S
Santanu Chaudhury
Researcher at Indian Institute of Technology, Jodhpur
Publications - 389
Citations - 4361
Santanu Chaudhury is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Ontology (information science) & Deep learning. The author has an hindex of 28, co-authored 380 publications receiving 3691 citations. Previous affiliations of Santanu Chaudhury include Central Electronics Engineering Research Institute & Indian Institute of Technology Delhi.
Papers
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Journal ArticleDOI
Neuro-adaptive hybrid controller for robot-manipulator tracking control
TL;DR: The paper is concerned with the design of a hybrid controller structure, consisting of the adaptive control law and a neural-network-based learning scheme for adaptation of time-varying controller parameters, implemented using both MLN and RBF networks.
Book ChapterDOI
Ontology Specification and Integration for Multimedia Applications
TL;DR: A new Bayesian Network based probabilistic reasoning framework with M-OWL for semantic interpretation of multimedia data and a new model for ontology integration, based on the similarity of the concepts in the media domain are proposed.
Journal ArticleDOI
Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm
TL;DR: An efficient and robust iris recognition model based on sparse representation using compressive sensing and k-nearest subspace (segments) has been proposed and results obtained on different databases show that the scheme is highly robust with FAR almost zero.
Journal ArticleDOI
Inversion of RBF networks and applications to adaptive control of nonlinear systems
TL;DR: The paper investigates the application of inversion of a radial basis function network (RBFN) to nonlinear control problems for which the structure of the nonlinearity is unknown and shows that the performance of the controller based on the proposed network inversion scheme is efficient.
Proceedings ArticleDOI
Gabor filter based fingerprint classification using support vector machines
TL;DR: A Gabor filter-based feature extraction scheme is used to generate a 384 dimensional feature vector for each fingerprint image through a novel two stage classifier in which K nearest neighbour acts as the first step and finds out the two most frequently represented classes amongst the K nearest patterns.