Precision-Aware and Quantization of Lifting Based DWT Hardware Architecture
Journal Title: International Journal of Computer & organization Trends(IJCOT) - Year 2013, Vol 3, Issue 6
Abstract
This paper presents precision-aware approaches and associated hardware implementations for performing the DWT. By implementing BP architecture and also presents DS design methodologies. These methods enable use of an optimal amount of hardware resources in the DWT computation.Experimental measurements of design performance in terms of area, speed, and power for 90-nm complementary metal–oxide semiconductor implementation are presented. Results indicate that BP designs exhibit inherent speed advantages than DS design.
Authors and Affiliations
REKHA. N , Dr. K B SHIVAKUMAR , M. Z KURIAN
Keywords
Related Articles
- EP ID EP136321
- DOI -
- Views 101
- Downloads 0
A Survey Of Key Management Schemes In Wireless Sensor Networks
In near future, the Wireless Sensor Networks (WSN) is widely used in many applications like military and civil domains. The wireless sensor networks are always deployed in hostile and pervasive environment. Security is m...
SE code optimization using Data Mining Approach
Data mining also holds promises for other software engineering processes, which have to deal with uncertainty and intangible data such as cost estimation, effort estimation and quality. It can also aid in interesti...
Cataloguing and Avoiding the Buffer Overflow Attacks in Network Operating Systems
The application software has a different dimension, size and intricacies is rising rapidly in current technology era and simultaneously increase a programming bugs also. The programming bugs cause vulnerabilities to the...
Performance Comparison of Eigen-faces vs. Fisher-faces for Face Recognition
Face recognition issue gained more interest recently due to its various applications and the demand of high security. In this paper two Face Recognition techniques, Eigen-faces commonly called Principal Component Analysi...
Role Of Clustering On Gene Data
This Data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Clustering algorithm used to find groups of objects such that the objects in a group will be simil...