190,957 research outputs found
The infrared imaging spectrograph (IRIS) for TMT: sensitivities and simulations
We present sensitivity estimates for point and resolved astronomical sources
for the current design of the InfraRed Imaging Spectrograph (IRIS) on the
future Thirty Meter Telescope (TMT). IRIS, with TMT's adaptive optics system,
will achieve unprecedented point source sensitivities in the near-infrared
(0.84 - 2.45 {\mu}m) when compared to systems on current 8-10m ground based
telescopes. The IRIS imager, in 5 hours of total integration, will be able to
perform a few percent photometry on 26 - 29 magnitude (AB) point sources in the
near-infrared broadband filters (Z, Y, J, H, K). The integral field
spectrograph, with a range of scales and filters, will achieve good
signal-to-noise on 22 - 26 magnitude (AB) point sources with a spectral
resolution of R=4,000 in 5 hours of total integration time. We also present
simulated 3D IRIS data of resolved high-redshift star forming galaxies (1 < z <
5), illustrating the extraordinary potential of this instrument to probe the
dynamics, assembly, and chemical abundances of galaxies in the early universe.
With its finest spatial scales, IRIS will be able to study luminous, massive,
high-redshift star forming galaxies (star formation rates ~ 10 - 100 M yr-1) at
~100 pc resolution. Utilizing the coarsest spatial scales, IRIS will be able to
observe fainter, less massive high-redshift galaxies, with integrated star
formation rates less than 1 M yr-1, yielding a factor of 3 to 10 gain in
sensitivity compared to current integral field spectrographs. The combination
of both fine and coarse spatial scales with the diffraction-limit of the TMT
will significantly advance our understanding of early galaxy formation
processes and their subsequent evolution into presentday galaxies.Comment: SPIE Astronomical Instrumentation 201
Characterization of Surface Karst Using LiDAR and Field Traverses, Fort Hood Military Installation, Coryell County, Texas
The Fort Hood Military Installation is a karst landscape that has been significantly altered for training exercises that include heavy vehicle maneuvers and simulated combat. Traditional karst surveys are often time-consuming and require extensive field analyses to adequately characterize large areas. Bias is given to areas that are most easily accessible and false negatives are common. Previous studies conducted in the eastern and western portion of the base have understated the abundance and spatial distribution of karst, particularly in the western portion.
This study used field traverses and 0.5-meter Light Detection and Ranging (LiDAR) data to characterize surface karst depressions, create a set of new and refined filters and buffering mechanisms to remove non-karst depressions, and determine the accuracy of the model. LiDAR data was used to create a digital elevation model (DEM), which was used to extract areas with localized depressions at a sub-meter scale. In order to isolate features that were formed through karst processes, data were processed through a series of filters with parameters based on features found during traverse surveys.
Field verifications to assess the accuracy of the LiDAR conducted with previous filters and buffering mechanisms had an overall accuracy of 77.3%, indicating this model overestimated the number of features in the study area. To assess the accuracy of the new filters and buffering parameters, field verified features from a random point survey and a remote verification survey of features within each of the filters was conducted. The overall accuracy was 84.1%, indicating that the new filters and buffering parameters improved depression characterization and the ability to determine those features that were influenced by natural and anthropogenic processes
A second-order PHD filter with mean and variance in target number
The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters
are popular solutions to the multi-target tracking problem due to their low
complexity and ability to estimate the number and states of targets in
cluttered environments. The PHD filter propagates the first-order moment (i.e.
mean) of the number of targets while the CPHD propagates the cardinality
distribution in the number of targets, albeit for a greater computational cost.
Introducing the Panjer point process, this paper proposes a second-order PHD
filter, propagating the second-order moment (i.e. variance) of the number of
targets alongside its mean. The resulting algorithm is more versatile in the
modelling choices than the PHD filter, and its computational cost is
significantly lower compared to the CPHD filter. The paper compares the three
filters in statistical simulations which demonstrate that the proposed filter
reacts more quickly to changes in the number of targets, i.e., target births
and target deaths, than the CPHD filter. In addition, a new statistic for
multi-object filters is introduced in order to study the correlation between
the estimated number of targets in different regions of the state space, and
propose a quantitative analysis of the spooky effect for the three filters
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Spectral filtering as a method of visualising and removing striped artefacts in digital elevation data
Spectral filtering was compared with traditional mean spatial filters to assess their ability to identify and remove striped artefacts in digital elevation data. The techniques were applied to two datasets: a 100 m contour derived digital elevation model (DEM) of southern Norway and a 2 m LiDAR DSM of the Lake District, UK. Both datasets contained diagonal data artefacts that were found to propagate into subsequent terrain analysis. Spectral filtering used fast Fourier transformation (FFT) frequency data to identify these data artefacts in both datasets. These were removed from the data by applying a cut filter, prior to the inverse transform. Spectral filtering showed considerable advantages over mean spatial filters, when both the absolute and spatial distribution of elevation changes made were examined. Elevation changes from the spectral filtering were restricted to frequencies removed by the cut filter, were small in magnitude and consequently avoided any global smoothing. Spectral filtering was found to avoid the smoothing of kernel based data editing, and provided a more informative measure of data artefacts present in the FFT frequency domain. Artefacts were found to be heterogeneous through the surfaces, a result of their strong correlations with spatially autocorrelated variables: landcover and landsurface geometry. Spectral filtering performed better on the 100 m DEM, where signal and artefact were clearly distinguishable in the frequency data. Spectrally filtered digital elevation datasets were found to provide a superior and more precise representation of the landsurface and be a more appropriate dataset for any subsequent geomorphological applications
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Cataloging the neuronal cell types that comprise circuitry of individual
brain regions is a major goal of modern neuroscience and the BRAIN initiative.
Single-cell RNA sequencing can now be used to measure the gene expression
profiles of individual neurons and to categorize neurons based on their gene
expression profiles. While the single-cell techniques are extremely powerful
and hold great promise, they are currently still labor intensive, have a high
cost per cell, and, most importantly, do not provide information on spatial
distribution of cell types in specific regions of the brain. We propose a
complementary approach that uses computational methods to infer the cell types
and their gene expression profiles through analysis of brain-wide single-cell
resolution in situ hybridization (ISH) imagery contained in the Allen Brain
Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH
image for each gene and model it as a spatial point process mixture, whose
mixture weights are given by the cell types which express that gene. By fitting
a point process mixture model jointly to the ISH images, we infer both the
spatial point process distribution for each cell type and their gene expression
profile. We validate our predictions of cell type-specific gene expression
profiles using single cell RNA sequencing data, recently published for the
mouse somatosensory cortex. Jointly with the gene expression profiles, cell
features such as cell size, orientation, intensity and local density level are
inferred per cell type
Regional variance for multi-object filtering
Recent progress in multi-object filtering has led to algorithms that compute
the first-order moment of multi-object distributions based on sensor
measurements. The number of targets in arbitrarily selected regions can be
estimated using the first-order moment. In this work, we introduce explicit
formulae for the computation of the second-order statistic on the target
number. The proposed concept of regional variance quantifies the level of
confidence on target number estimates in arbitrary regions and facilitates
information-based decisions. We provide algorithms for its computation for the
Probability Hypothesis Density (PHD) and the Cardinalized Probability
Hypothesis Density (CPHD) filters. We demonstrate the behaviour of the regional
statistics through simulation examples
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