Wei Xu

Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly. Machine Learning for Multimedia Content Analysis introduces machine learning techniques that are particularly powerful and effective for modeling spatial, temporal structures of multimedia data and for accomplishing common tasks of multimedia content analysis. This book systematically covers these techniques in an intuitive fashion and demonstrates their applications through case studies. This volume uses a large number of figures to illustrate and visualize complex concepts, and provides insights into the characteristics of many algorithms through examinations of their loss functions and straightforward comparisons. Machine Learning for Multimedia Content Analysis is designed for an academic and professional audience. Researchers will find this book an invaluable tool for applying machine learning techniques to multimedia content analysis. This volume is also suitable for practitioners in industry.

This book presents advances in control technologies for efficient operation of the brushless doubly-fed induction generator (BDFIG). For robust and low-cost operation of BDFIGs, it is required to keep high-quality output voltage and eliminate the speed/position encoder under different loads and operation conditions. Some advanced control technologies, from the authors’ latest work on these topics, are presented to achieve this goal with simple and accurate texts, illustrations, and tables. The qualified outcomes obtained from this book assure the high-performance operation of BDFIGs and also give the readers a straight insight toward challenges in this research area in the future.

This book presents advances in control technologies for efficient operation of the brushless doubly-fed induction generator (BDFIG). For robust and low-cost operation of BDFIGs, it is required to keep high-quality output voltage and eliminate the speed/position encoder under different loads and operation conditions. Some advanced control technologies, from the authors’ latest work on these topics, are presented to achieve this goal with simple and accurate texts, illustrations, and tables. The qualified outcomes obtained from this book assure the high-performance operation of BDFIGs and also give the readers a straight insight toward challenges in this research area in the future.

Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly. Machine Learning for Multimedia Content Analysis introduces machine learning techniques that are particularly powerful and effective for modeling spatial, temporal structures of multimedia data and for accomplishing common tasks of multimedia content analysis. This book systematically covers these techniques in an intuitive fashion and demonstrates their applications through case studies. This volume uses a large number of figures to illustrate and visualize complex concepts, and provides insights into the characteristics of many algorithms through examinations of their loss functions and straightforward comparisons. Machine Learning for Multimedia Content Analysis is designed for an academic and professional audience. Researchers will find this book an invaluable tool for applying machine learning techniques to multimedia content analysis. This volume is also suitable for practitioners in industry.

This book explores the dynamic landscape of language in Macau, focusing on how multilingualism shapes and is shaped by the city's educational, social, and economic transformations. Drawing on research, case studies, and lived experiences, the authors examine the interplay between language policy, classroom practice, and community life in one of Asia's most linguistically diverse urban centers. The volume offers insights into the challenges and opportunities of multilingual education, the impact of recent global events, and the evolving roles of Cantonese, Mandarin, Portuguese, and English in Macau's development. Bridging theory and practice, the book provides educators, policymakers, and researchers with a nuanced understanding of applied linguistics in action within a rapidly changing society.