3,407 research outputs found
Adaptive Optics for Astronomy
Adaptive Optics is a prime example of how progress in observational astronomy
can be driven by technological developments. At many observatories it is now
considered to be part of a standard instrumentation suite, enabling
ground-based telescopes to reach the diffraction limit and thus providing
spatial resolution superior to that achievable from space with current or
planned satellites. In this review we consider adaptive optics from the
astrophysical perspective. We show that adaptive optics has led to important
advances in our understanding of a multitude of astrophysical processes, and
describe how the requirements from science applications are now driving the
development of the next generation of novel adaptive optics techniques.Comment: to appear in ARA&A vol 50, 201
Estimation of the Distribution of Random Parameters in Discrete Time Abstract Parabolic Systems with Unbounded Input and Output: Approximation and Convergence
A finite dimensional abstract approximation and convergence theory is
developed for estimation of the distribution of random parameters in infinite
dimensional discrete time linear systems with dynamics described by regularly
dissipative operators and involving, in general, unbounded input and output
operators. By taking expectations, the system is re-cast as an equivalent
abstract parabolic system in a Gelfand triple of Bochner spaces wherein the
random parameters become new space-like variables. Estimating their
distribution is now analogous to estimating a spatially varying coefficient in
a standard deterministic parabolic system. The estimation problems are
approximated by a sequence of finite dimensional problems. Convergence is
established using a state space-varying version of the Trotter-Kato semigroup
approximation theorem. Numerical results for a number of examples involving the
estimation of exponential families of densities for random parameters in a
diffusion equation with boundary input and output are presented and discussed
Statistical properties of acoustic emission signals from metal cutting processes
Acoustic Emission (AE) data from single point turning machining are analysed
in this paper in order to gain a greater insight of the signal statistical
properties for Tool Condition Monitoring (TCM) applications. A statistical
analysis of the time series data amplitude and root mean square (RMS) value at
various tool wear levels are performed, �nding that ageing features can
be revealed in all cases from the observed experimental histograms. In
particular, AE data amplitudes are shown to be distributed with a power-law
behaviour above a cross-over value. An analytic model for the RMS values
probability density function (pdf) is obtained resorting to the Jaynes' maximum
entropy principle (MEp); novel technique of constraining the modelling function
under few fractional moments, instead of a greater amount of ordinary moments,
leads to well-tailored functions for experimental histograms.Comment: 16 pages, 7 figure
Estimating the Distribution of Random Parameters in a Diffusion Equation Forward Model for a Transdermal Alcohol Biosensor
We estimate the distribution of random parameters in a distributed parameter
model with unbounded input and output for the transdermal transport of ethanol
in humans. The model takes the form of a diffusion equation with the input
being the blood alcohol concentration and the output being the transdermal
alcohol concentration. Our approach is based on the idea of reformulating the
underlying dynamical system in such a way that the random parameters are now
treated as additional space variables. When the distribution to be estimated is
assumed to be defined in terms of a joint density, estimating the distribution
is equivalent to estimating the diffusivity in a multi-dimensional diffusion
equation and thus well-established finite dimensional approximation schemes,
functional analytic based convergence arguments, optimization techniques, and
computational methods may all be employed. We use our technique to estimate a
bivariate normal distribution based on data for multiple drinking episodes from
a single subject.Comment: 10 page
Cell Detection by Functional Inverse Diffusion and Non-negative Group SparsityPart I: Modeling and Inverse Problems
In this two-part paper, we present a novel framework and methodology to
analyze data from certain image-based biochemical assays, e.g., ELISPOT and
Fluorospot assays. In this first part, we start by presenting a physical
partial differential equations (PDE) model up to image acquisition for these
biochemical assays. Then, we use the PDEs' Green function to derive a novel
parametrization of the acquired images. This parametrization allows us to
propose a functional optimization problem to address inverse diffusion. In
particular, we propose a non-negative group-sparsity regularized optimization
problem with the goal of localizing and characterizing the biological cells
involved in the said assays. We continue by proposing a suitable discretization
scheme that enables both the generation of synthetic data and implementable
algorithms to address inverse diffusion. We end Part I by providing a
preliminary comparison between the results of our methodology and an expert
human labeler on real data. Part II is devoted to providing an accelerated
proximal gradient algorithm to solve the proposed problem and to the empirical
validation of our methodology.Comment: published, 15 page
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