Multi Layer Analysis

TL;DR

This thesis introduces Multi Layer Analysis (MLA), a novel methodology for analyzing one-dimensional signals, offering new insights and applications across pattern discovery, biology, and seismology.

Contribution

It proposes the MLA approach, linking it to tree kernels, randomness tests, and signal processing, with practical applications demonstrated in biology and seismology.

Findings

MLA provides a new framework for signal analysis.

MLA reveals relationships between signals and tree kernels.

Applications demonstrate MLA's effectiveness in real-world problems.

Abstract

This thesis presents a new methodology to analyze one-dimensional signals trough a new approach called Multi Layer Analysis, for short MLA. It also provides some new insights on the relationship between one-dimensional signals processed by MLA and tree kernels, test of randomness and signal processing techniques. The MLA approach has a wide range of application to the fields of pattern discovery and matching, computational biology and many other areas of computer science and signal processing. This thesis includes also some applications of this approach to real problems in biology and seismology.