1,541 research outputs found
Depth Sensitivity and Source-Detector Separations for Near Infrared Spectroscopy Based on the Colin27 Brain Template
Understanding the spatial and depth sensitivity of non-invasive near-infrared spectroscopy (NIRS) measurements to brain tissue–i.e., near-infrared neuromonitoring (NIN) – is essential for designing experiments as well as interpreting research findings. However, a thorough characterization of such sensitivity in realistic head models has remained unavailable. In this study, we conducted 3,555 Monte Carlo (MC) simulations to densely cover the scalp of a well-characterized, adult male template brain (Colin27). We sought to evaluate: (i) the spatial sensitivity profile of NIRS to brain tissue as a function of source-detector separation, (ii) the NIRS sensitivity to brain tissue as a function of depth in this realistic and complex head model, and (iii) the effect of NIRS instrument sensitivity on detecting brain activation. We found that increasing the source-detector (SD) separation from 20 to 65 mm provides monotonic increases in sensitivity to brain tissue. For every 10 mm increase in SD separation (up to ∼45 mm), sensitivity to gray matter increased an additional 4%. Our analyses also demonstrate that sensitivity in depth (S) decreases exponentially, with a “rule-of-thumb” formula S = 0.75*0.85depth. Thus, while the depth sensitivity of NIRS is not strictly limited, NIN signals in adult humans are strongly biased towards the outermost 10–15 mm of intracranial space. These general results, along with the detailed quantitation of sensitivity estimates around the head, can provide detailed guidance for interpreting the likely sources of NIRS signals, as well as help NIRS investigators design and plan better NIRS experiments, head probes and instruments
Spatial Locality of Galaxy Correlation Function in Phase Space: Samples from the 2MASS Extended Source Catalog
We analyze the statistical properties and dynamical implications of galaxy
distributions in phase space for samples selected from the 2MASS Extended
Source Catalog. The galaxy distribution is decomposed into modes which describe the number density perturbations of galaxies in phase
space cell given by scale band to and spatial
range to . In the nonlinear regime,
is highly non-Gaussian. We find, however, that the
correlations between and are always
very weak if the spatial ranges (, ) and
(, ) don't overlap. This feature is due to
the fact that the spatial locality of the initial perturbations is memorized
during hierarchical clustering. The highly spatial locality of the 2MASS galaxy
correlations is a strong evidence for the initial perturbations of the cosmic
mass field being spatially localized, and therefore, consistent with a Gaussian
initial perturbations on scales as small as about 0.1 h Mpc. Moreover,
the 2MASS galaxy spatial locality indicates that the relationship between
density perturbations of galaxies and the underlying dark matter should be
localized in phase space. That is, for a structure consisting of perturbations
on scales from to , the nonlocal range in the relation
between galaxies and dark matter should {\it not} be larger than . The stochasticity and nonlocality of the bias
relation between galaxies and dark matter fields should be no more than the
allowed range given by the uncertainty relation .Comment: 27 pages, 9 figures, accepted by Ap
Conductivity Imaging from Internal Measurements with Mixed Least-Squares Deep Neural Networks
In this work we develop a novel approach using deep neural networks to
reconstruct the conductivity distribution in elliptic problems from one
internal measurement. The approach is based on a mixed reformulation of the
governing equation and utilizes the standard least-squares objective to
approximate the conductivity and flux simultaneously, with deep neural networks
as ansatz functions. We provide a thorough analysis of the neural network
approximations for both continuous and empirical losses, including rigorous
error estimates that are explicit in terms of the noise level, various penalty
parameters and neural network architectural parameters (depth, width and
parameter bound). We also provide extensive numerical experiments in two- and
multi-dimensions to illustrate distinct features of the approach, e.g.,
excellent stability with respect to data noise and capability of solving
high-dimensional problems.Comment: 28 pages. 12 figure
Experimental Investigation of Longitudinal Space-Time Correlations of the Velocity Field in Turbulent Rayleigh-B\'{e}nard Convection
We report an experimental investigation of the longitudinal space-time
cross-correlation function of the velocity field, , in a cylindrical
turbulent Rayleigh-B\'{e}nard convection cell using the particle image
velocimetry (PIV) technique. We show that while the Taylor's frozen-flow
hypothesis does not hold in turbulent thermal convection, the recent elliptic
model advanced for turbulent shear flows [He & Zhang, \emph{Phys. Rev. E}
\textbf{73}, 055303(R) (2006)] is valid for the present velocity field for all
over the cell, i.e., the isocorrelation contours of the measured
have a shape of elliptical curves and hence can be related to
via with and
being two characteristic velocities. We further show that the fitted is
proportional to the mean velocity of the flow, but the values of are
larger than the theoretical predictions. Specifically, we focus on two
representative regions in the cell: the region near the cell sidewall and the
cell's central region. It is found that and are approximately
the same near the sidewall, while at cell center.Comment: 16 pages, 15 figures, submitted to J. Fluid Mec
A simulated study on the performance of diesel engine with ethanol-diesel blend fuel
This paper describes the simulated study on atomization, wall-film formation, combustion and emission forming process of ethanol-diesel blend fuels in a high speed light duty diesel engine. The result shows that increased ethanol volume percentage of the blend fuels could improve atomization and reduce wall-film formation. However, in the meanwhile, with the increased ethanol volume percentage, low heat values of blend fuels decrease, while both total heat releases and cylinder pressures drop. By adding codes into the FIRE software, the NOx and soot formation region mass fractions are outputted. The simulated results display a good correlation with the NOx and soot formation. Besides, the NOx, soot and CO emissions decrease with the increased ethanol volume percentage. The power output of engine penalize, while energy utilization of blend fuels improve and combustion noise reduce, owing to the increased ethanol volume percentage
Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback
The downlink channel covariance matrix (CCM) acquisition is the key step for
the practical performance of massive multiple-input and multiple-output (MIMO)
systems, including beamforming, channel tracking, and user scheduling. However,
this task is challenging in the popular frequency division duplex massive MIMO
systems with Type I codebook due to the limited channel information feedback.
In this paper, we propose a novel formulation that leverages the structure of
the codebook and feedback values for an accurate estimation of the downlink
CCM. Then, we design a cutting plane algorithm to consecutively shrink the
feasible set containing the downlink CCM, enabled by the careful design of
pilot weighting matrices. Theoretical analysis shows that as the number of
communication rounds increases, the proposed cutting plane algorithm can
recover the ground-truth CCM. Numerical results are presented to demonstrate
the superior performance of the proposed algorithm over the existing benchmark
in CCM reconstruction
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