SDM College of Engineering and Technology

About: SDM College of Engineering and Technology is a based out in . It is known for research contribution in the topics: Diesel fuel & Combustion. The organization has 350 authors who have published 351 publications receiving 2399 citations.

Adaptive self-tuning techniques for performance tuning of database systems: a fuzzy-based approach with tuning moderation

Abstract: Self-tuning of database management systems (DBMS) offers important advantages such as improved performance, reduced total cost of ownership, eliminates the need for an expert database administrator (DBA), and improves business prospects. Several techniques have been proposed by researchers and the database vendors to self-tune the DBMS. However, the research focus was confined to physical tuning techniques, and the algorithms used for self-tuning the shared memory of DBMS have high computational overheads as they use large statistical data. As a result, these approaches are not only computationally expensive but also do not adapt well to highly unpredictable workload types and user-load patterns. Hence, in this paper an important soft-computing method, namely, fuzzy-based self-tuning approach has been proposed wherein, three inputs namely, buffer-hit-ratio, number of users and database size are extracted from the database management system as sensor inputs that indicate degradation in performance, and key tuning parameters called the effectors are altered (Burlson and Donald 2010) according to the fuzzy rules. The fuzzy rules are framed after a detailed study of impact of each tuning parameter on the response-time of user queries. The proposed self-tuning architecture is based on modified Monitor, Analyze, Plan and Execute (MAPE) feedback control loop framework termed Monitor, Estimate and Execute (MEE). The self-tuning approach using this method has been tested under various workload types. The results have been validated by comparing the performance of the proposed self-tuning system with the workload-analysis-based self-tuning feature of the commercial database system, Oracle 10g. The results show significant improvement in performance under two workload types, namely, TPC-C and TPC-E and user-load variations in the range 2---100. The system is also tested under TPC-D workload for the user-load 1---10. This improved self-tuning helps in simplifying the job of the DBA, and results in cost saving and betters the business prospectus of the enterprise. A novel tuning moderation technique is also presented in this paper, that provides the necessary stability to the system while the tuning parameters are dynamically altered.

Development and experimental evaluation of titanium diboride particulate reinforcements on the Al6061 alloy composites properties

Abstract: In recent times, industries are focusing to develop newer materials with superior properties compared to the materials available at present. In this regard, the present investigation is aimed at th...

Wireless controlled military combat robot system

Abstract: Current defense applications are changing day by day and in future, no soldiers will go to battle field without using advance technology. The soldiers are able to know the position and movement of enemy in the battle field without entering to it. For this a micro wireless camera is fitted on the robot, where the video and voice transmitted to the control room. The present work designs a robot, constructed with the help of chain wheels and connected to motors, where movement of robot can be controlled in all directions with gear facility. The controlling of this robot is done with help of wireless RF module.

Decision fusion for robust horizon estimation using Dempster Shafer Combination Rule

Abstract: In this paper, we address the problem of decision fusion for robust horizon estimation using Dempster Shafer Combination Rule (DSCR). We provide a framework for decision fusion to select robust horizon estimate out of `n' estimates, based on confidence factor. Vision-based attitude estimation depends on robust horizon estimation and no single algorithm gives accurate results for different kind of scenarios. We propose to combine the evidence parameters to generate confidence factor using DSCR to justify the correctness of the estimated horizon. We compute Confidence Interval (CI) based on Gaussian Mixture Model (GMM). We also propose two techniques to provide evidence parameters for the estimated horizon using CI. We demonstrate the effectiveness of the decision framework on clear and noisy data sets of simulated and real images/videos captured by Micro Air Vehicle (MAV).

Wireless Sensor Network based Water Monitoring Systems: A survey

Abstract: India is experiencing rapid urbanization at the cost of it is Environment. Hazards related to Air and Water, are leading contributors to humanitarian crisis and that result in disaster. To overcome this, a quick check on the crisis is required by frequently monitoring the water and air for quality. This paper survey's different Wireless Sensor Network (WSN) based Water Monitoring Systems that address the challenges pertaining to specific water threats like physical, biological, chemical or radioactive contaminations, that are caused due to industrial, Sewer or agricultural wastes that are dumped in the river or lake water. These systems will continuously monitor the water for any changes using parameters like pH, DO, Temp, TDS, TBD… etc, if there are any changes in any one of the parameters, it is reported to the data center for further investigation. There are also many other issues related to water monitoring like Deployment of sensors nodes, QOS parameters and Underwater Communication protocols which also need to be solved. This paper tries to critically review the present system technologies used in WSN for monitoring water challenges, and try to find out the research gaps.