The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensi...The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensing system. The time domain signal of every code sequence is collected by the data acquisition card （DAQ）. A shift-averaging technique is applied in the frequency domain for the reason that the local oscillator （LO） in the coherent detection is fix-frequency deviated from the primary source. With the 31-bit simplex code, the signal-to-noise ratio （SNR） has 3.5-dB enhancement with the same single pulse traces, accordant with the theoretical analysis. The frequency fluctuation for simplex codes is 14.01 MHz less than that for a single pulse as to 4-m spatial resolution. The results are believed to be beneficial for the BOTDR performance improvement.展开更多
Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem ...Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.展开更多
基金supported by the National High Technology Research and Development Program of China(Grant No.2012AA041203)the National Natural Science Foundation of China(Grant Nos.61377062 and 31201377)+1 种基金the Program of Shanghai Excellent Technical Leaders,China(Grant No.13XD1425400)the Doctorial Fund of Zhengzhou University of Light Industry,China(Grant No.2013BSJJ012)
文摘The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensing system. The time domain signal of every code sequence is collected by the data acquisition card （DAQ）. A shift-averaging technique is applied in the frequency domain for the reason that the local oscillator （LO） in the coherent detection is fix-frequency deviated from the primary source. With the 31-bit simplex code, the signal-to-noise ratio （SNR） has 3.5-dB enhancement with the same single pulse traces, accordant with the theoretical analysis. The frequency fluctuation for simplex codes is 14.01 MHz less than that for a single pulse as to 4-m spatial resolution. The results are believed to be beneficial for the BOTDR performance improvement.
基金supported by the National Key R&D Program of China(2022YFB4701502)the“Leading Goose”R&D Program of Zhejiang(2023C01177)+1 种基金the Key Research Project of Zhejiang Lab(2021NB0AL03)the Key R&D Project on Agriculture and Social Development in Hangzhou City(Asian Games)(20230701 A05).
文摘Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.
