Localization is an important issue for many Ocean Internet of Things(OIoT)applications.The existing localization algorithms use localized beacon nodes to assist localization.
行走是日常生活中最常见的行为之一，它的状态可以反映人的身份、健康等重要信息。而行走的速度、方向和步数等细粒度的信息可以为室内追踪，步态分析、老人看护等情景感知场景提供重要参考，因此对行走的感知受到了研究人员的广泛关注。
随着传感器的普及，智慧城市、普适计算等领域应用不断涌现，对时序数据处理的需求也在不断增长。时序数据中反复出现的高度相似的模式称为主题模式。时序数据的主题模式蕴含有了大量的信息，对主题模式的识别是时序数据处理的重要分支领域。现有主题模式识别算法无法根据特定应用或领域的知识，指定主题模式识别的偏好，从而难以发现对分析领域问题最具价值的模式。针对这一问题，本文给出了一种可以根据领域偏好定义子序列相似性的
Smart watches have become one of the most representative devices in wearable devices because of their unique advantages such as integration,portability,reliability,stability,universality and low envir
User portrait is the virtual representation of one person,which is a model based on a series of data.
Crowd counting is the process of monitoring the number of people in a certain area,which is crucial in traffic supervision,etc.
In the era of mobile Internet,a large number of APP users pay more attention to product experience and express their usage and suggestions through comments.
The prediction of transnational population migration is of great value in the fields of social analysis,policy formulation and migration planning.
The rapid development of the modern e-commerce industry has led to a sharp increase in the demand for parcel delivery,and it is difficult to balance the main contradiction between parcel dispatching s
目的 地预测是许多新兴的基于位置的应用的基本任务,对目的地预测的常用方法是基于历史轨迹进行预测,而且大多集中于下一位置的预测,很少有工作关注面向特定时间间隔的预测。在实际的位置预测场景中,随着时间的推移,人们对目标的位置信息往往有新发现或新线索,会对目标的信息进行更新。比如,在追踪目标的过程中,目击者或道路摄像头会记录目标在某个时刻出现在的区域。这些更新的信息对位置预测是十分重要的信息。为了实现面