1,256 research outputs found
Magnetodielectric behavior in La2CoMnO6 nanoparticles
We have investigated magnetic, dielectric and magnetodielectric properties of
La2CoMnO6 nanoparticles prepared by sol-gel method. Magnetization measurements
revealed two distinct ferromagnetic transitions at 218 K and 135 K that can be
assigned to ordered and disordered magnetic phases of the La2CoMnO6
nanoparticles. Two dielectric relaxations culminating around the magnetic
transitions were observed with a maximum magnetodielectric response reaching
10% and 8% at the respective relaxation peaks measured at 100 kHz under 5T
magnetic field. The dc electrical resistivity followed an insulating behavior
and showed a negative magnetoresistance; there was no noticeable anomaly in
resistivity or magnetoresistance near the magnetic ordering temperatures.
Complex impedance analysis revealed a clear intrinsic contribution to the
magnetodielectric response; however, extrinsic contribution due to
Maxwell-Wagner effect combined with magnetoresistance property dominated the
magnetodielectric effect at high temperatures.Comment: 15 page
Planetary Nebulae with Ultra-Violet Imaging Telescope (UVIT): Far Ultra-violet halo around the Bow Tie nebula (NGC 40)
Context. NGC 40 is a planetary nebula with diffuse X-ray emission, suggesting
an interaction of the high speed wind from WC8 central star (CS) with the
nebula. It shows strong Civ 1550 {\AA} emission that cannot be explained by
thermal processes alone. We present here the first map of this nebula in C IV
emission, using broad band filters on the UVIT.
Aims. To map the hot C IV emitting gas and its correspondence with soft X-ray
(0.3-8 keV) emitting regions, in order to study the shock interaction with the
nebula and the ISM. This also illustrates the potential of UVIT for nebular
studies.
Methods. Morphological study of images of the nebula obtained at an angular
resolution of about 1.3" in four UVIT filter bands that include C IV 1550 {\AA}
and C II] 2326 {\AA} lines and UV continuum. Comparisons with X-ray, optical,
and IR images from literature.
Results. The C II] 2326 {\AA} images show the core of the nebula with two
lobes on either side of CS similar to [N II]. The C IV emission in the core
shows similar morphology and extant as that of diffuse X-ray emission
concentrated in nebular condensations. A surprising UVIT discovery is the
presence of a large faint FUV halo in FUV Filter with {\lambda}eff of 1608
{\AA}. The UV halo is not present in any other UV filter. FUV halo is most
likely due to UV fluorescence emission from the Lyman bands of H2 molecules.
Unlike the optical and IR halo, FUV halo trails predominantly towards
south-east side of the nebular core, opposite to the CS's proper motion
direction.
Conclusions. Morphological similarity of C IV 1550 {\AA} and X-ray emission
in the core suggests that it results mostly from interaction of strong CS wind
with the nebula. The FUV halo in NGC 40 highlights the existence of H2
molecules extensively in the regions even beyond the optical and IR halos.Comment: 4 pages, 5 figures, accepted for publication as a letter in Astronomy
& Astrophysic
CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
3D LiDARs and 2D cameras are increasingly being used alongside each other in
sensor rigs for perception tasks. Before these sensors can be used to gather
meaningful data, however, their extrinsics (and intrinsics) need to be
accurately calibrated, as the performance of the sensor rig is extremely
sensitive to these calibration parameters. A vast majority of existing
calibration techniques require significant amounts of data and/or calibration
targets and human effort, severely impacting their applicability in large-scale
production systems. We address this gap with CalibNet: a self-supervised deep
network capable of automatically estimating the 6-DoF rigid body transformation
between a 3D LiDAR and a 2D camera in real-time. CalibNet alleviates the need
for calibration targets, thereby resulting in significant savings in
calibration efforts. During training, the network only takes as input a LiDAR
point cloud, the corresponding monocular image, and the camera calibration
matrix K. At train time, we do not impose direct supervision (i.e., we do not
directly regress to the calibration parameters, for example). Instead, we train
the network to predict calibration parameters that maximize the geometric and
photometric consistency of the input images and point clouds. CalibNet learns
to iteratively solve the underlying geometric problem and accurately predicts
extrinsic calibration parameters for a wide range of mis-calibrations, without
requiring retraining or domain adaptation. The project page is hosted at
https://epiception.github.io/CalibNetComment: Appeared in the proccedings of the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
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