Monoscale and Polyscale Analyses of Physical Signals for Compressive Detection of Relevant Complexity Features
Soleiman Hosseinpour, Witold Kinsner, Nariman Sepehri · 2022
Dealing with the large volumes of data generated in the digital world requires efficient data interpretation techniques. Some compressive feature detection techniques are proposed to efficiently minimize the data size. Most of these algorithms in the signal processing area are based on monoscale or multi-scale methods. These methods are not sensitive to the reshuffling of the data, and critical features like correlation and covariance are lost. An alternative approach is to use polyscale algorithms. This paper provides a polyscale approach using the variance fractal dimension to be developed for compressive detection. Some critical aspects of using these measures are discussed, including robustness to noise and simplicity concerning other existing methods. These measures are applied to some speech utterances and experimental data measured from an electro hydrostatic actuator test rig to show the efficiency of this method. This work can provide insight into practical compressive detections to explore this promising area for future developments.