Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology

Laboratory of, China

Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology

Laboratory of, China

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Yang S.,Tianjin University of Technology | Yang S.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | Guo J.,Tianjin University of Technology | Guo J.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | And 2 more authors.
ICIC Express Letters, Part B: Applications | Year: 2016

In order to improve the rate of speaker recognition, this paper proposes a new scheme with Mel scale-based Wavelet Packet Decomposition and AR-Volterra model. First, the speech signal is decomposed by wavelet packet of Mel scale, and calculate energy spectrum of the sub-band signals. The sub-bands of low energy are discarded according to weighting the proportion ofenergy for all-bands. Second, chaos ofsub-band signals are determined by the largest Lyapunov exponent. Ifsub-band is chaotic, Volterra adaptive model will be used to extract features. Otherwise, the autoregressive model will be used to extract features. Third, linear and nonlinear characteristic parameters will be used to speaker recognition. Hidden Markov model is used to recognize speaker. The experimental results show the extracted features have been obviously improved. © 2016 ICIC International.


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.


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.


Yang W.,Tianjin University of Technology | Yang W.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | Wang H.,Tianjin University of Technology | Wang H.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | And 2 more authors.
Advanced Materials Research | Year: 2012

Path planning is the kernel problem of the robot technology area. In this paper, the grid method is used to make environmental modeling, Since the Genetic Algorithm (GA) has its immanent limitations and the Simulated Annealing (SA) Algorithm has the advantages in some aspects, combined these two algorithms together just achieve the perfection. In view of this, a hybrid of GA and SA (GA-SA Hybrid) is proposed in this paper to solve path planning problem for mobile robot. The algorithm making the crossover and mutation probability adjusted adaptively and nonlinearly with the completion time, can avoid such disadvantages as premature convergence. The new algorithm has better capability of searching globally and locally. The simulation results demonstrate that the proposed algorithm is valid and effective. © (2012) Trans Tech Publications.


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.


Liu L.-L.,Tianjin University of Technology | Liu L.-L.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | Wen X.-B.,Tianjin University of Technology | Wen X.-B.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

A new spectral clustering (SC) algorithm with Nyström method is proposed for SAR image segmentation in this paper. The proposed algorithm differs from previous approaches in that not only with Nyström method are employed for alleviating the computational and storage burdens of the SC algorithm, but also a new similarity function is constructed by combining the pixel value and the spatial location of each pixel to depict the intrinsic structure of the original SAR image better. Our algorithm and the classic spectral clustering algorithm with Nyström method are evaluated using the real-world SAR images. The results demonstrate the running time and the error rate of the proposed approach and the classic spectral clustering algorithm with Nyström method. © 2010 Springer-Verlag.


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.


Zhang D.-G.,Tianjin University of Technology | Zhang D.-G.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | Liang Y.-P.,Tianjin University of Technology | Liang Y.-P.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology
Mathematical and Computer Modelling | Year: 2013

Service-aware computing is a hot research topic under the banner of web-based uncertain mobile applications. As we know, in the research domain of uncertain mobile service, service-aware evidence with uncertainty is dynamic and changing randomly. In order to ensure the QoS of different mobile application fields based on decision making, we think the method of service-aware computing for uncertain mobile applications is very important. The key insight of this paper is that we modified the computing method of evidence information, which has been considered the reliability, time-efficiency, and relativity of service context. The method has improved the classical computing rule of D-S (Dempster-Shafer) Evidence Theory when being used in uncertain cases. The novel method may be called the extended D-S (EDS) method, which has overcome the drawbacks of classical D-S Evidence Theory. All these new ideas have been successfully used in our service-aware computing field of uncertain mobile applications. By comparing EDS with related methods, such as Bayesian Theory (BT), and Random Set Theory (RST), the advantage of the new service-aware computing method has been proved successfully. © 2012 Elsevier Ltd.


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.


Guo L.,Tianjin University of Technology | Wen X.,Tianjin University of Technology | Wen X.,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology | Yu J.,Tianjin University of Technology
Proceedings of the 2013 International Conference on Intelligent Control and Information Processing, ICICIP 2013 | Year: 2013

A novel SAR image compress and reconstruction algorithm based on compressive sampling (CS) is proposed in this paper. Firstly, the image is represented sparsely by G-level contourlet. Secondly, a Gaussian random matrices that proximate QR factorization is constructed to measure the high frequency coefficients and to realize data compression. Lastly, a modified Sparsity Adaptive Matching Pursuit algorithm(SAMP) is used to realize the precise reconstruction of SAR image. Experimental results demonstrate that the proposed algorithm can get better reconstruction performances and the convergence of the algorithm is much faster than the existed algorithms. © 2013 IEEE.

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