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Priadarshini A.,Sri Ramakrishna Engineering College | Jagadeeswari M.,Sri Ramakrishna Engineering College Coimbatore
2013 International Conference on Computer Communication and Informatics, ICCCI 2013

A low power CMOS Static Random Access Memory (SRAM) based Field Programmable Gate Arrays (FPGA) architecture is being presented in this paper. The architecture presented here is based on CMOS logic and CMOS SRAMs that are used for on-chip dynamic reconfiguration. This architecture employs the fast and low-power SRAM blocks that are based on 10T SRAM cells. These blocks are employed in fast access of the configuration bits by using the shadow SRAM technique. The dynamic reconfiguration delay is being hidden behind the computation delay through the use of shadow SRAM cells. The combined effect of both the SRAM memory cells and the shadow SRAM scheme enables to support in reducing the delay and also to achieve reduced power consumption. Experimental results show reduced delay of about 8.035ns and power consumption of about 0.015W for the 10T SRAM memory cell with an overhead in area, relative to 4T and 6T SRAM cells. Also, the experimental results include the values of delay of about 8.979ns and power consumption of about 0.052W, achieved for the LB of FPGA architecture which employs CMOS SRAMs using the 10T SRAM memory cells in it. © 2013 IEEE. Source

Rajathi G.M.,Sri Ramakrishna Engineering College Coimbatore | Rangarajan R.,VSB Engineering College
European Journal of Scientific Research

The major objective of the image enhancement techniques is to emphasize and sharpen the features of image for better display and investigation. It is the process of enhancing the quality of the image by applying the enhancement techniques to assist for the improvement of a solution to a computer imaging setback. As a result, the enhancement technique depends on the application and in practice it is developed empirically. It is the fundamental step which is used as a preprocessing step in computer vision applications, medical imaging, satellite imaging, and fingerprint identification, to ease the vision task. In this paper, proposed an enhanced adaptive wiener filter based on fast lifting wavelet transform by applying the thresholding. First step is to convert the noisy image into the wavelet domain with the use of Fast Lifting Wavelet Transform. Then the thresholding is applied in wavelet domain using VisuShrink and BayesShrink thresholding. After that, Lifting-based adaptive Wiener filter is applied to all the sub-band images. Finally, these sub-band images are inversely transformed to reconstruct the final improved image. From the simulation results, it is conformed that the performance of the adaptive Wiener filter with BayesShrink thresholding performs better in terms of peak-signal-to-noise-ratio (PSNR), execution time (ET) and Mean squared error (MSE) than the Wiener Filter, Adaptive Wiener Filter and adaptive Wiener filter with VisuShrink threshold. © 2012 EuroJournals Publishing, Inc. Source

Sudarmani R.,Avinashilingam University For Women | Kumar K.R.S.,Sri Ramakrishna Engineering College Coimbatore
European Journal of Scientific Research

Wireless sensor networks are energy constraint battery powered sensing, computing and communication infrastructure. Sensor nodes are randomly deployed and organized as clusters, and each node is responsible for transmitting the data to its cluster head. Most of the existing sensor networks focus on homogeneous and most of the existing clustering algorithms can be applied only for homogeneous sensor networks in which the cluster heads are changed periodically. In this paper, to reduce power consumption of Sensor Networks, we have investigated Heterogeneous Sensor networks with mobile sink using Energy Efficient Clustering algorithm (EECA). The simulation results show that the consumed energy is less in the case of Mobile Sink (MS) with EECA when compared to stationary sink. © 2012 EuroJournals Publishing, Inc. Source

Mahendiran T.V.,Sri Ramakrishna Engineering College Coimbatore | Thangam P.,Sri Ramakrishna Engineering College Coimbatore
European Journal of Scientific Research

This paper presents a novel control scheme for Linear and Non-Linear Drives control of separately excited DC motor. Buck chopper is considered as linear drive and controlled rectifier is considered as non-linear drive. Thus the control scheme includes Fuzzy PI Controller and Particle Swarm Optimization (PSO) method for formatting the optimal fuzzy controller tuning parameters. The main objective is to provide stability, to reduce overshoot in response to disturbance and sudden change in reference speed of the separately excited DC motor. The performances of both controllers are analyzed and compared on the basis of their applicability, adaptability, and controllability under various operating conditions such as varying speed at constant load, varying load at constant speed and varying speed and load simultaneously. The system is simulated using Matlab/Simulink GUI environment and the results are discussed in the paper. It has been found that PSO based Fuzzy PI controller performs well in regulating the speed above the rated values and resulting in better settling time of the speed response of the DC motor. The overshoot is eliminated in all the controllers. © EuroJournals Publishing, Inc. 2011. Source

Vishnudurai R.S.,Sri Ramakrishna Engineering College Coimbatore | Grace Selvarani A.,Sri Ramakrishna Engineering College Coimbatore
International Journal of Applied Engineering Research

Scheduling of tasks in cloud computing with available resources is a challenging task. An important aspect of task scheduling in cloud is the load balancing of non-preemptive independent tasks on Virtual Machines(VM). The scheduling process is done by the scheduler so that the available resources are fully utilized. Currently, the load balancing methods like Artificial Bee Colony(ABC) are good at exploration but poor at exploitation. To overcome this limitation, we propose an algorithm named Modified Artificial Bee Colony Algorithm(MABC). The MABC algorithm introducesthe best-so-far solution, inertia weight and acceleration coefficients to change the search process for available resources. It also improves the exploitation process. The evaluation process shows that the proposed algorithm is more feasible and efficient. © Research India Publications. Source

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