Digital library
Textbooks, references and open learning resources.
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Operational Amplifiers & Linear Integrated Circuits: Theory and Application
James Fiore · dissidents · 2018
The goal of this text, as its name implies, is to allow the reader to become proficient in the analysis and design of circuits utilizing modern linear ICs. It progresses from the fundamental circuit building blocks through to analog/digital conversion systems. The text is intended for use in a second year Operational Amplifiers course at the Associate level, or for a junior level course at the Baccalaureate level. In order to make effective use of this text, students should have already taken a course in basic discrete transistor circuits, and have a solid background in algebra and trigonometry, along with exposure to phasors. Calculus is used in certain sections of the text, but for the most part, its use is kept to a minimum. For students without a calculus background, these sections may be skipped without a loss of continuity. (The sole exception to this being Chapter Ten, Integrators and Differentiators, which hinges upon knowledge of calculus.) There is also a lab manual for this textbook.
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Semiconductor Devices: Theory and Application
James Fiore · dissidents · 2018
The goal of this text, as its name implies, is to allow the reader to become proficient in the analysis and design of circuits utilizing discrete semiconductor devices. It progresses from basic diodes through bipolar and field effect transistors. The text is intended for use in a first or second year course on semiconductors at the Associate or Baccalaureate level. In order to make effective use of this text, students should have already taken coursework in basic DC and AC circuits, and have a solid background in algebra and trigonometry along with exposure to phasors. Calculus is used in certain sections of the text but for the most part it is used for equation derivations and proofs, and is kept to a minimum. For students without a calculus background these sections may be skipped without a loss of continuity. There is also a lab manual for this textbook.
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Fundamentals of Electrical Engineering I
Don Johnson · OpenStax CNX · 2014
The course focuses on the creation, manipulation, transmission, and reception of information by electronic means. Elementary signal theory; time- and frequency-domain analysis; Sampling Theorem. Digital information theory; digital transmission of analog signals; error-correcting codes.
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Basics of Fluid Mechanics
Genick Bar-Meir · Potto Project · 2025
The topic of fluid mechanics is common to several disciplines: mechanical engineering, aerospace engineering, chemical engineering, and civil engineering. In fact, it is also related to disciplines like industrial engineering, and electrical engineering. While the emphasis is somewhat different in this book, the common material is presented and hopefully can be used by all. One can only admire the wonderful advances done by the previous geniuses who work in this field. In this book it is hoped to insert, what and when a certain model is suitable than other models. One of the difference in this book is the insertion of the introduction to multiphase flow. Clearly, multiphase is an advance topic. However, some minimal familiarity can be helpful for many engineers who have to deal with non pure single phase fluid. This book is the third book in the series of POTTO project books. POTTO project books are open content textbooks so everyone are welcome to joint in. The topic of fluid mechanics was chosen just to fill the introduction chapter to compressible flow. During the writing it became apparent that it should be a book in its own right. In writing the chapter on fluid statics, there was a realization that it is the best chapter written on this topic. It is hoped that the other chapters will be as good this one. This book is written in the spirit of my adviser and mentor E.R.G. Eckert. Eckert, aside from his research activity, wrote the book that brought a revolution in the education of the heat transfer. Up to Egret's book, the study of heat transfer was without any dimensional analysis. He wrote his book because he realized that the dimensional analysis utilized by him and his adviser (for the post doc), Ernst Schmidt, and their colleagues, must be taught in engineering classes. His book met strong criticism in which some called to "burn" his book. Today, however, there is no known place in world that does not teach according to Eckert's doctrine. It is assumed that
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Experimental Design and Technical Writing for Engineers
Susan Ely · University of Southern Indiana · 2024
This text is intended for undergraduate engineering students completing a technical writing course. As engineers complete technical writing in conjunction with research activities, this work also includes a basic overview of experimental design and the implications of robust quantitative methodologies. Hallmarks of quantitative research, best practices in presentation of data and a brief description of the use of descriptive statistics in data analysis are also provided. Technical communications for engineers often include industry related documents like memos, executive summaries, case studies, quality and process documents, standard operating procedures and requests for proposals. These documents are discussed, in addition to formal technical reports. The text also includes best practices in engineering oral presentations and the impact of Artificial Intelligence on technical communications. Ethical implications of engineering communications are also discussed from a framework of professional integrity as defined by the National Society of Professional Engineers Code of Ethics. Students using this text should be capable of communicating, in both verbal and written formats, in a way that clearly, concisely and correctly conveys engineering principles, projects and concepts to both technical and nontechnical audiences.
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ReStorying Education: Critical Perspectives in Public Education
Thor Gibbins; Ed Beck; Kjersti VanSlyke-Briggs · SUNY Oneonta · 2024
ReStorying Education: Critical Perspectives in Public Education offers a foundational exploration of public education in the United States using different perspectives and critical analysis of education and educational policy. From showcasing how popular media creates narratives about teachers and school to a case study of the education system outside the United States, this text offers multiple viewpoints and purposes of education to give readers studying education a multi-faceted story of the social, political, and personal purposes of public education. Each chapter details a restorying of key aspects of education critical to developing a more nuanced understanding of public education: popular media narratives, historical, curricular, philosophical, and political.
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Qualitative Research – a practical guide for health and social care researchers and practitioners
Darshini Ayton; Tess Tsindos; Danielle Berkovic · Monash University · 2023
This guide is designed to support health and social care researchers and practitioners to integrate qualitative research into the evidence base of health and social care research. Qualitative research designs are diverse and each design has a different focus that will inform the approach undertaken and the results that are generated. The aim is to move beyond the “what” of qualitative research to the “how”, by (1) outlining key qualitative research designs for health and social care research – descriptive, phenomenology, action research, case study, ethnography, and grounded theory; (2) a decision tool of how to select the appropriate design based on a guiding prompting question, the research question and available resources, time and expertise; (3) an overview of mixed methods research and qualitative research in evaluation studies; (4) a practical guide to data collection and analysis; (5) providing examples of qualitative research to illustrate the scope and opportunities; and (6) tips on communicating qualitative research.
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Oregon Writes Open Writing Text
Jenn Kepka · Open Oregon Educational Resources · 2018
This textbook guides students through rhetorical and assignment analysis, the writing process, researching, citing, rhetorical modes, and critical reading. Using accessible but rigorous readings by professionals throughout the college composition field, the Oregon Writes Writing Textbook aligns directly to the statewide writing outcomes for English Composition courses in Oregon.
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Steps to Success: Crossing the Bridge Between Literacy Research and Practice
Kristen Munger · Open SUNY · 2016
Steps to Success: Crossing the Bridge Between Literacy Research and Practice introduces instructional strategies linked to the most current research-supported practices in the field of literacy. The book includes chapters related to scientifically-based literacy research, early literacy development, literacy assessment, digital age influences on children's literature, literacy development in underserved student groups, secondary literacy instructional strategies, literacy and modern language, and critical discourse analysis. Chapters are written by authors with expertise in both college teaching and the delivery of research-supported literacy practices in schools. The book features detailed explanations of a wide variety of literacy strategies that can be implemented by both beginning and expert practitioners. Readers will gain knowledge about topics frequently covered in college literacy courses, along with guided practice for applying this knowledge in their future or current classrooms. The book's success-oriented framework helps guide educators toward improving their own practices and is designed to foster the literacy development of students of all ages.
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Scientific Computing for Chemists with Python
Charles Weiss · Charles J. Weiss · 2025
This book serves as an introduction to coding for chemists. The tools employed in this book are the powerful and popular combination of Jupyter notebooks and the Python programming language. No background beyond first-year college chemistry and occasionally some very basic spectroscopy (for advanced chapters) is assumed for most of this book. This book starts with a brief primer on Jupyter notebooks in chapter 0 and computer programming with Python in chapters 1 and 2. If you already have a background in these tools, feel free to skip ahead. The rest of the book dives into applications of Python to solving chemical problems. Python and Jupyter were chosen for a variety of reasons, including that they are: Relatively easy to use and learn Powerful and well-suited for solving chemical problems Free, open-source software Cross-platform (e.g., runs on Windows, macOS, and Linux) Supplemented with numerous, specialized libraries for handling specific types of data or problems (e.g., machine learning) Supported by a helpful and welcoming community Learning to use a number of popular Python scientific libraries to solve chemical problems is one of the themes of this book. A Python library can be thought of as a tool pack with premade functions for performing common tasks in scientific data processing, analysis, and visualization. For example, the matplotlib library provides a variety of functions for creating a wide range of plots, while the scikit-learn library contains functions and resources for machine learning.
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Operating Systems and Infrastructure in Data Science
Josef Spillner · vdf Hochschulverlag · 2023
Modern data scientists work with a number of tools and operating system facilities in addition to online platforms. Mastering these in combination to manage their data and to deploy software, models and data as ready-to-use online services as well as to perform data science and analysis tasks is in the focus of Operating Systems and Infrastructure in Data Science. Readers will come to understand the fundamental concepts of operating systems and to explore plenty of tools in hands-on tasks and thus gradually develop the skills necessary to compose them for programming in the large, an essential capability in their later career. The book guides students through semester studies, acts as reference knowledge base and aids in acquiring the necessary knowledge, skills and competences especially in self-study settings. A unique feature of the book is the associated access to Edushell, a live environment to practice operating systems and infrastructure tasks.
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Evidence-based Software Engineering
Derek Jones · Knowledge Software · 2020
This book discusses what is currently known about software engineering, based on an analysis of all the publicly available data. This aim is not as ambitious as it sounds, because there is not a great deal of data publicly available. The intent is to provide material that is useful to professional developers working in industry; until recently researchers in software engineering have been more interested in vanity work, promoted by ego and bluster. The material is organized in two parts, the first covering software engineering and the second the statistics likely to be needed for the analysis of software engineering data.
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The Crystal Ball Instruction Manual
Stephen Davies · University of Mary Washington · 2020
A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.
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A Primer for Computational Biology
Shawn O'Neil · Oregon State University · 2017
A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the “natural environment” of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful “pipe” operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.
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A First Course in Electrical and Computer Engineering
Louis Scharf · OpenStax CNX · 2009
This book was written for an experimental freshman course at the University of Colorado. The course is now an elective that the majority of our electrical and computer engineering students take in the second semester of their freshman year, just before their first circuits course. Our department decided to offer this course for several reasons: we wanted to pique student' interest in engineering by acquainting them with engineering teachers early in their university careers and by providing with exposure to the types of problems that electrical and computer engineers are asked to solve; we wanted students entering the electrical and computer engineering programs to be prepared in complex analysis, phasors, and linear algebra, topics that are of fundamental importance in our discipline; we wanted students to have an introduction to a software application tool, such as MATLAB, to complete their preparation for practical and efficient computing in their subsequent courses and in their professional careers; we wanted students to make early contact with advanced topics like vector graphics, filtering, and binary coding so that they would gain a more rounded picture of modern electrical and computer engineering. In order to introduce this course, we had to sacrifice a second semester of Pascal programming. We concluded that the sacrifice was worth making because we found that most of our students were prepared for high-level language computing after just one semester of programming. We believe engineering educators elsewhere are reaching similar conclusions about their own students and curriculums. We hope this book helps create a much needed dialogue about curriculum revision and that it leads to the development of similar introductory courses that encourage students to enter and practice our craft.Students electing to take this course have completed one semester of calculus, computer programming, chemistry, and humanities. Concurrently with this course, students take ph
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Blueprints: Creating, Describing, and Implementing Designs for Larger-scale Software Projects
Stephen Davies · University of Mary Washington · 2024
Blueprints is a concise yet comprehensive coverage of Object-Oriented Analysis and Design concepts, suitable for a second programming course in Computer Science. It introduces and teaches application development in a command-line environment, and assumes basic expertise with the Java programming language.
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Think Data Structures: Algorithms and Information Retrieval in Java
Allen Downey · Green Tea Press · 2016
Data structures and algorithms are among the most important inventions of the last 50 years, and they are fundamental tools software engineers need to know. But in my opinion, most of the books on these topics are too theoretical, too big, and too bottom-up: Too theoretical: Mathematical analysis of algorithms is based on simplifying assumptions that limit its usefulness in practice. Many presentations of this topic gloss over the simplifications and focus on the math. In this book I present the most practical subset of this material and eliminate the rest. Too big: Most books on these topics are at least 500 pages, and some are more than 1000. By focusing on the topics I think are most useful for software engineers, I kept this book under 250 pages. Too bottom-up: Many data structures books focus on how data structures work (the implementations), with less about how to use them (the interfaces). In this book, I go “top down”, starting with the interfaces. Readers learn to use the structures in the Java Collections Framework before getting into the details of how they work. Finally, many present this material out of context and without motivation: it’s just one damn data structure after another! I try to alleviate the boredom by organizing the topics around an application—web search—that uses data structures extensively, and is an interesting and important topic in its own right. This application also motivates some topics that are not usually covered in an introductory data structures class, including persistent data structures, with Redis, and streaming algorithms. This book also presents basic aspects of software engineering practice, including version control and unit testing. Each chapter ends with an exercise that allows readers to apply what they have learned. Each exercise includes automated tests that check the solution. And for most exercises, I present my solution at the beginning of the next chapter. This book is intended for college students in computer
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Python for Everybody: Exploring Data Using Python 3
Charles Severance · Charles Severance · 2016
I never seemed to find the perfect data-oriented Python book for my course, so I set out to write just such a book. Luckily at a faculty meeting three weeks before I was about to start my new book from scratch over the holiday break, Dr. Atul Prakash showed me the Think Python book which he had used to teach his Python course that semester. It is a well-written Computer Science text with a focus on short, direct explanations and ease of learning.The overall book structure has been changed to get to doing data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning. Chapters 2–10 are similar to the Think Python book, but there have been major changes. Number-oriented examples and exercises have been replaced with data- oriented exercises. Topics are presented in the order needed to build increasingly sophisticated data analysis solutions. Some topics like try and except are pulled forward and presented as part of the chapter on conditionals. Functions are given very light treatment until they are needed to handle program complexity rather than introduced as an early lesson in abstraction. Nearly all user-defined functions have been removed from the example code and exercises outside of Chapter 4. The word “recursion”1 does not appear in the book at all. In chapters 1 and 11–16, all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including regular expressions for searching and parsing, automating tasks on your computer, retrieving data across the network, scraping web pages for data, object-oriented programming, using web services, parsing XML and JSON data, creating and using databases using Structured Query Language, and visualizing data. The ultimate goal of all of these changes is a shift from a Computer Science to an Informatics focus is to only include topics into a first technology class that can be useful even if one choose
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Signal Computing: Digital Signals in the Software Domain
Michael Stiber; Bilin Stiber; Eric Larson · Michael Stiber, Eric Larson · 2020
In this book, you will learn how digital signals are captured, represented, processed, communicated, and stored in computers. The specific topics we will cover include: physical properties of the source information (such as sound or images), devices for information capture (microphones, cameras), digitization, compression, digital signal representation (JPEG, MPEG), digital signal processing (DSP), signal analysis and feature extraction via re-representation as functions of frequency, and network communication. By the end of this book, you should understand the problems and solutions facing signal computing systems development in the areas of data structures and algorithms, data analytics, feature extraction, information retrieval, user interfaces, and communications.
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Think Bayes: Bayesian Statistics Made Simple
Allen Downey · Green Tea Press · 2012
Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.