By M. A. Berlin, Sheila Anand (auth.), Natarajan Meghanathan, Nabendu Chaki, Dhinaharan Nagamalai (eds.)
The 3 quantity set LNICST eighty four - LNICST 86 represent the refereed complaints ofthe moment overseas convention on computing device technological know-how and InformationTechnology, CCSIT 2012, held in Bangalore, India, in January 2012. The fifty five revised complete papers offered during this quantity have been conscientiously reviewed andselected from various submissions. The papers are equipped in topical sectionson advances in computing device technological know-how and knowledge know-how; and advert hoc andubiquitous computing.
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Additional info for Advances in Computer Science and Information Technology. Computer Science and Information Technology: Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part III
Minimizing the number of one-paths in BDDs by an evolutionary algorithm. : An Algorithm for Minimizing of Boolean Functions Based on Graph DS. : On solving Covering Problems. In: Proc. of 33rd DAC, Las Vegas (June 1996) 48 D. Sensarma et al. : Graph-based algorithms for Boolean function manipulation. : Symbolic Boolean Manipulation with Ordered Binary-Decision Diagrams. : Binary decision diagrams and beyond: Enabeling techniques for formal verification. In: Int’l Conf. on CAD, pp. : Binary Decision Diagrams – Theory and Implementation.
J. : The Reconstruction Problem for Characteristic Polynomials of Graphs. Univ. Beograd. Publ. Elektrotehn. Fak. Ser. Mat. Fiz. : On the reconstruction of a graph from a collection of subgraphs. In: Fiedler, M. ) Theory of Graphs and its Applications, pp. 47–52. : A Survey of the Reconstruction Conjecture. , Harary, F. ) Graphs and Combinatorics, pp. 18–28. : Complexity results in graph reconstruction. Discrete Appl. Math. : Two-dimensional monomer-Dimer systems are computationally intractable.
Redundancy reduction removes duplication from image, and irrelevancy reduction omits parts of the signal that will not be noticed by Human Visual System (HVS). The redundancies in an image can be identified as spatial redundancy, coding redundancy and inter pixel redundancy. Image compression aims at reducing the number of bits needed to represent an image by removing the redundancies as much as possible.  Image compression methods can mainly be classified as lossy compression and lossless compression.