Being an inter-disciplinary subject, Signal Processing has application in almost all scientific fields. Applied Signal Processing tries to link between the analog and digital signal processing domains. Since the digital signal processing techniques have evolved from its analog counterpart, this book begins by explaining the fundamental concepts in analog signal processing and then progresses towards the digital signal processing. This will help the reader to gain a general overview of the whole subject and establish links between the various fundamental concepts. While the focus of this book is on the fundamentals of signal processing, the understanding of these topics greatly enhances the confident use as well as further development of the design and analysis of digital systems for various engineering and medical applications. Applied Signal Processing also prepares readers to further their knowledge in advanced topics within the field of signal processing.

During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines.

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.

Systems Performance, Second Edition, covers concepts, strategy, tools, and tuning for operating systems and applications, using Linux-based operating systems as the primary example. A deep understanding of these tools and techniques is critical for developers today. Implementing the strategies described in this thoroughly revised and updated edition can lead to a better end-user experience and lower costs, especially for cloud computing environments that charge by the OS instance. Systems performance expert and best-selling author Brendan Gregg summarizes relevant operating system, hardware, and application theory to quickly get professionals up to speed even if they have never analyzed performance before. Gregg then provides in-depth explanations of the latest tools and techniques, including extended BPF, and shows how to get the most out of cloud, web, and large-scale enterprise systems. Key topics covered include Hardware, kernel, and application internals, and how they perform Methodologies for rapid performance analysis of complex systems Optimizing CPU, memory, file system, disk, and networking usage Sophisticated profiling and tracing with perf, Ftrace, and BPF (BCC and bpftrace) Performance challenges associated with cloud computing hypervisors Benchmarking more effectively Featuring up-to-date coverage of Linux operating systems and environments, Systems Performance, Second Edition, also addresses issues that apply to any computer system. The book will be a go-to reference for many years to come and, like the first edition, required reading at leading tech companies. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

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.