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AuthorTempone, Raul (28)Alouini, Mohamed-Slim (11)Bagci, Hakan (7)Moraes, Alvaro (6)Nobile, Fabio (6)View MoreDepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division (54)Applied Mathematics and Computational Science Program (35)Electrical Engineering Program (19)Physical Sciences and Engineering (PSE) Division (7)Biological and Environmental Sciences and Engineering (BESE) Division (3)View MoreJournalEuropean Microscopy Congress 2016: Proceedings (1)PublisherWiley (1)SubjectWireless (11)Bayesian (9)Sampling (9)CEM (7)SDE (6)View MoreTypePoster (60)Year (Issue Date)

2016 (60)

Item AvailabilityOpen Access (59)Metadata Only (1)

Now showing items 51-60 of 60

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Energy Efficient Power Allocation for Cognitive MIMO Channels

Sboui, Lokman; Rezki, Zouheir; Salem, Ahmed Sultan; Alouini, Mohamed-Slim (2016-01-06) [Poster]

Two major issues are facing today s wireless communications evolution: -Spectrum scarcity: Need for more bandwidth. As a solution, the Cognitive Radio (CR) paradigm, where secondary users (unlicensed) share the spectrum with licensed users, was introduced. -Energy consumption and CO2 emission: The ICT produces 2% of global CO2 emission (equivalent to the aviation industry emission). The cellular networks produces 0.2%. As solution energy efficient systems should be designed rather than traditional spectral efficient systems. In this work, an energy efficient power allocation framework based on maximizing the average EE per parallel channel is presented.

Sparse Electromagnetic Imaging Using Nonlinear Landweber Iterations

Desmal, Abdulla; Bagci, Hakan (2016-01-06) [Poster]

Fast Bayesian Optimal Experimental Design for Seismic Source Inversion

Long, Quan; Motamed, Mohammad; Tempone, Raul (2016-01-06) [Poster]

We develop a fast method for optimally designing experiments [1] in the context of statistical seismic source inversion [2]. In particular, we efficiently compute the optimal number and locations of the receivers or seismographs. The seismic source is modeled by a point moment tensor multiplied by a time-dependent function. The parameters include the source location, moment tensor components, and start time and frequency in the time function. The forward problem is modeled by the elastic wave equations. We show that the Hessian of the cost functional, which is usually defined as the square of the weighted L2 norm of the difference between the experimental data and the simulated data, is proportional to the measurement time and the number of receivers. Consequently, the posterior distribution of the parameters, in a Bayesian setting, concentrates around the true parameters, and we can employ Laplace approximation and speed up the estimation of the expected Kullback-Leibler divergence (expected information gain), the optimality criterion in the experimental design procedure. Since the source parameters span several magnitudes, we use a scaling matrix for efficient control of the condition number of the original Hessian matrix. We use a second-order accurate finite difference method to compute the Hessian matrix and either sparse quadrature or Monte Carlo sampling to carry out numerical integration. We demonstrate the efficiency, accuracy, and applicability of our method on a two-dimensional seismic source inversion problem.

Bayesian inference of the heat transfer properties of a wall using experimental data

Iglesias, Marco; Sawlan, Zaid A; Scavino, Marco; Tempone, Raul; Wood, Christopher (2016-01-06) [Poster]

A hierarchical Bayesian inference method is developed to estimate the thermal resistance and volumetric heat capacity of a wall. We apply our methodology to a real case study where measurements are recorded each minute from two temperature probes and two heat flux sensors placed on both sides of a solid brick wall along a period of almost five days. We model the heat transfer through the wall by means of the one-dimensional heat equation with Dirichlet boundary conditions.
The initial/boundary conditions for the temperature are approximated by piecewise linear functions. We assume that temperature and heat flux measurements have independent Gaussian noise and derive the joint likelihood of the wall parameters and the initial/boundary conditions. Under the model assumptions, the boundary conditions are marginalized analytically from the joint likelihood. ApproximatedGaussian posterior distributions for the wall parameters and the initial condition parameter are obtained using the Laplace method, after incorporating the available prior information. The information gain is estimated under different experimental setups, to determine the best allocation of resources.

Multiscale Modeling of Wear Degradation

Moraes, Alvaro; Ruggeri, Fabrizio; Tempone, Raul; Vilanova, Pedro (2016-01-06) [Poster]

Cylinder liners of diesel engines used for marine propulsion are naturally subjected to a wear process, and may fail when their wear exceeds a specified limit. Since failures often represent high economical costs, it is utterly important to predict and avoid them. In this work [4], we model the wear process using a pure jump process. Therefore, the inference goal here is to estimate: the number of possible jumps, its sizes, the coefficients and the shapes of the jump intensities. We propose a multiscale approach for the inference problem that can be seen as an indirect inference scheme. We found that using a Gaussian approximation based on moment expansions, it is possible to accurately estimate the jump intensities and the jump amplitudes. We obtained results equivalent to the state of the art but using a simpler and less expensive approach.

An Efficient Forward-Reverse EM Algorithm for Statistical Inference in Stochastic Reaction Networks

Bayer, Christian; Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro (2016-01-06) [Poster]

In this work [1], we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce an efficient two-phase algorithm in which the first phase is deterministic and it is intended to provide a starting point for the second phase which is the Monte Carlo EM Algorithm.

Bayesian Inference for Linear Parabolic PDEs with Noisy Boundary Conditions

Ruggeri, Fabrizio; Sawlan, Zaid A; Scavino, Marco; Tempone, Raul (2016-01-06) [Poster]

In this work we develop a hierarchical Bayesian setting to infer unknown parameters in initial-boundary value problems (IBVPs) for one-dimensional linear parabolic partial differential equations. Noisy boundary data and known initial condition are assumed. We derive the likelihood function associated with the forward problem, given some measurements of the solution field subject to Gaussian noise. Such function is then analytically marginalized using the linearity of the equation. Gaussian priors have been assumed for the time-dependent Dirichlet boundary values. Our approach is applied to synthetic data for the one-dimensional heat equation model, where the thermal diffusivity is the unknown parameter. We show how to infer the thermal diffusivity parameter when its prior distribution is lognormal or modeled by means of a space-dependent stationary lognormal random field. We use the Laplace method to provide approximated Gaussian posterior distributions for the thermal diffusivity. Expected information gains and predictive posterior densities for observable quantities are numerically estimated for different experimental setups.

Indirect Inference for Stochastic Differential Equations Based on Moment Expansions

Ballesio, Marco; Tempone, Raul; Vilanova, Pedro (2016-01-06) [Poster]

We provide an indirect inference method to estimate the parameters of timehomogeneous scalar diffusion and jump diffusion processes. We obtain a system of ODEs that approximate the time evolution of the first two moments of the process by the approximation of the stochastic model applying a second order Taylor expansion of the SDE s infinitesimal generator in the Dynkin s formula. This method allows a simple and efficient procedure to infer the parameters of such stochastic processes given the data by the maximization of the likelihood of an approximating Gaussian process described by the two moments equations. Finally, we perform numerical experiments for two datasets arising from organic and inorganic fouling deposition phenomena.

Solving inverse problem via non-linear update of PCE coefficients

Litvinenko, Alexander; Matthies, Hermann G.; Rosic, Bojana V.; Zander, Elmar (2016-01-06) [Poster]

Hierarchical matrix approximation of large covariance matrices

Litvinenko, Alexander; Genton, Marc G.; Sun, Ying (2016-01-06) [Poster]

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