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Miguel R. D. Rodrigues

Miguel R. D. Rodrigues

D-Index & Metrics

Computer Science

D-Index
30
Citations
6979
World Ranking
13869
National Ranking
888

Overview

Miguel R. D. Rodrigues is affiliated with University College London in the United Kingdom. Their research predominantly spans Computer Science and Engineering, with a focus on several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Electrical and Electronic Engineering, and Biomedical Engineering.

The scientist's work involves multiple topics centered on machine learning and signal processing. Key areas include Sparse and Compressive Sensing Techniques, Adversarial Robustness in Machine Learning, Machine Learning and Algorithms, Machine Learning and Extreme Learning Machines (ELM), Neural Networks and Applications, Advanced Neural Network Applications, and Machine Learning and Data Classification.

Their recent publications cover a variety of technological and scientific problems, as reflected in papers such as:

  • Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules (2021, IEEE Transactions on Circuits and Systems for Video Technology)
  • FPGA-Based Acceleration for Bayesian Convolutional Neural Networks (2022, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)
  • Neural network-based classification of X-ray fluorescence spectra of artists' pigments: an approach leveraging a synthetic dataset created using the fundamental parameters method (2022, Heritage Science)
  • ADMM-Based Hyperspectral Unmixing Networks for Abundance and Endmember Estimation (2021, IEEE Transactions on Geoscience and Remote Sensing)
  • Theoretical Perspectives on Deep Learning Methods in Inverse Problems (2022, IEEE Journal on Selected Areas in Information Theory)

Miguel R. D. Rodrigues has frequently published in venues such as arXiv, IEEE Transactions on Signal Processing, SSRN Electronic Journal, Environmental Data Science, and the International Journal of Pharmaceutics.

The scientist collaborates regularly with several coauthors, including Martin Ferianc, Gholamali Aminian, Laura Toni, Yonina C. Eldar, and Wei Pu.

Best Publications

  • Wireless Information-Theoretic Security

    M. Bloch;J. Barros;M.R.D. Rodrigues;S.W. McLaughlin

  • Secrecy Capacity of Wireless Channels

    Joao Barros;Miguel D. Rodrigues

  • Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

    Jialong Xu;Bo Ai;Wei Chen;Ang Yang

  • Robust Large Margin Deep Neural Networks

    Jure Sokolic;Raja Giryes;Guillermo Sapiro;Miguel R. D. Rodrigues

  • MIMO Gaussian Channels With Arbitrary Inputs: Optimal Precoding and Power Allocation

    F. Perez-Cruz;M.R.D. Rodrigues;S. Verdu

  • Spectrally Efficient FDM Signals: Bandwidth Gain at the Expense of Receiver Complexity

    I. Kanaras;A. Chorti;M. R. D. Rodrigues;I. Darwazeh

  • Compressed Sensing With Prior Information: Strategies, Geometry, and Bounds

    Joao F. C. Mota;Nikos Deligiannis;Miguel R. D. Rodrigues

  • Generalization Error in Deep Learning

    Daniel Jakubovitz;Raja Giryes;Miguel R. D. Rodrigues

  • Multimodal Image Super-Resolution via Joint Sparse Representations Induced by Coupled Dictionaries

    Pingfan Song;Xin Deng;Joao F. C. Mota;Nikos Deligiannis

  • On Wireless Channels With ${M}$ -Antenna Eavesdroppers: Characterization of the Outage Probability and $ arepsilon $ -Outage Secrecy Capacity

    Vinay Uday Prabhu;Miguel R. D. Rodrigues

  • Projection Design for Statistical Compressive Sensing: A Tight Frame Based Approach

    Wei Chen;M. R. D. Rodrigues;I. J. Wassell

  • COMMUNICATIONS-INSPIRED PROJECTION DESIGN WITH APPLICATION TO COMPRESSIVE SENSING

    William R. Carson;William R. Carson;Minhua Chen;Miguel R. D. Rodrigues;A. Robert Calderbank

  • On the Use of Unit-Norm Tight Frames to Improve the Average MSE Performance in Compressive Sensing Applications

    Wei Chen;M. R. D. Rodrigues;I. J. Wassell

  • Hardware-Limited Task-Based Quantization

    Nir Shlezinger;Yonina C. Eldar;Miguel R. D. Rodrigues

  • Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction

    Pingfan Song;Lior Weizman;Joao F. C. Mota;Yonina C. Eldar

  • Comparison of Convolutional and Turbo Coding for Broadband FWA Systems

    I.A. Chatzigeorgiou;M.R.D. Rodrigues;I.J. Wassell;R.A. Carrasco

  • Joint channel equalization and detection of Spectrally Efficient FDM signals

    Arsenia Chorti;Ioannis Kanaras;Miguel R.D. Rodrigues;Izzat Darwazeh

  • FPGA-Based Acceleration for Bayesian Convolutional Neural Networks

    Unknown

  • Asymptotic Task-Based Quantization With Application to Massive MIMO

    Nir Shlezinger;Yonina C. Eldar;Miguel R. D. Rodrigues

  • Reconstruction of Signals Drawn From a Gaussian Mixture Via Noisy Compressive Measurements

    Francesco Renna;Robert Calderbank;Lawrence Carin;Miguel R. D. Rodrigues

  • Adversarially Learned Representations for Information Obfuscation and Inference

    Martín Bertrán;Natalia Martínez;Afroditi Papadaki;Qiang Qiu

  • Filter Design With Secrecy Constraints: The MIMO Gaussian Wiretap Channel

    Hugo Reboredo;Joao Xavier;Miguel R. D. Rodrigues

  • Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information

    Francesco Renna;Liming Wang;Xin Yuan;Jianbo Yang

  • Margin Preservation of Deep Neural Networks.

    Jure Sokolic;Raja Giryes;Guillermo Sapiro;Miguel R. D. Rodrigues

Frequent Co-Authors

Lawrence Carin
Lawrence Carin Duke University
Yonina C. Eldar
Yonina C. Eldar Weizmann Institute of Science
Izzat Darwazeh
Izzat Darwazeh University College London
Guillermo Sapiro
Guillermo Sapiro Princeton University
Raja Giryes
Raja Giryes Tel Aviv University
Ingrid Daubechies
Ingrid Daubechies Duke University
Xin Yuan
Xin Yuan Nanyang Technological University
Wayne Luk
Wayne Luk Imperial College London
Joao Barros
Joao Barros University of Porto
Volkan Cevher
Volkan Cevher École Polytechnique Fédérale de Lausanne

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