Pub Date : 2012-10-18DOI: 10.1109/CNNA.2012.6331470
J. Fernández-Berni, R. Carmona-Galán, Á. Rodríguez-Vázquez
This paper describes a real-time application programmed into Wi-FLIP, a wireless smart camera resulting from the integration of FLIP-Q, a prototype mixed-signal focal-plane array processor, and Imote2, a commercial WSN platform. The application consists in scanning the whole scene by sequentially analyzing small regions. Within each region, motion is detected by background subtraction. Subsequently, information related to that motion - intensity and location - is radio-propagated in order to remotely account for it. By aggregating this information along time, a motion map of the scene is built. This map permits to visualize the different activity patterns taking place. It also provides an elaborated representation of the scene for further remote analysis, preventing raw images from being transmitted. In particular, the scene inspected in this demo corresponds to vehicular traffic in a motorway. The remote representation progressively built enables the assessment of the traffic density.
{"title":"Real-time remote reporting of motion analysis with Wi-FLIP","authors":"J. Fernández-Berni, R. Carmona-Galán, Á. Rodríguez-Vázquez","doi":"10.1109/CNNA.2012.6331470","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331470","url":null,"abstract":"This paper describes a real-time application programmed into Wi-FLIP, a wireless smart camera resulting from the integration of FLIP-Q, a prototype mixed-signal focal-plane array processor, and Imote2, a commercial WSN platform. The application consists in scanning the whole scene by sequentially analyzing small regions. Within each region, motion is detected by background subtraction. Subsequently, information related to that motion - intensity and location - is radio-propagated in order to remotely account for it. By aggregating this information along time, a motion map of the scene is built. This map permits to visualize the different activity patterns taking place. It also provides an elaborated representation of the scene for further remote analysis, preventing raw images from being transmitted. In particular, the scene inspected in this demo corresponds to vehicular traffic in a motorway. The remote representation progressively built enables the assessment of the traffic density.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123551296","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 : 2012-10-18DOI: 10.1109/CNNA.2012.6331428
R. Yeniceri, M. Yalçin
Many path planning and navigation papers using Cellular Neural/Nonlinear Networks (CNN) are found in literature. High proportion of these works originated by wave processing feature of CNN. This paper proposes a special condition of a known Cellular Nonlinear Network model which makes the network very proper to obtain nested and repetitive travelling waves. The Doppler effect appears as a corollary using this special condition. The main contribution of the Doppler effect to the path planning applications that uses CNNs is giving an opportunity to adjust the tracker's speed or change the route completely, dependent to the target's motion. By this way, this paper gains a new qualification to the CNN-based wave computing techniques putting the wave source's motion into use.
{"title":"A new CNN based path planning algorithm improved by the Doppler effect","authors":"R. Yeniceri, M. Yalçin","doi":"10.1109/CNNA.2012.6331428","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331428","url":null,"abstract":"Many path planning and navigation papers using Cellular Neural/Nonlinear Networks (CNN) are found in literature. High proportion of these works originated by wave processing feature of CNN. This paper proposes a special condition of a known Cellular Nonlinear Network model which makes the network very proper to obtain nested and repetitive travelling waves. The Doppler effect appears as a corollary using this special condition. The main contribution of the Doppler effect to the path planning applications that uses CNNs is giving an opportunity to adjust the tracker's speed or change the route completely, dependent to the target's motion. By this way, this paper gains a new qualification to the CNN-based wave computing techniques putting the wave source's motion into use.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120960800","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 : 2012-10-18DOI: 10.1109/CNNA.2012.6331430
Young-Su Kim, K. Min
Cellular Neural Network (CNN) that can provide parallel processing in massive scale is known suitable to neuromorphic applications such as vision systems. In this paper, we propose a new synaptic weighting circuit that can perform analog multiplication for CNN applications. The common-mode feedback is used in the new weighting circuit to minimize the output offset. The multiplication accuracy can be degraded by finite High Resistance State (HRS) and non-zero Low Resistance State (LRS) of real memristors. To improve the multiplication accuracy, we added two MOSFET switches to the memristor weighting circuit and decided the weighting memristance very carefully considering the leakage current. Variations in memristance are analyzed to estimate how much they can affect the accuracy of analog multiplication. Finally, the Average and Laplacian template were tested and verified by the circuit simulation using the proposed weighting circuit.
{"title":"Synaptic weighting circuits for Cellular Neural Networks","authors":"Young-Su Kim, K. Min","doi":"10.1109/CNNA.2012.6331430","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331430","url":null,"abstract":"Cellular Neural Network (CNN) that can provide parallel processing in massive scale is known suitable to neuromorphic applications such as vision systems. In this paper, we propose a new synaptic weighting circuit that can perform analog multiplication for CNN applications. The common-mode feedback is used in the new weighting circuit to minimize the output offset. The multiplication accuracy can be degraded by finite High Resistance State (HRS) and non-zero Low Resistance State (LRS) of real memristors. To improve the multiplication accuracy, we added two MOSFET switches to the memristor weighting circuit and decided the weighting memristance very carefully considering the leakage current. Variations in memristance are analyzed to estimate how much they can affect the accuracy of analog multiplication. Finally, the Average and Laplacian template were tested and verified by the circuit simulation using the proposed weighting circuit.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126392638","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 : 2012-10-18DOI: 10.1109/CNNA.2012.6331429
M. Ventra, Y. Pershin
Memory circuit elements - resistors, capacitors and inductors with memory - are electronic components with great potential in a wide range of applications. In particular, they are ideally suited to enhance all three major computing paradigms: binary, analog and quantum. Here, we consider how to achieve a faster computation with these elements. Specifically, we will show that a binary logic architecture combining memristive and memcapacitive elements requires considerably less steps to process information compared to architectures employing only memristive elements. In addition, we demonstrate that a network of memristive - as well as memcapacitive or meminductive - systems can solve a complex optimization problem - the maze problem - with unprecedented speed due to the analog parallelism afforded by these elements.
{"title":"Fast computation with memory circuit elements","authors":"M. Ventra, Y. Pershin","doi":"10.1109/CNNA.2012.6331429","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331429","url":null,"abstract":"Memory circuit elements - resistors, capacitors and inductors with memory - are electronic components with great potential in a wide range of applications. In particular, they are ideally suited to enhance all three major computing paradigms: binary, analog and quantum. Here, we consider how to achieve a faster computation with these elements. Specifically, we will show that a binary logic architecture combining memristive and memcapacitive elements requires considerably less steps to process information compared to architectures employing only memristive elements. In addition, we demonstrate that a network of memristive - as well as memcapacitive or meminductive - systems can solve a complex optimization problem - the maze problem - with unprecedented speed due to the analog parallelism afforded by these elements.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115666047","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 : 2012-10-18DOI: 10.1109/CNNA.2012.6331447
Á. Zarándy, T. Zsedrovits, Zoltán Nagy, A. Kiss, T. Roska
A visual sense-and-avoid system is introduced in this paper. The system is designed to operate on small and medium sized UAVs, and to be able to detect and avoid small manned and unmanned aircrafts. The intruder detection is done on a 4650×1280 sized video flow which is processed by a many-core cellular processor array real-time.
{"title":"Visual sense-and-avoid system for UAVs","authors":"Á. Zarándy, T. Zsedrovits, Zoltán Nagy, A. Kiss, T. Roska","doi":"10.1109/CNNA.2012.6331447","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331447","url":null,"abstract":"A visual sense-and-avoid system is introduced in this paper. The system is designed to operate on small and medium sized UAVs, and to be able to detect and avoid small manned and unmanned aircrafts. The intruder detection is done on a 4650×1280 sized video flow which is processed by a many-core cellular processor array real-time.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122327638","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 : 2012-10-18DOI: 10.1109/CNNA.2012.6331408
M. Geese, Paul Ruhnau, B. Jähne
Image sensors come with a spatial inhomogeneity, known as Fixed Pattern Noise, that degrades the image quality. Especially the dark signal non uniformity (DSNU) component of the FPN drifts with time and depends highly on temperature and exposure time. In this paper we introduce a cellular neural network (CNN) to estimate the DSNU from a given set of recorded images. Therefore the foundations of a previously presented maximum likelihood estimation method are used. A rigorous mathematical derivation exploits the available sensor statistics and uses only well motivated statistical models to calculate the CNN's synaptic weights. The advantages of the resulting CNN-method are continuous DSNU updates and a reduction of the computational complexity. Furthermore, a comparison based on ground truth correction patterns shows a significant performance increase to related methods.
{"title":"CNN based dark signal non-uniformity estimation","authors":"M. Geese, Paul Ruhnau, B. Jähne","doi":"10.1109/CNNA.2012.6331408","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331408","url":null,"abstract":"Image sensors come with a spatial inhomogeneity, known as Fixed Pattern Noise, that degrades the image quality. Especially the dark signal non uniformity (DSNU) component of the FPN drifts with time and depends highly on temperature and exposure time. In this paper we introduce a cellular neural network (CNN) to estimate the DSNU from a given set of recorded images. Therefore the foundations of a previously presented maximum likelihood estimation method are used. A rigorous mathematical derivation exploits the available sensor statistics and uses only well motivated statistical models to calculate the CNN's synaptic weights. The advantages of the resulting CNN-method are continuous DSNU updates and a reduction of the computational complexity. Furthermore, a comparison based on ground truth correction patterns shows a significant performance increase to related methods.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114757850","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 : 2012-10-18DOI: 10.1109/CNNA.2012.6331425
A. Badalov, X. Vilasís-Cardona, J. Albó-Canals
Reinforcement learning is a powerful tool for teaching robotic agents to perform tasks in real environments. Visual information provided by a camera could be a cheap and rich source of information about an agent's surroundings, if this information were represented in a compact and generalizable form. We turn to cellular neural networks as the means of transforming visual input to a representation suitable for reinforcement learning. We investigate a CNN-based image processing algorithm and describe a method for efficiently computing CNNs using the DirectX 10 API.
{"title":"Visual learning with cellular neural networks","authors":"A. Badalov, X. Vilasís-Cardona, J. Albó-Canals","doi":"10.1109/CNNA.2012.6331425","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331425","url":null,"abstract":"Reinforcement learning is a powerful tool for teaching robotic agents to perform tasks in real environments. Visual information provided by a camera could be a cheap and rich source of information about an agent's surroundings, if this information were represented in a compact and generalizable form. We turn to cellular neural networks as the means of transforming visual input to a representation suitable for reinforcement learning. We investigate a CNN-based image processing algorithm and describe a method for efficiently computing CNNs using the DirectX 10 API.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129967887","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 : 2012-10-18DOI: 10.1109/CNNA.2012.6331422
M. Bonnin, F. Corinto, V. Lanza
Oscillatory networks represent a circuit architecture for image and information processing, that can be used to realize associative and dynamic memories. Phase noise is often a limiting key factors for the performances of oscillatory networks. The ideal framework to investigate phase noise effect in nonlinear oscillators are phase models. Classical phase models lead to the conclusion that, in presence of random disturbances such as white noise, the phase noise problem is simply a diffusion process. In this paper we develop a reduced order model for phase noise analysis in nonlinear oscillators. We derive a reduced Fokker-Planck equation for the phase variable and the corresponding reduced phase equations. We show that the phase noise problem is a convection-diffusion process, proving that white noise produces both phase diffusion and frequency shift.
{"title":"Phase model reduction for oscillatory networks subject to stochastic inputs","authors":"M. Bonnin, F. Corinto, V. Lanza","doi":"10.1109/CNNA.2012.6331422","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331422","url":null,"abstract":"Oscillatory networks represent a circuit architecture for image and information processing, that can be used to realize associative and dynamic memories. Phase noise is often a limiting key factors for the performances of oscillatory networks. The ideal framework to investigate phase noise effect in nonlinear oscillators are phase models. Classical phase models lead to the conclusion that, in presence of random disturbances such as white noise, the phase noise problem is simply a diffusion process. In this paper we develop a reduced order model for phase noise analysis in nonlinear oscillators. We derive a reduced Fokker-Planck equation for the phase variable and the corresponding reduced phase equations. We show that the phase noise problem is a convection-diffusion process, proving that white noise produces both phase diffusion and frequency shift.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130156361","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 : 2012-10-18DOI: 10.1109/CNNA.2012.6331460
D. Rohr
The online event reconstruction for the ALICE experiment at CERN requires processing capabilities to process central Pb-Pb collisions at a rate of more than 200 Hz, corresponding to an input data rate of about 25 GB/s. The reconstruction of particle trajectories in the Time Projection Chamber (TPC) is the most compute intensive step. The TPC online tracker implementation combines the principle of the cellular automaton and the Kalman filter. It has been accelerated by the usage of graphics cards (GPUs). A pipelined processing allows to perform the tracking on the GPU, the data transfer, and the preprocessing on the CPU in parallel. In order to use data locality, the tracking is split in multiple phases. At first, track segments are searched in local sectors of the detector, independently and in parallel. These segments are then merged at a global level. A shortcoming of this approach is that if a track contains only a very short segment in one particular sector, the local search possibly does not find this short part. The fast GPU processing allowed to add an additional step: all found tracks are extrapolated to neighboring sectors and the unassigned clusters which constitute the missing track segment are collected. For running QA, it is important that the output of the CPU and the GPU tracker is as consistent as possible. One major challenge was to implement the tracker such that the output is not affected by concurrency, while maintaining peak performance and efficiency. For instance, a naive implementation depended on the order of the tracks which is nondeterministic when they are created in parallel. Still, due to non-associative floating point arithmetic a direct binary comparison of the CPU and the GPU tracker output is impossible. Thus, the approach chosen for evaluating the GPU tracker efficiency is to compare the cluster to track assignment of the CPU and the GPU tracker cluster by cluster. With the above comparison scheme, the output of the CPU and the GPU tracker differ by 0.00024Compared to the offline tracker, the HLT tracker is orders of magnitudes faster while delivering good results. The GPU version outperforms its CPU analog by another factor of three. Recently, the ALICE HLT cluster was upgraded with new GPUs and is able to process central heavy ion events at a rate of approximately 200 Hz.
{"title":"ALICE TPC online tracker on GPUs for heavy-ion events","authors":"D. Rohr","doi":"10.1109/CNNA.2012.6331460","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331460","url":null,"abstract":"The online event reconstruction for the ALICE experiment at CERN requires processing capabilities to process central Pb-Pb collisions at a rate of more than 200 Hz, corresponding to an input data rate of about 25 GB/s. The reconstruction of particle trajectories in the Time Projection Chamber (TPC) is the most compute intensive step. The TPC online tracker implementation combines the principle of the cellular automaton and the Kalman filter. It has been accelerated by the usage of graphics cards (GPUs). A pipelined processing allows to perform the tracking on the GPU, the data transfer, and the preprocessing on the CPU in parallel. In order to use data locality, the tracking is split in multiple phases. At first, track segments are searched in local sectors of the detector, independently and in parallel. These segments are then merged at a global level. A shortcoming of this approach is that if a track contains only a very short segment in one particular sector, the local search possibly does not find this short part. The fast GPU processing allowed to add an additional step: all found tracks are extrapolated to neighboring sectors and the unassigned clusters which constitute the missing track segment are collected. For running QA, it is important that the output of the CPU and the GPU tracker is as consistent as possible. One major challenge was to implement the tracker such that the output is not affected by concurrency, while maintaining peak performance and efficiency. For instance, a naive implementation depended on the order of the tracks which is nondeterministic when they are created in parallel. Still, due to non-associative floating point arithmetic a direct binary comparison of the CPU and the GPU tracker output is impossible. Thus, the approach chosen for evaluating the GPU tracker efficiency is to compare the cluster to track assignment of the CPU and the GPU tracker cluster by cluster. With the above comparison scheme, the output of the CPU and the GPU tracker differ by 0.00024Compared to the offline tracker, the HLT tracker is orders of magnitudes faster while delivering good results. The GPU version outperforms its CPU analog by another factor of three. Recently, the ALICE HLT cluster was upgraded with new GPUs and is able to process central heavy ion events at a rate of approximately 200 Hz.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130725634","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 : 2012-10-18DOI: 10.1109/CNNA.2012.6331462
Ting Chang, P. Sheridan, W. Lu
We report the fabrication, modeling and implementation of nanoscale tungsten-oxide (WOx) memristive (memristor) devices for neuromorphic applications. The device behaviors can be predicted accurately by considering both ion drift and diffusion. Short-term memory and memory enhancement phenomena, and the effects of spike rate, timing and associativity have been demonstrated. SPICE modeling has been achieved that allows circuit-level implementations.
{"title":"Modeling and implementation of oxide memristors for neuromorphic applications","authors":"Ting Chang, P. Sheridan, W. Lu","doi":"10.1109/CNNA.2012.6331462","DOIUrl":"https://doi.org/10.1109/CNNA.2012.6331462","url":null,"abstract":"We report the fabrication, modeling and implementation of nanoscale tungsten-oxide (WOx) memristive (memristor) devices for neuromorphic applications. The device behaviors can be predicted accurately by considering both ion drift and diffusion. Short-term memory and memory enhancement phenomena, and the effects of spike rate, timing and associativity have been demonstrated. SPICE modeling has been achieved that allows circuit-level implementations.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130945200","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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