Loyola Institute of Technology

Chennai, India

Loyola Institute of Technology

Chennai, India
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Rajasekar R.,Sathyabama University | Karthik N.,Sathyabama University | Xavier G.,Loyola Institute of Technology
IOP Conference Series: Materials Science and Engineering | Year: 2017

Present work provides the effect of biodiesel blends and Sound Characteristics of P20 Biodiesel blend compared with Performance and emission Characteristics of diesel. Methods and analysis biodiesel blends was prepared by the Transesterification Process. Experiments were conducted in single cylinder constant speed direct injection diesel engine for various test fuels. Research is mainly focused on pongamia oil. It was observed that a 20% Pongamia oil blends and its properties were similar to diesel. The results showed that 20% Pongamia oil blends gave better performance, less in noise and emission compared with ester of Jatropha and neem oil blends. Hence Pongamia blends can be used in existing diesel engine without compromising the engine performance. © Published under licence by IOP Publishing Ltd.


Mily Velayudhan T.K.,Sathyabama University | Yameni M.D.,Loyola Institute of Technology
IOP Conference Series: Materials Science and Engineering | Year: 2017

The main objective or the purpose of this research is to investigate and identify the significance of work environment towards the performance and also to study the effectiveness of the QWL in the organization. Methods/Analysis:In order to meet the stated objectives a structured questionnaire was framed and data was collected using convenience sampling from 123 employees of the steel manufacturing organization in Chennai, and to study the significant association chi-square was used by the researcher. Findings:QWL of the employees of this steel company can be improved by conducting some more training classes for the employees who are falling in the category of more than 3 to 4 years of experience and >4 years of experience which would boost their self confidence and help them attain their level of satisfaction. Similarly the organization can give some more security to the employees falling in the category of 41 and above so that they feel quite secure in the hand of organization and they can give their paramount performance. Novelty/Improvement:This empirical article on Quality of Work life - A Study's structured questionnairecan be applied as an Employee opinion Survey taken in once in 6 months on knowing the quality of work life. By doing this survey organizations can get to know the quality of work life of the employees and take necessary steps to improve the QWL among all the Employees. It also helps the employers to know that their employees who are working in their organization are happily working leading to good QWL which will boost up their performance to come happily daily to their work place. © Published under licence by IOP Publishing Ltd.


Deepa A.R.,Loyola Institute of Technology | Sam Emmanuel W.R.M.,Nesamony Memorial Christian College
Concepts in Magnetic Resonance Part A: Bridging Education and Research | Year: 2016

Detection of brain tumor in Magnetic Resonance Imaging (MRI) is vital as it delivers data about unusual tissues which is essential for planning treatment. Automating this method is challenging due to the high variety in appearance of tumor tissue among dissimilar patients and in many circumstances, comparison between tumor and normal tissue. In this article, we presented a mixture model based segmentation and classification of brain MRI for tumor identification. The proposed robust mixture estimator combining trimming of the outliers is based on component wise confidence level ordering of observations. The proposed method consists of three stages. In the first stage, the brain MRI is segmented into white matter (WM), gray matter (GM), Cerebrospinal fluid (CSF), and outliers by ordering of observations. In second stage, outliers consists of tumor cells in which eight type of features Contrast, Correlation, Homogeneity, Energy, Entropy, Standard deviation, Skewness, and Kurtosis are extracted. In the third stage, the extracted features are trained by Artificial Neural Network (ANN)and based on this a brain tumor identification scheme is established to examine those features to judge whether brain tumor is present in the given image or not. Experimental results indicate that the proposed classification method has achieved 93.47% in sensitivity, 100% in specificity, and 96.34% accuracy with less computational time based on the number of extracted features when compared to earlier classification methods. © 2017 Wiley Periodicals, Inc.


Vanitha L.,Loyola Institute of Technology | Suresh G.R.,Easwari Engineering College
ICACCS 2013 - Proceedings of the 2013 International Conference on Advanced Computing and Communication Systems: Bringing to the Table, Futuristic Technologies from Around the Globe | Year: 2014

In recent years, stress has become ingrained part of our life, being stressed by our financial worries, our job, etc. Stress causes physical illnesses, such as heart attacks, arthritis, and chronic headaches or psychological diseases like mental illness, anger, anxiety, and depression. There are several research works coming up to resolve the limitations on measuring, analyzing and identifying the human stress levels Amongst the many stress monitoring methods the more reliable method to determine the human stress level is to use physiological signals. In this work, Heart Rate Variability (HRV) determined from ECG signal, an efficient parameter to detect the stress level is used. The features extracted from HRV are given as input, to the two stage classifier, to classify the stress into one of the four levels as no stress, low stress, medium stress and high stress. In the first stage of the classifier, Self Organizing Map is used to classify the stress into two classes as 'stress level 1'(no stress & low stress) and 'stress level 2' (medium stress & high stress). In the second stage Support Vector Machine is used with RBF kernel to subdivide the 'stress level 1' into two classes 'No Stress' and 'Low Stress'. The stress level 2 is subdivided into twoclasses 'Medium Stress' and 'High Stress'. The performance of this hybrid structure is better and the efficiency of classification is 91%. © 2013 IEEE.


Vanitha L.,Loyola Institute of Technology | Suresh G.R.,Easwari Engineering College
Proceedings of the IEEE International Caracas Conference on Devices, Circuits and Systems, ICCDCS | Year: 2014

Stress has become an embedded part of our life, being stressed by our financial worries, our job, etc. Stress causes physical illnesses, such as heart attacks, arthritis, and chronic headaches or psychological diseases like mental illness, anger, anxiety, and depression. There are several research works coming up to resolve the limitations on measuring, analyzing and identifying the human stress levels Amongst the many stress monitoring methods the more reliable method to determine the human stress level is to use physiological signals. The Heart Rate Variability (HRV) determined from ECG signal, an efficient parameter to detect the stress level is used in this work. The features extracted from HRV are given as input, to the Hierarchical classifier, to classify the stress into one of the four levels as no stress, low stress, medium stress and high stress. The performance of the hierarchical structure is better and the efficiency of classification is 92 %. © 2014 IEEE.


Sheela C.J.,Loyola Institute of Technology | Vanitha L.,Loyola Institute of Technology
2014 International Conference on Circuits, Power and Computing Technologies, ICCPCT 2014 | Year: 2014

Sudden death from cardiac arrest is a major health problem and is responsible for almost half of all heart disease deaths. In Sudden Cardiac Death (SCD), the cardiac arrest occurs for a very short time which is preceded and followed by normal ECG. Thus, it is difficult to detect such conditions, using only ECG. This work predicts sudden cardiac arrest before 30 minutes of its occurrence on the basis of time domain and frequency domain features of Heart rate variability (HRV) obtained from ECG and using SVM classifier to classify SCD patient from Normal patient The database of cardiac patients obtained from physionet is used to check the validity of the proposed work Performance of SVM is better giving the classification efficiency of 88%. © 2014 IEEE.


Kandasamy V.,Loyola Institute of Technology | Papitha E.,Loyola Institute of Technology
2013 International Conference on Information Communication and Embedded Systems, ICICES 2013 | Year: 2013

Online personal health record (PHR) enables patients to manage their own medical records in a centralized way, which greatly facilitates the storage, access and sharing of personal health data. With the emergence of cloud computing, it is attractive for the PHR service providers to shift their PHR applications and storage into the cloud, in order to enjoy the elastic resources and reduce the operational cost. However, by storing PHRs in the cloud, the patients lose physical control to their personal health data, which makes it necessary for each patient to encrypt her PHR data before uploading to the cloud servers. Under encryption, it is challenging to achieve fine-grained access control to PHR data in a scalable and efficient way by using FADE. For each patient, the PHR data should be encrypted so that it is scalable with the number of users having access. Also, since there are multiple owners (patients) in a PHR system and every owner would encrypt her PHR files using a different set of cryptographic keys, it is important to reduce the key distribution complexity in such multi-owner settings. Existing cryptographic enforced access control schemes are mostly designed for the single-owner scenarios. In order to realize scalable, flexible, and fine-grained access control of outsourced data in cloud computing, in this research, we propose hierarchical attribute-set-based encryption (HASBE) by extending cipher text-policy attribute-set-based encryption (ASBE) with a hierarchical structure of users. The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits flexibility and fine-grained access control in supporting compound attributes of ASBE. © 2013 IEEE.


Andal S.,Loyola Institute of Technology | Ilampoornan M.K.,Loyola Institute of Technology
2014 International Conference on Circuits, Power and Computing Technologies, ICCPCT 2014 | Year: 2014

In this paper single stage AC-DC converter is designed and developed for battery charging. Desired features for battery charger are low cost, fast charging, high power factor, high efficiency, minimum ripple and high reliability. These are achieved using proposed circuit. This work presents novel approach of a battery charger, which offers battery galvanic isolation and Power Factor Correction (PFC) in a simple structure. This converter used as a part of a Distributed Power Supply System (DPS), also overcomes the need of galvanic isolation in each dc/dc converters. Simulation of proposed converter is done and results obtained are satisfactory. © 2014 IEEE.


Paulraj T.,Loyola Institute of Technology | Ilampoornan M.K.,Loyola Institute of Technology
2014 International Conference on Circuits, Power and Computing Technologies, ICCPCT 2014 | Year: 2014

Current unbalance is frequently encountered power quality problem. Negative sequence current leads to increase in machine temperature and unnecessary tripping of circuit breakers. Unequal phase current and harmonic current causes increased line losses and skin effect. In this paper, a new stationary reference frame control is proposed and implemented based on Positive and Negative Sequence Component Extraction. Positive sequence current is taken as reference for VSI, used as current unbalance compensator. VSI is designed to eliminate negative sequence current component thereby balance of currents are obtained. Consequently, reduction in lines losses and improvement of power factor is also observed in the power system. © 2014 IEEE.


Prabu S.,Loyola Institute of Technology | Maheswari R.,Loyola Institute of Technology
2014 International Conference on Information Communication and Embedded Systems, ICICES 2014 | Year: 2014

In a wireless sensor network (WSN), sensor nodes are powered by batteries that can operate for only a short period of time, which results in short network lifetime. The short lifetime disables the application of WSNs for long term tasks such as structural health monitoring for bridges and tunnels, border surveillance, road condition monitoring, etc. In this paper the overall network lifetime is increased using Grade diffusion (GD) algorithm along with LZW compression technique. Grade diffusion algorithm select the more available energy node as the relay node in the routing process and LZW algorithm reduces the transmitting and receiving power by compressing the original data size. By reducing the transmitting and receiving power to extend the life time of the wireless sensor network. © 2014 IEEE.

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