Sri Ramakrishna Engineering College

About: Sri Ramakrishna Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Control theory. The organization has 1030 authors who have published 843 publications receiving 3822 citations.

Effect of titanium carbide reinforcement on micro structure and wear properties of annealed AA6082 by friction stir processing

Abstract: Friction stir processing (FSP) has evolved as a novel solid-state technique to fabricate AA6082 plates in recent years. FSP technique was applied to analyse Aluminium Matrix Composites (AMC) AA6082...

Network Lifetime Improved Optimal Routing in Wireless Sensor Network Environment

Abstract: In the field of Wireless Sensor Networks, major problem arises due to the unbalanced consumption of energy. The unbalanced consumption will reduce the lifetime of a network performance. In the existing work, replacement of node is a difficult process and conservation of network parameters is not concentrated. It leads to the failure of network in different situations. These issues are resolved by introducing a density aware optimal clustering which is used to perform clustering to ensure the data transmission in a reliable manner. In the proposed work, Energy Balancing and Optimal Routing Based Secured Data Transmission based on clustering is introduced. It enhances the lifetime uniformly deployed data-gathering sensor networks using a balanced energy consumption technique. Ant Colony Optimization method is used to select the optimum head of the cluster to enhance lifetime of a network. Different techniques have been proposed for optimal edge disjoint routing. The data packets are forwarded in an optimum way by these techniques. Hybrid Genetic Particle Swarm Optimization algorithm is used to select the optimum edge disjoint route paths. The parameters of Quality of Service like reliability, capacity of processing, bandwidth and energy are considered to make an optimum selection. Based on the similarity values, sensor data’s are grouped and these data are aggregated to ensure the consumption energy which makes an optimum data transmission.

Smart Emergency Vehicle Plan with App Monitoring and Centralized Network

Abstract: In this rapidly developing technological world, effective automated traffic systems are becoming a requirement. The present-day traffic signals require an efficient communication system for increased efficiency in response time to emergency vehicles. The proposed system not only eliminates the waiting period of emergency vehicles but also reduces the response time to emergency vehicles. Each node consists of a traffic signal system employed at a junction having a unique identification number which in turn is connected to a centralized network architecture constructed around a single server. This server in turn is connected and contains information of all nodes in a specific region. The traffic signal system is designed such that it tackles the delay in response and communication interruption that may occur with the help of the centralized server connected to them. The server plays a vital role in calculating the distance between the approaching emergency vehicle and the traffic signal and instructs the corresponding traffic signal which thereby turns the signal at the respective lane green. The range and the type of indicators in the emergency plan can be altered depending on the signal junction requirements. In case of interruption in communication between the server and the traffic signal due to climatic conditions or system failure, the traffic signal could be controlled via a mobile application developed. The various methods implemented to control the traffic signal are discussed in this paper.

A Novel Approach in Mining Specialized Coherent Rules in a Level-Crossing Hierarchy

Abstract: Multi-level association rule mining techniques that are developed for mining data at multiple-level taxonomy usually rely on minimum support and minimum confidence threshold for extracting frequent items at various levels. But the usage of above threshold alone does not produce meaningful and interesting rules. Moreover, the determination of minimum support and confidence is very hard in a dataset containing multiple-level taxonomy. In order to overcome the above drawbacks, coherent rule mining was proposed. The proposed approach first mines multiple-level coherent rules in a predefined taxonomy and then extracts specialized coherent rules. The rules are mentioned as specialized rules because they exist only in the bottommost level of taxonomy irrespective of their predecessor rules. The proposed algorithm works for the quantitative dataset, and a triangular membership function is used for converting the quantitative dataset at various levels into fuzzy itemset. The results obtained are compared with multi-level fuzzy association rule mining technique.