This paper proposes a novel nonlinear transient computation device described as the LTCM that uses the chaotic attractor provided by the Lorenz system of equations to perform pattern recognition. Previous work on nonlinear transient computation has demonstrated that such devices can process time varying input signals. This paper investigates the ability of the LTCM to correctly classify static,...

purpose: to assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. materials and methods: in this study a combination of power spectral density and a series of statistical features are proposed as statistical-frequency features. next, a feature selection method from pattern recognition (pr) tools is presented to extra...

in this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classifypatterns of different classes in the feature space. the introduced mutation operators and chaotic sequences allows us to overcomethe problem of early convergence into a local minima associated with particle swarm optimization algorithms. that is, the mutationope...

AbstrAct With the ever-increasing utilization of imagery in scientific, industrial, civilian, and military applications, visual pattern recognition has been thriving as a research field and has become an essential enabling technology for many applications. In this chapter, we present a brain-inspired pattern recognition architecture that can easily be adapted to solve various real-world visual ...

The basic properties of nonlinear global filtering techniques are analyzed. A nonlinear processor for pattern recognition that is optimum in terms of discrimination and that is tolerant of variations of the object to be recognized is presented. We compare this processor with power-law and nonlinear joint transform correlators.

II: An overview of available tools originating from the pattern recognition and artificial intelligence culture A. Dimensionality reduction 1. Linear methods for dimensionality reduction 2. Nonlinear methods for dimensionality reduction 3. Methods for checking the quality of a mapping and the optimal dimension of the reduced parameter space B. Automatic classification 1. Tools for supervised cl...

A new sliced orthogonal nonlinear generalized (SONG) decomposition for image processing can be useful for image representation. We examine the use of this representation for two-class pattern recognition in the presence of noise. The SONG correlation is defined and expressed by means of a diagonal matrix representation. Common linear correlation, binary correlation and morphological correlation...

In order to develop a solid earth science framework for understanding and studying of active tectonic and earthquake processes, this task develops simulation and analysis tools to study the physics of earthquakes using state-of-the-art modeling, data manipulation, and pattern recognition technologies. We develop clearly defined accessible data formats and code protocols as inputs to the simulat...