M. Sumathi ¹, R. Narmadha ¹, K. Sumathi ¹

Affiliations
1 Sathyabama University, Chennai - 600119, Tamil Nadu, IN

Abstract

Objectives: To design and analyze the performance of CMOS RF Oscillator circuits at low supply voltage. Methods/Statistical Analysis: The Current Mode Logic (CML) based oscillator and LC oscillator is designed for 5GHz WLAN applications. The CML design adopts a DCO topology and the schematic layout is drawn using Microwind 2.7. The performance analysis is carried out using Intel Core2 Duo CPU E7400 @ 2.80 GHz processor. Advanced Design System 9.0 is used to implement schematics for analyzing the performances of proposed LC tank oscillator. Findings: The simulated results show that the tri-state inverter based DCO has 20 to 30% power reduction which is more than other conventional oscillator circuits. The CML inverter based DCO consumed more power than tri-state inverter because it used tail current transistor that provides always the static path from supply to ground. The theoretical phase noise is compared with simulated value of –95.19 dBc/Hz at same offset frequency. Application/Improvements: These designs produce a substantial improvement in performance and may be easily integrated with RF front-end blocks with minimal interface problems.

Keywords

Current Mode Logic, CMOS Technology, Oscillator, Radio Frequency Design.

Full Text

   

N. G. S. Parameswaran ¹, M. Sumathi ²

Affiliations
1 Department of Computer Applications, VHNSN College, (Autonomous), Virudhunagar - 626001, Tamil Nadu, IN
2 Department of Computer Science, Sri Meenakshi Government Arts College for Women, Madurai - 625002, Tamil Nadu, IN

Abstract

The key factor of IaaS in cloud computing, deals with resource sharing and efficient utilization. Though, it was accessed only by legitimate user with cloud grant and revoke mechanism that depends on cloud data management. In this paper, a two way authentication method is proposed for accessing cloud data services with transactional database to provide resource access to legitimate user. The results show the comparison between single and two way authentication method for providing scalable and consistent performance for accessing cloud data store.

Keywords

Elastic Cloud Forecasting, Resource Sharing, Secure Resource Access, Transactional Database.

Full Text

   

S. Kumar Chandar ¹, M. Sumathi ², S. N. Sivanandam ³

Affiliations
1 Christ University, Bangalore - 560029, Karnataka, IN
2 Sri Meenakshi Government College for Arts for Women (Autonomous) Madurai - 625002, Tamil Nadu, IN
3 Karpagam College of Engineering, Coimbatore - 641032, Tamil, IN

Abstract

Background/Objectives: Foreign currency Exchange (FOREX) plays a vital role for currency trading in the international market. Accurate prediction of foreign currency exchange rate is a challenging task. The paper investigates the FOREX prediction using feed forward neural network. Methods/Statistical analysis: This paper employs artificial neural network to forecast foreign currency exchange rate in India during 2010-2015.The exchange rates considered between Indian Rupee and four major currencies Euro, Japanese Yen, Pound Sterling and US Dollar. The network developed consists of an input layer, hidden layer and output layer. The neural network was trained with Levenberg-Marquardt (LM) learning algorithm. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Forecasting Error (FE) are used as indicators for the performance of the networks. Findings: Simulation results are presented to show the performance of the proposed system. The paper also aims to suggest about network topology that must be chosen in order to fit time series kind of complicated data to a neural network model. The proposed technique gives the evidence that there is possibility of extracting information hidden in the foreign exchange rate and predicting into the future. Applications/Improvements: Finally, this paper presents the best network topology for FOREX prediction by comparing the effectiveness of various hidden layer performance algorithm using MATLAB neural network software as a tool.

Keywords

Exchange Rate, Forecasting Error, Mean Absolute Error, Network Topology and Levenberg - Marquardt Learning Algorithm

Full Text

   

S. Kumar Chandar ¹, M. Sumathi ², S. N. Sivanandam ³

Affiliations
1 Christ University, Bangalore - 560029, Karnataka, IN
2 Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai - 625002, Tamil Nadu, IN
3 Karpagam College of Engineering, Coimbatore - 641032, Tamil Nadu, IN

Abstract

Background/Objectives: Accurate prediction of stock market is highly challenging. This paper presents a forecasting model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for predicting financial time series. Methods/Statistical analysis: The idea of forecasting stock market prices with discrete wavelet transform is the central element of this paper. The proposed forecasting model uses the Discrete Wavelet Transform to decompose the financial time series data. The obtained approximation and detail coefficients after decomposition of the original time series data are used as input variables of back propagation neural network to forecast future stock prices. Approximation coefficients can characterize the coarse structure of the data and detail coefficients capture ruptures, discontinuities and singularities in the original data, to recognize the long-term trends in the original data. Findings: The proposed model was applied to five datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model. It also proved that the hybrid forecasting technique has achieved better results compared with the approach which is not using the wavelet transform. Applications/Improvements: The accuracy of the proposed hybrid method can also be improved by developing a model using artificial neural network with Adaptive Neuro Fuzzy Interference System.

Keywords

Artificial Neural Network, Discrete Wavelet Transform (DWT), Time Series and Stock Market Prediction

Full Text