基本信息

秦飞 男  电子与通信工程学院
电子邮件:fqin1982 at ucas.ac.cn
通信地址:学园2-365
邮政编码:


个人信息

现任中国科学院大学电子电气与通信工程学院教授。于2012年获得英国伦敦大学学院(UCL)博士学位,2006年获得北京理工大学硕士学位,2004年获得华中科技大学学士学位。在赴英国攻读博士学位前曾于美国crossbow科技公司北京代表处服务。

教育背景

2008-10--2012-02 伦敦大学学院University College London Ph.D
2004-09--2006-06 北京理工大学 硕士
2000-09--2004-06 华中科技大学 学士

工作经历

2012-09--今           中国科学院大学                              讲师、副教授、教授

2006-07--2008-08 美国crossbow科技公司北京代表处 无线产品经理


教授课程

系统仿真
单片机和嵌入式系统
单片机与嵌入式系统
信号与系统习题课
信号与系统
无线网络
开源硬件
移动通信和无线网络

研究工作


已资助项目

海上多波段云雾观测设备研制及示范应用,国家重点研发计划

机理建模数据驱动的工业无线网络延时分布控制方法研究,国家自然科学基金

三维多径场景的时间合成法空口模拟装置,中国科学院科研仪器设备研制项目

基于多载波复用水下移动网络的多用户共存技术研究,中国科学院水下航行器重点实验室开放课题

基于动态观测器的无线传感网链路质量估计系统研究,国家自然科学基金

传感网组网QoS保障及传输性能测试,科技部重大专项

应用分布式Machine learning的无线传感器网络路由算法研究,中国科学院无线传感网与通信重点实验室开放课题

JRP51 MORSE Metrology for optical and RF communication systems, No 912/2009/EC,European Metrology Research Program


Selected Publications

[1]  Y. Xiao, D. Zhang, Y. Wang, X. Dai, Z. Huang, W. Zhang, Y. Yang, A. Anjum, F. Qin, " NAIR: An Efficient Distributed Deep Learning Architecture for Resource Constrained IoT System", in IEEE Internet of Things Journal, 2024

[2]  S. Wang, S. Gao, W. Yang, T. H. Loh, Y. Yang, F. Qin, " Area Restoration of Channel Impulse Response with Time Decomposition based Super-Resolution Method", in IEEE Transactions on Wireless Communications (TWC), 2024

[3]    Q. Zhang, T. H. Loh, W. Zhang, Y. Yang, F. Qin, " A New Evaluation Framework for the Performance of Spatial Correlation in MIMO OTA Testing", in IEEE Transactions on Wireless Communications (TWC), 2023

[4]   Sun, X., Yin, D., Qin, F. et al. " Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery". Nature Communications Vol.14, Issue 1, 2023

[5]    F. Qin, Y. Xiao, X. Sun, X. Dai, W. Zhang, F. Shen, "Inverse-GMM: A Latency Distribution Shaping Method for Industrial Cooperative Deep Learning Systems", in IEEE Journal on Selected Areas in Communications (JSAC), vol. 41, no. 3, pp. 776-788, 2023

[6]    Z. Zhang, X. Dai, G. Shan, G. Li, X. Li, X. Liu, F. Qin, " A Low Cost UV-IR Dual Wavelength Optical Sensor with Chirp Modulation for In-Situ Chemical Oxygen Demand Measurements", in Sensors and Actuators B: Chemical, vol.371,2023

[7]    S. Wang, S. Gao, F. Qin and W. Yang, "A Decomposition-Based Analysis of Wireless Multipath Channel with Full-Wave Simulation," in IEEE Antennas and Wireless Propagation Letters, Vol. 21, No. 8, pp. 1615-1619, Aug. 2022

[8]    F. Liu, X. Dai, M. Jin, W. Zhang, Y. Yang, F. Qin, "TACAN: the Shaping of Delay Distribution under Multi-path Fading Channel for Industrial IoT Systems", in IEEE Internet of Things Journal,Vol.9, Issue 17, 2022

[9]    Q. Zhang, T. H. Loh, W. Zhang, Y. Yang, F. Qin, "A Low-cost and Efficient Single Probe based MIMO OTA Measurement Method", in IEEE Transactions on Instrumentation and Measurement, Vol.71, 2022,

[10]    G. Lu, W. Zhang, Y. Yang, X. Dai, F. Qin, "Non-data Aided Rician Parameters Estimation with Redundant GMM for Adaptive Modulation in Industrial Fading Channel", in IEEE Transactions on Industrial Informatics (TII), Vol.16 Issue 4, 2022

[11] J. Wei, F. Qin, G. Li, X. Li, X. Liu, X. Dai, "Chirp modulation enabled turbidity measurement for large scale monitoring of fresh water", in Measurement, Vol.184, 2021

[12] Y. Li, F. Qin, X. Wang, X. Lu, Z. Chu, “Feed-forward Timing Estimation for Burst Signals in Non-cooperative Communication", in IET Communications, Vol.14 Issue 17, 2871-2877,2020

[13] Q. Zhang, G. Lu, W. Zhang, F. Shen, J. Jiao, F. Qin, “Non-Data Aided Rician Parameters Estimation in Temporal Fading Channel With 3 DoFs Gaussian Mixture Model", in IEEE Access, 2019

[14] Y. Yang, Y. Li, W., Zhang, F. Qin, P. Zhu, C. Wang, “Generative Adversarial Network-based Wireless Channel Modelling: Challenges and Opportunities", in IEEE Communications Magazine, Vol. 57, Issue 3, 2019

[15] F. Qin, Q. Zhang, W. Zhang, Y. Yang, J. Ding, X, Dai, “Link Quality Estimation in Industrial Temporal Fading Channel with Augmented Kalman Filter”, in IEEE Transactions on Industrial Informatics (TII), Vol. 15, Issue 4, 2019

[16] P. Wei, F. Qin, F. Wan, Y. Zhu, J. Jiao and Q. Ye, "Correlated Topic Vector for Scene Classification", in IEEE Transactions on Image Processing (TIP), vol. 26, no. 7, pp. 3221-3234, 2017

[17] M. Shi , F. Qin, Q. Ye , Z. Han , J. Jiao , "A Scalable Convolutional Neural Network for Task-Specified Scenarios via Knowledge Distillation", in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)New Orleans2017

[18] T. H. Loh, K. Liu, F. Qin, H. Liu, "Adaptive routing in wireless sensor network using smart antennas", in IET Wireless Sensor Systems, Vol. 4, Issue 4, 2014

[19] F. Qin, J. E. Mitchell, "AS-MAC: Utilising the Adaptive Spreading Code Length for the MAC protocol design of WSNs", in ACM Transaction on Sensor Networks (TOSN), Vol.10, Issue 1, 2013

[20] F. Qin, X. Dai, and J. E. Mitchell, "Effective-SNR Estimation for Wireless Sensor Network Using Kalman Filter", in Ad Hoc Networks, Vol.11, Issue 3, 2013

[21] H. Liu, S. Gao, T. H. Loh, F. Qin, Low-Cost Intelligent Antenna with Low Profile and Broad Bandwidth", in IET Microwaves, Antennas & Propagation, Vol.7, Issue 5, 2013

[22] C.K Chau, F. Qin, S. Samir, M. H. Wahab, Y. Yang, "Harnessing Battery Recovery Effect in Sensor Networks", in IEEE Journal on Selected Areas in Communications (JSAC), Vol. 28, No. 7, 2010

 

 

 

 


研究方向

目前主要研究工作包括:


1. 自适应高斯混合模型估计

将经典高斯混合模型EM估计算法中需要手动指定的聚类数目纳入优化迭代过程,通过将相对收敛指标转换为绝对收敛指标,使得优化算法能够识别局部最优,始终收敛到未知的真实聚类分布上。进一步挖掘如信道估计时Rician分布的本质约束,将高维解空间映射到低维,加速高斯混合模型收敛速度。


2.工业场景下信道测量、建模、与模拟

针对工业信道下强反射的信道形成机理,设计实现多通道分布式信道测量装置,实现多空间位置的协同信道测量。构建基于全波仿真的信道解构算法,建立非平稳、异构的工业信道机理模型,改进经典的多径信道空间相关性假设。设计实现新体制信道空口模拟器,将并行空间合成映射为串行时间合成,在电磁域而非信号域复现任意信道场景。


3.复杂信道场景下传输质量优化

针对工业场景下如闭环控制系统、高维传感器信息传输、分布式协同深度学习等复杂业务需求,将经典的一阶优化目标函数扩展至二阶和KLD散度,为业务系统提供更强的延时控制自由度。


4.面测量传感器

基于欠采样估计原理,将传统主动辐射式点测量水质传感器的测量方法论进行扩维,获得基于中近红外、RGB等多光谱测量的水质面测量能力,有效提升污染物时空扩散过程的研究能力。进一步探索该测量方法论在医学创面监测、呼吸波复原与脉搏波全域测量场景下的应用。


I usually recruit one Ph.d and two master students each year.  If you can enjoy the pain and gain from research processes (preferred but not limited to the above topics), you are welcome to apply. I usually expect you to be good at theoretical modelling, be skilled in coding to implement algorithms, and be able to design hardware systems (at least two of three).