导师姓名：聂来森
性别：
人气指数：107

所属院校：西北工业大学
所属院系：电子信息学院
职称：副教授
导师类型：
招生专业：通信与信息系统、信息与通信工程


通讯方式 :
办公电话：**
电子邮件：nielaisen@nwpu.edu.cn


个人简述 :
1. 2015年8月 ? 获辽宁省自然科学学术成果奖三等奖（排名第一）2. 2014年9月 ? 获CSC-IBM中国优秀学生奖学金3. 2014年7月 ? 获辽宁省自然科学学术成果奖三等奖（排名第一）?4. 2014年6月 ? 获沈阳市自然科学学术成果奖三等奖（排名第一）?5. 2013年10月 获博士研究生国家奖学金6. 2013年10月 获国际学术会议COIN2013最佳论文奖

科研工作 :
2017年1月至今，西北工业大学电子信息学院 副教授 研究方向：1. 网络测量技术2. 网络安全 [1]?Laisen Nie, Dingde Jiang, Modeling network traffic for traffic matrix estimation and anomaly detection based on Bayesian network in cloud computing networks, Annals of Telecommunications, 72(2017): 297-305. (SCI)[2]?Laisen Nie, Dingde Jiang, Lei Guo, Shui Yu, Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks, Journal of Network and Computer Application, 2016, 76(2016): 16-22. (SCI)[3]?Laisen Nie, Dingde Jiang, Lei Guo, Traffic matrix estimation for IP-over-WDM networks via optical bypass techniques, China Communications, 2016, 13(7): 7-15. (SCI)[4]?Laisen Nie, Dingde Jiang, Lei Guo, A convex optimization-based traffic matrix estimation approach in IP-over-WDM backbone networks, Journal of Network and Computer Applications, 2015, 50(2015): 32-38. (SCI)[5]?Laisen Nie, Dingde Jiang, Lei Guo, A compressive sensing-based approach to end-to-end network traffic reconstruction utilizing partial measured origin-destination flows, Transactions on Emerging Telecommunications Technologies, 2015, 26(8): 1108-1117. (SCI)[6]?Laisen Nie, Dingde Jiang, A compressive sensing-based network tomography approach to estimating origin-destination flow traffic in large-scale backbone networks, International Journal of Communication Systems, 2015, 28(5): 889-900. (SCI)[7]?Laisen Nie, Dingde Jiang, Lei Guo, End-to-end network traffic reconstruction via network tomography based on compressive sensing, Journal of Network and Systems Management, 2015, 23(3): 709-730. (SCI)[8]?Laisen Nie, Dingde Jiang, Lei Guo, Zhengzheng Xu, A reconstructing approach to end-to-end network traffic based on multifractal wavelet model, International Journal of Communication Systems, 2014, 27(12): 3565-3580. (SCI)[9]?Laisen Nie, Dingde Jiang, Lei Guo, A power laws-based reconstruction approach to end-to-end network traffic, Journal of Network and Computer Applications, 2013, 36(2): 898-907. (SCI)[10]?Laisen Nie, Dingde Jiang, Zhengzheng Xu, A compressive sensing-based reconstruction approach to network traffic, Computers and Electrical Engineering, 2013, 39(5): 1422-1432. (SCI)[11]?Laisen Nie, Dingde Jiang, Shui Yu, and Houbing Song, Network traffic prediction based on Deep Belief Network in wireless mesh backbone networks, 2017 IEEE WCNC, 2017, pp. 1-5. (EI)[12]?Laisen Nie, Dingde Jiang, Lei Guo, Shui Yu, and Houbing Song, Traffic matrix prediction and estimation based on deep learning for data center networks, 2016 IEEE Globecom Workshops, 2016, pp. 1-6. (EI)[13]?Laisen Nie, A novel network tomography approach for traffic matrix estimation problem in large-scale IP backbone networks, 2015 International Conference on Computer Science and Mechanical Automation, 2015, Hangzhou, China, pp. 97-101. (EI)[14]?Laisen Nie, Traffic matrix estimation approach based on partial direct measurements in large-scale IP backbone networks, 2015 5th International Conference on Electronics Information and Eergency Communication, 2015, pp. 178-181. (EI)[15]?Laisen Nie, Dingde Jiang, and Lei Guo, A compressive sensing-based reconstruction approach to end-to-end network traffic, 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, 2012, Shanghai, China, Sept. 21-23, pp. 1-4. (EI)[16]?Dingde Jiang, Laisen Nie, Zhihan Lv, and Houbing Song, Spatio-Temporal Kronecker compressive sensing for traffic matrix recovery, IEEE Access, 2016,4(2016): 3046-3053. (SCI)[17]?Dingde Jiang, Zhengzheng Xu, Laisen Nie, and Jindi Liu, An approximation approach to end-to-end traffic in communication networks, Chinese Journal of Electronics, 2012, 21(4): 705-710. (SCI)[18]?Dingde Jiang, Wenda Qin, Qingyi Tang, Laisen Nie, and Zhang Jian, An estimation approach to traffic matrix in optical networks based on network tomography, Acta Photonica Sinica, 2014, 43(7): 1-10. (EI)