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We investigate constrained optimal control problems for linear stochastic dynamical systems evolving in discrete time. We consider minimization of an expected value cost over a finite horizon. Hard constraints are introduced first, and then reformulated in terms of probabilistic constraints. It is shown that, for a suitable parametrization of the control policy, a wide class of the resulting optimization...
This paper illustrates a stochastic Model Predictive Control (MPC) algorithm to control a linear system subject to additive zero-mean noise, state and input constraints. The algorithm proposed is computationally efficient since it can be formulated as a SemiDefinite Programming (SDP) problem and can thus be solved by interior-point methods. We also show that, under the hypotesis of bounded noise,...
In this paper, an optimal design approach for high-gain Proportional-Integral-Observer (PI-Observer), which is proposed by the authors, is improved and illustrated in details. PI-Observer, which can also be used as a type of high-gain observer, is successfully applied to estimate unknown inputs of systems. It is known that reasonable estimations of unknown inputs can only be derived assuming perfect...
The estimation approach discussed in this paper is based on a signal-theoretic and statistical analysis of the notion of orientation. In contrast to other approaches, it does not require the computation of gray value gradients, or the power spectrum of the given signal patch, or quadrature filter outputs, but directly estimates a small central part of the autocovariance function (acf) of the signal...
The transfer functions of all polynomial predictors are derived. Previously proposed IIR polynomial predictive filters based on the feedback extension of FIR predictors are shown to be a special case of this general class.
Reset compensation has been used to overcome limitations of LTI compensation. However, since a reset compensator may destabilize a stable base LTI system, stability needs to be guaranteed for a proper application of reset control. The goal of this work is the study of the stability of a nonlinear/hybrid compensator previously developed by some of the authors, referred to as the PI+CI compensator....
QRS detection in exercise stress test recordings remains a challenging task, because they are highly non-stationary and contaminated with noises, such as large baseline wander and muscular noise, among others. The aim of this work is to find an optimal set of parameters for QRS detection in very noisy ECG signals, such as those acquired during stress tests. Parameter optimization was addressed by...
In degraded listening conditions, speakers are known to adapt their speech via the Lombard reflex to make it more comprehensible. This characteristic has been used in previous work to modify speech recorded in quiet before it is rendered in a noisy environment. The spectral modifications used have been found to be effective in low-pass noise such as babble noise. In this work, we investigate intelligibility...
Optimization of filter parameters are important to improve the performance of the filter for the intended application. In general, the optimized parameters of the image filter for a particular application may not be suitable for other applications as well. Hence to have a better performance, it is always required to modify the filter coefficients or parameters. In this paper the author propose few...
Latent Low-Rank Representation (Lat LRR) has the empirical capability of identifying "salient" features. However, the reason behind this feature extraction effect is still not understood. Its optimization leads to non-unique solutions and has high computational complexity, limiting its potential in practice. We show that Lat LRR learns a transformation matrix which suppresses the most significant...
The parameters plays an important role to the performance of support vector regression(SVR). In order to solve the problem of the Parameter optimization for SVR, first, we transform the problem of Parameter optimization into a problem of nonlinear system state estimation, then, we propose a novel algorithm based on Dual Recursive Variational Bayesian Adaptive Square-Cubature Kalman Filter (DRVB-ASCKF),...
To improve the spectral efficiency of a cognitive radio system, sensing based spectrum sharing (SBSS) technique combines the advantages of spectrum overlay and spectrum underlay. In this paper, we study the performance of SBSS under primary users' (PUs') rate loss constraint. To be specific, efficient algorithms are introduced to find the optimal sensing time and power allocation in both single-carrier...
We present a deflation method for Nonnegative Matrix Factorization (NMF) that aims to discover latent components one by one in order of importance. To do so we perform a series of individual decompositions, each of which stands for a deflation step. In each deflation we obtain a dominant component and a nonnegative residual, and then the residual is further used as an input to the next deflation in...
The dimension of the density matrix of a quantum system increases with the qubits of the quantum system, which makes the quantum state estimation time consuming and requires huge computation. In order to reduce the computational time in quantum state estimation, the problem of quantum state estimation based on compressive sensing is changed to the optimization problem with error constraint. In this...
Particle Swarm Optimization uses noisy historical information to select potentially optimal function samples. Though information-theoretic principles suggest that less noise indicates greater certainty, PSO's momentum term is usually both the least informed and the most deterministic. This dichotomy suggests that while momentum has a profound impact on swarm diversity, it would benefit from a more...
In this paper, we consider the problem of unsupervised feature selection. Recently, spectral feature selection algorithms, which leverage both graph Laplacian and spectral regression, have received increasing attention. However, existing spectral feature selection algorithms suffer from two major problems: 1) since the graph Laplacian is constructed from the original feature space, noisy and irrelevant...
In this paper, we consider transmitter optimization in multiple-input single-output (MISO) broadcast channel with common and secret messages. The secret message is intended for K users and it is transmitted with perfect secrecy with respect to J eavesdroppers which are also assumed to be legitimate users in the network. The common message is transmitted at a fixed rate Äo and it is intended for all...
For compressed sensing of Poissonian measurements, there is a need for nonnegative measurement matrices. We seek an optimal measurement matrix that conserves energy. Moreover, the signals pass a known but uncontrolled mixing matrix, before being multiplexed and measured. This situation is relevant to various optical applications. We optimize the measurement matrix by mutual coherence minimization,...
The importance of any inferences that can be taken from underlying genetic networks of observed time-series data of gene expression patterns should not be overlooked. They are one of the largest topics within bioinformatics. The S-system model is one good choice for analyzing such genetic networks due to the fact that it can capture various dynamics. One problem this model faces is the fact that the...
In this paper, a new unsupervised design method of the weighted median filter (WMF) is proposed for recovering images from impulse noise. A design problem of WMFs is to determine a suitable window shape, and an appropriate weight for each element in the window. The purpose of the filter for the noise removal is generally to estimate the original values precisely for corrupted pixels while preserving...
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