MATLAB can be used to execute many mathematical and engineering calculations, as well as a handheld computer can-if not better. Moreover, like many other computer languages, it can perform tasks that a handheld computer cannot. Compared to other computer languages, MATLAB provides many built-in functions that make learning easier and reduce prototy
This practical book presents fundamental concepts and issues in computer modeling and simulation (M&S) in a simple and practical way for engineers, scientists, and managers who wish to apply simulation successfully to their real-world problems. It offers a concise approach to the coverage of generic (tool-independent) M&S concepts and enables engineering practitioners to easily learn, evaluate, and apply various available simulation concepts. Worked out examples are included to illustrate the concepts and an example modeling application is continued throughout the chapters to demonstrate the techniques. The book discusses modeling purposes, scoping a model, levels of modeling abstraction, the benefits and cost of including randomness, types of simulation, and statistical techniques. It also includes a chapter on modeling and simulation projects and how to conduct them for customer and engineer benefit and covers the stages of a modeling and simulation study, including process and system investigation, data collection, modeling scoping and production, model verification and validation, experimentation, and analysis of results.
This practical book presents fundamental concepts and issues in computer modeling and simulation (M&S) in a simple and practical way for engineers, scientists, and managers who wish to apply simulation successfully to their real-world problems. It offers a concise approach to the coverage of generic (tool-independent) M&S concepts and enables engineering practitioners to easily learn, evaluate, and apply various available simulation concepts. Worked out examples are included to illustrate the concepts and an example modeling application is continued throughout the chapters to demonstrate the techniques. The book discusses modeling purposes, scoping a model, levels of modeling abstraction, the benefits and cost of including randomness, types of simulation, and statistical techniques. It also includes a chapter on modeling and simulation projects and how to conduct them for customer and engineer benefit and covers the stages of a modeling and simulation study, including process and system investigation, data collection, modeling scoping and production, model verification and validation, experimentation, and analysis of results.
The traditional computer science courses for engineering focus on the fundamentals of programming without demonstrating the wide array of practical applications for fields outside of computer science. Thus, the mindset of “Java/Python is for computer science people or programmers, and MATLAB is for engineering” develops. MATLAB tends to dominate the engineering space because it is viewed as a batteries-included software kit that is focused on functional programming. Everything in MATLAB is some sort of array, and it lends itself to engineering integration with its toolkits like Simulink and other add-ins. The downside of MATLAB is that it is proprietary software, the license is expensive to purchase, and it is more limited than Python for doing tasks besides calculating or data capturing. This book is about the Python programming language. Specifically, it is about Python in the context of mechanical and aerospace engineering. Did you know that Python can be used to model a satellite orbiting the Earth? You can find the completed programs and a very helpful 595 page NSA Python tutorial at the book’s GitHub page at https://www.github.com/alexkenan/pymae. Read more about the book, including a sample part of Chapter 5, at https://pymae.github.io
This book offers a practical approach to understanding, designing, and building sound software based on solid principles. Using a unique Q&A format, this book addresses the issues that engineers need to understand in order to successfully work with software engineers, develop specifications for quality software, and learn the basics of the most common programming languages, development approaches, and paradigms. The new edition is thoroughly updated to improve the pedagogical flow and emphasize new software engineering processes, practices, and tools that have emerged in every software engineering area. Features: Defines concepts and processes of software and software development, such as agile processes, requirements engineering, and software architecture, design, and construction. Uncovers and answers various misconceptions about the software development process and presents an up-to-date reflection on the state of practice in the industry. Details how non-software engineers can better communicate their needs to software engineers and more effectively participate in design and testing to ultimately lower software development and maintenance costs. Helps answer the question: How can I better leverage embedded software in my design? Adds new chapters and sections on software architecture, software engineering and systems, and software engineering and disruptive technologies, as well as information on cybersecurity. Features new appendices that describe a sample automation system, covering software requirements, architecture, and design. This book is aimed at a wide range of engineers across many disciplines who work with software.
Accessibility is a core quality of digital products to be deliberately addressed throughout the development lifecycle. What Every Engineer Should Know About Digital Accessibility will prepare readers to integrate digital accessibility into their engineering practices. Readers will learn how to accurately frame accessibility as an engineering challenge so they are able to address the correct problems in the correct way. Illustrated with diverse perspectives from accessibility practitioners and advocates, this book describes how people with disabilities use technology, the nature of accessibility barriers in the digital world, and the role of engineers in breaking down those barriers. Accessibility competence for current, emerging, and future technologies is addressed through a combination of guiding principles, core attributes and requirements, and accessibility‐informed engineering practices. FEATURES Discusses how technology can support inclusion for people with disabilities and how rigorous engineering processes help create quality user experiences without introducing accessibility barriers Explains foundational principles and guidelines that build core competency in digital accessibility as they are applied across diverse and emerging technology platforms Highlights practical insights into how engineering teams can effectively address accessibility throughout the technology development lifecycle Uses international standards to define and measure accessibility quality Written to be accessible to non‐experts in the subject area, What Every Engineer Should Know About Digital Accessibility is aimed at students, professionals, and researchers in the field of software engineering.
Understanding the powerful computational and graphics capabilities of Microsoft Excel is an enormous benefit to engineers and technical professionals in almost any field and at all levels of experience. What Every Engineer Should Know About Excel is a practical guide to unlocking the features and functions of this program, using examples and screenshots to walk readers through the steps to build a strong understanding of the material. This second edition is updated to reflect the latest version of Excel (2016) and expands its scope to include data management, connectivity to external data sources, and integration with "the cloud" for optimal use of the Excel product. It also introduces the ribbon bar navigation prevalent in Microsoft products beginning with the 2007 version of MS Office. Covering a variety of topics in self-contained chapters, this handy guide will also prove useful for professionals in IT, finance, and real estate.
What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important machine learning approaches and concepts that can be exploited to build models to enable decision making in different domains. Utilizes practical examples from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making Introduces various approaches to build models that exploits different algorithms Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets Explores the augmentation of technical and mathematical materials with explanatory worked examples Includes a glossary, self-assessments, and worked-out practice exercises Written to be accessible to non-experts in the subject, this comprehensive introductory text is suitable for students, professionals, and researchers in engineering and data science.
Internet of Things (IoT) products and cyber-physical systems (CPS) are being utilized in almost every discipline and there continues to be significant increases in spending on design, development, and deployment of IoT applications and analytics within every domain, from our homes, schools, government, and industry. This practical text provides an introduction to IoT that can be understood by every engineering discipline and discusses detailed applications of IoT. Developed to help engineers navigate this increasingly important and cross-disciplinary topic, this work: Offers research-based examples and case studies to facilitate the understanding of each IoT primitive Highlights IoT’s connection to blockchain Provides and understanding of benefits and challenges of IoT and its importance to a variety of engineering disciplines Written to be accessible to non-experts in the subject, What Every Engineer Should Know About the Internet of Things communicates the importance of this technology and how it can support and challenge all interrelated actors as well as all involved assets across many domains.
Completely updated, this new edition uniquely explains how to assess and handle technical risk, schedule risk, and cost risk efficiently and effectively for complex systems that include Artificial Intelligence, Machine Learning, and Deep Learning. It enables engineering professionals to anticipate failures and highlight opportunities to turn failure into success through the systematic application of Risk Engineering. What Every Engineer Should Know About Risk Engineering and Management, Second Edition discusses Risk Engineering and how to deal with System Complexity and Engineering Dynamics, as it highlights how AI can present new and unique ways that failures can take place. The new edition extends the term "Risk Engineering" introduced by the first edition, to Complex Systems in the new edition. The book also relates Decision Tree which was explored in the first edition to Fault Diagnosis in the new edition and introduces new chapters on System Complexity, AI, and Causal Risk Assessment along with other chapter updates to make the book current. Features Discusses Risk Engineering and how to deal with System Complexity and Engineering Dynamics Highlights how AI can present new and unique ways of failure that need to be addressed Extends the term "Risk Engineering" introduced by the first edition to Complex Systems in this new edition Relates Decision Tree which was explored in the first edition to Fault Diagnosis in the new edition Includes new chapters on System Complexity, AI, and Causal Risk Assessment along with other chapters being updated to make the book more current The audience is the beginner with no background in Risk Engineering and can be used by new practitioners, undergraduates, and first-year graduate students.
