In this article, we first investigate the operational properties of algebroid functions. Then we prove two uniqueness theorems for algebroid functions.
Metal objects in X-ray computed tomography can cause severe artifacts.The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods.This paper proposes a proje...Metal objects in X-ray computed tomography can cause severe artifacts.The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods.This paper proposes a projectiondomain algorithm to reduce the metal artifacts.In this algorithm,the unknowns are the metal-affected projections,while the objective function is set up in the image domain.The data fidelity term is not utilized in the objective function.The objective function of the proposed algorithm consists of two terms:the total variation of the metalremoved image and the energy of the negative-valued pixels in the image.After the metal-affected projections are modified,the final image is reconstructed via the filtered backprojection algorithm.The feasibility of the proposed algorithm has been verified by real experimental data.展开更多
Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world.Although e...Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world.Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images,the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance.Consequently,this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques.We describe resources and techniques based on three visual feature extraction methods,namely calculation-,recognition-,and simulation-based.Additionally,we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow.展开更多
The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies.This new era allows the consumer to directly connect with other individuals,business corpor...The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies.This new era allows the consumer to directly connect with other individuals,business corporations,and the government.People are open to sharing opinions,views,and ideas on any topic in different formats out loud.This creates the opportunity to make the"Big Social Data"handy by implementing machine learning approaches and social data analytics.This study offers an overview of recent works in social media,data science,and machine learning to gain a wide perspective on social media big data analytics.We explain why social media data are significant elements of the improved data-driven decision-making process.We propose and build the"Sunflower Model of Big Data"to define big data and bring it up to date with technology by combining 5 V’s and 10 Bigs.We discover the top ten social data analytics to work in the domain of social media platforms.A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work."Text Analytics"is the most used analytics in social data analysis to date.We create a taxonomy on social media analytics to meet the need and provide a clear understanding.Tools,techniques,and supporting data type are also discussed in this research work.As a result,researchers will have an easier time deciding which social data analytics would best suit their needs.展开更多
基金supported by NSFC (10871076,10771011)SRFDP (20050574002)NKBRP (2005CB321902)
文摘In this article, we first investigate the operational properties of algebroid functions. Then we prove two uniqueness theorems for algebroid functions.
基金This research is partially supported by NIH,No.R15EB024283.
文摘Metal objects in X-ray computed tomography can cause severe artifacts.The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods.This paper proposes a projectiondomain algorithm to reduce the metal artifacts.In this algorithm,the unknowns are the metal-affected projections,while the objective function is set up in the image domain.The data fidelity term is not utilized in the objective function.The objective function of the proposed algorithm consists of two terms:the total variation of the metalremoved image and the energy of the negative-valued pixels in the image.After the metal-affected projections are modified,the final image is reconstructed via the filtered backprojection algorithm.The feasibility of the proposed algorithm has been verified by real experimental data.
文摘Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world.Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images,the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance.Consequently,this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques.We describe resources and techniques based on three visual feature extraction methods,namely calculation-,recognition-,and simulation-based.Additionally,we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow.
文摘The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies.This new era allows the consumer to directly connect with other individuals,business corporations,and the government.People are open to sharing opinions,views,and ideas on any topic in different formats out loud.This creates the opportunity to make the"Big Social Data"handy by implementing machine learning approaches and social data analytics.This study offers an overview of recent works in social media,data science,and machine learning to gain a wide perspective on social media big data analytics.We explain why social media data are significant elements of the improved data-driven decision-making process.We propose and build the"Sunflower Model of Big Data"to define big data and bring it up to date with technology by combining 5 V’s and 10 Bigs.We discover the top ten social data analytics to work in the domain of social media platforms.A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work."Text Analytics"is the most used analytics in social data analysis to date.We create a taxonomy on social media analytics to meet the need and provide a clear understanding.Tools,techniques,and supporting data type are also discussed in this research work.As a result,researchers will have an easier time deciding which social data analytics would best suit their needs.
