MRA-Wavelet subspace architecture for logic, probability, and symbolic sequence processing

National Chiao Tung University, Hsinchu, Taiwan
DOI
10.7287/peerj.preprints.520v1
Subject Areas
Genomics, Mathematical Biology, Computational Science
Keywords
multiresolution analysis, genomic signal processing, MRA, wavelets, lattice theory, order theory, subspaces, genomic sequence processing, GSP, multi-valued logic, ortho logic
Copyright
© 2014 Greenhoe
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Greenhoe DJ. 2014. MRA-Wavelet subspace architecture for logic, probability, and symbolic sequence processing. PeerJ PrePrints 2:e520v1

Abstract

The linear subspaces of a multiresolution analysis (MRA) and the linear subspaces of the wavelet analysis induced by the MRA, together with the set inclusion relation, form a very special lattice of subspaces which herein is called a "primorial lattice". This paper introduces an operator R that extracts a set of 2^{N-1} element Boolean lattices from a 2^N element Boolean lattice. Used recursively, a sequence of Boolean lattices with decreasing order is generated---a structure that is similar to an MRA. A second operator, which is a special case of a "difference operator", is introduced that operates on consecutive Boolean lattices L_2^n and L_2^{n-1} to produce a sequence of orthocomplemented lattices. These two sequences, together with the subset ordering relation, form a primorial lattice P. A logic or probability constructed on a Boolean lattice L_2^N likewise induces a primorial lattice P. Such a logic or probability can then be rendered at N different "resolutions" by selecting any one of the N Boolean lattices in P and at N different "frequencies" by selecting any of the N different orthocomplemented lattices in P. Furthermore, P can be used for symbolic sequence analysis by projecting sequences of symbols onto the sublattices in P using one of three lattice projectors introduced. P can be used for symbolic sequence processing by judicious rejection and selection of projected sequences. Examples of symbolic sequences include sequences of logic values, sequences of probabilistic events, and genomic sequences (as used in "genomic signal processing").

Author Comment

This paper is largely a mathematical one, but yet has "clear applicability to the core areas of Biological, Medical or Health sciences". In particular, please see pages 72-74, where application to "Genomic Signal Processing" (GSP) is proposed.