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This book was written for anyone interested in learning more about the Arduino and robotics in general. Though some projects are geared toward college students and adults, several early chapters cover robotics projects suitable for middle-school to high-school students. I will not, however, place an age restriction on the material in this book, since I have seen some absolutely awesome projects created by makers both young and old.

Let's investigate the inner-workings of your Arduino. It appears to be a simple machine, but it is not. Much effort has been invested to make it easy to learn and use. Unfortunately, these good intentions can mask some of the Arduino's underlying capabilities. You suspected that there was more, much more, under the simplistic veneer. You were right. This book is about how Arduino actually works. The Arduino is a successful composite of design decisions that has evolved over time. By giving you a more in-depth understanding of the complex technologies involved, you will see the Arduino "internals" as elements to be changed and re-arranged to suit your design goals.

This book explains how to build and operate a powerful eight or 16-node Pi2 or Pi3 supercomputer. You will be provided detailed systematic instructions on installing the Linux/Ubuntu operating system on your PC, and its use in configuring, communicating with, and ultimately operating your Pi supercomputer. Initially, you will learn how to write and run a serial and a Message Passing Interface (MPI) π code on your PC, which is then used as a one-node supercomputer. Armed with this knowledge, you will then configure a Pi one-node, 4-core supercomputer on which you subsequently run the previous mentioned MPI π code. Next, you will assemble a two-node, 8-core Pi supercomputer on which, again, you will execute said MPI π code, and finally, you will construct an eight or 16-node Pi supercomputer, which you will employ to solve complex calculations incorporating the MPI construct.

This is a book about “hacking” electronics. It is not a formal, theory-based book about electronics. Its sole aim is to equip the reader with the skills he or she needs to use electronics to make something, whether it’s starting from scratch, connecting together modules, or adapting existing electronic devices for some new use. You will learn how to experiment and get your ideas into some kind of order, so that what you make will work. Along the way, you’ll gain an appreciation for why things work and the limits of what they can do, and learn how to make prototypes on solderless breadboard, how to solder components directly to each other, and how to use protoboard to make more complex soldered circuits. You will also learn how to use the popular Arduino microcontroller board, which has become one of the most important tools available to the electronics hacker. There are over 20 examples of how to use an Arduino with electronics in this book. You will also learn how to use the Raspberry Pi (a tiny Linux computer) as a tool for electronics hacking. Electronics has changed. This is a modern book that avoids theory you will likely never use and instead concentrates on how you can build things using readymade modules when they are available. There is, after all, no point in reinventing the wheel.

Mention FPGAs to most people, and they will either give you a blank stare or think you are talking about some kind of golf league. To most of us Makers, the term conjures up thoughts of hardware creativity, exploration, and discovery, but many of you may have written it off as being way too complicated to even consider for your next project. This book is for you! It’s all about learning what amazing, easy, and affordable projects you can construct with field programmable gate array (FPGA) technology. We will be doing this with hands-on experiments, in a fun and practical way. This book is not a university textbook providing in-depth studies on hardware description languages (HDLs), HDL coding techniques, digital logic design theory, or validation methods. There are many very good resources both online and in textbook form that accomplish this goal. This will be more a learn-as-you-go experience. You can think of the book as a road map to a journey of design discovery, and I’ll be your guide. But before we jump in, I want to give you a little background on the history of FPGAs.

Microcontroller projects are ubiquitous in the hobbyist/hacker/Maker world, and with good reason. Microcontrollers stand directly in the middle ground between the hardware world of buttons, motors, and lights and the software world of algorithms, connectivity, and infinite possibility. Microcontrollers are part computer and part electrical component. They can also be the metaphorical glue between the real world and the virtual world.

WHEN I WAS 10 years old, one of my teachers sat me down in front of a computer at school. Now, this isn’t what you think. I wasn’t about to be inducted into the mysteries of computer programming, even though it was a BBC Micro (the most programmable and arguably the most architecturally sophisticated of the British 8-bit microcomputers, on which I would subsequently cut my teeth in BASIC and assembly language). Instead, I was faced with a half-hour barrage of multiple choice questions about my academic interests, hobbies and ambitions, after which the miraculous machine spat out a diagnosis of my ideal future career: microelectronic chip designer. This was a bit of a puzzler, not least because what I really wanted to be was a computer game programmer (okay, okay, astronaut) and there was nobody in my immediate environment who had any idea what a 10-year-old should do to set him on the path to the sunlit uplands of microelectronic chip design. Over the next few years, I studied a lot of maths and science at school, learned to program (games) at home, first on the BBC Micro and then the Commodore Amiga, and made repeated, not particularly successful, forays into electronics. As it turned out, and more by luck than judgment, I’d happened on a plausible road to my destination, but it wasn’t until I arrived at Cambridge at the age of 18 that I started to figure out where the gaps were in my understanding.

Traditional wisdom requires people using electronics to have at least an EE degree before they can do anything useful, but in this book the whole subject of electronics is given the highly respected O’Reilly Cookbook treatment and is broken down into recipes. These recipes make it possible for the reader to access the book at random, following the recipe that solves their problem and learning as much or as little about the theory as they are comfortable with. While it is impossible to cover in one volume everything in a complex and wideranging subject like electronics, I have tried to select recipes that seem to come up most frequently when I talk to other makers, hobbyists, and inventors.

This book was written by Michael Margolis with Nick Weldin to help you explore the amazing things you can do with Arduino. Arduino is a family of microcontrollers (tiny computers) and a software creation environment that makes it easy for you to create programs (called sketches) that can interact with the physical world. Things you make with Arduino can sense and respond to touch, sound, position, heat, and light. This type of technology, often referred to as physical computing, is used in all kinds of things from the iPhone to automobile electronics systems. Arduino makes it possible for anyone with an interest—even people with no programming or electronics experience—to use this rich and complex technology.

Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this book, we want to show you how easy it can be to build machine learning solutions yourself, and how to best go about it. With the knowledge in this book, you can build your own system for finding out how people feel on Twitter, or making predictions about global warming. The applications of machine learning are endless and, with the amount of data available today, mostly limited by your imagination

This book is about data science: a field that uses results from statistics, machine learning, and computer science to create predictive models. Because of the broad nature of data science, it’s important to discuss it a bit and to outline the approach we take in this book.

With more than 10 years experience programming in R, I’ve had the luxury of being able to spend a lot of time trying to figure out and understand how the language works. This book is my attempt to pass on what I’ve learned so that you can quickly become an effective R programmer. Reading it will help you avoid the mistakes I’ve made and dead ends I’ve gone down, and will teach you useful tools, techniques, and idioms that can help you to attack many types of problems. In the process, I hope to show that, despite its frustrating quirks, R is, at its heart, an elegant and beautiful language, well tailored for data analysis and statistics

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of R for Data Science is to help you learn the most important tools in R that will allow you to do data science. After reading this book, you’ll have the tools to tackle a wide variety of data science challenges, using the best parts of R.

The main goal of this book is to introduce the reader to the use of R as a tool for data mining. R is a freely downloadable1 language and environment for statistical computing and graphics. Its capabilities and the large set of available add-on packages make this tool an excellent alternative to many existing (and expensive!) data mining tools.

R is a powerful tool for statistics, graphics, and statistical programming. It is used by tens of thousands of people daily to perform serious statistical analyses. It is a free, open source system whose implementation is the collective accomplishment of many intelligent, hard-working people. There are more than 2,000 available add-ons, and R is a serious rival to all commercial statistical packages. But R can be frustrating. It’s not obvious how to accomplish many tasks, even simple ones. The simple tasks are easy once you know how, yet figuring out that “how” can be maddening.

The purpose of this book is to provide a self-contained entry into Monte Carlo computational techniques. First and foremost, it must not be confused with a programming addendum to our earlier book Monte Carlo Statistical Meth- ods whose second edition came out in 2004. The current book has a dierent purpose, namely to make a general audience familiar with the programming aspects of Monte Carlo methodology through practical implementation. Not only have we introduced R at the core of this book, but the emphasis and contents have changed drastically from Monte Carlo Statistical Methods, even though the overall vision remains the same. Theoretical foundations are intentionally avoided in the current book.

This book’s title is the synthesis of two influential and outstanding entities. To quote David Hendry in the Nobel Memorial Prize lecture for Clive W. J. Granger,“[the] modeling of non-stationary macroeconomic time series [. . . ] has now become the dominant paradigm in empirical macroeconomic research” (Hendry [2004]). Hence, a thorough command of integration and cointegration analysis is a must for the applied econometrician. On the other side is the open-source statistical programming environment R. Since the mid-1990s, it has grown steadily out of infancy and can now be considered mature, flexible, and powerful software with more than 600 contributed packages. However, it is fair to say that R has not yet received the attention among econometricians it deserves. This book tries to bridge this gap by showing how easily the methods and tools encountered in integration and cointegration analysis are implemented in R.

R is a scripting language for statistical data manipulation and analysis. It was inspired by, and is mostly compatible with, the statistical language S developed by AT&T. The name S, for statistics, was an allusion to another programming language with a one-letter name developed at AT&T—the famous C language. S later was sold to a small firm, which added a graphical user interface (GUI) and named the result S-Plus.

In a world where data is becoming increasingly important, business people and scientists need tools to analyze and process large volumes of data efficiently. R is one of the tools that have become increasingly popular in recent years for data processing, statistical analysis, and data science. While

Two common types of user interfaces in statistical computing are the command line interface (CLI) and the graphical user interface (GUI). The usual CLI consists of a textual console in which the user types a sequence of commands at a prompt, and the output of the commands is printed to the console as text. The R console is an example of a CLI. A GUI is the primary means of interacting with desktop environments, such as Windows and Mac OS X, and statistical software, such as JMP. GUIs are contained within windows, and resources, such as documents, are represented by graphical icons. User controls are packed into hierarchical drop-down menus, buttons, sliders, etc. The user manipulates the windows, icons, and menus with a pointer device, such as a mouse.

Machine learning, at its core, is concerned with the algorithms that transform information into actionable intelligence. This fact makes machine learning well-suited to the present-day era of big data. Without machine learning, it would be nearly impossible to keep up with the massive stream of information. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start using machine learning. R offers a powerful but easy-to-learn set of tools that can assist you with finding data insights

When I began writing this book, I spent quite a bit of time searching for a good quote to start things off. I ended up with two. R is a wonderfully flexible platform and language for exploring, visualizing, and understanding data. I chose the quote from Alice’s Adventures in Wonderland to capture the flavor of statistical analysis today—an interactive process of exploration, visualization, and interpretation. The second quote reflects the generally held notion that R is difficult to learn. What I hope to show you is that is doesn’t have to be. R is broad and powerful, with so many analytic and graphic functions available (more than 50,000 at last count) that it easily intimidates both novice and experienced users alike. But there is rhyme and reason to the apparent madness. With guidelines and instructions, you can navigate the tremendous resources available, selecting the tools you need to accomplish your work with style, elegance, efficiency—and more than a little coolness.

My primary intent in writing this book is to provide the reader with basic programming, financial, and mathematical tools that can be successfully leveraged both in industry and academia. I cover the use of the R programming language, as well as the R environment as a means for manipulating financial market data and for solving a subset of problems that quants and traders typically encounter in their day-to-day activities. The chapters that follow should be treated as a tutorial on a recommended set of tools that I have personally found useful and that have served me well during the last few years of my career as a quant trader/developer. I am writing this book from the vantage point of a quant practitioner and not that of an academic. A significant portion of the content is based on my lecture notes from a graduate level class in quantitative finance that I teach on a part-time basis at Loyola University in Chicago.

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in a general sense, to display or extract the signal in the data in the presence of noise and to nd out what the data show us in the midst of their apparent chaos.

Rcpp is an R add-on package which facilitates extending R with C++ functions. It is being used for anything from small and quickly constructed add-on functions written either to fluidly experiment with something new or to accelerate computing by replacing an R function with its C++ equivalent to large-scale bindings for existing libraries, or as a building block in entirely new research computing environments. While still relatively new as a project, Rcpp has already become widely deployed among users and developers in the R community. Rcpp is now the most popular language extension for the R system and used by over 100 CRAN packages as well as ten BioConductor packages. This books aims to provide a solid introduction to Rcpp

R has a command line interface that offers considerable advantages over menu systems in terms of efficiency and speed once the commands are known and the language understood. However, the command line system can be daunting for the first-time user, so there is a need for concise texts to enable the student or analyst to make progress with R in their area of study. This book aims to fulfil that need in the area of time series to enable the non-specialist to progress, at a fairly quick pace, to a level where they can confidently apply a range of time series methods to a variety of data sets. The book assumes the reader has a knowledge typical of a first-year university statistics course and is based around lecture notes from a range of time series courses that we have taught over the last twenty years. Some of this material has been delivered to postgraduate finance students during a concentrated six-week course and was well received, so a selection of the material could be mastered in a concentrated course, although in general it would be more suited to being spread over a complete semester.

A vast number of real-world (often complex) tasks can be viewed as an optimization problem, where the goal is to minimize or maximize a given goal. In effect, optimization is quite useful in distinct application domains, such as Agriculture, Banking, Control, Engineering, Finance, Marketing, Production and Science. Moreover, due to advances in Information Technology, nowadays it is easy to store and process data. Since the 1970s, and following the Moore’s law, the number of transistors in computer processors has doubled every 2 years, resulting in more computational power at a reasonable price. And it is estimated that the amount of data storage doubles at a higher rate. Furthermore, organizations and individual users are currently pressured to increase efficiency and reduce costs. Rather than taking decisions based on human experience and intuition, there is an increasing trend for adopting computational tools, based on optimization methods, to analyze real-world data in order to make better informed decisions

We are in the midst of an information explosion. Everything in our lives is becoming instrumented and connected in real-time with the Internet of Things, from our own biology to the world's environment. By some measures, it is projected that by 2020, world data will have grown by more than a factor of 10 from today to a staggering 44 Zettabytes—just one Zettabyte is the equivalent of 250 billion DVDs. In order to process this volume and velocity of big data, we need to harness a vast amount of compute, memory, and disk resources, and to do this, we need parallelism

I started using R several years ago to analyze data I had collected for my research in graduate school. My motivation at first was to escape from the restrictive environments and canned analyses offered by statistical programs like SPSS. And even better, because it’s freely available, I didn’t need to convince someone to buy me a copy of the software— very important for a poor graduate student! As I delved deeper into R, I discovered that it could also create excellent data graphics.

It is assumed that all readers have at least an elementary understanding of statistical or computer programming, specifically with respect to the R programming language. Those who do not will find it much more difficult to follow the sections of this book which give examples of code to use, and it is suggested that they return to this text upon gaining that information.

Probabilistic graphical models is one of the most advanced techniques in machine learning to represent data and models in the real world with probabilities. In many instances, it uses the Bayesian paradigm to describe algorithms that can draw conclusions from noisy and uncertain real-world data. The book covers topics such as inference (automated reasoning and learning), which is automatically building models from raw data. It explains how all the algorithms work step by step and presents readily usable solutions in R with many examples. After covering the basic principles of probabilities and the Bayes formula, it presents Probabilistic Graphical Models(PGMs) and several types of inference and learning algorithms. The reader will go from the design to the automatic fitting of the model.

MicroPython is a reimplementation of the Python programming language that targets microcontrollers and embedded systems. Microcontrollers are computers shrunk onto a single, very small chip. Embedded systems are computers that function within a larger mechanical or electrical system. Embedded systems often use microcontrollers. This book introduces, explores, and explains MicroPython through four typical yet different devices,1 all of which have a microcontroller at their core. Such devices are very different to other sorts of computer. Most computers contain lots of parts: memory, storage, and processing are physically separate components containing various specialist chips. They may also contain additional parts for sound, graphics, and networking capabilities. Such computers are significantly more powerful than the resource-constrained, microcontroller-based devices used in this book.

Internet of Things (IOT) solutions are not nearly as complicated as the name may seem to indicate. Indeed, the IOT is largely another name for what we have already been doing. You may have heard of “connected devices” or “Internet-ready” or even “cloud-enabled.” All of these refer to the same thing — be it a single device such as a toaster or a plant monitor or a complex, multidevice product like home automation solutions. They all share one thing in common: they can be accessed via the Internet to either display data or interact with the devices directly. The trick is applying knowledge of technologies to leverage them to the best advantages for your IOT solution. In this book, we explore how to build IOT solutions using an easy-to-understand programming language named MicroPython running on small, dedicated microcontroller boards.

The Raspberry Pi is amazing at two levels—the advanced functionality that you get in a credit card-sized SBC (Single Board Computer) and its price. Even with today’s Pi competitors, the Raspberry Pi reigns supreme because few can beat its price. Further, it enjoys great software and community support. Price is an important advantage of the Pi that competitors don’t always appreciate. Hobbyists and makers are applying the Pi in new and sometimes risky ways. Someone starting out doesn’t want to lose their SBC because of a rookie mistake. At the low Pi price point, the loss can be absorbed without losing heart. Imagine a student buying an Intel Joule1 (when it was offered) for $349 USD and toasting it by accident. That would be enough to make most people give up right there! Price allows everyone to proceed fearlessly in their learning.

The fundamental difference between analog and digital is ‘‘information.’’ With digital information the output is always the same: a set of ones and zeros that represents the information. This information is independent of the supply voltages or the circuitry that is used to generate it. With analog, the output information is basic electrical values—volts, current, charge—and is always related to some real world parameters.With analog, the methodology used to arrive at the answers is intrinsic to the quality of those answers. Errors such as temperature, noise, delay and time stability can all affect the analog output and all are a function of the circuitry that generates the output. It is this analog output that is difficult to derive and requires experience and circuit design talent.

During the last decades much has been prophesized that there will be little need for analog circuitry in the future because digital electronics is taking over. Far from having proven true, this contention has provoked controversial rebuttals, as epitomized by statements such as “If you cannot do it in digital, it’s got to be done in analog.” Add to this the common misconception that analog design, compared to digital design, seems to be more of a whimsical art than a systematic science, and what is the confused student to make of this controversy? Is it worth pursuing some coursework in analog electronics, or is it better to focus just on digital?

When I submitted proposals to publishers for the first edition of this book, they posed two questions to me: (1) What is the future demand for analog books in a digital world? and (2) Is it wise to publish a book dealing solely with CMOS? The words “analog” and “CMOS” in the book’s title were both in question. Fortunately, the book resonated with students, instructors, and engineers. It has been adopted by hundreds of universities around the world, translated to five languages, and cited 6,500 times. While many fundamentals of analog design have not changed since the first edition was introduced, several factors have called for a second: migration of CMOS technologies to finer geometries and lower supply voltages, new approaches to analysis and design, and the need for more detailed treatments of some topics. This edition provides: • Greater emphasis on modern CMOS technology, culminating in a new chapter, Chapter 11, on design methodologies and step-by-step op amp design in nanometer processes • Extensive study of feedback through the approaches by Bode and Middlebrook • A new section on the analysis of stability using Nyquist’s approach—as the oft-used Bode method falls short in some common systems • Study of FinFETs • Sidebars highlighting important points in nanometer design • A new section on biasing techniques • Study of low-voltage bandgap circuits • More than 100 new examples Some instructors ask why we begin with square-law devices. This is for two reasons: (1) such a path serves as an intuitive entry point and provides considerable value in the analysis of amplifiers in terms of allowable voltage swings, and (2) despite their very short channel lengths, FinFETs—the devices used in 16-nm nodes and below—exhibit nearly square-law characteristics. This book is accompanied with a solutions manual and a new set of PowerPoint slides, available at www.mhhe.com/razavi.

Microcontrollers are computers on a chip. When they power up they start running a program from internal program memory, also called ROM for read only memory, or Flash. Microcomputers are found in appliances, toys, automobiles, and computer peripherals, such as a keyboard or mouse, and are finding their way in as support electronics for almost everything electronic from battery chargers to RADAR systems. The Microchip PIC® microcontrollers have become the most popular choice for new designs based on their high speed, of up to 70 million instructions per second as of this writing; low cost, some under $1; and large number of interfaces like USB, Ethernet, and analog signals.

PostgreSQL bills itself as the world’s most advanced open source database. We couldn’t agree more. What we hope to accomplish in this book is to give you a firm grounding in the concepts and features that make PostgreSQL so impressive. Along the way, we should convince you that PostgreSQL does indeed stand up to its claim to fame. Because the database is advanced, no book short of the 3500 pages of documentation can bring out all its glory. But then again, most users don’t need to delve into the most abstruse features that PostgreSQL has to offer. So in our shorter 300-pager, we hope to get you, as the subtitle proclaims, Up and Running. Each topic is presented with some context so you understand when to use it and what it offers. We assume you have prior experience with some other database so that we can jump right to the key points of PostgreSQL. We generously litter the pages of this book with links to references so you can dig deeper into topics of interest. These links lead to sections in the manual, to helpful articles, to blog posts of PostgreSQL vanguards. We also link to our own site at Postgres OnLine Journal, where we have collected many pieces that we have written on PostgreSQL and its interoperability with other applications. This book focuses on PostgreSQL versions 9.5, 9.6, and 10, but we will cover some unique and advanced features that are also present in prior versions

Programming and Interfacing ATMEL AVR Microcontrollers introduces you to ATMEL’s AVR microprocessors—the same chip used in the Arduino. You’ll learn how to use the AVR to interact with devices such as motors, LCD screens, GPS sensors, touch pads, temperature sensors, accelerometers, distance sensors, and other interesting hardware. Other topics covered include analog to digital conversion, digital I/O, interrupts, serial peripheral interface, and serial communications.