J
Jayavardhana Gubbi
Researcher at Tata Consultancy Services
Publications - 99
Citations - 13746
Jayavardhana Gubbi is an academic researcher from Tata Consultancy Services. The author has contributed to research in topics: Computer science & Optical flow. The author has an hindex of 23, co-authored 82 publications receiving 11423 citations. Previous affiliations of Jayavardhana Gubbi include University of Melbourne.
Papers
More filters
Journal ArticleDOI
Internet of Things (IoT): A vision, architectural elements, and future directions
TL;DR: In this article, the authors present a cloud centric vision for worldwide implementation of Internet of Things (IoT) and present a Cloud implementation using Aneka, which is based on interaction of private and public Clouds, and conclude their IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
Posted Content
Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions
TL;DR: This paper presents a Cloud centric vision for worldwide implementation of Internet of Things, and expands on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
Journal ArticleDOI
An Information Framework for Creating a Smart City Through Internet of Things
TL;DR: A framework for the realization of smart cities through the Internet of Things (IoT), which encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system.
Journal ArticleDOI
Smoke detection in video using wavelets and support vector machines
TL;DR: A novel method for smoke characterization using wavelets and support vector machines is proposed and the results are impressive with limited false alarms.
Journal ArticleDOI
Complex Correlation Measure: a novel descriptor for Poincaré plot
TL;DR: Complex Correlation Measure (CCM) is defined based on the autocorrelation at different lags of the time series, hence giving an in depth measurement of the correlation structure of the Poincaré plot.