Wormhole Geometry and Three-Dimensional Embedding in Extended Symmetric Teleparallel Gravity

In the present manuscript, we study traversable wormhole solutions in the background of extended symmetric teleparallel gravity with matter coupling. With the anisotropic matter distribution we probe the wormhole geometry for two different gravity models. Primarily, we consider the linear model $f(Q,T) =Q + 2 \, \xi \,T$. Firstly, we presume a logarithmic form of shape function and analyze the scenario for different redshift functions. Secondly, for a specific form of energy density, we derive a shape function and note its satisfying behavior. Next, for the non-linear model $f(Q,T) = Q + \alpha ,Q^2 + \beta ,T$ and a specific shape function we examine the wormhole solution. Further, with the aid of embedding diagrams, we interpreted the geometry of wormhole models. Finally, we conclude results.Comment: New Astronomy published versio

Scene Classification in Remote Sensing Images using Dynamic Kernels

Classification of scenes across multi-sensor remote sensing images with different spatial, spectral, temporal resolutions involves identification of variable length spatial patterns of objects in a scene. So, it necessitates the use of local representations from different regions of a scene in order to comprehend the scene formation. In this paper, we propose a dynamic kernel based representation to handle the patterns of variable lengths in the scenes of remote sensing images. These kernels help to assimilate spatial variability captured using convolutional features in a Gaussian mixture model. The statistics of GMM facilitate the dynamic kernels in preserving the local spatial similarities while handling the changes in spatial content globally within the same scene. The efficacy of the proposed method using two variants of the dynamic kernels is demonstrated on three benchmark scene classification datasets, namely, UCM Land Use (21 classes), Aerial image dataset (30 classes), and NWPU-RESISC45 (45 classes). Our experiments show that the mean interval kernel is better discriminative as it makes use of first and second-order statistics of GMM. © 2021 IEEE

mSODANet: A network for multi-scale object detection in aerial images using hierarchical dilated convolutions

The object detection in aerial images is one of the most commonly used tasks in the wide-range of computer vision applications. However, the object detection is more challenging due to the following issues: (a) the pixel occupancy vary among the different scales of objects, (b) the distribution of objects is not uniform in aerial images, (c) the appearance of an object varies with different view-points and illumination conditions, and (d) the number of objects, even though they belong to same type, vary across the images. To address these issues, we propose a novel network for multi-scale object detection in aerial images using hierarchical dilated convolutions, called as mSODANet. In particular, we probe hierarchical dilated network using parallel dilated convolutions to learn the contextual information of different types of objects at multiple scales and multiple field-of-views. The introduced hierarchical dilated network captures the visual information of aerial image more effectively and enhances the detection capability of the model. Further, the extensive experiments conducted on three challenging publicly available datasets, i.e., Visdrone2019, DOTA (OBB & HBB), NWPU VHR-10, demonstrate the effectiveness of the proposed mSODANet and achieve the state-of-the-art performance on all three datasets. © 2022 Elsevier Lt