2023 - Research.com Computer Science in United Kingdom Leader Award
2023 - Research.com Electronics and Electrical Engineering in United Kingdom Leader Award
2013 - IEEE Fellow For contributions to multivariate and nonlinear learning systems
His work blends Artificial intelligence and Theoretical computer science studies together. He merges many fields, such as Theoretical computer science and Artificial intelligence, in his writings. Many of his studies involve connections with topics such as Hilbert–Huang transform and Energy (signal processing). As part of his studies on Hilbert–Huang transform, he often connects relevant areas like Energy (signal processing). His Telecommunications research extends to Sampling (signal processing), which is thematically connected. Telecommunications is often connected to Detector in his work. His Detector study frequently draws connections between related disciplines such as Sampling (signal processing). He integrates many fields, such as Statistics and Algorithm, in his works. In his research, Danilo P. Mandic performs multidisciplinary study on Algorithm and Statistics.
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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: Learning Algorithms,Architectures and Stability
Danilo P. Mandic;Jonathon Chambers.
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
Danilo P. Mandic;Jonathon A. Chambers.
Wiley Series in Adaptive and Learning Systems for Signal Processing, Communications, and Control (2001)
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)
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