Pub Date : 2022-07-06DOI: 10.1109/ICCE-Taiwan55306.2022.9869237
Chao-Chung Peng, Rong He
In recent years, the unmanned aerial vehicles (UAVs) have been widely used for environment reconstruction. From the commercial point of view, photography mapping with drones has been taken as a cost-efficient solution. In general, the associated system components consist of a multi-rotor drone, an inertial measurement unit (IMU), a flight control unit, an RGB camera and a global positioning system (GPS). In this system, IMU and GPS play important roles for UAV pose estimation while the RGB camera plays a role for further image processes such as feature extraction, matching, stitching and projection. Although this solution has been widely used for large scale map exploration, the mapping precision enhancement remains an important topic, which affects the application feasibility especially for land-use planning and infrastructure building evaluation. Therefore, in this note, an alternative mapping configuration, which considers multiple non-rotary solid state LiDARs (SSLs), is presented. Owing to the long measurement range and accuracy point cloud acquisition, precise 3D digital terrain can be expected.
{"title":"A Concept for High Precision Digital Terrain 3D Mapping using UAVs and Multiple Solid State LiDARs","authors":"Chao-Chung Peng, Rong He","doi":"10.1109/ICCE-Taiwan55306.2022.9869237","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869237","url":null,"abstract":"In recent years, the unmanned aerial vehicles (UAVs) have been widely used for environment reconstruction. From the commercial point of view, photography mapping with drones has been taken as a cost-efficient solution. In general, the associated system components consist of a multi-rotor drone, an inertial measurement unit (IMU), a flight control unit, an RGB camera and a global positioning system (GPS). In this system, IMU and GPS play important roles for UAV pose estimation while the RGB camera plays a role for further image processes such as feature extraction, matching, stitching and projection. Although this solution has been widely used for large scale map exploration, the mapping precision enhancement remains an important topic, which affects the application feasibility especially for land-use planning and infrastructure building evaluation. Therefore, in this note, an alternative mapping configuration, which considers multiple non-rotary solid state LiDARs (SSLs), is presented. Owing to the long measurement range and accuracy point cloud acquisition, precise 3D digital terrain can be expected.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122649202","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}
Pub Date : 2022-07-06DOI: 10.1109/ICCE-Taiwan55306.2022.9869215
Jingchuan Lin, S. Ho, Kuan-Yu Chou, Yon-Ping Chen
Mobile robots with artificial intelligence are more and more popular on the rescue and human-service in complex environment. Path planning techniques for robots become the important topic to achieve it. Recently, Q-learning becomes a popular topic since the property of model-free. In this paper, generating the collision-free and optimal path with Q-learning for an mobile robot is proposed. Q-learning is adopted to let the mobile robot achieve the destination successfully through designing the states, actions and reward function in this paper. The system structure is integrated by two parts. First, the Q-learning algorithm is applied to find the collision-free and optimal path for an mobile robot. Second, Robot Operation System (ROS) is used to be the data transmission system among the dynamic path planning system, global position system and mobile robot. In the simulation result, the dynamic path planning system generates the collision-free and optimal path for the mobile robot. In addition, the movable obstacles appear on the original path suddenly, then the dynamic path planning system would regenerate a new optimal path to achieve the goal successfully.
{"title":"Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment","authors":"Jingchuan Lin, S. Ho, Kuan-Yu Chou, Yon-Ping Chen","doi":"10.1109/ICCE-Taiwan55306.2022.9869215","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869215","url":null,"abstract":"Mobile robots with artificial intelligence are more and more popular on the rescue and human-service in complex environment. Path planning techniques for robots become the important topic to achieve it. Recently, Q-learning becomes a popular topic since the property of model-free. In this paper, generating the collision-free and optimal path with Q-learning for an mobile robot is proposed. Q-learning is adopted to let the mobile robot achieve the destination successfully through designing the states, actions and reward function in this paper. The system structure is integrated by two parts. First, the Q-learning algorithm is applied to find the collision-free and optimal path for an mobile robot. Second, Robot Operation System (ROS) is used to be the data transmission system among the dynamic path planning system, global position system and mobile robot. In the simulation result, the dynamic path planning system generates the collision-free and optimal path for the mobile robot. In addition, the movable obstacles appear on the original path suddenly, then the dynamic path planning system would regenerate a new optimal path to achieve the goal successfully.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122827821","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}
Electrical Overstress (EOS) avoiding power integrated circuits (ICs) are often designed with depletion mode NMOSFET. In some applications, there can be no depletion mode NMOSFETs. From device normal operations and EOS analyses, new circuits without depletion mode devices are successfully proposed for approaching device typical operations and EOS endurances.
{"title":"EOS Endurance Power Circuits without Depletion Mode Devices","authors":"Shao-Chang Huang, Jian-Hsing Lee, Ching-Ho Li, Sue-Yi Chen, Chih-Hsuan Lin, Chun-Chih Chen, Li-Fan Chen, Gong-Kai Lin, Chien-Wei Wang, K. Hsu, Szu-Chi Chen, S.C. Pai, Fu-Wei Pai, Yin-Wei Peng, Chih-Cherng Liao, Ke-Horng Chen","doi":"10.1109/ICCE-Taiwan55306.2022.9869061","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869061","url":null,"abstract":"Electrical Overstress (EOS) avoiding power integrated circuits (ICs) are often designed with depletion mode NMOSFET. In some applications, there can be no depletion mode NMOSFETs. From device normal operations and EOS analyses, new circuits without depletion mode devices are successfully proposed for approaching device typical operations and EOS endurances.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128807743","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}
Pub Date : 2022-07-06DOI: 10.1109/ICCE-Taiwan55306.2022.9869195
Chi-Mao Fan, Tsung-Jung Liu, Kuan-Hsien Liu
Image denoising is a long standing ill-posed prob-lem. Recently, the convolution neural networks (CNNs) gradually stand in the spotlight and almost dominated the computer vision field and had achieved impressive results in different levels of vision tasks. One of famous hierarchical CNN-backbones is the U-Net which shows awesome performance in both denoising and other areas of computer vision. However, the hierarchical architecture usually suffers from the loss of spatial information due to the repeated sampling. It seriously affects the denoising performance especially the element-wise task like denoising. In this paper, we proposed an improved hierarchical backbone: M-Net+ for image denoising to ameliorate the loss of spatial details. Furthermore, we test on two synthetic Gaussian noise datasets to demonstrate the competitive result of our model.
{"title":"Improved Hierarchical M-Net+ for Blind Image Denoising","authors":"Chi-Mao Fan, Tsung-Jung Liu, Kuan-Hsien Liu","doi":"10.1109/ICCE-Taiwan55306.2022.9869195","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869195","url":null,"abstract":"Image denoising is a long standing ill-posed prob-lem. Recently, the convolution neural networks (CNNs) gradually stand in the spotlight and almost dominated the computer vision field and had achieved impressive results in different levels of vision tasks. One of famous hierarchical CNN-backbones is the U-Net which shows awesome performance in both denoising and other areas of computer vision. However, the hierarchical architecture usually suffers from the loss of spatial information due to the repeated sampling. It seriously affects the denoising performance especially the element-wise task like denoising. In this paper, we proposed an improved hierarchical backbone: M-Net+ for image denoising to ameliorate the loss of spatial details. Furthermore, we test on two synthetic Gaussian noise datasets to demonstrate the competitive result of our model.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130546426","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}
Pub Date : 2022-07-06DOI: 10.1109/ICCE-Taiwan55306.2022.9869284
Xin-Yu Shih, Chen-Yen Song
This paper proposes a reconfigurable hardware architecture of modified KD-tree machine-learning classifier. As compared to current literature, this hardware is the first KD-tree-like hardware implementation. As compared with original KD-tree algorithm, our design can deliver a very low latency in hardware because we do not need the data traversal steps along the binary tree. Meanwhile, this scalable hardware can be easily constructed if supporting a greater number of data instances to be classified. In the hardware implementation with TSMC 40-nm CMOS technology, our synthesizable hardware achieves a maximum frequency of 401.6 MHz, only occupying an area of 0.562 mm2.
{"title":"Scalable and Reconfigurable Architecture of Modified KD-Tree ML-Classifier with 5-Point Searching","authors":"Xin-Yu Shih, Chen-Yen Song","doi":"10.1109/ICCE-Taiwan55306.2022.9869284","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869284","url":null,"abstract":"This paper proposes a reconfigurable hardware architecture of modified KD-tree machine-learning classifier. As compared to current literature, this hardware is the first KD-tree-like hardware implementation. As compared with original KD-tree algorithm, our design can deliver a very low latency in hardware because we do not need the data traversal steps along the binary tree. Meanwhile, this scalable hardware can be easily constructed if supporting a greater number of data instances to be classified. In the hardware implementation with TSMC 40-nm CMOS technology, our synthesizable hardware achieves a maximum frequency of 401.6 MHz, only occupying an area of 0.562 mm2.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114333033","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}
Pub Date : 2022-07-06DOI: 10.1109/ICCE-Taiwan55306.2022.9869139
Ting-Wen Chin, Ji-Heng Jiang, Chi-Hao Lung
This research has designed an interactive experience of step-by-step behavior content feedback through intuitive manipulation to propose another interactive interpretation of the new ecosystem. The research results hope viewers will no longer interact through non-intuitive methods but with the experience of body accumulated from experience to connect with digital content by internalizing themselves into the virtual world to enjoy the interactive feedback under intuitive control.
{"title":"A New Ecosystem of Virtual Daily Life: Exploring the Intuitive Manipulation Based on the Transformation of Physical Experience Between Virtuality and Reality","authors":"Ting-Wen Chin, Ji-Heng Jiang, Chi-Hao Lung","doi":"10.1109/ICCE-Taiwan55306.2022.9869139","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869139","url":null,"abstract":"This research has designed an interactive experience of step-by-step behavior content feedback through intuitive manipulation to propose another interactive interpretation of the new ecosystem. The research results hope viewers will no longer interact through non-intuitive methods but with the experience of body accumulated from experience to connect with digital content by internalizing themselves into the virtual world to enjoy the interactive feedback under intuitive control.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114793458","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}
Pub Date : 2022-07-06DOI: 10.1109/ICCE-Taiwan55306.2022.9869076
Weng Ju, Pan Chun Lin, Ju-Chun Ko, H. Tsao
This study proposes a new AR system application “Seventh Sense”, which is a system device that combines a 360 camera with AR glasses and software design. This system can collect people and things that appear in a circular area with a radius of one meter centered on the user in real-time, and provide timely and clear image feedback to the user after analysis so that the user can use this application to avoid various people or things in the real environment smoothly, even if they do not care about the surrounding environment, or even when they are concentrating on digital content as if they have obtained the Seventh Sense.
{"title":"7th Sense - Combining Augmented Reality Visual Feedback and 360 Camera User Solutions","authors":"Weng Ju, Pan Chun Lin, Ju-Chun Ko, H. Tsao","doi":"10.1109/ICCE-Taiwan55306.2022.9869076","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869076","url":null,"abstract":"This study proposes a new AR system application “Seventh Sense”, which is a system device that combines a 360 camera with AR glasses and software design. This system can collect people and things that appear in a circular area with a radius of one meter centered on the user in real-time, and provide timely and clear image feedback to the user after analysis so that the user can use this application to avoid various people or things in the real environment smoothly, even if they do not care about the surrounding environment, or even when they are concentrating on digital content as if they have obtained the Seventh Sense.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124045684","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}
Pub Date : 2022-07-06DOI: 10.1109/ICCE-Taiwan55306.2022.9869137
Kohichi Ogawa, Nobukuni Hamamoto, N. Yoshiura
Bring Your Own Device (BYOD) is widespread at many universities in Japan. Students set up their PC to connect to the university's wireless LAN. However, students are not familiar with IT, so the staff at the information center has to check the PC settings directly. However, universities with dispersed campuses cannot secure sufficient staff to support the PCs and provide good services to users. Improving the level of IT support for users has become an issue. Therefore, to deal with such problems, a “Smart Help Desk” is proposed to read the user's computer screen, recognize the status, and automatically teach the operation method. This is an ongoing project.
{"title":"Smart Help Desk to support user's PC settings","authors":"Kohichi Ogawa, Nobukuni Hamamoto, N. Yoshiura","doi":"10.1109/ICCE-Taiwan55306.2022.9869137","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869137","url":null,"abstract":"Bring Your Own Device (BYOD) is widespread at many universities in Japan. Students set up their PC to connect to the university's wireless LAN. However, students are not familiar with IT, so the staff at the information center has to check the PC settings directly. However, universities with dispersed campuses cannot secure sufficient staff to support the PCs and provide good services to users. Improving the level of IT support for users has become an issue. Therefore, to deal with such problems, a “Smart Help Desk” is proposed to read the user's computer screen, recognize the status, and automatically teach the operation method. This is an ongoing project.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128044216","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}
Pub Date : 2022-07-06DOI: 10.1109/ICCE-Taiwan55306.2022.9869204
Shu-Yen Lin, Guang-Fong Liu
Nowadays, Convolutional Neural Network (CNN) is widely used in many applications. Multi -layered convolutional neural networks need lots of memory capacity and bandwidth. A large number of the CNN parameters cause long latency for the memory accesses. To solve this problem, the 3D stacked DRAM-RRAM hybrid memory is discussed. However, the 3D stacked DRAM-RRAM hybrid memory may result in serious thermal problem for the thermal limitation of the DRAM and RRAM chips. In this work, we propose the dynamic thermal-predicted workload movement (DTPWM) to solve this problem. If the overheated banks of the DRAM and RRAM chips are predicted, DTPWM can move the workloads to other non-overheated memory banks. In our experiment, the latencies of the 3D stacked DRAM-RRAM hybrid memory is reduced by 27.7% under the thermal limitation.
{"title":"Dynamic Thermal-Predicted Workload Movement with Three-Dimensional DRAM-RRAM Hybrid Memories for Convolutional Neural Network Applications","authors":"Shu-Yen Lin, Guang-Fong Liu","doi":"10.1109/ICCE-Taiwan55306.2022.9869204","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869204","url":null,"abstract":"Nowadays, Convolutional Neural Network (CNN) is widely used in many applications. Multi -layered convolutional neural networks need lots of memory capacity and bandwidth. A large number of the CNN parameters cause long latency for the memory accesses. To solve this problem, the 3D stacked DRAM-RRAM hybrid memory is discussed. However, the 3D stacked DRAM-RRAM hybrid memory may result in serious thermal problem for the thermal limitation of the DRAM and RRAM chips. In this work, we propose the dynamic thermal-predicted workload movement (DTPWM) to solve this problem. If the overheated banks of the DRAM and RRAM chips are predicted, DTPWM can move the workloads to other non-overheated memory banks. In our experiment, the latencies of the 3D stacked DRAM-RRAM hybrid memory is reduced by 27.7% under the thermal limitation.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"50 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113934164","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}
Pub Date : 2022-07-06DOI: 10.1109/ICCE-Taiwan55306.2022.9869008
Myke D. M. Valadão, Diego A. Amoedo, Gustavo M. Torres, E. V. C. U. Mattos, Antônio M. C. Pereira, Matheus S. Uchôa, Lucas M. Torres, Victor L. G. Cavalcante, José E. B. S. Linhares, M. O. Silva, Agemilson P. Silva, Caio F. S. Cruz, Rômulo Fabrício, Ruan J. S. Belém, Thiago B. Bezerra, W. S. S. Júnior, Celso B. Carvalho
In this work, conducted by two partners, called UFAM/CETELI and, Envision (TPV Group), we present a method of automatic labeling of frames of worker's actions in factory environments using a model generated by a residual neural network. With this approach we used some manually labeled frames to training a model that provide the label of 4 classes of actions. We achieve accuracy rate over 96%, which give reliability to a supervised training of 3D dataset of actions.
{"title":"Automatic Video Labeling with Assembly Actions of Workers on a Production Line Using ResNet","authors":"Myke D. M. Valadão, Diego A. Amoedo, Gustavo M. Torres, E. V. C. U. Mattos, Antônio M. C. Pereira, Matheus S. Uchôa, Lucas M. Torres, Victor L. G. Cavalcante, José E. B. S. Linhares, M. O. Silva, Agemilson P. Silva, Caio F. S. Cruz, Rômulo Fabrício, Ruan J. S. Belém, Thiago B. Bezerra, W. S. S. Júnior, Celso B. Carvalho","doi":"10.1109/ICCE-Taiwan55306.2022.9869008","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869008","url":null,"abstract":"In this work, conducted by two partners, called UFAM/CETELI and, Envision (TPV Group), we present a method of automatic labeling of frames of worker's actions in factory environments using a model generated by a residual neural network. With this approach we used some manually labeled frames to training a model that provide the label of 4 classes of actions. We achieve accuracy rate over 96%, which give reliability to a supervised training of 3D dataset of actions.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132209383","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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