Multimode Optical Fiber Surface Plasmon Resonance Signal Processing Based on the Fourier Series Fitting

Zhou, Peng; Zhang, Wenbin; Wang, Junxing; Liu, Jin; Su, Rongxin; Wang, Xuemin

doi:10.1007/s11468-015-0100-6

Cited by 7 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For eliminating the time-lag effect, the premise is obtaining the phase difference between the temperature and the temperature-induced strain. To scientifically and accurately acquire the phase difference, a method based on Fourier fitting [23] is proposed.…”

Section: Methodsmentioning

confidence: 99%

Modeling of Temperature Time-Lag Effect for Concrete Box-Girder Bridges

et al. 2019

View full text Add to dashboard Cite

It is common to assume the relationship between temperature and temperature response is instantaneous in bridge health monitoring systems. However, a time-lag effect between temperature and thermal strain response has been documented by the analysis of monitored field data of concrete box-girder s. This effect is clearly reflected by the ring feature in the temperature-strain correlation curve. Inevitably, the time-lag effect has an adverse impact on the accuracy and reliability of state assessment and real-time warning for structural health monitoring (SHM) systems. To mitigate the influence of the time-lag effect, a phase-shifting method is proposed based on the Fourier series expansion fitting method. The time-domain signal is firstly converted into the frequency domain signal to compute the phase difference between temperature data and response strain data at each decomposed order. Subsequently, the total phase difference can be obtained by weighted summation. The signal processing effectively reduces the hysteresis loop area and enhances the correlation between the structural response data and the temperature data. When processing the daily data in different seasons, it is found that after subtraction by the proposed method, the linear feature becomes dominant in the relationship between temperature and the strain during long-term observation.

show abstract

Section: Methodsmentioning

confidence: 99%

Modeling of Temperature Time-Lag Effect for Concrete Box-Girder Bridges

et al. 2019

View full text Add to dashboard Cite

show abstract

“…On the contrast, eight smooth peaks appear on the outer envelope of PDM-8PSK signal because of 8 clusters. In order to reduce fluctuation caused by noise, Fourier fitting [30] which fit the amplitude of outer envelope in the form of 2-order Fourier series is employed. The amplitudes of outer envelope utilizing Fourier fitting for PDM-QPSK and PDM-8PSK signals are shown in Fig.…”

Section: Feature Extraction Of Modulation Formatsmentioning

confidence: 99%

Stokes Space Modulation Format Identification for Optical Signals Using Probabilistic Neural Network

Hao

Yan

et al. 2018

IEEE Photonics J.

View full text Add to dashboard Cite

A Stokes space modulation format identification (MFI) method using probabilistic neural network (PNN) is proposed for coherent optical receivers. According to amplitude histograms obtained by the distribution of Stokes vectors on the s 1 axis, the incoming signals are first classified into PDM-mPSK, PDM-16QAM, and PDM-64 QAM signals based on PNN. To further identify PDM-mPSK signals, the constellation feature of Stokes vectors on s 2s 3 plane is extracted by image processing techniques and then processed by PNN. The high identification accuracy is demonstrated via numerical simulations with 28-Gbaud PDM-QPSK, PDM-8PSK, PDM-16QAM, and PDM-64QAM signals over a wide optical signal-tonoise ratio range. Owing to the characteristic of PNN, the proposed MFI method has simple training process, small training data size and a small number of required symbols. Proofof-concept experiments have been implemented to verify the effectiveness of the proposed MFI method among 28-Gbaud PDM-QPSK, PDM-8PSK, and PDM-16QAM signals.

show abstract