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A New Improved Particle Swarm Optimization Algorithm for Multiprocessor Job Scheduling
K.Thanushkodi,K Deeba
International Journal of Computer Science Issues , 2011,
Abstract: Job Scheduling in a Multiprocessor architecture is an extremely difficult NP hard problem, because it requires a large combinatorial search space and also precedence constraints between the processes. For the effective utilization of multiprocessor system, efficient assignment and scheduling of jobs is more important. This paper proposes a new improved Particle Swarm Optimization (ImPSO) algorithm for the job scheduling in multiprocessor architecture in order to reduce the waiting time and finishing time of the process under consideration. In the Improved PSO, the movement of a particle is governed by three behaviors, namely, inertia, cognitive, and social. The cognitive behavior helps the particle to remember its previous visited best position. This paper proposes to split the cognitive behavior into two sections .This modification helps the particle to search the target very effectively. The proposed ImPSO algorithm is discussed in detail and results are shown considering different number of processes and also the performance results are compared with the conventional techniques such as longest processing time, shortest processing time and Particle Swarm Optimization.
An Improved k-Nearest Neighbor Classification Using Genetic Algorithm
N. Suguna,K.Thanushkodi
International Journal of Computer Science Issues , 2010,
Abstract: k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different data sets. The traditional KNN text classification algorithm has three limitations: (i) calculation complexity due to the usage of all the training samples for classification, (ii) the performance is solely dependent on the training set, and (iii) there is no weight difference between samples. To overcome these limitations, an improved version of KNN is proposed in this paper. Genetic Algorithm (GA) is combined with KNN to improve its classification performance. Instead of considering all the training samples and taking k-neighbors, the GA is employed to take k-neighbors straightaway and then calculate the distance to classify the test samples. Before classification, initially the reduced feature set is received from a novel method based on Rough set theory hybrid with Bee Colony Optimization (BCO) as we have discussed in our earlier work. The performance is compared with the traditional KNN, CART and SVM classifiers.
PERFORMANCE EVALUATION OF DIRECT PROCESSOR ACCESS FOR NON DEDICATED SERVER
P. S. BALAMURUGAN,Dr. K.THANUSHKODI
International Journal of Engineering Science and Technology , 2010,
Abstract: The objective of the paper is to design a co processor for a desktop machine which enables the machine to act as non dedicated server, such that the co processor will act as a server processor and the multi-core processor to act as desktop processor. By implementing this methodology a client machine can be made to act as a non dedicated server and a client machine. These type of machine can be used in autonomy networks. This design will lead to design of a cost effective server and machine which can parallel act as a non dedicated server and a client machine or it can be made to switch and act as client or server.
PRODUCTIVE CO PROCESSOR DESIGN BASED ON PROGRAM BENCHMARK
P. S. BALAMURUGAN,Dr. K.THANUSHKODI
International Journal on Computer Science and Engineering , 2010,
Abstract: The objective of this paper is to design a methodology where many co-processors are accessed by the processor in array mode. By using co processor, the work on the multi core processor gets reduced by accessing it in array manner. A multi core processor is an efficient processor which can enable parallel processing and perform multi threading effectively. In this paper, in order to improve the performance of multi-core processor two major factors are taken intoconsideration one is to improve the execution of array methodology by using co processor and other is yo design an array based co processor to improve the hit ratio of the co processor.
Direct Processor Access for Non Dedicated Server using Multi Core Processor
P. S. BALAMURUGAN,,Dr. K.THANUSHKODI
International Journal on Computer Science and Engineering , 2010,
Abstract: The objective of the paper is to design a co processor for a desktop machine which enables the machine to act as non dedicated server, such that the co processor will act as a server processor and the multi-core processor to act as desktop processor. By implementing this methodology a client machine can be made to act as a non dedicated server and a client machine. This type of machine can be used in autonomy networks. This design will lead to design of a cost effective server and machine which can parallel act as a non dedicatedserver and a client machine or it can be made to switch and act as client or server.
An Efficient DC- DC Converter with Bidirectional Power Flow
N.RAJARAJESWARI,K.THANUSHKODI
Annals of Dunarea de Jos , 2008,
Abstract: This paper introduces a Bi-directional DC-DC converter with adaptive fuzzy logic controller. Bidirectional power flow is obtained by same power components and provides a simple, efficient, and galvanically isolated converter. In the presence of DC mains the converter operates as buck converter and charges the battery. When the DC mains fails, the converter operates as boost converter and the down stream converter is fed by the battery. The power switches are controlled by Pulse Width Modulation technique and the pulses are generated by the application of fuzzy logic with an adoption algorithm. The proposed converter is simulated using MATLAB and laboratory prototype was developed to validate the simulation results.
Exploration on Selection of Medical Images employing New Transformation Technique
J.Jaya,K.Thanushkodi
International Journal of Computer Science Issues , 2010,
Abstract: Transformation model plays a vital role in medical image processing. This paper describes a new Transformation model (NTM) which is hybrid of linear and non linear Transformations techniques for the detection of tumor. In NTM, patient image is compared with reference images, which is block based. An image similarity measure quantifies the degree of similarity between intensity patterns in two images. The choice of an image similarity measure depends on the modality of the images to be registered. In this paper contrast checking, sum of squared intensity differences (SSD), calculation of white cells and point mapping are used.
IMAGE FUSION TECHNIQUES
A.Umaamaheshvari & K.Thanushkodi
International Journal of Research and Reviews in Applied Sciences , 2010,
Abstract:
Sleep Pass Gate Approach for Static Power Reduction in 8*8 Wallace Multiplier
R.Naveen,K.Thanushkodi,C. Saranya
International Journal of Engineering and Advanced Technology , 2012,
Abstract: As the VLSI technology and supply/threshold voltage continue scaling down, leakage power has become more and more significant in the power dissipation of today’s CMOS circuits. The leakage power dissipation is projected to grow exponentially during the next decade according to the International Technology Roadmap for Semiconductors (ITRS).This affects the portable battery operated devices directly. The multipliers are the main key for designing an energy efficient processor, where the multiplier design decides the digital signal processors efficiency. In this paper, a sleep pass gate method is used to reduce the static power dissipation in 8*8 Wallace tree multiplier architecture which has been designed by using 1-bit full adders. This method uses two complementary sleep transistors connected in parallel forming a gate pass structure. In our proposed leakage reduction method, the actual output logic state is maintained in both active and standby mode of operation. The main objective of our work is to calculate leakage power in 8*8 Wallace tree multiplier with sleep pass gate and it is compared with the 8*8 Wallace tree full adder multiplier. The proposed method reduces upto half of the static power dissipation with lesser area and delay.
Particle Swarm Optimization for Automatic Detection of Breast Cancer
K. Geetha,K. Thanushkodi
International Journal of Soft Computing , 2012,
Abstract: The presence of microcalcifications in breast tissue is one of the most important signs considered by radiologist for an early diagnosis of breast cancer, which is one of the most common forms of cancer among women. In this study, the Genetic Algorithm (GA) hybrid with Particle Swarm Optimization (PSO) is proposed to automatically detect the breast border and nipple position to identify the suspicious regions on digital mammograms based on asymmetries between left and right breast image. The basic idea of the asymmetry approach is corresponding left and right images are subtracted to extract the suspicious region. The proposed system consists of two steps: First, the mammogram images are enhanced using median filter, normalized the image, pectoral muscle region is removed and the border of the mammogram is detected for both left and right images from the binary image. Further PSO is applied to enhance the detected border. The figure of merit is calculated to evaluate whether the detected border is exact or not. And the nipple position is identified using ACS. The performance is compared with the existing methods. Second, using the border points and nipple position as the reference the mammogram images are aligned and subtracted to extract the suspicious region. The algorithms are tested on 60 abnormal digitized mammograms from MIAS database.
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