R. Kayalvizhi

Modified bacterial foraging algorithm based multilevel thresholding for image segmentation

Multilevel thresholding is one of the most popular image segmentation techniques. In order to determine the thresholds, most methods use the histogram of the image. This paper proposes multilevel thresholding for histogram-based image segmentation using modified bacterial foraging (MBF) algorithm. To improve the global searching ability and convergence speed of the bacterial foraging algorithm, the best bacteria among all the chemotactic steps are passed to the subsequent generations. The optimal thresholds are found by maximizing Kapurs (entropy criterion) and Otsus (between-class variance) thresholding functions using MBF algorithm. The superiority of the proposed algorithm is demonstrated by considering fourteen benchmark images and compared with other existing approaches namely bacterial foraging (BF) algorithm, particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The findings affirmed the robustness, fast convergence and proficiency of the proposed MBF over other existing techniques. Experimental results show that the Otsu based optimization method converges quickly as compared with Kapurs method.

PSO-Based Tsallis Thresholding Selection Procedure for Image Segmentation

Multilevel thresholding is a method that is widely used in image segmentation. The thresholding problem is treated as an optimization problem with an objective function. In this article, a simple and histogram based approach is presented for multilevel thresholding in image segmentation. The proposed method combines Tsallis objective function and Particle Swarm Optimization (PSO). The PSO algorithm is used to find the optimal threshold values which maximize the Tsallis objective function. Simulations are performed over various standard test images with different number of thresholds and comparisons are performed with Genetic Algorithm (GA). The experimental results show that the proposed PSO based thresholding method performs better than the GA method.

Comparison of intelligent techniques for multilevel thresholding problem

Segmentation is low-level image transformation routine that partitions an input image into distinct disjoint and homogeneous regions using thresholding algorithms. This paper presents both adaptation and comparison of four stochastic optimisation techniques to solve multilevel thresholding problem in image segmentation: Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Bacterial Foraging (BF) and Modified BF (MBF). Three objective functions such as Tsallis, Kapur’s and Otsu’s functions are considered and maximised by the above four algorithms. In order to compare the performances of all the algorithms, they are tested on various test images. Results show that the BF and MBF are much better in terms of robustness and time convergence than the PSO and GA. Among the last two algorithms, MBF is the most efficient with respect to the quality of the solution in terms of Peak Signal to Noise Ratio (PSNR) value and stability.

Design of Fractional Order PID controller using Soft computing methods for a Buck-Boost converter

Fractional order controller is widely used in most areas of science and engineering, being recognized its ability to yield a superior control in many dynamical systems. This work proposes the applications of a Fractional Order PID (FOPID) controller in the area of Power Electronics for a DC-DC power converter to evaluate the use of Fractional Order PID controller with soft computing techniques. To design Fractional Order PID controller is to determine the two important parameters λ (integrator order) and μ (derivative order). In this article that the response and performance of Fractional Order PID controller is compared with closed loop conventional PID controller. In all the cases the Fractional Order PID controller much better than conventional PID controller for the given system.

An Optimum Setting of PID Controller for Boost Converter Using Bacterial Foraging Optimization Technique

In this paper, a maiden attempt is made to examine and highlight the effective application of bacterial foraging (BF) algorithm to optimize the PID controller parameters for boost converter and to compare its performance to establish its superiority over other methods. The proposed BF-PID controller maintains the output voltage constant irrespective of line and load disturbances than particle swarm optimization (PSO)-based PID controller and conventional PID controllers.

Medical Image Segmentation using Modified Morphological Reconstruction

The objective of this research is to improve the accuracy of object segmentation in medical images by constructing an object segmentation algorithm. Image segmentation is a crucial step in the field of image processing and pattern recognition. Segmentation allows the identification of structures in an image which can be utilized for further processing. Both region-based and object-based segmentation are utilized in a robust and principled manner. Gradient based MultiScalE Graylevel mOrphological recoNstructions (GSEGON) is used for segmenting an image. SEGON roughly identifies the background and object regions in the image. The proposed method takes advantage of segmentation of both gray scale image and color image.

Optimization of PI Controller using PSO for Power Electronic Converter

DC-DC converters are widely used in application such as computer peripheral power supplies, car auxiliary power supplies and medical equipments. Positive output elementary Luo converter performs the conversion from positive source voltage to positive load voltage. Due to the time- varying and switching nature of the power electronic converters, their dynamic behavior is highly non-linear. Conventional controllers are incapable of providing good dynamic performance and hence optimized techniques have been developed to tune the PI parameter. In this work, Particle Swarm Optimization (PSO) is developed for PI optimization. Simulation results show that the performances of PSO-PI controllers are better than those obtained by the classical ZN-PI controller.