摘要 :
Location-based services have become an important part of the daily life.Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering en...
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Location-based services have become an important part of the daily life.Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering environment.In this paper,a single-site multiple-input multiple-output(MIMO)orthogonal frequency division multiplexing(OFDM)system is modeled,from which an angle delay channel power matrix(ADCPM)is extracted.Considering the changing environment,auto encoders are used to generate new fingerprints based on ADCPM fingerprints to improve the robustness of the fingerprints.When the scattering environment has changed beyond a certain extent,the robustness will not be able to make up for the positioning error.Under this circumstance,an updating of the fingerprint database is imperative.A new fingerprint database updating algorithm which combines a new clustering method and an updating rule based on probability is proposed.Simulation results show the desirable performance of the proposed methods.
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摘要 :
Constraint pushing techniques have been developed for mining frequent patterns and association rules. However, multiple constraints cannot be handled with existing techniques in frequent pattern mining. In this paper, a new algori...
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Constraint pushing techniques have been developed for mining frequent patterns and association rules. However, multiple constraints cannot be handled with existing techniques in frequent pattern mining. In this paper, a new algorithm MCFMC (mining complete set of frequent itemsets with multiple constraints) is introduced. The algorithm takes advantage of the fact that a convertible constraint can be pushed into mining algorithm to reduce mining research spaces. By using a sample database, the algorithm develops techniques which select an optimal method based on a sample database to convert multiple constraints into multiple convertible constraints, disjoined by conjunction and/or, and then partition these constraints into two parts. One part is pushed deep inside the mining process to reduce the research spaces for frequent itemsets, the other part that cannot be pushed in algorithm is used to filter the complete set of frequent itemsets and get the final result. Results from our detailed experiment show the feasibility and effectiveness of the algorithm.
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