Transactions on Case-Based Reasoning
(ISSN:1867-366X)
Volume 5 - Number 1 - October 2016 - Pages 19-31
A New Similarity Measure for Sequences and Time-Series Applied to Spectrometer Signal Analysis
Petra Perner
Institute of Computer Vision and applied Computer Sciences, IBaI, Germany
Abstract
Different spectrometer methods exist that have been developed over time to prac-tical applicable systems. Researchers in different fields try to apply these methods to different applications especially in the chemical and biological area. One of these methods is RAMAN spectroscopy for protein crystallization or Mid-Infrared spectroscopy for biomass identification. For the applications are required robust and machine learnable automatic signal interpretation methods. These methods should take into account that not so much spectrometer data about the application are available from scratch and that these data need to be learnt while using the spectrometer system. We propose to represent the spectrometer signal by a sequence of 0/1 characters obtained from a specific Delta Modulator. This prevents us from a particular symbolic description of peaks and background. The interpretation of the spectrometer signal is done by searching for a similar signal in a constantly increasing data base. The comparison between the two sequences is done based on a syntactic similarity measure. We describe in this paper how the signal representation is obtained by Delta Modulation, the similarity measure for the comparison of the signals and give results for searching the data base.
Keywords
Computational Methods, Delta Modulation, Feature Extraction, Incremental Knowledge Acquisition, Spectrometer Signal Analysis, Similarity-based Signal Interpretation, Case-Based Reasoning
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