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We study the problem of recovering point sources from samples of their convolution with a Gaussian kernel, showing that a convex program achieves exact deconvolution as long as the sources are not too clustered together and there are at least two samples close to the location of each source. The result is established using a novel dual-certificate construction.
We approximate the quasi-static equation of linear elasticity in translation invariant spaces on the torus. This unifies different FFT-based discretisation methods into a common framework and extends them to anisotropic lattices. We analyse the connection between the discrete solution spaces and demonstrate the numerical benefits. Finite element methods arise as a special case of periodised Box spline...
Due to the low weight of monocular camera, monocular Simultaneous Localization and Mapping (SLAM) is an area of popular research and promotes countless applications of micro Unmanned Aerial Vehicles (UAVs), especially in some GPS-denied indoor environments. Nevertheless, the motion of UAVs is often faster and more complex than that of ground-based robots. It would also lead to error accumulation if...
In this paper a semi-supervised regression model based on co-training is applied on the soft sensor context, together with a feature ranking approach which has the purpose of removing irrelevant features. The description of both the methods of semi-supervised regression and feature ranking, as well as the theorethical foundation of the proposed feature ranking approach are also given. To evaluate...
For the mathematical model of tug handling simulator, the locally optimal locally weighted learning (LWL) is proposed. Firstly, samples space rearrangement is taken to diminish the one-to-many mapping and non-separable of ship motion states. Secondly, distance metric is learned by leave-one-out cross validation for every sample, and this approach improves the nonlinearity mapping ability and robustness...
Unsupervised manifold learning has become accepted as an important tool for reducing dimensionality of a data set by finding its meaningful low dimensional representation lying on an unknown nonlinear subspace. Most manifold learning methods only embed an existing data set, but do not provide an explicit mapping function for novel out-of-sample data, thereby potentially resulting in an ineffective...
In this paper we present a sampling result about continuous-domain black and white images that form a convex shape. In particular, we will study shapes whose boundaries belong to the zero-level sets (roots) of bivariate polynomials. In [1] it was shown that generalized 2D moments of the image can lead to annihilation equations for the coefficients of the bivariate polynomial that determine the boundary...
For the patients with type 1 diabetes (T1D), it is very important to keep their blood glucose concentration in the normal level by insulin injections. As the glucose level can be checked consistently by continuous glucose monitoring (CGM) system, it enables estimation of near-future glucose prediction by developing a reliable prediction model. In this paper, a kernel-based adaptive filtering algorithm...
In this paper, we investigate a data-driven fault detection (FD) problem and its application to vehicle lateral dynamics for the improvement of vehicle lateral safety, reliability and feasibility. A more practical situation in this work is the generalized (non-affine) models and real-time implementation. In particular, a linear parameter-varying (LPV) model is established for vehicle lateral dynamics...
In this paper, the application of independent component analysis (ICA) to statistical process monitoring is studied. This paper mainly focuses on studying on the fault detection and isolation principle based on the data model of ICA. Contributions of this paper are: (1) for the purpose of fault detection, two monitoring statistics are designated by detailed analysis on the data model of ICA; (2) a...
Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. In this paper, we propose a unified Convolutional Neural Network (CNN) model for this task. In order to reliably detect modern steganographic algorithms, we design the proposed model from two aspects. For the first, different from existing CNN based steganalytic algorithms that use a predefined highpass...
The aim of this article is to design a moment transformation for Student-t distributed random variables, which is able to account for the error in the numerically computed mean. We employ Student-t process quadrature, an instance of Bayesian quadrature, which allows us to treat the integral itself as a random variable whose variance provides information about the incurred integration error. Advantage...
The mathematical model of nonlinear device (ND) plays an essential role in the power amplifier (PA) linearization by means of digital signal processing. In this paper we propose a model derived by the Wiener orthogonalization method. The one important feature of this method is that the resulted output decomposition depends on the statistics of the input signal, and initially it was derived by Wiener...
This study presents numerical algorithms for solving optimal control problems with a class of integro-differential equations of the second kind as costraints. This class of equations consists of an integro-differential term containing an Abeltype kernel. The first kind equations, with a weakly singular kernel, investigated here appear in the mathematical model of an aeroelasticity problem [1]. Two...
Many computational applications, such as loop current analysis in electric systems and truss analysis, involve solving a huge number of linear equations. These linear equations are considered to consume both time and resources, so finding fast solutions to these equations is a major challenge. Both CPUs and Graphical Processing Units (GPUs) can solve these linear equations, but choosing the best architecture...
This paper makes a Hopf bifurcation analysis of a two-neuron network with mixed time delays. The network contains two discrete delays and two distributed delays. The sum of discrete delays is chosen as the bifurcation parameter, and the conditions of the stability and Hopf bifurcation are achieved by analyzing its characteristic equation. Finally, numerical simulations are carried out to illustrate...
The use of dashboard-mounted video cameras is rapidly spreading in many countries around the world. Widespread usage of dash-cams brings new problems, for example, dash-cam videos are uploaded on public websites which contain footage of other cars with the number-plates visible. This can potentially compromise privacy. Further, dash-cam videos can be used as evidence in case of accidents. There have...
The paper considers the class of discrete-time, single-input, single-output, nonlinear dynamical systems described by a polynomial difference equation. This class, call polynomial time-invariant, is a proper generalization of the linear time-invariant model class. The identification data is assumed to be generated in the errors-in-variables setting, where the input and the output noise is zero mean,...
Signal strength difference (SSD) is widely utilized as the feature for Wi-Fi fingerprint localization to tackle the heterogeneity between training device and target device, but the correlation between SSDs is largely ignored. In this paper, a novel scheme named LC-KDE is proposed. It utilizes local Fisher discriminant analysis (LFDA) to transform the original SSDs into weakly correlated features,...
It is a challenging and important task to perceive and interact with other traffic participants in a complex driving environment. The human vision system plays one of the crucial roles to achieve this task. Particularly, visual attention mechanisms allow a human driver to cleverly attend to the salient and relevant regions of the scene to further make necessary decisions for the safe driving. Thus,...
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