Pub Date : 2017-05-22DOI: 10.1109/ATSIP.2017.8075605
Wael Brahim, M. Mestiri, N. Betrouni, K. Hamrouni
In this paper, a texture-based segmentation method of the Malignant Pleural Mesothelioma from thoracic CT scans is presented. For the texture analysis part, we have used an automatic sampling and a manual sampling to extract statistical features from the MPM texture. For the segmentation stage, the method iterates the whole CT volume and selects pixels satisfying the extracted statistical criteria. The assessment of the proposed method showed an acceptable degree of similarity rate (J=0.73) between the ground truth and the generated MPM volume.
{"title":"Malignant pleural mesothelioma segmentation from thoracic CT scans","authors":"Wael Brahim, M. Mestiri, N. Betrouni, K. Hamrouni","doi":"10.1109/ATSIP.2017.8075605","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075605","url":null,"abstract":"In this paper, a texture-based segmentation method of the Malignant Pleural Mesothelioma from thoracic CT scans is presented. For the texture analysis part, we have used an automatic sampling and a manual sampling to extract statistical features from the MPM texture. For the segmentation stage, the method iterates the whole CT volume and selects pixels satisfying the extracted statistical criteria. The assessment of the proposed method showed an acceptable degree of similarity rate (J=0.73) between the ground truth and the generated MPM volume.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132497436","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 : 2017-05-22DOI: 10.1109/ATSIP.2017.8075582
L. Hachad, O. Cherrak, H. Ghennioui, F. Mrabti, M. Zouak
In this work, the problem of direction finding is addressed. We show the Direction Of Arrival (DOA) estimation can be realized through the non-unitary joint diagonalization of spatial quadratic time-frequency. We use an approach of selection of time-frequency points to construct the set of matrices which will be jointly diagonalized to estimate the noise subspace. The main advantage of this method is that it does not require any whitening stage, and thus, it is intended to work even with a class of correlated signals. Finally, the noise subspace obtained is then used to estimate the directions using the MUltiple SIgnal Classification MUSIC spectrum. Numerical simulations are provided in order to illustrate the effectiveness and the behavior of the proposed approach.
{"title":"DOA estimation based on time-frequency music application to Massive MIMO systems","authors":"L. Hachad, O. Cherrak, H. Ghennioui, F. Mrabti, M. Zouak","doi":"10.1109/ATSIP.2017.8075582","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075582","url":null,"abstract":"In this work, the problem of direction finding is addressed. We show the Direction Of Arrival (DOA) estimation can be realized through the non-unitary joint diagonalization of spatial quadratic time-frequency. We use an approach of selection of time-frequency points to construct the set of matrices which will be jointly diagonalized to estimate the noise subspace. The main advantage of this method is that it does not require any whitening stage, and thus, it is intended to work even with a class of correlated signals. Finally, the noise subspace obtained is then used to estimate the directions using the MUltiple SIgnal Classification MUSIC spectrum. Numerical simulations are provided in order to illustrate the effectiveness and the behavior of the proposed approach.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116300243","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 : 2017-05-22DOI: 10.1109/ATSIP.2017.8075601
Rabeb Touati, Imen Massaoudi, A. Oueslati, Z. Lachiri
It is well recognized that the signal processing methods contributes in biology to the control of the DNA spatial structure. From the previous studies, it is inferred that the significant portion of the eukaryotic genomes is composed of transposable elements (TEs). The TEs play an important role as a driving force of genome evolution. An important sub class of ETs class II, Helitrons, have been revealed in diverse eukaryotic genomes. These elements have a remarkable ability to capture genes and they transpose by a rolling circle mechanism. In this context, helitron location is one of the main challenges of cell biology to better understand the different hereditary characteristics transmission modes in genomes. In this paper, we introduce the Frequency Chaos Game Signal (FCGS) method which provides a new way to present the DNA genomic sequence as a DNA signal (1-D presentation). Then, we use the Support Vector Machine (SVM) classification technique to classify helitron DNA. This choice came after studying the performance of SVM technique by varying the parameters of the kernel tricks. For this aim, different kernels have been taken into account. As for the nonlinear SVM approach, we choose the one-against-one strategy.
{"title":"SVM Helitrons recognition based on features extracted from the FCGS representation","authors":"Rabeb Touati, Imen Massaoudi, A. Oueslati, Z. Lachiri","doi":"10.1109/ATSIP.2017.8075601","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075601","url":null,"abstract":"It is well recognized that the signal processing methods contributes in biology to the control of the DNA spatial structure. From the previous studies, it is inferred that the significant portion of the eukaryotic genomes is composed of transposable elements (TEs). The TEs play an important role as a driving force of genome evolution. An important sub class of ETs class II, Helitrons, have been revealed in diverse eukaryotic genomes. These elements have a remarkable ability to capture genes and they transpose by a rolling circle mechanism. In this context, helitron location is one of the main challenges of cell biology to better understand the different hereditary characteristics transmission modes in genomes. In this paper, we introduce the Frequency Chaos Game Signal (FCGS) method which provides a new way to present the DNA genomic sequence as a DNA signal (1-D presentation). Then, we use the Support Vector Machine (SVM) classification technique to classify helitron DNA. This choice came after studying the performance of SVM technique by varying the parameters of the kernel tricks. For this aim, different kernels have been taken into account. As for the nonlinear SVM approach, we choose the one-against-one strategy.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134595061","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 : 2017-05-22DOI: 10.1109/ATSIP.2017.8075561
Adil Er-Rady, R. Faizi, R. Thami, H. Housni
Sign Language, which is a fully visual language with its own grammar, differs largely from that of spoken languages [21]. After nearly 30 years of research, SL recognition still in its infancy when compared to Automatic Speech Recognition. When producing Sign language (SL), different body parts are involved. Most importantly the hands, but also facial expressions and body movements/postures. The recognition of SL is still one of the most challenging problems in gesture recognition. In this survey, we are going to discuss the advancement of sign language recognition through the last decade. In this paper, we provide a review of the state-of-the-art building blocks of Automatic Sign Language Recognition (ASLR) system, from feature extraction up to sign.
{"title":"Automatic sign language recognition: A survey","authors":"Adil Er-Rady, R. Faizi, R. Thami, H. Housni","doi":"10.1109/ATSIP.2017.8075561","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075561","url":null,"abstract":"Sign Language, which is a fully visual language with its own grammar, differs largely from that of spoken languages [21]. After nearly 30 years of research, SL recognition still in its infancy when compared to Automatic Speech Recognition. When producing Sign language (SL), different body parts are involved. Most importantly the hands, but also facial expressions and body movements/postures. The recognition of SL is still one of the most challenging problems in gesture recognition. In this survey, we are going to discuss the advancement of sign language recognition through the last decade. In this paper, we provide a review of the state-of-the-art building blocks of Automatic Sign Language Recognition (ASLR) system, from feature extraction up to sign.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121868532","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 : 2017-05-22DOI: 10.1109/ATSIP.2017.8075518
Sameh Neili, S. Gazzah, M. El-Yacoubi, N. Amara
The aim of active and assisted living (AAL) is to develop tools to assist the elderly people in the ageing status. Human posture recognition algorithms can help monitor aged people in home environments. Different types of sensors can be used for such a task. A case in point is the RGBD sensors, which are cost-effective and provide rich information about the environment. This work aims to propose a posture recognition approach exploiting skeleton data extracted from Kinect. Our approach is based on the pose prediction using key joints features. We exploit the Convolution Neural Network for pose estimation and a multiclass Support Vector Machine to perform posture classification. The proposed approach has been tested on a publicly available dataset for activity recognition, namely CAD60. Our approach compares favorably previous works for both human pose estimation and posture recognition.
{"title":"Human posture recognition approach based on ConvNets and SVM classifier","authors":"Sameh Neili, S. Gazzah, M. El-Yacoubi, N. Amara","doi":"10.1109/ATSIP.2017.8075518","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075518","url":null,"abstract":"The aim of active and assisted living (AAL) is to develop tools to assist the elderly people in the ageing status. Human posture recognition algorithms can help monitor aged people in home environments. Different types of sensors can be used for such a task. A case in point is the RGBD sensors, which are cost-effective and provide rich information about the environment. This work aims to propose a posture recognition approach exploiting skeleton data extracted from Kinect. Our approach is based on the pose prediction using key joints features. We exploit the Convolution Neural Network for pose estimation and a multiclass Support Vector Machine to perform posture classification. The proposed approach has been tested on a publicly available dataset for activity recognition, namely CAD60. Our approach compares favorably previous works for both human pose estimation and posture recognition.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127188116","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 : 2017-05-22DOI: 10.1109/ATSIP.2017.8075523
I. Baklouti, M. Mansouri, H. Nounou, M. Slima, A. Hamida
In this paper, Unscented Kalman filter (UKF) based Exponentially Weighted Moving Average (EWMA) is proposed for fault detection in a Wastewater Treatment Plant (WWTP). In the developed UKF-based EWMA, the UKF technique is used to compute the residual between the true and the estimated variable and the EWMA control chart is applied to detect the faults. The fault detection technique will be tested using simulated COST wastewater treatment ASM1 model. The detection results of the UKF-based EWMA technique are evaluated using three fault detection criteria: the false alarm rate (FAR), Average Run Length (ARL1) and the missed detection rate (MDR).
{"title":"Fault detection in a wastewater treatment plant","authors":"I. Baklouti, M. Mansouri, H. Nounou, M. Slima, A. Hamida","doi":"10.1109/ATSIP.2017.8075523","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075523","url":null,"abstract":"In this paper, Unscented Kalman filter (UKF) based Exponentially Weighted Moving Average (EWMA) is proposed for fault detection in a Wastewater Treatment Plant (WWTP). In the developed UKF-based EWMA, the UKF technique is used to compute the residual between the true and the estimated variable and the EWMA control chart is applied to detect the faults. The fault detection technique will be tested using simulated COST wastewater treatment ASM1 model. The detection results of the UKF-based EWMA technique are evaluated using three fault detection criteria: the false alarm rate (FAR), Average Run Length (ARL1) and the missed detection rate (MDR).","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127365579","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 : 2017-05-22DOI: 10.1109/ATSIP.2017.8075591
Bilel Ameur, M. Belahcene, Sabeur Masmoudi, A. Derbel, A. Hamida
In uncontrolled environments, the major challenges in face recognition, such as illumination variation, occlusion, facial expressions and poses, greatly affect the performance of Facial Recognition Systems (FRS) especially those based on 2D information. We introduce, in this paper, a novel feature extraction approach named GLBSIF for face recognition in an uncontrolled environment. In our method, Gabor Wavelets (GW), Local Binary Patterns (LBP) and Binarized Statistical Image Features (BSIF) were combined. Moreover, the dimension reduction was applied in order to minimize the pattern vectors using PCA. Finally, we used KNN-SRC for classification. The introduced technique was assessed on LFW database using several experiments and tested on other databases, such as PUBFIG83, FERET, EXT.YALE B, ORL and IFD, in order to validate our approach. The best finding was provided when Recognition Rate (RR) is equal to 97.81%.
{"title":"A new GLBSIF descriptor for face recognition in the uncontrolled environments","authors":"Bilel Ameur, M. Belahcene, Sabeur Masmoudi, A. Derbel, A. Hamida","doi":"10.1109/ATSIP.2017.8075591","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075591","url":null,"abstract":"In uncontrolled environments, the major challenges in face recognition, such as illumination variation, occlusion, facial expressions and poses, greatly affect the performance of Facial Recognition Systems (FRS) especially those based on 2D information. We introduce, in this paper, a novel feature extraction approach named GLBSIF for face recognition in an uncontrolled environment. In our method, Gabor Wavelets (GW), Local Binary Patterns (LBP) and Binarized Statistical Image Features (BSIF) were combined. Moreover, the dimension reduction was applied in order to minimize the pattern vectors using PCA. Finally, we used KNN-SRC for classification. The introduced technique was assessed on LFW database using several experiments and tested on other databases, such as PUBFIG83, FERET, EXT.YALE B, ORL and IFD, in order to validate our approach. The best finding was provided when Recognition Rate (RR) is equal to 97.81%.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114668609","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 : 2017-05-22DOI: 10.1109/ATSIP.2017.8075565
Tounsi Yassine, Siari Ahmed, Nassim Abdelkrim
In this work, we present an effective method for speckle noise reduction in digital speckle pattern interferometry (DSPI), which is based on a Riesz wavelet transform thresholding technique. Riesz wavelet transform is a steerable pyramid wavelet transform. Before Riesz-wavelet decomposition is applied to the noised image; the given coefficients undergo to thresholding technique, where appropriate threshold limit at each level and threshold method (hard or soft thresholding) are used to remove the noise; therefore, the denoised image is obtained by reconstructing thresholded Riesz wavelets coefficients. The performance of the denoising method is analyzed by using computer-simulated correlation fringes, and the results are compared with those produced by discrete wavelet transform thresholding technique. An application of the proposed method to reduce speckle noise in experimental data is also presented.
{"title":"Speckle noise reduction in digital speckle pattern interferometry using Riesz wavelets transform","authors":"Tounsi Yassine, Siari Ahmed, Nassim Abdelkrim","doi":"10.1109/ATSIP.2017.8075565","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075565","url":null,"abstract":"In this work, we present an effective method for speckle noise reduction in digital speckle pattern interferometry (DSPI), which is based on a Riesz wavelet transform thresholding technique. Riesz wavelet transform is a steerable pyramid wavelet transform. Before Riesz-wavelet decomposition is applied to the noised image; the given coefficients undergo to thresholding technique, where appropriate threshold limit at each level and threshold method (hard or soft thresholding) are used to remove the noise; therefore, the denoised image is obtained by reconstructing thresholded Riesz wavelets coefficients. The performance of the denoising method is analyzed by using computer-simulated correlation fringes, and the results are compared with those produced by discrete wavelet transform thresholding technique. An application of the proposed method to reduce speckle noise in experimental data is also presented.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114452866","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 : 2017-05-22DOI: 10.1109/ATSIP.2017.8075511
Oumayma Bounouh, H. Essid, I. Farah
Prediction of Land use and cover change using remotely sensed imagery has attracted huge attention. From several decades, multiple researchers have investigated different approaches. The complex nature of the land use change process, due to human-nature interactions and the singularities of satellite images, demands a well-studied approach. Yet, a synthesis document is needed to provide a synthetic director paper combining the proposed and/or used models, their advantages and drawbacks. Hence, such studies are required to face the huge demands of land cover changes prediction needs. Therefore, this paper presents a review of prediction models used for land cover change variability purposes. A classification scheme is proposed to enable better specification of current forecasting models.
{"title":"Prediction of land use/land cover change methods: A study","authors":"Oumayma Bounouh, H. Essid, I. Farah","doi":"10.1109/ATSIP.2017.8075511","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075511","url":null,"abstract":"Prediction of Land use and cover change using remotely sensed imagery has attracted huge attention. From several decades, multiple researchers have investigated different approaches. The complex nature of the land use change process, due to human-nature interactions and the singularities of satellite images, demands a well-studied approach. Yet, a synthesis document is needed to provide a synthetic director paper combining the proposed and/or used models, their advantages and drawbacks. Hence, such studies are required to face the huge demands of land cover changes prediction needs. Therefore, this paper presents a review of prediction models used for land cover change variability purposes. A classification scheme is proposed to enable better specification of current forecasting models.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123144534","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 : 2017-05-22DOI: 10.1109/ATSIP.2017.8075527
Nada Zinelaabidine, M. Karim, B. Bossoufi, M. Taoussi
This work presents a comparison of several methods of tracking the maximum power point (MPPT) to extract the maximum power and improve the control performance of a photovoltaic system. The control techniques most used in MPPT control are reviewed, studied and developed such as: observation and perturbation (P&O) [1] and conductance increment (INC), Fuzzy Logic Control (FLC). The simulation results of these algorithms are interpreted by Matlab-Simulink, The three methods are used with a DC / DC converter “Boost” linked to the utility grid.
{"title":"MPPT algorithm control for grid connected PV module","authors":"Nada Zinelaabidine, M. Karim, B. Bossoufi, M. Taoussi","doi":"10.1109/ATSIP.2017.8075527","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075527","url":null,"abstract":"This work presents a comparison of several methods of tracking the maximum power point (MPPT) to extract the maximum power and improve the control performance of a photovoltaic system. The control techniques most used in MPPT control are reviewed, studied and developed such as: observation and perturbation (P&O) [1] and conductance increment (INC), Fuzzy Logic Control (FLC). The simulation results of these algorithms are interpreted by Matlab-Simulink, The three methods are used with a DC / DC converter “Boost” linked to the utility grid.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123407656","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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