57,024 research outputs found
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
In hyperspectral images, some spectral bands suffer from low signal-to-noise
ratio due to noisy acquisition and atmospheric effects, thus requiring robust
techniques for the unmixing problem. This paper presents a robust supervised
spectral unmixing approach for hyperspectral images. The robustness is achieved
by writing the unmixing problem as the maximization of the correntropy
criterion subject to the most commonly used constraints. Two unmixing problems
are derived: the first problem considers the fully-constrained unmixing, with
both the non-negativity and sum-to-one constraints, while the second one deals
with the non-negativity and the sparsity-promoting of the abundances. The
corresponding optimization problems are solved efficiently using an alternating
direction method of multipliers (ADMM) approach. Experiments on synthetic and
real hyperspectral images validate the performance of the proposed algorithms
for different scenarios, demonstrating that the correntropy-based unmixing is
robust to outlier bands.Comment: 23 page
Regularized adaptive long autoregressive spectral analysis
This paper is devoted to adaptive long autoregressive spectral analysis when
(i) very few data are available, (ii) information does exist beforehand
concerning the spectral smoothness and time continuity of the analyzed signals.
The contribution is founded on two papers by Kitagawa and Gersch. The first one
deals with spectral smoothness, in the regularization framework, while the
second one is devoted to time continuity, in the Kalman formalism. The present
paper proposes an original synthesis of the two contributions: a new
regularized criterion is introduced that takes both information into account.
The criterion is efficiently optimized by a Kalman smoother. One of the major
features of the method is that it is entirely unsupervised: the problem of
automatically adjusting the hyperparameters that balance data-based versus
prior-based information is solved by maximum likelihood. The improvement is
quantified in the field of meteorological radar
A Robust Variable Step Size Fractional Least Mean Square (RVSS-FLMS) Algorithm
In this paper, we propose an adaptive framework for the variable step size of
the fractional least mean square (FLMS) algorithm. The proposed algorithm named
the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step
size of the FLMS to achieve high convergence rate with low steady state error.
For the evaluation purpose, the problem of system identification is considered.
The experiments clearly show that the proposed approach achieves better
convergence rate compared to the FLMS and adaptive step-size modified FLMS
(AMFLMS).Comment: 15 pages, 3 figures, 13th IEEE Colloquium on Signal Processing & its
Applications (CSPA 2017
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
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