Mouse Anti-Human INS Hybridoma [DH8D8]

This paper investigates the observability and navigation performance analysis of the integrated navigation system for the onboard operation in urban building forests. INS mechanization with some rangefinders is employed to achieve navigation performance with an urban geographical map. First, presented is a model of the range-inertial sensor integrated navigation system for the purpose of performing the observability analysis in a complex urban environment. Next, it is derived from the analysis formula of the filter observability based on the proposed system and measurement model. In order to examine the validity of the model, we analyzed the relationship between observability and estimation performance through simulation and flight experiments. In addition, a comparative study using the ICP matching based integration is presented. The simulation study employs a simple map environment for an intuitive analysis, where error characteristics are related to the observability rank. Finally, a practical test result using the multi-copter system is presented to address the correlation between observability and navigation performance through a real flight adjacent to tall buildings.

Being a leading online service providing both local search and social networking functions, Foursquare has attracted tens of millions of users all over the world. Understanding the user behavior of Foursquare is helpful to gain insights for location-based social networks (LBSNs). Most of the existing studies focus on a biased subset of users, which cannot give a representative view of the global user base. Meanwhile, although the user-generated content (UGC) is very important to reflect user behavior, most of the existing UGC studies of Foursquare are based on the check-ins. There is a lack of a thorough study on tips, the primary type of UGC on Foursquare. In this article, by crawling and analyzing the global social graph and all published tips, we conduct the first comprehensive user behavior study of all 60+ million Foursquare users around the world. We have made the following three main contributions. First, we have found several unique and undiscovered features of the Foursquare social graph on a global scale, including a moderate level of reciprocity, a small average clustering coefficient, a giant strongly connected component, and a significant community structure. Besides the singletons, most of the Foursquare users are weakly connected with each other. Second, we undertake a thorough investigation according to all published tips on Foursquare. We start from counting the numbers of tips published by different users and then look into the tip contents from the perspectives of tip venues, temporal patterns, and sentiment. Our results provide an informative picture of the tip publishing patterns of Foursquare users. Last but not least, as a practical scenario to help third-party application providers, we propose a supervised machine learning-based approach to predict whether a user is an influential by referring to the profile and UGC, instead of relying on the social connectivity information. Our data-driven evaluation demonstrates that our approach can reach a good prediction performance with an F1-score of 0.87 and an AUC value of 0.88. Our findings provide a systematic view of the behavior of Foursquare users and are constructive for different relevant entities, including LBSN service providers, Internet service providers, and third-party application providers.