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Gomathi, R.
- Rewriting Common Sub-Expressions for Optimizing Multiple SPARQL Queries
Abstract Views :133 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science in Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
2 Department of Computer Science and Engineering from Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
3 Department of Electronics and Instrumentation Engineering in Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
1 Department of Computer Science in Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
2 Department of Computer Science and Engineering from Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
3 Department of Electronics and Instrumentation Engineering in Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
Source
Software Engineering, Vol 5, No 4 (2013), Pagination: 149-155Abstract
A World Wide Web Consortium standard for processing Resource Description Framework data is a SPARQL query language, a technique that is used to encode data in meaningful manner. Multi Query optimization (MQO) is a technique in which multiple query plans for satisfying a query are examined and a good query plan is identified. Query optimization is performed by grouping into individual clusters using common substructures in the multiple SPARQL queries. The foundations of SPARQL query and Query Optimization technique for Multiple SPARQL queries are investigated. An Efficient algorithm for identifying the common sub expressions is executed. A comprehensive set of query rewriting rules for the clustered group is proposed and finally Query execution provide the final result of optimized query. The proposed method is effective and efficient for optimized SPARQL query.
Keywords
W3C, RDF, SPARQL, MQO.
- Hardware Trojan Rectification and Reducing Trojan Activation Time Using Transition Probability and Dummy Flipflops
Abstract Views :174 |
PDF Views:3
Authors
R. Gomathi
1,
A. Jeyasingh
1
Affiliations
1 IFET College of Engineering, Villupuram-605108, Tamilnadu, IN
1 IFET College of Engineering, Villupuram-605108, Tamilnadu, IN
Source
Programmable Device Circuits and Systems, Vol 5, No 7 (2013), Pagination: 316-320Abstract
Fabless semiconductor industry and government agencies have raised serious concerns about tampering with inserting hardware Trojans in an integrated circuit supply chain. The proposed Trojan detection methods are based on Trojan activation to observe either a faulty output or measurable abnormality on side-channel signals. Time to activate a hardware Trojan circuit is a major concern from the authentication standpoint. The paper analyzes time to generate a transition in functional Trojans. Transition is modeled by geometric distribution and the number of clock cycles required to generate a transition is estimated. And a dummy scan flip-flop insertion procedure and switching activity is proposed aiming at decreasing transition generation time. The procedure increases transition probabilities of nets beyond a specific threshold. The relation between circuit topology, authentication time, and the threshold are determined. Proposed method can significantly increase Trojan activity and reduce Trojan activation time.Keywords
Dummy Flip-Flop Insertion, Hardware Trojan Security, Trojan Detection & Rectification, Transition Probability.- A Comparative Analysis of Image Compression Techniques: K Means Clustering and Singular Value Decomposition
Abstract Views :296 |
PDF Views:0
Authors
R. Gomathi
1,
R. Aparna
1
Affiliations
1 Department of Computer Applications, Madras University, IN
1 Department of Computer Applications, Madras University, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 4 (2020), Pagination: 2160-2164Abstract
The global drive to digitize almost all the existing processes has mandated the conversion of all concerned analog data into their respective digital formats. One such crucial data that is being digitized on a priority in today’s world is image. An image is a type of data which is composed of picture elements called pixels. It can be represented as a matrix for the manipulating process. The storage of a vast database of image files occupies a huge memory space in the disk. To overcome this hassle, image files can be compressed and saved. This image compression process is aimed at reducing the data size in terms of bytes and enable the efficient storage and transmission of image files. Image compression can be achieved through several algorithms. In this paper, we discuss two such algorithms, namely k means clustering and singular value decomposition. K Means Clustering technique helps in minimizing the colour components of the image. Singular Value Decomposition technique can be carried out by low rank approximation of the image matrix. This research work is performed using the Python platform and subsequently the efficiency of both the methods is compared. The comparative analysis of the simulation results are further compared with the existing methods to show the competence of different methodologies. Thus, this work strives to be of learned assistance to the concerned aspirants in choosing the best algorithm for their applications.Keywords
Image Compression, K-Means Clustering, Singular Value Decomposition.References
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