Pub Date : 2012-01-30DOI: 10.1109/iccic.2010.5705886
B. Singh
In the past few years government and military organizations have widely adopted LAN, WAN and Internet to take advantage of advancement in technology. Computers are integral part of everyday operations. Organizations depend on them. A computer failure will have a critical impact on the organization. The term “Security” brings to mind all shorts of issues that refer to data protection and prevention of unauthorised access. The common motives for computer crimes could be lure for money, revenge, terrorism, fun, recognition or curiosity. Information systems can be attacked by outsiders who may penetrate a computer system or by insiders who are authorised to use the resources but misuse their authorization. An attacker may disrupt the information system of an organization (active attack) or gain access to its sensitive information (passive attack). Although no direct damage is done in a passive attack, any leak in information could have drastic repercussions for the organization. In simple words, security can be defined as: protecting information system from intended access. The goal of network management is to provide users with a quality of service. To meet this goal, network services plan involve strategic and technical planning of engineering, operations and maintenance of the network. The field of network security and management is constantly undergoing changes in technology, applications and hence the need for continually changing skills set on the part of academics. We will try to cover the various points with respect to organizational structure, policies and techniques of network security and management. Impact of various factors including management has been discussed on network security.
{"title":"Network security and management","authors":"B. Singh","doi":"10.1109/iccic.2010.5705886","DOIUrl":"https://doi.org/10.1109/iccic.2010.5705886","url":null,"abstract":"In the past few years government and military organizations have widely adopted LAN, WAN and Internet to take advantage of advancement in technology. Computers are integral part of everyday operations. Organizations depend on them. A computer failure will have a critical impact on the organization. The term “Security” brings to mind all shorts of issues that refer to data protection and prevention of unauthorised access. The common motives for computer crimes could be lure for money, revenge, terrorism, fun, recognition or curiosity. Information systems can be attacked by outsiders who may penetrate a computer system or by insiders who are authorised to use the resources but misuse their authorization. An attacker may disrupt the information system of an organization (active attack) or gain access to its sensitive information (passive attack). Although no direct damage is done in a passive attack, any leak in information could have drastic repercussions for the organization. In simple words, security can be defined as: protecting information system from intended access. The goal of network management is to provide users with a quality of service. To meet this goal, network services plan involve strategic and technical planning of engineering, operations and maintenance of the network. The field of network security and management is constantly undergoing changes in technology, applications and hence the need for continually changing skills set on the part of academics. We will try to cover the various points with respect to organizational structure, policies and techniques of network security and management. Impact of various factors including management has been discussed on network security.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122445667","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 : 2011-03-14DOI: 10.1109/iccic.2010.5705762
V. Palanisamy, P. Annadurai, S. Vijayalakshmi
In wireless and distributed nature of Ad hoc networks, security has become a more sophisticated problem than security in other networks, due to the open nature and the lack of infrastructure of such networks. This paper is based on Blackhole attack. This attack node first needs to invade into the multicast forwarding group. It then absorbs all the data packets in the network and drops fully or partially. So, that the destination node does not get the data packets fully and this will affect the packet delivery ratio (PDR). In this paper, the goal is to measure the impact of Balckhole attack and their node positions which affect the performance metrics of packet delivery with respect to three scenarios: Black hole attack node near sender, blackhole attack node near receiver and anywhere within the network. The performance of PDR with respect to above three scenarios is also compared.
{"title":"Impact of black hole attack on multicast in ad hoc network (IBAMA)","authors":"V. Palanisamy, P. Annadurai, S. Vijayalakshmi","doi":"10.1109/iccic.2010.5705762","DOIUrl":"https://doi.org/10.1109/iccic.2010.5705762","url":null,"abstract":"In wireless and distributed nature of Ad hoc networks, security has become a more sophisticated problem than security in other networks, due to the open nature and the lack of infrastructure of such networks. This paper is based on Blackhole attack. This attack node first needs to invade into the multicast forwarding group. It then absorbs all the data packets in the network and drops fully or partially. So, that the destination node does not get the data packets fully and this will affect the packet delivery ratio (PDR). In this paper, the goal is to measure the impact of Balckhole attack and their node positions which affect the performance metrics of packet delivery with respect to three scenarios: Black hole attack node near sender, blackhole attack node near receiver and anywhere within the network. The performance of PDR with respect to above three scenarios is also compared.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122723338","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 : 2010-12-01DOI: 10.1109/ICCIC.2010.5705728
V. Manikandan, V. Venkatachalam, M. Kirthiga, K. Harini, N. Devarajan
Optical Character Recognition consists of various steps like skew detection, segmentation of columns, lines, words, and characters before feeding the isolated character to an optical character recognition system. Several methodologies are followed to perform these steps using conventional Hough Transformation. In this paper, a new algorithm is proposed to perform all those steps involved in document image processing. The algorithm is implemented for skew detection, column and line segmentation and Character Segmentation. This can be extended to all other steps like character recognition. The novelty of this approach lies in “the consideration of any image, as one formed by several black and white lines of various lengths and at various angles”. The pixel values of the binary image are stored in an array. All the pixel values in the array are compared with their horizontally adjacent pixel values, row by row, for the presence of collinear points (i.e., a line). It is done by detecting the continuity of either the white or black pixels accordingly. Once the continuity is detected, the starting and end coordinates are displayed as an intermediate result. A new image will be generated as a result, which indicates the pixel area of line, identified from the input image. The algorithm is applied for English and other regional languages.
{"title":"An enhanced algorithm for Character Segmentation in document image processing","authors":"V. Manikandan, V. Venkatachalam, M. Kirthiga, K. Harini, N. Devarajan","doi":"10.1109/ICCIC.2010.5705728","DOIUrl":"https://doi.org/10.1109/ICCIC.2010.5705728","url":null,"abstract":"Optical Character Recognition consists of various steps like skew detection, segmentation of columns, lines, words, and characters before feeding the isolated character to an optical character recognition system. Several methodologies are followed to perform these steps using conventional Hough Transformation. In this paper, a new algorithm is proposed to perform all those steps involved in document image processing. The algorithm is implemented for skew detection, column and line segmentation and Character Segmentation. This can be extended to all other steps like character recognition. The novelty of this approach lies in “the consideration of any image, as one formed by several black and white lines of various lengths and at various angles”. The pixel values of the binary image are stored in an array. All the pixel values in the array are compared with their horizontally adjacent pixel values, row by row, for the presence of collinear points (i.e., a line). It is done by detecting the continuity of either the white or black pixels accordingly. Once the continuity is detected, the starting and end coordinates are displayed as an intermediate result. A new image will be generated as a result, which indicates the pixel area of line, identified from the input image. The algorithm is applied for English and other regional languages.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115680753","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 : 2010-12-01DOI: 10.1109/ICCIC.2010.5705875
Darshankumar C. Dalwadi, H. Soni
In this paper we have presented an effect of Rician channel model which is used to model the wireless channels. We have evaluated its channel effect on Minimum Shift keying modulation technique. We have also considered the Gaussian Minimum Shift keying modulation (GMSK) and carried out simulation with Rayleigh channel for comparison purpose. We have carried out simulation with MATLAB.
{"title":"Performance evaluation of GMSK modulated signal under Rician channel model","authors":"Darshankumar C. Dalwadi, H. Soni","doi":"10.1109/ICCIC.2010.5705875","DOIUrl":"https://doi.org/10.1109/ICCIC.2010.5705875","url":null,"abstract":"In this paper we have presented an effect of Rician channel model which is used to model the wireless channels. We have evaluated its channel effect on Minimum Shift keying modulation technique. We have also considered the Gaussian Minimum Shift keying modulation (GMSK) and carried out simulation with Rayleigh channel for comparison purpose. We have carried out simulation with MATLAB.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116952412","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 : 2010-12-01DOI: 10.1109/ICCIC.2010.5705862
B. Gangamma, K. S. Murthy, G. C. P. Chandra, Shishir Kaushik, Saurabh Kumar
India is praised for its rich past and the culture. The rich heritage of the country has been carried over generations through the manuscripts and historic writings. A great deal of effort is being made to prevent their further degradation. A lot of research is also done in finding methods to digitize these documents so that they can be immortalized. But in order to achieve this, the noise in the documents (result of exposure to environmental factors and age) has to be eliminated. This paper deals with one such method aimed at denoising a degraded image. We describe a method to denoise an image using Curvelet and mathematical morphology. Proposed implementation offers reconstruction of the image from the degraded document and edge enhancement by eliminating background.
{"title":"A combined approach for degraded historical documents denoising using Curvelet and mathematical morphology","authors":"B. Gangamma, K. S. Murthy, G. C. P. Chandra, Shishir Kaushik, Saurabh Kumar","doi":"10.1109/ICCIC.2010.5705862","DOIUrl":"https://doi.org/10.1109/ICCIC.2010.5705862","url":null,"abstract":"India is praised for its rich past and the culture. The rich heritage of the country has been carried over generations through the manuscripts and historic writings. A great deal of effort is being made to prevent their further degradation. A lot of research is also done in finding methods to digitize these documents so that they can be immortalized. But in order to achieve this, the noise in the documents (result of exposure to environmental factors and age) has to be eliminated. This paper deals with one such method aimed at denoising a degraded image. We describe a method to denoise an image using Curvelet and mathematical morphology. Proposed implementation offers reconstruction of the image from the degraded document and edge enhancement by eliminating background.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125067434","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 : 2010-12-01DOI: 10.1109/ICCIC.2010.5705834
K. Borisagar, D. Kamdar, B. Sedani, G. R. Kulkarni
Voice activity detection (VAD) is an outstanding problem for speech transmission, enhancement and recognition. The variety and the varying nature of speech and background noise make it especially challenging. In the past years, many features emphasizing the differences between speech and noise have been proposed for their robustness. However an important problem in many areas of speech processing is the determination of presence of speech periods in a given signal. This task can be identified as a statistical hypothesis problem and its purpose is the determination to which category or class a given signal belongs. Also the classification task is often not as trivial as it appears since the increasing level of background noise leading to numerous detection errors. The selection of an adequate feature vector for signal detection and a robust decision rule is a challenging problem that affects the performance of VADs. Most algorithms are effective in numerous applications but often cause detection errors mainly due to the loss of discriminating power of the decision rule at lower SNRs. In this paper, it has been tried to extract the characteristics of noise by the VAD algorithm which can be used to smooth out the signal in silence part from the noisy environment. For further noise reduction signal then filtered in the wavelet domain using thresholding.
{"title":"Speech enhancement in noisy environment using voice activity detection and wavelet thresholding","authors":"K. Borisagar, D. Kamdar, B. Sedani, G. R. Kulkarni","doi":"10.1109/ICCIC.2010.5705834","DOIUrl":"https://doi.org/10.1109/ICCIC.2010.5705834","url":null,"abstract":"Voice activity detection (VAD) is an outstanding problem for speech transmission, enhancement and recognition. The variety and the varying nature of speech and background noise make it especially challenging. In the past years, many features emphasizing the differences between speech and noise have been proposed for their robustness. However an important problem in many areas of speech processing is the determination of presence of speech periods in a given signal. This task can be identified as a statistical hypothesis problem and its purpose is the determination to which category or class a given signal belongs. Also the classification task is often not as trivial as it appears since the increasing level of background noise leading to numerous detection errors. The selection of an adequate feature vector for signal detection and a robust decision rule is a challenging problem that affects the performance of VADs. Most algorithms are effective in numerous applications but often cause detection errors mainly due to the loss of discriminating power of the decision rule at lower SNRs. In this paper, it has been tried to extract the characteristics of noise by the VAD algorithm which can be used to smooth out the signal in silence part from the noisy environment. For further noise reduction signal then filtered in the wavelet domain using thresholding.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123324882","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 : 2010-12-01DOI: 10.1109/ICCIC.2010.5705757
P. Pal, N. Mohanty, A. Kushwaha, B. Singh, B. Mazumdar, T. Gandhi
Electromyography (EMG) signal is electrical manifestation of neuromuscular activation by which physiological processes are accessible. The bio-mechanical phenomenon induces the muscle to generate force and produce movement and help to interact with the world. Classification of EMGs is a big challenge due to its non stationary nature. Various features like root mean square, spectrogram, kurtosis, entropy and power are extracted from EMG signals of isometric contraction of two different abnormalities namely ALS (Amyotrophic Lateral Sclerosis) which is coming under Neuropathy and Myopathy. The classification accuracy is found to be satisfactory to design EMG signal classifier for various applications like knowledge-based expert system design and disease diagnosis.
{"title":"Feature extraction for evaluation of Muscular Atrophy","authors":"P. Pal, N. Mohanty, A. Kushwaha, B. Singh, B. Mazumdar, T. Gandhi","doi":"10.1109/ICCIC.2010.5705757","DOIUrl":"https://doi.org/10.1109/ICCIC.2010.5705757","url":null,"abstract":"Electromyography (EMG) signal is electrical manifestation of neuromuscular activation by which physiological processes are accessible. The bio-mechanical phenomenon induces the muscle to generate force and produce movement and help to interact with the world. Classification of EMGs is a big challenge due to its non stationary nature. Various features like root mean square, spectrogram, kurtosis, entropy and power are extracted from EMG signals of isometric contraction of two different abnormalities namely ALS (Amyotrophic Lateral Sclerosis) which is coming under Neuropathy and Myopathy. The classification accuracy is found to be satisfactory to design EMG signal classifier for various applications like knowledge-based expert system design and disease diagnosis.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125541087","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 : 2010-12-01DOI: 10.1109/ICCIC.2010.5705907
C. Babu, T. Subramanian, Parasuraman Kumar
In this paper a method for vehicle license plate identification is implemented and analyzed, on the basis of a novel adaptive image segmentation technique conjunction with character recognition. A novel method for license plate localization based on texture and edge information is proposed. The whole process is divided into two parts: candidate extraction and candidate verification. In the first part, the license plate being extracted from complex environment, several candidate areas instead of one with the max texture information are extracted. In the second part, autocorrelation based binary image and projection algorithm are used to verify the plate candidates. Adaptive median filter is applied to remove the noise from the image. Image processing technique such as edge detection, thresholding, resampling and filtering have been used to locate and isolate the license plate and the characters. The system can recognize single line number plates under widely varying illumination conditions with a success rate of about 80%.
{"title":"A feature based approach for license plate-recognition of Indian number plates","authors":"C. Babu, T. Subramanian, Parasuraman Kumar","doi":"10.1109/ICCIC.2010.5705907","DOIUrl":"https://doi.org/10.1109/ICCIC.2010.5705907","url":null,"abstract":"In this paper a method for vehicle license plate identification is implemented and analyzed, on the basis of a novel adaptive image segmentation technique conjunction with character recognition. A novel method for license plate localization based on texture and edge information is proposed. The whole process is divided into two parts: candidate extraction and candidate verification. In the first part, the license plate being extracted from complex environment, several candidate areas instead of one with the max texture information are extracted. In the second part, autocorrelation based binary image and projection algorithm are used to verify the plate candidates. Adaptive median filter is applied to remove the noise from the image. Image processing technique such as edge detection, thresholding, resampling and filtering have been used to locate and isolate the license plate and the characters. The system can recognize single line number plates under widely varying illumination conditions with a success rate of about 80%.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116463642","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 : 2010-12-01DOI: 10.1109/ICCIC.2010.5705895
K. Gandhi, M. Karnan
This paper proposes a image enhancement and segmentation method for mammographic images, which is based on the pixel intensity transformation, spatial filtering, edge detection, and region growing techniques. This method consists of two approaches. The first approach is applying intensity transformation, logarithmic transformation, contrast stretching, histogram equalization and spatial filtering techniques separately to enhance the mammogram images. In the second approach is applying edge detection operators, local and global thresholding techniques used to extract the suspicious regions from back ground tissue.
{"title":"Mammogram image enhancement and segmentation","authors":"K. Gandhi, M. Karnan","doi":"10.1109/ICCIC.2010.5705895","DOIUrl":"https://doi.org/10.1109/ICCIC.2010.5705895","url":null,"abstract":"This paper proposes a image enhancement and segmentation method for mammographic images, which is based on the pixel intensity transformation, spatial filtering, edge detection, and region growing techniques. This method consists of two approaches. The first approach is applying intensity transformation, logarithmic transformation, contrast stretching, histogram equalization and spatial filtering techniques separately to enhance the mammogram images. In the second approach is applying edge detection operators, local and global thresholding techniques used to extract the suspicious regions from back ground tissue.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122500750","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 : 2010-12-01DOI: 10.1109/ICCIC.2010.5705904
A. Mahesh, Dr. S. N. Sivanandam
Business intelligence may be defined as a set of mathematical models and analysis methodologies that systematically exploit the available data to retrieve information and knowledge useful in supporting complex decision-making processes. A business intelligence environment offers decision makers information and knowledge derived from data processing, through the application of mathematical models and algorithms. In some instances, these may merely consist of the calculation of totals and percentages, while more fully developed analyses make use of advanced models for optimization, inductive learning and prediction.
{"title":"Business intelligence: Identify valued customer from the data warehouse in financial institutions","authors":"A. Mahesh, Dr. S. N. Sivanandam","doi":"10.1109/ICCIC.2010.5705904","DOIUrl":"https://doi.org/10.1109/ICCIC.2010.5705904","url":null,"abstract":"Business intelligence may be defined as a set of mathematical models and analysis methodologies that systematically exploit the available data to retrieve information and knowledge useful in supporting complex decision-making processes. A business intelligence environment offers decision makers information and knowledge derived from data processing, through the application of mathematical models and algorithms. In some instances, these may merely consist of the calculation of totals and percentages, while more fully developed analyses make use of advanced models for optimization, inductive learning and prediction.","PeriodicalId":246468,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence and Computing Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128978587","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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