An LR parser shows error only during scanning input symbol. Error is never shown during the reduction of a handle (substring of stack) into nonterminal. It is because a symbol is put into the stack only when it is guaranteed to be the correct one. If the method of reduction of a handle is known then errors can also be shown during reduction. Hence a wrong symbol can be shifted on the stack and error can be detected during reduce operation. It may permit the merging of few states. The simplest type of reduction scheme is to remove few symbols (the number of symbols equal to the length of the handle) from the top of the stack and push the corresponding nonterminal on the stack. In this paper, a state merging scheme is proposed under this method of reduction. General Terms Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.
Optimized Link State Routing Protocol is developed for Mobile Ad Hoc Network. It operates as a table driven, proactive protocol. The core of the OLSR protocol is the selection of Multipoint Relays (MPRs), used as a flooding mechanism for distributing control traffic messages in the network, and reducing the redundancy in the flooding process. A node in an OLSR network selects its MPR set so that all two hop neighbor are reachable by the minimum number of MPR. However, if an MPR misbehaves during the execution of the protocol, the connectivity of the network is compromised. This paper introduces a new algorithm for the selection of Multipoint Relays (MPR) whose aims is to provide each node to selects alternative paths to reach any destination two hops away. This technique helps avoid the effect of malicious attacks and its easily to implement the corresponding algorithm without any additional overhead.
Software Defect Prediction has been an area of growing importance. It is always required to maintain high reliability and high quality for any software being developed. A software quality prediction model is built using software metrics and defect data collected from a previously developed system release or similar software project. Upon validation of such a model, it could be used for predicting the fault-proneness of program modules that are currently under development. A low quality or fault prone prediction can justify the application of available quality improvement resources to those programs. In contrast, a non fault prone prediction can justify non- application of the limited resources to these already high- quality programs. And finally high software reliability and quality are maintained with an effective use of the available resources. A feature extraction algorithm based on graph clustering is applied over the historical software data collected for defect prediction purpose and its impact on different data sets are analyzed in this paper.
Big data has brought a revolution in the world of data analytics. Data that was discarded a few years back is now considered a powerful asset. Big data is now being extensively used for knowledge discovery by all sectors of society. It is produced by almost all digital processes and is stored, shared on web. This reliance of big data on web model poses serious security concerns. Traditional security methods cannot be applied to big data due to its large volume, variety and volume. Also since big data contains person specific information, privacy is a major security concern. The three important privacy preservation methods are: data anonymization, notice and consent and differential privacy. In this paper we discuss these privacy preservation methods for big data and how differential privacy is a better solution for big data privacy.
Battery management system which consists of a controller that has algorithms to monitor and control several parameters such as, Soc, Soh, temperature, voltage and current. Battery management system (BMS) is having software and hardware. Software contains algorithms, limits for voltages, currents and temperatures and Hardware contains relays, cell balancing and signal conditioning circuits. In order to control battery performance and safety it is necessary to understand what need to be controlled and why it needs controlling. This user friendly battery system will helps to make charging very comfort by some algorithms and to evolve the performance characteristics of various batteries and display the voltages, currents, state of charge, state of health through various algorithms and display them.