Table of Content

15 February 2021 Volume 34 Issue 2

  

A Control Method of Power up Bias Sequence of Radio Frequency Amplifier

HE Qiang,YANG Qinghui,ZHANG Huaiwu

Electronic Science and Technology. 2021, 34(2):  1-6.  doi:10.16180/j.cnki.issn1007-7820.2021.02.001

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The RF amplifier has the problems of complicated power-on sequence and inaccurate delay, and the bias point is not easy to meet the requirements of the device. To solve these problems, a method of controlling the power on timing sequence and bias point of RF amplifier based on STM32F302K8 is proposed in the present study. The method controlls the power chip to delay power-on by the microcomputer IO to ensure that the HMC637 drain power supply and the gate power supply are sequentially powered up.According to the ADC sampling bias current the DAC is used to adjust the HMC637 gate voltaget to make the bias current reach the required value. The test results show that the proposed method can realize the power-on sequence of each power supply at intervals of 6 ms, and adjust the gate voltage of HMC637 to make the bias current reach the target value of 400 mA, which fully meets the requirements of the chip. Additionally, the design is not affected by the device technology, and has self-adaptability and broad application prospect.

Research Progress of Medical Image Segmentation Based on Deep Learning

YAN Chao,SUN Zhanquan,TIAN Engang,ZHAO Yangyang,FAN Xiaoyan

Electronic Science and Technology. 2021, 34(2):  7-11.  doi:10.16180/j.cnki.issn1007-7820.2021.02.002

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Medical image segmentation plays an important role in clinical diagnosis and is the basis of other medical image processing methods. With the improvement of computer hardware performance, image segmentation technology based on deep learning has already become a powerful tool for processing medical images and is widely used in various medical image segmentation tasks. This paper introduces several types of common medical images and their characteristics, analyzes and compares the image segmentation algorithms that have emerged in recent years. Some algorithms have been successfully applied to segmentation tasks such as brain tissue, lungs and blood vessels. In response to the current problems in the development of medical image segmentation technology based on deep learning, corresponding strategies are proposed, and the future development direction is also prospected.

A Novel Medical Image Segmentation Algorithm Based on Level Set

FANG Jinli,LÜ Yibin,WANG Yingzi,TANG Shengnan,WU Dean

Electronic Science and Technology. 2021, 34(2):  12-20.  doi:10.16180/j.cnki.issn1007-7820.2021.02.003

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In view of the common inhomogeneous intensity effect in medical images, a hybrid level set model combining global and local term is proposed, namely HLSGL. Based on the global information of the C-V level set segmentation algorithm, the local information energy term is introduced by calculating the local fitting average of each pixel of the image, so that the global and local information can be superimposed to form the driving force term, ensuring better localization effect on the edge of the image. A new speed stopping function is introduced into the driving force term to adaptively adjust the curve evolution rate in segmentation process, improve segmentation efficiency. In addition, HLSGL is applied to different kinds of medical images in the present study, and experiments results show that the HLSGL model can efficiently segment medical images with noise, weak boundaries and inhomogeneous intensity to obtain a more complete contour curve. Compared with other level set models,the accuracy, robustness and segmentation efficiency of the HLSGL model are significantly improved.

The Algorithm Based on CNN and LSTM for Sleep Apnea Syndrome Detection

GE Jing,LIU Zilong

Electronic Science and Technology. 2021, 34(2):  21-26.  doi:10.16180/j.cnki.issn1007-7820.2021.02.004

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Sleep apnea is a common sleep disorder, which is associated with multiple diseases. This study proposes deep learning methods to detect sleep apnea events. The two stages of ECG signal preprocessing are used as inputs to the CNN and LSTM models, respectively. The CNN model takes the original ECG signal as input and automatically extracts features through convolution to identify sleep apnea. The LSTM model uses ECG's indirect signal as input and automatically extractes features from RR intervals and respiratory signals. Experiments show that the LSTM model has a high accuracy of 87.4%, which is close to the performance of the traditional methods. The proposed method combines the advantages of artificial feature extraction and deep learning, and is more applicable than the traditional classification methods.

Implementation of Software Programmable FPGA Network Measurement Engine Technology

YAN Zijie,WANG Jingmei,CHEN Zhuo,LIU Yu

Electronic Science and Technology. 2021, 34(2):  27-32.  doi:10.16180/j.cnki.issn1007-7820.2021.02.005

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In order to solve the problems of large resource overhead and rough granularity in the existing network transmission and switching performance monitoring schemes, an implementation of software programmable FPGA network measurement engine technology scheme is proposed. First, this solution preprocesses the input rules by the measurement controller and compiles them into a custom instruction set, and sends them to the data collection points in each network node. Then, the data collection point processes the received instructions in a pipelined manner to measure the network flow. The proposed solution involves key technologies such as measurement rules pre-processing of network flow, pipeline high-speed processing engine with programmable hardware measurement rules, and can be used for efficient measurement of existing rules with complex rule definitions. Board-level verification is performed by injecting different parameter network flows into the system. The verification results show that the designed system can correctly receive and process the custom instruction set issued by the measurement controller to achieve the measurement function.

Research on Security Vulnerabilities and Control Flow Integrity of PLC in Industrial Control System

CHEN Dawei,XU Ruzhi

Electronic Science and Technology. 2021, 34(2):  33-37.  doi:10.16180/j.cnki.issn1007-7820.2021.02.006

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PLC plays an important role in industrial control systems. However, the security vulnerability of PLC disclosed in recent years has increased year by year. Carrying out the research on defense technology of vulnerability for PLC is of great significance for improving the security of industrial control system. Based on the control-flow integrity, this study proposes a defense mechanism using control-flow integrity for PLC to protect PLC from vulnerability hijacking. This defense mechanism protects the PLC from being hijacked by attackers through checking the control transfer instruction in the PLC program and inserting check instruction based on pile technology to ensure that the program is executed according to the original control-flow graph. In order to effectively guarantee the real-time performance of the PLC, a cyclic shadow stack is introduced. The proposed scheme effectively protects the PLC from vulnerability hijacking, and the performance overhead of the defense mechanism is only about 3.6% on average.

Research on Sorting of Retired Lithium Battery Based on Radial Basis Function Neural Network

HE Zhonglin,ZHOU Ping

Electronic Science and Technology. 2021, 34(2):  38-44.  doi:10.16180/j.cnki.issn1007-7820.2021.02.007

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With the aging of lithium-ion batteries of electric vehicles in the near future, research on the second use of retired LIBs has become more and more critical. However, the classification method of the retired LIBs is challenging before the second use due to large cell variations. The traditional sorting method requires a single battery to be tested one by one to complete the sorting, but this method is not suitable for rapid sorting of large-scale batteries. In order to improve the sorting speed of the retired battery, the equalization-charging sorting method is proposed in this study. The batteries to be sorted are parallel-balanced, and the constant current charging is performed after the voltage is consistent. According to the characteristics of different aging batteries with different voltage curves, combined with the non-linear function approximation capability of radial basis function neural network, battery capacity estimation are realized through model training, thereby completing battery sorting. The results of the simulation verification reveale that the capacity error doesnot exceed ±5%, and the results of the experimental verification show the capacity error is within ±3%, which indicates the proposed method can achieve the sorting of retired lithium batteries.

Stability Analysis of Transmission Structure of Rail Transport Conveyor Lifting Subsystem Based on Contact

PU Jianghua,WANG Xuejun,WU Peng,CHEN Mingfang,YANG Xiong

Electronic Science and Technology. 2021, 34(2):  45-51.  doi:10.16180/j.cnki.issn1007-7820.2021.02.008

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Rail transport conveyor is a kind of rail transport equipment, which is widely used in railway, metallurgy, mining and other fields. However, the stability of its transmission structure is an urgent problem to be solved in production. In this study, the transmission structure of the lifting subsystem of rail transport conveyor has been taken as the research object. Based on the multi-body dynamics theory and Hertz contact theory, the meshing process of the worm gear and worm in the lifting transmission structure of rail transport conveyor are studied. Additionally, the influence of stiffness coefficient, motor driving speed and transmission distance on the stability of transmission are analyzed. Then, the variation law of contact force between the worm gear and the worm is explored, so as to find out the reasons of affecting the stability of lifting structure. Through the research on the change law of the contact force of the worm gear and worm transmission mechanism in the transmission structure, it is found that the instantaneous impact of contact and the periodic fluctuation of contact force are the main reasons for the instability of the transmission structure of the lifting subsystem. The research work in this paper provids a theoretical basis for both the study of the dynamic characteristics of the worm gear and worm transmission and the design of the new transmission structure of rail transport conveyor.

Image Classification Method Using Convolutional Neural Network Based on New Initial Module

ZHU Bin,LIU Zilong

Electronic Science and Technology. 2021, 34(2):  52-56.  doi:10.16180/j.cnki.issn1007-7820.2021.02.009

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Among the problems involving image classification, the classification method based on convolutional neural networks is preferred. In order to solve the problems of poor processing capacity and low classification accuracy, a new type of image classification model of convolutional neural network is proposed. The new Inception module is added on the basis of the traditional network model, which enhances the transmission of the feature information of the model and improves the ability of feature expression. Additionally, the performance of the model and the classification accuracy are improved by activation function, data enhancement, batch regularization, initial weight optimization and Adadelta optimization method. The new network model based on the new initial module is used to test the data on the CIFAR-10 data set, and compared with the traditional network model method, which proves that the modulated model effectives improves the network performance.

Design of a Broadband Large Dynamic AGC Circuit

MAO Yunshan,WEI Xubo

Electronic Science and Technology. 2021, 34(2):  57-61.  doi:10.16180/j.cnki.issn1007-7820.2021.02.010

Abstract ( 335 )   HTML ( 7 )   PDF (863KB) ( 63 )  

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A high-frequency broadband large dynamic AGC circuit is designed for the high frequency, wide frequency band and large dynamics of automatic gain control circuits. In this paper, the problem of inaccurate power detection caused by the bottoming of the wideband circuit is solved by adopting the narrow banding method of the AGC coupling signal, and the large dynamic of the AGC circuit is realized by the cascade control of the two-stage voltage-controlled gain amplifier. The input and output amplification filter circuit is added to improve signal purity. According to the design, the measured AGC circuit has a dynamic range of 100 dB when the signal frequency is 2 GHz and the signal bandwidth is 200 MHz, which improves the applicable frequency and bandwidth when compared with the conventional AGC circuit.

Research Progress of Surface Electromyography Signal Classifier Based on Artificial Neural Network

ZHOU Xiaobo,ZOU Renling,LU Xuhua,WANG Haibin,ZHANG Junxiang

Electronic Science and Technology. 2021, 34(2):  62-67.  doi:10.16180/j.cnki.issn1007-7820.2021.02.011

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The surface electromyography signal is an important physiological electrical signal. The human body rehabilitation motion recognition system based on the surface electromyography is easy to operate, hurtless to the body and no interference to motion, and has broad application prospects. The limb rehabilitation motion recognition system heavily relies on signal feature extraction and the use of classifiers. In this paper, the surface electromyography signal based on artificial neural network including LVQ classifier, ELM classifier, WNN classifier, ANFIS classifier, Alex Net classifier, and GRNN classifier are reviewed and discussed. After the review and comparison of various classifiers, some shortcomings are found and the future research directions and development trends of the classifiers are analyzed and prospected, which provides a reference for relevant research in the future.

Experimental Study on Parameters Optimization of Magnetostrictive Sensor Backing Layer

JIANG Yinfang,HU Huajian,Guo Yongqiang,WU Bo

Electronic Science and Technology. 2021, 34(2):  68-73.  doi:10.16180/j.cnki.issn1007-7820.2021.02.012

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The backing layer of the traditional giant magnetostrictive sensor poorly absorbs the residual vibration, which affects the excitation performance. In view of this problem, the relationship between the backing layer parameters and the excitation performance of the magnetostrictive sensor is studied by using different parameters of the backing layer. The optimum backing layer ratio is determined to be number 3, the length is 25 mm, and the thickness is 6 mm. The sensor with this backing layer and the sensor with the traditional one are used to detect the same pipe respectively. The test results show that the echo coefficient is increased by 10.7%, the SNR is increased by 10.6 dB, and the dead zone detection time is shortened by 0.4×10^-4 s, which further verifies the practicability of the optimized giant magnetostrictive sensor backing layer.

Development of Digital Signal Processing Simulation Platform Based on MATLAB GUI

SUN Huixia,ZHOU Shangnan,ZHOU Ling,DOU Yongmei

Electronic Science and Technology. 2021, 34(2):  74-78.  doi:10.16180/j.cnki.issn1007-7820.2021.02.013

Abstract ( 514 )   HTML ( 24 )   PDF (2796KB) ( 107 )  

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In view of the rapid realization and visualization of basic operations and typical algorithms of digital signal processing, a digital processing simulation platform is developed with the help of MATLAB GUI. Through introducing actual engineering cases signal generation, basic operations, convolution calculation, CZT, FFT, power spectrum analysis, IIR filtering, FIR filtering and adaptive filtering are realized.The proprosed platform covers the common operations and typical algorithms of digital signal processing, and provides corresponding display functions. The simulation platform is intuitive, and easy to use, making the abstract and difficult algorithms visible and easy to understand and master.