Saqr Khalil Saeed Thabet, Emmanuel Osei-Mensah, Lei Luo, Ce Zhu
Unlike digital video transmission systems, SoftCast avoids the cliff effect and achieves a linear video quality transition that is commensurate with the wireless channel conditions. When SoftCast is applied to three-Dimension Video (3DV) transmission, resource allocation issues arise: 1) allocating the limited power budget to texture and depth to achieve the optimal overall quality. 2) distributing the suitable number of texture and depth chunks to adapt to bandwidth constraints. This work aims to efficiently solve the optimal joint power and bandwidth allocation problem. First, a power-distortion optimization problem is formulated to calculate the optimal Power Allocation Ratio (PAR) between texture and depth, then mapped to an unconstrained problem and solved using the Lagrangian multiplier. Finally, based on the closed-form of the optimal solution, an iterative algorithm is proposed to choose the suitable number of texture/depth chunks for a given bandwidth constraint. The proposed method achieves better performance than its counterpart default fixed-ratio power allocation between texture/depth. Further, we observe a graceful video quality transition with the improvement of channel conditions under bandwidth constraints.
{"title":"Joint Power and Bandwidth Allocation for 3D Video SoftCast","authors":"Saqr Khalil Saeed Thabet, Emmanuel Osei-Mensah, Lei Luo, Ce Zhu","doi":"10.1145/3529570.3529600","DOIUrl":"https://doi.org/10.1145/3529570.3529600","url":null,"abstract":"Unlike digital video transmission systems, SoftCast avoids the cliff effect and achieves a linear video quality transition that is commensurate with the wireless channel conditions. When SoftCast is applied to three-Dimension Video (3DV) transmission, resource allocation issues arise: 1) allocating the limited power budget to texture and depth to achieve the optimal overall quality. 2) distributing the suitable number of texture and depth chunks to adapt to bandwidth constraints. This work aims to efficiently solve the optimal joint power and bandwidth allocation problem. First, a power-distortion optimization problem is formulated to calculate the optimal Power Allocation Ratio (PAR) between texture and depth, then mapped to an unconstrained problem and solved using the Lagrangian multiplier. Finally, based on the closed-form of the optimal solution, an iterative algorithm is proposed to choose the suitable number of texture/depth chunks for a given bandwidth constraint. The proposed method achieves better performance than its counterpart default fixed-ratio power allocation between texture/depth. Further, we observe a graceful video quality transition with the improvement of channel conditions under bandwidth constraints.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115649013","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}
This paper proposes a prediction method by combining second-order Volterra (SOV) model and Kalman filter to further improve prediction accuracy of the traditional Kalman model in short-term traffic flow forecasting. Nonlinear relationship may exist in traffic flow data, but the traditional Kalman model cannot deal with this problem. Due to the second-order Volterra (SOV) filter can deal with a general class of nonlinear systems, the traditional Kalman combines with second-order Volterra model, named SOV-KF model, is presented. Furthermore, since the Gaussian assumption is not always be fulfilled in the traffic flow data and traditional minimum mean square error (MMSE) criterion do not perform well under non-Gaussian noises. By introducing maximum Versoria criterion, another prediction method called SOV-MVKF model is also proposed. Simulation results show that the SOV-KF model and SOV-MVKF model provide higher prediction accuracy compared to traditional Kalman model.
{"title":"Kalman Filter Using SOV Model with Maximum Versoria Criterion for Short-Term Traffic Flow Forecasting","authors":"Tingting Jiang, Zhao Zhang","doi":"10.1145/3529570.3529579","DOIUrl":"https://doi.org/10.1145/3529570.3529579","url":null,"abstract":"This paper proposes a prediction method by combining second-order Volterra (SOV) model and Kalman filter to further improve prediction accuracy of the traditional Kalman model in short-term traffic flow forecasting. Nonlinear relationship may exist in traffic flow data, but the traditional Kalman model cannot deal with this problem. Due to the second-order Volterra (SOV) filter can deal with a general class of nonlinear systems, the traditional Kalman combines with second-order Volterra model, named SOV-KF model, is presented. Furthermore, since the Gaussian assumption is not always be fulfilled in the traffic flow data and traditional minimum mean square error (MMSE) criterion do not perform well under non-Gaussian noises. By introducing maximum Versoria criterion, another prediction method called SOV-MVKF model is also proposed. Simulation results show that the SOV-KF model and SOV-MVKF model provide higher prediction accuracy compared to traditional Kalman model.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124376267","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}
Given the current computational complexity and the unsatisfactory anti-noise performance of the linear frequency modulated (LFM) signal parameter estimation method, this paper proposes a rather innovative and efficient method based on improved fractional Fourier transform (FrFT). This method first obtains the energy distribution of time-frequency domain (TFD) through short-time Fourier transform (STFT), which is used to determine the order search range of FrFT. On the other hand, incoherent accumulation is also employed to improve the anti-noise performance under low signal-to-noise-ratio (SNR) environments. Extensive computer simulations verified that the algorithm displays a good anti-noise performance while reducing the amount of calculation.
{"title":"Parameter Estimation of LFM Signal Based on Improved Fractional Fourier Transform","authors":"Guanyu Qiao, D. Dai, Caikun Zhang","doi":"10.1145/3529570.3529607","DOIUrl":"https://doi.org/10.1145/3529570.3529607","url":null,"abstract":"Given the current computational complexity and the unsatisfactory anti-noise performance of the linear frequency modulated (LFM) signal parameter estimation method, this paper proposes a rather innovative and efficient method based on improved fractional Fourier transform (FrFT). This method first obtains the energy distribution of time-frequency domain (TFD) through short-time Fourier transform (STFT), which is used to determine the order search range of FrFT. On the other hand, incoherent accumulation is also employed to improve the anti-noise performance under low signal-to-noise-ratio (SNR) environments. Extensive computer simulations verified that the algorithm displays a good anti-noise performance while reducing the amount of calculation.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124819071","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 interpretation of Synthetic Aperture Radar (SAR) image is considered to be a challenging task, especially when tracking the target shadow in Video SAR (ViSAR), the speckle noise needs to be considered. Based on this, this paper proposes a SAR image denoising algorithm based on the improved wavelet threshold function. Different from the existing denoising methods, this algorithm combines the characteristics of hard threshold function and soft threshold function in traditional wavelet transform denoising, constructs a new threshold function, and improves the equivalent number of looks (ENL) of denoised SAR image. When the denoised image is applied to the tracking task, the target features are enhanced by k-means algorithm and binarization method, so as to improve the tracking accuracy. Experimental results show that the algorithm improves the tracking accuracy on the basis of ensuring the real-time performance of tracking and makes the tracking task highly robust to the noise of SAR image.
{"title":"A SAR Image Denoising Method for Target Shadow Tracking Task","authors":"Yankun Huang, Guangcai Sun, M. Xing","doi":"10.1145/3529570.3529598","DOIUrl":"https://doi.org/10.1145/3529570.3529598","url":null,"abstract":"The interpretation of Synthetic Aperture Radar (SAR) image is considered to be a challenging task, especially when tracking the target shadow in Video SAR (ViSAR), the speckle noise needs to be considered. Based on this, this paper proposes a SAR image denoising algorithm based on the improved wavelet threshold function. Different from the existing denoising methods, this algorithm combines the characteristics of hard threshold function and soft threshold function in traditional wavelet transform denoising, constructs a new threshold function, and improves the equivalent number of looks (ENL) of denoised SAR image. When the denoised image is applied to the tracking task, the target features are enhanced by k-means algorithm and binarization method, so as to improve the tracking accuracy. Experimental results show that the algorithm improves the tracking accuracy on the basis of ensuring the real-time performance of tracking and makes the tracking task highly robust to the noise of SAR image.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114507615","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}
Efficient detection of cable surface defects can prevent and reduce the potential dangers in the process of high voltage transmission. In order to achieve efficient detection of cable surface defects and solve the problem of low detection accuracy of small and unobvious defects on cable surface, we propose an efficient cable surface defect detection model with deep learning. Firstly, the lightweight backbone feature extraction network is used to extract the preliminary defect features. Secondly, the parallel convolution module and serial convolution module are designed to obtain abundant defect features and reduce the number of model parameters. Then, the feature fusion module is designed to fuse the shallow features with deep features to enhance the features of small and unobvious defects. Finally, the obtained features are put into the corresponding detection head to get the final prediction results. The experimental results on local cable dataset show that our method achieves favorable trade-off between the accuracy, speed and model size of the cable surface defect detection, which meets the requirements of high accuracy, high speed and small model in industrial application.
{"title":"Efficient Cable Surface Defect Detection with Deep Learning","authors":"Guo-Chung Chen, Feng Xu, Guihua Liu, Yanjie Chen, Zhiqiang Liang","doi":"10.1145/3529570.3529595","DOIUrl":"https://doi.org/10.1145/3529570.3529595","url":null,"abstract":"Efficient detection of cable surface defects can prevent and reduce the potential dangers in the process of high voltage transmission. In order to achieve efficient detection of cable surface defects and solve the problem of low detection accuracy of small and unobvious defects on cable surface, we propose an efficient cable surface defect detection model with deep learning. Firstly, the lightweight backbone feature extraction network is used to extract the preliminary defect features. Secondly, the parallel convolution module and serial convolution module are designed to obtain abundant defect features and reduce the number of model parameters. Then, the feature fusion module is designed to fuse the shallow features with deep features to enhance the features of small and unobvious defects. Finally, the obtained features are put into the corresponding detection head to get the final prediction results. The experimental results on local cable dataset show that our method achieves favorable trade-off between the accuracy, speed and model size of the cable surface defect detection, which meets the requirements of high accuracy, high speed and small model in industrial application.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125598021","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}
N. Aoki, Kenichi Ikeda, H. Yasuda, Ying Shen, Jin Hou
This study investigates the possibility of visual communications using spectrogram arts drawn on sound signals. This approach attempts to increase the readability of the communications so that their messages are directly understandable by human users without any advanced decoders. This paper describes some pilot studies of our spectrogram art communication system that exchanges text messages in some chatting services. The experimental results indicate that the proposed technique may have certain appropriateness. It may be employed as a sort of steganography techniques enabling sub-channel communications in a covert manner.
{"title":"Some Evaluations on Spectrogram Art Communications Exchanging Secret Visual Messages","authors":"N. Aoki, Kenichi Ikeda, H. Yasuda, Ying Shen, Jin Hou","doi":"10.1145/3529570.3529583","DOIUrl":"https://doi.org/10.1145/3529570.3529583","url":null,"abstract":"This study investigates the possibility of visual communications using spectrogram arts drawn on sound signals. This approach attempts to increase the readability of the communications so that their messages are directly understandable by human users without any advanced decoders. This paper describes some pilot studies of our spectrogram art communication system that exchanges text messages in some chatting services. The experimental results indicate that the proposed technique may have certain appropriateness. It may be employed as a sort of steganography techniques enabling sub-channel communications in a covert manner.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116373675","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}
Distributed adaptive networks are widely used in many fields. Most of the existing distributed adaptive algorithms are designed to solve the problem of network optimization under unconstrained conditions. However, in actual situations, there exist some network optimization problem under constrained conditions need to be solved, and considering that the distributed network is usually interfered by impulsive noise, a novel diffusion algorithm called diffusion constrained least mean M-estimate (D-CLMM) is proposed by using the modified Huber (MH) function, which can provide robust learning ability when network is disturbed by impulsive interference. Finally, the performance of the proposed algorithm is verified under different non-Gaussian noise environments. Simulation results show that the D-CLMM algorithm performs better than the diffusion-constrained least mean square algorithm (D-CLMS) based on mean square error (MSE) criterion.
{"title":"Diffusion Constrained Least Mean M-estimate Algorithm for Adaptive Networks","authors":"Wenjing Xu, Haiquan Zhao","doi":"10.1145/3529570.3529601","DOIUrl":"https://doi.org/10.1145/3529570.3529601","url":null,"abstract":"Distributed adaptive networks are widely used in many fields. Most of the existing distributed adaptive algorithms are designed to solve the problem of network optimization under unconstrained conditions. However, in actual situations, there exist some network optimization problem under constrained conditions need to be solved, and considering that the distributed network is usually interfered by impulsive noise, a novel diffusion algorithm called diffusion constrained least mean M-estimate (D-CLMM) is proposed by using the modified Huber (MH) function, which can provide robust learning ability when network is disturbed by impulsive interference. Finally, the performance of the proposed algorithm is verified under different non-Gaussian noise environments. Simulation results show that the D-CLMM algorithm performs better than the diffusion-constrained least mean square algorithm (D-CLMS) based on mean square error (MSE) criterion.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131038824","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}
Emergency Medical Services’ response times to accidents are crucial to saving lives in vehicle accidents. Using deep learning to instantly detect accidents in public cameras and automatically alerting authorities could help this issue. However, this would require a large set of data on public cameras to train on, but this type of data hardly exists in a usable form. Current deep learning approaches to vehicle accidents typically use first-person cameras, which are not helpful for reducing response time as we do not have access to these cameras at all times. Also, public cameras such as closed-circuit television (CCTV) pick up a much larger amount of street activity than private cameras. Thus, we create a video dataset from live closed-circuit television, so we have access to the cameras at all times. We annotate the videos with metadata to help with future trend prediction as well as give further information for each video, as they are unlabeled. We create an unsupervised learning model to train on this video dataset, and visualize latent space representations of this data in order to cluster different types of street activity and pinpoint vehicle accidents. 1
{"title":"CCTV Latent Representations for Reducing Accident Response Times","authors":"Shafinul Haque","doi":"10.1145/3529570.3529582","DOIUrl":"https://doi.org/10.1145/3529570.3529582","url":null,"abstract":"Emergency Medical Services’ response times to accidents are crucial to saving lives in vehicle accidents. Using deep learning to instantly detect accidents in public cameras and automatically alerting authorities could help this issue. However, this would require a large set of data on public cameras to train on, but this type of data hardly exists in a usable form. Current deep learning approaches to vehicle accidents typically use first-person cameras, which are not helpful for reducing response time as we do not have access to these cameras at all times. Also, public cameras such as closed-circuit television (CCTV) pick up a much larger amount of street activity than private cameras. Thus, we create a video dataset from live closed-circuit television, so we have access to the cameras at all times. We annotate the videos with metadata to help with future trend prediction as well as give further information for each video, as they are unlabeled. We create an unsupervised learning model to train on this video dataset, and visualize latent space representations of this data in order to cluster different types of street activity and pinpoint vehicle accidents. 1","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132323442","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}
We investigate the generation of quantization matrices for image coding in the scenario to balance compression ratio and quality. We make use of machine learning to train and determine those quantization matrices that can achieve the best compression ratio while reaching the quality settings. By introducing the trainable parameters and considering the impact of the quantization module on task performance and compression ratio, the DCT and quantization modules are jointly optimized to minimize the total coding cost. We evaluate the well-trained quantization matrices under various quality settings of JPEG. The results indicate that the proposed scheme can be combined with quality settings to consistently achieve better compression performance.
{"title":"Improving Quantization Matrices for Image Coding by Machine Learning","authors":"Wei Ke, Ka‐Hou Chan","doi":"10.1145/3529570.3529590","DOIUrl":"https://doi.org/10.1145/3529570.3529590","url":null,"abstract":"We investigate the generation of quantization matrices for image coding in the scenario to balance compression ratio and quality. We make use of machine learning to train and determine those quantization matrices that can achieve the best compression ratio while reaching the quality settings. By introducing the trainable parameters and considering the impact of the quantization module on task performance and compression ratio, the DCT and quantization modules are jointly optimized to minimize the total coding cost. We evaluate the well-trained quantization matrices under various quality settings of JPEG. The results indicate that the proposed scheme can be combined with quality settings to consistently achieve better compression performance.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125549514","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}
Concerning poor interpretability and complexity of deep model in modulation recognition (MR) based on deep learning, an interpretable analysis and pruning framework of MR network based on Gradient-weighted Class Activation Mapping (Grad-CAM) is accordingly proposed in this paper. The framework first extracts the amplitude, phase and spectrum from the original modulated signal, and it uses the Smoothed Pseudo Wigner-Ville Distribution (SPWVD) to obtain the two-dimensional time-frequency spectrum of the modulated signal. Then, the key features in the deep model are visualized from the perspective of one-dimensional features and two-dimensional features at the input respectively. The framework visually displays and compares the differences and commonalities of the depth features of hidden layer with different models, extract the values of different filters of each layer in the deep neural network (DNN), and prune the network according to the values. The experiment results show that the interpretable and pruning framework of MR network based on Grad-CAM in this paper can achieve effective explanation and analysis on the MR network, and can greatly reduce the redundancy of the network. The running speed of the pruned network is 3.83 times higher than that of the original network. The size of the pruned network is 72% lower than that of the original network. Besides, the accuracy of the pruned network is 0.3% higher than that of the original network.
{"title":"Interpretable Analysis and Pruning of Modulation Recognition Network Based on Deep Learning","authors":"Fan Ni, Min Luo","doi":"10.1145/3529570.3529577","DOIUrl":"https://doi.org/10.1145/3529570.3529577","url":null,"abstract":"Concerning poor interpretability and complexity of deep model in modulation recognition (MR) based on deep learning, an interpretable analysis and pruning framework of MR network based on Gradient-weighted Class Activation Mapping (Grad-CAM) is accordingly proposed in this paper. The framework first extracts the amplitude, phase and spectrum from the original modulated signal, and it uses the Smoothed Pseudo Wigner-Ville Distribution (SPWVD) to obtain the two-dimensional time-frequency spectrum of the modulated signal. Then, the key features in the deep model are visualized from the perspective of one-dimensional features and two-dimensional features at the input respectively. The framework visually displays and compares the differences and commonalities of the depth features of hidden layer with different models, extract the values of different filters of each layer in the deep neural network (DNN), and prune the network according to the values. The experiment results show that the interpretable and pruning framework of MR network based on Grad-CAM in this paper can achieve effective explanation and analysis on the MR network, and can greatly reduce the redundancy of the network. The running speed of the pruned network is 3.83 times higher than that of the original network. The size of the pruned network is 72% lower than that of the original network. Besides, the accuracy of the pruned network is 0.3% higher than that of the original network.","PeriodicalId":430367,"journal":{"name":"Proceedings of the 6th International Conference on Digital Signal Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121274068","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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