Integer and Sparse Signal Recovery: Application, Theory and Algorithms

报告题目:Integer and Sparse Signal Recovery: Application, Theory and Algorithms

人:温金明 授(暨南大学)




   温金明,暨南大学教授,20156月毕业于加拿大麦吉尔大学数学与统计学院,获哲学博士学位。从20153月到20189月,先后在法国科学院里昂并行计算实验室、加拿大阿尔伯塔大学、多伦多大学从事博士后研究工作。从20189月至今,担任暨南大学网络空间安全学院教授,兼任IEEE Access(中科院二区)期刊的编辑。 研究方向主要是整数信号和稀疏信号恢复的算法设计与理论分析。以第一作者在IEEE Communications Magazine2篇)、Applied and Computational Harmonic Analysis (中科院数学一区期刊,2)IEEE Transactions on Information Theory2篇)、 IEEE Transactions on Signal Processing2篇)、IEEE Transactions on Wireless Communications2篇)、 IEEE Transactions on Communications等顶级期刊和会议发表论文25篇(含3ESI高被引论文),以通讯作者和合作者身份发表期刊和会议发表14篇。


In many applications, such as wireless communications, signal processing and GPS, we need to recover an integer parameter vector from an integer linear model. While in some other application domains including computer vision and machine learning, one is frequently required to recover a sparse signal from a few measurements. In the first part of this talk, we will first theoretically show that some commonly used lattice reductions, which are preprocess tools for recovering integer vectors, can always improve the success probability of some commonly used suboptimal recovery algorithms, and then show that they can decrease the complexity of the optimal method for detecting integer vectors. In the second part of the talk, we will introduce some algorithms and theory for recovering sparse algorithms from a few measurements.