M
Muralidhar Kulkarni
Researcher at National Institute of Technology, Karnataka
Publications - 67
Citations - 523
Muralidhar Kulkarni is an academic researcher from National Institute of Technology, Karnataka. The author has contributed to research in topics: Turbo code & Phase-shift keying. The author has an hindex of 8, co-authored 63 publications receiving 417 citations. Previous affiliations of Muralidhar Kulkarni include Delhi Technological University & University of Baghdad.
Papers
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Journal ArticleDOI
A Novel Optimal Fuzzy System for Color Image Enhancement Using Bacterial Foraging
TL;DR: A new approach is presented for the enhancement of color images using the fuzzy logic technique and is found to be better than the genetic algorithm (GA)-based and entropy-based approaches.
Proceedings ArticleDOI
A Simple Single Seeded Region Growing Algorithm for Color Image Segmentation using Adaptive Thresholding
TL;DR: A single seeded region growing technique for image segmentation is proposed, which starts from the center pixels of the image as the initial seed and grows region according to the grow formula and selects the next seed from connected pixel of the region.
Journal ArticleDOI
Effect of Black Hole Attack on MANET Routing Protocols
TL;DR: The effects of Black hole attack on mobile ad hoc routing protocols AODV and Improved A ODV have been analyzed and which protocol is least affected by these attacks is compared.
Proceedings ArticleDOI
MIMO radar with spatial-frequency diversity for improved detection performance
TL;DR: The system proposed will not only have several antennas appropriately spaced but also several operating frequencies appropriately spaced, providing a better detection performance than conventional MIMO radar systems for the same transmission power.
Proceedings ArticleDOI
Geographical location based hierarchical routing strategy for wireless sensor networks
TL;DR: A brief survey comprises of various reported methodologies that make uses of geographical location attributes for routing in WSN that incorporates an effective mechanism to select cluster heads for each cluster in such a way so as to avoid the localization of hot spot effects right from cluster level to network level.