Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Blockchain enabled Digitization of Land Registration

Abstract: In India, the Land Registry System is a very time-consuming procedure that requires many intermediaries, thus increasing the number of fraudulent cases. The use of Blockchain Technology for Land Registry Management can eliminate these problems. Blockchain is simply a data structure where each block is connected to another block. It is a distributed data ledger with an immutable public record of digital transactions. The key features of this technology are, the data in the block are immutable which is achieved by using hashing algorithms, cryptography techniques and consensus mechanisms which are done before adding a block into the blockchain. Even though there are limitations in Blockchain technology like, it is complex to implement and not as much fast as the centralized system. Also, there is a need of miners to validate the transactions but these can be overcome by using appropriate consensus mechanisms. In India, the Land registration process is one of the tedious process and most often people are not aware of the entire rules to be followed during registration process. Also, more documents need to be verified and thus it takes delay in completing registration. In addition to this, the middlemen collect bribes to complete this process. Mistakes also may occur while processing land records. The aim of this work is to develop a land registry system using Blockchain with a detailed and user-friendly feature with high reliability and good interface. It mainly focuses to cover the rules and procedures stated by the Indian Government regarding land registration. It ensures the enhanced security and accuracy of records.

Load balanced job scheduling approach for grid environment

Abstract: Grid computing is a way of combining computers across a network to form a distributed supercomputer to perform complex computations. In the commercial world, grid aims to maximize the utilization of an organization's computing resources by making them shareable across applications. A grid environment can be classified into two types: Computing grids and data grids. In computing grid, job scheduling is an important task. Load Balancing is a technique which is used to distribute the workload equally across multiple computers to enhance resource utilization and to reduce the response time in grid environment. Main goal of load balancing is to balance the load across all the processors. It improves the throughput of grid resources. A good Scheduling algorithm should assign jobs to resources efficiently and balance the system load. Hierarchical Load Balanced Algorithm is used to solve the problem in grid environment.The proposed system Enhanced Hierarchical Load Balance Algorithm is designed to schedule the jobs and also to improve the overall performance of the system in terms of resource utilization and user satisfaction. It also reduces the makespan of the jobs. If the resource capacity satisfies the need of the user then the job will be done within particular time period.

Throughput analysis of IEEE 802.11b WLAN under a non-saturated condition

Abstract: This paper provides a simple and accurate analysis using Markov chain modeling to compute IEEE 802.11 DCF performance, in the presence of channel errors. We extend the analytical model of IEEE 802.11 DCF from Bianchi model to study throughput performance under noisy situation, where the node's transmit queue may be at times empty and then extension of his model to a nonsaturated environment and also we develop an expression for the nonsaturation throughput as a function of the number of stations, packet sizes, and raw channel error rates. The derived analysis, which takes into account packet retry limits, is validated by comparison with NS-2 simulation results. Numerical results and simulations are provided to validate the accuracy of our model and is verified by previous work.

A Comparative Study on Human Spermatozoa Images Classification with Artificial Neural Network Based on FOS, GLCM and Morphological Features

Abstract: This paper aims to evaluate the accuracy of artificial neural network based classifiers using human spermatozoa images. Three different neural network based classifiers are used: Feed Forward Neural Network, Radial Basis Neural Network and Elman Back Propagation Neural Network. These three different classifiers were investigated to determine their ability to classify various categories of human spermatozoa images. The investigation was performed on the basis of the different feature vectors. The feature vector includes first order statistics (FOS), textural and morphological features. The extracted features are then used to train and test the artificial neural network. Experimental results are presented on a dataset of 91 images consisting of 71 abnormal images and 20 normal images. The radial basis network produced the highest classification accuracy of 60%, 75% and 70% when trained with FOS, Combined and Morphological features. When feed forward neural network is trained with GLCM features, a classification accuracy of 75% is achieved.

Energy and exergy analysis on hydrofluoroolefin/ hydrofluorocarbon (HFO/HFC) refrigerant mixtures in low and medium temperature small-scale refrigeration systems:

Abstract: The new refrigerants such as HFO-1234yf and HFO-1234ze have been considered as long-term replacements for HFC-134a to comply with the Kyoto protocol. In small size refrigeration systems, these refr...