摘要：
Crowd density estimation is one of the critical issues in social activities. The traditional solution to this problem is to leverage video surveillance to monitor a crowd. However, this is not accurate for crowd density estimation because it is still hard to identify people from background. In the past few years, more and more people use Wi-Fi enabled smartphones. Smartphones can send Wi-Fi request packets periodically, even when they are not connected to access points. This gives another promising solution to the crowd density estimation even for the public environment. In this paper, we first develop a Wi-Fi monitor detection that can capture smartphone passive Wi-Fi signal information including MAC address and received signal strength indicator. Then, we propose a positioning algorithm based on smartphone passive Wi-Fi probe and a dynamic fingerprint management strategy. In real-world public social activities, a person may have zero, one, two, or multiple smartphones with variant Wi-Fi signals. Therefore, we design a method of computing the probability of a user generating one Wi-Fi signal to identify people population. Finally, we propose a crowd density estimation solution based on Wi-Fi probe packets positioning algorithm. Experiments were conducted in an indoor laboratory class and three public social activities, clearly demonstrated that the proposed solution can effectively and accurately estimate crowd density.
会议论文集名称：
International Conference on Systems and Informatics
关键词：
information islands;data structure;Python;information resources in-depth processing;visualization
摘要：
The problem of "information islands" has become a bottleneck that restricts information construction and resource sharing in universities. Information islands cause waste of information resources. The data that has been used is only used in digital displays and cannot be used for management and decision-making. Students cannot absorb in-depth and the information obtained is not comprehensive. As a result, the information can only be used within the school. It can neither share with others nor use the resources of others to update and add to their information. A large number of established education networks are unable to function. Even the more networks are built, the greater waste are existed. Through the visualization of the data network under the campus environment, the free flow of data and information is an important way to solve information islands. This paper proposes an algorithmic theory of data structure visualization and provides an information flow project under the Python scene. It attempts to solve the problems of information islands in the campus environment and helps students to better obtain information and digest information.
摘要：
Recently, computational Grids have proven to be a good solution for processing large-scale, computation intensive problems. However, the heterogeneity, dynamics of resources and diversity of applications requirements have always been important factors affecting their performance. In response to these challenges, this work first builds a Grid job scheduling architecture that can dynamically monitor Grid computing center resources and make corresponding scheduling decisions. Second, a Grid job model is proposed to describe the application requirements. Third, this paper studies the characteristics of commercial interconnection networks used in Grids and forecast job transmission time. Fourth, this paper proposes an application-aware job scheduling mechanism (AJSM) that includes periodic scheduling flow and a heuristic application-aware deadline constraint job scheduling algorithm. The rigorous performance evaluation results clearly demonstrate that the proposed application-aware job scheduling mechanism can successful schedule more Grid jobs than the existing algorithms. For successful scheduled jobs, our proposed AJSM method is the best algorithm for job average processing time and makespan.
