Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204472
Engin Cemal Menguc, Nurettin Acır
In this study, a novel nonlinear adaptive filter algorithm is proposed guaranteeing the asymptotic stability in the sense of Lyapunov. The tracking capability of the proposed filter is tested by using a created artificial signal having a finite number of discontinuities. The proposed filter shows high performance both in Matlab environment and its FPGA realization. As a result, realization of the proposed filter with FPGA is confirmed.
{"title":"A novel nonlinear adaptive filter design and its implementation with FPGA","authors":"Engin Cemal Menguc, Nurettin Acır","doi":"10.1109/SIU.2012.6204472","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204472","url":null,"abstract":"In this study, a novel nonlinear adaptive filter algorithm is proposed guaranteeing the asymptotic stability in the sense of Lyapunov. The tracking capability of the proposed filter is tested by using a created artificial signal having a finite number of discontinuities. The proposed filter shows high performance both in Matlab environment and its FPGA realization. As a result, realization of the proposed filter with FPGA is confirmed.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131415076","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}
Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204666
Ezgi Ates, H. Özbay
In this paper a new filter structure is proposed for the H∞ estimation under delayed measurements for continuous time processes. As an example, target tracking problem is considered and results obtained from the classical H2-optimal and the proposed H∞-optimal filters are compared.
{"title":"H∞-filter based target tracking under time delayed measurements","authors":"Ezgi Ates, H. Özbay","doi":"10.1109/SIU.2012.6204666","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204666","url":null,"abstract":"In this paper a new filter structure is proposed for the H<sub>∞</sub> estimation under delayed measurements for continuous time processes. As an example, target tracking problem is considered and results obtained from the classical H<sub>2</sub>-optimal and the proposed H<sub>∞</sub>-optimal filters are compared.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131856733","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}
Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204556
Ibrahim Pektas, Mücahit K. Üner
In this study, the performance of constant false alarm rate (CFAR) processors in nonhomogeneous clutter background is analyzed. The nonhomogeneous clutter power is modeled as varying linearly in logarithmic scale from one low fixed level to a high fixed level, rather than modeled as an abrupt change. For different values of the linearly varying clutter power region width with respect to the reference window size of the CFAR processor, the false alarm probabilities of cell averaging (CA), greatest-of (GO) and ordered statistics (OS) CFAR processors, are computed analytically. These processors' capabilities of controlling false alarm probability are analyzed and compared.
{"title":"Performance of CFAR processors in nonhomogeneous clutter background","authors":"Ibrahim Pektas, Mücahit K. Üner","doi":"10.1109/SIU.2012.6204556","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204556","url":null,"abstract":"In this study, the performance of constant false alarm rate (CFAR) processors in nonhomogeneous clutter background is analyzed. The nonhomogeneous clutter power is modeled as varying linearly in logarithmic scale from one low fixed level to a high fixed level, rather than modeled as an abrupt change. For different values of the linearly varying clutter power region width with respect to the reference window size of the CFAR processor, the false alarm probabilities of cell averaging (CA), greatest-of (GO) and ordered statistics (OS) CFAR processors, are computed analytically. These processors' capabilities of controlling false alarm probability are analyzed and compared.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127388654","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}
Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204450
F. Özkaynak, A. Özer, S. Yavuz
Data compression and encryption are critical issues for efficiency and security requirements of information transmission. In order to improve the performance and the flexibility of multimedia applications, it is worthwhile to perform compression and encryption in a single process. Recently Hermassi et al. proposed a method for joint compression and encryption using chaotically mutated Huffman trees. The proposed method based on multiple Huffman tables simultaneously performs encryption and compression by a key-controlled swapping of the left and right branches of the Huffman tree. However, security problems were found. In this study describes the security weakness of the proposed method. By applying chosen-plaintext attacks, we show that secret key can be revealed.
{"title":"Analysis of chaotic methods for compression and encryption processes in data communication","authors":"F. Özkaynak, A. Özer, S. Yavuz","doi":"10.1109/SIU.2012.6204450","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204450","url":null,"abstract":"Data compression and encryption are critical issues for efficiency and security requirements of information transmission. In order to improve the performance and the flexibility of multimedia applications, it is worthwhile to perform compression and encryption in a single process. Recently Hermassi et al. proposed a method for joint compression and encryption using chaotically mutated Huffman trees. The proposed method based on multiple Huffman tables simultaneously performs encryption and compression by a key-controlled swapping of the left and right branches of the Huffman tree. However, security problems were found. In this study describes the security weakness of the proposed method. By applying chosen-plaintext attacks, we show that secret key can be revealed.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127414598","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}
Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204624
Ali Karaali, Ç. Erdem, Sezer Ulukaya
We propose a new efficient technique for localization of faces in arbitrary images. The technique is based on segmentation of images into skin colored blobs, which is followed by computation of scale, translation and rotation invariant moment-based features to learn a statistical model of faces and non-face regions. The superiority of the method to the state-of-the-art face detection methods is its ability to detect non-frontal faces in a person independent way. Experimental results on CVL database show that the proposed algorithm gives higher true positive rates as compared to the well-known Viola-Jones face detector.
{"title":"Multipose face detection using Zernike moment invariants","authors":"Ali Karaali, Ç. Erdem, Sezer Ulukaya","doi":"10.1109/SIU.2012.6204624","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204624","url":null,"abstract":"We propose a new efficient technique for localization of faces in arbitrary images. The technique is based on segmentation of images into skin colored blobs, which is followed by computation of scale, translation and rotation invariant moment-based features to learn a statistical model of faces and non-face regions. The superiority of the method to the state-of-the-art face detection methods is its ability to detect non-frontal faces in a person independent way. Experimental results on CVL database show that the proposed algorithm gives higher true positive rates as compared to the well-known Viola-Jones face detector.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133788048","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}
Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204572
Ilginç Demir, A. Sayar
Hadoop Distributed File System (HDFS) is widely used in large-scale data storage and processing. HDFS uses MapReduce programming model for parallel processing. The work presented in this paper proposes a novel Hadoop plugin to process image files with MapReduce model. The plugin introduces image related I/O formats and novel classes for creating records from input files. HDFS is especially designed to work with small number of large size files. Therefore, the proposed technique is based on merging multiple small size files into one large file to prevent the performance loss stemming from working with large number of small size files. In that way, each task becomes capable of processing multiple images in a single run cycle. The effectiveness of the proposed technique is proven by an application scenario for face detection on distributed image files.
{"title":"Hadoop plugin for distributed and parallel image processing","authors":"Ilginç Demir, A. Sayar","doi":"10.1109/SIU.2012.6204572","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204572","url":null,"abstract":"Hadoop Distributed File System (HDFS) is widely used in large-scale data storage and processing. HDFS uses MapReduce programming model for parallel processing. The work presented in this paper proposes a novel Hadoop plugin to process image files with MapReduce model. The plugin introduces image related I/O formats and novel classes for creating records from input files. HDFS is especially designed to work with small number of large size files. Therefore, the proposed technique is based on merging multiple small size files into one large file to prevent the performance loss stemming from working with large number of small size files. In that way, each task becomes capable of processing multiple images in a single run cycle. The effectiveness of the proposed technique is proven by an application scenario for face detection on distributed image files.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115237420","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}
Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204837
C. O. Sakar, Olcay Kursun, Ali Karaali, Ç. Erdem
Although several methods have been proposed for fusing different image representations obtained by different preprocessing methods for emotion recognition from the facial expression in a given image, the dependencies and relations among them have not been much investigated. In this study, it has been shown that covariates obtained by Canonical Correlation Analysis (CCA) that extracts relations between different representations have high predictive power for emotion recognition. As high prediction accuracy can be achieved using a small number of features extracted by it, CCA is considered to be a good dimensionality reduction method. For our simulations, we used the CK+ database and showed that covariates obtained from difference-images and geometric-features representations have high prediction accuracy.
{"title":"Feature extraction for facial expression recognition by canonical correlation analysis","authors":"C. O. Sakar, Olcay Kursun, Ali Karaali, Ç. Erdem","doi":"10.1109/SIU.2012.6204837","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204837","url":null,"abstract":"Although several methods have been proposed for fusing different image representations obtained by different preprocessing methods for emotion recognition from the facial expression in a given image, the dependencies and relations among them have not been much investigated. In this study, it has been shown that covariates obtained by Canonical Correlation Analysis (CCA) that extracts relations between different representations have high predictive power for emotion recognition. As high prediction accuracy can be achieved using a small number of features extracted by it, CCA is considered to be a good dimensionality reduction method. For our simulations, we used the CK+ database and showed that covariates obtained from difference-images and geometric-features representations have high prediction accuracy.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"342 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115405674","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}
Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204660
Qadri Mayyala, A. Hocanin, Ösman Kükrer
A new 2D frequency-response-shaped least mean square (2D FRS-LMS) adaptive filter is proposed by developing the 1D FRS-LMS. The new algorithm reuses data in both horizontal and vertical directions within the space plane to update the weight vector of the filter. Further, the proposed algorithm involves the multiplication of the filter coefficient vector by a variable matrix in the coefficient updating process. The new 2D FRS-LMS weight updating equation is derived and its performance is compared with that of the two dimensional LMS (2D LMS) and 2D leaky-LMS algorithms regarding image enhancement. The new algorithm gives improved performance over the other algorithms. The proposed 2D FRS-LMS is particularly useful in image processing, especially in data compression and image enhancement applications.
{"title":"Two dimensional FRS-LMS adaptive filter (2D FRS-LMS)","authors":"Qadri Mayyala, A. Hocanin, Ösman Kükrer","doi":"10.1109/SIU.2012.6204660","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204660","url":null,"abstract":"A new 2D frequency-response-shaped least mean square (2D FRS-LMS) adaptive filter is proposed by developing the 1D FRS-LMS. The new algorithm reuses data in both horizontal and vertical directions within the space plane to update the weight vector of the filter. Further, the proposed algorithm involves the multiplication of the filter coefficient vector by a variable matrix in the coefficient updating process. The new 2D FRS-LMS weight updating equation is derived and its performance is compared with that of the two dimensional LMS (2D LMS) and 2D leaky-LMS algorithms regarding image enhancement. The new algorithm gives improved performance over the other algorithms. The proposed 2D FRS-LMS is particularly useful in image processing, especially in data compression and image enhancement applications.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115459806","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}
Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204527
Berat Dogan, Mehmet Korürek
In this paper, an ECG beat clustering method based on fuzzy c-means algorithm and particle swarm optimization is proposed. For this purpose, ECG records which are selected from MIT-BIH arrhythmia database are firstly preprocessed and then four morphological features are extracted for six different types of beats. These features are then clustered with the proposed method. During the classification phase, in order to minimize the incongruity between the experiments and to better evaluate the performance of the proposed system a simple but stable classification method is used. After several experiments it is observed that the proposed method overcomes the restrictions of the fuzzy c-means algorithm which are sensitivity to initialization and trapping into local minima.
{"title":"ECG beat clustering using fuzzy c-means algorithm and particle swarm optimization","authors":"Berat Dogan, Mehmet Korürek","doi":"10.1109/SIU.2012.6204527","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204527","url":null,"abstract":"In this paper, an ECG beat clustering method based on fuzzy c-means algorithm and particle swarm optimization is proposed. For this purpose, ECG records which are selected from MIT-BIH arrhythmia database are firstly preprocessed and then four morphological features are extracted for six different types of beats. These features are then clustered with the proposed method. During the classification phase, in order to minimize the incongruity between the experiments and to better evaluate the performance of the proposed system a simple but stable classification method is used. After several experiments it is observed that the proposed method overcomes the restrictions of the fuzzy c-means algorithm which are sensitivity to initialization and trapping into local minima.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115555223","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}
Pub Date : 2012-04-18DOI: 10.1109/SIU.2012.6204785
M. S. Mercan, A. V. Atli, Ersin Öztürk
In this paper, we present a through-wall-imaging approach based on ultra wideband radar. We aim to locate stationary objects which are hidden behind a wall. The proposed approach consists of clutter reduction, imaging and image processing methods. We make use of principal component analysis (PCA) for clutter reduction, and back projection for imaging. Finally, for the purpose of image processing some of the basic methods are used such as thresholding, blurring, contour extraction, and ellipse fitting. Our approach is tested and evaluated on real data by making use of MATLAB and OpenCV.
{"title":"A through-wall-imaging approach based on ultra wideband radar","authors":"M. S. Mercan, A. V. Atli, Ersin Öztürk","doi":"10.1109/SIU.2012.6204785","DOIUrl":"https://doi.org/10.1109/SIU.2012.6204785","url":null,"abstract":"In this paper, we present a through-wall-imaging approach based on ultra wideband radar. We aim to locate stationary objects which are hidden behind a wall. The proposed approach consists of clutter reduction, imaging and image processing methods. We make use of principal component analysis (PCA) for clutter reduction, and back projection for imaging. Finally, for the purpose of image processing some of the basic methods are used such as thresholding, blurring, contour extraction, and ellipse fitting. Our approach is tested and evaluated on real data by making use of MATLAB and OpenCV.","PeriodicalId":256154,"journal":{"name":"2012 20th Signal Processing and Communications Applications Conference (SIU)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124335777","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}
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