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This paper focuses on the spectral unmixing technique for analyzing hyperspectral image (HSI). In this paper, we first prove that the reconstruction errors and the abundance anomalies (AAs, abundances that are negative or greater than one) are effective in measuring the purity of pixels. Then, due to the continuity of the objects in the space, the endmembers are assumed to be located at some noticeable...
This paper aims at non-asymptotically estimating the fractional integral and derivative of the output for a class of fractional order linear systems in noisy environment with unknown initial conditions. For this purpose, the considered system modeled by the pseudo-state space representation is firstly transformed into a fractional order differential equation. Secondly, based on the obtained equation...
Human action classification, which is vital for content-based video retrieval and human-machine interaction, finds problem in distinguishing similar actions. Previous works typically detect spatial-temporal interest points (STIPs) from action sequences and then adopt bag-of-visual words (BoVW) model to describe actions as numerical statistics of STIPs. Despite the robustness of BoVW, this model ignores...
Photoplethysmography(PPG) heart rate(HR) measurement technologies have the drawback of robustness. The traditional threshold method is not able to compute HR from the disorderly and unsystematic PPG signal (especially the PPG signal come from the people who has hypertension). To enhance HR estimation robustness, we present a strong robustness fusion method (SRFM) which fuses discrete fourier transform...
Allowing for a priori optimization of the robot manipulation to improve the performance in the unmanned environment, it is critical for the augmented reality system to estimate the attitude of point clouds in model reconstruction. The estimation of planar parameter is not always faithful for point cloud fitting, because the gross errors and outliers are not considered in by the traditional plane fitting...
For multi-model multisensor system with uncertain variance linearly correlated white noises, the problems of designing robust weighted fusion Kalman estimators (predictor, filter, smoother) are addressed. According to the minimax robust estimation principle, applying Lyapunov equation approach, a unified design approach to obtain the local and three weighted fusion robust Kalman estimators of the...
This paper presents a computational framework for accurately estimating the disparity map of plenoptic images. The proposed framework is based on the variational principle and provides intrinsic sub-pixel precision. The light-field motion tensor introduced in the framework allows us to combine advanced robust data terms as well as provides explicit treatments for different color channels. A warping...
Single-image blind deblurring could be considered as an important preprocessing step in imaging information fusion. Its purpose is to simultaneously estimate blur kernel and latent sharp image from only one observed blurred image. Blind deblurring has been attracting increasing attention in the fields of image processing, computer vision, computational photography, etc. However, it is a typically...
We propose a diffusion expectation-maximization algorithm with adaptive combiner for distributed estimation over sensor networks. Due to the spatial distribution of the nodes, variation of node profile across the network is a common phenomena in real applications. The unreliable nodes exist and provide inaccurate estimates, which may be caused by high levels of noise or malicious attacks. Instead...
This article considers the robust cooperative control problem of multiple double-integrator agents' formation moving around a set of given curves on spheres, where each agent suffers an unknown spatiotemporal flowfield and the communication topology among agents is directed. The flow specification is composed of a general direction matrix and a responding unknown flow speed vector, which includes...
Digitalisation of industrial processes, also called the fourth industrial revolution, is leading to availability of large volume of data containing measurements of many process variables. This offers new opportunities to gain deeper insights on process variability and its effects on quality and performance. Manufacturing facilities already use data driven approaches to study process variability and...
With the increasing number of public available training data for face alignment, the regression-based methods attracted much attention and have become the dominant methods to solve this problem. There are two main factors, the variance of the regression target and the capacity of the regression model, affecting the performance of the regression task. In this paper, we present a Stacked Hourglass Network...
In contemporary mechatronic applications decision-making is often based on information about the underlying model governing the dynamical evolution, in order to ensure optimal operation with respect to a prioritized objective. Modeling errors stemming from parameter uncertainty or varying operational conditions result in inevitable deviations from the theoretical estimate and consequently in suboptimal...
In this paper, we attack the estimation problem in Kalman filtering when the measurements are contaminated by outliers. We employ the Laplace distribution to model the underlying non-Gaussian measurement process. The maximum posterior estimation is solved by the majorization minimization (MM) approach. This yields an MM based robust filter, where the intractable ℓ1 norm problem is converted into an...
This paper investigates the robust weighted fusion estimation problem of multi-sensor systems with mixed uncertainties, including stochastic parameter uncertainties, missing measurements and uncertain noise variances. The stochastic parameter uncertainties are described by multiplicative noise. Especially, the variances of both the multiplicative and additive noises are uncertain. By introducing two...
The problem of disturbance attenuation and rejection for discrete systems with unknown nonlinear and missing measurement is studied in this paper. Here, two types of disturbance are considered. One of them is matched unknown disturbance generated by an exogenous systems, the other belongs to norm-bounded disturbance. The matched unknown disturbance is estimated by disturbance observer (DOB), and it...
This paper studies an event-triggered control problem for nonlinear systems in the presence of external disturbances. To avoid infinitely fast sampling caused by disturbances, a new event-triggering mechanism is proposed, which depends not only on the system state but also on an estimation of the influence of the external disturbance. Moreover, the closed-loop event-triggered system is proved to be...
Stereo rectification is a crucial step for a number of computer vision problems and in particular for dense 3D reconstruction which is a very powerful characterization tool for microscopic objects. Rectification simplifies and speeds up the correspondence search in a pair of images: the search space is reduced to a horizontal line. It is mainly developed for perspective camera model: a projective...
The paper deals with the design of a robust predictive fault-tolerant control for linear discrete-time systems with an application of the quadratic boundedness theory and an associated robust invariant set. The main problem is to maintain the state of the system inside the robust invariant set obtained under asymmetric input and state constraints. The proposed strategy relies on a three-stage procedure,...
This paper presents a complete method for automatic and robust control configuration selection for linear systems which relies upon acquired process data under Gaussian noise excitation. The selection of the configuration is based on the estimation of the Interaction Measure named Participation Matrix. This estimation is derived with uncertainty bounds, which allows to determine online whether the...
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