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/SIU55565.2022.9864956
Ugur Erbas, M. Tabakcioglu
With the developing communication technology in recent years, the importance of placing the base stations in the right location has increased in order to ensure a healthy communication. It is thought that this situation will become even more important with 5G technology. In this study, 2D maps with earth maps and transformation windows were created in MATLAB using 3D digital data. The diffracted, direct and reflected rays were determined, and the ray tracing algorithm was run for the superconducting surface. A 3D coverage area is mapped for a possible transmitter position. Electric field graphs are drawn for different heights. It has been observed that the electric field graph changes depending on the landforms, distance, diffraction and interference of the rays.
{"title":"Generation of 3D Coverage Map","authors":"Ugur Erbas, M. Tabakcioglu","doi":"10.1109/SIU55565.2022.9864956","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864956","url":null,"abstract":"With the developing communication technology in recent years, the importance of placing the base stations in the right location has increased in order to ensure a healthy communication. It is thought that this situation will become even more important with 5G technology. In this study, 2D maps with earth maps and transformation windows were created in MATLAB using 3D digital data. The diffracted, direct and reflected rays were determined, and the ray tracing algorithm was run for the superconducting surface. A 3D coverage area is mapped for a possible transmitter position. Electric field graphs are drawn for different heights. It has been observed that the electric field graph changes depending on the landforms, distance, diffraction and interference of the rays.","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":"114250365","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/SIU55565.2022.9864848
E. Dogan, H. F. Ugurdag, Hasan Unlu
Applications of artificial neural networks on low-cost embedded systems and microcontrollers (MCUs), has recently been attracting more attention than ever. Since MCUs have limited memory capacity as well as limited compute-speed compared to workstations, employment of current deep learning algorithms on MCUs becomes more practical with the help of model compression. This makes MCUs common and practical alternative solution for autonomous systems. In this paper, we add model compression, specifically Deep Compression, to an existing work, which efficiently deploys PyTorch models on MCUs, in order to increase neural network speed and save electrical power. First, we prune the weight values close to zero in convolutional and fully connected layers. Secondly, the remaining weights and activations are quantized to 8-bit integers from 32-bit floating-point. Finally, forward pass functions are compressed using special data structures for sparse matrices, which store only nonzero weights. In the case of the LeNet-5 model, the memory footprint was reduced by 12.5x, and the inference speed was boosted by 2.6x.
{"title":"Using Deep Compression on PyTorch Models for Autonomous Systems","authors":"E. Dogan, H. F. Ugurdag, Hasan Unlu","doi":"10.1109/SIU55565.2022.9864848","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864848","url":null,"abstract":"Applications of artificial neural networks on low-cost embedded systems and microcontrollers (MCUs), has recently been attracting more attention than ever. Since MCUs have limited memory capacity as well as limited compute-speed compared to workstations, employment of current deep learning algorithms on MCUs becomes more practical with the help of model compression. This makes MCUs common and practical alternative solution for autonomous systems. In this paper, we add model compression, specifically Deep Compression, to an existing work, which efficiently deploys PyTorch models on MCUs, in order to increase neural network speed and save electrical power. First, we prune the weight values close to zero in convolutional and fully connected layers. Secondly, the remaining weights and activations are quantized to 8-bit integers from 32-bit floating-point. Finally, forward pass functions are compressed using special data structures for sparse matrices, which store only nonzero weights. In the case of the LeNet-5 model, the memory footprint was reduced by 12.5x, and the inference speed was boosted by 2.6x.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"16 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":"122852364","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/SIU55565.2022.9864934
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}
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/SIU55565.2022.9864873
Saadet Aytaç Arpaci, Songül Varlı
The mixup data augmentation method is a method that creates new images via a linear function from multiple images. In this paper, it is examined whether the mixup data augmentation method improves the U-Net model’s segmentation capability. In this study, artifact segmentation was performed with histopathological images. The dataset used was examined into three different groups: (1) images that are produced through traditional data augmentation methods like flipping and rotation; (2) images that are produced through only the mixup method; and (3) images that are produced through both the traditional and mixup methods. According to the findings, the use of the mixup method in combination with the traditional data augmentation methods improved the model’s average Dice coefficient value for artifact segmentation of histopathological images.
{"title":"Semantic Segmentation with the Mixup Data Augmentation Method","authors":"Saadet Aytaç Arpaci, Songül Varlı","doi":"10.1109/SIU55565.2022.9864873","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864873","url":null,"abstract":"The mixup data augmentation method is a method that creates new images via a linear function from multiple images. In this paper, it is examined whether the mixup data augmentation method improves the U-Net model’s segmentation capability. In this study, artifact segmentation was performed with histopathological images. The dataset used was examined into three different groups: (1) images that are produced through traditional data augmentation methods like flipping and rotation; (2) images that are produced through only the mixup method; and (3) images that are produced through both the traditional and mixup methods. According to the findings, the use of the mixup method in combination with the traditional data augmentation methods improved the model’s average Dice coefficient value for artifact segmentation of histopathological images.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"118 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":"131278129","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/SIU55565.2022.9864730
Ensar Emirali, M. Karsligil
Developing systems for automatically detection of date, time, duration and set expressions containing time information in texts is within the scope of Natural Language Processing research field. When studies for Turkish in the literature are reviewed, it is observed that only date and time expressions are included in the expressions detected by the models developed within the scope of Named Entity Recognition. There are studies to develop only rule-based systems on the subject of detection of temporal expressions in Turkish. Within the scope of this study, first Artificial Neural Networks based model for the detection of temporal expressions in Turkish texts is developed. The input of the developed model is word embeddings. In this study, the developed model success with using word embeddings built by different methods is measured on a dataset consisting of Turkish complaint texts collected from internet websites. By comparing the success of word embeddings on the detection of temporal expressions with the coverage percentages of word embeddings on the dataset, it is concluded that there is no correlation between them.
{"title":"Using Word Embeddings in Detection of Temporal Expressions in Turkish Texts","authors":"Ensar Emirali, M. Karsligil","doi":"10.1109/SIU55565.2022.9864730","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864730","url":null,"abstract":"Developing systems for automatically detection of date, time, duration and set expressions containing time information in texts is within the scope of Natural Language Processing research field. When studies for Turkish in the literature are reviewed, it is observed that only date and time expressions are included in the expressions detected by the models developed within the scope of Named Entity Recognition. There are studies to develop only rule-based systems on the subject of detection of temporal expressions in Turkish. Within the scope of this study, first Artificial Neural Networks based model for the detection of temporal expressions in Turkish texts is developed. The input of the developed model is word embeddings. In this study, the developed model success with using word embeddings built by different methods is measured on a dataset consisting of Turkish complaint texts collected from internet websites. By comparing the success of word embeddings on the detection of temporal expressions with the coverage percentages of word embeddings on the dataset, it is concluded that there is no correlation between them.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"54 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":"131654819","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/SIU55565.2022.9864736
M. Elamassie, M. Uysal
In this paper, we investigate the outage performance of vertical stratified underwater optical links in the presence of moderate/strong turbulence conditions. Specifically, we consider the cascaded Gamma-Gamma turbulence channel model and derive a closed-form expression for outage probability. We then use our derived expression to investigate the achievable diversity order (DO) and asymptotic diversity order (ADO). We further confirm our derivations through Monte Carlo simulations.
{"title":"Outage Performance Analysis of Vertical Underwater VLC Links","authors":"M. Elamassie, M. Uysal","doi":"10.1109/SIU55565.2022.9864736","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864736","url":null,"abstract":"In this paper, we investigate the outage performance of vertical stratified underwater optical links in the presence of moderate/strong turbulence conditions. Specifically, we consider the cascaded Gamma-Gamma turbulence channel model and derive a closed-form expression for outage probability. We then use our derived expression to investigate the achievable diversity order (DO) and asymptotic diversity order (ADO). We further confirm our derivations through Monte Carlo simulations.","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":"131622520","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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