2015 International Conference on Sampling Theory and Applications (SampTA)最新文献_第8页

A class of frames for Paley-Wiener spaces with multiple lattice tiles support 一类具有多格块支持的paly - wiener空间的框架

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148856

Ezra Tampubolon, V. Pohl, H. Boche

引用次数: 0

Kramer's generalized sampling of stochastic processes 随机过程的克雷默广义抽样

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148888

Sinuk Kang

引用次数: 0

Sparse source localization in presence of co-array perturbations 存在共阵扰动的稀疏源定位

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148954

A. Koochakzadeh, P. Pal

引用次数: 12

An Amalgam Balian-Low Theorem for symplectic lattices of rational density 有理密度辛格的Amalgam Balian-Low定理

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148866

C. Cabrelli, U. Molter, G. Pfander

引用次数: 4

Nonlinear sampling for sparse recovery 稀疏恢复的非线性采样

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148872

S. A. Hosseini, Mahdi Barzegar Khalilsarai, A. Amini, F. Marvasti

{"title":"Nonlinear sampling for sparse recovery","authors":"S. A. Hosseini, Mahdi Barzegar Khalilsarai, A. Amini, F. Marvasti","doi":"10.1109/SAMPTA.2015.7148872","DOIUrl":"https://doi.org/10.1109/SAMPTA.2015.7148872","url":null,"abstract":"Linear sampling of sparse vectors via sensing matrices has been a much investigated problem in the past decade. The nonlinear sampling methods, such as quadratic forms are also studied marginally to include undesired effects in data acquisition devices (e.g., Taylor series expansion up to two terms). In this paper, we introduce customized nonlinear sampling techniques that provide possibility of sparse signal recovery. The main advantage of the nonlinear method over the conventional linear schemes is the reduction in the number of required samples to 2k for recovery of k-sparse signals. We also introduce a low-complexity reconstruction method similar to the annihilating filter in the sampling of signals with finite rate of innovation (FRI). The disadvantage of this nonlinear sampler is its sensitivity to additive noise; thus, it is suitable in scenarios dealing with noiseless data such as network packets, where the data is either noiseless or it is erased. We show that by increasing the number of samples and applying denoising techniques, one can improve the performance. We further introduce a modified version of the proposed method which has strong links with spectral estimation methods and exhibits a more stable performance under noise and numerical errors. Simulation results confirm that this method is much faster than ℓ1-norm minimization routines, widely used in linear compressed sensing and thus much less complex.","PeriodicalId":311830,"journal":{"name":"2015 International Conference on Sampling Theory and Applications (SampTA)","volume":"8 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114052144","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

Spatially distributed sampling and reconstruction of high-dimensional signals 高维信号的空间分布采样与重构

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148932

Cheng Cheng, Yingchun Jiang, Qiyu Sun

引用次数: 5

Parameter estimation from samples of stationary complex Gaussian processes 平稳复高斯过程样本参数估计

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148890

P. Hurley, Orhan Ocal

引用次数: 0

Analysis of low rank matrix recovery via Mendelson's small ball method 门德尔松小球法低秩矩阵恢复分析

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148918

Maryia Kabanava, H. Rauhut, Ulrich Terstiege

{"title":"Analysis of low rank matrix recovery via Mendelson's small ball method","authors":"Maryia Kabanava, H. Rauhut, Ulrich Terstiege","doi":"10.1109/SAMPTA.2015.7148918","DOIUrl":"https://doi.org/10.1109/SAMPTA.2015.7148918","url":null,"abstract":"We study low rank matrix recovery from undersampled measurements via nuclear norm minimization. We aim to recover an n1 x n2 matrix X from m measurements (Frobenius inner products) 〈X, Aj〉, j = 1...m. We consider different scenarios of independent random measurement matrices Aj and derive bounds for the minimal number of measurements sufficient to uniformly recover any rank r matrix X with high probability. Our results are stable under passing to only approximately low rank matrices and under noise on the measurements. In the first scenario the entries of the Aj are independent mean zero random variables of variance 1 with bounded fourth moments. Then any X of rank at most r is stably recovered from m measurements with high probability provided that m ≥ Cr max{n1, n2}. The second scenario studies the physically important case of rank one measurements. Here, the matrix X to recover is Hermitian of size n × n and the measurement matrices Aj are of the form Aj = aja*j for some random vectors aj. If the aj are independent standard Gaussian random vectors, then we obtain uniform stable and robust rank-r recovery with high probability provided that m ≥ crn. Finally we consider the case that the aj are independently sampled from an (approximate) 4-design. Then we require m ≥ crn log n for uniform stable and robust rank-r recovery. In all cases, the results are shown via establishing a stable and robust version of the rank null space property. To this end, we employ Mendelson's small ball method.","PeriodicalId":311830,"journal":{"name":"2015 International Conference on Sampling Theory and Applications (SampTA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115530777","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: 4

Resolution limits for atomic decompositions via Markov-Bernstein type inequalities 利用Markov-Bernstein型不等式求解原子分解的分辨率极限

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148951

Gongguo Tang

引用次数: 38

Filter recovery in infinite spatially invariant evolutionary system via spatiotemporal trade off 基于时空权衡的无限空间不变进化系统的滤波恢复

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI: 10.1109/SAMPTA.2015.7148930

Sui Tang

{"title":"Filter recovery in infinite spatially invariant evolutionary system via spatiotemporal trade off","authors":"Sui Tang","doi":"10.1109/SAMPTA.2015.7148930","DOIUrl":"https://doi.org/10.1109/SAMPTA.2015.7148930","url":null,"abstract":"We consider the problem of spatiotemporal sampling in an evolutionary process xn = Anx where an unknown operator A driving an unknown initial state x is to be recovered from a combined set of coarse spatial samples {χ|Ωο, x(1)|Ωι,· · ·, x(N)|ΩN}. In this paper, we will study the case of infinite dimensional spatially invariant evolutionary process, where the unknown initial signals x are modeled as ℓ2(Z) and A is an unknown spatial convolution operator given by a filter α ε ℓ1 (Z) so that Ax = a · x. We show that {x|Ωm, x(1)|Ωm, ···, x(N)|Ωm:N≥2m -, Ωm = mZ} contains enough information to recover the Fourier spectrum of a typical low pass filter a, if x is from a dense subset of ℓ2 (Z). The idea is based on a nonlinear, generalized Prony method similar to [2]. We provide an algorithm for the case when a and x are both compactly supported. Finally, We perform the accuracy analysis based on the spectral properties of the operator A and the initial state x and verify them in several numerical experiments.","PeriodicalId":311830,"journal":{"name":"2015 International Conference on Sampling Theory and Applications (SampTA)","volume":"23 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125926334","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