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Special Issue:Fractional Fourier Domain
Specially invited expert: Tao Ran (Professor, Beijing Institute of Technology, China) ,Li Bingzhao (Professor, Beijing Institute of Technology, China)

Since the theory and method of fractional Fourier domain signal processing have the unique characteristics of fractional domain, their related researches have been rapidly infiltrated from the early fields of mathematics, quantum mechanics and optical domain (optical image compression) to encryption, digital watermarking, communication, information security, medical imaging, radar and sonar and so on.
According to the increasing demand of fractional Fourier theory in various fields, this special issue further studies the basic theory and application of fractional domain signal processing based on the existing theoretical results. The experts and scholars in this field are invited to elaborate on the latest research results and development trends to further promoting the synergistic development of relevant theories, technologies and applications. Through this topic, the aim is to grasp the research opportunities together, face the challenges of research and promote the progress and development of this field.
The articles published include two reviews and twelve research papers.
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  Review  |  O438
Online Time:Jun 01, 2018
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Cite this article:
Ma Jinming, Miao Hongxia, Su Xinhua, et al. Research progress in theories and applications of the fractional Fourier transform[J]. Opto-Electronic Engineering, 2018, 45(6): 170747. 
  Review  |  O436.3
Online Time:Jun 01, 2018
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Sun Yannan, Li Bingzhao, Tao Ran. Research progress on discretization of linear canonical transform[J]. Opto-Electronic Engineering, 2018, 45(6): 170738. 
  Article  |  TN911.7
Online Time:Jun 01, 2018
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Song Weibin, Zhang Shengru, Deng Yiqiu, et al. Analog to information conversion for sparse signals band-limited in fractional Fourier transform domain[J]. Opto-Electronic Engineering, 2018, 45(6): 170740. 
  Article  |  TN911.7
Online Time:Jun 01, 2018
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Li Yonggang, Zhang Chuan. Hartley transform for linear canonical transformation and uncertainty principle[J]. Opto-Electronic Engineering, 2018, 45(6): 170743. 
  Article  |  TN911.7
Online Time:Jun 01, 2018
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Wang Rongbo, Feng Qiang. Convolution theorems for the linear canonical sine and cosine transform and its application[J]. Opto-Electronic Engineering, 2018, 45(6): 170722. 
  Article  |  TN253
Online Time:Jun 01, 2018
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Liu Zhangyun, Zhou Liming, Liu Weimin, et al. Research on ESPRIT estimation algorithm for the central frequency of gain spectrum in Brillouin optical time-domain reflectrometry[J]. Opto-Electronic Engineering, 2018, 45(6): 180007. 
  Article  |  TP391
Online Time:Jun 01, 2018
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Shi Chao, Chen Enqing, Qi Lin. Ship detection from infrared video[J]. Opto-Electronic Engineering, 2018, 45(6): 170748. 
  Article  |  TN959
Online Time:Jun 01, 2018
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Lu Xiangyang, Sun Lijuan, Fang Xiangqian, et al. Scattering effect analysis of elastic wave concentrator based on FRFT[J]. Opto-Electronic Engineering, 2018, 45(6): 170739. 
  Article  |  TN957.51
Online Time:Jun 01, 2018
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Yu Xiaohan, Chen Xiaolong, Chen Baoxin, et al. Radar maneuvering target detection method based on fast and high resolution sparse fractional Fourier transform[J]. Opto-Electronic Engineering, 2018, 45(6): 170702. 
  Article  |  O436.3
Online Time:Jun 01, 2018
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Chen Tianshi, Tie Yun, Qi Lin, et al. An improved method to render the sound of VR scene[J]. Opto-Electronic Engineering, 2018, 45(6): 170742. 
  Article  |  O436.3
Online Time:Jun 01, 2018
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Zhang Yanshan, Pang Dongdong, Ma Pengge, et al. Fractional magnetic resonance imaging based on inhomogeneous main magnetic field[J]. Opto-Electronic Engineering, 2018, 45(6): 170737. 
  Article  |  O438
Online Time:Jun 01, 2018
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Lang Jun, Fu Xiangxue, Guo Pan. Optical color image asymmetric compressed encryption in fractional Fourier transform domain[J]. Opto-Electronic Engineering, 2018, 45(6): 170732. 
  Article  |  TP391.41
Online Time:Jun 01, 2018
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Jiu Mingyuan, Chen Enqing, Qi Lin, et al. Multiple order fractional Fourier transformation for face recognition based on multiple kernel learning[J]. Opto-Electronic Engineering, 2018, 45(6): 170744.