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Search Results: 1 - 10 of 461640 matches for " A. Tamilarasi "
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Idempotent-separating extensions of regular semigroups
A. Tamilarasi
International Journal of Mathematics and Mathematical Sciences , 2005, DOI: 10.1155/ijmms.2005.2945
Abstract: For a regular biordered set E, the notion of E-diagram and the associated regular semigroup was introduced in our previous paper (1995). Given a regular biordered set E, an E-diagram in a category C is a collection of objects, indexed by the elements of E and morphisms of C satisfying certain compatibility conditions. With such an E-diagram A we associate a regular semigroup RegE(A) having E as its biordered set of idempotents. This regular semigroup is analogous to automorphism group of a group. This paper provides an application of RegE(A) to the idempotent-separating extensions of regular semigroups. We introduced the concept of crossed pair and used it to describe all extensions of a regular semigroup S by a group E-diagram A. In this paper, the necessary and sufficient condition for the existence of an extension of S by A is provided. Also we study cohomology and obstruction theories and find a relationship with extension theory for regular semigroups.
On-Line Data Acquisition Using Virtual Instrumentation and Sensor-less Speed Estimation of Three Phase Induction Motor-Neuro-Fuzzy Approach
S. Vijayachitra,A. Tamilarasi
International Journal of Systems Signal Control and Engineering Application , 2012,
Abstract: This study presents online data acquisition of 3 phase voltages (R,Y and B) of induction motor through Virtual Instrumentation (VI) with Lab VIEW 8.2 software package by interfacing with DAQ NI USB 6008 card and sensor-less speed measurement has been made by intelligent combination of Neural Networks and Fuzzy logic called as Adaptive Neuro-Fuzzy Inference System (ANFIS), i.e., Neuro-Fuzzy approach, which has been developed by MATLAB 7.5. The resulting conceptual neural fuzzy model contains the robustness of fuzzy systems, the learning ability of neural networks and can adapt to various situations in real time.
Visually Improved Image Compression by using Embedded Zero-tree Wavelet Coding
Janaki R,Tamilarasi A
International Journal of Computer Science Issues , 2011,
Abstract: Image compression is very important for efficient transmission and storage of images. Embedded Zero- tree Wavelet (EZW) algorithm is a simple yet powerful algorithm having the property that the bits in the stream are generated in the order of their importance. Image compression can improve the performance of the digital systems by reducing time and cost in image storage and transmission without significant reduction of the image quality. For image compression it is desirable that the selection of transform should reduce the size of resultant data set as compared to source data set. EZW is computationally very fast and among the best image compression algorithm known today. This paper proposes a technique for image compression which uses the Wavelet-based Image Coding. A large number of experimental results are shown that this method saves a lot of bits in transmission, further enhances the compression performance. This paper aims to determine the best threshold to compress the still image at a particular decomposition level by using Embedded Zero-tree Wavelet encoder. Compression Ratio (CR) and Peak-Signal-to-Noise (PSNR) is determined for different threshold values ranging from 6 to 60 for decomposition level 8.
On-Line Data Acquisition Using Virtual Instrumentation and Sensor-less Speed Estimation of Three Phase Induction Motor-Neuro-Fuzzy Approach
S. Vijayachitra and A. Tamilarasi
International Journal of Systems Signal Control and Engineering Application , 2008,
Abstract: This study presents online data acquisition of 3 phase voltages (R,Y and B) of induction motor through Virtual Instrumentation (VI) with Lab VIEW 8.2 software package by interfacing with DAQ NI USB 6008 card and sensor-less speed measurement has been made by intelligent combination of Neural Networks and Fuzzy logic called as Adaptive Neuro-Fuzzy Inference System (ANFIS), i.e., Neuro-Fuzzy approach, which has been developed by MATLAB 7.5. The resulting conceptual neural fuzzy model contains the robustness of fuzzy systems, the learning ability of neural networks and can adapt to various situations in real time.
An enhanced genetic algorithm with simulated annealing for job-shop scheduling
A Tamilarasi, T Anantha kumar
International Journal of Engineering, Science and Technology , 2010,
Abstract: The Job-Shop Scheduling Problem (JSSP) is one of the most difficult problems, as it is classified as NP-Hard problem. The main objective of the JSSP is to find a schedule of operations that can minimize the maximum completion time (called makespan) that is the completed time of carrying total operations out in the schedule for n jobs and m machines. In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult, because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time to obtain the optimum solution. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. In this paper we proposed a new method for solving job-shop scheduling problem using hybrid Genetic Algorithm (GA) with Simulated Annealing (SA). This method introduces a reasonable combination of local search and global search for solving JSSP.
An enhanced genetic algorithm with simulated annealing for job-shop scheduling
A. Tamilarasi,T. Anantha kumar
International Journal of Engineering, Science and Technology , 2010,
Abstract: The Job-Shop Scheduling Problem (JSSP) is one of the most difficult problems, as it is classified as NP-Hard problem. The main objective of the JSSP is to find a schedule of operations that can minimize the maximum completion time (called makespan) that is the completed time of carrying total operations out in the schedule for n jobs and m machines. In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult, because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time to obtain the optimum solution. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. In this paper we proposed a new method for solving job-shop scheduling problem using hybrid Genetic Algorithm (GA) with Simulated Annealing (SA). This method introduces a reasonable combination of local search and global search for solving JSSP.
DESIGNING AN M-LEARNING APPLICATION FOR A UBIQUITOUS LEARNING ENVIRONMENT IN THE ANDROID BASED MOBILE DEVICES USING WEB SERVICES
SHANMUGAPRIYA M,,Dr TAMILARASI A
Indian Journal of Computer Science and Engineering , 2011,
Abstract: Educational Technology is constantly evolving and growing, and this progression will continually offer new and interesting advances in our learning environment. Traditional E-Learning systems developed for laptop and desktop computers were based on stand-alone software application or through websites and lack the ability to provide a comprehensive ubiquitous learning environment. A ubiquitous learning environment based on early days mobile phones lack the processing power of notebooks or desktop computers, low data transfer speeds and capacity. However, the ability to provide a comprehensive ubiquitous learning environment on the 3G (3rd Generation) mobile device will offer powerful collaborative and interactive learning opportunities.Thus the main objective of the research work is to develop an nteractive mobile learning application based on Web Services inAndroid base mobile devices to facilitate the ubiquitous learning. This paper deals with the prototype development of an MLearning application for mobile phones running with Android platform using Web services.
Fuzzy Based Task Scheduling for Hierarchical Resource Allocation in Mobile Grid Environment
S. Thenmozhi,A. Tamilarasi,K. Thangavel
International Journal of Soft Computing , 2012, DOI: 10.3923/ijscomp.2012.97.103
Abstract: In mobile grid environment, the main challenging issues are scheduling, adaptation, security and mobility. The job scheduling problem becomes more complicated due to the limitations of node mobility. In order to minimize the resource utilization, gaining the maximum profit to be cost effective and satisfying the user constraints, an efficient job scheduling technique is required for mobile grid environment. In this study, researchers propose a fuzzy based task scheduling algorithm for resource allocation depending upon the workload and the resource availability of the grid members. In this scheduling, the computation sensitive task is assigned for grid members with least workload and the communication sensitive task is assigned for grid members with high resource availability. Using the workload and resource availability as input variables, fuzzy decision rule table is created. After defuzzification, the output gives us a perfect matching for scheduling the tasks according to the load and availability. Thus, the algorithm proves to be more effective in task scheduling of mobile grids. From the simulation results, researchers show that the proposed scheduling technique attained maximum throughput and less delay when compared with the existing technique.
Innovative Migrants Operators for Improved Genetic Optimization of Fuzzy Logic Controller
S. Vijayachitra,A. Tamilarasi,N. Kasthuri
International Journal of Soft Computing , 2012,
Abstract: The Fuzzy Logic Controller (FLC) is very much useful in more complex situations which cannot be dealt mathematically. By providing the knowledge about the complex system, Fuzzy logic controller (FLC) can be developed and formed into a number of fuzzy rules. Since, FLC needs more human approach to control, Genetic algorithm is introduced for the purpose of the design and optimization of fuzzy rule base. But due to some problems like messy overlapping of membership functions and redundant rules, simple GA is not enough. To overcome the above situations, it is better to introduce the migrants formation which is the movement of individuals between subpopulations. In this study, migrants are introduced in 3 populations (7 7, 5 5 and 3 3) for a second order process and verified the improvement in the genetic optimization of the process.
DEVELOPING A MOBILE COURSEWARE FOR ICT STUDENTS USING PROBLEM BASED LEARNING APPROACH
SHANMUGAPRIYA M, DR.TAMILARASI A
International Journal of Innovative Research in Computer and Communication Engineering , 2013,
Abstract: Mobile Devices are pervasive in nature and supports ubiquitous learning environment. In this article the designing and developing a mobile courseware for ICT students using problem-based learning approach is discussed. The courseware is designed to evaluate the feasibility of adopting the problem-based learning pedagogies in a mobile learning environment for ICT students. A case study is built for Java Programming and the courseware is implemented on the M-learning framework designed. The m-learning framework is developed using service-oriented architecture. The design and delivery of learning objects for the mobile learning is being depicted in the PBL environment.
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