Pub Date : 2017-11-01DOI: 10.1109/ICAIT.2017.8388817
Yu Tao, Y.-M. Niu, Yaoliang Song
In this paper, a novel radiometer system using compressed sensing technique is simulated. Millimeter-wave radiometer is a high-sensitivity noise power receiver that can detect the microwave radiation from objects. Millimeter wave radiometer is one of the key technologies in millimeter wave passive detection and imaging. The novel system this paper proposed using compressed sensing is a focal plane system, which simultaneously receives signals through multiple feeds. Each feed receives the corresponding pixel point of millimeter wave radiation energy. The system achieves the similar function of the dam-board by on-off the switch. The system reduces the number of receiver channels via the use of compressed sensing. The proposed system not only overcomes the shortcomings of the traditional systems, but also recovers the image according to the theory of compressed sensing. The system performance is validated through simulations.
{"title":"Simulations of novel radiometer systems using compressed sensing","authors":"Yu Tao, Y.-M. Niu, Yaoliang Song","doi":"10.1109/ICAIT.2017.8388817","DOIUrl":"https://doi.org/10.1109/ICAIT.2017.8388817","url":null,"abstract":"In this paper, a novel radiometer system using compressed sensing technique is simulated. Millimeter-wave radiometer is a high-sensitivity noise power receiver that can detect the microwave radiation from objects. Millimeter wave radiometer is one of the key technologies in millimeter wave passive detection and imaging. The novel system this paper proposed using compressed sensing is a focal plane system, which simultaneously receives signals through multiple feeds. Each feed receives the corresponding pixel point of millimeter wave radiation energy. The system achieves the similar function of the dam-board by on-off the switch. The system reduces the number of receiver channels via the use of compressed sensing. The proposed system not only overcomes the shortcomings of the traditional systems, but also recovers the image according to the theory of compressed sensing. The system performance is validated through simulations.","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"519 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134106126","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 : 2017-11-01DOI: 10.1109/ICAIT.2017.8388899
Yue Xiu, Wenyuan Wang, Jiao Wu, Yongliang Shen
Downlink channel estimation in massive multiple-input multiple-output (MIMO) systems is challenging due to the large training and feedback overhead. So, it is necessary to reduce the pilot overhead. we propose a new compressive sensing (CS)CSI estimation scheme for frequency division duplexing (FDD)massive MIMO systems, which combines the algorithm of supports identify and the complex approximate message passing-multiple measurement vector (CAMP-MMV) algorithm. The approach by using information of supports position to improve the performance of CAMP-MMV. The analytic performance guarantees of the proposed scheme are the length of non orthogonal pilot and signal noise ratio (SNR). The numerical results show that performance of CSI estimation and achieve higher estimation accuracy as compared to an existing sparse Bayesian algorithm.
{"title":"Massive MIMO downlink channel estimation based on improved CAMP-MMV algorithm","authors":"Yue Xiu, Wenyuan Wang, Jiao Wu, Yongliang Shen","doi":"10.1109/ICAIT.2017.8388899","DOIUrl":"https://doi.org/10.1109/ICAIT.2017.8388899","url":null,"abstract":"Downlink channel estimation in massive multiple-input multiple-output (MIMO) systems is challenging due to the large training and feedback overhead. So, it is necessary to reduce the pilot overhead. we propose a new compressive sensing (CS)CSI estimation scheme for frequency division duplexing (FDD)massive MIMO systems, which combines the algorithm of supports identify and the complex approximate message passing-multiple measurement vector (CAMP-MMV) algorithm. The approach by using information of supports position to improve the performance of CAMP-MMV. The analytic performance guarantees of the proposed scheme are the length of non orthogonal pilot and signal noise ratio (SNR). The numerical results show that performance of CSI estimation and achieve higher estimation accuracy as compared to an existing sparse Bayesian algorithm.","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117125826","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 : 2017-11-01DOI: 10.1109/ICAIT.2017.8388885
Jiajing Cheng, Huanhuan Liu, F. Pang, Tingyun Wang
We have proposed a temperature-insensitive sensor with enhanced sensitivity for refractive index (RI) measurement based on thinned double-cladding fiber. The RI sensitivity is increased by 6 times to 6000 nm/RIU compared with none-thinned double-cladding fiber.
{"title":"Temperature-insensitive thinned double-cladding fiber with enhanced sensitivity for refractive index measurement","authors":"Jiajing Cheng, Huanhuan Liu, F. Pang, Tingyun Wang","doi":"10.1109/ICAIT.2017.8388885","DOIUrl":"https://doi.org/10.1109/ICAIT.2017.8388885","url":null,"abstract":"We have proposed a temperature-insensitive sensor with enhanced sensitivity for refractive index (RI) measurement based on thinned double-cladding fiber. The RI sensitivity is increased by 6 times to 6000 nm/RIU compared with none-thinned double-cladding fiber.","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116050648","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 : 2017-11-01DOI: 10.1109/ICAIT.2017.8388822
Xinyu Zhang, Chunhua Hu, Ran Zhao, Qing Dai, P. Sun
The generation of an optical vortex array (OVA) by using diffraction grating is presented. At first a diffraction grating is produced with a computer and displayed on a spatial light modulator (SLM) which is controlled by the computer. Then let a plane light irradiate the SLM with the diffraction grating vertically. The transmitted lights after the SLM can produce a regular optical vortex array which can be observed by adjusting the diffraction distance of the grating. The principle of the generation is discussed and the distribution of the intensity of the optical vortex array is presented. The properties of the produced optical vortex array at different diffraction distance are also discussed. The simulations are completed and the results show that the optical vortex array can be generated well by using the diffraction grating, which is simple and easy to implement. And we measure the deformation phase by using the zero-contour of the real parts of the OVA, which provides a new method for deformation phase measurement.
{"title":"Generation of optical vortex array by using a diffraction grating","authors":"Xinyu Zhang, Chunhua Hu, Ran Zhao, Qing Dai, P. Sun","doi":"10.1109/ICAIT.2017.8388822","DOIUrl":"https://doi.org/10.1109/ICAIT.2017.8388822","url":null,"abstract":"The generation of an optical vortex array (OVA) by using diffraction grating is presented. At first a diffraction grating is produced with a computer and displayed on a spatial light modulator (SLM) which is controlled by the computer. Then let a plane light irradiate the SLM with the diffraction grating vertically. The transmitted lights after the SLM can produce a regular optical vortex array which can be observed by adjusting the diffraction distance of the grating. The principle of the generation is discussed and the distribution of the intensity of the optical vortex array is presented. The properties of the produced optical vortex array at different diffraction distance are also discussed. The simulations are completed and the results show that the optical vortex array can be generated well by using the diffraction grating, which is simple and easy to implement. And we measure the deformation phase by using the zero-contour of the real parts of the OVA, which provides a new method for deformation phase measurement.","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122558829","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 : 2017-11-01DOI: 10.1109/ICAIT.2017.8388922
Haoye Chai, S. Leng, Jie Hu, Kun Yang
Fog computing has emerging as a promising technique to meet the ultra-low latency services in wireless network such as Augmented Reality (AR). The fog paradigm tends to distribute computing, storage, control, network resources and services closer to terminal devices as much as possible while most of User Equipments (UEs) do not have constant power supply thus the power supplement has developed as a nontrivial challenge to realize the paradigm. In this paper, simultaneous wireless information and power transfer (SWIPT) is introduced as a power resource to guarantee the UEs complete their computing tasks. We proposed a power, time and data allocation scheme to minimize the total consumption of energy at source node while maintaining the latency requirement. A Quantum particle swarm optimization (QPSO) algorithm in introduced to solve the non-convex problem, numerical results reveal that our proposed allocation scheme consumes less energy than the conventional particle swarm optimization approach.
{"title":"Resources allocation in SWIPT aided fog computing networks","authors":"Haoye Chai, S. Leng, Jie Hu, Kun Yang","doi":"10.1109/ICAIT.2017.8388922","DOIUrl":"https://doi.org/10.1109/ICAIT.2017.8388922","url":null,"abstract":"Fog computing has emerging as a promising technique to meet the ultra-low latency services in wireless network such as Augmented Reality (AR). The fog paradigm tends to distribute computing, storage, control, network resources and services closer to terminal devices as much as possible while most of User Equipments (UEs) do not have constant power supply thus the power supplement has developed as a nontrivial challenge to realize the paradigm. In this paper, simultaneous wireless information and power transfer (SWIPT) is introduced as a power resource to guarantee the UEs complete their computing tasks. We proposed a power, time and data allocation scheme to minimize the total consumption of energy at source node while maintaining the latency requirement. A Quantum particle swarm optimization (QPSO) algorithm in introduced to solve the non-convex problem, numerical results reveal that our proposed allocation scheme consumes less energy than the conventional particle swarm optimization approach.","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128477270","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 : 2017-11-01DOI: 10.1109/ICAIT.2017.8388952
Yongxin Chang, Shijie Feng, Jing Zhang
Recognizing objects from arbitrary aspects is always a highly challenging problem in applied engineering and computer vision fields. At present, most existing algorithms mainly focus on specific viewpoint detection. Hence, in this paper we propose a novel recognizing model, which combines Gabor transform with LBP variance to handle the problem of different viewpoints and pose changing. Then, the images of inaccurate recognizing are evaluated by learning and fed back the detector to avoid the same mistakes in the future. The principal idea is to extract intrinsic viewpoint invariant features from the unseen poses of object, and then to take advantage of these features to support recognition. Compared with other recognition models, the proposed approach can efficiently tackle the multi-view problem and promote the recognition performance. For a quantitative evaluation, this novel algorithm has been tested on two benchmark datasets such as Caltech 101 and PASCAL VOC 2011datasets. The experimental results validate that our approach can recognize objects more precisely and outperforms others single view recognition methods.
{"title":"An efficient object recognition based on Gabor transform and LBP variance","authors":"Yongxin Chang, Shijie Feng, Jing Zhang","doi":"10.1109/ICAIT.2017.8388952","DOIUrl":"https://doi.org/10.1109/ICAIT.2017.8388952","url":null,"abstract":"Recognizing objects from arbitrary aspects is always a highly challenging problem in applied engineering and computer vision fields. At present, most existing algorithms mainly focus on specific viewpoint detection. Hence, in this paper we propose a novel recognizing model, which combines Gabor transform with LBP variance to handle the problem of different viewpoints and pose changing. Then, the images of inaccurate recognizing are evaluated by learning and fed back the detector to avoid the same mistakes in the future. The principal idea is to extract intrinsic viewpoint invariant features from the unseen poses of object, and then to take advantage of these features to support recognition. Compared with other recognition models, the proposed approach can efficiently tackle the multi-view problem and promote the recognition performance. For a quantitative evaluation, this novel algorithm has been tested on two benchmark datasets such as Caltech 101 and PASCAL VOC 2011datasets. The experimental results validate that our approach can recognize objects more precisely and outperforms others single view recognition methods.","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130546678","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 : 2017-11-01DOI: 10.1109/ICAIT.2017.8388926
Jing Zhang, Linghua Zhang
DV-Hop node localization algorithm is one of representative node positioning algorithms that are ranged-free in wireless sensor network. in actual measurement, we discover that if we define the hop number as one hop in the case of the distance that is less than or equal to communication radius, it will cause great positioning error. So, the improved algorithm introduces multiple communication radius in order to subdivide hop numbers and deduces a formula of hop numbers with the numbers of radius changing, amends average hop distance by correcting the weighted value. Simulation results proof that the algorithm availably cuts down the positioning error under the same network topology, comparing to traditional algorithm and the common weighted algorithm.
{"title":"A modified DV-hop localization algorithm based on communication radius dynamic adjustment","authors":"Jing Zhang, Linghua Zhang","doi":"10.1109/ICAIT.2017.8388926","DOIUrl":"https://doi.org/10.1109/ICAIT.2017.8388926","url":null,"abstract":"DV-Hop node localization algorithm is one of representative node positioning algorithms that are ranged-free in wireless sensor network. in actual measurement, we discover that if we define the hop number as one hop in the case of the distance that is less than or equal to communication radius, it will cause great positioning error. So, the improved algorithm introduces multiple communication radius in order to subdivide hop numbers and deduces a formula of hop numbers with the numbers of radius changing, amends average hop distance by correcting the weighted value. Simulation results proof that the algorithm availably cuts down the positioning error under the same network topology, comparing to traditional algorithm and the common weighted algorithm.","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131368535","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 : 2017-11-01DOI: 10.1109/ICAIT.2017.8388920
Tianzhu Qin, Bin Ba, Zhiyu Lu, Daming Wang
Multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system, a new concept in recent years, is used extensively in civilian and military applications. We develop a novel direct position determination (DPD) approach in MIMO-OFDM system to locate a stationary target, reaching a superior performance than other conventional localization algorithms. Firstly, we obtain extend noise subspaces from all receivers by constructing and decomposing the extended covariance matrices. Then, the target position is estimated directly via fusing the extend noise subspace data, thereby avoiding the limitations of two-step method. Our proposed algorithm realizes low complexity and high robustness to noise by using the subspace of the extended Hadamard product. Simulation results demonstrate our proposed algorithm outperforms other localization methods, especially under the condition of low signal-to-noise ratio (SNR).
{"title":"Direct positioning of a stationary target in MIMO-OFDM system","authors":"Tianzhu Qin, Bin Ba, Zhiyu Lu, Daming Wang","doi":"10.1109/ICAIT.2017.8388920","DOIUrl":"https://doi.org/10.1109/ICAIT.2017.8388920","url":null,"abstract":"Multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system, a new concept in recent years, is used extensively in civilian and military applications. We develop a novel direct position determination (DPD) approach in MIMO-OFDM system to locate a stationary target, reaching a superior performance than other conventional localization algorithms. Firstly, we obtain extend noise subspaces from all receivers by constructing and decomposing the extended covariance matrices. Then, the target position is estimated directly via fusing the extend noise subspace data, thereby avoiding the limitations of two-step method. Our proposed algorithm realizes low complexity and high robustness to noise by using the subspace of the extended Hadamard product. Simulation results demonstrate our proposed algorithm outperforms other localization methods, especially under the condition of low signal-to-noise ratio (SNR).","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126933293","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 : 2017-11-01DOI: 10.1109/ICAIT.2017.8388954
Yang Fan, Li Ming
This paper presents a visual simultaneously localization and mapping (SLAM) system based on Oriented FAST and Rotated BRIEF (ORB) and the RGB-D camera accessory of LeTV. Frame matching is performed through ORB feature and random sample and consensus (RANSAC) algorithm. G2o library is used as graph based pose optimization framework. Alternative key frame generation method is proposed to get increased processing capacity. Dense point cloud map which can be used for following intelligent robot related applications is generated from the depth information captured with color image simultaneously by the calibrated RGB-D camera.
提出了一种基于定向FAST和旋转BRIEF (ORB)和乐视RGB-D相机配件的视觉同步定位与制图系统(SLAM)。通过ORB特征和RANSAC (random sample and consensus)算法进行帧匹配。采用g20库作为基于图的姿态优化框架。提出了替代关键帧生成方法以提高处理能力。通过标定后的RGB-D相机同时采集彩色图像的深度信息，生成可用于后续智能机器人相关应用的密集点云图。
{"title":"An ORB based visual SLAM system by RGB-D camera of LeTV","authors":"Yang Fan, Li Ming","doi":"10.1109/ICAIT.2017.8388954","DOIUrl":"https://doi.org/10.1109/ICAIT.2017.8388954","url":null,"abstract":"This paper presents a visual simultaneously localization and mapping (SLAM) system based on Oriented FAST and Rotated BRIEF (ORB) and the RGB-D camera accessory of LeTV. Frame matching is performed through ORB feature and random sample and consensus (RANSAC) algorithm. G2o library is used as graph based pose optimization framework. Alternative key frame generation method is proposed to get increased processing capacity. Dense point cloud map which can be used for following intelligent robot related applications is generated from the depth information captured with color image simultaneously by the calibrated RGB-D camera.","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124362937","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 : 2017-11-01DOI: 10.1109/icait.2017.8388956
Yang Miaoben, Xiong Wei
As an important component of the national military defense system, the space-based early warning system can play an important role in safeguarding national security and acquiring potential threat of targets. Therefore, the modeling and analysis of the system network of the space-based early warning system becomes an important method to understand the system network and measure the performance of the system. Based on this background, this paper puts forward the abstract mapping and feature information extraction of the space-based early warning system based on the complex network theory to complete the construction and rendering of the system network topology model, the discovery and extraction of the important nodes and the acquisition of the basic attributes of the system network model and so on.
{"title":"Research on model mapping and analysis method of space-based early warning system based on complex network","authors":"Yang Miaoben, Xiong Wei","doi":"10.1109/icait.2017.8388956","DOIUrl":"https://doi.org/10.1109/icait.2017.8388956","url":null,"abstract":"As an important component of the national military defense system, the space-based early warning system can play an important role in safeguarding national security and acquiring potential threat of targets. Therefore, the modeling and analysis of the system network of the space-based early warning system becomes an important method to understand the system network and measure the performance of the system. Based on this background, this paper puts forward the abstract mapping and feature information extraction of the space-based early warning system based on the complex network theory to complete the construction and rendering of the system network topology model, the discovery and extraction of the important nodes and the acquisition of the basic attributes of the system network model and so on.","PeriodicalId":376884,"journal":{"name":"2017 9th International Conference on Advanced Infocomm Technology (ICAIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130674649","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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