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Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs). Formulas for estimating the model according to the maximum likelihood criterion are derived from the EM algorithm. To segment an image, optimal classes are searched jointly for all the blocks by the maximum a posteriori (MAP) rule. The 2-D HMM is extended to multiresolution so that more context information is exploited in classification and fast progressive segmentation schemes can be formed naturally. The second issue addressed in the book is the design of joint compression and classification systems using the 2-D HMM and vector quantization. A classifier designed with the side goal of good compression often outperforms one aimed solely at classification because overfitting to training data is suppressed by vector quantization. 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China has been selected as a case of inquiry for multilingual crisis communication because of its high level of linguistic diversity. Taking up critical sociopolitical approaches, this book conceptualizes multilingual crisis communication from three dimensions: identifying communication barriers, engaging communication repertoires, and empowering communication justice.\u003c\/p\u003e\u003cp\u003eComprising eight main chapters, along with an introduction and an epilogue, this edited book is divided into three parts in terms of the demographic and social conditions of linguistic minorities, as indigenous, migrant, and those with communicative disabilities. This book brings together a range of critical perspectives of sociolinguistic scholars, language teachers, and public health workers. Each team of authors includes at least one member of the research community with many years of field work experience, and some of them belong to ethnic minorities. 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Focused on researchers, engineers, and students in materials science and energy storage fields, this book systematically covers the synthesis, properties, and applications of rGO, emphasizing its potential to revolutionize supercapacitor performance through enhanced energy density, capacitance, and stability. This book is crucial for those involved in energy storage research and development because it addresses the growing need for materials that can offer higher energy density and better cycle life in supercapacitors. By focusing on rGO, a material that combines the best properties of graphene and graphene oxide, the book provides a pathway to overcoming the limitations of current energy storage systems. 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