Sleeping scheduling has been widely employed in target tracking due to its energy conservation. However, the randomness of target’s trajectory makes it difficult to implement with accuracy and real time guarantee. We...
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With the rapid development of the Internet, Mobile Crowdsensing System (MCS) is widely used in various fields. Because of the insufficient number of users’ participation and the insufficient amount of uploaded sensin...
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In this paper, a radio-over-fiber distributed antenna system (ROF-DAS) and application of wireless positioning are proposed. The modified Medium Access Control (MAC) frame, optical switch array, ROF network based on s...
ISBN:
(数字)9781728143903
ISBN:
(纸本)9781728143910
In this paper, a radio-over-fiber distributed antenna system (ROF-DAS) and application of wireless positioning are proposed. The modified Medium Access Control (MAC) frame, optical switch array, ROF network based on star topology and Reflective Semiconductor Optical Amplifier (RSOA) are employed to design the system. Based on the structure, wireless positioning could be achieved by Time Division Multiplexing-Time Difference of Arrival (TDM-TDOA) technology combined with Chan assisted Taylor algorithm. The quality of signal transmission is evaluated by Q Factor, and the positioning accuracy is performed in terms of the antennas amount using the positioning algorithm.
Community search is to explore valuable target community structure from a large social network. In real community, every point has a geographic location information, and many edges are uncertain. Such a network is cal...
Community search is to explore valuable target community structure from a large social network. In real community, every point has a geographic location information, and many edges are uncertain. Such a network is called spatial uncertain network. In order to meet the existing needs, this paper studies the community search in the spatial uncertain network so that the searched communities can be relatively close in the geographical location and the relationship between each other is also very close. Based on the spatial uncertain graph, this paper proposes K-R-τ community, and proposes a linear algorithm and a two-dimensional algorithm for community search in the spatial uncertain network. A large number of experiments are carried out on several real datasets, and the results show that the algorithm proposed in this paper is effective and accurate.
Research on the optimization and parallelization of the MRF network community detection algorithm for a specific network is carried out in this paper. Firstly, the principle of the existing algorithm is expounded, the...
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Influence maximization is one of the key research problems in social networks due to its wide applications like spread of ideas and viral marketing of products. Most of the existing work focus on social networks conta...
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Recognizing human complex activities has become an essential topic in pervasive computing research area. With the growing popularity of mobile phones, more and more studies have been dedicated to identifying human com...
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Influence maximization is one of the key research problems in social networks due to its wide applications like spread of ideas and viral marketing of products. Most of the existing work focus on social networks conta...
Influence maximization is one of the key research problems in social networks due to its wide applications like spread of ideas and viral marketing of products. Most of the existing work focus on social networks containing only positive relationships(e.g. friend or trust) between users, but in reality social networks containing both positive and negative relationships(e.g. foe or distrust). Ignoring the negative relations may lead to over-estimation of positive influence in practical applications. Thus, in this paper, we study influence maximization problem with negative effects(NIM). To address the NIM problem, we use the polarity Independent Cascade(IC-P) diffusion model which extends the standard Independent Cascade(IC) model with negative *** we propose the positive influence maximization algorithm(PIM) and negative influence maximization(NIM) problem. We prove that influence function of the NIM problem is monotonic and submodular, so we propose a CELF based algorithm(CELFIM) to solve it. Experiments results show that our algorithm has matching influence spread compared with greedy algorithm and achieves several orders of magnitude time improvement.
Recognizing human complex activities has become an essential topic in pervasive computing research area. With the growing popularity of mobile phones, more and more studies have been dedicated to identifying human com...
Recognizing human complex activities has become an essential topic in pervasive computing research area. With the growing popularity of mobile phones, more and more studies have been dedicated to identifying human complex activities using mobile phones in recent years. However, previous works often restrain the position and orientation of cell phones which limit the applicability of their methods. To overcome this limitation, we propose a novel position-irrelevant activities identification method named PSHCAR, which efficiently utilize information from multiple sensors on smartphones. Moreover,besides commonly-used features such as accelerometer and gyroscope,PSHCAR also employ the knowledge about scenes of activities, which is helpful but ignored by previous works, to identify complex activities of mobile phone users. Comparative experiments show that our method performs better than several strong baselines on the task of human complex activities recognition. In conclusion, our method achieves state-of-the-art performance without any limitation on position or orientation of mobile phones.
Probabilistic tracking algorithms typically using linear structure to update the learning *** linear structure is not appropriate for long-term robust tracking as the occlusion and other challe
ISBN:
(纸本)9781509053643;9781509053636
Probabilistic tracking algorithms typically using linear structure to update the learning *** linear structure is not appropriate for long-term robust tracking as the occlusion and other challe
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