Compressed sensing, Artificial intelligence, Algorithm, Computer vision and Iterative reconstruction are his primary areas of study. His work deals with themes such as Theoretical computer science, Sparse approximation, Signal processing, Computation and Wavelet, which intersect with Compressed sensing. In his research, Time series and Matched filter is intimately related to Pattern recognition, which falls under the overarching field of Artificial intelligence.
Marco F. Duarte has included themes like Information theory and Speech recognition in his Algorithm study. His studies in Computer vision integrate themes in fields like Optical computing and Digital micromirror device. As a member of one scientific family, Marco F. Duarte mostly works in the field of Iterative reconstruction, focusing on Signal and, on occasion, Decoding methods.
His primary areas of investigation include Compressed sensing, Artificial intelligence, Algorithm, Pattern recognition and Computer vision. His Compressed sensing study combines topics from a wide range of disciplines, such as Theoretical computer science, Signal reconstruction, Sparse matrix, Sparse approximation and Signal. His Artificial intelligence study frequently involves adjacent topics like Machine learning.
His Algorithm research includes elements of Bandlimiting, Nyquist–Shannon sampling theorem, Sampling, Nyquist rate and Mathematical optimization. His research in Nyquist–Shannon sampling theorem intersects with topics in Analog signal, Greedy algorithm, Signal processing, Robustness and Restricted isometry property. In his work, Power graph analysis, Graph, Feature vector and Feature selection is strongly intertwined with Autoencoder, which is a subfield of Pattern recognition.
His scientific interests lie mostly in Artificial intelligence, Algorithm, Pattern recognition, Feature extraction and Training set. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Metric. His research integrates issues of Principal component analysis and Wideband in his study of Algorithm.
The concepts of his Pattern recognition study are interwoven with issues in Autoencoder and Linear model. The various areas that he examines in his Sparse approximation study include Leverage, Parameter space, Estimation theory, Compressed sensing and Euclidean distance. Marco F. Duarte combines Compressed sensing and Convex optimization in his research.
Marco F. Duarte focuses on Artificial intelligence, Pattern recognition, Dimensionality reduction, Machine learning and Feature extraction. Many of his studies involve connections with topics such as Metric and Artificial intelligence. His work on Wavelet transform and Wavelet as part of his general Pattern recognition study is frequently connected to Redundancy, thereby bridging the divide between different branches of science.
The study incorporates disciplines such as Deep learning, Activity recognition, Relevance and Hyperspectral imaging in addition to Feature extraction. His work in Training set tackles topics such as Compact space which are related to areas like Algorithm. His work on Sparse approximation as part of general Algorithm research is often related to Extension, thus linking different fields of science.
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.
Single-Pixel Imaging via Compressive Sampling
M.F. Duarte;M.A. Davenport;D. Takhar;J.N. Laska.
IEEE Signal Processing Magazine (2008)
Model-Based Compressive Sensing
R.G. Baraniuk;V. Cevher;M.F. Duarte;C. Hegde.
IEEE Transactions on Information Theory (2010)
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
J.A. Tropp;J.N. Laska;M.F. Duarte;J.K. Romberg.
IEEE Transactions on Information Theory (2010)
Structured Compressed Sensing: From Theory to Applications
M. F. Duarte;Y. C. Eldar.
IEEE Transactions on Signal Processing (2011)
A new compressive imaging camera architecture using optical-domain compression
Dharmpal Takhar;Jason N. Laska;Michael B. Wakin;Marco F. Duarte.
electronic imaging (2006)
Introduction to compressed sensing
Mark A. Davenport;Marco F. Duarte;Yonina C. Eldar;Gitta Kutyniok.
Compressed Sensing (2012)
Distributed Compressed Sensing of Jointly Sparse Signals
M.F. Duarte;S. Sarvotham;D. Baron;M.B. Wakin.
asilomar conference on signals, systems and computers (2005)
Vehicle classification in distributed sensor networks
Marco F. Duarte;Yu Hen Hu.
Journal of Parallel and Distributed Computing (2004)
Analog-to-Information Conversion via Random Demodulation
S. Kirolos;J. Laska;M. Wakin;M. Duarte.
2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software (2006)
Theory and Implementation of an Analog-to-Information Converter using Random Demodulation
J.N. Laska;S. Kirolos;M.F. Duarte;T.S. Ragheb.
international symposium on circuits and systems (2007)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: