Optimal constant shape parameter for multiquadric based RBF-FD method

Radial basis functions (RBFs) have become a popular method for interpolation and solution of partial differential equations (PDEs). Many types of RBFs used in these problems contain a shape parameter, and there is much experimental evidence showing that accuracy strongly depends on the value of this shape parameter. In this paper, we focus on PDE problems solved with a multiquadric based RBF finite difference (RBF-FD) method. We propose an efficient algorithm to compute the optimal value of the shape parameter that minimizes the approximation error. The algorithm is based on analytical approximations to the local RBF-FD error derived in [1]. We show through several examples in 1D and 2D, both with structured and unstructured nodes, that very accurate solutions (compared to finite differences) can be achieved using the optimal value of the constant shape parameter.This work has been supported by Spanish MICINN grants FIS2010-18473, CSD2010-00011 and by Madrid Autonomous Region grant S2009-1597

Gaussian RBF-FD weights and its corresponding local truncation errors

In this work we derive analytical expressions for the weights of Gaussian RBF-FD and Gaussian RBF-HFD formulas for some differential operators. These weights are used to derive analytical expressions for the leading order approximations to the local truncation error in powers of the inter-node distance h and the shape parameter є. We show that for each differential operator, there is a range of values of the shape parameter for which RBF-FD formulas and RBF-HFD formulas are significantly more accurate than the corresponding standard FD formulas. In fact, very often there is an optimal value of the shape parameter є+ for which the local error is zero to leading order. This value can be easily computed from the analytical expressions for the leading order approximations to the local error. Contrary to what is generally believed, this value is, to leading order, independent of the internodal distance and only dependent on the value of the function and its derivatives at the node.This work has been supported by Spanish MICINN Grants FIS2010-18473, CSD2010-00011 and by Madrid Autonomous Region Grant S2009-1597. M.K. acknowledges Fundacion Caja Madrid for its financial support

Robust multifrequency imaging with MUSIC

In this paper, we study the MUltiple SIgnal Classification (MUSIC) algorithm often used to image small targets when multiple measurement vectors are available. We show that this algorithm may be used when the imaging problem can be cast as a linear system that admits a special factorization. We discuss several active array imaging configurations where this factorization is exact, as well as other configurations where the factorization only holds approximately and, hence, the results provided by MUSIC deteriorate. We give special attention to the most general setting where an active array with an arbitrary number of transmitters and receivers uses signals of multiple frequencies to image the targets. This setting provides all the possible diversity of information that can be obtained from the illuminations. We give a theorem that shows that MUSIC is robust with respect to additive noise provided that the targets are well separated. The theorem also shows the relevance of using appropriate sets of controlled parameters, such as excitations, to form the images with MUSIC robustly. We present numerical experiments that support our theoretical results.Part of this material is based upon work supported by the National Science Foundation under Grant No. DMS-1439786 while the authors were in residence at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Providence, RI, during the Fall 2017 semester. The work of M Moscoso was partially supported by Spanish grant FIS2016- 77892-R. The work of A Novikov was partially supported by NSF grant DMS-1813943. The work of C Tsogka was partially supported by AFOSR FA9550-17-1-0238

Imaging with highly incomplete and corrupted data

We consider the problem of imaging sparse scenes from a few noisy data using an L1-minimization approach. This problem can be cast as a linear system of the form Ap = b, where A is an N x K measurement matrix. We assume that the dimension of the unknown sparse vector p E Ck is much larger than the dimension of the data vector b E Cn, i.e. K >>N. We provide a theoretical framework that allows us to examine under what conditions the L1-minimization problem admits a solution that is close to the exact one in the presence of noise. Our analysis shows that L1-minimization is not robust for imaging with noisy data when high resolution is required. To improve the performance of L1-minimization we propose to solve instead the augmented linear system [A|C]p = b, where the N = Σ matrix C is a noise collector. It is constructed so as its column vectors provide a frame on which the noise of the data, a vector of dimension N, can be well approximated. Theoretically, the dimension Σ of the noise collector should be eN which would make its use not practical. However, our numerical results illustrate that robust results in the presence of noise can be obtained with a large enough number of columns Σ~10K.Part of this material is based upon work supported by the National Science Foundation under Grant No. DMS-1439786 while the authors were in residence at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Providence, RI, during the Fall 2017 semester. The work of M Moscoso was partially supported by Spanish MICINN grant FIS2016-77892-R. The work of A Novikov was partially supported by NSF grants DMS-1515187, DMS-1813943. The work of C Tsogka was partially supported by AFOSR FA9550-17-1-0238

The noise collector for sparse recovery in high dimensions

The ability to detect sparse signals from noisy, high-dimensional data is a top priority in modern science and engineering. It is well known that a sparse solution of the linear system Alpharho=b0 can be found efficiently with an l1-norm minimization approach if the data are noiseless. However, detection of the signal from data corrupted by noise is still a challenging problem as the solution depends, in general, on a regularization parameter with optimal value that is not easy to choose. We propose an efficient approach that does not require any parameter estimation. We introduce a no-phantom weight tau and the Noise Collector matrix C and solve an augmented system Alpharho+Ceta=b0+e, where e is the noise. We show that the l1-norm minimal solution of this system has zero false discovery rate for any level of noise, with probability that tends to one as the dimension of b0 increases to infinity. We obtain exact support recovery if the noise is not too large and develop a fast Noise Collector algorithm, which makes the computational cost of solving the augmented system comparable with that of the original one. We demonstrate the effectiveness of the method in applications to passive array imaging.The work of M.M. was partially supported by Spanish Ministerio de Ciencia e Innovación Grant FIS2016-77892-R. The work of A.N. was partially supported by NSF Grants DMS-1515187 and DMS-1813943. The work of G.P. was partially supported by Air Force Office of Scientific Research (AFOSR) Grant FA9550-18-1-0519. The work of C.T. was partially supported by AFOSR Grants FA9550-17-1-0238 and FA9550-18-1-0519

Recovering fluorophore location and orientation from lifetimes

In this paper, we study the possibility of using lifetime data to estimate the position and orientation of a fluorescent dipole source within a disordered medium. The vector Foldy-Lax equations are employed to calculate the interaction between the fluorescent source and the scatterers that are modeled as point-scatterers. The numerical experiments demonstrate that if good prior knowledge about the positions of the scatterers is available, the position and orientation of the dipole source can be retrieved from its lifetime data with precision. If there is uncertainty about the positions of the scatterers, the dipole source position can be estimated within the same level of uncertainty.Publicad

Three-dimensional imaging from single-element holographic data

We present a holographic imaging approach for the case in which a single source-detector pair is used to scan a sample. The source-detector pair collects intensity-only data at different frequencies and positions. By using an appropriate illumination strategy, we recover field cross correlations over different frequencies for each scan location. The problem is that these field cross correlations are asynchronized, so they have to be aligned first in order to image coherently. This is the main result of the paper: a simple algorithm to synchronize field cross correlations at different locations. Thus, one can recover full field data up to a global phase that is common to all scan locations. The recovered data are, then, coherent over space and frequency so they can be used to form high-resolution three-dimensional images. Imaging with intensity-only data is therefore as good as coherent imaging with full data. In addition, we use an ℓ1-norm minimization algorithm that promotes the low dimensional structure of the images, allowing for deep high-resolution imaging.The work of MM was partially supported by spanish grant MICINN FIS2016-77892-R. The work of AN was partially supported by NSF DMS-1813943 and AFOSR FA9550-20-1-0026. The work of G. Papanicolaou was partially supported by AFOSR FA9550-18-1-0519. The work of C. Tsogka was partially supported by AFOSR FA9550-17-1-0238 and FA9550-18-1-0519

Optimal variable shape parameter for multiquadric based RBF-FD method

In this follow up paper to our previous study in Bayona et al. (2011) [2], we present a new technique to compute the solution of PDEs with the multiquadric based RBF finite difference method (RBF-FD) using an optimal node dependent variable value of the shape parameter. This optimal value is chosen so that, to leading order, the local approximation error of the RBF-FD formulas is zero. In our previous paper (Bayona et al., 2011) [2] we considered the case of an optimal (constant) value of the shape parameter for all the nodes. Our new results show that, if one allows the shape parameter to be different at each grid point of the domain, one may obtain very significant accuracy improvements with a simple and inexpensive numerical technique. We analyze the same examples studied in Bayona et al. (2011) [2], both with structured and unstructured grids, and compare our new results with those obtained previously. We also find that, if there are a significant number of nodes for which no optimal value of the shape parameter exists, then the improvement in accuracy deteriorates significantly. In those cases, we use generalized multiquadrics as RBFs and choose the exponent of the multiquadric at each node to assure the existence of an optimal variable shape parameter.This work has been supported by Spanish MICINN Grants FIS2010-18473, CSD2010-00011 and by Madrid Autonomous Region grant S2009-1597. M.K. acknowledges Fundacion Caja Madrid for its financial support

A closed-form formula for the RBF-based approximation of the Laplace-Beltrami operator

In this paper we present a method that uses radial basis functions to approximatethe Laplace&-Beltrami operator that allows to solve numerically diffusion (and reaction&-diffusion) equations on smooth, closed surfaces embedded in R3. The novelty of the methodis in a closed-form formula for the Laplace&-Beltrami operator derived in the paper, whichinvolve the normal vector and the curvature at a set of points on the surface of interest.An advantage of the proposed method is that it does not rely on the explicit knowledgeof the surface, which can be simply defined by a set of scattered nodes. In that case, thesurface is represented by a level set function from which we can compute the needed normalvectors and the curvature. The formula for the Laplace&-Beltrami operator is exact for radialbasis functions and it also depends on the first and second derivatives of these functionsat the scattered nodes that define the surface. We analyze the converge of the method andwe present numerical simulations that show its performance. We include an application thatarises in cardiology.This work has been supported by Spanish MICINN Grant FIS2016-77892-R. We thank the anonymous reviewer for his or her careful reading of our manuscript and his or her many insightful comments and suggestions

Quantitative subsurface imaging in strongly scattering media

We present a method to obtain quantitatively accurate images of small obstacles or inhomogeneities situated near the surface of a strongly scattering medium. The method uses time-resolved measurements of backscattered light to form the images. Using the asymptotic solution of the radiative transfer equation for this problem, we determine that the key information content in measurements is modeled by a diffusion approximation that is valid for small source-detector distances, and shallow penetration depths. We simplify this model further by linearizing the effect of the inhomogeneities about the known background optical properties using the Born approximation. The resulting model is used in a two-stage imaging algorithm. First, the spatial location of the inhomogeneities are determined using a modification of the multiple signal classification (MUSIC) method. Using those results, we then determine the quantitative values of the inhomogeneities through a least-squares approximation. We find that this two-stage method is most effective for reconstructing a sequence of one-dimensional images along the penetration depth corresponding to none source-detector separations rather than simultaneously using measurements over several source-detector distances. This method is limited to penetration depths and distances between boundary measurements on the order of the scattering mean-free path. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing AgreementC. Tsogka and A. D. Kim are supported by the Air Force Office of Scientific Research (Grant: FA9550-17-1-0238). P. Gonzalez-Rodriguez and M. Moscoso are supported by the MINECO grant FIS2016-77892-R