Abstract:
We show that a ring of phase oscillators coupled with transmission delays can be used as a pattern recognition system. The introduced model encodes patterns as stable periodic orbits. We present a detailed analysis of the underlying dynamics. In particular, we show that the system possesses a multitude of periodic solutions, prove stability results and present a bifurcation analysis. Furthermore, we show successful recognition results using artificial patterns and speech recordings.

Abstract:
Pattern Recognition is the science of recognizing patterns by machines. This is very wide research area as of today, because every newresearch tries to make machine as intelligent as human for recognizing patterns. Pattern recognition is an active research and an importanttrait of ‘artificial intelligence’. This review paper introduces pattern recognition, its fundamental definitions, and provides understanding of related research work. This paper presents different types of algorithms, their limitations & applications of pattern recognition.

Abstract:
The objective of this paper is to discuss and compare some aspect of pattern recognition, among the various framework in which pattern recognition has been traditional formulated. The primary goal of pattern recognition is supervised or unsupervised classification. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation.

Abstract:
I propose that pattern recognition, memorization and processing are key concepts that can be a principle set for the theoretical modeling of the mind function. Most of the questions about the mind functioning can be answered by a descriptive modeling and definitions from these principles. An understandable consciousness definition can be drawn based on the assumption that a pattern recognition system can recognize its own patterns of activity. The principles, descriptive modeling and definitions can be a basis for theoretical and applied research on cognitive sciences, particularly at artificial intelligence studies.

Abstract:
Biological and machine pattern recognition systems face a common challenge: Given sensory data about an unknown object, classify the object by comparing the sensory data with a library of internal representations stored in memory. In many cases of interest, the number of patterns to be discriminated and the richness of the raw data force recognition systems to internally represent memory and sensory information in a compressed format. However, these representations must preserve enough information to accommodate the variability and complexity of the environment, or else recognition will be unreliable. Thus, there is an intrinsic tradeoff between the amount of resources devoted to data representation and the complexity of the environment in which a recognition system may reliably operate. In this paper we describe a general mathematical model for pattern recognition systems subject to resource constraints, and show how the aforementioned resource-complexity tradeoff can be characterized in terms of three rates related to number of bits available for representing memory and sensory data, and the number of patterns populating a given statistical environment. We prove single-letter information theoretic bounds governing the achievable rates, and illustrate the theory by analyzing the elementary cases where the pattern data is either binary or Gaussian.

Abstract:
The classical model of signaling games assumes that the receiver exactly know the type space (private information) of the sender and be able to discriminate each type of the sender distinctly. However, the justification of this assumption is questionable. It is more reasonable to let the receiver recognize the pattern of the sender. In this paper, we investigate what happens if the assumption is relaxed. A framework of signaling games with pattern recognition and an example are given.

Abstract:
Structural pattern recognition in case of molecules is an important task in the field of bio-engineering. Several techniques are employed in order to get the exact structural conformation and structural parameters of molecules. Present paper discusses some of the available techniques such as Fourier Infra-Red Spectrocopy, Raman Spectroscopy and Theoretical Structure simulation which are employed in this technologically important field. An attempt has been made to look for the exact structural conformation in case of Polyformaldehyde using First-principles calculations based on Density Functional Theory.

Abstract:
Different mathematical models of recognition processes are known. In the present paper we consider a pattern recognition algorithm as an oracle computation on a Turing machine. Such point of view seems to be useful in pattern recognition as well as in recursion theory. Use of recursion theory in pattern recognition shows connection between a recognition algorithm comparison problem and complexity problems of oracle computation. That is because in many cases we can take into account only the number of sign computations or in other words volume of oracle information needed. Therefore, the problem of recognition algorithm preference can be formulated as a complexity optimization problem of oracle computation. Furthermore, introducing a certain "natural" preference relation on a set of recognizing algorithms, we discover it to be nontransitive. This relates to the well known nontransitivity paradox in probability theory. Keywords: Pattern Recognition, Recursion Theory, Nontransitivity, Preference Relation

Abstract:
This work deals with bifurcation and pattern changing in models described by two real scalar fields. We consider generic models with quartic potentials and show that the number of independent polynomial coefficients affecting the ratios between the various domain wall tensions can be reduced to 4 if the model has a superpotential. We then study specific one-parameter families of models and compute the wall tensions associated with both BPS and non-BPS sectors. We show how bifurcation can be associated to modification of the patterns of domain wall networks and illustrate this with some examples which may be relevant to describe realistic situations of current interest in high energy physics. In particular, we discuss a simple solution to the cosmological domain wall problem.

Abstract:
This work deals with bifurcation and pattern changing in models described by two real scalar fields. We consider generic models with quartic potentials and show that the number of independent polynomial coefficients affecting the ratios between the various domain wall tensions can be reduced to 4 if the model has a superpotential. We then study specific one-parameter families of models and compute the wall tensions associated with both BPS and non-BPS sectors. We show how bifurcation can be associated to modification of the patterns of domain wall networks and illustrate this with some examples which may be relevant to describe realistic situations of current interest in high energy physics. In particular, we discuss a simple solution to the cosmological domain wall problem.