Efficient blind signal reconstruction with wavelet transforms regularization for educational robot infrared vision sensing
Fourier transform infrared (FTIR) imaging spectrometers are often corrupted by the problems
of band overlap and random noise during the infrared spectrum acquisition process. Such …
of band overlap and random noise during the infrared spectrum acquisition process. Such …
Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings
Rolling element bearings (REBs) play an essential role in modern machinery and their
condition monitoring is significant in predictive maintenance. Due to the harsh operating …
condition monitoring is significant in predictive maintenance. Due to the harsh operating …
Sparse plus low rank network identification: A nonparametric approach
Modeling and identification of high-dimensional stochastic processes is ubiquitous in many
fields. In particular, there is a growing interest in modeling stochastic processes with simple …
fields. In particular, there is a growing interest in modeling stochastic processes with simple …
Proximal algorithms for large-scale statistical modeling and sensor/actuator selection
Several problems in modeling and control of stochastically driven dynamical systems can be
cast as regularized semidefinite programs. We examine two such representative problems …
cast as regularized semidefinite programs. We examine two such representative problems …
A new family of high-resolution multivariate spectral estimators
M Zorzi - IEEE Transactions on Automatic Control, 2013 - ieeexplore.ieee.org
In this paper, we extend the Beta divergence family to multivariate power spectral densities.
Similarly to the scalar case, we show that it smoothly connects the multivariate Kullback …
Similarly to the scalar case, we show that it smoothly connects the multivariate Kullback …
Time and spectral domain relative entropy: A new approach to multivariate spectral estimation
The concept of spectral relative entropy rate is introduced for jointly stationary Gaussian
processes. Using classical information-theoretic results, we establish a remarkable …
processes. Using classical information-theoretic results, we establish a remarkable …
Multivariate spectral estimation based on the concept of optimal prediction
M Zorzi - IEEE Transactions on Automatic Control, 2014 - ieeexplore.ieee.org
In this technical note, we deal with a spectrum approximation problem arising in THREE-like
multivariate spectral estimation approaches. The solution to the problem minimizes a …
multivariate spectral estimation approaches. The solution to the problem minimizes a …
Identification of sparse reciprocal graphical models
In this letter we propose an identification procedure of a sparse graphical model associated
to a Gaussian stationary stochastic process. The identification paradigm exploits the …
to a Gaussian stationary stochastic process. The identification paradigm exploits the …
Rational approximations of spectral densities based on the Alpha divergence
M Zorzi - Mathematics of Control, Signals, and Systems, 2014 - Springer
We approximate a given rational spectral density by one that is consistent with prescribed
second-order statistics. Such an approximation is obtained by selecting the spectral density …
second-order statistics. Such an approximation is obtained by selecting the spectral density …
[HTML][HTML] Covariance structure regularization via entropy loss function
The need to estimate structured covariance matrices arises in a variety of applications and
the problem is widely studied in statistics. A new method is proposed for regularizing the …
the problem is widely studied in statistics. A new method is proposed for regularizing the …