Bits, bytes, logic, RAM, CPUs, hard drives and SSD drives. Master the geeky acronyms and simplify computer hardware & terminology with ease. Computer hardware with all its technical jargon can be baffling, even for the moderately experienced user. This book is ideal for a computing course, whether in high school, college or first degree. Step-by-step, visual approach to help you quickly decode the jargon Plenty of full color, illustrated screenshots and photographs to help you Presented in an easy and simple to read format. This book looks at Computer fundamentals: logic gates, binary arithmetic, hexadecimal, and number base conversions Data compression and encryption Hardware components: CPUs, RAM, Hard Drives, Portable Drives, video cards memory cards, motherboards, and the BIOS Inside the CPU, CPU architecture, instructions sets, and the fetch execute cycle Data Storage: bits, bytes, kilo bytes, megabytes, giga bytes and tera bytes Computer ports: VGA, HDMI, DVI, USB 2&3, FireWire, RJ45 ethernet, eSATA and more Different types of computer: desktops, laptops, netbooks, tablets, hybrids and supercomputers Operating systems: process management, memory management, file management Computer Software: applications, system software Computer peripherals: laser and inkjet printers Types of computer networks, Network topologies, LANs, WANs, MANs, fibre optics and ethernet WiFi and Cellular internet connections The internet: email, the cloud, the world-wide web, and packet switching IP Addressing, web servers, DNS servers and DHCP servers, TCP/IP model, OSI model and more... Techniques are illustrated step-by-step using full color photography and screen prints throughout, together with concise, easy to follow text from an established expert in the field, provide a comprehensive guide to computer systems.

A Computer Science Tapestry is designed for use in a first course in computer science (CS1) that uses C++ as its programming language. This book covers basic concepts in programming, program design and computer science and gives students a good introduction to the C++ language. In the second edition, Astrachan has put more emphasis on object-oriented programming by introducing a graphics library and including a new chapter on object-oriented techniques. He has also added new case studies and design tips.

What happens at a library? How can you find a good book? What does a librarian do? Learn the answers to these questions and more in this book about all of the fun things you can do and learn at your own library!

Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming introduces computational problem solving as a vehicle of discovery in a wide variety of disciplines. With a principles-oriented introduction to computational thinking, the text provides a broader and deeper introduction to computer science than typical introductory programming books. Organized around interdisciplinary problem domains, rather than programming language features, each chapter guides students through increasingly sophisticated algorithmic and programming techniques. The author uses a spiral approach to introduce Python language features in increasingly complex contexts as the book progresses. The text places programming in the context of fundamental computer science principles, such as abstraction, efficiency, and algorithmic techniques, and offers overviews of fundamental topics that are traditionally put off until later courses. The book includes thirty well-developed independent projects that encourage students to explore questions across disciplinary boundaries. Each is motivated by a problem that students can investigate by developing algorithms and implementing them as Python programs. The book's accompanying website — http://discoverCS.denison.edu — includes sample code and data files, pointers for further exploration, errata, and links to Python language references. Containing over 600 homework exercises and over 300 integrated reflection questions, this textbook is appropriate for a first computer science course for computer science majors, an introductory scientific computing course or, at a slower pace, any introductory computer science course.

This book shows how the web-based PhysGL programming environment (http://physgl.org) can be used to teach and learn elementary mechanics (physics) using simple coding exercises. The book's theme is that the lessons encountered in such a course can be used to generate physics-based animations, providing students with compelling and self-made visuals to aid their learning. Topics presented are parallel to those found in a traditional physics text, making for straightforward integration into a typical lecture-based physics course. Users will appreciate the ease at which compelling OpenGL-based graphics and animations can be produced using PhysGL, as well as its clean, simple language constructs. The author argues that coding should be a standard part of lower-division STEM courses, and provides many anecdotal experiences and observations, that include observed benefits of the coding work.

The goal of visualization is the accurate, interactive, and intuitive presentation of data. Complex numerical simulations, high-resolution imaging devices and incre- ingly common environment-embedded sensors are the primary generators of m- sive data sets. Being able to derive scienti?c insight from data increasingly depends on having mathematical and perceptual models to provide the necessary foundation for effective data analysis and comprehension. The peer-reviewed state-of-the-art research papers included in this book focus on continuous data models, such as is common in medical imaging or computational modeling. From the viewpoint of a visualization scientist, we typically collaborate with an application scientist or engineer who needs to visually explore or study an object which is given by a set of sample points, which originally may or may not have been connected by a mesh. At some point, one generally employs low-order piecewise polynomial approximationsof an object, using one or several dependent functions. In order to have an understanding of a higher-dimensional geometrical “object” or function, ef?cient algorithms supporting real-time analysis and manipulation (- tation, zooming) are needed. Often, the data represents 3D or even time-varying 3D phenomena (such as medical data), and the access to different layers (slices) and structures (the underlying topology) comprising such data is needed.