Scott C. Douglas focuses on Algorithm, Adaptive filter, Blind signal separation, Signal processing and Control theory. His studies deal with areas such as Signal, Filter and Stability as well as Algorithm. His study in Adaptive filter is interdisciplinary in nature, drawing from both Mathematical optimization, Kernel adaptive filter, Filter design and Finite impulse response.
His Blind signal separation research incorporates elements of Deconvolution, Blind deconvolution, Independent component analysis and Source separation. His Signal processing study combines topics in areas such as Subspace topology, Filtering theory and Robustness. The various areas that Scott C. Douglas examines in his Control theory study include Stochastic process, Active noise control, Harmonics, Applied mathematics and Rate of convergence.
His scientific interests lie mostly in Algorithm, Adaptive filter, Blind signal separation, Signal processing and Control theory. His Algorithm course of study focuses on Mathematical optimization and Estimation theory. His studies in Adaptive filter integrate themes in fields like Adaptive algorithm, Finite impulse response and Kernel adaptive filter, Filter design.
The Blind signal separation study combines topics in areas such as Speech recognition, Source separation, Artificial intelligence and Pattern recognition. His research integrates issues of Artificial neural network, Subspace topology, Covariance matrix and Electronic engineering in his study of Signal processing. Scott C. Douglas combines subjects such as Active noise control and Filter with his study of Control theory.
His primary scientific interests are in Algorithm, Adaptive filter, Least mean squares filter, Signal processing and Control theory. Scott C. Douglas performs integrative study on Algorithm and Function in his works. Within one scientific family, he focuses on topics pertaining to Least squares under Adaptive filter, and may sometimes address concerns connected to Adaptive algorithm, Leverage and Adaptive beamformer.
His research in Least mean squares filter intersects with topics in Mean squared error, Estimator and System identification. His study in Signal processing is interdisciplinary in nature, drawing from both Independent component analysis, Noise, Spectral density and Approximation algorithm. His Control theory study combines topics in areas such as Linear model and Electric power system.
Algorithm, Covariance, Least mean squares filter, Adaptive filter and System identification are his primary areas of study. His Algorithm research integrates issues from Artificial neural network, Control theory and Signal processing. Scott C. Douglas has researched Control theory in several fields, including Instantaneous phase, Electronic engineering, Harmonics and Estimator.
His Covariance study also includes fields such as
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Adaptive algorithms for the rejection of sinusoidal disturbances with unknown frequency
Marc Bodson;Scott C. Douglas.
Multichannel blind deconvolution and equalization using the natural gradient
S. Amari;S.C. Douglas;A. Cichocki;H.H. Yang.
international workshop on signal processing advances in wireless communications (1997)
Introduction to Adaptive Filters
Scott C. Douglas.
Why natural gradient
S. Amari;S.C. Douglas.
international conference on acoustics speech and signal processing (1998)
Adaptive filters employing partial updates
IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing (1997)
A family of normalized LMS algorithms
IEEE Signal Processing Letters (1994)
Active noise control for periodic disturbances
M. Bodson;J.S. Jensen;S.C. Douglas.
IEEE Transactions on Control Systems and Technology (2001)
Normalized data nonlinearities for LMS adaptation
S.C. Douglas;T.H.-Y. Meng.
IEEE Transactions on Signal Processing (1994)
Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators
Yili Xia;S. C. Douglas;D. P. Mandic.
IEEE Signal Processing Magazine (2012)
Novel On-Line Adaptive Learning Algorithms for Blind Deconvolution Using the Natural Gradient Approach
Shun-ichi Amari;Scott C. Douglas;Andrzej Cichocki;Howard H. Yang.
IFAC Proceedings Volumes (1997)
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