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