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Methods based on order statistics are often used in finance, quality control, data and signal processing, especially when signals of interest are immersed in impulsive noise. These allow to include rank information by increasing the dimension of the problem. In large dimension problems, we are usually required to know only the second order statistics. In this article we use a rank-one quadratic measurement...
Saliency detection aims to find the useful and attractive regions from an image. In many situations, there may be multiple objects in the image, and these objects may have equal attractiveness. Moreover, the appearance of pixels in one object may demonstrate large difference, which could lead to lose the object integrality when detecting saliency. To this end, this paper proposes a multi-saliency...
In general cases, particularly in developing countries, the weakness of the national electric grid is due to overloading, high temperatures and bad weather. In such systems, the interruptions of electricity supply can take several hours to days. To prevent the power deficit, consumers can use diesel generators that are very noisy and highly polluting. But, as alternative, this paper proposes a combination...
In robotics, non-linear least squares estimation is a common technique for simultaneous localization and mapping. One of the remaining challenges are measurement outliers leading to inconsistency or even divergence within the optimization process. Recently, several approaches for robust state estimation dealing with outliers inside the optimization back-end were presented, but all of them include...
Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
We consider tracking of a target with elliptical nonlinear constraints on its motion dynamics. The state estimates are generated by sensors and sent over long-haul links to a remote fusion center for fusion. We show that the constraints can be projected onto the known ellipse and hence incorporated into the estimation and fusion process. In particular, two methods based on (i) direct connection to...
This paper proposes a Doppler estimation algorithm for underwater acoustic communication by constructing the guide function of the objective function based on linear frequency modulation (LFM) signal. The algorithm employs the least square principle and the particle swarm method, to build the objective function and solves the global optimal solution, respectively. Computer simulations show that the...
The modeling of fluid catalytic cracking unit (FCCU) is important due to the significant role of FCCU in oil refining. On account of the strong nonlinearity of FCCU mechanisms the solved optimized parameters are usually local optimum, which is sensible to the parameter initial value. A parameter estimation framework and algorithm is proposed in this paper for getting a stable optimal solution. According...
Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While variations due to scale and rotation have been examined, there is a lack of practical solutions for more complex deformations such as affine transformations because of the tremendous size of the associated solution space. To...
We propose a lightweight method for dense online monocular depth estimation capable of reconstructing 3D meshes on computationally constrained platforms. Our main contribution is to pose the reconstruction problem as a non-local variational optimization over a time-varying Delaunay graph of the scene geometry, which allows for an efficient, keyframeless approach to depth estimation. The graph can...
Solving blind image deblurring usually requires defining a data fitting function and image priors. While existing algorithms mainly focus on developing image priors for blur kernel estimation and non-blind deconvolution, only a few methods consider the effect of data fitting functions. In contrast to the state-of-the-art methods that use a single or a fixed data fitting term, we propose a data-driven...
Hand-Eye Calibration (HEC) which is necessary in robotics is proposed to determine the relative transformations between the camera and the Inertial Measurement Unit (IMU) in this paper. We know HEC could be degenerated into the problem of solving an optimization issue for the homogeneous matrix composed of rotation and translation. After acquiring an initial estimation with Kronecker product and singular...
Optimization of anti-windup controller design is achieved for non-linear systems with actuator saturation. The systems are represented as Euler-Lagrange systems, and the proportional and integral (PI) controller, in which the gravity term is cancelled by a non-linear compensation technique, is used. The goal of optimization is to minimize the saturated input, i.e., the difference between the control...
Transparency of optical see-through head-mounted displays (OST-HMDs) makes them suffer from background blending. Existing works have tackled this problem by color correction, but have not addressed how to estimate the background color accurately. In this paper, we apply colorimetric estimation to the subtraction compensation for background blending. Moreover, we propose an optimization framework that...
In this paper, we consider an optimal parameter and state estimation problem arising in an one-dimensional (1D) magnetohydrodynamic (MHD) flow system, whose dynamics can be modeled by a coupled partial differential equations (PDEs). In this model, the coefficients of the Reynolds number and initial conditions as well as state variables are supposed to be unknown and need to be estimated. An adjoint-based...
Although several powerful joint filters for cross-modal image pairs have been proposed, the existing joint filters generate severe artifacts when there are misalignments between a target and a guidance images. Our goal is to generate an artifact-free output image even from the misaligned target and guidance images. We propose a novel misalignment-robust joint filter based on weight-volume-based image...
One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown...
The paper presents a variation of the systems' synthesis problem setting, where it is proposed to use the values of the entropy potentials of the output parameters as an optimization criterion. The use of such criteria makes it possible to improve the quality of the process dynamics assessment, what creates the prerequisites for the control efficiency improvement. With the reference to this specific...
Large scale distributed simulation should be well planned before the execution, since applying unnecessary hardware only wastes our time and money. On the other side, we need enough hardware to achieve an acceptable performance. Thus, it is considerable to estimate the performance of a large scale distributed simulation before the execution. Such an estimation also improves the efficiency of the applied...
The estimation of the domain of attraction of a class of susceptible-infectious-removed-susceptible immigration is investigated. On assumption the disease-free equilibrium and the endemic equilibrium existences, hence a Lyapunov function too, the domain of attraction of the epidemic model is estimated by means of LF-LMI-moment and SOS optimization approaches. An invariant subset of the domain of attraction,...
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