2013 - Fellow, National Academy of Inventors
2003 - IEEE Fellow For contributions to the development of methods for solving inverse problems in the field of nondestructive evaluation.
Satish S. Udpa mostly deals with Artificial neural network, Finite element method, Eddy-current testing, Magnetic flux leakage and Eddy current. His work carried out in the field of Artificial neural network brings together such families of science as Inverse problem, Signal processing, Algorithm and Wavelet, Pattern recognition. As part of the same scientific family, he usually focuses on Finite element method, concentrating on Nuclear magnetic resonance and intersecting with Giant magnetoresistance and Canalisation.
His Acoustics research extends to the thematically linked field of Eddy-current testing. His research in Magnetic flux leakage focuses on subjects like Magnetic flux, which are connected to Iterative method. The various areas that Satish S. Udpa examines in his Eddy current study include Signal, Representation, Electromagnetic coil and Massively parallel.
Satish S. Udpa mainly focuses on Eddy current, Acoustics, Electronic engineering, Finite element method and Eddy-current testing. Satish S. Udpa has included themes like Signal, Excitation and Magnetic flux leakage in his Acoustics study. His study focuses on the intersection of Magnetic flux leakage and fields such as Wavelet with connections in the field of Artificial neural network.
Satish S. Udpa combines subjects such as Pipeline transport and Signal processing with his study of Electronic engineering. He works mostly in the field of Finite element method, limiting it down to topics relating to Inverse problem and, in certain cases, Integral equation and Iterative method, as a part of the same area of interest. His studies deal with areas such as Giant magnetoresistance, Algorithm and Bobbin as well as Eddy-current testing.
Satish S. Udpa mainly investigates Eddy current, Acoustics, Eddy-current testing, Finite element method and Electromagnetic coil. His Eddy current study incorporates themes from Electronic engineering, Excitation, Rotating magnetic field and Boiler. His Acoustics research incorporates elements of Finite-difference time-domain method, Sensitivity, Signal, Signal processing and Fastener.
His study in Eddy-current testing is interdisciplinary in nature, drawing from both Giant magnetoresistance, Solver, Bobbin and Computational model. His research in Finite element method intersects with topics in Ferrite core, Inverse problem and Magnetoresistance. His Electromagnetic coil research is multidisciplinary, incorporating elements of Magnetic potential and Nuclear magnetic resonance.
Satish S. Udpa spends much of his time researching Eddy current, Acoustics, Eddy-current testing, Electromagnetic coil and Finite element method. The concepts of his Eddy current study are interwoven with issues in Artificial neural network, Boiler, Transient and Rotating magnetic field. The study incorporates disciplines such as Wave propagation, Excitation, Finite difference method and Finite-difference time-domain method in addition to Acoustics.
His work focuses on many connections between Eddy-current testing and other disciplines, such as Giant magnetoresistance, that overlap with his field of interest in Signal, Fastener, Signal processing and Frequency domain. His research in Electromagnetic coil tackles topics such as Nuclear magnetic resonance which are related to areas like Magnetic potential. His Finite element method study combines topics from a wide range of disciplines, such as Iterative method and Numerical stability.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Advanced signal processing of magnetic flux leakage data obtained from seamless gas pipeline
Muhammad Afzal;Satish Udpa.
Ndt & E International (2002)
Electromagnetic NDE signal inversion by function-approximation neural networks
P. Ramuhalli;L. Udpa;S.S. Udpa.
IEEE Transactions on Magnetics (2002)
Pulsed Eddy-Current Based Giant Magnetoresistive System for the Inspection of Aircraft Structures
Guang Yang;A. Tamburrino;L. Udpa;S.S. Udpa.
IEEE Transactions on Magnetics (2010)
Adaptive Wavelets for Characterizing Magnetic Flux Leakage Signals From Pipeline Inspection
A.V. Joshi;L. Udpa;S. Udpa;A. Tamburrino.
ieee international magnetics conference (2006)
Frequency invariant classification of ultrasonic weld inspection signals
R. Polikar;L. Udpa;S.S. Udpa;T. Taylor.
IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control (1998)
Three-dimensional defect reconstruction from eddy-current NDE signals using a genetic local search algorithm
Yue Li;L. Udpa;S.S. Udpa.
IEEE Transactions on Magnetics (2004)
Eddy current defect characterization using neural networks
L. Udpa;S. S. Udpa.
Materials evaluation (1990)
Invariance transformations for magnetic flux leakage signals
S. Mandayam;L. Udpa;S.S. Udpa;W. Lord.
IEEE Transactions on Magnetics (1996)
Characterization of gas pipeline inspection signals using wavelet basis function neural networks
K. Hwang;S. Mandayam;S.S. Udpa;L. Udpa.
Ndt & E International (2000)
Neural networks for the classification of nondestructive evaluation signals
L. Udpa;S.S. Udpa.
IEE Proceedings F Radar and Signal Processing (1991)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: