Int. J. Digit. Multim. Broadcast.最新文献_第4页

A Scheduling Method of Cross-Layers Optimization of Polling Weight for AOS Multiplexing 一种面向AOS复用的轮询权跨层优化调度方法

Int. J. Digit. Multim. Broadcast. Pub Date : 2019-03-24 DOI: 10.1155/2019/2560623

Yuntao Zhao, Bo Bo, Yongxin Feng

引用次数: 1

Emerging Technologies, Infrastructures, and Applications for Video Streaming over Future Communication Networks 未来通信网络视频流的新兴技术、基础设施和应用

Int. J. Digit. Multim. Broadcast. Pub Date : 2019-02-25 DOI: 10.1155/2019/1294380

Yuanlong Cao, Yuming Fang, Ji-Yong Wu, W. Quan, Kai Wang

引用次数: 0

Loss-Aware CMT-Based Multipathing Scheme for Efficient Data Delivery to Heterogeneous Wireless Networks 异构无线网络中基于丢失感知cmt的高效数据传输多路径方案

Int. J. Digit. Multim. Broadcast. Pub Date : 2019-02-20 DOI: 10.1155/2019/9474057

Qinghua Liu, Fenfen Ke, Zhihua Liu, Jiaxin Zeng

{"title":"Loss-Aware CMT-Based Multipathing Scheme for Efficient Data Delivery to Heterogeneous Wireless Networks","authors":"Qinghua Liu, Fenfen Ke, Zhihua Liu, Jiaxin Zeng","doi":"10.1155/2019/9474057","DOIUrl":"https://doi.org/10.1155/2019/9474057","url":null,"abstract":"With the rapid development of wireless networks, multiple network interfaces are gradually being designed into more and more mobile devices. When it comes to data delivery, Stream Control Transmission Protocol (SCTP)-based Concurrent Multipath Transfer (CMT) has proven to be quite useful solution for multiple home networks, and it could become the key transport protocol for the next generation of wireless communications. The CMT delay caused by data rearrangement has been noticed by researchers, but they have seldom considered the frequent occurrence of packet loss that occurs in the high-loss networks. In this paper, we proposed an original loss-aware solution for multipath concurrent transmission (CMT-LA) that achieves the following goals: (1) identifying packet loss on all paths, (2) distributing packets adaptively across multiple available paths according to their packet loss and loss variation, and (3) maintaining the features of bandwidth aggregation and parallel transmission of CMT while improving the throughput performance. The results of our simulations showed that the proposed CMT-LA reduces reordering delay and unnecessary fast retransmissions, thereby demonstrating that CMT-LA is a more efficient data delivery scheme than classic CMT.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124589870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Dynamic Soft Real-Time Scheduling with Preemption Threshold for Streaming Media 基于抢占阈值的流媒体动态软实时调度

Int. J. Digit. Multim. Broadcast. Pub Date : 2019-01-01 DOI: 10.1155/2019/5284968

Wenle Wang, Y. Wang, Jiangyan Dai, Zhonghua Cao

引用次数: 3

QoS Prediction for Neighbor Selection via Deep Transfer Collaborative Filtering in Video Streaming P2P Networks 视频流P2P网络中基于深度传输协同过滤的邻居选择QoS预测

Int. J. Digit. Multim. Broadcast. Pub Date : 2019-01-01 DOI: 10.1155/2019/1326831

Wenming Ma, Qian Zhang, Chunxiao Mu, Meng Zhang

{"title":"QoS Prediction for Neighbor Selection via Deep Transfer Collaborative Filtering in Video Streaming P2P Networks","authors":"Wenming Ma, Qian Zhang, Chunxiao Mu, Meng Zhang","doi":"10.1155/2019/1326831","DOIUrl":"https://doi.org/10.1155/2019/1326831","url":null,"abstract":"To expand the server capacity and reduce the bandwidth, P2P technologies are widely used in video streaming systems in recent years. Each client in the P2P streaming network should select a group of neighbors by evaluating the QoS of the other nodes. Unfortunately, the size of video streaming P2P network is usually very large, and evaluating the QoS of all the other nodes is resource-consuming. An attractive way is that we can predict the QoS of a node by taking advantage of the past usage experiences of a small number of the other clients who have evaluated this node. Therefore, collaborative filtering (CF) methods could be used for QoS evaluation to select neighbors. However, we might use different QoS properties for different video streaming policies. If a new video steaming policy needs to evaluate a new QoS property, but the historical experiences include very few evaluation data for this QoS property, CF methods would incur severe overfitting issues, and the clients then might get unsatisfied recommendation results. In this paper, we proposed a novel neural collaborative filtering method based on transfer learning, which can evaluate the QoS with few historical data by evaluating the other different QoS properties with rich historical data. We conduct our experiments on a large real-world dataset, the QoS values of which are obtained from 339 clients evaluating on the other 5825 clients. The comprehensive experimental studies show that our approach offers higher prediction accuracy than the traditional collaborative filtering approaches.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133421706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

An Improved Artificial Bee Colony Algorithm in LoRa Wireless Communication System for Efficient Multimedia Transmission 一种改进的LoRa无线通信系统中人工蜂群算法的高效多媒体传输

Int. J. Digit. Multim. Broadcast. Pub Date : 2018-12-03 DOI: 10.1155/2018/9678694

Yan Song, Lidong Huang, Pan-Feng Xu, Lili Li, Min-yan Song, Yue Long

引用次数: 4

PO-MPTCP: Priorities-Oriented Data Scheduler for Multimedia Multipathing Services 多媒体多路径服务的面向优先级的数据调度程序

Int. J. Digit. Multim. Broadcast. Pub Date : 2018-12-02 DOI: 10.1155/2018/1413026

Wei-Kun Lu, Dandan Yu, Minghe Huang, Bin Guo

{"title":"PO-MPTCP: Priorities-Oriented Data Scheduler for Multimedia Multipathing Services","authors":"Wei-Kun Lu, Dandan Yu, Minghe Huang, Bin Guo","doi":"10.1155/2018/1413026","DOIUrl":"https://doi.org/10.1155/2018/1413026","url":null,"abstract":"With the diversified wireless network access technology and large-scale equipment of multinetwork interface devices expanding, network transmission performance for multi-homed terminals has been widely concerned by academic circles. More and more scholars have paid attention to Multipath Transmission Control Protocol (MPTCP) as one of the representative methods for studying path transmission performance. However, their studies ignore the impact of dynamic network environments on data transmission performance and seldom consider the priority of data transmission. Undoubtedly, not prioritizing packets will have a dramatic effect on the users experience in the heterogeneous networks. In this paper, we propose a novel priority-aware streaming media multipath data scheduler mechanism (PO-MPTCP) to achieve the following goals: (1) detecting the priority of all streaming media data; (2) achieving multiattribute-aware path evaluation and switching mechanism; (3) introducing a path-quality priority-driven data distribution mechanism to improve streaming multipath transmission performance. The simulation experiment shows that PO-MPTCP proposed by this paper improves the transmission performance of streaming media and reduces the transmission delay. For the result of simulation experiment, it is easy for us to find that PO-MPTCP is more efficient in data delivery than the standard MPTCP mechanism.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128050310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Novel Method of Complexity Metric for Object-Oriented Software 面向对象软件复杂性度量的新方法

Int. J. Digit. Multim. Broadcast. Pub Date : 2018-11-01 DOI: 10.1155/2018/7624768

Tong Yi, Chun Fang

引用次数: 3

Video Data Integrity Verification Method Based on Full Homomorphic Encryption in Cloud System 云系统中基于全同态加密的视频数据完整性验证方法

Int. J. Digit. Multim. Broadcast. Pub Date : 2018-10-22 DOI: 10.1155/2018/7543875

Ruoshui Liu, Jianghui Liu, Jingjie Zhang, Moli Zhang

引用次数: 4

Small Object Detection with Multiscale Features 基于多尺度特征的小目标检测

Int. J. Digit. Multim. Broadcast. Pub Date : 2018-09-30 DOI: 10.1155/2018/4546896

Guoxiong Hu, Zhong Yang, Lei Hu, Li Huang, Jiaming Han

{"title":"Small Object Detection with Multiscale Features","authors":"Guoxiong Hu, Zhong Yang, Lei Hu, Li Huang, Jiaming Han","doi":"10.1155/2018/4546896","DOIUrl":"https://doi.org/10.1155/2018/4546896","url":null,"abstract":"The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. The detection models can get better results for big object. However, those models fail to detect small objects that have low resolution and are greatly influenced by noise because the features after repeated convolution operations of existing models do not fully represent the essential characteristics of the small objects. In this paper, we can achieve good detection accuracy by extracting the features at different convolution levels of the object and using the multiscale features to detect small objects. For our detection model, we extract the features of the image from their third, fourth, and 5th convolutions, respectively, and then these three scales features are concatenated into a one-dimensional vector. The vector is used to classify objects by classifiers and locate position information of objects by regression of bounding box. Through testing, the detection accuracy of our model for small objects is 11% higher than the state-of-the-art models. In addition, we also used the model to detect aircraft in remote sensing images and achieved good results.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125541273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 56