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Optimal Privacy-Enhancing And Cost-Efficient Energy Management Strategies For Smart Grid Consumers 智能电网用户的最优隐私增强和成本效益能源管理策略
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450736
Yang You, Zuxing Li, T. Oechtering
{"title":"Optimal Privacy-Enhancing And Cost-Efficient Energy Management Strategies For Smart Grid Consumers","authors":"Yang You, Zuxing Li, T. Oechtering","doi":"10.1109/SSP.2018.8450736","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450736","url":null,"abstract":"The design of optimal energy management strategies that trade-off consumers’ privacy and expected energy cost by using an energy storage is studied. The Kullback-Leibler divergence rate is used to assess the privacy risk of the unauthorized testing on consumers’ behavior. We further show how this design problem can be formulated as a belief state Markov decision process problem so that standard tools of the Markov decision process framework can be utilized, and the optimal solution can be obtained by using Bellman dynamic programming. Finally, we illustrate the privacy-enhancement and cost-saving by numerical examples.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"297 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133199972","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
Hyperspectral Super-Resolution: Exact Recovery In Polynomial Time 高光谱超分辨率:多项式时间精确恢复
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450697
Qiang Li, Wing-Kin Ma, Qiong Wu
{"title":"Hyperspectral Super-Resolution: Exact Recovery In Polynomial Time","authors":"Qiang Li, Wing-Kin Ma, Qiong Wu","doi":"10.1109/SSP.2018.8450697","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450697","url":null,"abstract":"In hyperspectral remote sensing, the hyperspectral super-resolution (HSR) problem has recently received growing interest. Simply speaking, the problem is to recover a super-resolution image—which has high spectral and spatial resolutions—from some lower spectral and spatial resolution measurements. Many of the current HSR studies consider matrix factorization formulations, with an emphasis on algorithms and performance in practice. On the other hand, the question of whether a factorization model is equipped with provable recovery guarantees of the true super-resolution image is much less explored. In this paper we show that unique and exact recovery of the super-resolution image is not only possible, it can also be done in polynomial time. We employ the matrix factorization model commonly used in the context of hyperspectral unmixing, and show that if certain local sparsity conditions are satisfied then the matrix factors constituting the true super-resolution image can be recovered by a simple two-step procedure.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127173823","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: 12
Convolutional Gaussian Mixture Models with Application to Compressive Sensing 卷积高斯混合模型及其在压缩感知中的应用
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450817
Ren Wang, X. Liao, Jingbo Guo
{"title":"Convolutional Gaussian Mixture Models with Application to Compressive Sensing","authors":"Ren Wang, X. Liao, Jingbo Guo","doi":"10.1109/SSP.2018.8450817","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450817","url":null,"abstract":"Gaussian mixture models (GMM) have been used to statistically represent patches in an image. Extending from small patches to an entire image, we propose a convolutional Gaussian mixture models (convGMM) to model the statistics of an entire image and apply it for compressive sensing (CS). We present the algorithm details for learning a convGMM from training images by maximizing the marginal log-likelihood estimation (MMLE). The learned convGMM is used to perform model-based compressive sensing, using the convGMM as a model of the underlying image. In addition, a key feature of our method is that all of the training and reconstruction process could be fast and efficient calculated in the frequency-domain by 2-dimensional fast Fourier transforms (2d-FFTs). The performance of the convGMM on CS is demonstrated on several image sets.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084969","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
On-Line Blind Unmixing For Hyperspectral Pushbroom Imaging Systems 高光谱推扫成像系统的在线盲解混
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450702
Ludivine Nus, S. Miron, D. Brie
{"title":"On-Line Blind Unmixing For Hyperspectral Pushbroom Imaging Systems","authors":"Ludivine Nus, S. Miron, D. Brie","doi":"10.1109/SSP.2018.8450702","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450702","url":null,"abstract":"In this paper, the on-line hyperspectral image blind unmixing is addressed. Inspired by the Incremental Non-negative Matrix Factorization (INMF) method [2], we propose an on-line NMF which is adapted to the acquisition scheme of a pushbroom imager. Because of the non-uniqueness of the NMF model, a minimum volume constraint on the endmembers is added allowing to reduce the set of admissible solutions. This results in a stable algorithm yielding results similar to those of standard off-line NMF methods, but drastically reducing the computation time. The algorithm is applied to wood hyperspectral images showing that such a technique is effective for the on-line prediction of wood piece rendering after finishing.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128947489","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: 3
Rényi Divergence to Compare Moving-Average Processes 比较移动平均过程的雷氏散度
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450711
Fernando Merchan, É. Grivel, R. Diversi
{"title":"Rényi Divergence to Compare Moving-Average Processes","authors":"Fernando Merchan, É. Grivel, R. Diversi","doi":"10.1109/SSP.2018.8450711","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450711","url":null,"abstract":"Comparing processes or models is of interest in various applications. Among the existing approaches, one of the most popular methods is to use the Kullback-Leibler (KL) divergence which is related to Shannon’s entropy. Similarly, the Rényi divergence of order α can be deduced from the Rényi entropy. When α tends to 1, it leads to the KL divergence. In this paper, our purpose is to derive the expression of the Rényi divergence between the probability density functions of k consecutive samples of two real first-order moving average (MA) processes by using the eigen-decompositions of their Toeplitz correlation matrices. The resulting expression is compared with the expressions of the Rao distance and the Jeffrey’s divergence (JD) based on the eigenvalues. The way these quantities evolve when k increases is then presented. When dealing with unit-zero MA processes, the derivate is infinite for the JD and finite for the others. The influence of α is also studied.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127575793","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
One-Bit Sigma-Delta Modulation on a Closed Loop 闭环上的一位σ - δ调制
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450721
S. Krause-Solberg, Olga Graf, F. Krahmer
{"title":"One-Bit Sigma-Delta Modulation on a Closed Loop","authors":"S. Krause-Solberg, Olga Graf, F. Krahmer","doi":"10.1109/SSP.2018.8450721","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450721","url":null,"abstract":"In this paper, we propose a scheme for the first order one-bit ΣΔ modulation on a closed loop. In contrast to schemes designed for the real line, which rely entirely on a recurrence relation, the difficulty of this setting is to avoid mismatches at the initialization point. In order to distribute the error equally around the loop, we propose an update of the samples using information we gain from the classical approach. We prove that the proposed scheme leads to a smaller reconstruction error and we discuss how this scheme can be extended to higher orders.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"74 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115629089","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
Hierarchical Sparsity Within And Across Overlapping Groups 重叠组内和组间的层次稀疏性
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450707
I. Bayram
{"title":"Hierarchical Sparsity Within And Across Overlapping Groups","authors":"I. Bayram","doi":"10.1109/SSP.2018.8450707","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450707","url":null,"abstract":"Recently, different penalties have been proposed for signals whose non-zero coefficients reside in a small number of groups, where within each group, only few of the coefficients are active. In this paper, we extend such a penalty, and introduce an additional layer of grouping on the coefficients. Specifically, we first partition the signal into groups, and then apply the penalty on the $ell _{2}$ norms of the groups. We discuss how this extended penalty can be used in energy minimization formulations, and demonstrate the effects of the proposed extension on a dereverberation experiment.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"2672 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122554911","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
Union of Subspaces Signal Detection In Subspace Interference 子空间干扰中子空间信号检测的并集
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450694
M. Lodhi, W. Bajwa
{"title":"Union of Subspaces Signal Detection In Subspace Interference","authors":"M. Lodhi, W. Bajwa","doi":"10.1109/SSP.2018.8450694","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450694","url":null,"abstract":"This paper investigates detection theory for signals belonging to a union of subspaces (UoS) in the presence of an interference subspace and white noise of unknown variance. Generalized likelihood ratio tests are provided for both signal detection and “active” subspace detection under the UoS model. The paper also derives performance bounds on the associated detection problems and relates them to the geometry of subspaces in the union and the interfering subspace. These relations are then corroborated through numerical experiments on synthetic data.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115914967","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: 3
Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization 基于熵最大化的半参数密度估计的独立分量分析
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450858
Zois Boukouvalas, Y. Levin-Schwartz, Rami Mowakeaa, Gengshen Fu, T. Adalı
{"title":"Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization","authors":"Zois Boukouvalas, Y. Levin-Schwartz, Rami Mowakeaa, Gengshen Fu, T. Adalı","doi":"10.1109/SSP.2018.8450858","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450858","url":null,"abstract":"Independent component analysis (ICA) is one of the most popular methods for blind source separation with a diverse set of applications, such as: biomedical signal processing, video and image analysis, and communications. The success of ICA is tied to proper characterization of the probability density function (PDF) of the latent sources; information that is generally unknown. In this work, we propose a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which uses both global and local measuring functions as constraints to dynamically estimate the PDF of the sources. We present a mathematical justification of its convergence and demonstrate its superior performance over competing ICA algorithms using simulated as well as real-world data.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131729165","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: 3
Sequential MCMC With The Discrete Bouncy Particle Sampler 离散弹性粒子采样器的顺序MCMC
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450772
Soumyasundar Pal, M. Coates
{"title":"Sequential MCMC With The Discrete Bouncy Particle Sampler","authors":"Soumyasundar Pal, M. Coates","doi":"10.1109/SSP.2018.8450772","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450772","url":null,"abstract":"Sequential MCMC (SMCMC) methods are a useful alternative to particle filters for performing sequential inference in a Bayesian framework in nonlinear and non-Gaussian state-space models. The weight degeneracy phenomenon which impacts the performance of even the most advanced particle filters in higher dimensions is avoided. In this paper, we explore the applicability of the discrete bouncy particle sampler, which is based on constructing a guided random walk and performing delayed rejection, to perform more effective sampling within SMCMC. We perform numerical simulations to examine when the proposed method offers advantages compared to state-of-the-art SMCMC techniques.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131173311","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: 3
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