Thesis Pattern Recognition Topics

PhD Projects in Pattern Recognition

PhD Projects in Pattern Recognition lays the hi-tech dias for the PhD/MS scholars. … Our main concern lies in finding the apt solution for any research issue.

Stress Field Problems of a Plate-Strip Having Finite Dimensions (Paperback)

The problems involving non-linear effects in the dynamics of the elastic medium is one of the major subjects of the computational and applied mathematics and cannot be solved within the framework of the linear theory of elastodynamics. There is a wide range of applications for problems including initially stressed bodies in practice. For example, initial stresses occur in structural elements after manufacturing and assembly. Initial stresses are also present in composite materials. A large number of theoretical and experimental investigations had been made in this field. Almost all of these investigations were made within the framework of linearized theory of elastic waves. For the solution of the problems involving layers with finite length numerical methods are to be employed. An efficient method for such problems is the Finite Element Method. This book concerns with some stress field problems for initially pre-stressed plate-strip with finite-length for both isotropic and anisotropic case. Stress Field Problems of a Plate-Strip Having Finite Dimensions (Paperback)

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Nonlinear optical beams in nanodisordered photorefractive ferroelectrics.- Microscopy.- Miniaturized photogenerated electro-optic axicon lens Gaussian-to-Bessel beam conversion.- Diffraction-free light droplets for axially-resolved volume imaging.- Self-suppression of Bessel beam side lobes for high-contrast light sheet microscopy.- Microscopic reversibility, nonlinearity, and the conditional nature of single particle entanglement.- Super-crystals in composite ferroelectrics.- Intrinsic negative-mass from nonlinearity.- Rogue waves: transition to turbulence and control through spatial incoherence.- Appendix. • Author: Giuseppe Di Domenico • ISBN:9783030231880 • Format:Hardcover • Publication Date:2019-07-17

Predictive Artificial Neural Networks (Paperback)

Data compression deals with removal of redundancy, reducing bandwidth and thus lowering transmission and storage costs. Telemetry data can be sensitive to inaccuracies and require lossless compression for exact reconstruction at the receiver. One technology that has been successfully applied in a wide range of applications is artificial neural networks (ANN), a massively parallel system with pattern recognition capabilities. This monograph is a reproduction of the author’s postgraduate thesis work at Multimedia University, Malaysia. A two-stage predictor-encoder combination is proposed, incorporating a variety of feedforward, recurrent and radial basis ANN architectures, as the predictors. The encoders are well known compression a
lgorithms. Characteristic features of the models, transmission issues and other practical considerations are taken into account to determine optimised configuration of the schemes. Significant compression results are reported, along with a critical review of the strengths and weaknesses of over 50 implementations simulated with satellite telemetry data. Predictive Artificial Neural Networks (Paperback)

Emd-chaos Based Analysis of Eeg Signals for Early Seizure Detection (Paperback)

In this thesis, a method has been developed to analyze EEG signals for early detection of seizure using empirical mode decomposition (EMD) and chaos. Chaos in EEG is de ned by the tendency to gravitate towards speci c regions in phase space. Lyapunov exponent and Kol-mogorov complexity are the important factors regarding chaotic behavior of any dynamical system. In this thesis, the Largest Lyapunov Exponent (LLE) of the EEG signal over time is observed and decision about Epileptic Seizure is taken. It is seen that from normal to seizure state transition, the amount of chaos in EEG is drastically reduced. Thus, the behavior of chaos in EEG signal described above can be used for seizure detection. Emd-Chaos Based Analysis of Eeg Signals for Early Seizure Detection (Paperback)

Group Behavior Recognition Using Dynamic Bayesian Networks (Paperback)

In this PhD thesis we analyze the concepts involved in the decision making of groups of agents and apply these concepts in creating a framework for performing group behavior recognition. We present an overview of the intention theory, as studied by some great theorists such as Searle, Bratmann and Cohen, and show the link with more recent researches. We study the advantages and drawbacks of some techniques in the domain and create a new model for representing and detecting group behaviors, the aim being to create a unified approach of the problem. Most of this thesis is consecrated in the detailed presentation of the model as well as the algorithm responsible for behavior recognition. Our model is tested on two different applications involving human gesture analysis and multimodal fusion of audio and video data. By means of these applications, we advance the argument that multivariate sets of correlated data can be efficiently analyzed under a unified framework of behavior recognition. We show that the correlation between different sets of data can be modeled as cooperation inside a team and that behavior recognition is a modern approach of classification and pattern recognition. Group Behavior Recognition Using Dynamic Bayesian Networks (Paperback)