The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We consider the problem of estimating queue-lengths at an intersection from a pair of advance and stop bar detectors that count vehicles, when these measurements are noisy and biased. The key assumption is that we know weather the queue is empty or not. We propose a real-time queue estimation algorithm based on stochastic gradient descent. The algorithm provably learns the detector bias, and efficiently...
In this paper, we propose a novel decentralized active perception strategy that maximizes the convergence rate in estimating the (unmeasurable) formation scale in the context of bearing-based formation localization for robots evolving in ℝ3 × S1. The proposed algorithm does not assume presence of a global reference frame and only requires bearing-rigidity of the formation (for the localization problem...
PET/MR scanner has been developed for both molecular and morphological assessment with great potentials. In the PET/MR scan, the attenuation correction is still a problem. One method is the MR-based attenuation correction that generates the synthetic CT images from MR images. However, the lack of bone signal and the bias from the synthetic CT image can degrade the PET image quality. Another method...
In industry and engineering, reliability analysis of complex systems is one of the most concerning problems. System reliability depends on the reliability of components and system structure. Due to high testing costs, only component data is available and sample sizes are strictly limited. In this article, we describe a new form of WCF expansion for determining the confidence bounds of system reliability...
In this paper, we propose a new distributed sensor fusion algorithm for environmental monitoring by wireless sensor networks (WSNs). The considered WSNs are assumed to have limited communication capabilities. For each sensor node, a set of local estimation algorithms are maintained as its fused model for environmental variable to be sensed. The proposed algorithm relies on local information exchange...
A new rotor flux linkage estimator based on sliding mode control (SMC) is proposed for vector controlled induction machine (IM) to improve the performance of the flux orientation and torque control for IM. The stator current and rotor flux sliding mode estimators were constructed in stator coordinate frame. Furthermore, the adjusting parameters for SMC are optimized to improve the estimation accuracy...
We have previously showed that it is possible to achieve parameter identification of discrete-time structured uncertainties without requiring persistency of excitation when using Concurrent Learning. Instead, granted a less restrictive condition compared to that of persistency of excitation is verified, exponential convergence of parameter estimates to their true values ensues. The present study applies...
Parameter convergence is of great importance as it enhances the overall stability and robustness properties of adaptive control systems. However, a stringent persistent-excitation (PE) condition usually has to be satisfied to achieve parameter convergence in adaptive control. In this paper, a least-squares learning control strategy without regressor filtering is presented to achieve parameter convergence...
A combine-then-adapt (CTA) diffusion proportionate affine projection algorithm (DP APA) is proposed for distributed estimation, which uses gain matrices in the CTA diffusion affine projection algorithm (DAPA) to proportionately adapt the weight vectors of agents in the network. Then, a variable step-size (VSS) is presented for the DPAPA to address the problem of tradeoff between fast convergence rate...
Moving Horizon Estimation (MHE) is a powerful, yet computationally expensive approach for state and parameter estimation that is based on online optimization. In applications with multi-rate measurements that may include outliers, the Huber penalty is often a better candidate for the MHE objective than the commonly used Euclidean norm. Treating this non-smooth objective in Newton-type optimization...
State-of-the-art single image dehazing algorithms have some challenges to deal with images captured under complex weather conditions because their assumptions usually do not hold in those situations. In this paper, we develop a deep transmission network for robust single image dehazing. This deep transmission network simultaneously copes with three color channels and local patch information to automatically...
A sparsity-aware least-mean mixed-norm (LMMN) adaptive filter algorithm is proposed for sparse channel estimation applications. The proposed algorithm is realized by incorporating a sum-log function constraint into the cost function of a LMMN which is a mixed norm controlled by a scalar-mixing parameter. As a result, a shrinkage is given to enhance the performance of the LMMN algorithm when the majority...
The paper deals with the problem of simultaneous state and process fault estimation for uncertain dynamic systems. Contrarily to the approaches presented in the literature, the nonlinear estimation problem is reduced to the linear one by introducing a suitable system reparameterization and new estimator structure. Instead of estimating the fault directly, its product with state and the state itself...
Closed-form analytical expressions are derived for the numerical integration of the spectral Green's function with Contour-FFT. Results indicates that a proper uniform grid of spatial points enhances the convergence rate of the truncation error, that the Nyquist-Shannon theorem can be generalized to inverse Laplace transforms on linear path and that the relative error specific to Contour-FFT is a...
Improving the execution time and the numerical complexity of the well-known kurtosis-based maximization method, the RobustICA, is investigated in this paper. A Newton-based scheme is proposed and compared to the conventional RobustICA method. A new implementation using the nonlinear Conjugate Gradient one is investigated also. Regarding the Newton approach, an exact computation of the Hessian of the...
Multipath is the dominant error source for high-accuracy positioning systems. It is significant for eliminating the multipath error and improving the positioning accuracy to estimate multipath parameters. The existing multipath estimation algorithms are usually designed for Gaussian noise, and their performances degrade dramatically in non-Gaussian noise. To solve the problem, a multipath estimation...
The on-line identification of the moment of inertia is researched, in order to improve the performance of servo system. The identification of the moment of inertia based on the recursive forgetting factor least squares(RFFLS), gradient descent (GDM)and model reference adaptive (MRA)methods is designed respectively. And the advantages and disadvantages of the three methods are summarized. The factors...
For the multi-sensor system with unknown observation noises and parameters, based on ARMA model, a multi-step algorithm is presented. The first step is to use recursive instrumental variable method and the method of average local model parameter estimations to get the initial fusion estimations. The second step is to get the estimations of the observation noises variance by system identification method...
Distributed estimation and processing in networks modeled by graphs have received a great deal of interest recently, due to the benefits of decentralised processing in terms of performance and robustness to communications link failure between nodes of the network. Diffusion-based algorithms have been demonstrated to be among the most effective for distributed signal processing problems, through the...
The complex multivariate generalized Gaussian distribution (CMGGD) is a flexible parametrized distribution suitable for a variety of applications. Previous work in this area is either limited to the univariate case or, in the multivariate case, restricts the complex vectors, unjustifiably, to be circular. In both cases, algorithms for parameter estimation also suffer from convergence or accuracy limitations...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.