2013 - IEEE Fellow For contributions to multivariate and nonlinear learning systems
His primary areas of study are Algorithm, Artificial intelligence, Signal processing, Control theory and Nonlinear system. His study in Algorithm is interdisciplinary in nature, drawing from both Scatter plot, Quaternion, Kernel adaptive filter and Benchmark. His Artificial intelligence research incorporates elements of Machine learning, Hilbert–Huang transform, Speech recognition and Pattern recognition.
The concepts of his Signal processing study are interwoven with issues in Digital signal processing and Data mining. His research integrates issues of Estimator and Linear model in his study of Control theory. His Nonlinear system research includes themes of Recurrent neural network, Time series, Mathematical optimization, Extension and Range.
Algorithm, Artificial intelligence, Adaptive filter, Control theory and Nonlinear system are his primary areas of study. Linear model is closely connected to Quaternion in his research, which is encompassed under the umbrella topic of Algorithm. Danilo P. Mandic has included themes like Machine learning, Electroencephalography and Pattern recognition in his Artificial intelligence study.
Danilo P. Mandic studied Pattern recognition and Hilbert–Huang transform that intersect with Speech recognition and Time–frequency analysis. His biological study spans a wide range of topics, including Convergence, Algorithm design, Kernel adaptive filter and Finite impulse response. His Control theory study incorporates themes from Estimator and Electric power system.
The scientist’s investigation covers issues in Algorithm, Signal processing, Theoretical computer science, Tensor and Artificial intelligence. He is interested in Adaptive filter, which is a branch of Algorithm. His Signal processing study combines topics from a wide range of disciplines, such as Digital signal processing, Graph theory, Data analysis and Restricted isometry property.
His work is dedicated to discovering how Theoretical computer science, Tensor are connected with Curse of dimensionality and other disciplines. His research in Tensor tackles topics such as Space which are related to areas like Square and Computation. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
His primary areas of investigation include Algorithm, Signal processing, Tensor, Theoretical computer science and Graph. His study in the field of Adaptive filter is also linked to topics like Wigner distribution function. His studies deal with areas such as Graph theory and Data analysis as well as Signal processing.
His work carried out in the field of Tensor brings together such families of science as Matrix decomposition, Matrix, Approximation algorithm, Rank and Volatility. His Graph research integrates issues from Recurrent neural network and Graph. In his study, Hilbert–Huang transform and Multivariate statistics is inextricably linked to Pattern recognition, which falls within the broad field of Artificial intelligence.
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.
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis
Andrzej Cichocki;Danilo Mandic;Lieven De Lathauwer;Guoxu Zhou.
IEEE Signal Processing Magazine (2015)
Recurrent Neural Networks for Prediction
Danilo P. Mandic;Jonathon A. Chambers.
Wiley Series in Adaptive and Learning Systems for Signal Processing, Communications, and Control (2001)
Multivariate empirical mode decomposition
N. Rehman;D. P. Mandic.
Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences (2010)
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Danilo P. Mandic;Jonathon Chambers.
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models
Danilo Mandic;Vanessa Su Lee Goh.
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
A. Cichocki;D. Mandic;A-H. Phan;C. Caiafa.
arXiv: Numerical Analysis (2014)
Complex Valued Nonlinear Adaptive Filters
Danilo P. Mandic;Vanessa Su Lee Goh.
Filter Bank Property of Multivariate Empirical Mode Decomposition
Naveed ur Rehman;D P Mandic.
IEEE Transactions on Signal Processing (2011)
Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis
Danilo P. Mandic;Naveed Ur Rehman;Zhaohua Wu;Norden E. Huang.
IEEE Signal Processing Magazine (2013)
Complex Empirical Mode Decomposition
T. Tanaka;D.P. Mandic.
IEEE Signal Processing Letters (2007)
Profile was last updated on December 6th, 2021.
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