Multidimensional continuous systems arising from physical applications with distributed parameters are conventionally modelled by partial differential equations. This paper presents an alternate description by transfer functions based on suitably chosen functional transformations. Signal processing techniques lead to discrete simulation models which are suitable for computer implementation. Numerical results show considerable savings in computer time over existing numerical methods.
{"title":"Discrete models for multidimensional system simulation","authors":"R. Rabenstein","doi":"10.5281/ZENODO.36068","DOIUrl":"https://doi.org/10.5281/ZENODO.36068","url":null,"abstract":"Multidimensional continuous systems arising from physical applications with distributed parameters are conventionally modelled by partial differential equations. This paper presents an alternate description by transfer functions based on suitably chosen functional transformations. Signal processing techniques lead to discrete simulation models which are suitable for computer implementation. Numerical results show considerable savings in computer time over existing numerical methods.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132075741","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}
This paper introduces a new structure for stack filtering, where the filter adapts to the local characteristics encountered in data. Both supervised and unsupervised techniques for optimal design are investigated. We split the image into small regions and select the stack filter to process each region according to the spatial domain or threshold level domain characteristics of the input signal. This method provides a significant improvement potential over the global stack filtering approach. Some local statistics are computed, to build a reduced input space which efficiently describes the most important local characteristics of data. Vector quantization is used for clustering the reduced input space into a small number of regions, and then finding a mapping between reduced input space clusters and the filter space, will result in rules for selecting the best suited stack filter for a given region. The supervised clustering procedures are shown to surpass significantly the global filtering approach.
{"title":"Locally adaptive techniques for stack filtering","authors":"D. Petrescu, I. Tabus, M. Gabbouj","doi":"10.5281/ZENODO.36042","DOIUrl":"https://doi.org/10.5281/ZENODO.36042","url":null,"abstract":"This paper introduces a new structure for stack filtering, where the filter adapts to the local characteristics encountered in data. Both supervised and unsupervised techniques for optimal design are investigated. We split the image into small regions and select the stack filter to process each region according to the spatial domain or threshold level domain characteristics of the input signal. This method provides a significant improvement potential over the global stack filtering approach. Some local statistics are computed, to build a reduced input space which efficiently describes the most important local characteristics of data. Vector quantization is used for clustering the reduced input space into a small number of regions, and then finding a mapping between reduced input space clusters and the filter space, will result in rules for selecting the best suited stack filter for a given region. The supervised clustering procedures are shown to surpass significantly the global filtering approach.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133909475","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}
Partial-response signaling is known as correlative level coding wherein the constraint on waveforms is relaxed so as to allow a controlled amount of ISI. In this paper, the Lagrange multiplier approach, which is easy to incorporate both time- and frequency-domain constraints by minimizing a quadratic measure of the error in the design bands, is applied to design a large class of such digital filters for communication in this paper. Also, the iterative Lagrange multiplier approach combining the Lagrange multiplier approach and a tree search algorithm is proposed for designing discrete coefficient pulse shaping FIR digital filters. System experiments such as an SSB radio system using partial response signaling are demonstrated to present the usefulness of the proposed algorithm.
{"title":"Design of pulse shaping filters and their applications in radio systems","authors":"Jong-Jy Shyu, Yo-Chuan Lai","doi":"10.5281/ZENODO.35960","DOIUrl":"https://doi.org/10.5281/ZENODO.35960","url":null,"abstract":"Partial-response signaling is known as correlative level coding wherein the constraint on waveforms is relaxed so as to allow a controlled amount of ISI. In this paper, the Lagrange multiplier approach, which is easy to incorporate both time- and frequency-domain constraints by minimizing a quadratic measure of the error in the design bands, is applied to design a large class of such digital filters for communication in this paper. Also, the iterative Lagrange multiplier approach combining the Lagrange multiplier approach and a tree search algorithm is proposed for designing discrete coefficient pulse shaping FIR digital filters. System experiments such as an SSB radio system using partial response signaling are demonstrated to present the usefulness of the proposed algorithm.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123990516","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}
The corrected least squares (CLS) approach using an over-determined model is investigated to decide the number of sinusoids in additive white noise. Like the total least squares (TLS) approach, the CLS estimation is different from the ordinary least squares (LS) method in that the noise variance is subtracted from the diagonal elements of the correlation matrix of the noisy observed data. Therefore the inversion of the resultant matrix becomes ill-conditioned and then adequate truncation of the eigenvalue decomposition (EVD) should be done. This paper clarifies how to simultaneously estimate the noise variance and truncate the eigenvalues, since they are mutually dependent. By introducing a multiple number of regularization parameters and determining them to minimize the MSE of the model parameters, we can give an optimal scheme for the truncation of eigenvalues. Furthermore, an iterative algorithm using only observed data is also clarified.
{"title":"MSE-based regularization approach to rank determination in CLS and TLS estimation","authors":"H. Kagiwada, Y. Aoki, J. Xin, A. Sano","doi":"10.5281/ZENODO.35974","DOIUrl":"https://doi.org/10.5281/ZENODO.35974","url":null,"abstract":"The corrected least squares (CLS) approach using an over-determined model is investigated to decide the number of sinusoids in additive white noise. Like the total least squares (TLS) approach, the CLS estimation is different from the ordinary least squares (LS) method in that the noise variance is subtracted from the diagonal elements of the correlation matrix of the noisy observed data. Therefore the inversion of the resultant matrix becomes ill-conditioned and then adequate truncation of the eigenvalue decomposition (EVD) should be done. This paper clarifies how to simultaneously estimate the noise variance and truncate the eigenvalues, since they are mutually dependent. By introducing a multiple number of regularization parameters and determining them to minimize the MSE of the model parameters, we can give an optimal scheme for the truncation of eigenvalues. Furthermore, an iterative algorithm using only observed data is also clarified.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116780875","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}
Usual coherence estimation in SAR interferometry is a time consuming task since an accurate estimation of the local frequency of the interferometrie fringes is required. In this paper a fast algorithm for generating coherence maps, mainly intended to data browsing, is presented. The proposed estimator is based on the speckle similarity of coherent SAR data and is, thus, independent of the fringes frequency. Advantages with respect to the usual estimates are achieved in terms of computational costs (up to 100 times lower), robustness (the estimator presented is not affected by possible local frequency estimation errors) and flexibility (the estimator can be applied both to complex and to detected images). The statistical properties of the frequency independent estimator are given in the stationary case. A preprocessing technique that reduces the degradions due to non-stationarities is then shown.
{"title":"Coherence estimation of interferometric SAR images","authors":"F. Gatelli, A. M. Guarnieri, C. Prati","doi":"10.5281/ZENODO.36271","DOIUrl":"https://doi.org/10.5281/ZENODO.36271","url":null,"abstract":"Usual coherence estimation in SAR interferometry is a time consuming task since an accurate estimation of the local frequency of the interferometrie fringes is required. In this paper a fast algorithm for generating coherence maps, mainly intended to data browsing, is presented. The proposed estimator is based on the speckle similarity of coherent SAR data and is, thus, independent of the fringes frequency. Advantages with respect to the usual estimates are achieved in terms of computational costs (up to 100 times lower), robustness (the estimator presented is not affected by possible local frequency estimation errors) and flexibility (the estimator can be applied both to complex and to detected images). The statistical properties of the frequency independent estimator are given in the stationary case. A preprocessing technique that reduces the degradions due to non-stationarities is then shown.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122470944","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}
The problem of source location of wideband signals impinging on an array of sensors is addressed. The proposed method exploits the cyclostationarity exhibited by most communication signals to discriminate signals of interest from noise and interfering signals. The new method performs coherent combination of the spatial contributions at different frequencies and exploits signal-subspace properties of the resulting focused matrix. Numerical results show that the proposed technique is superior to existing algorithms and assures good performances also when the signals of interest are fully correlated.
{"title":"A cyclic coherent method for wideband source location","authors":"G. Gelli, L. Izzo","doi":"10.5281/ZENODO.36134","DOIUrl":"https://doi.org/10.5281/ZENODO.36134","url":null,"abstract":"The problem of source location of wideband signals impinging on an array of sensors is addressed. The proposed method exploits the cyclostationarity exhibited by most communication signals to discriminate signals of interest from noise and interfering signals. The new method performs coherent combination of the spatial contributions at different frequencies and exploits signal-subspace properties of the resulting focused matrix. Numerical results show that the proposed technique is superior to existing algorithms and assures good performances also when the signals of interest are fully correlated.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123818074","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}
In this paper we solve the constrained deconvolution problem by state space approach in an H∞ setting. The problem addressed is the design of a nonlinear estimator that guarantees H∞ performance on infinite horizon for the estimation error by using the Game Theory technic. The method proposed is useful in cases where the statistics of the disturbance and the noise signal are not completely known. We used the technic proposed to estimate heat production rate from the knowledge of the temperature.
{"title":"Constrained deconvolution: A game theory approach in an H∞ setting","authors":"E. Sekko, G. Thomas","doi":"10.5281/ZENODO.36194","DOIUrl":"https://doi.org/10.5281/ZENODO.36194","url":null,"abstract":"In this paper we solve the constrained deconvolution problem by state space approach in an H∞ setting. The problem addressed is the design of a nonlinear estimator that guarantees H∞ performance on infinite horizon for the estimation error by using the Game Theory technic. The method proposed is useful in cases where the statistics of the disturbance and the noise signal are not completely known. We used the technic proposed to estimate heat production rate from the knowledge of the temperature.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131866107","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}
N. Laskaris, S. Fotopoulos, Anastasios Bezerianos, A. Manolis
In this work we introduce a method for the enhancement of Late Potentials in the Signal Averaged electrocardiography. The method involves computation of weights prior averaging. Two fuzzy control techniques are proposed for the derivation of weights. The experimental results indicate the contribution of the method to a more reliable prognosis.
{"title":"Fuzzy — weighted averaging for high-resolution ECG based on exploratory data analysis","authors":"N. Laskaris, S. Fotopoulos, Anastasios Bezerianos, A. Manolis","doi":"10.5281/ZENODO.36166","DOIUrl":"https://doi.org/10.5281/ZENODO.36166","url":null,"abstract":"In this work we introduce a method for the enhancement of Late Potentials in the Signal Averaged electrocardiography. The method involves computation of weights prior averaging. Two fuzzy control techniques are proposed for the derivation of weights. The experimental results indicate the contribution of the method to a more reliable prognosis.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"34 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131673544","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}
There are many research papers published in modulation classification, and most of them have a common framework. In this paper we will give an overview, and the paper contains four topics: 1) Some fundamental principles, 2) features used for classification, 3) the algorithm structure, and finally 4) a literature survey.
{"title":"Modulation classification — An unified view","authors":"Peter A. J. Nagy","doi":"10.5281/ZENODO.35980","DOIUrl":"https://doi.org/10.5281/ZENODO.35980","url":null,"abstract":"There are many research papers published in modulation classification, and most of them have a common framework. In this paper we will give an overview, and the paper contains four topics: 1) Some fundamental principles, 2) features used for classification, 3) the algorithm structure, and finally 4) a literature survey.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129978317","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}
This paper addresses the problem of noise attenuation for multichannel data. Two multichannel filters which utilize adaptively determined data dependent coefficients are introduced. The special case of colour image processing is studied as an important example of multichannel signal processing. Simulation results indicate that the new filters are computationally attractive and have excellent performance.
{"title":"Nearest neighbour multichannel filters for image processing","authors":"K. Plataniotis, D. Androutsos, A. Venetsanopoulos","doi":"10.5281/ZENODO.36384","DOIUrl":"https://doi.org/10.5281/ZENODO.36384","url":null,"abstract":"This paper addresses the problem of noise attenuation for multichannel data. Two multichannel filters which utilize adaptively determined data dependent coefficients are introduced. The special case of colour image processing is studied as an important example of multichannel signal processing. Simulation results indicate that the new filters are computationally attractive and have excellent performance.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134243481","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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