765 research outputs found

    The empirical distribution of the eigenvalues of a Gram matrix with a given variance profile

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    Consider a N×nN\times n random matrix Yn=(Yijn)Y_n=(Y_{ij}^{n}) where the entries are given by Yijn=σ(i/N,j/n)nXijnY_{ij}^{n}=\frac{\sigma(i/N,j/n)}{\sqrt{n}} X_{ij}^{n}, the XijnX_{ij}^{n} being centered i.i.d. and σ:[0,1]2(0,)\sigma:[0,1]^2 \to (0,\infty) being a continuous function called a variance profile. Consider now a deterministic N×nN\times n matrix Λn=(Λijn)\Lambda_n=(\Lambda_{ij}^{n}) whose non diagonal elements are zero. Denote by Σn\Sigma_n the non-centered matrix Yn+ΛnY_n + \Lambda_n. Then under the assumption that limnNn=c>0\lim_{n\to \infty} \frac Nn =c>0 and 1Ni=1Nδ(iN,(Λiin)2)nH(dx,dλ), \frac{1}{N} \sum_{i=1}^{N} \delta_{(\frac{i}{N}, (\Lambda_{ii}^n)^2)} \xrightarrow[n\to \infty]{} H(dx,d\lambda), where HH is a probability measure, it is proven that the empirical distribution of the eigenvalues of ΣnΣnT \Sigma_n \Sigma_n^T converges almost surely in distribution to a non random probability measure. This measure is characterized in terms of its Stieltjes transform, which is obtained with the help of an auxiliary system of equations. This kind of results is of interest in the field of wireless communication.Comment: 25 pages, revised version. Assumption (A2) has been relaxe

    The empirical eigenvalue distribution of a Gram matrix: From independence to stationarity

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    Consider a N×nN\times n random matrix Zn=(Zj1j2n)Z_n=(Z^n_{j_1 j_2}) where the individual entries are a realization of a properly rescaled stationary gaussian random field. The purpose of this article is to study the limiting empirical distribution of the eigenvalues of Gram random matrices such as ZnZnZ_n Z_n ^* and (Zn+An)(Zn+An)(Z_n +A_n)(Z_n +A_n)^* where AnA_n is a deterministic matrix with appropriate assumptions in the case where nn\to \infty and Nnc(0,)\frac Nn \to c \in (0,\infty). The proof relies on related results for matrices with independent but not identically distributed entries and substantially differs from related works in the literature (Boutet de Monvel et al., Girko, etc.).Comment: 15 page

    A sharp-front moving boundary model for malignant invasion

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    We analyse a novel mathematical model of malignant invasion which takes the form of a two-phase moving boundary problem describing the invasion of a population of malignant cells into a population of background tissue, such as skin. Cells in both populations undergo diffusive migration and logistic proliferation. The interface between the two populations moves according to a two-phase Stefan condition. Unlike many reaction-diffusion models of malignant invasion, the moving boundary model explicitly describes the motion of the sharp front between the cancer and surrounding tissues without needing to introduce degenerate nonlinear diffusion. Numerical simulations suggest the model gives rise to very interesting travelling wave solutions that move with speed cc, and the model supports both malignant invasion and malignant retreat, where the travelling wave can move in either the positive or negative xx-directions. Unlike the well-studied Fisher-Kolmogorov and Porous-Fisher models where travelling waves move with a minimum wave speed cc>0c \ge c^* > 0, the moving boundary model leads to travelling wave solutions with c<c|c| < c^{**}. We interpret these travelling wave solutions in the phase plane and show that they are associated with several features of the classical Fisher-Kolmogorov phase plane that are often disregarded as being nonphysical. We show, numerically, that the phase plane analysis compares well with long time solutions from the full partial differential equation model as well as providing accurate perturbation approximations for the shape of the travelling waves.Comment: 48 pages, 21 figure

    Invading and receding sharp-fronted travelling waves

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    Biological invasion, whereby populations of motile and proliferative individuals lead to moving fronts that invade into vacant regions, are routinely studied using partial differential equation (PDE) models based upon the classical Fisher--KPP model. While the Fisher--KPP model and extensions have been successfully used to model a range of invasive phenomena, including ecological and cellular invasion, an often--overlooked limitation of the Fisher--KPP model is that it cannot be used to model biological recession where the spatial extent of the population decreases with time. In this work we study the \textit{Fisher--Stefan} model, which is a generalisation of the Fisher--KPP model obtained by reformulating the Fisher--KPP model as a moving boundary problem. The nondimensional Fisher--Stefan model involves just one single parameter, κ\kappa, which relates the shape of the density front at the moving boundary to the speed of the associated travelling wave, cc. Using numerical simulation, phase plane and perturbation analysis, we construct approximate solutions of the Fisher--Stefan model for both slowly invading and slowly receding travelling waves, as well as for rapidly receding travelling waves. These approximations allow us to determine the relationship between cc and κ\kappa so that commonly--reported experimental estimates of cc can be used to provide estimates of the unknown parameter κ\kappa. Interestingly, when we reinterpret the Fisher--KPP model as a moving boundary problem, many disregarded features of the classical Fisher--KPP phase plane take on a new interpretation since travelling waves solutions with c<2c < 2 are not normally considered. This means that our analysis of the Fisher--Stefan model has both practical value and an inherent mathematical value.Comment: 47 pages, 13 figure

    Exact sharp-fronted travelling wave solutions of the Fisher-KPP equation

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    A family of travelling wave solutions to the Fisher-KPP equation with speeds c=±5/6c=\pm 5/\sqrt{6} can be expressed exactly using Weierstrass elliptic functions. The well-known solution for c=5/6c=5/\sqrt{6}, which decays to zero in the far-field, is exceptional in the sense that it can be written simply in terms of an exponential function. This solution has the property that the phase-plane trajectory is a heteroclinic orbit beginning at a saddle point and ends at the origin. For c=5/6c=-5/\sqrt{6}, there is also a trajectory that begins at the saddle point, but this solution is normally disregarded as being unphysical as it blows up for finite zz. We reinterpret this special trajectory as an exact sharp-fronted travelling solution to a \textit{Fisher-Stefan} type moving boundary problem, where the population is receding from, instead of advancing into, an empty space. By simulating the full moving boundary problem numerically, we demonstrate how time-dependent solutions evolve to this exact travelling solution for large time. The relevance of such receding travelling waves to mathematical models for cell migration and cell proliferation is also discussed

    Detection of Local Wall Stiffness Drop in Steel-Lined Pressure Tunnels and Shafts of Hydroelectric Power Plants Using Steep Pressure Wave Excitation and Wavelet Decomposition

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    A new monitoring approach for detecting, locating, and quantifying structurally weak reaches of steel-lined pressure tunnels and shafts is presented. These reaches arise from local deterioration of the backfill concrete and the rock mass surrounding the liner. The change of wave speed generated by the weakening of the radial-liner supports creates reflection boundaries for the incident pressure waves. The monitoring approach is based on the generation of transient pressure with a steep wave front and the analysis of the reflected pressure signals using the fast Fourier transform and wavelet decomposition methods. Laboratory experiments have been carried out to validate the monitoring technique. The multilayer system (steel-concrete-rock) of the pressurized shafts and tunnels is modeled by a one-layer system of the test pipe. This latter was divided into several reaches having different wall stiffnesses. Different longitudinal placements of a steel, aluminum, and PVC pipe reach were tested to validate the identification method of the weak section. DOI: 10.1061/(ASCE)HY.1943-7900.0000478. (C) 2012 American Society of Civil Engineers
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