Pythagorean harmonic summability of Fourier series

This paper explores the possibility for summing Fourier series nonlinearly via the Pythagorean harmonic mean. It reports on new results for this summability with the introduction of new concepts like the smoothing operator and semi-harmonic summation. The smoothing operator is demonstrated to be Kalman filtering for linear summability, logistic processing for Pythagorean harmonic summability and linearized logistic processing for semi-harmonic summability. An emerging direct inapplicability of harmonic summability to seismic-like signals is shown to be resolvable by means of a regularizational asymptotic approach.

On local well-posedness of logarithmic inviscid regularizations of generalized SQG equations in borderline Sobolev spaces

<p style='text-indent:20px;'>This paper studies a family of generalized surface quasi-geostrophic (SQG) equations for an active scalar <inline-formula><tex-math id="M1">\begin{document}$ \theta $\end{document}</tex-math></inline-formula> on the whole plane whose velocities have been mildly regularized, for instance, logarithmically. The well-posedness of these regularized models in borderline Sobolev regularity have previously been studied by D. Chae and J. Wu when the velocity <inline-formula><tex-math id="M2">\begin{document}$ u $\end{document}</tex-math></inline-formula> is of lower singularity, i.e., <inline-formula><tex-math id="M3">\begin{document}$ u = -\nabla^{\perp} \Lambda^{ \beta-2}p( \Lambda) \theta $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M4">\begin{document}$ p $\end{document}</tex-math></inline-formula> is a logarithmic smoothing operator and <inline-formula><tex-math id="M5">\begin{document}$ \beta \in [0, 1] $\end{document}</tex-math></inline-formula>. We complete this study by considering the more singular regime <inline-formula><tex-math id="M6">\begin{document}$ \beta\in(1, 2) $\end{document}</tex-math></inline-formula>. The main tool is the identification of a suitable linearized system that preserves the underlying commutator structure for the original equation. We observe that this structure is ultimately crucial for obtaining continuity of the flow map. In particular, straightforward applications of previous methods for active transport equations fail to capture the more nuanced commutator structure of the equation in this more singular regime. The proposed linearized system nontrivially modifies the flux of the original system in such a way that it coincides with the original flux when evaluated along solutions of the original system. The requisite estimates are developed for this modified linear system to ensure its well-posedness.</p>

Variational convergence of discrete elasticae

We discuss a discretization of the Euler–Bernoulli bending energy and of Euler elasticae under clamped boundary conditions by polygonal lines. We show Hausdorff convergence of the set of almost minimizers of the discrete bending energy to the set of smooth Euler elasticae under mesh refinement in (i) the $W^{1,\infty }$-topology for piecewise-linear interpolation; and in (ii) the $W^{2,p}$-topology, $p \in [2,\infty [$, using a suitable smoothing operator to create $W^{2,p}$-curves from polygons.

Resolvent Approximations in L2-Norm for Elliptic Operators Acting in a Perforated Space

We study homogenization of a second-order elliptic differential operator Aε = - div a(x/ε)∇ acting in an ε-periodically perforated space, where ε is a small parameter. Coefficients of the operator Aε are measurable ε-periodic functions. The simplest case where coefficients of the operator are constant is also interesting for us. We find an approximation for the resolvent (Aε + 1)-1 with remainder term of order ε2 as ε → 0 in operator L2-norm on the perforated space. This approximation turns to be the sum of the resolvent (A0 + 1)-1 of the homogenized operator A0 = - div a0 ∇, a0 > 0 being a constant matrix, and some correcting operator εCε. The proof of this result is given by the modified method of the first approximation with the usage of the Steklov smoothing operator.

A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing

Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved. To this end, we first introduce the truncated Huber penalty function which has seldom been used in image smoothing. A robust framework is then proposed. When combined with the strong flexibility of the truncated Huber penalty function, our framework is capable of a range of applications and can outperform the state-of-the-art approaches in several tasks. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. The effectiveness and superior performance of our approach are validated through comprehensive experimental results in a range of applications.

Convergence Rates in Homogenization of the Mixed Boundary Value Problems

We will study the convergence rates of solutions for homogenization of the mixed boundary value problems. By utilizing the smoothing operator as well as duality argument, we deal with the mixed boundary conditions in a uniform fashion. As a consequence, we establish the sharp rate of convergence in H1 and L2, with no smoothness assumption on the coefficients.

The Adaptive Analysis of Shock Signals on the Basis of Improved Morlet Wavelet Clusters

Morlet wavelets do not satisfy the permissibility condition of wavelet analysis, and there are therefore no inverse transformations for Morlet wavelet transforms. In this paper, we put forward the Yang and Pan transform (YPT), which is an adaptive discrete analysis method for shock signals. First, we improved the Morlet wavelet so that the centre and radius of the frequency window can be easily adjusted in the frequency domain. Second, we proposed the extremum frequency concept and analysed the extremum situation of the improved Morlet wavelet. Third, combining the improved Morlet wavelet and extremum frequency, we advanced the theory of the YPT, which does not need to satisfy the permissibility condition. We then continued by using a smoothing operator that can smooth the potentially distorted signal reconstructed after being analysed by the YPT and filtered by using the threshold filtering theory. This operator proved to be simple and efficient. Finally, a noisy signal was reconstructed after being analysed and filtered using the YPT and threshold filtering, respectively, to verify the validity of the theory, and the YPT was compared with the discrete wavelet transform (DWT). As a supplement to the theory in engineering, the shock signals about a gun automatic mechanism were also analysed using the theory in this paper. Good results were obtained, thereby demonstrating that the YPT can be helpful to further extract the features of shock signals in pattern recognition and fault diagnosis.