Pub Date : 2011-06-28DOI: 10.1109/SSP.2011.5967644
D. Ramírez, J. Vía, I. Santamaría, L. Scharf
In this work, we derive a maximum likelihood formula for beamsteering in a multi-sensor array. The novelty of the work is that the impinging signal and noises are wide sense stationary (WSS) time series with unknown power spectral densities, unlike in previous work that typically considers white signals. Our approach naturally provides a way of fusing frequency-dependent information to obtain a broadband beamformer. In order to obtain the compressed likelihood, it is necessary to find the maximum likelihood estimates of the unknown parameters. However, this problem turns out to be an ML estimation of a block-Toeplitz matrix, which does not have a closed-form solution. To overcome this problem, we derive the asymptotic likelihood, which is given in the frequency domain. Finally, some simulation results are presented to illustrate the performance of the proposed technique. In these simulations, it is shown that our approach presents the best results.
{"title":"Multi-sensor beamsteering based on the asymptotic likelihood for colored signals","authors":"D. Ramírez, J. Vía, I. Santamaría, L. Scharf","doi":"10.1109/SSP.2011.5967644","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967644","url":null,"abstract":"In this work, we derive a maximum likelihood formula for beamsteering in a multi-sensor array. The novelty of the work is that the impinging signal and noises are wide sense stationary (WSS) time series with unknown power spectral densities, unlike in previous work that typically considers white signals. Our approach naturally provides a way of fusing frequency-dependent information to obtain a broadband beamformer. In order to obtain the compressed likelihood, it is necessary to find the maximum likelihood estimates of the unknown parameters. However, this problem turns out to be an ML estimation of a block-Toeplitz matrix, which does not have a closed-form solution. To overcome this problem, we derive the asymptotic likelihood, which is given in the frequency domain. Finally, some simulation results are presented to illustrate the performance of the proposed technique. In these simulations, it is shown that our approach presents the best results.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115301359","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 : 2011-06-28DOI: 10.1109/SSP.2011.5967737
Young-Chang Hou, Pei-hsiu Huang
In this paper, a novel intellectual property protection scheme for digital images based on visual cryptography and statistical property is proposed. The result of comparing two pixels that are selected randomly from the host image determines the content of the master share. Then, the master share and the watermark are used to generate the ownership share according to the encryption rules of visual cryptography. Our method does not need to alter the original image and can identify the ownership without restoring to the original image. Besides, our method allows multiple watermarks to be registered for a single host image without causing any damage to other hidden watermarks. Moreover, it is also possible for our scheme to cast a larger watermark into a smaller host image. Finally, experimental results will show the robustness of our scheme against several common attacks.
{"title":"Image protection based on visual cryptography and statistical property","authors":"Young-Chang Hou, Pei-hsiu Huang","doi":"10.1109/SSP.2011.5967737","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967737","url":null,"abstract":"In this paper, a novel intellectual property protection scheme for digital images based on visual cryptography and statistical property is proposed. The result of comparing two pixels that are selected randomly from the host image determines the content of the master share. Then, the master share and the watermark are used to generate the ownership share according to the encryption rules of visual cryptography. Our method does not need to alter the original image and can identify the ownership without restoring to the original image. Besides, our method allows multiple watermarks to be registered for a single host image without causing any damage to other hidden watermarks. Moreover, it is also possible for our scheme to cast a larger watermark into a smaller host image. Finally, experimental results will show the robustness of our scheme against several common attacks.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115496388","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 : 2011-06-28DOI: 10.1109/SSP.2011.5967638
M. Roubaud, B. Torrésani
We propose a new approach for merging gene expression data originating from independent microarray experiments. The proposed approach is based upon a model assuming dataset-independent gene expression distribution, and dataset-dependent observation noise and nonlinear observation functions. The estimation algorithm combines smoothing spline estimation for the observation functions with an iterative method for gene expression estimation. The approach is illustrated by numerical results on simulation studies and real data originating from prostate cancer datasets.
{"title":"A new approach for merging gene expression datasets","authors":"M. Roubaud, B. Torrésani","doi":"10.1109/SSP.2011.5967638","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967638","url":null,"abstract":"We propose a new approach for merging gene expression data originating from independent microarray experiments. The proposed approach is based upon a model assuming dataset-independent gene expression distribution, and dataset-dependent observation noise and nonlinear observation functions. The estimation algorithm combines smoothing spline estimation for the observation functions with an iterative method for gene expression estimation. The approach is illustrated by numerical results on simulation studies and real data originating from prostate cancer datasets.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115604126","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 : 2011-06-28DOI: 10.1109/SSP.2011.5967712
Luiz Felipe da Silva, J. Bermudez
Many existing speech enhancement techniques, especially Wiener filtering, suffer from introducing annoying musical noise and speech distortion in low SNR due to their rigid gain functions. In this paper we propose a modification to the parametric Wiener filter that emphasizes the spectral contributions in spectral regions which are important for intelligibility. This is done by defining an adaptive parameter that is a function of the pitch. Objective measures and statistical tests are used to assess subjective speech quality and intelligibility. The results indicate that the proposed algorithm results in speech intelligibility improvement and in musical noise reduction, as compared to the parametric Wiener filter.
{"title":"Speech enhancement using a frame adaptive gain function for Wiener filtering","authors":"Luiz Felipe da Silva, J. Bermudez","doi":"10.1109/SSP.2011.5967712","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967712","url":null,"abstract":"Many existing speech enhancement techniques, especially Wiener filtering, suffer from introducing annoying musical noise and speech distortion in low SNR due to their rigid gain functions. In this paper we propose a modification to the parametric Wiener filter that emphasizes the spectral contributions in spectral regions which are important for intelligibility. This is done by defining an adaptive parameter that is a function of the pitch. Objective measures and statistical tests are used to assess subjective speech quality and intelligibility. The results indicate that the proposed algorithm results in speech intelligibility improvement and in musical noise reduction, as compared to the parametric Wiener filter.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127206567","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 : 2011-06-28DOI: 10.1109/SSP.2011.5967659
F. Lindsten, Henrik Ohlsson, L. Ljung
We present a novel clustering method, formulated as a convex optimization problem. The method is based on over-parameterization and uses a sum-of-norms (SON) regularization to control the tradeoff between the model fit and the number of clusters. Hence, the number of clusters can be automatically adapted to best describe the data, and need not to be specified a priori. We apply SON clustering to cluster the particles in a particle filter, an application where the number of clusters is often unknown and time varying, making SON clustering an attractive alternative.
{"title":"Clustering using sum-of-norms regularization: With application to particle filter output computation","authors":"F. Lindsten, Henrik Ohlsson, L. Ljung","doi":"10.1109/SSP.2011.5967659","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967659","url":null,"abstract":"We present a novel clustering method, formulated as a convex optimization problem. The method is based on over-parameterization and uses a sum-of-norms (SON) regularization to control the tradeoff between the model fit and the number of clusters. Hence, the number of clusters can be automatically adapted to best describe the data, and need not to be specified a priori. We apply SON clustering to cluster the particles in a particle filter, an application where the number of clusters is often unknown and time varying, making SON clustering an attractive alternative.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124742770","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 : 2011-06-28DOI: 10.1109/SSP.2011.5967774
D. Ciuonzo, F. Palmieri
In pattern classification problems lack of knowledge about the prior distribution is typically filled up with uniform priors. However this choice may lead to unsatisfactory inference results when the amount of observed data is scarce. The application of Maximum Entropy (ME) principle to prior determination results in the so-called en-tropic priors, which provide a much more cautious inference in comparison to uniform priors. The idea, introduced mainly within the context of theoretical physics, is applied here to signal processing scenarios. We derive efficient formulas for computing and updating entropic priors when the the likelihoods follow on Independent, Markov and Hidden Markov models and we apply them to a target-track classification task.
{"title":"Entropic priors for hidden-Markov model classification","authors":"D. Ciuonzo, F. Palmieri","doi":"10.1109/SSP.2011.5967774","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967774","url":null,"abstract":"In pattern classification problems lack of knowledge about the prior distribution is typically filled up with uniform priors. However this choice may lead to unsatisfactory inference results when the amount of observed data is scarce. The application of Maximum Entropy (ME) principle to prior determination results in the so-called en-tropic priors, which provide a much more cautious inference in comparison to uniform priors. The idea, introduced mainly within the context of theoretical physics, is applied here to signal processing scenarios. We derive efficient formulas for computing and updating entropic priors when the the likelihoods follow on Independent, Markov and Hidden Markov models and we apply them to a target-track classification task.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126065432","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 : 2011-06-28DOI: 10.1109/SSP.2011.5967669
Arash Mohammadi, A. Asif
In this paper, we propose a consensus-based, distributed implementation of the unscented particle filter (CD/UPF) that extends the distributed Kalman filtering framework to non-linear, distributed dynamical systems with non-Gaussian excitations. Compared to the existing distributed implementations of the particle filter, the CD/UPF offers two advantages. First, it uses all available local observations including the most recent ones in deriving the proposal distribution. Second, computation of global estimates from local estimates during the consensus step is based on an optimal fusion rule. In our bearing-only tracking simulations, the performance of the proposed CD/UPF is virtually indistinguishable from its centralized counterpart.
{"title":"Consensus-based distributed unscented particle filter","authors":"Arash Mohammadi, A. Asif","doi":"10.1109/SSP.2011.5967669","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967669","url":null,"abstract":"In this paper, we propose a consensus-based, distributed implementation of the unscented particle filter (CD/UPF) that extends the distributed Kalman filtering framework to non-linear, distributed dynamical systems with non-Gaussian excitations. Compared to the existing distributed implementations of the particle filter, the CD/UPF offers two advantages. First, it uses all available local observations including the most recent ones in deriving the proposal distribution. Second, computation of global estimates from local estimates during the consensus step is based on an optimal fusion rule. In our bearing-only tracking simulations, the performance of the proposed CD/UPF is virtually indistinguishable from its centralized counterpart.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"121 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123445453","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 : 2011-06-28DOI: 10.1109/SSP.2011.5967748
David S. Choi, P. Wolfe
The latent position model is a well known model for social network analysis which has also found application in other fields, such as analysis of marketing and e-commerce data. In such applications, the data sets are increasingly massive and only partially observed, giving rise to the possibility of overfitting by the model. Using tools from statistical learning theory, we bound the VC dimension of the latent position model, leading to bounds on the overfit of the model. We find that the overfit can decay to zero with increasing network size even if only a vanishing fraction of the total network is observed. However, the amount of observed data on a per-node basis should increase with the size of the graph.
{"title":"Learnability of latent position network models","authors":"David S. Choi, P. Wolfe","doi":"10.1109/SSP.2011.5967748","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967748","url":null,"abstract":"The latent position model is a well known model for social network analysis which has also found application in other fields, such as analysis of marketing and e-commerce data. In such applications, the data sets are increasingly massive and only partially observed, giving rise to the possibility of overfitting by the model. Using tools from statistical learning theory, we bound the VC dimension of the latent position model, leading to bounds on the overfit of the model. We find that the overfit can decay to zero with increasing network size even if only a vanishing fraction of the total network is observed. However, the amount of observed data on a per-node basis should increase with the size of the graph.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"686 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126928308","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 : 2011-06-28DOI: 10.1109/SSP.2011.5967648
F. Abda, S. Femmam
We expose in this paper an explanation of the Cramer-Rao lower bound calculation for Doppler velocity estimation using a recently proposed hybrid method [1, 2]. We provide extensive simulation results using a model for the backscat-tered ultrasonic signal from a set of moving particles. It will be shown that the proposed theoretical model fits the simulation variances for larger signal-to-noise ratio ranges and using two different pulse compression techniques. A formula is also provided for expressing the expected minimum standard deviation on velocity estimation with respect to the various Doppler sonar parameters.
{"title":"Performance of Hybrid Wideband Doppler Sonars","authors":"F. Abda, S. Femmam","doi":"10.1109/SSP.2011.5967648","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967648","url":null,"abstract":"We expose in this paper an explanation of the Cramer-Rao lower bound calculation for Doppler velocity estimation using a recently proposed hybrid method [1, 2]. We provide extensive simulation results using a model for the backscat-tered ultrasonic signal from a set of moving particles. It will be shown that the proposed theoretical model fits the simulation variances for larger signal-to-noise ratio ranges and using two different pulse compression techniques. A formula is also provided for expressing the expected minimum standard deviation on velocity estimation with respect to the various Doppler sonar parameters.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116144353","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 : 2011-06-28DOI: 10.1109/SSP.2011.5967722
S. Somasundaram
Recent robust Capon beamformers (RCBs) systematically allow for array steering vector (ASV) errors by exploiting ASV uncertainty ellipsoids, which are typically characterized in element space (ES). Reduced dimension (RD) techniques are often used to reduce computational complexity and speed up algorithm convergence. Here, a general framework is proposed for combining RD and RCB techniques, producing RD-RCBs. The key to this framework is a complex propagation theorem, which propagates the ES ellipsoid through the dimension reducing transform, so that the appropriate ASV uncertainty information is exploited in the RD space.
{"title":"A framework for reduced dimension robust Capon beamforming","authors":"S. Somasundaram","doi":"10.1109/SSP.2011.5967722","DOIUrl":"https://doi.org/10.1109/SSP.2011.5967722","url":null,"abstract":"Recent robust Capon beamformers (RCBs) systematically allow for array steering vector (ASV) errors by exploiting ASV uncertainty ellipsoids, which are typically characterized in element space (ES). Reduced dimension (RD) techniques are often used to reduce computational complexity and speed up algorithm convergence. Here, a general framework is proposed for combining RD and RCB techniques, producing RD-RCBs. The key to this framework is a complex propagation theorem, which propagates the ES ellipsoid through the dimension reducing transform, so that the appropriate ASV uncertainty information is exploited in the RD space.","PeriodicalId":274050,"journal":{"name":"2011 IEEE Statistical Signal Processing Workshop (SSP)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116420150","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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