Informatics Publishing Limited

E. Preethi ¹, A. Mohamed Abbas ¹, S. Prabhu Kumar ¹, J. T. Arun Raghesh ¹

Affiliations
1 Department of ECE, Vel Tech Multitech, Avadi, Chennai-600062, Tamil Nadu, IN

Abstract

Background/Objectives: This paper presents timing storage circuit based on memristor emulator. The memristor emulator includes most of the characteristics of real memristor. Methods/Statistical analysis: The considerable properties that a memristor embrace are wide range of memristance, bimodal operability of pulse and continuous input signals, long period of volatility, operability with other devices. The proposed timing storage circuit stores and reproduces timing information in analog manner without performing quantization. Findings: In design of continuous-time digital FIR filter, the analog delags:y blocks, which are implemented using memristor, are replaced with memristor emulator based timing storage circuit. Application/Improvements. It extends its benefits of storing and reproducing the CT digital signals, wide range of memristance, and anti aliasing processing. A CT FIR filter has been designed with memristor emulator based delay block as an example

Keywords

Biomedical Signal Processing, Continuous-time Digital Signal Processing, Memristance, Memristor Emulator, Timing Storage

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Dhananjay Kumar ¹, B. Kavitha ², M. Padmavathy ¹, B. Harshini ¹, E. Preethi ¹, P. Varalakshmi ²

Affiliations
1 Department of Information Technology, Anna University, MIT Campus, Chennai, IN
2 Department of Computer Technology, Anna University, MIT Campus, Chennai, IN

Abstract

Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service) when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider's resources aren't enough to satisfy the customer's demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO) combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO). These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

Keywords

Hybrid Cloud, Particle Swarm Optimization, Ant Colony Optimization, Task Scheduling.

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V. Mareeswari ¹, R. Saranya ¹, R. Mahalakshmi ¹, E. Preethi ¹

Affiliations
1 Department of Software Engineering, School of Information Technology and Engineering, VIT University, Vellore, IN

Abstract

Diabetes mellitus is one of the world's major diseases. Millions of people are affected by the disease. The risk of diabetes is increasing day by day and is found mostly in women than men. The diagnosis of diabetes is a tedious process. So with improvement in science and technology it is made easy to predict the disease. The purpose is to diagnose whether the person is affected by diabetes or not using K Nearest Neighbor classification technique. The diabetes dataset is a taken as the training data and the details of the patient are taken as testing data. The training data are classified by using the KNN classifier and secondly the target data is predicted. KNN algorithm used here would be more efficient for both classification and prediction. The results are analyzed with different values for the parameter k.

Keywords

Data Mining Techniques, K Nearest Neighbor, Prediction of Diabetes, Classification, UCI Repository.

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