The text examines the basics of analog image analysis while placing greater emphasis on digitally based systems and analysis techniques. The presentation is discipline neutral, so students in any field of study can gain a clear understanding of these systems and their virtually unlimited applications.

    Jon C. Thompson presents the latest data in thoroughly updated diagnostic and treatment algorithms for all conditions while preserving the popular at-a-glance table format from the previous edition. You'll get even more art from the Netter Collection as well as new radiologic images that visually demonstrate the key clinical correlations and applications of anatomical imaging. For a fast, memorable review of orthopaedic anatomy, this is a must-have.Maintains the popular at-a-glance table format that makes finding essential information quick and convenient.Contains useful clinical information on disorders, trauma, history, physical exam, radiology, surgical approaches, and minor procedures in every chapter.Lists key information on bones, joints, muscles, and nerves in tables correlate to each Netter image.Highlights key material in different colors-pearls in green and warnings in red-for easy reference. Features both plain film and advanced radiographic (CT and MRI) images, along with cross-sectional anatomic plates for an even more thorough visual representation of the material.Includes additional common surgical approaches to give you a broader understanding of techniques.Incorporates reorganized Complicated Arthology tables for large joints, such as the shoulder, knee, and hip, for increased clarity and to incorporate new artwork and additional clinical correlations.Reflects new data and current diagnostic and treatment techniques through updates to the Disorders and Fractures sections and the Physical Exam and Anatomic tables in each chapter.Presents the very latest developments in the field through thoroughly updated diagnostic and treatment algorithms for all clinical conditions.

    The manuscript was a palimpsest-a book made from an earlier codex whose script had been scraped off and the pages used again. Behind the script of the thirteenth-century monk's prayer book, the palimpsest revealed the faint writing of a much older, tenth-century manuscript. Part archaeological detective story, part science, and part history, The Archimedes Codex tells the extraordinary story of this lost manuscript, from its tenth-century creation in Constantinople to the auction block at Christie's, and how a team of scholars used the latest imaging technology to reveal and decipher the original text. What they found was the earliest surviving manuscript by Archimedes (287 b.c.-212 b.c.), the greatest mathematician of antiquity-a manuscript that revealed, for the first time, the full range of his mathematical genius, which was two thousand years ahead of modern science.

    An x-ray microscope would do the trick, or since we're dreaming, perhaps an Asimov-style nanosubmarine (unfortunately, neither is currently feasible). Think of the wonders we could witness firsthand: antibodies atta- ing a virus, electrical signals racing down nerve fibers, proteins building new strands of DNA. Many of the questions puzzling the current cadre of sci- tists would be answered at a glance. But the nanoscale world of molecules is separated from our everyday world of experience by a daunting million-fold difference in size, so the world of molecules is completely invisible. I created the illustrations in this book to help bridge this gulf and allow us to see the molecular structure of cells, if not directly, then in an artistic rendition. I have included two types of illustrations with this goal in mind: watercolor paintings which magnify a small portion of a living cell by one million times, showing the arrangement of molecules inside, and comput- generated pictures, which show the atomic details of individual molecules. In this second edition of The Machinery of Life, these illustrations are presented in full color, and they incorporate many of the exciting scientific advances of the 15 years since the first edition.

    I also love the sense of humor. It's very good at disarming the fear. And it's gorgeous. I'll be recommending this book highly." --Tom Igoe, author of Physical Computing and Making Things TalkWant to learn the fundamentals of electronics in a fun, hands-on way? With Make: Electronics, you'll start working on real projects as soon as you crack open the book. Explore all of the key components and essential principles through a series of fascinating experiments. You'll build the circuits first, then learn the theory behind them!Build working devices, from simple to complex You'll start with the basics and then move on to more complicated projects. Go from switching circuits to integrated circuits, and from simple alarms to programmable microcontrollers. Step-by-step instructions and more than 500 full-color photographs and illustrations will help you use -- and understand -- electronics concepts and techniques.Discover by breaking things: experiment with components and learn from failureSet up a tricked-out project space: make a work area at home, equipped with the tools and parts you'll needLearn about key electronic components and their functions within a circuitCreate an intrusion alarm, holiday lights, wearable electronic jewelry, audio processors, a reflex tester, and a combination lockBuild an autonomous robot cart that can sense its environment and avoid obstaclesGet clear, easy-to-understand explanations of what you're doing and why

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    Building on his classic first edition, Theodore S. Rappaport reviews virtually every important new wireless standard and technological development, including W-CDMA, cdma2000, UMTS, and UMC 136/EDGE; IEEE 802.11 and HIPERLAN WLANs; Bluetooth, LMDS, and more. Includes dozens of practical new examples, solved step by step.

    Its fundamental goals are to provide an integrated view of macroeconomics, and to make close contact with current macroeconomic events.

    Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.

    Why, then, do their designs diverge so sharply? Humans, for instance, love right angles, while nature's angles are rarely right and usually rounded. Our technology goes around on wheels—and on rotating pulleys, gears, shafts, and cams—yet in nature only the tiny propellers of bacteria spin as true wheels. Our hinges turn because hard parts slide around each other, whereas nature's hinges (a rabbit's ear, for example) more often swing by bending flexible materials. In this marvelously surprising, witty book, Steven Vogel compares these two mechanical worlds, introduces the reader to his field of biomechanics, and explains how the nexus of physical law, size, and convenience of construction determine the designs of both people and nature. "This elegant comparison of human and biological technology will forever change the way you look at each."—Michael LaBarbera, American Scientist

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    It is designed as a reference for students in the physical sciences and engineering.

    Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a detailed list of commands to follow blindly. Instead, their aim is to implement high-level routines that teach computers how to approach new and unknown problems – these are called algorithms. In practice, they want to give computers the ability to Learn and to Adapt. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before – or even knew existed. Unfortunately, the true power and applications of today’s Machine Learning Algorithms remain deeply misunderstood by most people. Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry. I will show you exactly how each algorithm works, why it works and when you should use it. Supervised Learning Algorithms K-Nearest Neighbour Naïve Bayes Regressions Unsupervised Learning Algorithms: Support Vector Machines Neural Networks Decision Trees

    Verification that algorithms work is emphasized more than their complexity. An effective use of examples, and huge number of interesting exercises, demonstrate the topics of trees and distance, matchings and factors, connectivity and paths, graph coloring, edges and cycles, and planar graphs. For those who need to learn to make coherent arguments in the fields of mathematics and computer science.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data