Zobrazeno 1 - 10
of 7 434
pro vyhledávání: '"Kumar, Ashish A."'
Autor:
Kumar, Ashish, Behera, Laxmidhar
Publikováno v:
IEEE Robotics & Automation Letters, 2024
In this paper, we present a comprehensive UAV system design to perform the highly complex task of off-centered aerial grasping. This task has several interdisciplinary research challenges which need to be addressed at once. The main design challenges
Externí odkaz:
http://arxiv.org/abs/2410.05738
Autor:
Kumar, Ashish, Behera, Laxmidhar
Publikováno v:
IEEE Robotics & Automation Letters, 2023
In this work, we propose an end-to-end Thrust Microstepping and Decoupled Control (TMDC) of quadrotors. TMDC focuses on precise off-centered aerial grasping of payloads dynamically, which are attached rigidly to the UAV body via a gripper contrary to
Externí odkaz:
http://arxiv.org/abs/2410.05737
Publikováno v:
IEEE Robotics & Automation Letters, 2023
We present an accurate and GPU-accelerated Stereo Visual SLAM design called Jetson-SLAM. It exhibits frame-processing rates above 60FPS on NVIDIA's low-powered 10W Jetson-NX embedded computer and above 200FPS on desktop-grade 200W GPUs, even in stere
Externí odkaz:
http://arxiv.org/abs/2410.04090
Autor:
Kumar, Ashish, Park, Jaesik
In the era of vision Transformers, the recent success of VanillaNet shows the huge potential of simple and concise convolutional neural networks (ConvNets). Where such models mainly focus on runtime, it is also crucial to simultaneously focus on othe
Externí odkaz:
http://arxiv.org/abs/2410.04089
Autor:
Kumar, Ashish, Park, Jaesik
Detection Transformers (DETR) are renowned object detection pipelines, however computationally efficient multiscale detection using DETR is still challenging. In this paper, we propose a Cross-Resolution Encoding-Decoding (CRED) mechanism that allows
Externí odkaz:
http://arxiv.org/abs/2410.04088
Autor:
Kumar, Ashish, Toshniwal, Durga
Hierarchical Text Classification (HTC) aims to categorize text data based on a structured label hierarchy, resulting in predicted labels forming a sub-hierarchy tree. The semantics of the text should align with the semantics of the labels in this sub
Externí odkaz:
http://arxiv.org/abs/2409.00788
This paper investigates neuron dropout as a post-processing bias mitigation for deep neural networks (DNNs). Neural-driven software solutions are increasingly applied in socially critical domains with significant fairness implications. While neural n
Externí odkaz:
http://arxiv.org/abs/2407.04268
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Channel pruning approaches for convolutional neural networks (ConvNets) deactivate the channels, statically or dynamically, and require special implementation. In addition, channel squeezing in representative ConvNets is carried out via 1x1 convoluti
Externí odkaz:
http://arxiv.org/abs/2406.10935
Autor:
Kumar, Ashish, Behera, Laxmidhar
Autonomous aerial harvesting is a highly complex problem because it requires numerous interdisciplinary algorithms to be executed on mini low-powered computing devices. Object detection is one such algorithm that is compute-hungry. In this context, w
Externí odkaz:
http://arxiv.org/abs/2402.14591
Publikováno v:
Journal of Quality in Maintenance Engineering, 2024, Vol. 30, Issue 4, pp. 584-604.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JQME-02-2024-0016
