Indian Institute of Technology (BHU) Varanasi

About: Indian Institute of Technology (BHU) Varanasi is a education organization based out in Varanasi, India. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 2309 authors who have published 4540 publications receiving 41418 citations.

SymPy: Symbolic Computing in Python

Abstract: SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.

A review on hybrid nanofluids: Recent research, development and applications

Abstract: Researches on the nanofluids have been increased very rapidly over the past decade. In spite of some inconsistency in the reported results and insufficient understanding of the mechanism of the heat transfer in nanofluids, it has been emerged as a promising heat transfer fluid. In the continuation of nanofluids research, the researchers have also tried to use hybrid nanofluid recently, which is engineered by suspending dissimilar nanoparticles either in mixture or composite form. The idea of using hybrid nanofluids is to further improvement of heat transfer and pressure drop characteristics by trade-off between advantages and disadvantages of individual suspension, attributed to good aspect ratio, better thermal network and synergistic effect of nanomaterials. This review summarizes recent researches on synthesis, thermophysical properties, heat transfer and pressure drop characteristics, possible applications and challenges of hybrid nanofluids. Review showed that proper hybridization may make the hybrid nanofluids very promising for heat transfer enhancement, however, lot of research works is still needed in the fields of preparation and stability, characterization and applications to overcome the challenges.

Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

Abstract: Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner. Recent approaches to single view depth estimation explore the possibility of learning without full supervision via minimizing photometric error. In this paper, we explore the use of stereo sequences for learning depth and visual odometry. The use of stereo sequences enables the use of both spatial (between left-right pairs) and temporal (forward backward) photometric warp error, and constrains the scene depth and camera motion to be in a common, real-world scale. At test time our framework is able to estimate single view depth and two-view odometry from a monocular sequence. We also show how we can improve on a standard photometric warp loss by considering a warp of deep features. We show through extensive experiments that: (i) jointly training for single view depth and visual odometry improves depth prediction because of the additional constraint imposed on depths and achieves competitive results for visual odometry; (ii) deep feature-based warping loss improves upon simple photometric warp loss for both single view depth estimation and visual odometry. Our method outperforms existing learning based methods on the KITTI driving dataset in both tasks. The source code is available at https://github.com/Huangying-Zhan/Depth-VO-Feat.