Hong Qin – författare
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11 produkter
11 produkter
Häftad, Engelska, 2019
967 kr
Skickas inom 10-15 vardagar
From Design Methods and Generation Schemes to State-of-the-Art ApplicationsWavelets are powerful tools for functional analysis and geometry processing, enabling researchers to determine the structure of data and analyze 3D shapes. Suitable for researchers in computer graphics, computer vision, visualization, medical imaging, and geometric modeling as well as graduate and senior undergraduate students in computer science, Diffusion-Driven Wavelet Design for Shape Analysis presents recent research results in wavelet designs on 3D shapes and their applications in shape analysis. It explains how to apply the design methods to various types of 3D data, such as polygonal meshes, point clouds, manifolds, and volumetric images.Extensions of Wavelet Generation on Volumetric and Manifold DataThe first part of the book introduces design methods of wavelets on manifold data, incorporating interdisciplinary knowledge from differential geometry, functional analysis, Fourier transform, spectral graph theory, and stochastic processes. The authors show how wavelets are purely determined by the shape geometry and how wavelet transforms are computed as inner products of wavelet kernels and input functions.Wavelets for Solving Computer Graphics ProblemsThe second part presents applications in shape analysis/representation. The book looks at wavelets as spectral tools for geometry processing with filters in a joint space-frequency domain and examines wavelets as detail extractors for shape feature definition and detection. Going beyond these fundamental applications, the book also covers middle- and high-level applications, including shape matching, shape registration, and shape retrieval.Easy-to-Understand Implementations and AlgorithmsUnlike many other wavelet books, this one does not involve complicated mathematics. Instead, the book uses simplified formulations and illustrative example
Inbunden, Engelska, 2026
1 679 kr
Skickas inom 10-15 vardagar
The volume explores a wide-range of applications of computer vision in the field of healthcare. Computer vision, an interdisciplinary field that combines image processing, pattern recognition, and artificial intelligence, has the potential to revolutionize healthcare by enabling automated analysis and interpretation of medical images and videos. This chapter will provide an overview of the current advancements and potential future directions of computer vision applications in healthcare. Specifically, the book examines the application of computer vision techniques in various healthcare domains, including diagnostic imaging, surgical interventions, clinical decision support, and healthcare management. It reviews the challenges and limitations in implementing computer vision systems in real-world healthcare settings and discusses potential solutions.Overall, this book aims to shed light on the current and potential applications of computer vision in healthcare. It will summarize the state-of-the-art techniques, their benefits, challenges, and implications, while also discussing potential ethical considerations. The objective is to provide healthcare professionals, researchers, and practitioners with an understanding of the transformative impact of computer vision in the healthcare sector and inspire further research and development in this exciting field.
E-bok
Engelska, 20141 143 kr
Läs direkt efter köp
From Design Methods and Generation Schemes to State-of-the-Art ApplicationsWavelets are powerful tools for functional analysis and geometry processing, enabling researchers to determine the structure of data and analyze 3D shapes. Suitable for researchers in computer graphics, computer vision, visualization, medical imaging, and geometric modeling
E-bok
PDF, Engelska, 20261 921 kr
Läs direkt efter köp
The volume explores a wide-range of applications of computer vision in the field of healthcare. Computer vision, an interdisciplinary field that combines image processing, pattern recognition, and artificial intelligence, has the potential to revolutionize healthcare by enabling automated analysis and interpretation of medical images and videos. This chapter will provide an overview of the current advancements and potential future directions of computer vision applications in healthcare. Specifically, the book examines the application of computer vision techniques in various healthcare domains, including diagnostic imaging, surgical interventions, clinical decision support, and healthcare management. It reviews the challenges and limitations in implementing computer vision systems in real-world healthcare settings and discusses potential solutions.Overall, this book aims to shed light on the current and potential applications of computer vision in healthcare. It will summarize the state-of-the-art techniques, their benefits, challenges, and implications, while also discussing potential ethical considerations. The objective is to provide healthcare professionals, researchers, and practitioners with an understanding of the transformative impact of computer vision in the healthcare sector and inspire further research and development in this exciting field.
E-bok
Engelska, 20261 991 kr
Läs direkt efter köp
The volume explores a wide-range of applications of computer vision in the field of healthcare. Computer vision, an interdisciplinary field that combines image processing, pattern recognition, and artificial intelligence, has the potential to revolutionize healthcare by enabling automated analysis and interpretation of medical images and videos. This chapter will provide an overview of the current advancements and potential future directions of computer vision applications in healthcare. Specifically, the book examines the application of computer vision techniques in various healthcare domains, including diagnostic imaging, surgical interventions, clinical decision support, and healthcare management. It reviews the challenges and limitations in implementing computer vision systems in real-world healthcare settings and discusses potential solutions.Overall, this book aims to shed light on the current and potential applications of computer vision in healthcare. It will summarize the state-of-the-art techniques, their benefits, challenges, and implications, while also discussing potential ethical considerations. The objective is to provide healthcare professionals, researchers, and practitioners with an understanding of the transformative impact of computer vision in the healthcare sector and inspire further research and development in this exciting field.
Inbunden, Engelska, 2014
1 127 kr
Skickas inom 10-15 vardagar
From Design Methods and Generation Schemes to State-of-the-Art ApplicationsWavelets are powerful tools for functional analysis and geometry processing, enabling researchers to determine the structure of data and analyze 3D shapes. Suitable for researchers in computer graphics, computer vision, visualization, medical imaging, and geometric modeling as well as graduate and senior undergraduate students in computer science, Diffusion-Driven Wavelet Design for Shape Analysis presents recent research results in wavelet designs on 3D shapes and their applications in shape analysis. It explains how to apply the design methods to various types of 3D data, such as polygonal meshes, point clouds, manifolds, and volumetric images.Extensions of Wavelet Generation on Volumetric and Manifold DataThe first part of the book introduces design methods of wavelets on manifold data, incorporating interdisciplinary knowledge from differential geometry, functional analysis, Fourier transform, spectral graph theory, and stochastic processes. The authors show how wavelets are purely determined by the shape geometry and how wavelet transforms are computed as inner products of wavelet kernels and input functions.Wavelets for Solving Computer Graphics ProblemsThe second part presents applications in shape analysis/representation. The book looks at wavelets as spectral tools for geometry processing with filters in a joint space-frequency domain and examines wavelets as detail extractors for shape feature definition and detection. Going beyond these fundamental applications, the book also covers middle- and high-level applications, including shape matching, shape registration, and shape retrieval.Easy-to-Understand Implementations and AlgorithmsUnlike many other wavelet books, this one does not involve complicated mathematics. Instead, the book uses simplified formulations and illustrative example
Del 22 - Springer Tracts on Transportation and Traffic
Applications of Computational Learning and IoT in Smart Road Transportation System
Inbunden, Engelska, 2025
2 160 kr
Skickas inom 5-8 vardagar
This book discusses machine learning and AI in real-time image processing for road transportation and traffic management. There is a growing need for affordable solutions that make use of cutting-edge technology like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The efficiency, sustainability, and safety of transport networks can be greatly increased by implementing an Internet of Things (IoT) and machine learning (ML)-based smart road transport system. Install sensors on roadways and intersections to gather data on traffic conditions in real time, such as vehicle density, speed, and flow. Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. This can lessen traffic, enhance overall traffic management, and optimize traffic signal timings. Vehicles equipped with Internet of Things devices can have their health monitored in real time. Parameters including lane changes, brake condition, tire pressure, and engine performance can all be monitored by sensors. Based on the gathered data, ML models are used to forecast probable maintenance problems. By scheduling preventive maintenance, failures can be avoided and overall road safety can be increased. Create a smartphone app that would enable drivers to locate parking spots in their area. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms. Integrate Internet of Things (IoT) sensors into fleet vehicles to monitor their performance, location, and fuel consumption. To maximize fleet efficiency, reduce fuel consumption, and plan routes more effectively, apply machine learning algorithms. Train ML models to forecast the quickest and most efficient routes with the help of historical data analysis. Route recommendations for drivers or fleet management systems can be constantly adjusted with real-time updates, which contain real-time data on road conditions, accidents, and construction. To guarantee smooth integration and efficient implementation, government organizations, transportation providers, and technology firms must work together.
E-bok
Engelska, 20252 840 kr
Läs direkt efter köp
This book discusses machine learning and AI in real-time image processing for road transportation and traffic management. There is a growing need for affordable solutions that make use of cutting-edge technology like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The efficiency, sustainability, and safety of transport networks can be greatly increased by implementing an Internet of Things (IoT) and machine learning (ML)-based smart road transport system. Install sensors on roadways and intersections to gather data on traffic conditions in real time, such as vehicle density, speed, and flow. Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. This can lessen traffic, enhance overall traffic management, and optimize traffic signal timings. Vehicles equipped with Internet of Things devices can have their health monitored in real time. Parameters including lane changes, brake condition, tire pressure, and engine performance can all be monitored by sensors. Based on the gathered data, ML models are used to forecast probable maintenance problems. By scheduling preventive maintenance, failures can be avoided and overall road safety can be increased. Create a smartphone app that would enable drivers to locate parking spots in their area. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms. Integrate Internet of Things (IoT) sensors into fleet vehicles to monitor their performance, location, and fuel consumption. To maximize fleet efficiency, reduce fuel consumption, and plan routes more effectively, apply machine learning algorithms. Train ML models to forecast the quickest and most efficient routes with the help of historical data analysis. Route recommendations for drivers or fleet management systems can be constantly adjusted with real-time updates, which contain real-time data on road conditions, accidents, and construction. To guarantee smooth integration and efficient implementation, government organizations, transportation providers, and technology firms must work together.
Del 22 - Springer Tracts on Transportation and Traffic
Applications of Computational Learning and IoT in Smart Road Transportation System
Häftad, Engelska, 2026
2 160 kr
Skickas inom 10-15 vardagar
This book discusses machine learning and AI in real-time image processing for road transportation and traffic management. There is a growing need for affordable solutions that make use of cutting-edge technology like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The efficiency, sustainability, and safety of transport networks can be greatly increased by implementing an Internet of Things (IoT) and machine learning (ML)-based smart road transport system. Install sensors on roadways and intersections to gather data on traffic conditions in real time, such as vehicle density, speed, and flow. Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. This can lessen traffic, enhance overall traffic management, and optimize traffic signal timings. Vehicles equipped with Internet of Things devices can have their health monitored in real time. Parameters including lane changes, brake condition, tire pressure, and engine performance can all be monitored by sensors. Based on the gathered data, ML models are used to forecast probable maintenance problems. By scheduling preventive maintenance, failures can be avoided and overall road safety can be increased. Create a smartphone app that would enable drivers to locate parking spots in their area. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms. Integrate Internet of Things (IoT) sensors into fleet vehicles to monitor their performance, location, and fuel consumption. To maximize fleet efficiency, reduce fuel consumption, and plan routes more effectively, apply machine learning algorithms. Train ML models to forecast the quickest and most efficient routes with the help of historical data analysis. Route recommendations for drivers or fleet management systems can be constantly adjusted with real-time updates, which contain real-time data on road conditions, accidents, and construction. To guarantee smooth integration and efficient implementation, government organizations, transportation providers, and technology firms must work together.
Häftad, Engelska, 2018
814 kr
Skickas inom 10-15 vardagar
The book provides necessary knowledge for readers interested in developing the theory of uniform experimental design. It discusses measures of uniformity, various construction methods of uniform designs, modeling techniques, design and modeling for experiments with mixtures, and the usefulness of the uniformity in block, factorial and supersaturated designs.Experimental design is an important branch of statistics with a long history, and is extremely useful in multi-factor experiments. Involving rich methodologies and various designs, it has played a key role in industry, technology, sciences and various other fields. A design that chooses experimental points uniformly scattered on the domain is known as uniform experimental design, and uniform experimental design can be regarded as a fractional factorial design with model uncertainty, a space-filling design for computer experiments, a robust design against the model specification, and a supersaturated design and can be applied to experiments with mixtures.
E-bok
Engelska, 20181 029 kr
Läs direkt efter köp
The book provides necessary knowledge for readers interested in developing the theory of uniform experimental design. It discusses measures of uniformity, various construction methods of uniform designs, modeling techniques, design and modeling for experiments with mixtures, and the usefulness of the uniformity in block, factorial and supersaturated designs.
Experimental design is an important branch of statistics with a long history, and is extremely useful in multi-factor experiments. Involving rich methodologies and various designs, it has played a key role in industry, technology, sciences and various other fields. A design that chooses experimental points uniformly scattered on the domain is known as uniform experimental design, and uniform experimental design can be regarded as a fractional factorial design with model uncertainty, a space-filling design for computer experiments, a robust design against the model specification, and a supersaturated design and can be applied to experiments with mixtures.