Pub Date : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920476
V. Biolková, D. Biolek, Z. Kolka
The paper proposes a methodology for implementing true current amplifiers, i.e., amplifiers with low-impedance current inputs and high-impedance current outputs, from commercial integrated circuits. The possibilities of implementing a true current instrumentation amplifier are analyzed, the simplification of which results in a true current operational amplifier (TCOA) as an adjoint circuit to a known voltage operational amplifier. Basic measurements are made on the implemented TCOA specimen and its operation in a current-mode Sallen-Key filter is verified.
{"title":"Implementation of True Current Amplifiers via Commercial Integrated Circuits","authors":"V. Biolková, D. Biolek, Z. Kolka","doi":"10.23919/NTSP54843.2022.9920476","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920476","url":null,"abstract":"The paper proposes a methodology for implementing true current amplifiers, i.e., amplifiers with low-impedance current inputs and high-impedance current outputs, from commercial integrated circuits. The possibilities of implementing a true current instrumentation amplifier are analyzed, the simplification of which results in a true current operational amplifier (TCOA) as an adjoint circuit to a known voltage operational amplifier. Basic measurements are made on the implemented TCOA specimen and its operation in a current-mode Sallen-Key filter is verified.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115768531","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 : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920388
M. Turčaník, J. Baráth
This paper presents a new approach to intrusion detection using methods of artificial intelligence. Neural networks are suitable for use in intrusion detection systems. To analyze the suitability of using neural networks several data sets were created. They consist of a set of legitimate and malicious communications represented by equally represented samples of data streams, with the number of parameters used varying according to the input parameter optimization method used. For training of the neural networks were used 3 training algorithms: Levenberg–Marquardt algorithm, Bayesian regularization, and scaled conjugate gradient backpropagation algorithm. Dimensionality reduction can decrease the number of features to decrease computational complexity. Two methods are analyzed in the paper: principal component analysis and the stepwise selection method. These methods are compared with results achieved from the training of neural networks for a full set of parameters of the input data sets. The proposed topology of the artificial neural network obtains the probability of correct classification from 80.8 to 84.6% for selected test sets.
{"title":"Intrusion Detection by Artificial Neural Networks","authors":"M. Turčaník, J. Baráth","doi":"10.23919/NTSP54843.2022.9920388","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920388","url":null,"abstract":"This paper presents a new approach to intrusion detection using methods of artificial intelligence. Neural networks are suitable for use in intrusion detection systems. To analyze the suitability of using neural networks several data sets were created. They consist of a set of legitimate and malicious communications represented by equally represented samples of data streams, with the number of parameters used varying according to the input parameter optimization method used. For training of the neural networks were used 3 training algorithms: Levenberg–Marquardt algorithm, Bayesian regularization, and scaled conjugate gradient backpropagation algorithm. Dimensionality reduction can decrease the number of features to decrease computational complexity. Two methods are analyzed in the paper: principal component analysis and the stepwise selection method. These methods are compared with results achieved from the training of neural networks for a full set of parameters of the input data sets. The proposed topology of the artificial neural network obtains the probability of correct classification from 80.8 to 84.6% for selected test sets.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128556985","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 : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920433
Pavel Kozak, V. Platenka, Marketa Vrsecka
This article explores control signal protocols for controlling UAV (Unmanned Aerial Vehicles). It is aimed at commercially available UAVs that are intended for high-quality private or affordable professional use, but at the same time operate in the 2.4 GHz license-free band. Representatives of control sets with different types of communication protocols were selected for the measurement so that they could be compared. The initial measurement revealed the behavior of the control signals (modulation type, security, FH (Frequency Hopping), existence of a return channel...), thereby creating three different groups of control devices for measurement. Only some control units can actively react to interference. The communication protocols of the UAV control sets are designed to be able to ensure a reliable connection in an environment where unintentional collisions can occur relatively often. The analysis of the control protocol took place on the basis of the interference of a very small part of the frequency spectrum (one channel intended for FH) and the subsequent analysis of the behavior of this interference. The knowledge gained about the ability to avoid collisions in the spectrum can be used to create effective intentional jamming in the future.
{"title":"Analysis of Communication Protocols of UAV Control Sets","authors":"Pavel Kozak, V. Platenka, Marketa Vrsecka","doi":"10.23919/NTSP54843.2022.9920433","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920433","url":null,"abstract":"This article explores control signal protocols for controlling UAV (Unmanned Aerial Vehicles). It is aimed at commercially available UAVs that are intended for high-quality private or affordable professional use, but at the same time operate in the 2.4 GHz license-free band. Representatives of control sets with different types of communication protocols were selected for the measurement so that they could be compared. The initial measurement revealed the behavior of the control signals (modulation type, security, FH (Frequency Hopping), existence of a return channel...), thereby creating three different groups of control devices for measurement. Only some control units can actively react to interference. The communication protocols of the UAV control sets are designed to be able to ensure a reliable connection in an environment where unintentional collisions can occur relatively often. The analysis of the control protocol took place on the basis of the interference of a very small part of the frequency spectrum (one channel intended for FH) and the subsequent analysis of the behavior of this interference. The knowledge gained about the ability to avoid collisions in the spectrum can be used to create effective intentional jamming in the future.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132697133","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 : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920469
Jan Malucha
This paper describes a learning tool developed in MATLAB environment for training English pronunciation of specific terminology, focused on special vocabulary from signal processing and electronics by default. The tool enables to measure three phonetic parameters, namely accent, intonation and voicing. This is done using various computational methods and algorithms including basic filtering, short-time energy, average magnitude difference function or harmonic-to-noise ratio. Spoken words are compared with reference pronunciation in terms of phonetic parameters. Each parameter can be evaluated separately or all parameters together. The learner gets immediate feedback in two forms – percentage correctness of its pronunciation and verbal recommendation at which points to improve its pronunciation, supported by an indicative visual feedback in form of graphs showing each of the phonetic parameters along the spoken word. Two regimes of practices are possible.
{"title":"Software Tool for Pronunciation Training of Specific English Terminology","authors":"Jan Malucha","doi":"10.23919/NTSP54843.2022.9920469","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920469","url":null,"abstract":"This paper describes a learning tool developed in MATLAB environment for training English pronunciation of specific terminology, focused on special vocabulary from signal processing and electronics by default. The tool enables to measure three phonetic parameters, namely accent, intonation and voicing. This is done using various computational methods and algorithms including basic filtering, short-time energy, average magnitude difference function or harmonic-to-noise ratio. Spoken words are compared with reference pronunciation in terms of phonetic parameters. Each parameter can be evaluated separately or all parameters together. The learner gets immediate feedback in two forms – percentage correctness of its pronunciation and verbal recommendation at which points to improve its pronunciation, supported by an indicative visual feedback in form of graphs showing each of the phonetic parameters along the spoken word. Two regimes of practices are possible.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114214549","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 : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920440
Stanislav Drozd, Juraj Tomlain, Martin Marko, O. Teren, J. Tomlain
This paper deals with using low-cost radio modules for indoor localization purposes. A comparison of the various positioning techniques is introduced in the beginning parts. In addition, the trilateration method’s mathematical background is briefly introduced. Bluetooth Low Energy has been chosen for real environment tests and deployment of the proposed methods. The system consists of newly developed and manufactured hardware units. The paper then describes the microprocessor and post-processing algorithms used. The designed system was installed in the laboratory room for verification and measurement purposes. Finally, the platform was evaluated regarding the final accuracy in the real installation.
{"title":"Evaluation of the Cost-Effective Indoor Wireless Positioning System Using RSSI Method","authors":"Stanislav Drozd, Juraj Tomlain, Martin Marko, O. Teren, J. Tomlain","doi":"10.23919/NTSP54843.2022.9920440","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920440","url":null,"abstract":"This paper deals with using low-cost radio modules for indoor localization purposes. A comparison of the various positioning techniques is introduced in the beginning parts. In addition, the trilateration method’s mathematical background is briefly introduced. Bluetooth Low Energy has been chosen for real environment tests and deployment of the proposed methods. The system consists of newly developed and manufactured hardware units. The paper then describes the microprocessor and post-processing algorithms used. The designed system was installed in the laboratory room for verification and measurement purposes. Finally, the platform was evaluated regarding the final accuracy in the real installation.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125279755","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 : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920436
F. Paulů, J. Hospodka
This article presents a new concept of fully analogue adaptive filters. The adaptation is based on fully analogue neural networks. With the use of a filter bank, it can be used for high frequency and real-time adaptation. The properties of this concept are verified using electronic circuit simulations.
{"title":"New Concept of Analogue Adaptive Filter Based on Fully Analogue Artificial Neural Network","authors":"F. Paulů, J. Hospodka","doi":"10.23919/NTSP54843.2022.9920436","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920436","url":null,"abstract":"This article presents a new concept of fully analogue adaptive filters. The adaptation is based on fully analogue neural networks. With the use of a filter bank, it can be used for high frequency and real-time adaptation. The properties of this concept are verified using electronic circuit simulations.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129340663","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 : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920384
D. Kyselica, Linda Jurkasová, R. Ďurikovič, J. Silha
Our work focuses on application of modern experimental Machine Learning (ML) algorithms toward the space objects classification. Two types of data are analyzed, frame objects present on the astronomical Flexible Image Transport System (FITS) frames and space objects’ light curves, which could be considered as a footprint for given object. In our work we will present ML algorithm used for recognition of frame objects present in the FITS frames by using their specific shape. Presented algorithm is a Convolutional Neural Network (CNN) of 9 layers. The input to the network is a small 50x50 image which must contain only one object for the network to correctly classify it. This could later be used as subnet in region-based CNN after finding regions of interest in full FITS image. Additionally, we present results of applying CNN neural network based on ResNet architecture to classify light curves to categories based on their shape. For deep learning we used primarily public catalogue light curves of selected populations of upper stages e.g., Falcon 9, Atlas Centaur 5, Delta 4 which usually contain simpler features in their photometric series. The modeling software Blender was also used to generate synthetic light curves for training purposes. Algorithm can identify correctly more than 84% of tested objects. In near future we plan to extend the algorithm to identify more complex objects such as box-wing and single box-wing satellites.
{"title":"Astronomical Objects Classification by Convolutional Neural Network Algorithms Layers","authors":"D. Kyselica, Linda Jurkasová, R. Ďurikovič, J. Silha","doi":"10.23919/NTSP54843.2022.9920384","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920384","url":null,"abstract":"Our work focuses on application of modern experimental Machine Learning (ML) algorithms toward the space objects classification. Two types of data are analyzed, frame objects present on the astronomical Flexible Image Transport System (FITS) frames and space objects’ light curves, which could be considered as a footprint for given object. In our work we will present ML algorithm used for recognition of frame objects present in the FITS frames by using their specific shape. Presented algorithm is a Convolutional Neural Network (CNN) of 9 layers. The input to the network is a small 50x50 image which must contain only one object for the network to correctly classify it. This could later be used as subnet in region-based CNN after finding regions of interest in full FITS image. Additionally, we present results of applying CNN neural network based on ResNet architecture to classify light curves to categories based on their shape. For deep learning we used primarily public catalogue light curves of selected populations of upper stages e.g., Falcon 9, Atlas Centaur 5, Delta 4 which usually contain simpler features in their photometric series. The modeling software Blender was also used to generate synthetic light curves for training purposes. Algorithm can identify correctly more than 84% of tested objects. In near future we plan to extend the algorithm to identify more complex objects such as box-wing and single box-wing satellites.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127928373","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 : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920393
M. Richterová, J. Olivová, M. Popela, V. Blažek
This paper describes the fabrication of a Sierpinski fractal antenna using a 3D commercial printer. The Sierpinski fractal antenna was chosen for its design simplicity and broadband performance. The paper describes the fabrication procedure for 3D printing and subsequent metal spray coating of the Sierpinski fractal antenna of the 1st and 2nd iterations. The design of the Sierpinski fractal antenna in the MATLAB application using the Antenna Toolbox extension is also described, including 3D printing procedures, post processing procedures (plating) and practical testing of its functionality. The measured results are compared to simulations and then analyzed.
{"title":"Use of 3D Printing for Sierpinski Fractal Antenna Manufacturing","authors":"M. Richterová, J. Olivová, M. Popela, V. Blažek","doi":"10.23919/NTSP54843.2022.9920393","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920393","url":null,"abstract":"This paper describes the fabrication of a Sierpinski fractal antenna using a 3D commercial printer. The Sierpinski fractal antenna was chosen for its design simplicity and broadband performance. The paper describes the fabrication procedure for 3D printing and subsequent metal spray coating of the Sierpinski fractal antenna of the 1st and 2nd iterations. The design of the Sierpinski fractal antenna in the MATLAB application using the Antenna Toolbox extension is also described, including 3D printing procedures, post processing procedures (plating) and practical testing of its functionality. The measured results are compared to simulations and then analyzed.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128141444","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 : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920403
Nenad Stojanović, Boban P. Bondzulic, B. Pavlović, V. Ristić
The paper proposes an objective image quality assessment measure with full referencing. The measure is based on a comparison of the contrast of the original image and the image with the degradation. Discrete cosine transform coefficients are used for contrast estimation. By applying the measure, a scalar value is obtained that reflects the quality of the test (degraded) image. The proposed measure is tested on an infrared image dataset developed by the Military Academy in Belgrade, Serbia. The performance of the measure was compared with other well-known objective full-reference image quality assessment metrics, which were developed for the images in visible domain. It was shown that measure performance can be improved with the adequate selections of the block dimensions and the number of discrete cosine transform coefficients during the calculation of image quality value. The proposed measure obtained a correlation with the subjective scores near 82%, which puts the measure into the top three of all tested image quality assessment measures. The proposed measure showed the best performance on the images distorted by Gaussian blurring, where the level of agreement with the subjective scores is over 97%, according to which the measure stands out as the best compared to other tested measures.
{"title":"Contrast Quality Measure: Full-Reference Image Quality Assessment Metric for Infrared Images","authors":"Nenad Stojanović, Boban P. Bondzulic, B. Pavlović, V. Ristić","doi":"10.23919/NTSP54843.2022.9920403","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920403","url":null,"abstract":"The paper proposes an objective image quality assessment measure with full referencing. The measure is based on a comparison of the contrast of the original image and the image with the degradation. Discrete cosine transform coefficients are used for contrast estimation. By applying the measure, a scalar value is obtained that reflects the quality of the test (degraded) image. The proposed measure is tested on an infrared image dataset developed by the Military Academy in Belgrade, Serbia. The performance of the measure was compared with other well-known objective full-reference image quality assessment metrics, which were developed for the images in visible domain. It was shown that measure performance can be improved with the adequate selections of the block dimensions and the number of discrete cosine transform coefficients during the calculation of image quality value. The proposed measure obtained a correlation with the subjective scores near 82%, which puts the measure into the top three of all tested image quality assessment measures. The proposed measure showed the best performance on the images distorted by Gaussian blurring, where the level of agreement with the subjective scores is over 97%, according to which the measure stands out as the best compared to other tested measures.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127029693","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 : 2022-10-12DOI: 10.23919/NTSP54843.2022.9920396
J. Perdoch, S. Gazovová, M. Pacek, Z. Matousek, J. Ochodnicky
This paper presents algorithms for improving Constant False Alarm Rate (CFAR) detections on raw radar data matrices. Acceleration of radar signal processing was assessed by the application of Cell-Averaging CFAR (CA-CFAR) in four specific optimization cases. Reduction of clutter impact in CA-CFAR was also implemented in order to enhance CA-CFAR operation. For the simulation setup, synthetic radar signals with different Signal-to-Noise Ratio (SNR) values were used. It is further demonstrated that radar signal processing computational complexity can be reduced by applying CA-CFAR on the vector consisting of computed statistical values.
{"title":"CFAR Algorithm for Improving Detections on Radar Raw Data Matrices","authors":"J. Perdoch, S. Gazovová, M. Pacek, Z. Matousek, J. Ochodnicky","doi":"10.23919/NTSP54843.2022.9920396","DOIUrl":"https://doi.org/10.23919/NTSP54843.2022.9920396","url":null,"abstract":"This paper presents algorithms for improving Constant False Alarm Rate (CFAR) detections on raw radar data matrices. Acceleration of radar signal processing was assessed by the application of Cell-Averaging CFAR (CA-CFAR) in four specific optimization cases. Reduction of clutter impact in CA-CFAR was also implemented in order to enhance CA-CFAR operation. For the simulation setup, synthetic radar signals with different Signal-to-Noise Ratio (SNR) values were used. It is further demonstrated that radar signal processing computational complexity can be reduced by applying CA-CFAR on the vector consisting of computed statistical values.","PeriodicalId":103310,"journal":{"name":"2022 New Trends in Signal Processing (NTSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129777176","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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