Aims Carotid–femoral pulse wave velocity (PWV), a direct measure of aortic stiffness, has become increasingly important for total cardiovascular (CV) risk estimation. Its application as a routine tool for clinical patient evaluation has been hampered by the absence of reference values. The aim of the present study is to establish reference and normal values for PWV based on a large European population. Methods and results We gathered data from 16 867 subjects and patients from 13 different centres across eight European countries, in which PWV and basic clinical parameters were measured. Of these, 11 092 individuals were free from overt CV disease, non-diabetic and untreated by either anti-hypertensive or lipid-lowering drugs and constituted the reference value population, of which the subset with optimal/normal blood pressures (BPs) (n = 1455) is the normal value population. Prior to data pooling, PWV values were converted to a common standard using established conversion formulae. Subjects were categorized by age decade and further subdivided according to BP categories. Pulse wave velocity increased with age and BP category; the increase with age being more pronounced for higher BP categories and the increase with BP being more important for older subjects. The distribution of PWV with age and BP category is described and reference values for PWV are established. Normal values are proposed based on the PWV values observed in the non-hypertensive subpopulation who had no additional CV risk factors. Conclusion The present study is the first to establish reference and normal values for PWV, combining a sizeable European population after standardizing results for different methods of PWV measurement.

Determinants of pulse wave velocity in healthy people and in the presence of cardiovascular risk factors

Thank you for downloading regularization of inverse problems. Maybe you have knowledge that, people have search hundreds times for their favorite novels like this regularization of inverse problems, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some infectious bugs inside their computer. regularization of inverse problems is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the regularization of inverse problems is universally compatible with any devices to read.

Regularization Of Inverse Problems

Thank you for downloading introduction to electrodynamics. Maybe you have knowledge that, people have look numerous times for their chosen books like this introduction to electrodynamics, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their computer. introduction to electrodynamics is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the introduction to electrodynamics is universally compatible with any devices to read.

Introduction To Electrodynamics

Thank you very much for downloading a course in functional analysis. As you may know, people have look numerous times for their favorite books like this a course in functional analysis, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their desktop computer. a course in functional analysis is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the a course in functional analysis is universally compatible with any devices to read.

A Course In Functional Analysis

Sparsity of representations of signals has been shown to be a key concept of fundamental importance in fields such as blind source separation, compression, sampling and signal analysis. The aim of this paper is to compare several commonly-used sparsity measures based on intuitive attributes. Intuitively, a sparse representation is one in which a small number of coefficients contain a large proportion of the energy. In this paper, six properties are discussed: (Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates, and Babies), each of which a sparsity measure should have. The main contributions of this paper are the proofs and the associated summary table which classify commonly-used sparsity measures based on whether or not they satisfy these six propositions. Only two of these measures satisfy all six: the pq-mean with p les 1, q > 1 and the Gini index.

Comparing Measures of Sparsity

In this paper, we consider nonlinear inverse problems where the solution is assumed to have a sparse expansion with respect to a preassigned basis or frame. We develop a scheme which allows to minimize a Tikhonov functional where the usual quadratic regularization term is replaced by a one-homogeneous (typically weighted l p ) penalty on the coefficients (or isometrically transformed coefficients) of such expansions. For (p < 2), the regularized solution will have a sparser expansion with respect to the basis or frame under consideration. The computation of the regularized solution amounts in our setting to a Landweber-fixed-point iteration with a projection applied in each fixed-point iteration step. The performance of the resulting numerical scheme is demonstrated by solving the nonlinear inverse single photon emission computerized tomography (SPECT) problem.

http://userwww.hs-nb.de/~teschke/ps/35.pdf

A Tikhonov-based projection iteration for nonlinear Ill-posed problems with sparsity constraints

A Mumford-Shah level-set approach for the inversion and segmentation of X-ray tomography data

In this paper we deal with Morozov's discrepancy principle as an a posteriori parameter choice rule for Tikhonov regularization with general convex penalty terms Ψ for nonlinear inverse problems It is shown that a regularization parameter α fulfilling the discprepancy principle exists, whenever the operator F satisfies some basic conditions, and that for suitable penalty terms the regularized solutions converge to the true solution in the topology induced by Ψ It is illustrated that for this parameter choice rule it holds α → 0, δq/α → 0 as the noise level δ goes to 0 Finally, we establish convergence rates with respect to the generalized Bregman distance and a numerical example is presented

/pdf/morozov-s-discrepancy-principle-for-tikhonov-type-4qprpy4536.pdf

Morozov's discrepancy principle for Tikhonov-type functionals with nonlinear operators

In this paper we shall be concerned with the construction of an adaptive Landweber iteration for solving linear ill-posed and inverse problems. Classical Landweber iteration schemes provide in combination with suitable regularization parameter rules order optimal regularization schemes. However, for many applications the implementation of Landweber's method is numerically very intensive. Therefore we propose an adaptive variant of Landweber's iteration that may reduce the computational expense significantly, i.e. leading to a compressed version of Landweber's iteration. We borrow the concept of adaptivity that was primarily developed for well-posed operator equations (in particular, for elliptic PDE's) essentially exploiting the concept of wavelets (frames), Besov regularity, best N-term approximation and combine it with classical iterative regularization schemes. As the main result of this paper we define an adaptive variant of Landweber's iteration. In combination with an adequate refinement/stopping rule (a priori as well as a posteriori principles) we prove that the proposed procedure is a regularization method which converges in norm for exact and noisy data. The proposed approach is verified in the field of computerized tomography imaging.

/pdf/a-compressive-landweber-iteration-for-solving-ill-posed-2ldmaffial.pdf

A compressive Landweber iteration for solving ill-posed inverse problems

This paper is concerned with the regularization of linear ill-posed problems by a combination of data smoothing and fractional filter methods. For the data smoothing, a wavelet shrinkage denoising is applied to the noisy data with known error level δ. For the reconstruction, an approximation to the solution of the operator equation is computed from the data estimate by fractional filter methods. These fractional methods are based on the classical Tikhonov and Landweber method, but avoid, at least partially, the well-known drawback of oversmoothing. Convergence rates as well as numerical examples are presented.

/pdf/regularization-by-fractional-filter-methods-and-data-4kvqpyyam7.pdf

Ronny Ramlau

Papers

A Tikhonov-based projection iteration for nonlinear Ill-posed problems with sparsity constraints

A Mumford-Shah level-set approach for the inversion and segmentation of X-ray tomography data

Morozov's discrepancy principle for Tikhonov-type functionals with nonlinear operators

A compressive Landweber iteration for solving ill-posed inverse problems

Regularization by fractional filter methods and data smoothing