Pub Date : 2022-06-16DOI: 10.1109/comm54429.2022.9817306
Catalin Rizea, Călin Bîră, M. Stanciu
This paper proposes a new robust algorithm for hiding information in the visual information of images. Our robust version (RV) supports hiding data in 40×40 pixel black and white image and even after resizing and jpeg transform, around 80% of the original watermark can be recovered. Our dimension version (DV) increases the amount of data that may be hidden, up to 75% of LSB steganography but offers a high level of imperceptibility by hiding data inside the wavelet coefficients.
{"title":"Robust Steganographic Algorithm based on Wavelet Transform","authors":"Catalin Rizea, Călin Bîră, M. Stanciu","doi":"10.1109/comm54429.2022.9817306","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817306","url":null,"abstract":"This paper proposes a new robust algorithm for hiding information in the visual information of images. Our robust version (RV) supports hiding data in 40×40 pixel black and white image and even after resizing and jpeg transform, around 80% of the original watermark can be recovered. Our dimension version (DV) increases the amount of data that may be hidden, up to 75% of LSB steganography but offers a high level of imperceptibility by hiding data inside the wavelet coefficients.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127765231","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 : 2022-06-16DOI: 10.1109/comm54429.2022.9817330
Melisa Unsalan, A. Radoi, M. Datcu
The recent technological advancements in remote sensing lead to an increased importance regarding the analysis of satellite data targeting security and surveillance tasks. Although the availability of data products is constantly augmented and the advances in Deep Learning technologies are constant, Synthetic Aperture Radar (SAR) image classification remains a challenge in the remote sensing domain because standard convolutional neural network-based architectures may encounter difficulties in recognizing objects that are characterized by similar texture, but different backscattering patterns. Moreover, training deep learning architectures requires a large volume of annotated data, which, in general, represents an obstacle, especially in the case of the remote sensing domain. This article addresses complex-valued SAR image classification through both spatial and Fourier-domain features, extracted by means of pretrained neural networks. While spatial features allow extracting knowl-edge regarding the structure and texture of the objects from intensity images, the physical properties of the objects are learned from radar spectrograms. In addition, we show that considering different polarizations of the SAR sensor, we are able to obtain better visual classifications. The experiments are conducted over Sentinel-1images, which are freely available for download under the Copernicus initiative.
{"title":"SAR Image Classification Using Mixed Spatial-Spectral Information and Pre-trained Convolutional Neural Networks","authors":"Melisa Unsalan, A. Radoi, M. Datcu","doi":"10.1109/comm54429.2022.9817330","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817330","url":null,"abstract":"The recent technological advancements in remote sensing lead to an increased importance regarding the analysis of satellite data targeting security and surveillance tasks. Although the availability of data products is constantly augmented and the advances in Deep Learning technologies are constant, Synthetic Aperture Radar (SAR) image classification remains a challenge in the remote sensing domain because standard convolutional neural network-based architectures may encounter difficulties in recognizing objects that are characterized by similar texture, but different backscattering patterns. Moreover, training deep learning architectures requires a large volume of annotated data, which, in general, represents an obstacle, especially in the case of the remote sensing domain. This article addresses complex-valued SAR image classification through both spatial and Fourier-domain features, extracted by means of pretrained neural networks. While spatial features allow extracting knowl-edge regarding the structure and texture of the objects from intensity images, the physical properties of the objects are learned from radar spectrograms. In addition, we show that considering different polarizations of the SAR sensor, we are able to obtain better visual classifications. The experiments are conducted over Sentinel-1images, which are freely available for download under the Copernicus initiative.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127788521","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 : 2022-06-16DOI: 10.1109/comm54429.2022.9817274
Catalin Pescari, A. Silaghi, A. Sabata, Ciprian Bleoju
This article shows how to study the Power Integrity (PI) capability of an automotive device by means of Electromagnetic simulation software. A process description on how to manage PI simulations for a device under test (DUT) exemplifies the novelty. The innovation arises from the demonstration that using EM modeling early in designing phase permits the enhancement of PI performance without the need for sophisticated measurements.
{"title":"On Using EMC Simulation for Solving Power Integrity Issues","authors":"Catalin Pescari, A. Silaghi, A. Sabata, Ciprian Bleoju","doi":"10.1109/comm54429.2022.9817274","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817274","url":null,"abstract":"This article shows how to study the Power Integrity (PI) capability of an automotive device by means of Electromagnetic simulation software. A process description on how to manage PI simulations for a device under test (DUT) exemplifies the novelty. The innovation arises from the demonstration that using EM modeling early in designing phase permits the enhancement of PI performance without the need for sophisticated measurements.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134051327","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 : 2022-06-16DOI: 10.1109/comm54429.2022.9817310
Cristian Manolache, Mihai Boldeanu, C. Talianu, H. Cucu
Identification of atmospheric particles and aerosols is a very important topic in climatology. However, before classification, the various homogenous layers of aerosols need to be segmented. In this paper we present an initial work towards the development of an automated segmentation system for aerosols. Provided that there are no annotated datasets available for this task, we approach the problem using unsupervised machine learning techniques. Several machine learning (ML) models, previously used in other similar segmentation tasks, have been trained for the purpose of identifying various types of aerosols based on the input data. Initial model performance showed unsatisfactory results and thus several adjustments were made to fit our requirements. The ML models for aerosol segmentation have been evaluated objectively, only in terms of reconstruction efficiency, more precisely, how well does the model recreate the input data. Since there is no annotated dataset (neither for training, nor for evaluation), the segmentation efficiency of the models was not evaluated objectively. Consequently, the segmentation results have been evaluated by a human expert.
{"title":"Unsupervised deep learning models for aerosol layers segmentation","authors":"Cristian Manolache, Mihai Boldeanu, C. Talianu, H. Cucu","doi":"10.1109/comm54429.2022.9817310","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817310","url":null,"abstract":"Identification of atmospheric particles and aerosols is a very important topic in climatology. However, before classification, the various homogenous layers of aerosols need to be segmented. In this paper we present an initial work towards the development of an automated segmentation system for aerosols. Provided that there are no annotated datasets available for this task, we approach the problem using unsupervised machine learning techniques. Several machine learning (ML) models, previously used in other similar segmentation tasks, have been trained for the purpose of identifying various types of aerosols based on the input data. Initial model performance showed unsatisfactory results and thus several adjustments were made to fit our requirements. The ML models for aerosol segmentation have been evaluated objectively, only in terms of reconstruction efficiency, more precisely, how well does the model recreate the input data. Since there is no annotated dataset (neither for training, nor for evaluation), the segmentation efficiency of the models was not evaluated objectively. Consequently, the segmentation results have been evaluated by a human expert.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134346118","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 : 2022-06-16DOI: 10.1109/comm54429.2022.9817267
R. Dogaru, Adrian-Dumitru Mirica, I. Dogaru
Comparative synapses are proposed and investigated in the context of convolutional neural networks as replacements for the traditional, multiplier-based synapses. A comparative synapse is an operator inspired from the min() operator used in fuzzy-logic a replacement for product to implement AND function. Its implementation complexity is linear in the number of bits unlike multipliers, requiring quadratic complexity. In effect, using a typical resolution of 8 bits the use of comparative synapse would reduce 8 times the number of hardware resources allocated for the operator. A C-CNN model was constructed to support comparative synapses and their update and error propagation rules. GPU acceleration of the C-CNN model was achieved using CUPY. The model was trained with several widely known image recognition datasets including MNIST, CIFAR and USPS. It turns out that functional performance (accuracy) is not dramatically affected in C-CNN against a similar traditional CNN model with multiplicative operators, thus opening an interesting implementation perspective, particularly for the TinyML and HW-oriented solutions with significant reduction in energy, silicon area and costs. The approach is scalable to more sophisticated CNN models providing adequate optimized operators adapted to this new synaptic model.
{"title":"The C-CNN model: Do we really need multiplicative synapses in convolutional neural networks?","authors":"R. Dogaru, Adrian-Dumitru Mirica, I. Dogaru","doi":"10.1109/comm54429.2022.9817267","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817267","url":null,"abstract":"Comparative synapses are proposed and investigated in the context of convolutional neural networks as replacements for the traditional, multiplier-based synapses. A comparative synapse is an operator inspired from the min() operator used in fuzzy-logic a replacement for product to implement AND function. Its implementation complexity is linear in the number of bits unlike multipliers, requiring quadratic complexity. In effect, using a typical resolution of 8 bits the use of comparative synapse would reduce 8 times the number of hardware resources allocated for the operator. A C-CNN model was constructed to support comparative synapses and their update and error propagation rules. GPU acceleration of the C-CNN model was achieved using CUPY. The model was trained with several widely known image recognition datasets including MNIST, CIFAR and USPS. It turns out that functional performance (accuracy) is not dramatically affected in C-CNN against a similar traditional CNN model with multiplicative operators, thus opening an interesting implementation perspective, particularly for the TinyML and HW-oriented solutions with significant reduction in energy, silicon area and costs. The approach is scalable to more sophisticated CNN models providing adequate optimized operators adapted to this new synaptic model.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127845969","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 : 2022-06-16DOI: 10.1109/comm54429.2022.9817308
Mihai-Alexandrui Ilie, C. Rîncu
The rapid increase in network scale as well as the necessity of safe, secure communication through mediums that are prone to cyberattacks has determined the development of new methods for faster network convergence as well as lower deployment time for security measures. This paper presents a solution to the abovementioned concerns by using automations in order to obtain secure communications in the lowest time possible in an ever increasingly infrastructure.
{"title":"Convergence and security improvements by using automation in DMVPN networks","authors":"Mihai-Alexandrui Ilie, C. Rîncu","doi":"10.1109/comm54429.2022.9817308","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817308","url":null,"abstract":"The rapid increase in network scale as well as the necessity of safe, secure communication through mediums that are prone to cyberattacks has determined the development of new methods for faster network convergence as well as lower deployment time for security measures. This paper presents a solution to the abovementioned concerns by using automations in order to obtain secure communications in the lowest time possible in an ever increasingly infrastructure.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123896306","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 : 2022-06-16DOI: 10.1109/comm54429.2022.9817356
Cristian-Alexandru Tanase, Alexandru Vulpe
As large buildings, hospitals offer many situations where patients can get lost or medical personnel has trouble locating a patient inside a hospital or around its premises. Lost patients can lead to increased cost from wasting hospital employee time but more importantly to situations that can be dangerous or life threatening for some patients. The paper studies the use of a ZigBee network for patient location detection as well as indoor navigation within a hospital building. The system employs hospital mapping based on graph theory, a distinct three-technology network and a patient device that both measures the patient temperature and is also part of the location subsystem. The system obtained has lower power consumption and an average cost lower than other similar solutions.
{"title":"Indoor Location Monitoring and Navigation System for Hospitals","authors":"Cristian-Alexandru Tanase, Alexandru Vulpe","doi":"10.1109/comm54429.2022.9817356","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817356","url":null,"abstract":"As large buildings, hospitals offer many situations where patients can get lost or medical personnel has trouble locating a patient inside a hospital or around its premises. Lost patients can lead to increased cost from wasting hospital employee time but more importantly to situations that can be dangerous or life threatening for some patients. The paper studies the use of a ZigBee network for patient location detection as well as indoor navigation within a hospital building. The system employs hospital mapping based on graph theory, a distinct three-technology network and a patient device that both measures the patient temperature and is also part of the location subsystem. The system obtained has lower power consumption and an average cost lower than other similar solutions.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124447096","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 : 2022-06-16DOI: 10.1109/comm54429.2022.9817277
A. Al-Jawad, I. Comsa, P. Shah, R. Trestian
With the advent of 5G networks and beyond, there is an increasing demand to leverage Machine Learning (ML) capabilities and develop new and innovative solutions that could achieve efficient use of network resources and improve users' Quality of Experience (QoE). One of the key enabling technologies for 5G networks is Software Defined Networking (SDN) as it enables fine-grained monitoring and control of the network. Given the variety of dynamic networking conditions within 5G-SDN environments and the diversity of routing algorithms, an intelligent control of these strategies should exist to maximize the Quality of Service (QoS) provisioning of multimedia traffic with more stringent requirements without penalizing the performance of the background traffic. This paper proposes LearnSDN, an innovative ML-based solution that enables QoS provisioning over multimedia-based 5G-SDN environments. LearnSDN uses ML to learn the most convenient routing algorithm to be employed on the background traffic based on the dynamic network conditions in order to cater for the QoS requirements of the multimedia traffic. The performance of the proposed LearnSDN solution is evaluated under a realistic emulation-based SDN environment. The results indicate that LearnSDN outperforms other state-of-the-art solutions in terms of QoS provisioning, PSNR and MOS.
{"title":"LearnSDN: Optimizing Routing Over Multimedia-based 5G-SDN using Machine Learning","authors":"A. Al-Jawad, I. Comsa, P. Shah, R. Trestian","doi":"10.1109/comm54429.2022.9817277","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817277","url":null,"abstract":"With the advent of 5G networks and beyond, there is an increasing demand to leverage Machine Learning (ML) capabilities and develop new and innovative solutions that could achieve efficient use of network resources and improve users' Quality of Experience (QoE). One of the key enabling technologies for 5G networks is Software Defined Networking (SDN) as it enables fine-grained monitoring and control of the network. Given the variety of dynamic networking conditions within 5G-SDN environments and the diversity of routing algorithms, an intelligent control of these strategies should exist to maximize the Quality of Service (QoS) provisioning of multimedia traffic with more stringent requirements without penalizing the performance of the background traffic. This paper proposes LearnSDN, an innovative ML-based solution that enables QoS provisioning over multimedia-based 5G-SDN environments. LearnSDN uses ML to learn the most convenient routing algorithm to be employed on the background traffic based on the dynamic network conditions in order to cater for the QoS requirements of the multimedia traffic. The performance of the proposed LearnSDN solution is evaluated under a realistic emulation-based SDN environment. The results indicate that LearnSDN outperforms other state-of-the-art solutions in terms of QoS provisioning, PSNR and MOS.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124580296","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 : 2022-06-16DOI: 10.1109/comm54429.2022.9817154
Kanar Al-Sammak, Sama AL-Gburi, I. Marghescu
Smart metering is an essential component of advanced grid infrastructure to provide better services to the consumers, which digitally provides the consumption data to both consumer and utility companies. The communications technologies used in smart metering needs a robust authentication approach to secure the data. In this paper, we have realized a systematic literature review (SLR) concerning the communications technologies involved in smart metering and the issues associated with them, like the data security concerns and their practical solutions. We have searched the main international databases such as IEEE Xplore, Elsevier and Springer Libraries with appropriate key words and processed the references by means of a systematic review process in order to identify the current solutions for gathering the data, the main issues related to the data security and the aspects that are still to be investigated.
{"title":"Communications Systems in Smart Metering: A Concise Systematic Literature review","authors":"Kanar Al-Sammak, Sama AL-Gburi, I. Marghescu","doi":"10.1109/comm54429.2022.9817154","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817154","url":null,"abstract":"Smart metering is an essential component of advanced grid infrastructure to provide better services to the consumers, which digitally provides the consumption data to both consumer and utility companies. The communications technologies used in smart metering needs a robust authentication approach to secure the data. In this paper, we have realized a systematic literature review (SLR) concerning the communications technologies involved in smart metering and the issues associated with them, like the data security concerns and their practical solutions. We have searched the main international databases such as IEEE Xplore, Elsevier and Springer Libraries with appropriate key words and processed the references by means of a systematic review process in order to identify the current solutions for gathering the data, the main issues related to the data security and the aspects that are still to be investigated.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"2004 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125834223","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 : 2022-06-16DOI: 10.1109/comm54429.2022.9817233
Maria Oniga, Razvan-Florian Micu, Andreea Griparis
Skin cancer is one of the major threats to men's and women's health on a global scale, and as with all other cancers, early diagnosis leads to a high rate of recovery. To reduce the required time for diagnosis, we developed an architecture for the automated classification of dermatological images with multiple skin lesions. The proposed system is based on a classical Unet architecture trained with patches extracted from four images with various skin lesions to identify the areas of interest whose condition is determined by an adapted EfficientNetB5 architecture trained with the HAM10000 dataset. Our results showed that the dermatoscopic image models learned from the HAM10000 dataset can be successfully used to diagnose skin cancer from images with multiple lesions, captured with usual cameras.
{"title":"Deep neural networks for classification of dermatological images with multiple skin lesions","authors":"Maria Oniga, Razvan-Florian Micu, Andreea Griparis","doi":"10.1109/comm54429.2022.9817233","DOIUrl":"https://doi.org/10.1109/comm54429.2022.9817233","url":null,"abstract":"Skin cancer is one of the major threats to men's and women's health on a global scale, and as with all other cancers, early diagnosis leads to a high rate of recovery. To reduce the required time for diagnosis, we developed an architecture for the automated classification of dermatological images with multiple skin lesions. The proposed system is based on a classical Unet architecture trained with patches extracted from four images with various skin lesions to identify the areas of interest whose condition is determined by an adapted EfficientNetB5 architecture trained with the HAM10000 dataset. Our results showed that the dermatoscopic image models learned from the HAM10000 dataset can be successfully used to diagnose skin cancer from images with multiple lesions, captured with usual cameras.","PeriodicalId":118077,"journal":{"name":"2022 14th International Conference on Communications (COMM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124709752","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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