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Wang J.H.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | Zhao Y.C.,Tianjin University of Technology
Advanced Materials Research | Year: 2011

In this paper, a novel blind separation approach using wavelet and cross-wavelet is presented. This method extends the separate technology from time-frequency domain to time-scale domain. The simulation showed that this method is suitable for dealing with non-stationary signal. © 2011 Trans Tech Publications. Source


Zhu K.,Tianjin University of Technology | Xiao Y.,Tianjin University of Technology | Xiao Y.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | Ai P.,Tianjin University of Technology | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

The ever growing popularity of smart mobile devices and rapid advent of wireless technology have given rise to a new class of advertising system, i.e., mobile advertisement recommender system. The traditional internet advertising systems have largely ignored the fact that users interact with the system within a particular “context”. In this paper, we implemented a mobile advertisement recommender prototype system called MARS. MARS captures different user’s contextual information to improve recommendation results. The demonstration shows that MARS makes advertisement recommendation more effectively. © Springer International Publishing Switzerland 2015. Source


Xiao Y.,Tianjin University of Technology | Xiao Y.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | Ai P.,Tianjin University of Technology | Wang H.,Donghua University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

We study the problem of location-dependent skyline query (LDSQ) processing in wireless broadcast environments in this paper. Compared with answering the skyline queries in a conventional setting, two new issues arise while processing location-dependent skyline in wireless broadcast environments. First, the result of an LDSQ is closely related to the query point’s location; secondly, query processing strategies must take the linear property of wireless broadcast media and limited battery life of mobile devices into consideration. To address these new issues, this paper proposes an efficient solution for LDSQ processing in wireless broadcast environments. In particular, data objects to be disseminated are first divided into two parts via pre-computation in the broadcast server, and then a novel air data organization scheme is designed in the broadcast disk. At the mobile client end, an energy-efficient LDSQ processing algorithm is presented. To demonstrate the efficiency of our solution, extensive experiments are conducted along with detailed performance analysis. © Springer International Publishing Switzerland 2015. Source


Ai P.,Tianjin University of Technology | Xiao Y.,Tianjin University of Technology | Xiao Y.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | Zhu K.,Tianjin University of Technology | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

The tags of news articles give readers the most important and relevant information regarding the news articles, which are more useful than a simple bag of keywords extracted from news articles. Moreover, latent dependency among tags can be used to assign tags with different weight. Traditional content-based recommendation engines have largely ignored the latent dependency among tags. To solve this problem, we implemented a prototype system called PRST, which is presented in this paper. PRST builds a tag dependency graph to capture the latent dependency among tags. The demonstration shows that PRST makes news recommendation more effectively. © Springer International Publishing Switzerland 2015. Source


Guo L.,Tianjin University of Technology | Wen X.,Tianjin University of Technology | Wen X.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology
Journal of Information and Computational Science | Year: 2014

The appearance of multiplicative speckle noise in Synthetic Aperture Radar (SAR) imagery makes it very difficult to visualization and compression. This paper proposes a novel framework of Compressed Sensing (CS) to compress the SAR image. It combined the Nonsubsampled Contourlet Transform (NSCT) shrinkage method to SAR image sparse represent. And a modified Smoothed LO norm (SLO) algorithm is used for SAR image accurate reconstruction. The experimental results show the proposed method is very effective and can get better reconstruction performances. Copyright © 2014 Binary Information Press. Source

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