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

TL;DR

This paper introduces a novel lattice-based framework called primorial lattice for multi-resolution logic, probability, and symbolic sequence processing, enabling analysis at various resolutions and frequencies.

Contribution

It develops a new mathematical structure combining Boolean and orthocomplemented lattices for multi-resolution symbolic and probabilistic analysis.

Findings

Introduces the primorial lattice P for multi-resolution analysis.

Defines operators R and difference for lattice generation.

Demonstrates applications to logic, probability, and genomic sequences.

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").