Liu Guili

Design of virtual oscilloscope based on GPIB interface and SCPI

A design of virtual oscilloscope is proposed, which uses the technology of GPIB interface and SCPI (Standard Commands for Programmable Instruments) on the LabVIEW platform. It has the functions of waveform display, parameter measurement and data storage. As far as the oscilloscope involving the GPIB interface, the VISA technology in LabVIEW is adopted to programming. And virtual oscilloscope has the function of real-time data acquisition, display and storage without extra data collecting card in PC. It was proved that the design has the characteristics of reliable operation, flexible extensions and has practical value in automatic test areas.

Research on compensation method with additional phase for quartz crystal test network

IEC recommended π network zero phase method is the standard one for testing parameters of quartz crystal resonator. Main difficulty in realizing such method is the compensation for additional phase shift of π network. π network can be inspired by multiplexed output standard signal adjusted by phase difference. After testing the additional phase shift of π network, and then adjusting the phase difference of signal source, actively compensating the additional phase shift of network, the test precision can be significantly improved and requirements for π network manufacturing technology and operating environment will be lowered.

Dichotomous research on near infrared spectrogram of tomato leaf based on SVM algorithm

A method for classfying anti-nematodes tomato and normal tomato based on near infrared (NIR) spectroscopy technology is presented. Forty anti-nematodes tomatoes and forty normal tomatoes were selected, and leaves spectrograms were achieved.The original spectrograms were preprocessed by normalization and wavelet transform. Support vector machine (SVM) model was established.The optimal parameter combination of the model was found by grid search method. The research indicates that the penalty parameter C mainly affects the recognition rate of model, while the kernel parameter γ mainly affects the prediction rate of model. The recognition rate and prediction rate of SVM model are 98% and 100% respectively. SVM model can identify anti-nematodes tomato and normal tomato well.