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2020年海內專家系列學術講座(九)Xiaoping Zhang: Thresholding Neural Networks for Adaptive Signal Processing

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講座標題問題(Title of Lecture):Thresholding Neural Networks for Adaptive Signal Processing

講座時候(Time of Lecture):2020年12月31日禮拜四10:00-11:00

講座地址(Site of Lecture):騰訊集會ID:730 498 856

主講人(Lecturer):Prof. Xiao-Ping (Steven) Zhang, Ryerson University, Canada

報告人簡介(Introduction of Lecturer):

張曉平傳授是加拿大工程院院士, 加拿大工程研討院院院士,和IEEE會士。他別離于1992和1996年從清華大學電子工程系獲學士和博士學位。他從芝加哥大學布斯商學院取得金融和經濟學專業工商辦理碩士學位(MBA)并獲優異畢業生聲譽。 

張曉平院士現為加拿大Ryerson大學電氣、計較機和生物工程系正傳授(畢生教職),通訊和旌旗燈號處置及利用嘗試室主任,并兼任Ted Rogers辦理學院金融系傳授。曾任系研討生和科研主管。2015和2017年任麻省理工學院電子學嘗試室拜候迷信家。張曉平院士努力于旌旗燈號處置和大數據的實際和利用研討開辟,首要處置統計模子、旌旗燈號處置、機械進修和野生智能、物聯網和電子信息體系、生物信息及金融經濟模子和大數據等方面的研發。張曉平院士是其研討范疇的國際著名專家并曾在華爾街和硅谷產業界任職。曾任麻省理工學院和哈佛大學拜候迷信家。頒發國際頂級期刊和集會學術論文200余篇。具有多項美國專利,此中大局部已轉化進入貿易產物。張曉平傳授現任《IEEE旌旗燈號處置匯刊 》和《IEEE圖象處置匯刊 》的高等副主編(Senior Area Editor),曾任《IEEE旌旗燈號處置匯刊 》、《IEEE多媒體處置匯刊》、《IEEE圖象處置匯刊 》、《IEEE電路與體系視頻手藝匯刊》、《IEEE旌旗燈號處置快報》等國際著名學術期刊的副主編。他現任IEEE旌旗燈號處置學會圖象視頻及多維旌旗燈號處置手藝委員會副主席,是國際旌旗燈號處置最大旗艦年會IEEE ICASSP集會2021年大會配合主席(General Co-Chair),2017和2019年IEEE環球旌旗燈號和信息處置年會(GlobalSIP)金融和貿易信息處置大會主席,2015 IEEE多媒體旌旗燈號處置年會(MMSP2015)主席。現任IEEE國際多媒體大會(ICME)指點委員會(Steering Committee)委員。張傳授曾在多個著名國際集會如ACM多媒體年會ACMMM2011, IEEE電路與體系年會ISCAS2013和ISCAS2019, IEEE圖象處置年會ICIP2013, IEEE旌旗燈號處置年會ICASSP2014,國際神經收集結合大會IJCNN2017應邀作教程報告(Tutorial)。他被挑選為IEEE旌旗燈號處置學會精采講座學者和IEEE電路和體系學會精采講座學者。2020年獲Ryerson大學學術科研最高獎–Sarwan Sahota Ryerson精采學者獎。 www.ryerson.ca/~xzhang

講座內容(Content of Lecture):

In this talk, a system framework of nonlinear thresholding for adaptive signal processing, namely thresholding neural network (TNN), is presented. Several types of thresholding functions are created to serve as activation functions. Unlike the standard thresholding functions, the new thresholding functions are infinitely differentiable. By using the new thresholding functions, some gradient-based learning algorithms become possible or more effective. General optimal performances of TNNs are analyzed.  Gradient-based adaptive learning algorithms are presented to seek the optimal solution for noise reduction. The algorithms include supervised and unsupervised batch learning as well as supervised and unsupervised stochastic learning. It is indicated that the TNN with the stochastic learning algorithms can be used as a novel nonlinear adaptive filter.  Numerical results show that the TNN is very effective in finding the optimal solutions of thresholding methods in an MSE sense and usually outperforms other noise reduction methods.  Especially, it is shown that the TNN based nonlinear adaptive filtering outperforms the conventional linear adaptive filtering in both optimal solution and learning performance.

胡蘿卜視頻app破解版:

  2020年專利講座 王凱盛: 專利的檢索、撰寫與回答 前往目次 胡蘿卜視頻app破解版:2021年BCI腦控機械人大賽(秦皇島賽區)講座報告