work, a new approach for detecting clouds in high-resolution satellite images has been proposed. The proposed method initially subsets the image into small patches and performs analyses based on edge and color information. Homogenous Texture Descriptors are then extracted for the patches that have the potential to contain cloud areas and the final classification of the patch is performed by using a Support Vector Machine (SVM) type classifier.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125591925","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
Through wall imaging with MIMO UWB radar 通过墙成像与MIMO超宽带雷达
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531260
M. S. Mercan, Ahmet Caliskan, Ersin Öztürk
{"title":"Through wall imaging with MIMO UWB radar","authors":"M. S. Mercan, Ahmet Caliskan, Ersin Öztürk","doi":"10.1109/SIU.2013.6531260","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531260","url":null,"abstract":"A real-time through wall imaging system based on ultra wideband (UWB) multiple-input multiple-output (MIMO) radar is presented in this paper. The goal is to find the positions of moving objects hidden behind a wall or any other nonconducting obstacle. The system consists of data acquisition, signal processing, image reconstruction and image processing blocks. As a result of the tests conducted in a real environment it was shown that moving objects behind a wall can be detected and tracked in real-time.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129985484","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
Turkish Sign Language recognition using spatio-temporal features on Kinect RGB video sequences and depth maps 使用Kinect RGB视频序列和深度图的时空特征识别土耳其手语
2013 21st Signal Processing and Communications Applications Conference (SIU) Pub Date : 2013-04-24 DOI: 10.1109/SIU.2013.6531360
Abbas Memiş, S. Albayrak
{"title":"Turkish Sign Language recognition using spatio-temporal features on Kinect RGB video sequences and depth maps","authors":"Abbas Memiş, S. Albayrak","doi":"10.1109/SIU.2013.6531360","DOIUrl":"https://doi.org/10.1109/SIU.2013.6531360","url":null,"abstract":"This paper presents a Turkish Sign Language recognition system that uses spatio-temporal features on Kinect sensor RGB video sequences and depth maps. Proposed system uses cumulative motion images which based on motion differences and represent the temporal characteristics of dynamic signs in motion sequences. Cumulative motion images represent the whole motions of signers. 2-D Discrete Cosine Transform (DCT) is applied to cumulative sign images in order to obtain spatial features of signs and transformed images that represent the energy density of signs are obtained. Two transform images are obtained by applying referred methods to both of RGB video sequences and depth maps seperately. Feature vectors of dynamic signs are produced by combining a certain amount of DCT coefficients that contain higher energy via zig-zag scanning on transform images. K-Nearist Neighbor classifier with Manhattan distance used for recognition process. System performance is evaluated on a sign database that contains 1002 signs belongs to 111 words in three different categories of Turkish Sign Language (TID). Proposed sign language recognition system has a recognition rate about %90.","PeriodicalId":168462,"journal":{"name":"2013 21st Signal Processing and Communications Applications Conference (SIU)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129430018","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
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