[1] X. M. Tao, Y. Liu, C. Jiang, Z. Wang and X. Qin, "QoE-Oriented Multimedia Assessment: A Facial Expression Recognition Approach", IEEE Trans. Multimedia, vol.26, pp.41–50, 2019.
[2] X. M. Tao, Y. Duan, M. Xu, Z. Meng and J. Lu, "Learning QoE of Mobile Video Transmission with Deep Neural Network: A Data-driven Approach", IEEE Journal on Selected Areas in Communications, vol.37, pp.1337–1348, 2019.
[3] X. M. Tao, C. Jiang, J. Liu, A. Xiao and J. Lu, "QoE Driven Resource Allocation in Next Generation Wireless Networks", IEEE Wireless Communications, vol.26, pp.78–85, 2019.
[4] X. M. Tao, L. Dong, Y. Li, J. Zhou, N. Ge and J. Lu, "Real-time personalized content catering via viewer sentiment feedback: a QoE perspective", IEEE Network, vol.29, pp.14–19, 2015.
[5] X. M. Tao, Y. P, Duan, C. Yang, H. J. Zhang, S. Liu, and J. H. Lu, “Representation Learning in Wireless Multimedia Communications”, IEEE Wireless Communications Magazine, Accepted, 2020.
[6] Y. P. Duan, C. Y. Han, X. M. Tao, B. R. Geng, Y. F. Du, and J. H. Lu, “Panoramic Image Generation: from 2-D Sketch to Spherical Image”, IEEE Journal of Selected Topics in Signal Processing, Vol. 14, no. 1, 2019.
[7] C. Han, Y. Duan, X. Tao, M. XU and J. Lu, "Toward Variable-Rate Generative Compression by Reducing the Channel Redundancy", IEEE Transactions on on Circuits and Systems for Video Technology, Accepted, 2019.
[8] Y. N. Miao, X. M. Tao, and J. H. Lu, “Joint 3D Shape Estimation and Landmark Localization from Monocular Cameras of Intelligent Vehicles”, IEEE Internet of Things Journal, vol. 6, no. 1, pp. 15-25, Feb. 2019.
[9] B. H. Lin, X. M. Tao, and J. H. Lu, “Hyperspectral Image Denoising via Matrix Factorization and Deep Prior Regularization”, IEEE Transactions on Image Processing, vol. 29, pp. 565-578, Jul. 2019.
[10] C. Zhao, X. M. Tao, J. C, Xiao, VCM. Leung, “Joint Minimization of Wired and Wireless Traffic for Content Delivery by Multicast Pushing”, IEEE Transactions on Wireless Communications, vol. 18, no. 5, pp. 2828-2841, May. 2019.

陶晓明博士长期致力于探索发展计算通信新理论方法及关键技术。研究以多媒体体验质量（QoE）为优化目标的计算通信技术框架，以人工智能、深度学习、计算机视觉等技术为支撑，发展QoE度量、QoE建模等新方法，探索基于智能计算的多媒体传输和网络优化新技术，为显著提升宽带移动网络可持续发展能力，特别是对于大容量多媒体业务的支持能力奠定重要基础。目前，已发表SCI论文55篇，EI论文60余篇；获得国际会议最佳论文奖；获得发明专利40余项；获得国家杰出青年科学基金资助；获得国家优秀青年科学基金资助；获国家重点研发计划课题、国家自然科学基金面上项目等项目。

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