J
Jose C. M. Bermudez
Researcher at Universidade Federal de Santa Catarina
Publications - 231
Citations - 4410
Jose C. M. Bermudez is an academic researcher from Universidade Federal de Santa Catarina. The author has contributed to research in topics: Adaptive filter & Monte Carlo method. The author has an hindex of 28, co-authored 226 publications receiving 3672 citations. Previous affiliations of Jose C. M. Bermudez include Federal University of Rio de Janeiro & Universidade Católica de Pelotas.
Papers
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Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis
TL;DR: Wang et al. as discussed by the authors proposed a library augmentation strategy to increase the diversity of existing spectral libraries, thus improving their ability to represent the materials in real images, and leveraged the power of deep generative models to learn the statistical distribution of the EMs based on the spectral signatures available in the existing libraries.
Proceedings ArticleDOI
Online Graph-Based Change Point Detection in Multiband Image Sequences
TL;DR: In this article, an online framework for detecting changes in multispectral remote sensing images is proposed. And the graph is estimated directly from the images using superpixel decomposition algorithms.
Proceedings ArticleDOI
A design methodology for the Gaussian KLMS algorithm
TL;DR: This paper proposes, test, and validate a methodology for the design of the Gaussian KLMS algorithm, and examines the time to convergence, the residual mean-squared-error (MSE), and the filter order.
Proceedings ArticleDOI
Statistical analysis of the deficient length affine projection adaptive algorithm
TL;DR: It is shown that the AP coefficients converge in the mean to the initial plant coefficients, producing an unbiased solution even for the correlated input signal case, and the steady-state mean square error has a term that is proportional to the power of the unpredictable part of the input signal filtered by the un-modeled part ofthe unknown impulse response.
Proceedings ArticleDOI
A fully analytical recursive stochastic model to the normalized signed regressor LMS algorithm
Marcio Costa,Jose C. M. Bermudez +1 more
TL;DR: A new statistical analysis of the normalized signed regressor least mean square adaptive algorithm for Gaussian input signals shows very good agreement between model and simulations during transient and steady-state even for large step sizes and small number of coefficients.