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Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


A Discrete Fourier Transform Based Dithering Signal Generation Method for Reduced Quantization Error

Journal of Vibration Testing and System Dynamics 2(1) (2018) 43--51 | DOI:10.5890/JVTSD.2018.03.005

Chang-Qing Ye, Xing-Zhong Xiong

Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, China

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Addressing the impact of quantization error in analog-to-digital converter (ADC) for signal processing, a Discrete Fourier Transform (DFT) based method for the generation of dithering signals is presented. Effective in reducing quantization error, the dithering signal generated using the method is not only independent of the input signal but also can improve the performance of the ADC. To illustrate the advantages of the dithering signal reconstructed using the method, three kinds of typical dithering effects, namely, Triangular Distribution Dithering, Gaussian Distribution Dithering and Uniform Distribution Dithering on the quantization error in an ADC are considered. The properties of three practical dithering signals are investigated. It is shown that while these typical dithering may be useful they are some potential problems. The dithering signals reconstructed using the DFT based generation scheme is shown to contribute significantly to reducing quantization error. Numerical experiments and theoretical analyses demonstrate that the DFT based dithering reconstruction can obtain prominent performance for ADC in comparison to typical dithering signals.


This work was supported by the Innovation Group Building Plan of Sichuan (No. 2015TD0022), Project of Talents of Sichuan University of Science and Engineering (No. 2014RC13), Initiation Project of Artificial Intelligence Key Laboratory of Sichuan Province (2017WZJ01), Initiation Project of Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things (2017WZJ01), and Sichuan University of Science and Engineering Project of Talent Introduction (2017RCL11).


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