任意维成对协素数整数矩阵及其最小公右倍数的构造

IF 4.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Guangpu Guo;Xiang-Gen Xia
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引用次数: 0

摘要

与协素数矩阵相比,协素数矩阵由于其不可交换性而更具挑战性。本文给出了一类新的大尺寸任意维的成对协素数矩阵。这些矩阵是不可交换的,具有低扩展,也就是说,它们的分量的峰值绝对值与平均值(或最小的非零绝对值)的比率很低。当矩阵维数大于2时,这类矩阵不同于现有的循环矩阵、Toeplitz矩阵或三角矩阵,因此在应用中提供了更多的变化。本文首先证明了所构造矩阵的成对共性,然后用一个封闭的简单形式确定了它们的行列式绝对值,以及它们的最小公右乘。当这些矩阵用作多维信号的采样矩阵时,我们还分析了它们的采样率。所提出的一对协素数矩阵族可以应用于多维中国剩余定理(MD-CRT)中,该定理可用于从整数向量余数对一组整数矩阵模确定整数向量,也可以应用于多维稀疏感知和多速率系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Construction of Pairwise Co-Prime Integer Matrices of Any Dimension and Their Least Common Right Multiple
Compared with co-prime integers, co-prime integer matrices are more challenging due to the non-commutativity. In this paper, we present a new family of pairwise co-prime integer matrices of any dimension and large size. These matrices are non-commutative and have low spread, i.e., their ratios of peak absolute values to mean absolute values (or the smallest non-zero absolute values) of their components are low. When matrix dimension is larger than 2, this family of matrices differs from the existing families, such as circulant, Toeplitz matrices, or triangular matrices, and therefore, offers more varieties in applications. In this paper, we first prove the pairwise coprimality of the constructed matrices, then determine their determinant absolute values, and their least common right multiple (lcrm) with a closed and simple form. We also analyze their sampling rates when these matrices are used as sampling matrices for a multi-dimensional signal. The proposed family of pairwise co-prime integer matrices may have applications in multi-dimensional Chinese remainder theorem (MD-CRT) that can be used to determine integer vectors from their integer vector remainders modulo a set of integer matrix moduli, and also in multi-dimensional sparse sensing and multirate systems.
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来源期刊
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing 工程技术-工程:电子与电气
CiteScore
11.20
自引率
9.30%
发文量
310
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.
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