{"title":"An experimental study on combining Euclidean distances","authors":"Wan-Jui Lee, R. Duin, A. Ibba, M. Loog","doi":"10.1109/CIP.2010.5604238","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604238","url":null,"abstract":"Combining different distance matrices or dissimilarity representations can often increase the performance of individual ones. In this work, we experimentally study on the performance of combining Euclidean distances and its relationship with the non-Euclideaness produced from combining Euclidean distances. The relationship between the degree of non-Euclideaness from combining Euclidean distances and the correlations between these Euclidean distances are also investigated in the experiments. From the results, we observe that combining dissimilarities computed with Euclidean distances usually performs better than combining dissimilarities computed with squared Euclidean distances. Also, the improvements are found to be highly related to the degree of non-Euclideaness. Moreover, the degree of non-Euclideaness is relatively large if two highly uncorrelated dissimilarity matrices are combined and the degree of non-Euclideaness remains lower if two dissimilarity matrices to be combined are more correlated.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130558205","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}
{"title":"Locally optimum detection in heavy-tailed noise for spectrum sensing in cognitive radio","authors":"F. Y. Suratman, Y. Chakhchoukh, A. Zoubir","doi":"10.1109/CIP.2010.5604214","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604214","url":null,"abstract":"Cognitive radio today is considered to be the solution to solving the problem of spectrum scarcity. One of the most important features of cognitive radio is spectrum sensing. In spectrum sensing it is sometimes necessary to operate in a low SNR regime, in which the performance of most of the classical detectors decreases, especially when they have to deal with imprecise knowledge of the noise characteristics. In fact, in practical applications, the underlying noise often can not be assumed to be Gaussian. In this paper, we design a locally optimum detector, assuming that the underlying noise follows a Student's t-distribution, which is very suitable for modeling heavy-tailed noise. We also assume that BPSK signals are used by primary users and that we have a flat fading channel. Simulation results show that our proposed detector outperforms energy detector in all pre-determined scenarios. It is also more robust in dealing with outliers than both the energy detector and the locally optimum detector based on the assumption of complex Gaussian noise.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124339416","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}