Yoshinori Mine

Bo Jiang

Eunice Li-Chan

Bioactive Proteins and Peptides as Functional Foods and Nutraceuticals highlights recent developments of nutraceutical proteins and peptides for the promotion of human health. The book considers fundamental concepts and structure-activity relations for the major classes of nutraceutical proteins and peptides. Coverage includes functional proteins and peptides from numerous sources including: soy, Pacific hake, bovine muscle, peas, wheat, fermented milk, eggs, casein, fish collagen, bovine lactoferrin, and rice. The international panel of experts from industry and academia also reviews current applications and future opportunities within the nutraceutical proteins and peptides sector.

Bioactive Proteins and Peptides as Functional Foods and Nutraceuticals

Yongfeng Yin

This book introduces embedded software engineering and management methods, proposing the relevant testing theory and techniques that promise the final realization of automated testing of embedded systems. The quality and reliability of embedded systems have become a great concern, faced with the rising demands for the complexity and scale of system hardware and software. The authors propose and expound on the testing theory and techniques of embedded software systems and relevant environment construction technologies, providing effective solutions for the automated testing of embedded systems. Through analyzing typical testing examples of the complex embedded software systems, the authors verify the effectiveness of the theories, technologies and methods proposed in the book. In combining the fundamental theory and technology and practical solutions, this book will appeal to researchers and students studying computer science, software engineering, and embedded systems, as well as professionals and practitioners engaged in the development, verification, and maintenance of embedded systems in the military and civilian fields.

Automatic Testing Solution Based on Formal Method

Embedded Software System Testing

Li M. Chen

Zhixun Su

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.

Bo Jiang

Books

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