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.

Linear Matrix Inequality Based Controller used in Multivariable Systems

Abstract: In industries most of the process are non-linear in nature. Inorder to overcome this problem most of the industries modify the loop and make it as Single Input and Single Output (SISO) controllers to regulate such processes inorder to obtain the fast and quicker settling. But some processes with numerous variables causes lot of complexities, there is a high demand for designing a multivariable system in process industries. One such multivariable process is the quadruple tank systems which are a multivariable process. It is non-linear and dynamic, but the challenging task is to maintain the level of tanks due to the interaction between the process variable. This paper is based on using LMI (Linear Matrix inequalities) and Model Reference Adaptive control (MRAC) tuned PI controller for controlling the level of the four tanks. All the experimental results for various tanks are obtained using MATLAB software.

Digital image based simple scanometric device for the express detection of aqueous contamination of Hg2

Abstract: Detection of mercury ion (Hg2+) contamination is of utmost importance as it poses a major ecological problem, and its increased accumulation would affect the flora and fauna. Herein we report an instrument free colorimetric sensing of Hg2+ using bio-functionalized gold nanoparticle (A-GNP). Digital image scanner (DIS) is used in the detection of Hg2+ based on the change in RGB AVG (Red, Blue and Green) intensities of A-GNP solution upon the addition of different concentrations of Hg2+. The limit of detection (LOD) for naked eye detection was 5 nM, while the DIS system had a LOD of 3 ppt and the limit of quantification (LOQ) 3.68 aM. This concentration is very much lower as compared to the permissible limits prescribed by the USA-EPA. RGB changes for lower concentrations of Hg2+ undifferentiated to naked eye was monitored using our digital image scanometer (DIS) and were cross verified using UV–vis spectroscopy. The DIS system was employed for testing tap water as well as ground water and the results produced were satisfactory in nature. The proposed DIS system can be used for on spot detection of water samples and the analysis does not involve tedious procedures such as sample collection, processing and storage prior to detection using traditional methods. The DIS system is both sensitive and selective and is specific to the detection of mercury. Thus, our study provides a highly sensitive and cost effective system for mercury detection in rural areas where the access to sophisticated instruments is negligible.

A Survey on Wireless Sensor Networks and Instrumentation Techniques for Smart Agriculture

Abstract: Agriculture is the primary source for the development of our country’s economy. It involves production, processing, marketing, and distribution of agricultural produce. Smart agriculture is one of the modern farming practices that increase the agricultural production in a most efficient manner. Smart agriculture involves wireless sensor networks (WSN) for various operations. Wide variety of sensors are used in WSN that assists farmers to know the statistical details of their field which helps them to take accurate decisions and provides instantaneous feedback for various agricultural parameters. Numerous researchers and manufacturers aim to develop real-time sensors based on different approaches, namely electromagnetic, electric, optical, mechanistic, acoustic, or electrochemical, which calculates the physical and chemical properties of soil in agricultural fields. The main objective of this study is to review the application of wireless sensors used in agriculture, the principle and various instrumentation techniques in measuring different parameters in agriculture.

Road Sign Recognition System for Autonomous Vehicle using Raspberry Pi

Abstract: Road sign recognition is one of the important tasks of intelligent transportation systems (ITS). The project aims at implementation of road sign detection and control of an autonomous vehicle using Haar Cascade Classifier algorithm. In this proposed work, the system automatically detects the road signs, controls the vehicle and command certain actions. The system consists of Raspberry Pi 3 processor and web camera which automatically captures the video data and converts them into number of frames which are processed by the proposed algorithm in OpenCV to detect the road sign and control the vehicle. Based on the detected sign, the vehicle is controlled by two DC motors interfaced with Raspberry Pi. The experimental results for Peak Signal to Noise Ratio (PSNR) and Minimum Mean Square Error indicate the proposed system gives more accurate results with higher PSNR value compared to Hough Transformation. The performance metrics of the algorithm implemented in ARM processor is much better compared to the results obtained using MATLAB software.