Pub Date : 2022-05-15DOI: 10.1109/SIU55565.2022.9864954
K. Kayabol, Ensar Burak Aytekin, Sertaç Arisoy, E. Kuruoğlu
In this paper, autoencoder-based multivariate skewed t-distribution is proposed for hyperspectral anomaly detection. In the proposed method, the reconstruction error between the hyperspectral images reconstructed by the autoencoder and the original hyperspectral images is calculated and is modeled with a multivariate skewed t-distribution. The parameters of the distribution are estimated using the variational Bayes approach, and a distribution-based rule is determined for anomaly detection. The experimental results show that the proposed method has better performance when compared to the RX, LRASR and DAEAD anomaly detection methods.
{"title":"Hyperspectral Anomaly Detection with Multivariate Skewed t Background Model","authors":"K. Kayabol, Ensar Burak Aytekin, Sertaç Arisoy, E. Kuruoğlu","doi":"10.1109/SIU55565.2022.9864954","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864954","url":null,"abstract":"In this paper, autoencoder-based multivariate skewed t-distribution is proposed for hyperspectral anomaly detection. In the proposed method, the reconstruction error between the hyperspectral images reconstructed by the autoencoder and the original hyperspectral images is calculated and is modeled with a multivariate skewed t-distribution. The parameters of the distribution are estimated using the variational Bayes approach, and a distribution-based rule is determined for anomaly detection. The experimental results show that the proposed method has better performance when compared to the RX, LRASR and DAEAD anomaly detection methods.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125183880","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864962
Muhammad Adeel Hafeez, C. Kayasandik, Merve Yusra Dogan
The brain tumor has become one of the most prominent types of cancers affecting a huge population across the globe every year. It has the lowest life expectancy rate and the risk of death is highly associated with the type, shape, and location of the tumor. The Magnetic Resonance Imaging (MRI) is a strong tool to detect different brain lesions and is extensively used by radiologists and physicians. For the early and accurate diagnosis of the brain tumor using MRI, it is important to consider automated computer-assisted diagnosis which is more flexible and efficient. In this paper, we have proposed a Convolutional Neural Network (CNN) based approach for the classification of three types of brain tumors (meningiomas, gliomas, and pituitary tumors). A publicly available dataset that contains 3064 T1-weighted brain CE-MRI images collected from 233 patients has been used in the study. We propose a 15 layers CNN model for the classification of three types of brain tumors from the mentioned dataset. We obtained an accuracy, precision, recall, and f1-score of 98.6%, 99%, 98.3%, and 98.6% from our proposed model which is higher than previously reported results.
{"title":"Brain Tumor Classification Using MRI Images and Convolutional Neural Networks","authors":"Muhammad Adeel Hafeez, C. Kayasandik, Merve Yusra Dogan","doi":"10.1109/SIU55565.2022.9864962","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864962","url":null,"abstract":"The brain tumor has become one of the most prominent types of cancers affecting a huge population across the globe every year. It has the lowest life expectancy rate and the risk of death is highly associated with the type, shape, and location of the tumor. The Magnetic Resonance Imaging (MRI) is a strong tool to detect different brain lesions and is extensively used by radiologists and physicians. For the early and accurate diagnosis of the brain tumor using MRI, it is important to consider automated computer-assisted diagnosis which is more flexible and efficient. In this paper, we have proposed a Convolutional Neural Network (CNN) based approach for the classification of three types of brain tumors (meningiomas, gliomas, and pituitary tumors). A publicly available dataset that contains 3064 T1-weighted brain CE-MRI images collected from 233 patients has been used in the study. We propose a 15 layers CNN model for the classification of three types of brain tumors from the mentioned dataset. We obtained an accuracy, precision, recall, and f1-score of 98.6%, 99%, 98.3%, and 98.6% from our proposed model which is higher than previously reported results.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129965021","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864953
Muna Layth Abdulateef Al-Zubaidi, Selim Aras
The reason why real feelings and mood changes can be seen through our eyes is that the eyes provide the most revealing and accurate information of all human communication signs. It is possible to control a human-computer interface by voluntarily moving the eyes, which have an important place in communication. In this study, the appropriate feature and classification methods were investigated to use the Electooculography signs obtained from seven different voluntary eye movements in the human-computer interface. The success of the system is increased by determining the combination that gives the best result from many features by using the sequential forward feature selection method. The developed method reached 93.9% success in the seven-class dataset. The results show that human-computer interface control can be done with high accuracy with voluntary eye movements. Also, the development of a real-time working model is inspiring for work.
{"title":"Investigation of Appropriate Classification Method for EOG Based Human Computer Interface","authors":"Muna Layth Abdulateef Al-Zubaidi, Selim Aras","doi":"10.1109/SIU55565.2022.9864953","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864953","url":null,"abstract":"The reason why real feelings and mood changes can be seen through our eyes is that the eyes provide the most revealing and accurate information of all human communication signs. It is possible to control a human-computer interface by voluntarily moving the eyes, which have an important place in communication. In this study, the appropriate feature and classification methods were investigated to use the Electooculography signs obtained from seven different voluntary eye movements in the human-computer interface. The success of the system is increased by determining the combination that gives the best result from many features by using the sequential forward feature selection method. The developed method reached 93.9% success in the seven-class dataset. The results show that human-computer interface control can be done with high accuracy with voluntary eye movements. Also, the development of a real-time working model is inspiring for work.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124527512","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864960
Latif Akçay, Bartu Sürer, B. Yalçin
In this study, it is aimed to implement the low-RISC system-on-chip, which is based on the Rocket processor created with the RISC-V instruction set architecture developed by Berkeley University, on FPGA and to run image processing algorithms on this system. While making this implementation, the main target is a system that is very simple, consumes low power, and can be quickly redirected to other purposes. Therefore, it is based on the effective evaluation of the existing system without using any extra customized accelerators. Thus, a free, open source, and powerful enough platform for many embedded system applications is proposed to the designers. For this purpose, a lane detection application designed with standard C libraries such as Gaussian blur filter, Sobel operation filter and other elements, which are widely used in image processing applications, is run with embedded Linux operating system and the results are shared.
{"title":"Implementation of a SoC by Using lowRISC Architecture on an FPGA for Image Filtering Applications","authors":"Latif Akçay, Bartu Sürer, B. Yalçin","doi":"10.1109/SIU55565.2022.9864960","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864960","url":null,"abstract":"In this study, it is aimed to implement the low-RISC system-on-chip, which is based on the Rocket processor created with the RISC-V instruction set architecture developed by Berkeley University, on FPGA and to run image processing algorithms on this system. While making this implementation, the main target is a system that is very simple, consumes low power, and can be quickly redirected to other purposes. Therefore, it is based on the effective evaluation of the existing system without using any extra customized accelerators. Thus, a free, open source, and powerful enough platform for many embedded system applications is proposed to the designers. For this purpose, a lane detection application designed with standard C libraries such as Gaussian blur filter, Sobel operation filter and other elements, which are widely used in image processing applications, is run with embedded Linux operating system and the results are shared.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115961969","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864827
Rabia Toprak, S. S. Gültekin, D. Uzer
Pathology science has an important place in the medical field. Its importance is increasing day by day because it evaluates the information about diseases at the cellular level. The reports prepared from the tissue samples examined by the pathologists contain very important information for both the patient and the doctor. This information may include the level of the disease and the mode of treatment. Therefore, the time to reach the pathological reports is important. Microstrip patch antennas are used for various purposes in the biomedical field. In this study, the far and near field outputs of the evaluations of the pathological tissue samples were tested with the microstrip patch antenna structure. For this, a microstrip patch antenna with an operating frequency of 2.45 GHz was used. Pathological tissue samples were modeled in the free-space measurement technique created using the antenna structure. The electric field and scattering parameter values obtained as a result of the simulations using the Ansys HFSS program were evaluated for the near and far field. When the evaluation results are examined, it has been shown that near field measurements for electric field data and far field measurements for scattering parameter data are more efficient.
{"title":"Comparison of Far Field and Near Field Values of Skin Tissue Measured Using Microstrip Antenna Structure","authors":"Rabia Toprak, S. S. Gültekin, D. Uzer","doi":"10.1109/SIU55565.2022.9864827","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864827","url":null,"abstract":"Pathology science has an important place in the medical field. Its importance is increasing day by day because it evaluates the information about diseases at the cellular level. The reports prepared from the tissue samples examined by the pathologists contain very important information for both the patient and the doctor. This information may include the level of the disease and the mode of treatment. Therefore, the time to reach the pathological reports is important. Microstrip patch antennas are used for various purposes in the biomedical field. In this study, the far and near field outputs of the evaluations of the pathological tissue samples were tested with the microstrip patch antenna structure. For this, a microstrip patch antenna with an operating frequency of 2.45 GHz was used. Pathological tissue samples were modeled in the free-space measurement technique created using the antenna structure. The electric field and scattering parameter values obtained as a result of the simulations using the Ansys HFSS program were evaluated for the near and far field. When the evaluation results are examined, it has been shown that near field measurements for electric field data and far field measurements for scattering parameter data are more efficient.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126704810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The inability of conventional orthogonal multiple access (OMA) techniques to guarantee a low latency rate, high spectral efficiency, massive device connectivity, and a better quality of service (QoS) led to the introduction of the non-orthogonal multiple access (NOMA) technique. Multiple-input multiple-output (MIMO) technologies can increase the capacity and decrease the error rate of wireless systems. Due to the advantages mentioned earlier, integrating NOMA and MIMO is indispensable in future wireless communication systems. In this context, this paper considers MIMO-NOMA networks, in which all nodes are equipped with multiple antennas. In the considered network, the majority-based transmit antenna selection and maximal ratio combining schemes are employed at the base station and users, respectively. Then, the bit error rate performance is investigated over Nakagami-m fading channels by Monte Carlo simulations.
{"title":"Bit Error Rate Performance of MIMO-NOMA with Majority Based TAS/MRC Scheme in Nakagami-m Fading Channels","authors":"Princewill Kum Kumson, Rusul Al-Afah Russ, Mahmoud Aldababsa","doi":"10.1109/SIU55565.2022.9864766","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864766","url":null,"abstract":"The inability of conventional orthogonal multiple access (OMA) techniques to guarantee a low latency rate, high spectral efficiency, massive device connectivity, and a better quality of service (QoS) led to the introduction of the non-orthogonal multiple access (NOMA) technique. Multiple-input multiple-output (MIMO) technologies can increase the capacity and decrease the error rate of wireless systems. Due to the advantages mentioned earlier, integrating NOMA and MIMO is indispensable in future wireless communication systems. In this context, this paper considers MIMO-NOMA networks, in which all nodes are equipped with multiple antennas. In the considered network, the majority-based transmit antenna selection and maximal ratio combining schemes are employed at the base station and users, respectively. Then, the bit error rate performance is investigated over Nakagami-m fading channels by Monte Carlo simulations.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127176309","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864720
Mehmet Z. Akpolat, Abdullah Bülbül
Pruning methods for neural network models are important for devices with performance and storage problems. Recently, unlike traditional pruning methods, The Goal Driven Pruning method has been proposed. This approach, inspired by the attention mechanism in humans, is based on decreasing the sensitivity to the features of distractors in the environment. For this purpose, in this method, pruning is performed not only in the middle layers, but also in the output layers for the task irrelevant classes. In this study, we present Global Goal-driven Pruning, which, unlike Goal-driven Pruning, prunes by evaluating the model as a whole, instead of layer-based pruning. The effectiveness of the proposed model has been demonstrated by the tests.
{"title":"A Global Approach for Goal-Driven Pruning of Object Recognition Networks","authors":"Mehmet Z. Akpolat, Abdullah Bülbül","doi":"10.1109/SIU55565.2022.9864720","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864720","url":null,"abstract":"Pruning methods for neural network models are important for devices with performance and storage problems. Recently, unlike traditional pruning methods, The Goal Driven Pruning method has been proposed. This approach, inspired by the attention mechanism in humans, is based on decreasing the sensitivity to the features of distractors in the environment. For this purpose, in this method, pruning is performed not only in the middle layers, but also in the output layers for the task irrelevant classes. In this study, we present Global Goal-driven Pruning, which, unlike Goal-driven Pruning, prunes by evaluating the model as a whole, instead of layer-based pruning. The effectiveness of the proposed model has been demonstrated by the tests.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126084455","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864911
Golara Ghorban Dordinejad, Hakan Cevikalp
Image set based face recognition has recently become a popular topic as it has better performance than single image based face recognition. However, preprocessing is needed to remove the effects of some adverse conditions such as different pose angles, illumination, and expression differences within the set. One of the most effective preprocessing to improve the face recognition rate is face frontalization. Face frontalization is defined as the artificial acquisition of a face image with a different pose angle to a frontal pose. It has been observed that this process increases the face recognition performance. In this paper, image set based face recognition was performed by applying face frontalization to all images in the sets. Firstly, the faces in the IJBA database were frontalized by using the Rotate and Render hybrid frontalization method, which is based on a Three-Dimensional and Generative Adversial Network. Then, discriminative convex classifier is used for set based face recognition. In face recognition experiments, when the frontalized IJBA database and its non-frontalized version were compared, it was observed that the accuracy of face recognition increased with the frontalized face images.
{"title":"Face Frontalization for Image Set Based Face Recognition","authors":"Golara Ghorban Dordinejad, Hakan Cevikalp","doi":"10.1109/SIU55565.2022.9864911","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864911","url":null,"abstract":"Image set based face recognition has recently become a popular topic as it has better performance than single image based face recognition. However, preprocessing is needed to remove the effects of some adverse conditions such as different pose angles, illumination, and expression differences within the set. One of the most effective preprocessing to improve the face recognition rate is face frontalization. Face frontalization is defined as the artificial acquisition of a face image with a different pose angle to a frontal pose. It has been observed that this process increases the face recognition performance. In this paper, image set based face recognition was performed by applying face frontalization to all images in the sets. Firstly, the faces in the IJBA database were frontalized by using the Rotate and Render hybrid frontalization method, which is based on a Three-Dimensional and Generative Adversial Network. Then, discriminative convex classifier is used for set based face recognition. In face recognition experiments, when the frontalized IJBA database and its non-frontalized version were compared, it was observed that the accuracy of face recognition increased with the frontalized face images.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124022411","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864950
Haydar Kiliç, S. Ceyhan
In this study, a new anisotropic metric for color images was defined and the filtering results of a noisy image were examined. Unlike the others (Riemann), the metric created in filtering was chosen as Finsler type and mathematical inferences were made until the filter creation stage. The scale parameter beta and step size dt were tried for different images, and the parameters that gave the best results for the new metric were examined for this study. This new filter was compared with some known filters and the results were examined. As a result, the new filter provided the best image enhancement.
{"title":"Color Image Enhancement Using A New Anisotropic Metric","authors":"Haydar Kiliç, S. Ceyhan","doi":"10.1109/SIU55565.2022.9864950","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864950","url":null,"abstract":"In this study, a new anisotropic metric for color images was defined and the filtering results of a noisy image were examined. Unlike the others (Riemann), the metric created in filtering was chosen as Finsler type and mathematical inferences were made until the filter creation stage. The scale parameter beta and step size dt were tried for different images, and the parameters that gave the best results for the new metric were examined for this study. This new filter was compared with some known filters and the results were examined. As a result, the new filter provided the best image enhancement.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126742311","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864934
Serhat Karaçam, T. S. Navruz
In this study, variation of estimation errors of resampling methods which is one of the most important steps of FastSLAM algorithm, in different process and measurement noise values under different particle numbers is examined. It is seen that variation of process noise affected error values more than variation of measurement noise for all resampling methods, and Metropolis resampling is the method least affected by variation of measurement noise. It has been determined that resampling method that provides the closest error value to the correct position changes according to the noise conditions in which the system operates.
{"title":"Effect of Resampling Methods to Performance of FastSLAM Under Different Noise Conditions","authors":"Serhat Karaçam, T. S. Navruz","doi":"10.1109/SIU55565.2022.9864934","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864934","url":null,"abstract":"In this study, variation of estimation errors of resampling methods which is one of the most important steps of FastSLAM algorithm, in different process and measurement noise values under different particle numbers is examined. It is seen that variation of process noise affected error values more than variation of measurement noise for all resampling methods, and Metropolis resampling is the method least affected by variation of measurement noise. It has been determined that resampling method that provides the closest error value to the correct position changes according to the noise conditions in which the system operates.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126533639","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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