This book is intended to be used as a textbook in undergraduate civil engineering and construction courses to introduce cutting edge mechanical, electrical, and computer science topics that are needed for civil and construction engineers to collaborate in inter-disciplinary automation projects.Part I introduces the basics of hardware and software technologies that are needed for implementing automation in buildings and construction. The content begins with the fundamental concepts ...

Computers are ubiquitous throughout all life-cycle stages of engineering, from conceptual design to manufacturing maintenance, repair and replacement. It is essential for all engineers to be aware of the knowledge behind computer-based tools and techniques they are likely to encounter. The computational technology, which allows engineers to carry out design, modelling, visualisation, manufacturing, construction and management of products and infrastructure is known as Computer-Aided Engine...

Geographic information systems (GIS) have become increasingly important in helping us understand complex social, economic, and natural dynamics where spatial components play a key role. The critical algorithms used in GIS, however, are notoriously difficult to both teach and understand, in part due to the lack of a coherent representation. GIS Algorithms attempts to address this problem by combining rigorous formal language with example case studies and student exercises.

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.Key FeaturesSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningGet to know the best practices to improve and optimize you...

A practical text suitable for an introductory or advanced course in formal methods, this book presents a mathematical approach to modelling and designing systems using an extension of the B formal method: Event-B. Based on the idea of refinement, the author's systematic approach allows the user to construct models gradually and to facilitate a systematic reasoning method by means of proofs. Readers will learn how to build models of programs and, more generally, discrete systems, but this i...

This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard has been designed as a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions.Divided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providing high-performance data analytics on c...

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guideKey Features\[\*\] Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.\[\*\] Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.\[\*\] Get a solid foundation for your entr...

Many systems, devices and appliances used routinely in everyday life, ranging from cell phones to cars, contain significant amounts of software that is not directly visible to the user and is therefore called "embedded". For coordinating the various software components and allowing them to communicate with each other, support software is needed, called an operating system (OS). Because embedded software must function in real time (RT), a RTOS is needed. This book describes a formally devel...

Computation in Science provides a theoretical background in computation to scientists who use computational methods. It explains how computing is used in the natural sciences, and provides a high-level overview of those aspects of computer science and software engineering that are most relevant for computational science. The focus is on concepts, results, and applications, rather than on proofs and derivations.The unique feature of this book is that it connects the dots be...

Build, implement and scale distributed deep learning models for large-scale datasets Key FeaturesGet to grips with the deep learning concepts and set up Hadoop to put them to useImplement and parallelize deep learning models on Hadoop’s YARN frameworkA comprehensive tutorial to distributed deep learning with HadoopBook DescriptionThis book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop fo...

Make the most of the common architectures used for deriving meaningful data from sensors. This book provides you with the tools to understand how sensor data is converted into actionable knowledge and provides tips for in-depth work in this field.Making Sense of Sensors starts with an overview of the general pipeline to extract meaningful data from sensors. It then dives deeper into some commonly used sensors and algorithms designed for knowledge extraction. Practical exam...

Get up and running with machine learning with F\# in a fun and functional wayKey FeaturesDesign algorithms in F\# to tackle complex computing problemsBe a proficient F\# data scientist using this simple-to-follow guideSolve real-world, data-related problems with robust statistical models, built for a range of datasetsBook DescriptionThe F\# functional programming language enables developers to write simple code to solve compl...