Formulation, analysis, and hardware implementation of chaotic dynamics based algorithm for compression and feature recognition in digital images

In this paper we will discuss the utilization of a set of waveforms derived from chaotic dynamical systems for compression and feature recognition in digital images. We will also describe the design and testing of an embedded systems implementation of the algorithm. We will show that a limited set of combined chaotic oscillations are sufficient to form a basis for the compression of thousands of digital images. We will demonstrate this in the analysis of images extracted from the solar heliospheric observatory (SOHO), showing that we are able to detect coronal mass ejections (CMEs) in quadrants of the image data during a severe solar event. We undertake hardware design in order to optimize the speed of the algorithm, taking advantage of its parallel nature. We compare the calculation speed of the algorithm in compiled C, enhanced Matlab, Simulink, and in hardware.