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The evolution of the shape of high-intensity acoustic pulses at the apex of an anisotropic elastic wedge is described by an evolution equation which contains an effective nonlinearity of second order, if the symmetry of the geometry is sufficiently low. The strength of this nonlinearity is governed by a kernel function. For silicon as a strongly anisotropic wedge material, this kernel function has...
The Datagram Congestion Control Protocol (DCCP) is no longer too young to be usable: the first RFCs were published in 2006, and a stable and quite complete Linux implementation exists. DCCP over UDP has also recently been specified to address network traversal problems. But how good is the service provided to applications by this protocol? This paper identifies some deficiencies of the current implementation—the...
In this paper, we propose a novel blind image restoration method based on total variation (TV) regularization. It involves alternate iteration of point spread function (PSF) estimation and deconvolution. Using this method, we can obtain clear images from blurred images without an increase in noise and ringing. Thus, blurred images captured using digital cameras can be restored effectively.
A method is considered for determining the statistical distribution function of the field enhancement factor matrix nanoscale field emission structures from the results of emission testing. The method is based on the work of academician A.N. Tikhonov, associated with the decision of inverse ill-posed problems described by the Fredholm equation 1-st kind. In deciding it was taken into account that...
In this paper, we propose a learning based technique for imagedeblurring using artificial neural networks. We model the original image as Markov Random field and the blurred image as degraded version of the original MRF. We do not make any prior assumptions for the blur kernel and develop the proposed algorithm by taking into account the space varying nature of the blur kernel. We re-formulate the...
This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compared to modeling them individually. Multi-task Gaussian processes provide a powerful...
This paper is concerned with Fault Detection and Isolation (FDI) and more specifically it focuses on a parameter-free residual generation method. The residual signals are obtained by projecting the measured signals onto the kernel of an extended input matrix, which depends on the structure of the system model. The method was not easily applicable in real-world applications due to a high computational...
In this paper shows the analysis of the implementation of a adapted filter to a chirp signal in the domain of the optimal order of the FrFT. Here express the equations for a chirp signal receptor in the frequency domain and a general equation in the Fourier fractional domain using the Phase stationary principle (PSP), with the objective of expose the adapted filter limitations, describe the affecting...
This paper transforms sequential power flow problem to a parallel problem and solves it on GPU. In particular, we implement parallel Gauss-Seidel solver, Newton-Raphson solver, and P-Q decoupled solver using CUDA (Compute Unified Device Architecture) on GPU. The aim is to investigate the performance of the three different parallel power flow solvers. We use four IEEE standard power systems and one...
In this paper, we introduce two kinds of models, beta distribution estimation and the kernel density distribution estimation, to simulate the distribution of the ultimate recovery rates of bonds. As we know, the model based on the beta distribution is common in the daily use by the investors and financial agents. However, it has a fatal defect that it can't fit the two-peaked distribution. In order...
In this paper, a method for designing two-channel zero-phase 3-D Face Centered Orthorhombic (FCO) filter bank is illustrated. We present Eigenfilter based method for designing symmetric transformation kernels which can be used in generalized McClellan transformation method for designing 3-D FCO filter banks. Design examples are provided to demonstrate the effectiveness of the design method and it...
In this manuscript, we propose a class of linear integral transforms that extends the capabilities of the Fractional Fourier Transform(FrFT), while preserving some key properties of the FrFT. The FrFT has diverse applications in literature, including the parameter estimation of linear chirp signals. Here, we also illustrate ways to employ the proposed new transforms to estimate higher order chirps,...
A new method of the near-field electromagnetic sounding and tomography of subsurface dielectric inhomogeneities is studied. It is based on the transformation of the multifrequency inverse scattering problem to that for the complex-valued synthesized pulse. This new statement of the inverse scattering problem leads to the much better resolution of the scanning tomography based on its solution. Input...
To solve the learning control problem of a bioreactor system, a novel framework of heuristic dynamic programming (HDP) with sparse kernel machines is presented, which integrates kernel methods into critic learning of HDP. As a class of adaptive critic designs (ACDs), HDP has been used to realize online learning control of dynamical systems, where neural networks are commonly employed to approximate...
An identification method is discussed that deals with the Wiener-Hammerstein systems of general nonlinearity. By introducing a suitable instrumental variable a new algorithm is presented to recursively estimate the linear subsystems using stochastic approximation algorithm. The kernel nonparametric method is used to estimate the nonlinear function. The consistent analysis of the method is given under...
For complex nonlinear systems of chemical industry process, traditional kernel principal component analysis (KPCA) methods are very difficult to calculate the kernel matrix for fault detection with large sample sets. So an improved fault detection method based on feature vector selection-KPCA (FVS-KPCA) is developed. This method can evidently reduce calculational complexity of fault detection and...
Edge detection is one of the most commonly used operations in image analysis. Most algorithms contain two basic steps: denoise and derivative computing. We apply kernel regression to remove noise and to get gray-level and derivative intensity surface of images. We explore the Nadaraya-Watson kernel regression which conquers the more negative impact caused by noises for derivative computing than general...
This paper investigates the role that nonlinear camera response functions (CRFs) have on image deblurring. In particular, we show how nonlinear CRFs can cause a spatially invariant blur to behave as a spatially varying blur. This can result in noticeable ringing artifacts when deconvolution is applied even with a known point spread function (PSF). In addition, we show how CRFs can adversely affect...
Image noise can present a serious problem in motion deblurring. While most state-of-the-art motion deblurring algorithms can deal with small levels of noise, in many cases such as low-light imaging, the noise is large enough in the blurred image that it cannot be handled effectively by these algorithms. In this paper, we propose a technique for jointly denoising and deblurring such images that elevates...
While classical kernel-based clustering algorithms are based on a single kernel, in practice it is often desirable to base clustering on combination of multiple kernels. In [1], we considered a fuzzy c-means with multiple kernels in observation space (FCMK-OS) algorithm which constructs the kernel from a number of Gaussian kernels and learns a resolution specific weight for each kernel function in...
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