Sampling with Walsh Transforms

TL;DR

This paper introduces a novel sampling method for noisy discrete signals using Walsh transforms, establishing conditions for effective sampling based on Shannon's channel coding theorem and highlighting cryptographic implications.

Contribution

It presents a new sampling framework for incomplete noisy signals leveraging Walsh transforms and Shannon's theorem, with a focus on large Walsh coefficients as signal identifiers.

Findings

Large Walsh coefficients characterize discrete statistical signals.

Necessary and sufficient conditions for sampling are established.

Cryptographic significance of sparse Walsh transform is discussed.

Abstract

With the advent of massive data outputs at a regular rate, admittedly, signal processing technology plays an increasingly key role. Nowadays, signals are not merely restricted to physical sources, they have been extended to digital sources as well. Under the general assumption of discrete statistical signal sources, we propose a practical problem of sampling incomplete noisy signals for which we do not know a priori and the sample size is bounded. We approach this sampling problem by Shannon's channel coding theorem. We use an extremal binary channel with high probability of transmission error, which is rare in communication theory. Our main result demonstrates that it is the large Walsh coefficient(s) that characterize(s) discrete statistical signals, regardless of the signal sources. Note that this is a known fact in specific application domains such as images. By the connection of Shannon's theorem, we establish the necessary and sufficient condition for our generic sampling problem for the first time. Finally, we discuss the cryptographic significance of sparse Walsh transform.