Pub Date : 2017-12-01DOI: 10.1109/ISS1.2017.8389441
S. Inguva, J. Seventline
In this paper, we have designed an efficient CORDIC algorithm, which is used to minimize the CORDIC rotation angle with the help of several rotations. The main idea of this new CORDIC algorithm is to use an area efficient carry select adder (CSLA), instead of using a normal adder. This adder can achieve fast arithmetic operation in various data processing techniques. Finally, the comparison of various parameters like area, power and delay are calculated and they are reduced in the proposed method when compared to the existing method.
{"title":"Enhanced CORDIC algorithm using an area efficient carry select adder","authors":"S. Inguva, J. Seventline","doi":"10.1109/ISS1.2017.8389441","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389441","url":null,"abstract":"In this paper, we have designed an efficient CORDIC algorithm, which is used to minimize the CORDIC rotation angle with the help of several rotations. The main idea of this new CORDIC algorithm is to use an area efficient carry select adder (CSLA), instead of using a normal adder. This adder can achieve fast arithmetic operation in various data processing techniques. Finally, the comparison of various parameters like area, power and delay are calculated and they are reduced in the proposed method when compared to the existing method.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123116087","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 : 2017-12-01DOI: 10.1109/ISS1.2017.8389465
Venkatesh Vadde, S. Srivatsa, Y. Vijay, Nidhin Anisham, K. Arun
We describe an electric driver assist system (EDAS) concept, where a vehicle can autonomously navigate and trail a leading vehicle with minimal human intervention. A DC hub-motor driven control system is designed, modelled and simulated using Simulink. The system designed for rush-hour urban settings with speeds of 0–5 Kmph, maintains about 1m separation from the front vehicle. The system uses inexpensive ultrasonic sensors and a tachometer to estimate distance and speed dynamically. The speed-control algorithm is successfully able to track typical rush-hour vehicular profiles. The EDAS has also been implemented on a model car using an Arduino and Beaglebone with acceptable real-time performance results.
{"title":"Design and analysis of an electric driver assist system for rush-hour navigation in dense urban traffic","authors":"Venkatesh Vadde, S. Srivatsa, Y. Vijay, Nidhin Anisham, K. Arun","doi":"10.1109/ISS1.2017.8389465","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389465","url":null,"abstract":"We describe an electric driver assist system (EDAS) concept, where a vehicle can autonomously navigate and trail a leading vehicle with minimal human intervention. A DC hub-motor driven control system is designed, modelled and simulated using Simulink. The system designed for rush-hour urban settings with speeds of 0–5 Kmph, maintains about 1m separation from the front vehicle. The system uses inexpensive ultrasonic sensors and a tachometer to estimate distance and speed dynamically. The speed-control algorithm is successfully able to track typical rush-hour vehicular profiles. The EDAS has also been implemented on a model car using an Arduino and Beaglebone with acceptable real-time performance results.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129718609","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 : 2017-12-01DOI: 10.1109/ISS1.2017.8389411
Nischitha, N. Padmavathi
In medical field, the modality based image analysis is attaining much importance due to the clinical data has to be processed to analyze various outcomes. Fusion of multimodal images is performed to combine all relevant information from single or multiple imaging modalities into a new single. In this work fusion of different modality images like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) of abdomen cancer is carried using Laplacian Pyramid fusion rule and Multi-resolution Singular Value Decomposition (MSVD) fusion rule. The fusion performance is analyzed using various quality metrics like Entropy, Fusion Factor (FF), Standard Deviation (SD), Structural Similarity Index and Correlation measure. Fused images are classified as benign or malignant lesion using Support Vector Machine (SVM) classifier.
{"title":"Fusion of multimodal abdominal cancerous images and classification using support vector machine","authors":"Nischitha, N. Padmavathi","doi":"10.1109/ISS1.2017.8389411","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389411","url":null,"abstract":"In medical field, the modality based image analysis is attaining much importance due to the clinical data has to be processed to analyze various outcomes. Fusion of multimodal images is performed to combine all relevant information from single or multiple imaging modalities into a new single. In this work fusion of different modality images like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) of abdomen cancer is carried using Laplacian Pyramid fusion rule and Multi-resolution Singular Value Decomposition (MSVD) fusion rule. The fusion performance is analyzed using various quality metrics like Entropy, Fusion Factor (FF), Standard Deviation (SD), Structural Similarity Index and Correlation measure. Fused images are classified as benign or malignant lesion using Support Vector Machine (SVM) classifier.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128362185","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 : 2017-12-01DOI: 10.1109/ISS1.2017.8389385
S. Devi, Poornima Mohan
Compressive Sensing is an effective method which allows us to sample below Nyquist rate and thus store less information, thereby saving space. The recovery of the signal from the compressed measurements is done efficiently by means of optimization algorithms. This paper employs the convex optimisation algorithm for reconstruction, which is implemented using the CVX package. This paper also deals with denoising of signals and images using different algorithms used for compressive sensing based denoising namely direct L1, joint L1 and separation based method and discovers the range of signal to noise ratio in which each algorithm is applicable. Compressive sensing based method of denoising proved to be a more effective method than the existing method of wavelet based denoising. The performance comparison of wavelet and compressive sensing based method are done using the parameters signal to noise ratio and mean square error. Denoising finds application in Medical Image analysis, which will enable us to recover the original image after removing the noise caused due to various disturbances. Denosing finds application in Radio Astronomy, in which it enables us to obtain the spatial information without the effect of back ground radiation.
{"title":"A comparison of compressive sensing application for image denoising with wavelet denoising","authors":"S. Devi, Poornima Mohan","doi":"10.1109/ISS1.2017.8389385","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389385","url":null,"abstract":"Compressive Sensing is an effective method which allows us to sample below Nyquist rate and thus store less information, thereby saving space. The recovery of the signal from the compressed measurements is done efficiently by means of optimization algorithms. This paper employs the convex optimisation algorithm for reconstruction, which is implemented using the CVX package. This paper also deals with denoising of signals and images using different algorithms used for compressive sensing based denoising namely direct L1, joint L1 and separation based method and discovers the range of signal to noise ratio in which each algorithm is applicable. Compressive sensing based method of denoising proved to be a more effective method than the existing method of wavelet based denoising. The performance comparison of wavelet and compressive sensing based method are done using the parameters signal to noise ratio and mean square error. Denoising finds application in Medical Image analysis, which will enable us to recover the original image after removing the noise caused due to various disturbances. Denosing finds application in Radio Astronomy, in which it enables us to obtain the spatial information without the effect of back ground radiation.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130335047","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 : 2017-12-01DOI: 10.1109/ISS1.2017.8389312
C. Therasa, C. Vijayabanu, S. Manikandan, S. Gopalakrishnan
The study focuses on the cross-selling practices in banking service with respect to employee perception. The study is made to diagnose the present cross-selling practices in banks and to know the issues faced by them for improvement of cross-selling performance. The study was limited to bank employees indulge in cross-selling practices. The number of respondents who were answered were 100 totally. The study provided various details like training, motivation factors, knowledge in cross selling and their effectiveness.
{"title":"Analysing cross selling performance-fitting in a regression equation","authors":"C. Therasa, C. Vijayabanu, S. Manikandan, S. Gopalakrishnan","doi":"10.1109/ISS1.2017.8389312","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389312","url":null,"abstract":"The study focuses on the cross-selling practices in banking service with respect to employee perception. The study is made to diagnose the present cross-selling practices in banks and to know the issues faced by them for improvement of cross-selling performance. The study was limited to bank employees indulge in cross-selling practices. The number of respondents who were answered were 100 totally. The study provided various details like training, motivation factors, knowledge in cross selling and their effectiveness.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129236531","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 : 2017-12-01DOI: 10.1109/ISS1.2017.8389437
C. F. Chu, S. Yuen, Y. Wong
Neural Network has been widely used to model the dynamics of chlorophyll-a concentration for over a decade. Previous studies were always based on shallow network structures (i.e. 3–5 layers) and used time-lagged data from the localized region as model inputs. Recent ecological studies have shown that the coastal ocean current circulation is one of the key factors for the formation of algae bloom (red tide), hence the level of chlorophyll-a concentration increases. This suggests that the data from nearby regions should be included in the modeling process along with the localized data. This study investigates the classification performance among models with and without the use of data from nearby regions under deep neural network learning models. Networks with 3 to 12 layers are employed in two distinct structures respectively for conducting the empirical analysis on 1990–2016 monthly marine water quality data obtained from the Hong Kong Environmental Protection Department. The deep networks are shown to be able to extract useful information from 108 input attributes consolidated from 5 nearby coastal regions in Deep Bay water control zone. The 5-fold cross valuation results indicated that the use of additional layers tends to improve the classification performance and the optimal result is achieved by using 11 layers under the two proposed network structures.
{"title":"Deep neural network for marine water quality classification with the consideration of coastal current circulation effect","authors":"C. F. Chu, S. Yuen, Y. Wong","doi":"10.1109/ISS1.2017.8389437","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389437","url":null,"abstract":"Neural Network has been widely used to model the dynamics of chlorophyll-a concentration for over a decade. Previous studies were always based on shallow network structures (i.e. 3–5 layers) and used time-lagged data from the localized region as model inputs. Recent ecological studies have shown that the coastal ocean current circulation is one of the key factors for the formation of algae bloom (red tide), hence the level of chlorophyll-a concentration increases. This suggests that the data from nearby regions should be included in the modeling process along with the localized data. This study investigates the classification performance among models with and without the use of data from nearby regions under deep neural network learning models. Networks with 3 to 12 layers are employed in two distinct structures respectively for conducting the empirical analysis on 1990–2016 monthly marine water quality data obtained from the Hong Kong Environmental Protection Department. The deep networks are shown to be able to extract useful information from 108 input attributes consolidated from 5 nearby coastal regions in Deep Bay water control zone. The 5-fold cross valuation results indicated that the use of additional layers tends to improve the classification performance and the optimal result is achieved by using 11 layers under the two proposed network structures.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130577833","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 : 2017-12-01DOI: 10.1109/ISS1.2017.8389348
R. Singh, Namrata Dhanda, K. Agrawal
An internet consists of various types of network and connecting devices like router. A packets starts from the source host, passes through many physical network and finally, reaches the destination host. At the network level, the routers are recognized by their IP address. The main limitation of ARP is traffic jam in nodes due to unless point to point communication. ARP Request is point to point communication which packet doesn't contain hardware address of receiver per as my research when a packet extend it content and include the hardware address it can maintain point to point communication with the nodes(machine). ARP Reply includes point to point in network, as my research the packet reply at broadcasting to every node which copy the sender and receiver hardware address. As per research, the COUNT helps to check the no of nodes viewed the packet.
{"title":"Evaluation of address resolution protocol and essential security issues","authors":"R. Singh, Namrata Dhanda, K. Agrawal","doi":"10.1109/ISS1.2017.8389348","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389348","url":null,"abstract":"An internet consists of various types of network and connecting devices like router. A packets starts from the source host, passes through many physical network and finally, reaches the destination host. At the network level, the routers are recognized by their IP address. The main limitation of ARP is traffic jam in nodes due to unless point to point communication. ARP Request is point to point communication which packet doesn't contain hardware address of receiver per as my research when a packet extend it content and include the hardware address it can maintain point to point communication with the nodes(machine). ARP Reply includes point to point in network, as my research the packet reply at broadcasting to every node which copy the sender and receiver hardware address. As per research, the COUNT helps to check the no of nodes viewed the packet.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116305672","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 : 2017-12-01DOI: 10.1109/ISS1.2017.8389392
A. Ghasemi, C. Kumar
Suspicious activities seriously endanger at public areas and personal security. There are millions of video surveillance systems used in public areas, such as streets, prisons, holy sites, airports, and supermarkets. It is essential to investigate the detection and recognition of suspicious activities contents from surveillance video. The common suspicious activities at public areas with an aspect of security are fighting, running, leave luggage and run, put an unusual packet in somewhere like a dustbin and leave. We focus on the recognition of suspicious activity and aim to find a method that can automatically detect suspicious activity using computer vision methods. Complex background, illumination changes and different distances between the human and the camera have made this topic very challenging, especially in the case of real-time applications. We adopted GMM to produce candidate regions whose has suspicious activity of motion features extracted from the magnitude information of Optical Flow, and we call this method Suspicious Activity Region Detector (SARD). Experimental results on several benchmark datasets have demonstrated the robustness of our proposed framework over the state-of-the-arts in terms of both detection accuracy and processing speed, even in crowded scenes.
{"title":"A novel algorithm to predict and detect suspicious behaviors of people at public areas for surveillave cameras","authors":"A. Ghasemi, C. Kumar","doi":"10.1109/ISS1.2017.8389392","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389392","url":null,"abstract":"Suspicious activities seriously endanger at public areas and personal security. There are millions of video surveillance systems used in public areas, such as streets, prisons, holy sites, airports, and supermarkets. It is essential to investigate the detection and recognition of suspicious activities contents from surveillance video. The common suspicious activities at public areas with an aspect of security are fighting, running, leave luggage and run, put an unusual packet in somewhere like a dustbin and leave. We focus on the recognition of suspicious activity and aim to find a method that can automatically detect suspicious activity using computer vision methods. Complex background, illumination changes and different distances between the human and the camera have made this topic very challenging, especially in the case of real-time applications. We adopted GMM to produce candidate regions whose has suspicious activity of motion features extracted from the magnitude information of Optical Flow, and we call this method Suspicious Activity Region Detector (SARD). Experimental results on several benchmark datasets have demonstrated the robustness of our proposed framework over the state-of-the-arts in terms of both detection accuracy and processing speed, even in crowded scenes.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116309452","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 : 2017-12-01DOI: 10.1109/ISS1.2017.8389309
A. N. Niranjani, M. Sivachitra
A brain-computer interface (BCI) is both a hardware and software based communication system that allows cerebral activity to control computers or external devices. The instantaneous aim of BCI research is to offer communication abilities to severely disabled people who are ‘locked in’ by neurological disorders such as amyotrophic lateral sclerosis, brain stem stroke or spinal cord injury. “Electroencephalography”, a non-invasive approach, has been widely used for BCI system. In recent times, several classifiers have been used in analyzing EEG signals measured in the planning and relaxed state. The key work addressed is the classification of EEG signals (motor imagery signals) using spiking neural classifier. The dataset (Planning and relaxed state data) is a benchmark data taken from UCI (University of California, Irvine) repository. Online Meta-neuron based Learning Algorithm (OMLA), is a newly evolved network applied for the EEG signal classification task. Spiking neural classifier performs better than the other classifiers due to the use of both global and local information of the network.
{"title":"Motor imagery signal classification using spiking neural network","authors":"A. N. Niranjani, M. Sivachitra","doi":"10.1109/ISS1.2017.8389309","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389309","url":null,"abstract":"A brain-computer interface (BCI) is both a hardware and software based communication system that allows cerebral activity to control computers or external devices. The instantaneous aim of BCI research is to offer communication abilities to severely disabled people who are ‘locked in’ by neurological disorders such as amyotrophic lateral sclerosis, brain stem stroke or spinal cord injury. “Electroencephalography”, a non-invasive approach, has been widely used for BCI system. In recent times, several classifiers have been used in analyzing EEG signals measured in the planning and relaxed state. The key work addressed is the classification of EEG signals (motor imagery signals) using spiking neural classifier. The dataset (Planning and relaxed state data) is a benchmark data taken from UCI (University of California, Irvine) repository. Online Meta-neuron based Learning Algorithm (OMLA), is a newly evolved network applied for the EEG signal classification task. Spiking neural classifier performs better than the other classifiers due to the use of both global and local information of the network.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752330","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}
In recent years there has been a rapid increase in use of therapies for mentally challenged. This paper describes the use of a Snoezelen bubble tube. The simplest way to cope up with the mental handicapped is to respond according to their actions. In order to bring positive development in them, one of the therapy is using snoezelen bubble tube, in this paper we like to add some more new features to Snoezelen bubble tube for the betterment of people suffering with learning disabilities and mentally challenged.
{"title":"Smart snoezelen bubble tube for mentally challenged and learning disability","authors":"Lalithkalyan Anirudh Pusuluri, Tatavarthi Dhiraj, Yeswanth Sinha Kothuri, Amarnath Malisetty, S. Kalaivani","doi":"10.1109/ISS1.2017.8389466","DOIUrl":"https://doi.org/10.1109/ISS1.2017.8389466","url":null,"abstract":"In recent years there has been a rapid increase in use of therapies for mentally challenged. This paper describes the use of a Snoezelen bubble tube. The simplest way to cope up with the mental handicapped is to respond according to their actions. In order to bring positive development in them, one of the therapy is using snoezelen bubble tube, in this paper we like to add some more new features to Snoezelen bubble tube for the betterment of people suffering with learning disabilities and mentally challenged.","PeriodicalId":321227,"journal":{"name":"2017 International Conference on Intelligent Sustainable Systems (ICISS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128157861","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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