TITLE:
Non-Parametric Local Maxima and Minima Finder with Filtering Techniques for Bioprocess
AUTHORS:
K. K. L. B. Adikaram, M. A. Hussein, M. Effenberger, T. Becker
KEYWORDS:
Extrema Point, First Derivative, Peak Finder, Peaks and Valleys, Maxima and Minima, Second Derivative
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.7 No.4,
October
11,
2016
ABSTRACT: Typically extrema filtration techniques are
based on non-parametric properties such as magnitude of prominences and the
widths at half prominence, which cannot be used with data that possess a
dynamic nature. In this work, an extrema identification that is totally
independent of derivative-based approaches and independent of quantitative
attributes is introduced. For three consecutive positive terms arranged in a
line, the ratio (R) of the sum of the maximum and minimum to the sum of the three
terms is always 2/n, where n is the number of terms and 2/3 ≤ R ≤ 1 when n = 3.
R > 2/3 implies that one term is away from the other two terms. Applying
suitable modifications for the above stated hypothesis, the method was
developed and the method is capable of identifying peaks and valleys in any
signal. Furthermore, three techniques were developed for filtering
non-dominating, sharp, gradual, low and high extrema. Especially, all the
developed methods are non-parametric and suitable for analyzing processes that
have dynamic nature such as biogas data. The methods were evaluated using
automatically collected biogas data. Results showed that the extrema
identification method was capable of identifying local extrema with 0% error.
Furthermore, the non-parametric filtering techniques were able to distinguish
dominating, flat, sharp, high, and low extrema in the biogas data with high
robustness.