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QoE Management for 5G New Radio 5G新无线电的QoE管理
Pub Date : 2021-10-11 DOI: 10.12142/ZTECOM.202103008
Zhang Man, Li Dapeng, Liu Zhuang, Gao Yin
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引用次数: 2
Semiconductor Optical Amplifier and Gain Chip Used in Wavelength Tunable Lasers 用于波长可调谐激光器的半导体光放大器和增益芯片
Pub Date : 2021-10-11 DOI: 10.12142/ZTECOM.202103010
S. Kenji, Z. Xiaobo
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引用次数: 2
A Survey of Intelligent Sensing Technologies in Autonomous Driving 自动驾驶中的智能传感技术综述
Pub Date : 2021-10-11 DOI: 10.12142/ZTECOM.202103007
Shao Hong, Xie Daxiong, Huang Yihua
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引用次数: 3
Using UAV to Detect Truth for Clean Data Collection in Sensor‑Cloud Systems 在传感器云系统中使用无人机检测真相以收集干净的数据
Pub Date : 2021-10-11 DOI: 10.12142/ZTECOM.202103005
Liu Xiuxian, Li Zhetao, Ouyang Yan, Duan Haohua, Xiang Liyao
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引用次数: 6
Feedback‑Aware Anomaly Detection Through Logs for Large‑Scale Software Systems 大型软件系统的日志反馈感知异常检测
Pub Date : 2021-10-11 DOI: 10.12142/ZTECOM.202103011
Han Jing, Jia Tong, Wu Yifan, Hou Chuanjia, Li Ying
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引用次数: 2
Artificial Intelligence Rehabilitation Evaluation and Training System for Degeneration of Joint Disease 关节疾病退行性变的人工智能康复评估与训练系统
Pub Date : 2021-10-11 DOI: 10.12142/ZTECOM.202103006
Liu Weichen, Shen Mengqi, Zhang Anda, Chen Yiting, Z. Wenqiang
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引用次数: 0
BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing BPPF:用于移动群组感知的双边隐私保护框架
Pub Date : 2021-07-27 DOI: 10.12142/ZTECOM.202102004
Liu Junyu, Yongjian Yang, Wang En
With the emergence of mobile crowdsensing (MCS), merchants can use their mo⁃ bile devices to collect data that customers are interested in. Now there are many mobile crowdsensing platforms in the market, such as Gigwalk, Uber and Checkpoint, which pub⁃ lish and select the right workers to complete the task of some specific locations (for example, taking photos to collect the price of goods in a shopping mall). In mobile crowdsensing, in or⁃ der to select the right workers, the platform needs the actual location information of workers and tasks, which poses a risk to the location privacy of workers and tasks. In this paper, we study privacy protection in MCS. The main challenge is to assign the most suitable worker to a task without knowing the task and the actual location of the worker. We propose a bilateral privacy protection framework based on matrix multiplication, which can protect the location privacy between the task and the worker, and keep their relative distance unchanged.
随着移动众包(MCS)的出现,商家可以使用他们的移动设备来收集客户感兴趣的数据。现在市场上有许多移动众包平台,如Gigwalk、优步和Checkpoint,哪些酒吧和选择合适的员工来完成某些特定地点的任务(例如,在购物中心拍照收集商品价格)。在移动众筹中,为了选择合适的工作人员,平台需要工作人员和任务的实际位置信息,这对工作人员和工作任务的位置隐私构成了风险。在本文中,我们研究了MCS中的隐私保护。主要的挑战是在不知道任务和工人的实际位置的情况下,为任务分配最合适的工人。我们提出了一种基于矩阵乘法的双边隐私保护框架,该框架可以保护任务和工作人员之间的位置隐私,并保持他们的相对距离不变。
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引用次数: 0
RecCac: Recommendation-Empowered Cooperative Edge Caching for Internet of Things RecCac:物联网的推荐协作边缘缓存
Pub Date : 2021-07-27 DOI: 10.12142/ZTECOM.202102002
Suning Han, Xiuhua Li, Sun Chuan, Xiaofei Wang, Victor C. M. Leung
Edge caching is an emerging technology for supporting massive content access in mobile edge networks to address rapidly growing Internet of Things (IoT) services and con⁃ tent applications. However, the edge server is limited with the computation/storage capacity, which causes a low cache hit. Cooperative edge caching jointing neighbor edge servers is re⁃ garded as a promising technique to improve cache hit and reduce congestion of the net⁃ works. Further, recommender systems can provide personalized content services to meet us⁃ er’s requirements in the entertainment-oriented mobile networks. Therefore, we investigate the issue of joint cooperative edge caching and recommender systems to achieve additional cache gains by the soft caching framework. To measure the cache profits, the optimization problem is formulated as a 0–1 Integer Linear Programming (ILP), which is NP-hard. Spe⁃ cifically, the method of processing content requests is defined as server actions, we deter⁃ mine the server actions to maximize the quality of experience (QoE). We propose a cachefriendly heuristic algorithm to solve it. Simulation results demonstrate that the proposed framework has superior performance in improving the QoE.
边缘缓存是一种新兴技术,用于支持移动边缘网络中的大规模内容访问,以解决快速增长的物联网(IoT)服务和内容应用。然而,边缘服务器受到计算/存储容量的限制,这导致缓存命中率较低。连接相邻边缘服务器的协作边缘缓存被认为是一种很有前途的提高缓存命中率和减少网络拥塞的技术。此外,推荐系统可以提供个性化的内容服务,以满足我们在面向娱乐的移动网络中的需求。因此,我们研究了联合协作边缘缓存和推荐系统的问题,以通过软缓存框架实现额外的缓存增益。为了衡量缓存利润,优化问题被公式化为0–1整数线性规划(ILP),这是NP困难的。具体而言,处理内容请求的方法被定义为服务器行为,我们确定服务器行为以最大限度地提高体验质量(QoE)。我们提出了一种缓存友好的启发式算法来解决这个问题。仿真结果表明,该框架在提高QoE方面具有优越的性能。
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引用次数: 0
A Novel De-Embedding Technique of Packaged GaN Transistors 一种新型的GaN封装晶体管去嵌入技术
Pub Date : 2021-07-27 DOI: 10.12142/ZTECOM.202102010
Wei Xinghui, Chen Xiaofan, Chen Wenhua, Zhou Junmin
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引用次数: 3
Differentially Authorized Deduplication System Based on Blockchain 基于区块链的差异授权重复数据删除系统
Pub Date : 2021-07-27 DOI: 10.12142/ZTECOM.202102009
Z. Tian, Li Hui, Yang Xin, Wang Han, Z. Ming, Guo Haisheng, Wang Dezheng
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引用次数: 1
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