离散余弦变换的一种推广

离散余弦变换(DCT)在数字信号、图像处理、频谱分析、数据压缩和信息隐藏等领域有着广泛的应用.推广离散余弦变换,给出一个包含三个参数的统一表达式,并证明在许多情形新变换是正交变换.最后给出一种新型离散余弦变换,并证明它是正交变换.     

计算一类数字变换的收缩阵列

本文以DFT的收缩（Systolic）阵列结构为基础，给出了一类数字变换的收缩阵列，这些变换包括离散富里叶变换，离散余弦变换，离散正弦变换，离散Hartley变换，数论变换和多项式变换．     

码本在压缩感知矩阵中的应用

压缩感知矩阵的构造在压缩感知理论中起着举足轻重的作用.基于线性码和最优码本构造了一类新的压缩感知矩阵,并与DeVore构造的压缩感知矩阵和Gaussian随机矩阵的进比较,从不同的角度分别证明了当参数满足一定的条件时,新构造的压缩感知矩阵具有更好的性能.     

多面体块稀疏表示和非凸压缩感知

压缩感知(compressed sensing,CS)理论表明稀疏信号可以从欠定系统中被准确恢复,但在很多实际应用中,信号不一定有标准稀疏性而可能拥有一些其他的结构特点,典型的一种就是块稀疏信号,它的非零元仅在很少的一些块中出现.本文考虑从很少的线性测量中恢复块稀疏信号,并得到经混合l2/lq(0＜q≤1)最小化准确重...     

基于冗余紧框架的?2/?1极小化块稀疏压缩感知

压缩感知是(近似)稀疏信号处理的研究热点之一,它突破了Nyquist/Shannon采样率,实现了信号的高效采集和鲁棒重构.本文采用l2/l1极小化方法和BlockD-RIP理论研究了在冗余紧框架下的块稀疏信号,所获结果表明,当BlockD-RIP常数δ2k/τ满足0<δ2k/τ<0.2时,l2/l1极小化方法能够鲁棒重构原始信号,同时改进了已有的重构条件和误差上界.基于离散傅里叶变换(DFT)字典,执行了一系列仿真实验充分证实了理论结果.     

块H-矩阵与块矩阵的谱

利用G-函数概念研究块H-矩阵,引入若干块矩阵概念。获得了块H-矩阵的等价刻划,得到了一般块矩阵特征值的由G-函数描述的分布域,由于用G-函数刻划,所获结果具有一般性。     

基于SCAD罚函数的有噪压缩感知

研究有噪声的压缩感知,提出了基于SCAD罚函数的压缩感知策略,并给出一种高效的阈值迭代算法,从理论上证明了算法的有效性.大量实验验证基于SCAD罚函数的压缩感知策略解的稀疏性及稳健性.     

缺项块矩阵的投影补

设Ａ、Ｂ、Ｃ分别为ｎ×ｎ，ｍ×ｍ，ｎ×ｍ复数矩阵，本文得到缺项矩阵存在投影补的充分必要条件，并且给出这些投影补的完全刻画．     

基于矩阵斜消变换的最大公因式求解

提出了矩阵的第一、第二斜消变换概念,并利用其得到求解多个多项式的最大公因式的方法.提供了相关的证明及具体的应用实例.     

块α-对角占优矩阵的讨论

应用矩阵块对角占优理论,讨论了块α-对角占优矩阵之间的蕴含关系,并得到了条件最弱的块严格α1-双对角占优的两个等价表征,并作为应用给出了块H矩阵新的判定准则,最后用数值例子说明结果的有效性.     

Deterministic constructions of compressed sensing matrices

Compressed sensing is a new area of signal processing. Its goal is to minimize the number of samples that need to be taken from a signal for faithful reconstruction. The performance of compressed sensing on signal classes is directly related to Gelfand widths. Similar to the deeper constructions of optimal subspaces in Gelfand widths, most sampling algorithms are based on randomization. However, for possible circuit implementation, it is important to understand what can be done with purely deterministic sampling. In this note, we show how to construct sampling matrices using finite fields. One such construction gives cyclic matrices which are interesting for circuit implementation. While the guaranteed performance of these deterministic constructions is not comparable to the random constructions, these matrices have the best known performance for purely deterministic constructions.     

Improvement of the Discrete Cosine Transform calculation by means of a recursive method

This paper presents a recursive method for function evaluation. The proposal argues for the use of a more complete primitive, namely a weighted sum, which converts the calculation of the function values into a recursive operation defined by a two input table. The weighted sum can be tuned for different values of the weighting parameters that hold the features of the concrete evaluated function. The calculation of the Discrete Cosine Transform (DCT) is improved by this method which provides a decrease of the operation count as well as an acceptable error bound. These results have repercussions on the compression standard JPEG, for the purposes of storage and transmission in web-based applications     

Robust Mean-Squared Error Estimation of Multiple Signals in Linear Systems Affected by Model and Noise Uncertainties

This paper is a continuation of the work in [11] and [2] on the problem of estimating by a linear estimator, N unobservable input vectors, undergoing the same linear transformation, from noise-corrupted observable output vectors. Whereas in the aforementioned papers, only the matrix representing the linear transformation was assumed uncertain, here we are concerned with the case in which the second order statistics of the noise vectors (i.e., their covariance matrices) are also subjected to uncertainty. We seek a robust mean-squared error estimator immuned against both sources of uncertainty. We show that the optimal robust mean-squared error estimator has a special form represented by an elementary block circulant matrix, and moreover when the uncertainty sets are ellipsoidal-like, the problem of finding the optimal estimator matrix can be reduced to solving an explicit semidefinite programming problem, whose size is independent of N. The research was partially supported by BSF grant #2002038     

Semiconvergence of block SOR method for singular linear systems with p-cyclic matrices

In this paper, we discuss semiconvergence of the block SOR method for solving singular linear systems with p-cyclic matrices. Some sufficient conditions for the semiconvergence of the block SOR method for solving a general p-cyclic singular system are proved.     

A coupled method of Laplace transform and Legendre wavelets for nonlinear Klein–Gordon equations

Klein–Gordon equation models many phenomena in both physics and applied mathematics. In this paper, a coupled method of Laplace transform and Legendre wavelets, named (LLWM), is presented for the approximate solutions of nonlinear Klein–Gordon equations. By employing Laplace operator and Legendre wavelets operational matrices, the Klein–Gordon equation is converted into an algebraic system. Hence, the unknown Legendre wavelets coefficients are calculated in the form of series whose components are computed by applying a recursive relation. Block pulse functions are used to calculate the Legendre wavelets coefficient vectors of nonlinear terms. The convergence analysis of the LLWM is discussed. The results show that LLWM is very effective and easy to implement. Copyright © 2013 John Wiley & Sons, Ltd.