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2010 IEEE 18th Signal Processing and Communications Applications Conference最新文献

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A fast-implemented recursive inverse adaptive filtering algorithm 一种快速实现的递归逆自适应滤波算法
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5651743
Mohammad Shukri Ahmad, A. Hocanin, O. Kukrer
{"title":"A fast-implemented recursive inverse adaptive filtering algorithm","authors":"Mohammad Shukri Ahmad, A. Hocanin, O. Kukrer","doi":"10.1109/SIU.2010.5651743","DOIUrl":"https://doi.org/10.1109/SIU.2010.5651743","url":null,"abstract":"The recently proposed Recursive Inverse (RI) adaptive algorithm [1] has shown improved performance in channel equalization and system identification settings. Although its computational complexity is lower than those of the RLS and Robust RLS algorithms, its computational complexity can be reduced further. A fast implementation method is applied in this paper to decrease its computational complexity. The performance of the fast implemented RI algorithm is compared to those of the Variable Step-Size LMS (VSSLMS), Discrete Cosine Transform LMS (DCTLMS) and Recursive-Least-Squares (RLS) algorithms in Additive White Gaussian Noise (AWGN), Additive Correlated Gaussian Noise (ACGN), Additive White Impulsive Noise (AWIN) and Additive Correlated Impulsive Noise (ACIN) environments in a noise cancellation setting. Simulation results show that the Fast RI algorithm performs better than the VSSLMS and DCTLMS algorithms. Its performance is the same as in the RLS algorithm with a considerable reduction in complexity.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127311632","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
Signal denoising by piecewise continuous polynomial fitting 分段连续多项式拟合的信号去噪方法
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5651446
Aykut Yildiz, O. Arikan
{"title":"Signal denoising by piecewise continuous polynomial fitting","authors":"Aykut Yildiz, O. Arikan","doi":"10.1109/SIU.2010.5651446","DOIUrl":"https://doi.org/10.1109/SIU.2010.5651446","url":null,"abstract":"Piecewise smooth signal denoising is cast as a non-linear optimization problem in terms of transition boundaries and a parametric smooth signal family. Optimal transition boundaries for a given number of transitions are obtained by using particle swarm optimization. The piece-wise smooth section parameters are obtained as the maximum likelihood estimates conditioned on the optimal transition boundaries. The proposed algorithm is extended to the case where the number of transition boundaries are unknown by sequentially increasing number of sections until the residual error is at the level of noise standard deviation. Performance comparison with the state of the art techniques reveals the important advantages of the proposed technique.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125285487","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
A comparison of two CA-CFAR loss calculation methods in spatially correlated K-Distributed sea clutter 空间相关k分布海杂波中两种CA-CFAR损耗计算方法的比较
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5649278
Aysin Cetin Hamurcu, A. Hizal
{"title":"A comparison of two CA-CFAR loss calculation methods in spatially correlated K-Distributed sea clutter","authors":"Aysin Cetin Hamurcu, A. Hizal","doi":"10.1109/SIU.2010.5649278","DOIUrl":"https://doi.org/10.1109/SIU.2010.5649278","url":null,"abstract":"Radar target detection of targets in sea clutter modelled by compound K-distribution is examined from a statistical viewpoint by Monte Carlo simulations. The target detection is processed by Cell Averaging Constant False Alarm Rate (CA-CFAR) processor and the performance evaluations are quantified by CFAR loss. Curves for CFAR loss versus the spatial correlation and spikiness of sea clutter, number of cells of CA-CFAR processor and the number of non-coherently integrated pulses are presented.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114916495","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
Biometric face recognition using randomfaces 使用随机面孔的生物特征人脸识别
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5652682
Elena Battini Sonmez, S. Albayrak, B. Sankur
{"title":"Biometric face recognition using randomfaces","authors":"Elena Battini Sonmez, S. Albayrak, B. Sankur","doi":"10.1109/SIU.2010.5652682","DOIUrl":"https://doi.org/10.1109/SIU.2010.5652682","url":null,"abstract":"This paper investigates the use of the Compressive Sensing (CS) technique to the classification issue. In this context, CS is used as a means to probe the nonlinear manifold on which faces under various illumination effects reside. The scheme of randomly sampled faces (Randomfaces) with nearest neighbor classifier are compared with two classical feature extraction approaches, as Eigenfaces and Fisherfaces. It is shown that randomfaces outperform the eigenface approach in classifying faces under illumination disturbances and their performance approaches that of the Fisherfaces.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116052087","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
Survey evaluation by character recognition 基于字符识别的调查评价
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5651566
Z. Iscan
{"title":"Survey evaluation by character recognition","authors":"Z. Iscan","doi":"10.1109/SIU.2010.5651566","DOIUrl":"https://doi.org/10.1109/SIU.2010.5651566","url":null,"abstract":"In the study, an automated system was proposed for the evaluation of survey sheets filled by different students. In this method, the regions related to the answers on the survey sheets digitized by a scanner are determined. For this purpose, after finding the right edge of the survey, upper-right corner of the survey is marked by a developed edge tracking algorithm. Afterwards, rows of the survey are found and the cells which contain the answers between the rows are segmented. After the pre-processing step that includes filling and thinning operations, the answers in these parts are categorized using histogram-based features and area ratio. Obtained performances using limited survey sheets indicate that the survey evaluation by character recognition can be an appropriate option.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116438342","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
Hardware implementation of Discrete Wavelet Transform and Inverse Discrete Wavelet Transform on FPGA 离散小波变换和逆离散小波变换在FPGA上的硬件实现
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5653126
M. A. Çavuslu, F. Karakaya
{"title":"Hardware implementation of Discrete Wavelet Transform and Inverse Discrete Wavelet Transform on FPGA","authors":"M. A. Çavuslu, F. Karakaya","doi":"10.1109/SIU.2010.5653126","DOIUrl":"https://doi.org/10.1109/SIU.2010.5653126","url":null,"abstract":"In this paper, hardware implementation of the Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) based on FPGA is explained. DWT and IDWT algorithms are implemented on the Altera Cyclone-II FPGA. Filtering processes of rows and columns are seriatim applied as in level-by-level architecture. But both addressing for read/write and DWT/IDWT processes are implemented via only one filter by checking kind of filter to be applied. This usage has got advantages of both elapsed times for read/write processes and cost of hardware area. Implementation DWT and IDWT on the hardware is required only 2% hardware area with this approximation.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116558831","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: 6
Environmental sound classification for recognition of house appliances 家用电器识别用环境声分类
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5652796
M. A. Guvensan, Z. C. Taysi
{"title":"Environmental sound classification for recognition of house appliances","authors":"M. A. Guvensan, Z. C. Taysi","doi":"10.1109/SIU.2010.5652796","DOIUrl":"https://doi.org/10.1109/SIU.2010.5652796","url":null,"abstract":"Monitoring of daily activities is highly important to build environmental intelligence. Especially monitoring of house appliances is a key point for creating an intelligent home environment. Run levels of home appliances can be useful to detect such activities. Many house appliances produce different sounds during their differerent run levels. In this paper, we focus on recognition of running house appliances based on sound samples collected from house environment. MFCC and physical features of the sound are tested. Performance of both k-NN and SVM are evaluated. Our proposed system is able to identify working house appliances with 98% success rate.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122658408","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
Effects of signal randomization on performance of binary communications systems 信号随机化对二进制通信系统性能的影响
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5653977
Cagri Goken, S. Gezici, O. Arikan
{"title":"Effects of signal randomization on performance of binary communications systems","authors":"Cagri Goken, S. Gezici, O. Arikan","doi":"10.1109/SIU.2010.5653977","DOIUrl":"https://doi.org/10.1109/SIU.2010.5653977","url":null,"abstract":"In this paper, effects of signal randomization are studied for binary communications systems. First, it is stated that the average probability of error for a power-constrained binary communications system is minimized when each symbol is randomized between at most two signal values. Then, a fixed detector is considered, and sufficient conditions under which its performance can or cannot be improved via signal randomization are presented. After that, the joint design of detectors and signal structures is studied, andan optimization problem is formulated to determine the optimal system parameters. Finally, numerical results are presented to exemplify the improvements via signal randomization.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114451533","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
Multi pose face detection and pose estimation using Multi-class LogitBoost algorithm 基于Multi-class LogitBoost算法的多姿态人脸检测与姿态估计
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5652357
C. Demirkir, B. Sankur
{"title":"Multi pose face detection and pose estimation using Multi-class LogitBoost algorithm","authors":"C. Demirkir, B. Sankur","doi":"10.1109/SIU.2010.5652357","DOIUrl":"https://doi.org/10.1109/SIU.2010.5652357","url":null,"abstract":"We handle the problem of detecting and classifying face pose views in images at the same time developing a Multi-class view detection. In order to solve this problem we use Multi-class LogitBoost algorithm in order to construct corresponding classifier structure. Although approaches generally use binary classifiers for each view class detection, we develop one multi-class classifier using LogitBoost algorithm. We collect large number of background images under a cascade of classifiers constructed with multi-class boosting algorithm. Using this classification approach each pose view of the face images can be detected and classified at the same time and rejecting the background images at each stage of the multi-class classifier cascade as well. Experiments on video images have shown that the performance of this classification approach is similar to the other state-of-art approaches for the detection and pose estimation of face images.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114585912","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
A new spatio-temporal wave based target tracking algorithm 一种新的基于时空波的目标跟踪算法
2010 IEEE 18th Signal Processing and Communications Applications Conference Pub Date : 2010-04-22 DOI: 10.1109/SIU.2010.5652251
R. Yeniceri, V. Kılıç, M. Yalçin
{"title":"A new spatio-temporal wave based target tracking algorithm","authors":"R. Yeniceri, V. Kılıç, M. Yalçin","doi":"10.1109/SIU.2010.5652251","DOIUrl":"https://doi.org/10.1109/SIU.2010.5652251","url":null,"abstract":"In this paper, the model and implementation of a Cellular Nonlinear Network which is able to solve the shortest path finding problem between two points in a two dimensional space including obstacles is presented with a navigation algorithm towards the target via the shortest path. Then, the target tracking scenario with tracker and target robots on a platform which is specially prepared for this application is realized using mentioned algorithm.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129721371","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
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