125,447 research outputs found
Inter-Pixel Crosstalk in Teledyne Imaging Sensors (TIS) H4RG-10 Detectors
CMOS-hybrid arrays have recently surfaced as competitive optical detectors
for use in ground- and space-based astronomy. One source of error in these
detectors that does not appear in more traditional CCD arrays is the
inter-pixel capacitance component of crosstalk. In this paper we use a single
pixel reset method to model inter-pixel capacitance (IPC). We combine this IPC
model with a model for charge diffusion to estimate the total crosstalk on H4RG
arrays. Finally, we compare our model results to Fe55 data obtained using an
astrometric camera built to test the H4RG-B0 generation detectors.Comment: Accepted to Applied Optics 03-26-1
Agonistic behavior of captive saltwater crocodile, crocodylus porosus in Kota Tinggi, Johor
Agonistic behavior in Crocodylus porosus is well known in the wild, but the available data regarding this behavior among the captive individuals especially in a farm setting is rather limited. Studying the aggressive behavior of C. porosus in captivity is important because the data obtained may contribute for conservation and the safety for handlers and visitors. Thus, this study focuses on C. porosus in captivity to describe systematically the agonistic behaviour of C. porosus in relation to feeding time, daytime or night and density per pool. This study was carried out for 35 days in two different ponds. The data was analysed using Pearson’s chi-square analysis to see the relationship between categorical factors. The study shows that C. porosus was more aggressive during daylight, feeding time and non-feeding time in breeding enclosure (Pond C, stock density =0.0369 crocodiles/m2) as compared to non-breeding pond (Pond B, stock density =0.3317 crocodiles/m2) where it is only aggressive during the nighttime. Pond C shows the higher domination in the value of aggression in feeding and non-feeding time where it is related to its function as breeding ground. Chi-square analysis shows that there is no significant difference between ponds (p=0.47, χ2= 2.541, df= 3), thus, there is no relationship between categorical factors. The aggressive behaviour of C. porosus is important for the farm management to evaluate the risk in future for the translocation process and conservation of C. porosus generally
Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of Hybrid Diffusion Imaging based on BFOR Signal Basis
We propose a large deformation diffeomorphic metric mapping algorithm to
align multiple b-value diffusion weighted imaging (mDWI) data, specifically
acquired via hybrid diffusion imaging (HYDI), denoted as LDDMM-HYDI. We then
propose a Bayesian model for estimating the white matter atlas from HYDIs. We
adopt the work given in Hosseinbor et al. (2012) and represent the q-space
diffusion signal with the Bessel Fourier orientation reconstruction (BFOR)
signal basis. The BFOR framework provides the representation of mDWI in the
q-space and thus reduces memory requirement. In addition, since the BFOR signal
basis is orthonormal, the L2 norm that quantifies the differences in the
q-space signals of any two mDWI datasets can be easily computed as the sum of
the squared differences in the BFOR expansion coefficients. In this work, we
show that the reorientation of the -space signal due to spatial
transformation can be easily defined on the BFOR signal basis. We incorporate
the BFOR signal basis into the LDDMM framework and derive the gradient descent
algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally,
we extend the previous Bayesian atlas estimation framework for scalar-valued
images to HYDIs and derive the expectation-maximization algorithm for solving
the HYDI atlas estimation problem. Using real HYDI datasets, we show the
Bayesian model generates the white matter atlas with anatomical details.
Moreover, we show that it is important to consider the variation of mDWI
reorientation due to a small change in diffeomorphic transformation in the
LDDMM-HYDI optimization and to incorporate the full information of HYDI for
aligning mDWI
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