Informatics Publishing Limited

S. Sridevi ¹, M. Sumathi ¹, V. J. Vishnuprasad ²

Affiliations
1 Dept. of ECE, Dhirajlal Gandhi College of Technology, Salem, Tamilnadu, IN
2 Physics Dept., Dhirajlal Gandhi College of Technology, Salem, Tamilnadu, IN

Abstract

In the current scenario, curriculum design of Indian engineering education need to have high impact to change the lives of engineers and scientists to sustain in the professional field. Moreover engineers are in a position to meet the elevated demand in various sectors right from financial services to manufacturing techniques. In order to meet the above demand in this competitive world, it is essential to improve the engineering curriculum design which actually refers to the entire plan of a course and its desired learning outcomes. Effective improvement in the curriculum design of undergraduate engineering education can be done with the inclusion of various experiential learning methodologies. This paper deals with the inclusion of experiential learning in terms of guiding and training the students to design the equipments needed for the laboratory of optical communication and thereby enhancing the learning of theory course of Fiber optic communication. This methodology can be facilitated during the semester holidays for the students. This paper also describes about various experiential learning methodologies and high impact teaching skills required for engineering curriculum design.

Keywords

Curriculum, Engineering Education, Experiential Learning, Optical Communication.

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M. Sumathi ¹, S. Malarvizhi ²

Affiliations
1 Sathyabama University, IN
2 SRM University, Chennai, IN

Abstract

This paper describes the design evaluation of a 1.5V,2.4 GHz CMOS RF receiver front-end system consists of a low noise amplifier (LNA), down-conversion mixer and digitally controlled ring oscillator for Direct Conversion specification of IEEE 802.11b/g application. The designed LNA uses inductively degenerated Common Source-Common Gate stage (CS-CG) structure to enhance the gain and impedance matching. The mixer uses Switched – Transconductance Gilbert cell architecture for the betterment of noise figure. Also the tristate –inverter based 8-Bit Digitally controlled ring oscillator designed for generating local oscillator frequency. The front end blocks performance are analyzed by using Intel Core2DuoCPU E7400@2.80GHz processor with Agilent’s Advanced Design System (ADS) EDA tools and Microwind 2.7 version software. The design simulation process has been carried out in a standard TSMC 0.18-μm CMOS process technology. The LNA achieves a power gain of 18.35 dB and input reflection coefficient of -12.5 dB at 2.4 GHz RFfrequency. Also the mixer exhibits a noise figure of 13 dB and conversion gain of 39 dB. The schematic and layout developed for ring oscillator whose frequency of oscillation observed as 2.25GHz.

Keywords

CMOS, Down-Conversion, Front-end, LNA, Mixer, Ring Oscillator.

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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.

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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.

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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

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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

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M. Sumathi ¹, S. Umagowrie ²

Affiliations
1 Centre for Environmental Studies, Anna University, Chennai (T.N.), IN
2 Department of Plant Biology and Plant Biotechnology, Ethiraj College for Women, Chennai (T.N.), IN

Abstract

Tanneries are the oldest industries started to promote cottage business in India. Only after several years it was realized that the effluent had adverse effect on the environment. Many initiative steps were taken to treat the effluent in Common Effluent Treatment Plants (CETPs). CETPs discharge treated effluent with organics and chromium toxicant higher than the statutory limit for discharge into the surface water bodies. This study aims to remediate the contaminated environment by biological means with the indigenous bacterial population from the tannery effluent treatment plant by reduction of hexavalent chromium to trivalent form. The bacterial strains isolated from different stages of the treatment plant were characterized based on the morphological and biochemical properties. The chromium resistance and reduction potential of the isolates were done to identify the efficient chromium reducer. Bacillus sp. tolerated up to 2000 μg/ ml and reduced hexavalent chromium to trivalent form by 77.4 %. This strain can be used to remediate the chromium contaminated environment. Further molecular identification and approaches for strain improvement can enhance the reduction potential of the strain effectively.

Keywords

Tanneries, CETPs, Hexavalent Chromium, Strain Improvement.

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P. Chitra ¹, M. R. Ebenezer Jebarani ¹, P. Kavipriya ¹, K. Srilatha ¹, M. Sumathi ¹, S. Lakshmi ¹

Affiliations
1 ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, IN

Abstract

A novel method of detecting Acute myelogenous leukemia (AML) disease using image processing algorithms is discussed in this paper. AML is an high risk disease which should be diagnosed early. AML detection is challenging, and should be performed by a qualified hematopathologist or hematologist. This paper discusses an automatic detection of AML using image processing methods. The algorithm consists contrast enhancement, Local binary pattern detection and Fuzzy C mean clustering technique. This Automatic detection method will helps the hematologists for easier identification and early detection of leukemia from blood microscopic images which will improve the chances of survival for the patient. A fuzzy based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia.

Keywords

Acute Myelogenous Leukemia, Fuzzy C Mean Clustering, Local Binary Pattern Feature Extraction.

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