Mobile multimedia streaming is an open topic in vehicular environment.Due to the high intermittent links,it has become a critical challenge to deliver high quality video streaming in vehicular networks.In this paper,w...Mobile multimedia streaming is an open topic in vehicular environment.Due to the high intermittent links,it has become a critical challenge to deliver high quality video streaming in vehicular networks.In this paper,we reform the Information Centric Networking(ICN) concept for multimedia delivery in urban vehicular networks.By leveraging the ICN perspective,we highlight that vehicular peers can obtain multimedia chunks via the vehicle-to-cloud(V2C) approach to improve the delivery quality.Based on this,we propose a lightweight multipath selection strategy to guide the network system to adaptively adjust the forwarding means.Extensive simulations show that the proposed solution can optimize the utilization of network paths,lighten network loads as well as avoid wasting resources.展开更多
Recently,the Cooperative Training Algorithm(CTA),a well-known Semi-Supervised Learning(SSL)technique,has garnered significant attention in the field of image classification.However,traditional CTA approaches face chal...Recently,the Cooperative Training Algorithm(CTA),a well-known Semi-Supervised Learning(SSL)technique,has garnered significant attention in the field of image classification.However,traditional CTA approaches face challenges such as high computational complexity and low classification accuracy.To overcome these limitations,we present a novel approach called Weighted fusion based Cooperative Training Algorithm(W-CTA),which leverages the cooperative training technique and unlabeled data to enhance classification performance.Moreover,we introduce the K-means Cooperative Training Algorithm(km-CTA)to prevent the occurrence of local optima during the training phase.Finally,we conduct various experiments to verify the performance of the proposed methods.Experimental results show that W-CTA and km-CTA are effective and efficient on CIFAR-10 dataset.展开更多
基金partially supported by the Fundamental Research Funds for the Central Universities under Grant No.2015JBM009the National Natural Science Foundation of China(NSFC) under Grant 61602030 U1404611,61301081+1 种基金the Project Funded by China Postdoctoral Science Foundation under Grant No.2016T90031,2015M570028 and 2015M580970the Program for Science & Technology Innovation Talents in the University of Henan Province under Grant No.16HASTIT035
文摘Mobile multimedia streaming is an open topic in vehicular environment.Due to the high intermittent links,it has become a critical challenge to deliver high quality video streaming in vehicular networks.In this paper,we reform the Information Centric Networking(ICN) concept for multimedia delivery in urban vehicular networks.By leveraging the ICN perspective,we highlight that vehicular peers can obtain multimedia chunks via the vehicle-to-cloud(V2C) approach to improve the delivery quality.Based on this,we propose a lightweight multipath selection strategy to guide the network system to adaptively adjust the forwarding means.Extensive simulations show that the proposed solution can optimize the utilization of network paths,lighten network loads as well as avoid wasting resources.
基金supported in part by the National Natural Science Foundation of China(NSFC)(Nos.62033010,62102134)in part by the Leading talents of science and technology in the Central Plain of China(No.224200510004)+2 种基金in part by the Key R&D projects in Henan Province,China(No.231111222600)in part by the Aeronautical Science Foundation of China(No.2019460T5001)in part by the Scientific and Technological Innovation Talents of Colleges and Universities in Henan Province,China(No.22HASTIT014).
文摘Recently,the Cooperative Training Algorithm(CTA),a well-known Semi-Supervised Learning(SSL)technique,has garnered significant attention in the field of image classification.However,traditional CTA approaches face challenges such as high computational complexity and low classification accuracy.To overcome these limitations,we present a novel approach called Weighted fusion based Cooperative Training Algorithm(W-CTA),which leverages the cooperative training technique and unlabeled data to enhance classification performance.Moreover,we introduce the K-means Cooperative Training Algorithm(km-CTA)to prevent the occurrence of local optima during the training phase.Finally,we conduct various experiments to verify the performance of the proposed methods.Experimental results show that W-CTA and km-CTA are effective and efficient on CIFAR-10 dataset.
