Image features are essential components for physical detection, classification of objectives and downstream tasks. Specifically, the image features can be utilized to automatically detect the characteristics of images and realize physical information mapping into information domains. However, existing image features are concentrated on the decrease the contrast, which can reduce the influence of lights. Another extraction process converts images into digital images and utilize digital information techniques to obtain the features. In this paper, we utilize the machine learning model to extract the features of images with enough training iterations. Initially, we utilize the CIFAR-10 data set, which contains the 10 categories of physical objectives and simulate as the training set. Indeed, the establish machine learning model is utilize to train through inputting the 80% of total data set. After training process, the output of machine learning mode can obtain the features of any physical images. Finally, we compare our proposed model with existing image features extraction methods and utilize 20% data to evaluate our model. From our extensive experimental results, we can conclude that our established model can effectively achieve the image features extraction with higher extraction accuracy and acceptable computation time through comparing with traditional mathematical analysis methods.
{"title":"Research on image features extraction based on machine learning algorithms","authors":"Xiao-Chuang Chang","doi":"10.1117/12.2682449","DOIUrl":"https://doi.org/10.1117/12.2682449","url":null,"abstract":"Image features are essential components for physical detection, classification of objectives and downstream tasks. Specifically, the image features can be utilized to automatically detect the characteristics of images and realize physical information mapping into information domains. However, existing image features are concentrated on the decrease the contrast, which can reduce the influence of lights. Another extraction process converts images into digital images and utilize digital information techniques to obtain the features. In this paper, we utilize the machine learning model to extract the features of images with enough training iterations. Initially, we utilize the CIFAR-10 data set, which contains the 10 categories of physical objectives and simulate as the training set. Indeed, the establish machine learning model is utilize to train through inputting the 80% of total data set. After training process, the output of machine learning mode can obtain the features of any physical images. Finally, we compare our proposed model with existing image features extraction methods and utilize 20% data to evaluate our model. From our extensive experimental results, we can conclude that our established model can effectively achieve the image features extraction with higher extraction accuracy and acceptable computation time through comparing with traditional mathematical analysis methods.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126432213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seed is the key link to ensure the yield and quality of crops, and seed quality plays a vital role in ensuring agricultural production. For seed quality testing, traditional methods have problems such as irreversible destruction, strong professionalism, complex process, long cycle, high cost, and inability to detect seeds online and real-time, which are difficult to meet the requirements of modern agriculture for non-destructive, rapid and efficient production of seeds. Spectral imaging technology has been widely used in the non-destructive and rapid detection of agricultural products, food, etc., with its advantages of fast, efficient, nondestructive and simple. The research progress of three spectral imaging technologies in seed quality detection was reviewed from the perspective of multispectral image recognition, hyperspectral image recognition and near-infrared spectral imaging. The basic principles, key technologies, implementation methods and application research results of the three spectral imaging technologies were compared, analyzed and summarized. The advantages and problems of spectral imaging technology detection and identification in crop seed quality detection were pointed out, In view of these problems, the future research directions are given to improve the understanding of agricultural information detection process using spectral imaging technology and promote the development of agricultural seed sources and other key agricultural core technologies.
{"title":"Research progress of crop seed quality detection based on spectral imaging technology","authors":"Hui Li, Chengjun Zou, Xuliang Duan","doi":"10.1117/12.2682450","DOIUrl":"https://doi.org/10.1117/12.2682450","url":null,"abstract":"Seed is the key link to ensure the yield and quality of crops, and seed quality plays a vital role in ensuring agricultural production. For seed quality testing, traditional methods have problems such as irreversible destruction, strong professionalism, complex process, long cycle, high cost, and inability to detect seeds online and real-time, which are difficult to meet the requirements of modern agriculture for non-destructive, rapid and efficient production of seeds. Spectral imaging technology has been widely used in the non-destructive and rapid detection of agricultural products, food, etc., with its advantages of fast, efficient, nondestructive and simple. The research progress of three spectral imaging technologies in seed quality detection was reviewed from the perspective of multispectral image recognition, hyperspectral image recognition and near-infrared spectral imaging. The basic principles, key technologies, implementation methods and application research results of the three spectral imaging technologies were compared, analyzed and summarized. The advantages and problems of spectral imaging technology detection and identification in crop seed quality detection were pointed out, In view of these problems, the future research directions are given to improve the understanding of agricultural information detection process using spectral imaging technology and promote the development of agricultural seed sources and other key agricultural core technologies.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"12715 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128862110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emotion recognition from social network texts aims to mine netizens’ subjective emotions, such as stances and emotional tendencies, over an event, which is imperative for monitoring Internet public opinion. Convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are widely used in the field of text classification. The combination of the two can exploit the CNNs’ feature extraction ability and BiLSTM’s ability to extract context dependency. However, for emotional recognition, it is also necessary to consider some specific words in online social text. Therefore, we constructed a model for emotion recognition based on Internet public opinion in three steps. First, the CNN was used to extract local features of social network texts. Second, context-related global features were extracted using the BiLSTM. Finally, we introduced an attention mechanism to obtain important features. Experiments were conducted using netizens’ comments from a microblog during the COVID-19 epidemic as the dataset. Experimental results showed that the feature vector of the proposed model (i.e., the CNN-BiLSTM-Attention model) contains richer semantic information of texts, which can effectively improve the performance of emotion recognition from Internet public opinions.
{"title":"Sentiment analysis of online public opinion based on CNN-BiLSTM and attention mechanism","authors":"Lingwei Wei, Lei Yang","doi":"10.1117/12.2682437","DOIUrl":"https://doi.org/10.1117/12.2682437","url":null,"abstract":"Emotion recognition from social network texts aims to mine netizens’ subjective emotions, such as stances and emotional tendencies, over an event, which is imperative for monitoring Internet public opinion. Convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are widely used in the field of text classification. The combination of the two can exploit the CNNs’ feature extraction ability and BiLSTM’s ability to extract context dependency. However, for emotional recognition, it is also necessary to consider some specific words in online social text. Therefore, we constructed a model for emotion recognition based on Internet public opinion in three steps. First, the CNN was used to extract local features of social network texts. Second, context-related global features were extracted using the BiLSTM. Finally, we introduced an attention mechanism to obtain important features. Experiments were conducted using netizens’ comments from a microblog during the COVID-19 epidemic as the dataset. Experimental results showed that the feature vector of the proposed model (i.e., the CNN-BiLSTM-Attention model) contains richer semantic information of texts, which can effectively improve the performance of emotion recognition from Internet public opinions.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117175085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To solve the problem of time synchronization between two stations in an optical two-way time synchronization system, an optical time synchronization scheme based on spread spectrum code ranging is presented. Time difference between sites is measured by spread spectrum code ranging, and link delay is eliminated by two-way method to achieve time synchronization between two sites. The accuracy of time synchronization between stations is affected by the accuracy of time difference measurement. By analyzing the structure and measurement principle of receiver code tracking loop, it is found that the accuracy of time difference measurement is mainly related to the signal system, carrier-noise ratio and receiver parameter settings. The carrier-noise ratio of links and receiver parameter conditions are determined. Through the analysis of different signal systems time difference accuracy, the signal system that can meet the requirements of highprecision time synchronization is selected.
{"title":"Analysis and research on the accuracy of optical fiber two-way time synchronization signal based on spread spectrum code ranging","authors":"H. Sun, H. Gong, Jing Peng, Wenchi Zang, Ming Ma","doi":"10.1117/12.2682432","DOIUrl":"https://doi.org/10.1117/12.2682432","url":null,"abstract":"To solve the problem of time synchronization between two stations in an optical two-way time synchronization system, an optical time synchronization scheme based on spread spectrum code ranging is presented. Time difference between sites is measured by spread spectrum code ranging, and link delay is eliminated by two-way method to achieve time synchronization between two sites. The accuracy of time synchronization between stations is affected by the accuracy of time difference measurement. By analyzing the structure and measurement principle of receiver code tracking loop, it is found that the accuracy of time difference measurement is mainly related to the signal system, carrier-noise ratio and receiver parameter settings. The carrier-noise ratio of links and receiver parameter conditions are determined. Through the analysis of different signal systems time difference accuracy, the signal system that can meet the requirements of highprecision time synchronization is selected.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122633973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problem that the terahertz wave beam is narrow and directionality is strong, which makes it easy for the enemy to eavesdrop on the data information, and thus the information cannot be transmitted safely, a pseudo-random sequence generation method based on n-LFSR and AV-NL chaotic mapping is proposed. Different algorithms are selected according to the number of LFSRs. At the same time, the absolute value nonlinear chaotic map is generalized. Finally, the generated two pseudo-random sequences are XOR combined to form the final pseudo-random sequence for source encryption. The simulation results show that this method takes into account the advantages of simple design of linear feedback shift register and high chaotic randomness. The generated pseudo-random sequence has both high linear complexity and the advantages of chaotic non-linearity. The high randomness used in source encryption can improve the security of the system.
{"title":"Research on encryption algorithms in terahertz communication systems","authors":"Yuanming Ding, Jing Cui, Jianxin Jianxin","doi":"10.1117/12.2682373","DOIUrl":"https://doi.org/10.1117/12.2682373","url":null,"abstract":"Aiming at the problem that the terahertz wave beam is narrow and directionality is strong, which makes it easy for the enemy to eavesdrop on the data information, and thus the information cannot be transmitted safely, a pseudo-random sequence generation method based on n-LFSR and AV-NL chaotic mapping is proposed. Different algorithms are selected according to the number of LFSRs. At the same time, the absolute value nonlinear chaotic map is generalized. Finally, the generated two pseudo-random sequences are XOR combined to form the final pseudo-random sequence for source encryption. The simulation results show that this method takes into account the advantages of simple design of linear feedback shift register and high chaotic randomness. The generated pseudo-random sequence has both high linear complexity and the advantages of chaotic non-linearity. The high randomness used in source encryption can improve the security of the system.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122446773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose an image feature extraction method based on 3D chaotic attractor, and carry out face video recognition. By adjusting the auxiliary function, the feature point set is located on a plane in the 3D coordinate system. The experiment shows that the feature point set on the plane can extract face features better and recognize faces more efficiently. This method is faster and has higher recognition rate than the method that uses trigonometric function to iteratively generate image feature point set to recognize face in video.
{"title":"Video face recognition based on 3D chaotic attractor","authors":"Xiang Li, Xiaoran Chen, Wanbo Yu","doi":"10.1117/12.2682356","DOIUrl":"https://doi.org/10.1117/12.2682356","url":null,"abstract":"In this paper, we propose an image feature extraction method based on 3D chaotic attractor, and carry out face video recognition. By adjusting the auxiliary function, the feature point set is located on a plane in the 3D coordinate system. The experiment shows that the feature point set on the plane can extract face features better and recognize faces more efficiently. This method is faster and has higher recognition rate than the method that uses trigonometric function to iteratively generate image feature point set to recognize face in video.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123643574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
According to the principle of digital modulation and demodulation system, the Quadrature Phase Shift Keying (QPSK) modulation and demodulation module is implemented with Verilog HDL language, and the error caused by incoherent demodulation is reduced by compensating the circuit carrier error. In the process of development, intellectual property core (IP) is used to develop the communication system, including bit synchronization technology, baseband signal generator, phase-locked loop, etc., which greatly increases the convenience of development. The reception performance of visible light receiving circuit is improved by combining three-stage amplifier circuit, and the communication and anti-noise performance of visible light communication system are enhanced by using high-order modulation mode with multi-stage photoelectric conversion receiving circuit. Through the FPGA (Field Programmable Gate Array) development board for on-board testing, the whole set of visible light communication system is designed with photoelectric conditioning circuit
{"title":"FPGA implementation of QPSK visible light communication system","authors":"Haoshen Wei, Shanchun Zong, Xiaohong Sun","doi":"10.1117/12.2682313","DOIUrl":"https://doi.org/10.1117/12.2682313","url":null,"abstract":"According to the principle of digital modulation and demodulation system, the Quadrature Phase Shift Keying (QPSK) modulation and demodulation module is implemented with Verilog HDL language, and the error caused by incoherent demodulation is reduced by compensating the circuit carrier error. In the process of development, intellectual property core (IP) is used to develop the communication system, including bit synchronization technology, baseband signal generator, phase-locked loop, etc., which greatly increases the convenience of development. The reception performance of visible light receiving circuit is improved by combining three-stage amplifier circuit, and the communication and anti-noise performance of visible light communication system are enhanced by using high-order modulation mode with multi-stage photoelectric conversion receiving circuit. Through the FPGA (Field Programmable Gate Array) development board for on-board testing, the whole set of visible light communication system is designed with photoelectric conditioning circuit","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123389224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaojuan Fu, Yinglang Huang, Jiabao Huang, Zhang Li
Based on the information provided in Problem D of the 2022 National College Mathematical Modeling Contest, only considering the transmission and reception of meteorological message between the main stations, the success rate of sending and receiving is 100% , set half-time information as one unit, then each main station can transmit up to three units、150 characters (less than the limit of 158) at a time.Using the method of sequential transmission in turn, the first round and the last round are studied separately, the other rounds meet the maximum transmission of cases, the relationship between the minimum value of K and N is modeled, as shown in the formula (1) . As result, it takes at least 4.67(rounded up to 5) minutes to transmit 6 rounds, the information sharing between 9 master stations can be realized. Then, according to the state characteristics of each main station after each round of transmission,the state transfer equations between the main stations are established, such as formulas (8) and (9), and the scheme of meteorological message transmission for each round of main station 1 is obtained.
{"title":"Study on satellite communication transmission of meteorological information between main stations based on state transfer","authors":"Xiaojuan Fu, Yinglang Huang, Jiabao Huang, Zhang Li","doi":"10.1117/12.2682409","DOIUrl":"https://doi.org/10.1117/12.2682409","url":null,"abstract":"Based on the information provided in Problem D of the 2022 National College Mathematical Modeling Contest, only considering the transmission and reception of meteorological message between the main stations, the success rate of sending and receiving is 100% , set half-time information as one unit, then each main station can transmit up to three units、150 characters (less than the limit of 158) at a time.Using the method of sequential transmission in turn, the first round and the last round are studied separately, the other rounds meet the maximum transmission of cases, the relationship between the minimum value of K and N is modeled, as shown in the formula (1) . As result, it takes at least 4.67(rounded up to 5) minutes to transmit 6 rounds, the information sharing between 9 master stations can be realized. Then, according to the state characteristics of each main station after each round of transmission,the state transfer equations between the main stations are established, such as formulas (8) and (9), and the scheme of meteorological message transmission for each round of main station 1 is obtained.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129635080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing diversification of signal modulation types, the importance of signal modulation recognition is increasing, which is an important part between signal detection and demodulation. It has great applied value in jamming identification, electronic countermeasures, intelligent modem and other fields. Aiming at the improvement of recognition accuracy for some modulation types, a communication signal modulation recognition method based on cyclic spectrum features and bagged decision tree is proposed. The method extracts the cyclic spectrum features of signals and inputs them into the bagged decision tree for model training. Simulation results show that the accuracy of the proposed method reaches 93.8%, which is 39.4% higher than that of the traditional recognition method with high-order cumulants and 22.2% higher than that of the method using the original signal directly.
{"title":"Communication signal modulation recognition based on cyclic spectrum features and bagged decision tree","authors":"Tianyi Huang, F. Xin, Jiachen Wang","doi":"10.1117/12.2682404","DOIUrl":"https://doi.org/10.1117/12.2682404","url":null,"abstract":"With the increasing diversification of signal modulation types, the importance of signal modulation recognition is increasing, which is an important part between signal detection and demodulation. It has great applied value in jamming identification, electronic countermeasures, intelligent modem and other fields. Aiming at the improvement of recognition accuracy for some modulation types, a communication signal modulation recognition method based on cyclic spectrum features and bagged decision tree is proposed. The method extracts the cyclic spectrum features of signals and inputs them into the bagged decision tree for model training. Simulation results show that the accuracy of the proposed method reaches 93.8%, which is 39.4% higher than that of the traditional recognition method with high-order cumulants and 22.2% higher than that of the method using the original signal directly.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128717581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The semantic segmentation task of medical image is to segment the focus, organ or substructure of human body in medical image. It plays an important role in locating and identifying the diseased area and making medical plan. In various medical image segmentation tasks, the U-shaped architecture has achieved great success. Transunet introduces Transformer with global attention mechanism into the U-shaped architecture, which overcomes the inherent limitations of convolution, but because it still continues the original skip connections structure, it will bring the strong noise from features in the shallow network into the high semantic features of the deep network, thus affecting the segmentation accuracy. UTSN-net model based on the combination of Transformer and nonlocal attention mechanism is proposed. UTSN-net uses Transformer to overcome the inherent limitations of convolution, and introduces the skip connections module based on nonlocal attention mechanism into the U-shaped network, which can comprehensively consider the deep features with global context information and the shallow features with accurate high-resolution positioning information to improve the accuracy of segmentation results. Experiments on synapse multi-organ abdominal CT dataset verify that UTSN-net has better semantic segmentation performance.
{"title":"UTSN-net: medical image semantic segmentation model based on skip non-local attention module","authors":"Li Zhang, BinBing Zhu, Chunpeng Ma","doi":"10.1117/12.2682365","DOIUrl":"https://doi.org/10.1117/12.2682365","url":null,"abstract":"The semantic segmentation task of medical image is to segment the focus, organ or substructure of human body in medical image. It plays an important role in locating and identifying the diseased area and making medical plan. In various medical image segmentation tasks, the U-shaped architecture has achieved great success. Transunet introduces Transformer with global attention mechanism into the U-shaped architecture, which overcomes the inherent limitations of convolution, but because it still continues the original skip connections structure, it will bring the strong noise from features in the shallow network into the high semantic features of the deep network, thus affecting the segmentation accuracy. UTSN-net model based on the combination of Transformer and nonlocal attention mechanism is proposed. UTSN-net uses Transformer to overcome the inherent limitations of convolution, and introduces the skip connections module based on nonlocal attention mechanism into the U-shaped network, which can comprehensively consider the deep features with global context information and the shallow features with accurate high-resolution positioning information to improve the accuracy of segmentation results. Experiments on synapse multi-organ abdominal CT dataset verify that UTSN-net has better semantic segmentation performance.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125646230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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