36,651 research outputs found
Low-Complexity Sub-band Digital Predistortion for Spurious Emission Suppression in Noncontiguous Spectrum Access
Noncontiguous transmission schemes combined with high power-efficiency
requirements pose big challenges for radio transmitter and power amplifier (PA)
design and implementation. Due to the nonlinear nature of the PA, severe
unwanted emissions can occur, which can potentially interfere with neighboring
channel signals or even desensitize the own receiver in frequency division
duplexing (FDD) transceivers. In this article, to suppress such unwanted
emissions, a low-complexity sub-band DPD solution, specifically tailored for
spectrally noncontiguous transmission schemes in low-cost devices, is proposed.
The proposed technique aims at mitigating only the selected spurious
intermodulation distortion components at the PA output, hence allowing for
substantially reduced processing complexity compared to classical linearization
solutions. Furthermore, novel decorrelation based parameter learning solutions
are also proposed and formulated, which offer reduced computing complexity in
parameter estimation as well as the ability to track time-varying features
adaptively. Comprehensive simulation and RF measurement results are provided,
using a commercial LTE-Advanced mobile PA, to evaluate and validate the
effectiveness of the proposed solution in real world scenarios. The obtained
results demonstrate that highly efficient spurious component suppression can be
obtained using the proposed solutions
Mesoscopic simulation of diffusive contaminant spreading in gas flows at low pressure
Many modern production and measurement facilities incorporate multiphase
systems at low pressures. In this region of flows at small, non-zero Knudsen-
and low Mach numbers the classical mesoscopic Monte Carlo methods become
increasingly numerically costly. To increase the numerical efficiency of
simulations hybrid models are promising. In this contribution, we propose a
novel efficient simulation approach for the simulation of two phase flows with
a large concentration imbalance in a low pressure environment in the low
intermediate Knudsen regime. Our hybrid model comprises a lattice-Boltzmann
method corrected for the lower intermediate Kn regime proposed by Zhang et al.
for the simulation of an ambient flow field. A coupled event-driven
Monte-Carlo-style Boltzmann solver is employed to describe particles of a
second species of low concentration. In order to evaluate the model, standard
diffusivity and diffusion advection systems are considered.Comment: 9 pages, 8 figure
Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection
Hierarchies, such as the tree of shapes, are popular representations for
image simplification and segmentation thanks to their multiscale structures.
Selecting meaningful level lines (boundaries of shapes) yields to simplify
image while preserving intact salient structures. Many image simplification and
segmentation methods are driven by the optimization of an energy functional,
for instance the celebrated Mumford-Shah functional. In this paper, we propose
an efficient approach to hierarchical image simplification and segmentation
based on the minimization of the piecewise-constant Mumford-Shah functional.
This method conforms to the current trend that consists in producing
hierarchical results rather than a unique partition. Contrary to classical
approaches which compute optimal hierarchical segmentations from an input
hierarchy of segmentations, we rely on the tree of shapes, a unique and
well-defined representation equivalent to the image. Simply put, we compute for
each level line of the image an attribute function that characterizes its
persistence under the energy minimization. Then we stack the level lines from
meaningless ones to salient ones through a saliency map based on extinction
values defined on the tree-based shape space. Qualitative illustrations and
quantitative evaluation on Weizmann segmentation evaluation database
demonstrate the state-of-the-art performance of our method.Comment: Pattern Recognition Letters, Elsevier, 201
Scalable Object Detection using Deep Neural Networks
Deep convolutional neural networks have recently achieved state-of-the-art
performance on a number of image recognition benchmarks, including the ImageNet
Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on
the localization sub-task was a network that predicts a single bounding box and
a confidence score for each object category in the image. Such a model captures
the whole-image context around the objects but cannot handle multiple instances
of the same object in the image without naively replicating the number of
outputs for each instance. In this work, we propose a saliency-inspired neural
network model for detection, which predicts a set of class-agnostic bounding
boxes along with a single score for each box, corresponding to its likelihood
of containing any object of interest. The model naturally handles a variable
number of instances for each class and allows for cross-class generalization at
the highest levels of the network. We are able to obtain competitive
recognition performance on VOC2007 and ILSVRC2012, while using only the top few
predicted locations in each image and a small number of neural network
evaluations
A conjugate gradient minimisation approach to generating holographic traps for ultracold atoms
Direct minimisation of a cost function can in principle provide a versatile
and highly controllable route to computational hologram generation. However, to
date iterative Fourier transform algorithms have been predominantly used. Here
we show that the careful design of cost functions, combined with numerically
efficient conjugate gradient minimisation, establishes a practical method for
the generation of holograms for a wide range of target light distributions.
This results in a guided optimisation process, with a crucial advantage
illustrated by the ability to circumvent optical vortex formation during
hologram calculation. We demonstrate the implementation of the conjugate
gradient method for both discrete and continuous intensity distributions and
discuss its applicability to optical trapping of ultracold atoms.Comment: 11 pages, 4 figure
Overlapping resonances in the control of intramolecular vibrational redistribution
Coherent control of bound state processes via the interfering overlapping
resonances scenario [Christopher et al., J. Chem. Phys. 123, 064313 (2006)] is
developed to control intramolecular vibrational redistribution (IVR). The
approach is applied to the flow of population between bonds in a model of
chaotic OCS vibrational dynamics, showing the ability to significantly alter
the extent and rate of IVR by varying quantum interference contributions.Comment: 10 pages, 7 figure
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