Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation.

Due to the increasing availability of affordable internet services, the number of users, and the need for a wider range of multimedia-based applications, internet usage is on the rise. With so many users and such a large amount of data, the requirements of analyzing large data sets leads to the need for further advancements to information processing. Big Data Processing with Hadoop is an essential reference source that discusses possible solutions for millions of users working with a variety of data applications, who expect fast turnaround responses, but encounter issues with processing data at the rate it comes in. Featuring research on topics such as market basket analytics, scheduler load simulator, and writing YARN applications, this book is ideally designed for IoT professionals, students, and engineers seeking coverage on many of the real-world challenges regarding big data.

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.

Changing business environments and information technology advancements fundamentally reshaped the traditional information landscape in our contemporary society, urging companies to seek innovative ways to diffuse and manage assets on a global scale. It is crucial for society to understand the new methodologies and common practices that organizations can utilize to leverage their knowledge into practice. Global Information Diffusion and Management in Contemporary Society is an essential reference source featuring research on the development and implementation of contemporary global information management initiatives in organizations. Including coverage on a multitude of topics such as data security, global manufacturing, and information governance, this book explores the importance of information management in a global context. This book is ideally designed for managers, information systems specialists, professionals, researchers, and administrators seeking current research on the theories and applications of global information management.

Acoustics: Sound Fields, Transducers and Vibration, Second Edition guides readers through the basics of sound fields, the laws governing sound generation, radiation, and propagation, and general terminology. Specific sections cover microphones (electromagnetic, electrostatic, and ribbon), earphones, and horns, loudspeaker enclosures, baffles and transmission lines, miniature applications (e.g. MEMS microphones and micro speakers in tablets and smart phones), sound in enclosures of all sizes, such as school rooms, offices, auditoriums and living rooms, and fluid-structure interaction. Numerical examples and summary charts are given throughout the text to make the material easily applicable to practical design. New to this edition: A chapter on electrostatic loudspeakers A chapter on vibrating surfaces (membranes, plates, and shells) Readers will find this to be a valuable resource for experimenters, acoustical consultants, and to those who anticipate being engineering designers of audio equipment. It will serve as both a text for students in engineering departments and as a valuable reference for practicing engineers.

Businesses have had to face many challenges due to the COVID-19 pandemic; to survive in the changing landscape, they had to adapt quickly and implement new tactics and best practices to stay competitive. Networking is one of the many areas that looks vastly different in a post-pandemic world and companies must understand this change or risk falling behind. Further study is required to uncover the various difficulties and potential future directions of networking and innovation within the business landscape. The Handbook of Research on Digital Innovation and Networking in Post-COVID-19 Organizations provides a thorough overview of the ways in which organizations have had to change and adapt to the new business environments and considers how networking looks different in a post-COVID-19 world. Covering key topics such as organizational structures, consumer behavior, teleworking, and collaborations, this major reference work is ideal for managers, business owners, industry professionals, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications covers timely topics, including the neural network (NN), particle swarm optimization (PSO), evolutionary algorithm (GA), fuzzy sets (FS) and rough sets (RS), etc. Furthermore, the book highlights recent research on representative techniques to elaborate how a data-centric system formed a powerful platform for the processing of cloud hosted multimedia big data and how it could be analyzed, processed and characterized by CI. The book also provides a view on how techniques in CI can offer solutions in modeling, relationship pattern recognition, clustering and other problems in bioengineering. It is written for domain experts and developers who want to understand and explore the application of computational intelligence aspects (opportunities and challenges) for design and development of a data-centric system in the context of multimedia cloud, big data era and its related applications, such as smarter healthcare, homeland security, traffic control trading analysis and telecom, etc. Researchers and PhD students exploring the significance of data centric systems in the next paradigm of computing will find this book extremely useful.