Table of Content

15 August 2023 Volume 36 Issue 8

  

Design and FPGA Implementation of Dehazing Based on Channel Difference Model and Guided Filtering

CAO Hongfang,WANG Xiaolei,DU Gaoming,LI Zhenmin,NI Wei

Electronic Science and Technology. 2023, 36(8):  1-6.  doi:10.16180/j.cnki.issn1007-7820.2023.08.001

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Computer vision systems are affected by foggy weather, resulting in poor quality images captured. To solve this problem, this study proposes a guided filtering dehazing algorithm based on channel difference model and its FPGA design. The channel difference model is obtained by separating the bright channel and dark channel of foggy image, and the model is used as a guide map for guided filtering to smooth the foggy image. Finally, a high boost filtering operation is performed to obtain a dehazed image. The hardware architecture is designed and implemented on FPGA. The experimental results show that the image scene after dehazing has uniform illumination, high degree of texture information recovery and high color fidelity. For an image of 480×270 size, the integrated frequency of the circuit is 108.448 MHz, the throughput is 323.47 MB·s^-1, and the time to complete the entire dehazing is 0.001 2 s. These results indicate that the proposed algorithm and its hardware design can effectively improve image visibility and dehazing speed.

Super-Resolution Imaging of Laminate Debonding Defects via Deconvolutional Neural Network and Ultrasound Guided Waves

YUE Shengyao,XU Baiqiang,XU Guidong,XU Chenguang,ZHANG Sai

Electronic Science and Technology. 2023, 36(8):  7-13.  doi:10.16180/j.cnki.issn1007-7820.2023.08.002

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Traditional ultrasonic guided wave imaging detection methods are difficult to accurately characterize structural damage details. In order to obtain detailed features of the damage, the deconvolutional neural network model via deep learning is proposed to investigate the super-resolution imaging problem of subwavelength debonding defects in laminate plates. Initial imaging results are obtained by finite element simulation with the total focusing method. The labeled 12 550 damage images are trained and tested based on extended database expanded by data enhancement method. The results show that compared with the original full-focus imaging algorithm, the deconvolution neural network model improves the accuracy of damage location by 5%, the imaging accuracy is higher than 91%, and the positioning error is lower than 1.8 mm, indicating that the proposed method can significantly improve the resolution of network imaging results and better display the details of subwavelength damage. The above results show that the proposed method has high detection efficiency and does not require manual experience, and has good application value in engineering practice.

A Firmware Security Update Scheme for Embedded Devices

ZENG Xiangyi,LIU Wei,XIAO Hao

Electronic Science and Technology. 2023, 36(8):  14-18.  doi:10.16180/j.cnki.issn1007-7820.2023.08.003

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In view of the security problem of embedded device firmware update, this study proposes a multi-check firmware security update scheme based on hash, symmetric and asymmetric encryption algorithms. In this study, the master key pairs, temporary key pairs, shared key and hash chain are designed to protect firmware update from identity authentication, data encryption, integrity check and other aspects, which can effectively prevent illegal users, firmware tampering, firmware data leakage, replay attack, firmware rollback and other attacks. In this study, the concrete implementation of the security update scheme is carried out. The experiment results show that compared with the ISP(In System Programming) and IAP(In Application Pragramming) technologies without any security protection, the scheme achieves the security protection of the whole process of firmware update at the time cost of about 7% and 11%, it provides a safe and reliable update method for embedded device firmware update.

Binocular Vision Localization Method of Underwater Obstacles Based on Red Channel Prior

WANG Yuhai,ZHANG Meiyan,CAI Wenyu,XIE Qinan

Electronic Science and Technology. 2023, 36(8):  19-28.  doi:10.16180/j.cnki.issn1007-7820.2023.08.004

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During the underwater cruise of the autonomous underwater vehicle based on binocular vision, the images acquired by the binocular camera have low contrast and color distortion due to the attenuation effect of the water and the scattering effect of the suspended particles on the light, which leads to the low accuracy of underwater obstacle localization. In view of the above problems, this study adopts the red channel prior restoration algorithm to improve the quality of underwater imaging, obtains the binocular disparity map of obstacles according to the calibration parameters of the binocular camera, and proposes an underwater obstacle localization method based on depth disparity map fusion. The proposed method fuses the depth disparity map and the underwater restoration contour map, performs convex polygon detection on the fused image, obtains the contour of the obstacle, and extracts the effective depth information of the obstacle based on the contour information to realize the spatial positioning of the obstacle. The experimental results of underwater binocular localization show that the method can make the binocular stereo matching more ideal and effectively improve the accuracy of underwater obstacle localization.

Distributed-Based Decentralized Federated Machine Learning

WANG Lihua,CHENG Xiang,YANG Ningbin,GONG Biyao,HUANG Zeyu,CHEN Meiyan

Electronic Science and Technology. 2023, 36(8):  29-34.  doi:10.16180/j.cnki.issn1007-7820.2023.08.005

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Federated learning is a new machine learning paradigm that allows multiple participants to collaboratively train a shared machine learning model in a private and secure way without sharing raw data. Federated learning has wide application value because it can solve the problem of data island. However, in traditional federated learning, using a single central server aggregation model can lead to a single point of failure problem. In order to overcome the possible single point of failure problem in traditional federated learning, this study proposes a blockchain-based distributed federation learning (DFL), which takes advantage of the characteristics of blockchain to delegate the task of storing the model to nodes in the blockchain network. An asynchronous aggregation strategy is proposed, which enables participants to join federated learning at any time reducing the waiting time of participants. To overcome the blockchain storage limitation, a model chunking strategy is designed to chunk the large-scale model to fit the blockchain storage requirements. The proposed DFL is evaluated by training multiple machine learning models on multiple data sets, and the experimental results show that DFL achieves better performance than traditional methods while overcoming single point of failure.

A Lightweight Crowd Detection Network for Dense Scenes

PAN Hao,LIU Xiang,ZHAO Jingwen,ZHANG Xing

Electronic Science and Technology. 2023, 36(8):  35-42.  doi:10.16180/j.cnki.issn1007-7820.2023.08.006

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For the occlusion problem of pedestrian detection in dense scenes, this study proposes the SC-YOLOv4 crowd detection network based on YOLO. Based on the CSPNet structure of YOLOv4 and combined with the idea of ShuffleNetv2 network, the common convolution structure is improved, and the original common residual module is replaced with the Shuffle Module. A backbone network structure based on S-CSPDarkNet53 is proposed, which preserves the accuracy and reduces the number of network parameters. The centroid prediction module is designed on the basis of retaining the original PANet structure, and the original three output feature layers are replaced with a centroid-based prediction method, that is, the regression and training of the target center point are carried out to calculate the loss, and the original NMS operation is discarded to further improve the detection accuracy in the case of occlusion. The experimental results show that YOLOv4 with S-CSPDarkNet53 structure on the CrowdHuamn data set reduces the amount of parameters and improves the detection speed by 5.2 frame·s^-1 when compared with the original network. Compared with YOLOv4, the final SC-YOLOv4 network improves the detection speed by 4.9 frame·s^-1.

Structured Compression and Acceleration of Network Based on Tiny-YOLOv3

HU Yongyang,LI Miao,MENG Fankai,ZHANG Feng,MENG Yiwei,SONG Yukun

Electronic Science and Technology. 2023, 36(8):  43-48.  doi:10.16180/j.cnki.issn1007-7820.2023.08.007

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In particular application scenarios, Tiny-YOLOv3 network has problems of high resource cost and slow running speed when deployed on embedded platform. This study proposes a structured compression scheme combining pruning and quantization, and establishes a convolutional layer acceleration system for compressed network. The structured compression scheme uses sparse training and channel pruning to reduce the amount of computation in the network, and utilizes fixed-point quantization of activation value and integer power quantization of weight two to reduce the storage of parameters in the network convolution layer. In the convolution layer accelerator system, the programmable logic part designs a convolution layer accelerator core according to the parallel plus pipeline method, and the processing system part is responsible for the scheduling of the convolution layer accelerator system. The experimental results show that the mean average precision of Tiny-YOLOv3 network after structured compression is 0.46, and the parameter compression ratio reaches 5%. When the convolution layer acceleration system is deployed on Xilinx ZYNQ chip, the hardware can run stably at 250 MHz clock frequency, and the calculation force of the convolution operation unit is 36 GOPS. In addition, the overall power consumption of the acceleration platform is 2.6 W, and the hardware design greatly saves hardware resources.

Study of Lamb-Wave Complex Dispersion Relations for Plate Structures Based on Bloch-Floquent Theory and Finite Element Simulation

GU Xin,XU Baiqiang,XU Guidong,XU Chenguang,ZHANG Sai

Electronic Science and Technology. 2023, 36(8):  49-55.  doi:10.16180/j.cnki.issn1007-7820.2023.08.008

Abstract ( 138 )   HTML ( 3 )   PDF (1124KB) ( 70 )  

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In order to solve the problem of Lamb wave dispersion and multi-mode restriction in plate structure and its practical application in ultrasonic nondestructive testing technology, a method based on Bloch-Floquent theory and finite element characteristic frequency method is used to calculate the dispersion curve of plate structure containing mode information in real and pure imaginary wave number domains. Based on the Bloch-Floquent periodic boundary conditions, the dispersion curves of infinite steel plate structures in irreducible Brillouin region and pure imaginary wave number domain are quickly calculated. In this study, Bloch-Floquent extension and Fourier transform are used to obtain the real wave number information of each characteristic mode and overcome the aliasing problem of dispersion curve. In this study, the modal identification is carried out according to the out-of-plane displacements of the upper and lower surfaces of each mode, and the complete dispersion relation of the steel plate is obtained. The calculated results of this method are in good agreement with those obtained by solving the dispersion equation with the traditional dichotomy method, which verifies the validity of this method.

PID Controller Tuning of Time Delay Integral System Based on Multi Dominant Pole Method

DING Xiangxin,ZHANG Wei,WANG Yagang

Electronic Science and Technology. 2023, 36(8):  56-64.  doi:10.16180/j.cnki.issn1007-7820.2023.08.009

Abstract ( 151 )   HTML ( 6 )   PDF (968KB) ( 68 )  

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As the most basic control problem of the time delay integral system, it is difficult to control servo and disturbance rejection. In this study, a differential forward PID tuning method based on direct synthesis method and multiple dominant pole-placement method is proposed. This method mainly compares the coefficients of the characteristic equation composed of series filter and the time-delay integral controlled process with the actual desired characteristic equation. The third-order dominant pole is placed at -1/λ, and the second-order non-dominant pole is placed at -5/λ (λ is the adjustment parameter), so as to obtain the desired characteristic equation. Parameters of the controller are tuned to achieve the desired robustness. The corresponding Ms values can be obtained by selecting different tuning parameters, and the relationship curves between Ms and the tuning parameters can be fitted under the limited condition that the parameters are nominal, and the analytical form of the tuning rules can be given. The simulations of PIPTD, DIPTD and FOPTDI systems show that the IAE index can be reduced by average 35.79% and the TV index can be reduced by average 18.97%.

Overview of Gallium Nitride Solid State Power Amplifier Development

XIE Hongxing,LU Hongmin,LIU Liang,REN Yongda,LI Min,ZHANG Jiahai

Electronic Science and Technology. 2023, 36(8):  65-71.  doi:10.16180/j.cnki.issn1007-7820.2023.08.010

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Solid-state power amplifiers using semiconductor technology have the advantages of small size and high stability, and have replaced traditional travelling wave tube amplifiers in many microwave applications. Among all types of semiconductor materials, the third-generation semiconductor material GaN(Gallium Nitride) has been widely used in power amplifiers because of its advantages of wide band gap, high electron mobility, and high breakdown voltage. Based on the development of power amplifiers, this study describes the development history of solid-state power amplifiers, summarizes the performance comparison between GaN technology and other semiconductor technologies, and focuses on power amplifiers using GaN HEMT(GaN High Electron Mobility Transistor) technology. This study discusses various types of GaN HEMT power amplifiers, including class A, B, C, D, and E, etc., introduces the efficiency and linearity improvement techniques applied to GaN power amplifiers, including Doherty power amplifiers and envelopes tracking technology, and digital pre-distortion technology, etc., and makes a summary and comparison of related technologies.

Research on Single Leg Trajectory Planning of Bionic Quadruped Robot

CHEN Jia,SAN Hongjun,CHEN Jiupeng,XU Yangyang

Electronic Science and Technology. 2023, 36(8):  72-80.  doi:10.16180/j.cnki.issn1007-7820.2023.08.011

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Quadruped robot has become a hot topic in the international community because it can adapt to the complex terrain environment using the way of detached feet.In view of the lack of bionic characteristics of the foot trajectory of the quadruped robot and the inconsistency between the walking effect and that of mammals, this study establishes the three-joint leg mechanism with the help of SolidWorks, obtains the leg kinematic model, and analyzes the leg trajectory motion by referring to the laws of biological movement. Combined with bionic characteristics and based on compound cycloid and quintic polynomial, a foot trajectory of compound cycloid quintic polynomial is planned in this study, which not only meets the law of bionic motion, but also meets the requirements of trajectory planning. In this study, MATLAB and ADAMS co-simulation is used to realize the kinematic simulation of single leg, and the prototype and control system are used to realize the physical motion of single leg. The experimental results show that the single-leg motion of the quadruped robot can realize the planned trajectory curve motion, and the acceleration is 0 at the second second, which is basically consistent with the simulation trajectory effect, which verifies the effectiveness of the proposed algorithm to a certain extent.

Fixed-Time Synchronization for Fractional-Order Memristive Neural Networks with Time-Delays

WANG Kejie,TONG Dongbing

Electronic Science and Technology. 2023, 36(8):  81-87.  doi:10.16180/j.cnki.issn1007-7820.2023.08.012

Abstract ( 139 )   HTML ( 6 )   PDF (1490KB) ( 72 )  

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The fixed-time synchronization problems are solved for fractional-order memristive neural networks. According to the voltage and current characteristics of memristors, the model of fractional-order memristive neural networks with time-varying delays is established. Different from the traditional calculation method of memristive synaptic weights based on the maximum absolute value, by introducing some transformations, using differential inclusion theory and set-valued maps, the fractional-order memristive neural networks are transformed into a type of fractional-order systems with uncertain parameters in the framework of Filippov solution. Based on the fixed-time stability theory and the theory of measurable selection, the sufficient conditions of fixed-time synchronization are given by constructing Lyapunov function and using inequality techniques, and the calculation formula of the upper bound for the synchronization time is given. By designing an appropriate state feedback controller, the master-slave systems can reach fixed-time synchronization, and the upper bound for the synchronization time is independent of the initial state for the systems. The simulation example shows that the designed controller makes the systems achieve synchronization quickly.