Zobrazeno 1 - 10
of 494
pro vyhledávání: '"Shi, Zhiguo"'
As three-dimensional acquisition technologies like LiDAR cameras advance, the need for efficient transmission of 3D point clouds is becoming increasingly important. In this paper, we present a novel semantic communication (SemCom) approach for effici
Externí odkaz:
http://arxiv.org/abs/2409.03319
In this paper, we propose a semantic communication approach based on probabilistic graphical model (PGM). The proposed approach involves constructing a PGM from a training dataset, which is then shared as common knowledge between the transmitter and
Externí odkaz:
http://arxiv.org/abs/2408.04499
Time-Sensitive Networking (TSN) serves as a one-size-fits-all solution for mixed-criticality communication, in which flow scheduling is vital to guarantee real-time transmissions. Traditional approaches statically assign priorities to flows based on
Externí odkaz:
http://arxiv.org/abs/2407.00987
Despite their exceptional performance on various tasks after fine-tuning, pre-trained language models (PLMs) face significant challenges due to growing privacy concerns with data in centralized training methods. We consider federated learning (FL) to
Externí odkaz:
http://arxiv.org/abs/2404.18848
While existing studies have highlighted the advantages of deep learning (DL)-based joint source-channel coding (JSCC) schemes in enhancing transmission efficiency, they often overlook the crucial aspect of resource management during the deployment ph
Externí odkaz:
http://arxiv.org/abs/2403.20198
This paper addresses the problem of group target tracking (GTT), wherein multiple closely spaced targets within a group pose a coordinated motion. To improve the tracking performance, the labeled random finite sets (LRFSs) theory is adopted, and this
Externí odkaz:
http://arxiv.org/abs/2403.13562
Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images. These promptable models exhibit denoising abilities for imprecise prompt inputs, such
Externí odkaz:
http://arxiv.org/abs/2403.04194
Autor:
Chen, Yuhao, Yan, Yuxuan, Yang, Qianqian, Shu, Yuanchao, He, Shibo, Shi, Zhiguo, Chen, Jiming
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers have proposed partitioning a la
Externí odkaz:
http://arxiv.org/abs/2311.05827
Autor:
Yin, Xunzhao, Qian, Yu, Vardar, Alptekin, Gunther, Marcel, Muller, Franz, Laleni, Nellie, Zhao, Zijian, Jiang, Zhouhang, Shi, Zhiguo, Shi, Yiyu, Gong, Xiao, Zhuo, Cheng, Kampfe, Thomas, Ni, Kai
Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications, including logistical planning, resource allocation, chip design, drug explorations, and more. Due to their critical significance and the inability of
Externí odkaz:
http://arxiv.org/abs/2309.13853
In recent years, semantic communication has been a popular research topic for its superiority in communication efficiency. As semantic communication relies on deep learning to extract meaning from raw messages, it is vulnerable to attacks targeting d
Externí odkaz:
http://arxiv.org/abs/2308.04304