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Contrast of echographic images has been highly improved by the injection of microbubbles, due to their nonlinear behavior. However, this contrast enhancement is limited by the nonlinear acoustic propagation in tissue. To overcome this drawback, sub and ultra-harmonic contrast imaging can be used, since only microbubbles can generate these components. Nonlinear modeling is a primordial step in the...
A newly-invented, distributed, high-performance graphical processing framework that simulates complex radio frequency (RF) propagation has been developed and demonstrated. The approach uses an advanced computer architecture and intensive multi-core system to enable highperformance data analysis at the fidelity necessary to design and develop modern sensor systems. This widely applicable simulation...
The paper deals with the problem of stability during the solving of pattern recognition tasks from the point of view of transformation groups. It shows the possibility to avoid the necessity of regularization by using the geometric equaffine Lorentz transformation, exploiting as example the alpha-procedure.
Under particle-based framework, level set is generally defined for fluid surfaces and is integrated with marching cubes algorithm to extract fluid surfaces. In these methods, anisotropic kernels method has proven successful for reconstructing fluid surfaces with high quality. It can perfectly represent smooth surfaces, thin stream and sharp features of fluids compare to other methods. In this paper,...
The increasing integration of distributed energy resources (DERs) calls for new monitoring and operational planning tools to ensure stability and sustainability in distribution grids. One idea is to use existing monitoring tools in transmission grids and some primary distribution grids. However, they usually depend on the knowledge of the system model, e.g., the topology and line parameters, which...
Accuracy in segmenting of brain vessels from medical angiographic data is crucial for further modelling and assessment of the human vasculature. It was demonstrated that the level set (LS) approach enhanced by the implementation of the vesselness function (VF) provides a robust segmentation framework enabling the high-quality vessel network extracted from CT and MR images. This work investigates the...
A double-sided input-output kernel functional representation is developed for the class of totally observable linear time-varying systems with inputs. The double-sided kernel representation is immediately applicable as part of a non-asymptotic state observer for observable LTV systems. In the absence of output measurement noise the observer provides exact state values of the system state in arbitrarily...
The novel adaptive multiple-model target tracking algorithm presented here employs a non-asymptotic state and parameter estimator whose design hinges on a non-standard integral system representation. The same estimator can be used for target maneuver detection and isolation and hence constitutes the principal ingredient of the tracking algorithm. The algorithm does not maintain a model bank, but creates...
Granger causality approaches have been widely used to estimate effective connectivity in complex dynamic systems. These techniques are based on the building of predictive models which not only depend on a proper selection of the predictive vectors size but also on the chosen class of regression functions. The question addressed in this paper is the estimation of the model order in the computation...
We present the theory of sequences of random graphs and their convergence to limit objects. Sequences of random dense graphs are shown to converge to their limit objects in both their structural properties and their spectra. The limit objects are bounded symmetric functions on [0,1]2. The kernel functions define an equivalence class and thus identify collections of large random graphs who are spectrally...
Scattering of an incident TM wave from multiple dielectric cylinders with arbitrary cross sections is elaborated. Parametrization of each cross section is assumed to be infinitely smooth, i.e. it is infinitely differentiable according to its argument. We suggest the super-algebraically convergent algorithm to solve both electric/magnetic field integral equations (EFIE/MFIE) by proper factorization...
In this work, we propose a deep learning approach for the detection of the activities of daily living (ADL) in a home environment starting from the skeleton data of an RGB-D camera. In this context, the combination of ad hoc features extraction/selection algorithms with supervised classification approaches has reached an excellent classification performance in the literature. Since the recurrent neural...
In the recent literature, drug design relying on molecular docking (MD) techniques is becoming a very promising field. Most of these techniques rely on the way ligands interact with protein target using only one binding site, in addition, they ignore the fact that assorted ligands interact with unconnected parts of the target. However, by taking the latter fact into consideration, the computational...
In this paper, we explored the development of an anxiety detection (AnD) system using the respiratory signal as its input. Time and frequency domain statistical features derived from breath-to-breath (BB) interval series of respiratory signal is input to a support vector machine (SVM) backend classifier. We used data from normative population, individuals with anxiety disorders and regular meditators...
In this study we use triangular basis function set to solve second kind fuzzy integral equation that can be converted to a system of two integral equations in crisp case. We also consider collocation method for approximately solving the equation.
Recently, kernelized correlation Filter-based trackers have aroused the interest of many researchers and achieved good results in the field of tracking. However, the current tracking model based on kernelized correlation filters can not deal with the changes of the target appearance and scale effectively. Therefore, in this paper, we intend to solve these two problems and improve the robustness of...
To satisfy growing computational demands of modern applications, significant enhancements have been introduced in the contemporary processor architectures with the aim to increase their attainable performance, such as increased number of cores, improved capability of memory subsystem and enhancements in the processor pipeline [1]. Therefore, the performance improvements are usually coupled with an...
Active contour models (ACM) have been proven to be the most promising model in solving the different problems encountered in image segmentation. This paper proposes a new region-based active contour model for level set formulation in which the energy function is formulated using both local and global intensity fitting terms. The generalized Gaussian distribution has been used as the kernel function...
Twin support vector regression and its extensions have been widely applied in machine learning and data mining. However, most of them can not achieve the satisfactory performances when the noise is involved. To this end, this paper presents a weighted least squares twin support vector regression (WLSTSVR) which can reduce the influence of the noise on prediction accuracy by using the information of...
Least squares support vector machines (LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method...
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