Scott C. Douglas

What is he best known for?

The fields of study he is best known for:

• Statistics
• Algorithm
• Artificial intelligence

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 most cited work include:

• Adaptive algorithms for the rejection of sinusoidal disturbances with unknown frequency (469 citations)
• Multichannel blind deconvolution and equalization using the natural gradient (308 citations)
• Adaptive filters employing partial updates (169 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

• Algorithm (57.41%)
• Adaptive filter (37.96%)
• Blind signal separation (23.61%)

What were the highlights of his more recent work (between 2009-2019)?

• Algorithm (57.41%)
• Adaptive filter (37.96%)
• Least mean squares filter (12.50%)

In recent papers he was focusing on the following fields of study:

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.

Between 2009 and 2019, his most popular works were:

• Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators (127 citations)
• Performance analysis of the conventional complex LMS and augmented complex LMS algorithms (43 citations)
• On Approximate Diagonalization of Correlation Matrices in Widely Linear Signal Processing (31 citations)

In his most recent research, the most cited papers focused on:

• Statistics
• Artificial intelligence
• Algorithm

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

• Singular value decomposition together with Mathematical optimization and Covariance matrix,
• Applied mathematics, which have a strong connection to Decorrelation, Matrix decomposition and Singular value. The study incorporates disciplines such as Algorithm design and Least squares in addition to Adaptive filter. His biological study spans a wide range of topics, including Mean squared error, Noise, Linear model and Probability distribution.