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Updated: Jul 3, 2026

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

Signal analysis on strings for immune-type pattern recognition.

Nikolaos D Atreas¹, Costas Karanikas, Persefoni Polychronidou

¹Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.

Comparative and Functional Genomics

|July 17, 2008

Summary

This summary is machine-generated.

This study introduces wavelet-type discrete transforms for analyzing finite strings, enabling edge and hidden Markov process detection. New methods for string matching and assessing chaotic string diversity are also presented.

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Area of Science:

Signal processing
String analysis
Applied mathematics

Background:

Finite strings present unique challenges for signal analysis.
Traditional methods may not fully capture complex patterns within strings.
The need for robust methods in detecting specific processes and diversity measures is growing.

Purpose of the Study:

To introduce wavelet-type discrete transforms for finite string signal analysis.
To apply these transforms for detecting edges and hidden Markov processes.
To develop novel approaches for string matching and measuring chaotic string diversity.

Main Methods:

Wavelet-type discrete transforms applied to finite-length strings.
Application of transforms for edge detection.
Application of transforms for hidden Markov process detection.
Development of new string matching algorithms.
Development of measures for chaotic string diversity.

Main Results:

Successful application of wavelet transforms for signal analysis on finite strings.
Effective detection of edges and hidden Markov processes using the proposed transforms.
Introduction of novel and effective methods for string matching.
Quantification of diversity in chaotic strings.

Conclusions:

Wavelet-type discrete transforms offer a powerful tool for finite string signal analysis.
The developed methods enhance capabilities in process detection, string matching, and diversity assessment.
This work provides new analytical tools for various computational and data analysis fields.