In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Medical Image Fusion: A survey of the state of the art

Medical image fusion: A survey of the state of the art

A comprehensive supervisor control for a hybrid system that comprises wind and photovoltaic generation subsystems, a battery bank, and an ac load is developed in this paper. The objectives of the supervisor control are, primarily, to satisfy the load power demand and, second, to maintain the state of charge of the battery bank to prevent blackout and to extend the life of the batteries. For these purposes, the supervisor controller determines online the operation mode of both generation subsystems, switching from power regulation to maximum power conversion. Decision criteria for the supervisor based on measurable system variables are presented. Finally, the performance of the supervisor controller is extensively assessed through computer simulation using a comprehensive nonlinear model of the plant.

Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy

A review on brain tumor segmentation of MRI images.

The effects of variation in grid voltage and frequency on the performance of the grid connected wind electric generating system has been studied for various wind velocities The induction generator, wind turbine and other network components are modeled using MATLAB/SIMULINK The model has been made realistic by considering the nonlinear variation of R/sub m/ and X/sub m/ of the induction generator A simple simulation procedure is developed to predict the system performance and the results are presented The dynamic VAr compensation by using TSC is also studied and the best location for the compensating capacitor is suggested

Modeling and simulation of grid connected wind electric generating system

Region growing is an important application of image segmentation in medical research for detection of tumour. In this paper, we propose an effective modified region growing technique for detection of brain tumour. Modified region growing includes an orientation constraint in addition to the normal intensity constrain. The performance of the proposed technique is systematically evaluated using the MRT brain images received from the public sources. For validating the effectiveness of the modified region growing, the quantity rate parameter has been considered. For the evaluation of the proposed technique of tumor detection, the sensitivity, specificity and accuracy values were used. Comparative analyses were made for the normal and the modified region growing using both the Feed Forward Neural Network (FFNN) and Radial Basis Function (RBF) neural network. The results show that the modified region growing achieved better results when compared to the normal technique.

An efficient approach for brain tumour detection based on modified region growing and neural network in MRI images

This paper aims at the modeling and power flow analysis of a stand-alone hybrid generating system (SAHGS) comprising of wind and photovoltaic systems. The wind driven self-excited induction generator (SEIG), photovoltaic array and other network components are modeled and simulated using Matlab/Simulink. The variable voltage and frequency of a generator is first rectified and controlled by a DC/DC converter before being fed to a common DC bus. The variable output voltage of the photovoltaic module is also controlled by a DC/DC converter. The DC bus collects the total power from the wind and photovoltaic systems and uses it partly to supply the required load demand and partly to charge the battery bank. The individual systems are simulated for varying wind velocities and solar intensities respectively and the results are used to identify the operating modes. A neuro controller is designed to adjust the duty ratios of the choppers and the firing angle of the converter at which the maximum power generation occurs.

Intelligent controller for a stand-alone hybrid generation system

Medical image fusion is used to derive useful information from multimodality medical image data. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide precise information to the doctor and clinical treatment planning system. This paper proposes image fusion based on Integer Wavelet Transform (IWT) and Neuro-Fuzzy. The anatomical and functional images are decomposed using Integer Wavelet Transform. The wavelet coefficients are then fused using neuro-fuzzy algorithm. Then Inverse Integer Wavelet Transform (IIWT) is applied to the fused coefficients to get the fused Image. The performance of this algorithm is compared with image fusion based on Discrete Wavelet Transform (DWT) and neuro-fuzzy using entropy metric. Fusion Symmetry (FS) which quantifies the relative distance in terms of mutual information of the fused image with respect to input images is measured. Fusion Factor (FF) the criterion of maximizing the joint mutual information is also quantified.

C. Chellamuthu

Papers

Modeling and simulation of grid connected wind electric generating system

An efficient approach for brain tumour detection based on modified region growing and neural network in MRI images

Intelligent controller for a stand-alone hybrid generation system

Multimodal medical image fusion based on Integer Wavelet Transform and Neuro-Fuzzy

Electrical braking of large wind turbines