107,862 research outputs found
At risk of being risky: The relationship between "brain age" under emotional states and risk preference.
Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N=212; 10-25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the "brain age" of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that "brain age" across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts) relates to a group mean difference in risk perception - a pattern exemplified greatest in young-adults (ages 18-21). The results are suggestive of a specified functional brain phenotype that relates to being at "risk to be risky.
Multiuser Switched Diversity Scheduling Schemes
Multiuser switched-diversity scheduling schemes were recently proposed in
order to overcome the heavy feedback requirements of conventional opportunistic
scheduling schemes by applying a threshold-based, distributed, and ordered
scheduling mechanism. The main idea behind these schemes is that slight
reduction in the prospected multiuser diversity gains is an acceptable
trade-off for great savings in terms of required channel-state-information
feedback messages. In this work, we characterize the achievable rate region of
multiuser switched diversity systems and compare it with the rate region of
full feedback multiuser diversity systems. We propose also a novel proportional
fair multiuser switched-based scheduling scheme and we demonstrate that it can
be optimized using a practical and distributed method to obtain the feedback
thresholds. We finally demonstrate by numerical examples that
switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the
ultimate network capacity of full feedback systems in Rayleigh fading
conditions.Comment: Accepted at IEEE Transactions on Communications, to appear 2012,
funded by NPRP grant 08-577-2-241 from QNR
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Technical Design Report for PANDA Electromagnetic Calorimeter (EMC)
This document presents the technical layout and the envisaged performance of the Electromagnetic Calorimeter (EMC) for the
PANDA target spectrometer. The EMC has been designed to meet the physics goals of the PANDA experiment. The performance figures are based on extensive prototype tests and radiation hardness studies. The document shows that the EMC is ready for construction up to the front-end electronics interface
A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor
In this paper we present a new methodology for edge detection in digital
images. The first originality of the proposed method is to consider image
content as a parametric surface. Then, an original parametric local model of
this surface representing image content is proposed. The few parameters
involved in the proposed model are shown to be very sensitive to
discontinuities in surface which correspond to edges in image content. This
naturally leads to the design of an efficient edge detector. Moreover, a
thorough analysis of the proposed model also allows us to explain how these
parameters can be used to obtain edge descriptors such as orientations and
curvatures.
In practice, the proposed methodology offers two main advantages. First, it
has high customization possibilities in order to be adjusted to a wide range of
different problems, from coarse to fine scale edge detection. Second, it is
very robust to blurring process and additive noise. Numerical results are
presented to emphasis these properties and to confirm efficiency of the
proposed method through a comparative study with other edge detectors.Comment: 21 pages, 13 figures and 2 table
Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis
The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving fairing of surfaces is discussed to underline the applicability of the presented approach.
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