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Hadamard Matrices and Their Applications

A. S. Hedayat, +1 more
- 01 Nov 1978 - 
- Vol. 6, Iss: 6, pp 1184-1238
TLDR
Hadamard matrices have been widely studied in the literature and many of their applications can be found in this paper, e.g., incomplete block designs, Youden designs, orthogonal $F$-square designs, optimal saturated resolution III (SRSIII), optimal weighing designs, maximal sets of pairwise independent random variables with uniform measure, error correcting and detecting codes, Walsh functions, and other mathematical and statistical objects.
Abstract
An $n \times n$ matrix $H$ with all its entries $+1$ and $-1$ is Hadamard if $HH' = nI$. It is well known that $n$ must be 1, 2 or a multiple of 4 for such a matrix to exist, but is not known whether Hadamard matrices exist for every $n$ which is a multiple of 4. The smallest order for which a Hadamard matrix has not been constructed is (as of 1977) 268. Research in the area of Hadamard matrices and their applications has steadily and rapidly grown, especially during the last three decades. These matrices can be transformed to produce incomplete block designs, $t$-designs, Youden designs, orthogonal $F$-square designs, optimal saturated resolution III designs, optimal weighing designs, maximal sets of pairwise independent random variables with uniform measure, error correcting and detecting codes, Walsh functions, and other mathematical and statistical objects. In this paper we survey the existence of Hadamard matrices and many of their applications.

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A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain

TL;DR: In this article, a new iterative low complexity algorithm for computing the Walsh-Hadamard transform (WHT) of an N-dimensional signal with a K-sparse WHT was presented.
Journal ArticleDOI

A Fast Hadamard Transform for Signals With Sublinear Sparsity in the Transform Domain

TL;DR: A new iterative low-complexity algorithm for computing the Walsh–Hadamard transform (WHT) of an $N$ dimensional signal with a $K$ -sparse WHT based on the subsampling (aliasing) property of the WHT, where a suitable aliasing pattern is induced in the transform domain.