Pub Date : 2018-09-01DOI: 10.1109/ICFSP.2018.8552063
O. Chernoyarov, A. Faulgaber, Y. Korchagin, A. Makarov
We found the structure and the asymptotic characteristics of the quasi-likelihood estimates of the time of arrival and the duration of the video pulse with the unknown amplitude. We determined the losses in accuracy of the produced estimates of the time parameters due to the difference of the true amplitude of the received signal from the expected one. We also established the usefulness of the introduced quasi-likelihood measurer depending on the available prior information.
{"title":"Quasi-Likelihood Estimates of the Time of Arrival and the Duration of the Signal with the Unknown Amplitude","authors":"O. Chernoyarov, A. Faulgaber, Y. Korchagin, A. Makarov","doi":"10.1109/ICFSP.2018.8552063","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552063","url":null,"abstract":"We found the structure and the asymptotic characteristics of the quasi-likelihood estimates of the time of arrival and the duration of the video pulse with the unknown amplitude. We determined the losses in accuracy of the produced estimates of the time parameters due to the difference of the true amplitude of the received signal from the expected one. We also established the usefulness of the introduced quasi-likelihood measurer depending on the available prior information.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"123 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116378131","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 : 2018-09-01DOI: 10.1109/ICFSP.2018.8552044
Md. Rashedul Islam, Umme Kulsum Mitu, R. Bhuiyan, Jungpil Shin
In this era, Human-Computer Interaction (HCI) is a fascinating field about the interaction between humans and computers. Interacting with computers, human Hand Gesture Recognition (HGR) is the most significant way and the major part of HCI. Extracting features and detecting hand gesture from inputted color videos is more challenging because of the huge variation in the hands. For resolving this issue, this paper introduces an effective HGR system for low-cost color video using webcam. In this proposed model, Deep Convolutional Neural Network (DCNN) is used for extracting efficient hand features to recognize the American Sign Language (ASL) using hand gestures. Finally, the Multi-class Support Vector Machine (MCSVM) is used for identifying the hand sign, where CNN extracted features are used to train up the machine. Distinct person hand gesture is used for validation in this paper. The proposed model shows satisfactory performance in terms of classification accuracy, i.e., 94.57%
{"title":"Hand Gesture Feature Extraction Using Deep Convolutional Neural Network for Recognizing American Sign Language","authors":"Md. Rashedul Islam, Umme Kulsum Mitu, R. Bhuiyan, Jungpil Shin","doi":"10.1109/ICFSP.2018.8552044","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552044","url":null,"abstract":"In this era, Human-Computer Interaction (HCI) is a fascinating field about the interaction between humans and computers. Interacting with computers, human Hand Gesture Recognition (HGR) is the most significant way and the major part of HCI. Extracting features and detecting hand gesture from inputted color videos is more challenging because of the huge variation in the hands. For resolving this issue, this paper introduces an effective HGR system for low-cost color video using webcam. In this proposed model, Deep Convolutional Neural Network (DCNN) is used for extracting efficient hand features to recognize the American Sign Language (ASL) using hand gestures. Finally, the Multi-class Support Vector Machine (MCSVM) is used for identifying the hand sign, where CNN extracted features are used to train up the machine. Distinct person hand gesture is used for validation in this paper. The proposed model shows satisfactory performance in terms of classification accuracy, i.e., 94.57%","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116553284","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 : 2018-09-01DOI: 10.1109/ICFSP.2018.8552074
Jun Seong Lee, M. Seo, S. W. Kim, Minho Choi
Detection of fetal QRS complexes in a noninvasive fetal electrocardiogram (NI-FECG) signal is an important task to check fetal conditions and to prevent birth defects. However, the detection is not easy because the NI-FECG signal contains a maternal ECG signal that has greater amplitude than that of a fetal ECG signal. This paper proposes an algorithm to detect the fetal QRS complexes in the NI-FECG signal. The proposed algorithm is based on convolutional neural networks (CNN) and can reliably detect the fetal QRS complexes without separating the maternal ECG signal. To verify the algorithm, NI-FECG data (PhysioNet/computing in cardiology challenge 2013) were used. The proposed algorithm showed the average sensitivity of 89.06 % and positive predictive value of 92.77 %. The proposed algorithm can help to check fetal conditions and to prevent birth defects.
在无创胎儿心电图(NI-FECG)信号中检测胎儿QRS复合物是检查胎儿状况和预防出生缺陷的重要任务。然而，检测并不容易，因为NI-FECG信号中含有比胎儿ECG信号幅度更大的母体ECG信号。本文提出了一种检测NI-FECG信号中胎儿QRS复合物的算法。该算法基于卷积神经网络(CNN)，可以在不分离母体心电信号的情况下可靠地检测胎儿QRS复合物。为了验证该算法，使用了NI-FECG数据(PhysioNet/computing in cardiology challenge 2013)。该算法的平均灵敏度为89.06%，阳性预测值为92.77%。提出的算法可以帮助检查胎儿状况，防止出生缺陷。
{"title":"Fetal QRS Detection Based on Convolutional Neural Networks in Noninvasive Fetal Electrocardiogram","authors":"Jun Seong Lee, M. Seo, S. W. Kim, Minho Choi","doi":"10.1109/ICFSP.2018.8552074","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552074","url":null,"abstract":"Detection of fetal QRS complexes in a noninvasive fetal electrocardiogram (NI-FECG) signal is an important task to check fetal conditions and to prevent birth defects. However, the detection is not easy because the NI-FECG signal contains a maternal ECG signal that has greater amplitude than that of a fetal ECG signal. This paper proposes an algorithm to detect the fetal QRS complexes in the NI-FECG signal. The proposed algorithm is based on convolutional neural networks (CNN) and can reliably detect the fetal QRS complexes without separating the maternal ECG signal. To verify the algorithm, NI-FECG data (PhysioNet/computing in cardiology challenge 2013) were used. The proposed algorithm showed the average sensitivity of 89.06 % and positive predictive value of 92.77 %. The proposed algorithm can help to check fetal conditions and to prevent birth defects.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133577062","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 : 2018-09-01DOI: 10.1109/ICFSP.2018.8552042
P. Kiruthika, K B Jayanthi, Madian Nirmala
Karyotyping of Banded Metaphase Chromosomes is one of the preliminary steps used in cytogenetics to analyze the chromosomes for diagnostic purposes. Deep learning is a subfield of machine learning concerned with structure and function of brain. It exploits a way to automate predictive analysis. The key aspect of deep learning is that the layers of features are not designed by human engineers. They are learned from data using a general purpose learning procedure. This paper proposes a convolution based deep learning to classify the chromosomes for automated karyotyping. The developed architecture allows us to train and test images that helps in predicting the chromosome abnormality. The performance analysis is based on loss and accuracy curves and the graphical representation clearly exhibits better classification results for this architecture.
{"title":"Classification of Metaphase Chromosomes Using Deep Learning Neural Network","authors":"P. Kiruthika, K B Jayanthi, Madian Nirmala","doi":"10.1109/ICFSP.2018.8552042","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552042","url":null,"abstract":"Karyotyping of Banded Metaphase Chromosomes is one of the preliminary steps used in cytogenetics to analyze the chromosomes for diagnostic purposes. Deep learning is a subfield of machine learning concerned with structure and function of brain. It exploits a way to automate predictive analysis. The key aspect of deep learning is that the layers of features are not designed by human engineers. They are learned from data using a general purpose learning procedure. This paper proposes a convolution based deep learning to classify the chromosomes for automated karyotyping. The developed architecture allows us to train and test images that helps in predicting the chromosome abnormality. The performance analysis is based on loss and accuracy curves and the graphical representation clearly exhibits better classification results for this architecture.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131305230","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 : 2018-09-01DOI: 10.1109/ICFSP.2018.8552052
R. Paringer, M. Boori, Y. Donon, A. Kupriyanov, D. Kirsh, Kravtsova Natalia
This work aims to increase the reliability of dendritic crystallogram’s images classification. Crystallographic methods are used for medical diagnosis and we propose here to improve the reliability of their classification through an improved description of de dendritic structures’ features. In this paper, we use the parameters of the mathematical model describing objects with dendritic structure. We developed a technology of parameters identification from a model image of dendritic structures, that was then implemented through the use of geometric and statistical features, together with a nearest neighbor classification algorithm.
{"title":"Development of Technology for the Identification of Model Parameters for Dendritic Structures Images","authors":"R. Paringer, M. Boori, Y. Donon, A. Kupriyanov, D. Kirsh, Kravtsova Natalia","doi":"10.1109/ICFSP.2018.8552052","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552052","url":null,"abstract":"This work aims to increase the reliability of dendritic crystallogram’s images classification. Crystallographic methods are used for medical diagnosis and we propose here to improve the reliability of their classification through an improved description of de dendritic structures’ features. In this paper, we use the parameters of the mathematical model describing objects with dendritic structure. We developed a technology of parameters identification from a model image of dendritic structures, that was then implemented through the use of geometric and statistical features, together with a nearest neighbor classification algorithm.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122416083","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 : 2018-09-01DOI: 10.1109/ICFSP.2018.8552059
Ivan Ralašić, A. Tafro, D. Seršić
Compressive sensing (CS) represents a signal processing technique for simultaneous signal acquisition and compression that relies on signal dimensionality reduction. Statistical compressive sensing (SCS) uses statistical models to develop an efficient sampling strategy for signals that follow some statistical distribution. In this paper, statistical model based on Gaussian mixtures is employed to design an efficient framework for the CS signal reconstruction and classification. A robust classification method based on sparse signal representation using overcomplete eigenvector dictionaries andl1-norm is presented. Optimal non-adaptive measurement matrix for observed Gaussian mixture model is discussed. A series of experiments to analyze the performance of the proposed method has been performed and presented in the experimental results section.
{"title":"Statistical Compressive Sensing for Efficient Signal Reconstruction and Classification","authors":"Ivan Ralašić, A. Tafro, D. Seršić","doi":"10.1109/ICFSP.2018.8552059","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552059","url":null,"abstract":"Compressive sensing (CS) represents a signal processing technique for simultaneous signal acquisition and compression that relies on signal dimensionality reduction. Statistical compressive sensing (SCS) uses statistical models to develop an efficient sampling strategy for signals that follow some statistical distribution. In this paper, statistical model based on Gaussian mixtures is employed to design an efficient framework for the CS signal reconstruction and classification. A robust classification method based on sparse signal representation using overcomplete eigenvector dictionaries andl1-norm is presented. Optimal non-adaptive measurement matrix for observed Gaussian mixture model is discussed. A series of experiments to analyze the performance of the proposed method has been performed and presented in the experimental results section.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114879425","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}
A novel photonic approach for generating a frequency-septupling or frequency-nonupling millimeter-wave (mm-wave) signal with tunable phase shift is proposed. Two fourth-order sidebands and an optical carrier are generated by using a dual-parallel Mach-Zehnder modulator (DPMZM). An optical bandstop filter (OBSF) is used to filter out optical carrier and a Mach-Zehnder interferometer (MZI) is employed to separate the +4th-order sideband and the −4th-order sideband. Then the −4th-order sideband is modulated by an optical phase modulator (PM), and the phase of the −4th-order sideband can be controlled by changing the dc voltage that drives the PM. The +4th-order sideband is modulated by the second DPMZM, then a +3rd-order sideband or +5th-order sideband is generated by controlling the dc voltage that drives the main-MZM of the second DPMZM. After an optical coupler and a photodiode (PD), a frequency-septupling or frequency-nonupling mm-wave signal with tunable phase shift is gotten. A simulation experiment is performed, and tunable 360-degree phase shift is realized, and the amplitude variation of the generated mm-wave signal is less than 0.2dB.
{"title":"Photonic Generation of Millimeter-Wave Signals With Frequency-Multiplying and Tunable Phase Shift","authors":"Conghui Zhang, Ruiying He, Xiaoyu Zhang, Yongfeng Wei","doi":"10.1109/ICFSP.2018.8552069","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552069","url":null,"abstract":"A novel photonic approach for generating a frequency-septupling or frequency-nonupling millimeter-wave (mm-wave) signal with tunable phase shift is proposed. Two fourth-order sidebands and an optical carrier are generated by using a dual-parallel Mach-Zehnder modulator (DPMZM). An optical bandstop filter (OBSF) is used to filter out optical carrier and a Mach-Zehnder interferometer (MZI) is employed to separate the +4th-order sideband and the −4th-order sideband. Then the −4th-order sideband is modulated by an optical phase modulator (PM), and the phase of the −4th-order sideband can be controlled by changing the dc voltage that drives the PM. The +4th-order sideband is modulated by the second DPMZM, then a +3rd-order sideband or +5th-order sideband is generated by controlling the dc voltage that drives the main-MZM of the second DPMZM. After an optical coupler and a photodiode (PD), a frequency-septupling or frequency-nonupling mm-wave signal with tunable phase shift is gotten. A simulation experiment is performed, and tunable 360-degree phase shift is realized, and the amplitude variation of the generated mm-wave signal is less than 0.2dB.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116996845","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 : 2018-09-01DOI: 10.1109/ICFSP.2018.8552077
A. Zeroual, F. Harrou, Ying Sun
Efficient and accurate estimation of traffic density plays an important role in the development of intelligent transportation systems by providing relevant information for rapid decision-making. The purpose of this study is to design a model-based procedure to estimate traffic density. Here, we design an innovative observer that combines the benefits of piecewise switched linear traffic model with Luenberger observer estimator for improving road traffic density estimation. We evaluated the proposed estimator by using traffic data from the four-lane SR-60 freeway in southern California.
{"title":"An Improved Macroscopic Modeling for Highway Traffic Density Estimation","authors":"A. Zeroual, F. Harrou, Ying Sun","doi":"10.1109/ICFSP.2018.8552077","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552077","url":null,"abstract":"Efficient and accurate estimation of traffic density plays an important role in the development of intelligent transportation systems by providing relevant information for rapid decision-making. The purpose of this study is to design a model-based procedure to estimate traffic density. Here, we design an innovative observer that combines the benefits of piecewise switched linear traffic model with Luenberger observer estimator for improving road traffic density estimation. We evaluated the proposed estimator by using traffic data from the four-lane SR-60 freeway in southern California.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134167895","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 : 2018-09-01DOI: 10.1109/ICFSP.2018.8552057
Patryk Bąk, Jȩdrzej Bieniasz, M. Krzemiński, K. Szczypiorski
Currently designed malware utilizes various mechanisms allowing to increase the level of its undetectability through static and dynamic analysis. One of such mechanisms may be hiding in overt network traffic proper communication between the attacker and an active malware application on the infected terminal side. In this paper, a design of such a covert channel of communication is proposed, using a StegBlocks method, which is characterized by a proven feature of perfectly undetectable network steganography. An environment was implemented to test the proof of concept of the designed system of covert transmission. Characteristics and limitations of the method were discussed and directions for development were proposed.
{"title":"Application of Perfectly Undetectable Network Steganography Method for Malware Hidden Communication","authors":"Patryk Bąk, Jȩdrzej Bieniasz, M. Krzemiński, K. Szczypiorski","doi":"10.1109/ICFSP.2018.8552057","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552057","url":null,"abstract":"Currently designed malware utilizes various mechanisms allowing to increase the level of its undetectability through static and dynamic analysis. One of such mechanisms may be hiding in overt network traffic proper communication between the attacker and an active malware application on the infected terminal side. In this paper, a design of such a covert channel of communication is proposed, using a StegBlocks method, which is characterized by a proven feature of perfectly undetectable network steganography. An environment was implemented to test the proof of concept of the designed system of covert transmission. Characteristics and limitations of the method were discussed and directions for development were proposed.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129556010","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 : 2018-09-01DOI: 10.1109/ICFSP.2018.8552049
O. Chernoyarov, A. Salnikova, V. Kostylev, A. Faulgaber
We studied the possibility of applying power method for the radio signal detection against Gaussian white noise. It is presupposed that the signal amplitude is random and distributed by the Nakagami law. For this case, we found the distribution of the decision statistics of the energy detector. We obtained the expressions for the probability of correct detection under the discrete processing of the observable data realization within the limited time interval. We also analyzed the influence of the average power signal-to-noise ratio value and the time-bandwidth product upon the detection characteristics.
{"title":"Energy Detection Characteristics of the Quasi-Deterministic Signal with the Nakagami Amplitude","authors":"O. Chernoyarov, A. Salnikova, V. Kostylev, A. Faulgaber","doi":"10.1109/ICFSP.2018.8552049","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552049","url":null,"abstract":"We studied the possibility of applying power method for the radio signal detection against Gaussian white noise. It is presupposed that the signal amplitude is random and distributed by the Nakagami law. For this case, we found the distribution of the decision statistics of the energy detector. We obtained the expressions for the probability of correct detection under the discrete processing of the observable data realization within the limited time interval. We also analyzed the influence of the average power signal-to-noise ratio value and the time-bandwidth product upon the detection characteristics.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125112988","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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