Table of Content

15 April 2022 Volume 35 Issue 4

  

EMI Margin Assessment of Vehicular Communication System

Jianpeng WAN,Hongmin LU,Guohua LIU,Min LI

Electronic Science and Technology. 2022, 35(4):  1-7.  doi:10.16180/j.cnki.issn1007-7820.2022.04.001

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With the development of science and technology, the electromagnetic interference in the vehicular communication system becomes more and more obvious. In view of the interference margin assessment of the vehicular communication system, this study proposes a novel evaluation method of electromagnetic interference margin of the vehicular communication system based on four-level screening method. The working conditions, frequency, signal power and communication performance of the vehicular receiver are evaluated respectively to display the disturbed situation and interference margin of the vehicular receiver quantitatively. Aiming at the influence of interference source on receiver sensitivity, the communication distance, signal-to-noise ratio and bit error rate are selected to evaluate the performance of vehicular communication system by this method. The results show that compared with the traditional four-level screening method, this method can more comprehensively reflect the performance changes of communication system with interference.

Design of Ternary Logic-in-Memory Based on RRAM Dual-Crossbars

Weiyi LIU,Yanan SUN,Weifeng HE

Electronic Science and Technology. 2022, 35(4):  8-13.  doi:10.16180/j.cnki.issn1007-7820.2022.04.002

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Implementing logic within RRAM crossbar is an attractive approach to overcome the memory wall in conventional Von Neumann architecture. Ternary logic can reduce the number of logic operations and enhance the computation speed compared to binary logic. In this study, a ternary logic-in-memory scheme is proposed based on the RRAM dual-crossbar structure, in which the inputs and outputs are represented by the multi-level cells of RRAMs. Two ternary logic gates and one binary logic gate are supported in the proposed structure to effectively increase the computation speed. Experimental results show that the operation steps of the ternary logic-in-memory adder are reduced by up to 68.84%, as compared with previously published binary logic-in-memory designs. The energy consumed by the ternary logic-in-memory adder is also reduced by 33.05% when compared with previously published IMPLY-based design.

VSLAM for Indoor Dynamic Scenes

Hongjun SAN,Wanglin WANG,Jiupeng CHEN,Feiya XIE,Yangyang XU,Jia CHEN

Electronic Science and Technology. 2022, 35(4):  14-19.  doi:10.16180/j.cnki.issn1007-7820.2022.04.003

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The traditional VSLAM algorithm is implemented based on static scenes, and the positioning accuracy is degraded in indoor dynamic scenes, and the 3D sparse point cloud map has problems such as mismatching of dynamic feature points. In this study, the ORB-SLAM2 framework is improved, which is combined with Mask R-CNN to perform semantic segmentation of images to remove dynamic feature points located on dynamic objects, optimize the camera pose, and obtain a static 3D sparse point cloud map. The experimental results on the public TUM dataset show that ORB-SLAM2 combined with Mask R-CNN effectively improves the pose estimation accuracy of intelligent mobile robots. The root mean square error of the absolute trajectory can be increased by 96.3%. The root mean square error of relative translation trajectory can be increased by 41.2%, and the relative rotation trajectory error has also been significantly improved. Compared with ORB-SLAM2, the proposed method can more accurately establish a 3D sparse point cloud map without the interference of dynamic object feature points.

Globaland Local Scene Representation Method Based on Deep Convolutional Features

Chaowei LIN,Feifei LI,Qiu CHEN

Electronic Science and Technology. 2022, 35(4):  20-27.  doi:10.16180/j.cnki.issn1007-7820.2022.04.004

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Scene Recognition is a fundamental task in computer vision. Different from image classification, scene recognition needs to take a comprehensive consideration of factors such as global layout information, local scene features, and object features, which leads to the poor performance of classic convolutional neural network for scene recognition. In order to solve this issue, this study proposes a global and local scene representation method based on deep convolutional features. The proposed method transforms deep convolutional features of scene image to generate a comprehensive representation for each image. Specifically, CAM is used to discovery local key regions, and LSTM is used to encode convolutional features extracted from local key regions to produce the local representation for scene images. Attention mechanism is adopted to fuse scene features and object features to form a global representation for scene images. Finally, the evaluation experiments are conducted on MIT indoor 67 data set and the results show that the test accuracy is up to 87.59% using the proposed method.

Design of Biological Behavior Analysis System Based on Vision and IMU Sensors

Sunyun YANG,Xiu KAN

Electronic Science and Technology. 2022, 35(4):  28-34.  doi:10.16180/j.cnki.issn1007-7820.2022.04.005

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In order to solve the problem that the digital image processing technology alone cannot detect and analyze the behavior of mice in behavioral experiments, a biological behavior detection method based on the combination of image and IMU sensor is proposed in this study. In Python programming environment, this method uses digital image processing technology to detect and track the motion video of mice, and obtain the motion behavior parameters of mice. Meanwhile, the wireless bluetooth micro IMU sensor designed by the laboratory is used to measure the three-axis acceleration, three-axis angular velocity and three-axis magnetic force of mice. The extended Kalman filter algorithm is used to solve the measured data to obtain the posture and other posture change information of mice. The experimental results show that the system can automatically analyze the motor behavior and posture information of mice, and can display the behavior information on the interface of biological behavior analysis system in real time.

Video Retrieval Algorithm Based on 3D Convolution and Hash Method

Hanqing CHEN,Feifei LI,Qiu CHEN

Electronic Science and Technology. 2022, 35(4):  35-39.  doi:10.16180/j.cnki.issn1007-7820.2022.04.006

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Different from other multimedia information retrieval, video retrieval requires a large amount of computation in similarity calculation due to the large amount of information contained in videos. In addition, the temporal correlation between video frames is often ignored in feature extraction, which leads to insufficient feature extraction and affects the accuracy of video retrieval. For this problem, this study proposes a video retrieval method based on 3D convolution and Hash method. This method constructs an end-to-end framework, uses a 3D convolutional neural network to extract the features of the representative frames selected from the video, and then maps the features to the low-dimensional Hamming space to calculate the similarity in the Hamming space. Experimental results on two video data sets show that compared with the latest video retrieval algorithms, the proposed method has a greater improvement in accuracy.

Track Disease Diagnosis Method Based on VMD and BP Neural Network

Li HUA,Jian YANG,Tianchen YUAN,Ruigang SONG

Electronic Science and Technology. 2022, 35(4):  40-46.  doi:10.16180/j.cnki.issn1007-7820.2022.04.007

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In view of the problem of difficulty in extracting disease features from non-linear and unsteady sleeper vibration signals, this study proposes a track disease feature extraction method based on variational modal decomposition and multi-scale permutation entropy, and adopts the BP neural network disease diagnosis model to perform disease identification. The variational modal decomposition method is used to decompose the collected vibration acceleration signals to obtain several eigenmode components. The multi-scale permutation entropy value of these eigenmode components is calculated and used as the high-dimensional feature vector of the track disease to realize the noise reduction of the sleeper vibration signal and the extraction of the disease feature. Through the establishment of a BP neural network disease diagnosis model, high-dimensional feature vectors are input into the BP network for training, fitting, and verification, and compared with the method of combining empirical mode decomposition and BP neural network. The analysis results show that the proposed method has a higher recognition accuracy rate and can effectively diagnose disease.

Optical Design of Reflective ADB Module

Cunsheng YU,Hongbing YAO,Limin HUANG,Xiajun YANG,Wanyu LÜ

Electronic Science and Technology. 2022, 35(4):  47-52.  doi:10.16180/j.cnki.issn1007-7820.2022.04.008

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In order to solve the problem of car driving safety at night under complex road conditions and avoid the dazzling effect of the drivers of the opposing vehicle when meeting cars, the study is based on the non-imaging theory and combined with the law of refraction and reflection to establish the corresponding projection relationship between the emitting light of LED and the far-light distribution point on the receiving surface. The discrete points of the free-form surface are solved by numerical iteration, and are imported into 3D software for fitting modeling. A reflective adaptive driving beam optical system is designed to combine and form different high beam types by controlling the on and off of the corresponding LEDs. The test results of the final sample show that when the optical system is in the maximum working condition, the luminous efficiency reaches 34.3%, and the maximum illuminance value is 134 lx, which demonstrates that the light distribution results meet the requirements of high beam regulations.

The Point Cloud Coarse Registration Method Based on Boundary Centroid

Shanghong LU,Wenguo LI

Electronic Science and Technology. 2022, 35(4):  53-59.  doi:10.16180/j.cnki.issn1007-7820.2022.04.009

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The quality of point cloud registration directly affects the quality of 3D reconstruction. To solve the problem that the traditional K-4PC is time-consuming and prone to mismatching, a coarse point cloud registration method based on boundary centroid is proposed. By extracting the boundary of the point cloud, the surface features of the point cloud are preserved and the size of the point cloud data is reduced, which improves the speed of coarse registration. In order to speed up the extraction of boundary points, the K-D tree algorithm is used to search for k nearest neighbors. By registering the centroid of the boundary points, the initial distance of the point cloud is reduced and the degree of overlap is increased, ensuring the accuracy of coarse registration. The experimental results show that the proposed method is better than the traditional K-4PCS algorithm in terms of speed and accuracy. The speed of this method is about twice that of traditional K-4PCS. Both the translation and rotation accuracy are 40% higher than that of traditional K-4PCS. The proposed method has certain reference value for improving the speed and accuracy of point cloud coarse registration.

Drift Processing of Gyro While Drilling Based on Synaptic Plasticity Pulsed Neural Network

Jinxian YANG,Yuxin HAN,Pengwei LIU

Electronic Science and Technology. 2022, 35(4):  60-66.  doi:10.16180/j.cnki.issn1007-7820.2022.04.010

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In view of the data drift problem of MEMS gyroscope caused by vibration while drilling, a spiking neural network algorithm is proposed in this study. First, according to the time characteristics of the drift error of the gyroscope, the pulse time of the spiking neural network is used to encode the information intensity of the gyroscope. Then, the synaptic plasticity of the Izhikevich neuron model is used to adjust the excitatory synaptic conductance and inhibitory synaptic conductance to enhance the robustness of the network, thereby improving the anti-interference ability of the gyroscope signal against noise. Finally, under different vibration frequencies, the correlation between the firing rate of the Gaussian white noise output neuron and the membrane potential is analyzed. Experimental results show that under strong vibrations of different frequencies, noise has little effect on the firing rate of output neurons and the relative change of firing rate of output layer neurons, and has little effect on the membrane potential of output layer neurons, but has a greater impact on the correlation between membrane potentials. These results indicate that the proposed method improves the anti-interference ability of the gyroscope under vibration and noise, and can provide a new idea for the processing of gyroscope drift.

Simulation of Dynamic Process of Micro-Cutting Single Crystal Germanium Based on SPH Method

Xiaojing YANG,Hongxiu YANG

Electronic Science and Technology. 2022, 35(4):  67-71.  doi:10.16180/j.cnki.issn1007-7820.2022.04.011

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In view of the difficulty of observing chip formation in flying cutting of single crystal germanium, a meshless simulation method (SPH method) is adopted in this study. The influence of cutting depth and cutting speed on cutting force and chip formation during plastic removal is studied by establishing a micro- cutting simulation model of single crystal germanium (111). The results show that when the cutting speed is 4 μm·μs^-1 and the cutting depth is 0.5 μm, 1 μm, 2 μm and 5 μm, the tangential force and normal force gradually increase and then decrease to a gentle fluctuation trend. It is concluded that the larger the cutting depth is, the greater the stable fluctuation value of cutting force is, and the critical cutting depth for chip generation is between 0.5 μm and 1μm. When the cutting depth of single crystal germanium is 1 μm and the cutting speed is 2 μm·μs^-1, 4 μm·μs^-1, 6 μm·μs^-1 and 8 μm·μs^-1, it is found that the tangential force and normal force are not affected by the cutting speed, and the critical cutting speed for chip formation is between 2 μm·μs^-1 and 4 μm·μs^-1.

Research and Implementation of APFC Low Voltage System

Fuzhuan WU,Haoyang LI,Sheng PENG,Mengna CHEN

Electronic Science and Technology. 2022, 35(4):  72-77.  doi:10.16180/j.cnki.issn1007-7820.2022.04.012

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In view of the harmonic pollution caused by nonlinear rectifier devices in low-voltage application scenarios such as low-voltage micro-grid, lathe lighting, teaching experiment and temporary residence at construction sites, this study investigates the low-voltage APFC system using single-cycle control. The control principle of single-cycle controller UCC28180 is analyzed in detail, and the parameters of main circuit and control circuit are calculated. Based on the analysis of the average current loop and the voltage loop, the loop compensation of the zero-pole in the loop is carried out by combining the nonlinear current loop gain factor M₁ of the low voltage system, the PWM slope compensation slope M₂ of the voltage loop and the nonlinear gain M₃, and the corresponding Bode diagram is given. Finally, the prototype system is established and the system efficiency, power factor, and total harmonic distortion rate of input current are analyzed. The results show that the low-voltage system meets the design requirements.

Characteristic Signal Extraction of Non-Ideal Three-Phase Grid

Qingqing YUAN,Min JIANG,Yumei YANG

Electronic Science and Technology. 2022, 35(4):  78-86.  doi:10.16180/j.cnki.issn1007-7820.2022.04.013

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When the three-phase grid voltage is distorted or unbalanced, there are many non-ideal characteristic signals in the grid-connected system, such as harmonics and negative sequence components. In view of such problems, this study proposes a harmonic and negative sequence component extraction algorithm based on ISDFT. This algorithm redesigns the transfer function of the SDFT, which retains good signal filtering characteristics while improving the response rate of the system, and has strong flexibility. Experimental results show that ISDFT can quickly and accurately extract characteristic harmonics and negative sequence components, and ISDFT has good dynamic stability when the load changes suddenly. In addition, in terms of the extraction results of the 5th and 7th harmonics, DFT, SDFT, and ISDFT have the same effect of extracting harmonics, but the response rate of ISDFT is 44.56% faster than SDFT and 65.32% faster than DFT.