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D-Index & Metrics

Computer Science

D-Index
33
Citations
11350
World Ranking
12369
National Ranking
5009

Overview

Monson H. Hayes is affiliated with George Mason University in the United States, specializing in research intersecting engineering and computer science. Their body of work covers several fields and subfields, including artificial intelligence, electrical and electronic engineering, biomedical engineering, signal processing, and mechanical engineering.

The scientist has published papers in a variety of venues, with notable contributions appearing in IEEE Access, Sensors, and IEEE Signal Processing Magazine. Their recent research outputs include:

  • A Learning Approach for Physical Layer Authentication Using Adaptive Neural Network, 2020, IEEE Access
  • Enhanced Security Authentication Based on Convolutional-LSTM Networks, 2021, Sensors
  • Meeting the Challenges of a Growing ICASSP: Highlights from ICASSP 2024 [Conference Highlights], 2024, IEEE Signal Processing Magazine

The main research topics addressed in their work consist of wireless signal modulation classification, wireless communication security techniques, speech and audio processing, hate speech and cyberbullying detection, biomedical and engineering education, mechatronics education and applications, as well as soft robotics and applications.

Frequent collaborators include Xiaoying Qiu, Jianmei Dai, Xuan Sun, Hanseok Ko, and John H. L. Hansen, indicating a collaborative research approach across various projects.

Best Publications

  • Statistical Digital Signal Processing and Modeling

    Monson H. Hayes

  • The reconstruction of a multidimensional sequence from the phase or magnitude of its Fourier transform

    M. Hayes

  • Signal reconstruction from phase or magnitude

    M. Hayes;Jae Lim;A. Oppenheim

  • Hidden Markov models for face recognition

    A.V. Nefian;M.H. Hayes

  • Eigenface-domain super-resolution for face recognition

    B.K. Gunturk;A.U. Batur;Y. Altunbasak;M.H. Hayes

  • Exploiting human actions and object context for recognition tasks

    D.J. Moore;I.A. Essa;M.H. Hayes

  • A Novel Lane Detection System With Efficient Ground Truth Generation

    Amol Borkar;M. Hayes;M. T. Smith

  • Using iterated function systems to model discrete sequences

    D.S. Mazel;M.H. Hayes

  • An embedded HMM-based approach for face detection and recognition

    A.V. Nefian;M.H. Hayes

  • Face detection and recognition using hidden Markov models

    A.V. Nefian;M.H. Hayes

  • Robust lane detection and tracking with ransac and Kalman filter

    Amol Borkar;Monson Hayes;Mark T. Smith

  • Schaum's Outline of Theory and Problems of Digital Signal Processing

    Monson H. Hayes

  • Reducible polynomials in more than one variable

    M.H. Hayes;J.H. McClellan

  • A hidden markov model-based approach for face detection and recognition

    Ara Victor Nefian;Monson H. Hayes

  • Maximum likelihood training of the embedded HMM for face detection and recognition

    A.V. Nefian;M.H. Hayes

  • Convergence of iterative nonexpansive signal reconstruction algorithms

    V. Tom;T. Quatieri;M. Hayes;J. McClellan

  • Linear subspaces for illumination robust face recognition

    A.U. Batur;M.H.I.I.I. Hayes

  • A layered approach to robust lane detection at night

    Amol Borkar;Monson Hayes;Mark T. Smith;Sharathchandra Pankanti

  • Adaptive active appearance models

    A.U. Batur;M.H. Hayes

  • Signal reconstruction from signed Fourier transform magnitude

    P. Van Hove;M. Hayes;Jae Lim;A. Oppenheim

Frequent Co-Authors

Joonki Paik
Joonki Paik Chung-Ang University
Russell M. Mersereau
Russell M. Mersereau Georgia Institute of Technology
James H. McClellan
James H. McClellan Georgia Institute of Technology
Faouzi Kossentini
Faouzi Kossentini Intel (United States)
Yucel Altunbasak
Yucel Altunbasak Georgia Institute of Technology
Mark A. Clements
Mark A. Clements Georgia Institute of Technology
Irfan Essa
Irfan Essa Georgia Institute of Technology

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