1,199 research outputs found
A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling
In this paper, we present a novel statistical model, (GG-Rician) distribution, for the
characterization of synthetic aperture radar (SAR) images. Since accurate
statistical models lead to better results in applications such as target
tracking, classification, or despeckling, characterizing SAR images of various
scenes including urban, sea surface, or agricultural, is essential. The
proposed statistical model is based on the Rician distribution to model the
amplitude of a complex SAR signal, the in-phase and quadrature components of
which are assumed to be generalized-Gaussian distributed. The proposed
amplitude GG-Rician model is further extended to cover the intensity SAR
signals. In the experimental analysis, the GG-Rician model is investigated for
amplitude and intensity SAR images of various frequency bands and scenes in
comparison to state-of-the-art statistical models that include ,
Weibull, Gamma, and Lognormal. In order to decide on the most suitable model,
statistical significance analysis via Kullback-Leibler divergence and
Kolmogorov-Smirnov statistics are performed. The results demonstrate the
superior performance and flexibility of the proposed model for all frequency
bands and scenes and its applicability on both amplitude and intensity SAR
images.Comment: 20 Pages, 9 figures, 8 table
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
In this work, we investigate the value of uncertainty modeling in 3D
super-resolution with convolutional neural networks (CNNs). Deep learning has
shown success in a plethora of medical image transformation problems, such as
super-resolution (SR) and image synthesis. However, the highly ill-posed nature
of such problems results in inevitable ambiguity in the learning of networks.
We propose to account for intrinsic uncertainty through a per-patch
heteroscedastic noise model and for parameter uncertainty through approximate
Bayesian inference in the form of variational dropout. We show that the
combined benefits of both lead to the state-of-the-art performance SR of
diffusion MR brain images in terms of errors compared to ground truth. We
further show that the reduced error scores produce tangible benefits in
downstream tractography. In addition, the probabilistic nature of the methods
naturally confers a mechanism to quantify uncertainty over the super-resolved
output. We demonstrate through experiments on both healthy and pathological
brains the potential utility of such an uncertainty measure in the risk
assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201
Changes in the phenolic content and free radical-scavenging activity of vacuum packed walnut kernels during storage
In this study, the effects of storage temperature, O2 permeability of packaging materials and variety on phenolic content and free radical-scavenging activity of vacuum-packaged walnut kernels were studied over a 12 months storage period. Methyl gallate (23.70 . 93.75 mg/kg), ellagic acid (137.95 . 569.22 mg/kg), and an ellagic acid pentoside (270.59 . 637.17 mg ellagic acid equivalent/kg) were identified in walnut varieties. While a slight decrease in the amount of ellagic acid was observed during 12 months storage, decreases in the amount of ellagic acid pentoside, total phenolic content and free radical-scavenging activity were severe. The present study concluded that it is possible to protect the phenolic content and antiradical activity of walnut kernels by packaging in Polyamide/Polyethylene laminate pouches having an oxygen permeability lower than 63.40±0.40 (mL/m2/24h at 23°C) under vacuum at 20°C up to twelve months
Correlations between Elastic, Calorimetric, and Polar Properties of Ferroelectric PbSc0.5Ta0.5O3 (PST)
Calorimetric, elastic, and polar properties of ferrolectric lead scandium tantalate PbSc0.5Ta0.5O3 (PST) with 65% cation
order have been investigated in the vicinity of the paraelectric-ferroelectric transition at Ttrans = 295K. Comparison of
temperature dependencies of the excess specific heat and elastic properties indicate that both anomalies stem from ther-
mal fluctuations of order parameters in three dimensions. These fluctuations are consistent with tweed microstructure.
This transition is driven by several coupled thermodynamic order parameters, as evidenced by a strongly non-linear
scaling of the excess entropy with the squared ferroelectric polarization.National Natural Science Foundation of China (51850410520, 51320105014 and 51621063
Graphene and Related Materials for the Internet of Bio-Nano Things
Internet of Bio-Nano Things (IoBNT) is a transformative communication
framework, characterized by heterogeneous networks comprising both biological
entities and artificial micro/nano-scale devices, so-called Bio-Nano Things
(BNTs), interfaced with conventional communication networks for enabling
innovative biomedical and environmental applications. Realizing the potential
of IoBNT requires the development of new and unconventional communication
technologies, such as molecular communications, as well as the corresponding
transceivers, bio-cyber interfacing technologies connecting the biochemical
domain of IoBNT to the electromagnetic domain of conventional networks, and
miniaturized energy harvesting and storage components for the continuous power
supply to BNTs. Graphene and related materials (GRMs) exhibit exceptional
electrical, optical, biochemical, and mechanical properties, rendering them
ideal candidates for addressing the challenges posed by IoBNT. This perspective
article highlights recent advancements in GRM-based device technologies that
are promising for implementing the core components of IoBNT. By identifying the
unique opportunities afforded by GRMs and aligning them with the practical
challenges associated with IoBNT, particularly in the materials domain, our aim
is to accelerate the transition of envisaged IoBNT applications from
theoretical concepts to practical implementations, while also uncovering new
application areas for GRMs
Integrable Hierarchies and Information Measures
In this paper we investigate integrable models from the perspective of
information theory, exhibiting various connections. We begin by showing that
compressible hydrodynamics for a one-dimesional isentropic fluid, with an
appropriately motivated information theoretic extension, is described by a
general nonlinear Schrodinger (NLS) equation. Depending on the choice of the
enthalpy function, one obtains the cubic NLS or other modified NLS equations
that have applications in various fields. Next, by considering the integrable
hierarchy associated with the NLS model, we propose higher order information
measures which include the Fisher measure as their first member. The lowest
members of the hiearchy are shown to be included in the expansion of a
regularized Kullback-Leibler measure while, on the other hand, a suitable
combination of the NLS hierarchy leads to a Wootters type measure related to a
NLS equation with a relativistic dispersion relation. Finally, through our
approach, we are led to construct an integrable semi-relativistic NLS equation.Comment: 11 page
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