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Anthony G. Constantinides
Researcher at Imperial College London
Publications - 321
Citations - 5374
Anthony G. Constantinides is an academic researcher from Imperial College London. The author has contributed to research in topics: Adaptive filter & Signal processing. The author has an hindex of 28, co-authored 319 publications receiving 5012 citations. Previous affiliations of Anthony G. Constantinides include General Post Office & University of Hull.
Papers
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Book
MIMO Wireless Communications
Ezio Biglieri,Robert Calderbank,Anthony G. Constantinides,Andrea Goldsmith,Arogyaswami Paulraj,H. Vincent Poor +5 more
TL;DR: In this article, a detailed introduction to the analysis and design of multiple-input multiple-output (MIMO) wireless systems is presented, and the fundamental capacity limits of MIMO systems are examined.
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Lagrange programming neural networks
TL;DR: A class of neural networks appropriate for general nonlinear programming, i.e., problems including both equality and inequality constraints, is analyzed in detail and the methodology is based on the Lagrange multiplier theory in optimization and seeks to provide solutions satisfying the necessary conditions of optimality.
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Spectral transformations for digital filters
TL;DR: In this paper, the authors describe general transformations for digital filters in the frequency domain, which operate on a lowpass-digital-filter prototype to give either another lowpass or a highpass, bandpass or band-elimination characteristic.
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Variable size block matching motion compensation with applications to video coding
TL;DR: A new motion compensation scheme based on block matching is presented, where the size for each block is variable and the proposed algorithm adaptively divides the image into blocks of vari- able size to meet the assumption on uniform motion for all blocks.
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Least mean mixed-norm adaptive filtering
TL;DR: A new family of stochastic gradient adaptive filter algorithms is proposed which is based on mixed error norms which combine the advantages of different error norms, for example the conventional, relatively well-behaved, least mean square algorithm and the more sensitive, but better converging, least means fourth algorithm.