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Ramkumar S.,Oxford Engineering College | Rajarajan G.,Vidhya Mandhir Institute of Technology | Singh H.K.,National Physical Laboratory India
Journal of Materials Science: Materials in Electronics | Year: 2016

The paper contributes the preparation and characterization of Sm0.43Nd0.10Sr0.47MnO3 thin film. The thin film deposited on the single crystal of LaAlO3 (001) substrates by DC-magnetron sputtering at 1053 K at a pressure of 200 m Torr. The deposited thin film was found to have insulating behavior when annealed in oxygen atmosphere at 1223 K for 24 h. The X-ray diffraction (XRD) study shows that, both 30 nm and 60 nm thin films reveal compressive strain. The magnetic measurement (M-H) shows that the paramagnetic to ferromagnetic transition temperature of 60 nm film is 145 K. At higher film thickness (~ 60 nm) a sharp insulator-to-metal transition is observed at 142 K (TIM = 142 K). 60 nm film show a huge temperature co-efficient of resistance (TCR) and low field magneto resistance/an-isotropic magneto resistance (MR/AMR) is observed. The difference in the magneto transport properties of the two films have been explained in terms of presence of the magnetically and structurally disordered layer at the film-substrate interface. © 2016 Springer Science+Business Media New York Source

Gayathri Devi K.,Coimbatore Institute of Technology | Radhakrishnan R.,Vidhya Mandhir Institute of Technology
Computational and Mathematical Methods in Medicine | Year: 2015

Purpose. Colon segmentation is an essential step in the development of computer-aided diagnosis systems based on computed tomography (CT) images. The requirement for the detection of the polyps which lie on the walls of the colon is much needed in the field of medical imaging for diagnosis of colorectal cancer. Methods. The proposed work is focused on designing an efficient automatic colon segmentation algorithm from abdominal slices consisting of colons, partial volume effect, bowels, and lungs. The challenge lies in determining the exact colon enhanced with partial volume effect of the slice. In this work, adaptive thresholding technique is proposed for the segmentation of air packets, machine learning based cascade feed forward neural network enhanced with boundary detection algorithms are used which differentiate the segments of the lung and the fluids which are sediment at the side wall of colon and by rejecting bowels based on the slice difference removal method. The proposed neural network method is trained with Bayesian regulation algorithm to determine the partial volume effect. Results. Experiment was conducted on CT database images which results in 98% accuracy and minimal error rate. Conclusions. The main contribution of this work is the exploitation of neural network algorithm for removal of opacified fluid to attain desired colon segmentation result. © 2015 K. Gayathri Devi and R. Radhakrishnan. Source

Gokuldev S.,Amrita University | Radhakrishnan R.,Vidhya Mandhir Institute of Technology
International Journal of Applied Engineering Research | Year: 2015

Grid computing has emerged as the next generation parallel and distributed computing methodology that aggregates geographically distributed heterogeneous resources which have the potential to solve large scientific applications. In addition to the challenges of job scheduling, the effective utilization of resources and fault tolerance plays a major role due to the unreliable nature of the grid environment. Load balancing is a technique to enhance the resources, utilizing parallelism, exploiting throughput improvisation and to reduce the response time through an appropriate distribution of the application. Several existing algorithms address the grid system with no resource broker and few with single resource broker to provide solution with increase in efficiency with a variety of load variations. The proposed work holds a grid scenario with multiple resources brokers explicitly created for each set of grid resources interconnected with each other, categorized based on the capacities of the Processing Elements (PEs) to balance the load and to achieve reduced execution time of jobs that are being processed. A Weighed Rank Based Scheduling (WRBS) algorithm is proposed for the Meta broker to route the incoming jobs to sub- level of resource brokers and hence as a result an optimum solution is obtained thus achieving the effective scheduling and optimum load balancing with effective utilization of resources under heterogeneous grid environment. The simulation result shows significant results with a minimum of four percentage increase in efficiency through reducing the overall execution of jobs for minimum load and thereby shows an increase in efficiency for greater loads depending on the number of incoming jobs. © Research India Publications. Source

Nagajayanthi B.,iversity | Radhakrishnan R.,Vidhya Mandhir Institute of Technology | Vijayakumari V.,Sri Krishna College of Engineering And Technology
International Journal of Applied Engineering Research | Year: 2015

Network security is the key challenge in IoT. Possible source for security issues and vulnerabilities in IoT networks are caused due to its dynamic network topology, mobility and weak physical security of low power devices. Authentication, Authorization and Access control are important and critical functionalities in the context of IoT to enable secure communication between devices. It is infeasible to use conventional cryptography in IoT networks since security and privacy of critical data are crucial. In this paper, a secure and efficient authentication mechanism in IoT based healthcare system is proposed. Security and privacy of patients‟ medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. Primary focus of this work is to provide a Multilayer Security using Linear Programmable Pre-coded Matrix Decomposition (MS_LPMD) method for secure authentication and authorization of remote in or out patients. The proposed architecture uses an efficient and secure key management scheme between IoT node and IoT s_gateway. In this approach during the pre-authentication phase, connection between IoT node and IoT secure gateway is established. The Secure Gateway being the primary authentication server validates unique pattern coefficients generated using LPMD process against the coefficients received from IoT Node for authentication. Authenticated IoT Node employs a simple light-weight computational process for cipher key generation used for subsequent encrypted data communication through a secure communication channel. Proposed MS_LPMD scheme is built to protect the confidentiality, integrity and availability of network data by making the system reliable and protecting the system from malicious attacks which can lead to information disclosure. The proposed MS_LPMD approach has outperformed other existing approaches such as AES, EDCH etc, in terms of higher security level and trust worthiness factor. The performance analysis revealed that our proposed MS_LPMD approach has less communication overhead and latency between the IoT Node and secure gateway. Using software-hardware co-designed IoT test bed, the computed results on standard simulation setup shows an average reduced overhead of 29% and latency of 21% when compared to existing EDCH scheme. © Research India Publications. Source

Kalaiarasu M.,Sri Ramakrishna Engineering College | Radhakrishnan R.,Vidhya Mandhir Institute of Technology
Research Journal of Applied Sciences, Engineering and Technology | Year: 2015

In recent years, data are collected to a greater extent from several sources or represented by multiple views, in which different views express different point of views of the data. Even though each view might be individually exploited for discovering patterns by clustering, the clustering performance could be further perfect by exploring the valuable information among multiple views. On the other hand, several applications offer only a partial mapping among the two levels of variables such as the view weights and the variables weights views, developing a complication for current approaches, since incomplete view of the data are not supported by these approaches. In order to overcome this complication, proposed a Kernel-based Independent Component Analysis (KICA) based on steepest descent subspace two variables weighted clustering in this study and it is named as KICASDSTWC that can execute with an incomplete mapping. Independent Component Analysis (ICA) which exploit distinguish operations depending on canonical correlations in a reproducing kernel Hilbert space. Centroid values of the subspace clustering approaches are optimized depending on steepest descent algorithm and Artificial Fish Swarm Optimization (AFSO) algorithm for the purpose of weight calculation to recognize the compactness of the view and a variable weight. This framework permits the integration of complete and incomplete views of data. Experimental observations on three real-life data sets and the outcome have revealed that the proposed KICASDSTWC considerably outperforms all the competing approaches in terms of Precision, Recall, F Measure, Average Cluster Entropy (ACE) and Accuracy for both complete and incomplete view of the data with respect to the true clusters in the data. © Maxwell Scientific Organization, 2015. Source

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