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Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoring—guiding readers from the basics of rotating machines to the generation of knowledge using vibration signals Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.

Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Nowadays, tensors play a central role for the representation, mining, analysis, and fusion of multidimensional, multimodal, and heterogeneous big data in numerous fields. This set on Matrices and Tensors in Signal Processing aims at giving a self-contained and comprehensive presentation of various concepts and methods, starting from fundamental algebraic structures to advanced tensor-based applications, including recently developed tensor models and efficient algorithms for dimensionality reduction and parameter estimation. Although its title suggests an orientation towards signal processing, the results presented in this set will also be of use to readers interested in other disciplines. This first book provides an introduction to matrices and tensors of higher-order based on the structures of vector space and tensor space. Some standard algebraic structures are first described, with a focus on the hilbertian approach for signal representation, and function approximation based on Fourier series and orthogonal polynomial series. Matrices and hypermatrices associated with linear, bilinear and multilinear maps are more particularly studied. Some basic results are presented for block matrices. The notions of decomposition, rank, eigenvalue, singular value, and unfolding of a tensor are introduced, by emphasizing similarities and differences between matrices and tensors of higher-order.

The updated 2020 edition of the popular step-by-step tutorial for Revit Architecture Shortly after its first publication , Autodesk Revit for Architecture: No Experience Required quickly became the market-leading, real-world guide for learning and building with Revit—the powerful and sophisticated Building Information Modeling (BIM) software used by professionals the world over. Fully updated for Revit 2020, this popular, user-friendly book helps you learn the Revit interface, understand the fundamental concepts and features of the software, and design, document, and present a 3D BIM project. A continuous, step-by-step tutorial guides you through every phase of the project: from placing walls, doors, windows, structural elements, dimensions, and text, to generating documentation, advanced detailing, site grading, construction scheduling, material takeoffs, and much more. Updated and revised to include new content, this invaluable guide covers all the fundamental skills every Revit user needs. Whether used as a complete, start-to-finish lesson or as a quick-reference for unfamiliar tasks, this book will help you: Learn each phase of designing, documenting, and presenting a four-story office building using a simple yet engaging continuous tutorial Follow the tutorial sequentially or jump to any chapter by downloading the project files from the Sybex website Use the start-to-finish tutorial project as a reference for your own real-world projects and to develop a powerful Revit skillset Gain thorough knowledge of Revit’s essential concepts and features to make the move from 2D drafting to 3D building information modeling Get up to speed with advanced features, including new coverage of advanced walls, families, sites, topography, and more Autodesk Revit 2020 for Architecture No Experience Required is the go-to guide for both professionals and students seeking to learn Revit's essential functions quickly and effectively, to understand real workplace projects, processes, and workflows, and to set the stage for continuing on to more advanced skills.

Effectively manage business finances with Quickbooks If you need a guide for essential bookkeeping, QuickBooks Online For Dummies, UK Edition helps you get finances under control. This book offers guidance in the areas of budgeting, payroll, taxes, inventory, accounts and more. It assists small business owners, accountants and bookkeepers who could benefit from QuickBooks Online tutorials. It provides easy-to-follow and practical instruction using the QuickBooks cloud-based solution. Track and control finances alerts, reminders, and detailed reports for a clear view into the financial status of your business.