Bøker utgitt av Springer Verlag, Singapore

Comprehensively discussing all aspects of drilling technologies, and providing abundant figures, illustrations and tables, examples and exercises to facilitate the learning process, it is a valuable resource for students, scholars and engineers in the field of petroleum engineering.

This open access book presents multidisciplinary research on the cultural history, ethnic connectivity, and oceanic transportation of the ancient Indigenous Bai Yue ( ) in the prehistoric maritime region of southeast China and southeast Asia.

Today, the development of robots is making steady advances. In particular, the Robot Operating System (ROS) offers a unified platform that greatly facilitates the development of robots and has become a new hotspot for learning and application in the field of robotics research.This book introduces readers to the key technologies and development methods for ROS-based intelligent robots. Covering both the development history of robots and various aspects of programming robots, it offers effective support for beginners.The book is divided into three parts, the first of which introduces the basics of robots, including their definition, development, composition, and key technologies. In turn, the second part covers the hardware and software components and the implementation of functions such as vision, speech, grasping, and autonomous navigation. These functions need to work together to provide user-friendlier and more intelligent service. The third part shows how to develop robots with different functions in different application scenarios.Combining theoretical and practical aspects, with a strong focus on application, this work can be used as a reference book for robotics-related courses. Moreover, it will benefit all readers who are interested in intelligent robot development, sharing essential insights into developing service robots based on ROS.

This is a popular science book surrounding astronauts¿ life on the space station. The book not only highlights the weightlessness experience of astronauts and the extraordinary phenomena they witness, but also illustrates the physics behind these events, which opens a new window for readers to explore the outer space. This book is especially fun for those who are curious about the life details of astronauts on the space station. The book is based on real events, and features images and cartoons that vividly depict unusual scenes between the outer space and the earth. Physical principles become easier to understand with these visual aids. During the reading, readers can immerse themselves in the enjoyable adventure of space travel and the strange feeling of weightlessness while having their doubts of every oddity solved. The book elaborates on the interesting contrast between the space and the earth, and provides readers with a stunning new perspective with easily comprehensible language and examples.

This textbook offers advanced content on computer vision (basic content can be found in its prerequisite textbook, ¿2D Computer Vision: Principles, Algorithms and Applications¿), including the basic principles, typical methods and practical techniques. It is intended for graduate courses on related topics, e.g. Computer Vision, 3-D Computer Vision, Graphics, Artificial Intelligence, etc. The book is mainly based on my lecture notes for several undergraduate and graduate classes I have offered over the past several years, while a number of topics stem from my research publications co-authored with my students. This book takes into account the needs of learners with various professional backgrounds, as well as those of self-learners. Furthermore, it can be used as a reference guide for practitioners and professionals in related fields. To aid in comprehension, the book includes a wealth of self-test questions (with hints and answers). On the one hand, these questions help teachers to carry out online teaching and interact with students during lectures; on the other, self-learners can use them to assess whether they have grasped the key content.

This book provides insights into deep learning techniques that impact the implementation strategies toward achieving the Sustainable Development Goals (SDGs) laid down by the United Nations for its 2030 agenda, elaborating on the promises, limits, and the new challenges. It also covers the challenges, hurdles, and opportunities in various applications of deep learning for the SDGs. A comprehensive survey on the major applications and research, based on deep learning techniques focused on SDGs through speech and image processing, IoT, security, AR-VR, formal methods, and blockchain, is a feature of this book. In particular, there is a need to extend research into deep learning and its broader application to many sectors and to assess its impact on achieving the SDGs. The chapters in this book help in finding the use of deep learning across all sections of SDGs. The rapid development of deep learning needs to be supported by the organizational insight and oversight necessary for AI-based technologies in general; hence, this book presents and discusses the implications of how deep learning enables the delivery agenda for sustainable development.

In 2021, the University Grants Committee of Hong Kong, the funding body for higher education in Hong Kong, initiated a scheme and associated grant fund with the aim of enhancing the use of technology for teaching in higher education institutions in Hong Kong. In the Faculty of Social Sciences, Hong Kong Baptist University, the funding was used to support colleagues in various disciplines to develop teaching and learning projects that capitalized on technology to improve the educational experiences of students. In this book, seven project teams from five disciplines, Education, Geography, History, Social Work, and Sociology, share their technological innovations. Each chapter presents the design, implementation, challenges, benefits, and impact on student learning and experiences of each innovative project. Lecturers, professors and curriculum designers engaged in teaching and learning will find this book an invaluable resource as it provides ways to integrate technology into their teaching practices. Scholars of teaching and learning (SoTL) will also find the book a useful reference for up-to-date technological and pedagogical practices in the social science disciplines.

This book introduces basic concepts, principle, and methods of medical statistics systematically and practically, especially in the statistical design of the experiment in terms of the specific problems, adequate use of statistical methods based on actual data and the reasonable explanation for statistical results.This textbook combines statistical methods with the common application of SPSS software, which is flexible, convenient, and user-friendly; thus, students can focus on the deep understanding of statistics.The authors emphasize the application and generalization of statistical methods, and combine these methods with the modern statistical theory, such as sequential contingency table and multivariate statistical modelling, etc.This book is a useful textbook for graduate and undergraduate students in medical schools, including MBBS (Bachelor of Medicine and Bachelor of Surgery) student.

This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.