Journal papers

2019

  • R. Q. Zhao, Q. Wang. Learning Separable Dictionaries for Sparse Tensor Representation: An Online Approach [J]. IEEE Transactions of Circuits and Systems II-Express Briefs, vol. 66, pp. 502-506, Mar 2019.

2018

  • Q. Wang, Z. J. Wu, J. Jin, T. C. Wang, Y. Shen. Low rank constraint and spatial spectral total variation for hyperspectral image mixed denoising [J]. Signal Processing, vol. 142, pp. 11-26, Jan 2018.
  • C. C. Lv, Q. Wang, W. J. Yan, J. Li. A sparsity feedback-based data gathering algorithm for Wireless Sensor Networks [J]. Computer Networks. vol. 141, pp.145-156, Aug 2018.

2017

  • Z. J. Wu, Q. Wang, J. Jin, Y. Shen. Structure tensor total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising [J]. Signal Processing, vol. 131, pp. 202-219, Feb 2017.
  • Q. Wang, D. Li, Y. Shen. Intelligent nonconvex compressive sensing using prior information for image reconstruction by sparse representation [J]. Neurocomputing, vol. 224, pp. 71-81, Feb 2017.
  • C. C. Lv, Q. Wang, W. J. Yan, Y. Shen. Diffusion wavelet basis algorithm for sparse representation of sensory data in WSNs [J]. Signal Processing, vol. 140, pp. 12-31, Nov 2017.
  • D. Li, Q. Wang, Y. Shen. Geometric structure based intelligent collaborative compressive sensing for image reconstruction by l(0) minimization [J]. Neurocomputing, vol. 260, pp. 221-234, Oct 2017.

2016

  • R. Q. Zhao, Q. Wang, Y. Shen, J. Li. Multidimensional dictionary learning algorithm for compressive sensing-based hyperspectral imaging [J]. Journal of Electronic Imaging, 2016, 25(6).
  • R. Q. Zhao, Q. Wang, Y. Shen, J. Li. Multiatom tensor orthogonal matching pursuit algorithm for compressive-sensing-based hyperspectral image reconstruction [J]. Journal of Applied Remote Sensing, vol. 10, Oct 2016.
  • Z. J. Wu, Q. Wang, Z. H. Wu, Y. Shen. Total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising [J]. Journal of Electronic Imaging, 2016, 25(1).
  • C. C. Lv, Q. Wang, W. J. Yan, Y. Shen. Energy-balanced compressive data gathering in Wireless Sensor Networks [J]. Journal of Network and Computer Applications, vol. 61, pp. 102-114, Feb 2016.
  • D. Li, Q. Wang, Y. Shen. Predicted multi-variable intelligent matching pursuit algorithm for image sequences reconstruction based on l(0) minimization [J]. Journal of Visual Communication and Image Representation, vol. 38, pp. 316-327, Jul 2016.
  • D. Li, Q. Wang, Y. Shen. Multi-variable intelligent matching pursuit algorithm using prior knowledge for image reconstruction by l(0) minimization [J]. Neurocomputing, vol. 207, pp. 548-559, Sep 2016.
  • D. Li, Q. Wang, Y. Shen. Intelligent greedy pursuit model for sparse reconstruction based on l(0) minimization [J]. Signal Processing, vol. 122, pp. 138-151, May 2016.

2015

  • R. Q. Zhao, Q. Wang, Y. Shen. Kronecker compressive sensing-based mechanism with fully independent sampling dimensions for hyperspectral imaging [J]. Journal of Electronic Imaging, 2015, 24(6).

2013

  • D. Yu, X. Zhang, J. Liu, and Q. Wang, “Feature analysis and adaptive distance learning algorithm based on mdi magnetogram,” ICIC Express Letters, vol. 7, pp. 2099-2104, 2013.
  • Y. P. Liu, J. Jin, Q. Wang, and Y. Shen, “Phases measure of image sharpness based on quaternion wavelet,” Pattern Recognition Letters, vol. 34, pp. 1063-1070, Jul 2013.
  • J. Li, Q. Wang, Y. Shen, and B. Li, “Collaborative construction of measurement matrix and reconstruction algorithm in compressive sensing,” Tien Tzu Hsueh Pao/Acta Electronica Sinica, vol. 41, pp. 29-34, 2013.
  • Z. He, Q. Wang, Y. Shen, and Y. Wang, “Discrete multivariate gray model based boundary extension for bi-dimensional empirical mode decomposition,” Signal Processing, vol. 93, pp. 124-138, Jan 2013.
  • Z. He, Q. Wang, Y. Shen, J. Jin, and Y. Wang, “Multivariate Gray Model-Based BEMD for Hyperspectral Image Classification,” Ieee Transactions on Instrumentation and Measurement, vol. 62, pp. 889-904, May 2013.

2012

  • Z.-H. Wang, Y. Shen, X.-L. Zhang, and Q. Wang, “Robust H filter for uncertain linear descriptor systems,” Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, vol. 34, pp. 1878-1883, 2012.
  • Z. Wang, Y. Shen, X. Zhang, and Q. Wang, “Observer design for discrete-time descriptor systems: An LMI approach,” Systems and Control Letters, vol. 61, pp. 683-687, 2012.
  • Q. Wang, W. J. Yan, and Y. Shen, “N-Person Card Game Approach for Solving SET K-COVER Problem in Wireless Sensor Networks,” Ieee Transactions on Instrumentation and Measurement, vol. 61, pp. 1522-1535, May 2012.
  • Q. Wang, Y. Liu, and Y. Shen, “A survey on extrinsic self-calibration algorithms for cameras without overlapping field of views,” Zidonghua Xuebao/Acta Automatica Sinica, vol. 38, pp. 1-11, 2012.
  • Z.-L. Tu, Q. Wang, and Y. Shen, “A distributed simultaneous optimization algorithm for tracking and monitoring of moving target in mobile sensor networks,” Zidonghua Xuebao/Acta Automatica Sinica, vol. 38, pp. 452-461, 2012.
  • Z.-L. Tu, Q. Wang, and Y. Shen, “A distributed coverage optimization algorithm for target monitoring in mobile sensor network,” Kongzhi yu Juece/Control and Decision, vol. 27, pp. 1353-1358, 2012.
  • Z. L. Tu, Q. Wang, H. R. Qi, and Y. Shen, “Flocking based sensor deployment in mobile sensor networks,” Computer Communications, vol. 35, pp. 849-860, Apr 2012.
  • Z. Tu, Q. Wang, H. Qi, and Y. Shen, “Flocking based distributed self-deployment algorithms in mobile sensor networks,” Journal of Parallel and Distributed Computing, 2012.
  • Y. Shen, Z.-L. Tu, and Q. Wang, “Distributed nonuniform deployment for target monitoring in mobile sensor networks,” Journal of Harbin Institute of Technology (New Series), vol. 19, pp. 1-9, 2012.
  • Y. P. Liu, J. Jin, Q. Wang, and Y. Shen, “Phase-preserving speckle reduction based on soft thresholding in quaternion wavelet domain,” Journal of Electronic Imaging, vol. 21, Oct-Dec 2012.
  • Z. He, Y. Shen, Q. Wang, Y. Wang, N. Feng, and L. Ma, “Mitigating end effects of EMD using non-equidistance grey model,” Journal of Systems Engineering and Electronics, vol. 23, pp. 603-611, 2012.
  • Z. He, Y. Shen, and Q. Wang, “Boundary extension for Hilbert-Huang transform inspired by gray prediction model,” Signal Processing, vol. 92, pp. 685-697, Mar 2012.

2011

  • Y.-R. Lin, Q. Wang, Y.-E. Lin, and X.-Z. Liang, “Adaptive weight based hyperspectral image classification,” Guangdianzi Jiguang/Journal of Optoelectronics Laser, vol. 22, pp. 935-939, 2011.
  • Y.-R. Lin and Q. Wang, “Extracting details from images based on 1-DEMD,” Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), vol. 41, pp. 1766-1770, 2011.
  • Y. Lin and Q. Wang, “Hyperspectral image classification based on adaptive weight coefficient based on kernel method,” Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, vol. 40, pp. 2535-2539, 2011.
  • H. Chen, Q. Wang, and Y. Shen, “Decision tree support vector machine based on genetic algorithm for multi-class classification,” Journal of Systems Engineering and Electronics, vol. 22, pp. 322-326, 2011.

2010

  • C. Zhu, Y. Shen, and Q. Wang, “New fast algorithm for hypercomplex decomposition and hypercomplex cross-correlation,” Journal of Systems Engineering and Electronics, vol. 21, pp. 514-519, 2010.

2009

  • M. Zhang, Y. Shen, and Q. Wang, “Evaluating the performance of hyperspectral feature selection algorithm using Tsallis entropy redundancy,” Guangdianzi Jiguang/Journal of Optoelectronics Laser, vol. 20, pp. 784-788, 2009.
  • M. Zhang, Y. Shen, and Q. Wang, “Hyperspectral datum classification using kernel method based on mutual information of neighbor bands,” Optoelectronics Letters, vol. 5, pp. 309-312, 2009.
  • M. Zhang, Y. Shen, and Q. Wang, “Nonlinear correlation coefficient based kernel method for hyperspectral data classification,” Guangxue Xuebao/Acta Optica Sinica, vol. 29, pp. 2607-2614, 2009.
  • Q. Wang, H.-H. Chen, and T. Wang, “Multi-class support vector machine for fault diagnosis,” Dianji yu Kongzhi Xuebao/Electric Machines and Control, vol. 13, pp. 302-306, 2009.
  • X.-F. Li, Y. Shen, and Q. Wang, “Computer-aided diagnosis system of breast tumor ultrasound images,” Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), vol. 39, pp. 770-775, 2009.

2008 and before

  • P. Wang, Y. Shen, and Q. Wang, “Gaussian wavelet based dynamic filtering (GWDF) method for medical ultrasound systems,” Ultrasonics, vol. 46, pp. 168-176, 2007.
  • J. Jin, Q. Wang, and Y. Shen, “MNCIE method for registration of ultrasound images,” Journal of Harbin Institute of Technology (New Series), vol. 14, pp. 252-258, 2007.
  • J. Jin, Q. Wang, and Y. Shen, “Registering multiple medical images using the shared chain mutual information,” Chinese Optics Letters, vol. 5, pp. 389-392, 2007.
  • J. Hao, Y. Shen, and Q. Wang, “Segmentation for MRA image: An improved level-set approach,” IEEE Transactions on Instrumentation and Measurement, vol. 56, pp. 1316-1321, 2007.
  • Q. Wang and Y. Shen, “Performance assessment of image fusion,” in Advances in Image and Video Technology, Proceedings. vol. 4319, L. W. Chang and W. N. Lie, Eds., ed, 2006, pp. 373-382.
  • B. J. Xu, Q. Wang, Y. Shen, and X. X. Liao, “Global exponential stability of delayed impulsive Hopfield type neural networks,” in Advances in Neural Networks – Isnn 2005, Pt 1, Proceedings. vol. 3496, J. Wang, et al., Eds., ed, 2005, pp. 181-186.
  • Q. Wang, Y. Shen, and J. Q. Zhang, “A nonlinear correlation measure for multivariable data set,” Physica D: Nonlinear Phenomena, vol. 200, pp. 287-295, 2005.
  • Q. Wang, Y. Shen, Y. Zhang, and J. Q. Zhang, “Fast quantitative correlation analysis and information deviation analysis for evaluating the performances of image fusion techniques,” IEEE Transactions on Instrumentation and Measurement, vol. 53, pp. 1441-1447, 2004.
  • Q. Wang, Y. Shen, Y. Zhang, and J. Q. Zhang, “A quantitative method for evaluating the performances of hyperspectral image fusion,” IEEE Transactions on Instrumentation and Measurement, vol. 52, pp. 1041-1047, 2003.
  • Q. Wang, Y. Zhang, S. Li, and Y. Shen, “The reduction of hyperspectral data dimensionality and classification based on recursive subspace fusion,” Chinese Journal of Electronics, vol. 11, pp. 12-15, 2002.
  • Q. Wang, Y. Zhang, S. Li, and Y. Shen, “Quantitative and comparative analysis of hyperspectral data fusion performance,” Journal of Harbin Institute of Technology (New Series), vol. 9, pp. 234-238, 2002.

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