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.

Detection of black hole attack in enhanced AODV protocol

Abstract: Wireless Adhoc network is decentralized in nature which means it is independent of base station and do not rely on fixed routers to route packets from source to destination and also is dynamic in nature which is made up of mobile nodes. Although there are multiple proficient reactive protocols like AODV, TORA, DSR etc. there are some loop holes in these reactive protocols which are of on-demand type. One of the serious problem identified in such protocols is black hole attack, in which a malicious node (or group of malicious nodes) in the network act as legitimate node but actually does some superfluous actions in the network. It is very important to identify this type of malicious nodes in the initial stage so that further complications can be avoided. At present, normal AODV which is based on hop-count metric to detect the packets suffer from black hole type of attack. In this proposed work, care is taken to enhance the AODV protocol by making it delay aware. Detecting the black hole attack and avoiding them in participating in the network transactions is the primary focus of this paper. All nodes in network are ranked based on their distance from particular node which wishes to send packets through the network.

Power Generation From Renewable Energy Sources Derived From Biodiesel and Low Energy Content Producer Gas for Rural Electrification

Abstract: The work presented mainly focuses on the power generation from engine-gasifier integrated system suitably modified to operate on selected renewable as well as fossil-derived fuel combinations. In this direction, biodiesel of Honge oil methyl ester (HOME), fuel oil (FO) also called pyrolysis plastic oil (PPO), were selected as pilot injected fuels in the modified diesel engine to operate in dual fuel mode with low energy content producer gas as inducted fuel. Accordingly, the performance, combustion, and emission characteristics of a single cylinder, four stroke water cooled direct injection (DI) compression ignition (CI) engine operated in dual fuel mode using these fuel combinations were investigated as potential sources for power generation applications. In the first phase of the work, production of fuel oil from low-density polyethylene waste (LDPE) using thermal and catalytic process has been investigated. Thermal cracking yielded 97.2 wt% conversions compared to 99.1 wt% by catalytic cracking with catalyst to plastic ratio of 0.2 and biodiesel was obtained using transesterification process from the Honge oil, which is locally and abundantly available in India. Further, in the next phase of the work, performance of dual fuel engine was investigated using HOME and fuel oil (ranging from 0% to 30% in HOME) blend and producer gas induction. Optimized performance of dual fuel operation using combination of HOME and FO blend called HFO and producer gas induction was done using different engine operating parameters such as reentrant type combustion chamber (RCC), 230 bar injection pressure, 4 hole, and 0.25 mm nozzle orifice which showed a 4.9% increased performance with acceptable levels of emissions compared to HOME-PG operation with conventional unmodified diesel engine provided with hemispherical combustion chamber (HCC). Results of investigation on HOME + FO-PG operation with RCC showed significant changes in ignition delay, increased cylinder pressure, heat release rate, and decreased combustion duration compared to HOME-PG operation with conventional existing engine facility. However, extended investigations in engine technology with further development would enhance the performance and feasibility of these fuels for dual fuel operation and future power generation exploitations.

Mobile Agent Based Distributed Data Mining

Abstract: The analysis of large datasets has become an important tool in understanding complex systems in areas such as economics, business, science and engineering Such datasets are often collected geographically distributed way and cannot in practice be gathered in to a single repository Applications that work with such datasets cannot control most aspects of the data's partitioning and arrangements So far, attention in data mining process has always focused on extracting information from data physically located at one central site and they often do not consider the resource constraints of distributed and mobile environments Few attempts were also made in parallel data mining However most real life applications rely on data distributed in several locations As a consequence both new architectures and new algorithms are needed In this paper author proposes a method that explores the capabilities of mobile agents to build an appropriate frame work and an algorithm that better suits the distributed data mining applications it also makes the performance analysis and comparison with the existing such method

Entropy coding of H.264/AVC using Exp-Golomb coding and CAVLC coding

Abstract: As the costs for both processing power and memory have reduced, network support for coded video data has diversified, and advances in video coding technology have progressed, the need has arisen for an industry standard for compressed video representation with substantially increased coding efficiency and enhanced robustness to network environments. The H.264/AVC standard aims to enable significantly improved compression performance compared to all existing video coding standards. In this project we employ two schemes of coding transform coefficients namely exponential Golomb coding & context adaptive variable length coding (CAVLC).And the major part of the contribution is the decoding strategy applied for decoding which results in performance enhancement saving the memory and decoding time which are the most important factors for bandwidth utilization. The transform coefficients are obtained using a simple zigzag scan technique. The consensus among the major players of the communications and video industry on H.264 might provide the major thrust for this new standard.

Adaptive Self-Tuning Techniques for Performance Tuning of Database Systems: A Fuzzy-Based Approach

Abstract: Self-tuning of Database Management Systems(DBMS) offers important advantages such as improved performance, reduced Total Cost of Ownership(TCO), eliminating the need for an exert Database Administrator(DBA) and improved 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 in existing methods for self-tuning of memory need analysis of large statistical data. As 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 a 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 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 Monitor, Analyze, Plan and Execute(MAPE) feedback control loop framework [1] and has been tested under various workload types. The results have been validated by comparing the performance of the proposed self-tuning system with the auto-tuning feature of commercial database systems. The results show significant improvement in performance under various workload-types, user-load variations.