The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.
Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.
This book constitutes the refereed proceedings of the International Symposium on Surgery Simulation and Soft Tissue Modeling, IS4TM 2003, held in Juan-Les-Pins, France in June 2003.
The 33 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on soft tissue models, haptic rendering, cardiac modeling, and patient specific simulators.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, 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 a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
There are many biochemistry textbooks in the market. Some of them are purely basic while others are applied, and there are very few books which cover both these aspects together. For this reason, the students learning biochemistry in their undergraduate courses have to depend on multiple books to acquire a sound knowledge of the subject.
This book, ‘Biochemistry’ is unique with a simultaneous and equal emphasis on basic and applied aspects of biochemistry. This textbook offers an integration of medical and pure sciences, comprehensively written to meet the curriculum requirements of undergraduate courses in medical, dental, pharmacy, life-sciences and other categories (agriculture, veterinary, etc.).
This book is designed to develop in students a sustained interest and enthusiasm to learn and develop the concepts in biochemistry in a logical and stepwise manner. It incorporates a variety of pedagogic aids, besides colour illustrations to help the students understand the subject quickly and to the maximum. The summary and biomedical/clinical concepts are intended for a rapid absorption and assimilation of the facts and concepts in biochemistry. The self-assessment exercises will stimulate the students to think rather than merely learn the subject. In addition, these exercises (essays, short notes, fill in the blanks, multiple choice questions) set at different difficulty levels, will cater to the needs of all the categories of learners.
New to This Edition
The book offers an integration of medical and pure sciences, and is comprehensively written, revised and updated to meet the curriculum requirements of Medical, Pharmacy, Dental, Veterinary, Biotechnology, Agricultural Sciences, Life Sciences, and others studying Biochemistry as one of the subjects.
It is the first text book on Biochemistry in English with multi-colour illustrations by an author from Asia. The use of multicolours is for a clearer understanding of the complicated biochemical reactions.
It is written in a lucid style with the subject being presented as an engaging story growing from elementary information to the most recent advances, and with theoretical discussions being supplemented with illustrations, flowcharts, and tables for easy understanding of Biochemistry.
It has each chapter beginning with a four-line verse followed by the text, biomedical concepts, a summary, and self-assessment exercises. The lively illustrations and text with appropriate headings and sub-headings in bold type faces facilitate reading path clarity and quick recall.
It provides the most recent and essential information on Molecular Biology and Biotechnology, Diabetes, Cancer, Free Radicals, Free radicals and Antioxidants, Prostaglandins, etc.
It describes a wide variety of case studies and biochemical correlations and several newer biomedical aspects- Metabolic syndrome, Therapeutic diets, Atkins diet, Trans fatty acids, Epigenetics, Nutrigenomics, Recombinant ribozymes, Membrane transport disorders, Pleural fluid etc.
It contains the basics (Bioorganic and Biophysical Chemistry, Tools of Biochemistry, Immunology, and Genetics) for beginners to learn easily Biochemistry, origins of biochemical words, confusables in Biochemistry, principles of Practical Biochemistry, and Clinical Biochemistry Laboratory.
This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful.What You Will LearnInstall and adopt TensorFlow in your Python environment to solve mathematical problemsGet to know the basic machine and deep learning conceptsTrain and test neural networks to fit your data modelMake predictions using regression algorithmsAnalyze your data with a clustering procedureDevelop algorithms for clustering and data classificationUse GPU computing to analyze big dataIn Detail
Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks.
This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples.
By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application.Style and approach
Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry—something you can connect to and will find familiar.
The aim of this book is to explain the process of biomedical imaging, from image acquisition to automated diagnosis. This process consists of three thematic areas. The first is dedicated to the acquisition process and the underlying properties of images from a physics-oriented perspective. The second part addresses the dominant state-of-the-art methodologies behind content extraction and interpretation of medical images. The third section presents an application-based example, which develops solutions to address the particular needs of various diagnoses.
This complete volume is an exceptional tool for radiologists, research scientists, senior undergraduate and graduate students in health sciences and engineering, and university professors. This book offers a unique guide to the entire chain of biomedical imaging, explaining how image formation is done, and how the most appropriate algorithms are used to address demands and diagnoses.
With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.
This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.Learn OpenCV data types, array types, and array operationsCapture and store still and video images with HighGUITransform images to stretch, shrink, warp, remap, and repairExplore pattern recognition, including face detectionTrack objects and motion through the visual fieldReconstruct 3D images from stereo visionDiscover basic and advanced machine learning techniques in OpenCV
This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful.What You Will LearnBecome familiar with the basics of the TensorFlow machine learning libraryGet to know Linear Regression techniques with TensorFlowLearn SVMs with hands-on recipesImplement neural networks and improve predictionsApply NLP and sentiment analysis to your dataMaster CNN and RNN through practical recipesTake TensorFlow into productionIn Detail
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow.
This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.
Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.Style and approach
This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.
Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data.
Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.
Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:A thorough introduction to OpenCVGetting input from camerasTransforming imagesSegmenting images and shape matchingPattern recognition, including face detectionTracking and motion in 2 and 3 dimensions3D reconstruction from stereo visionMachine learning algorithms
Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.
Each chapter ends with clinical cases and the related questions, which evokes yet another method of active learning rather than didactic methods of imparting knowledge.
Key points have been highlighted and boxed at the end of each topic for quick revision of the core concepts.
This book comes with a free companion website which contains self-assessment exercises, detailed case discussions related to the clinical cases given inside the book, glossary and various other features for enhanced learning.
The 11 revised full papers were carefully selected out of 13 initial submissions. The papers are organized on topical secttion such as computer vision, graphics, robotics, medical imaging, external tracking systems, medical device controls systems, information processing techniques, endoscopy planning and simulation.
The 7 full papers presented at CARE 2017 and the 10 full papers presented at CLIP 2017 were carefully reviewed and selected. The papers deal with interventional and diagnostic endoscopy integrating the latest advances in computer vision, robotics, medical imaging and information processing and the development and evaluation of new translational image-based techniques in the modern hospital.
Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills.Learn techniques used in robot navigation, medical image analysis, and other computer vision applicationsWork with image mappings and transforms, such as texture warping and panorama creationCompute 3D reconstructions from several images of the same sceneOrganize images based on similarity or content, using clustering methodsBuild efficient image retrieval techniques to search for images based on visual contentUse algorithms to classify image content and recognize objectsAccess the popular OpenCV library through a Python interface
A Doody's Core Title for 2017!
THE BEST REVIEW FOR THE USMLE!
The Thirtieth Edition of Harper’s Illustrated Biochemistry combines outstanding full-colorillustrations with authoritative integrated coverage of biochemical disease and clinical information. Using brevity and numerous medically relevant examples, Harper's presents a clear, succinct review of the fundamentals of biochemistry that every student must understand in order to succeed in medical school.
All fifty-eight chapters emphasize the medical relevance of biochemistryFull-color presentation includes more than 600 illustrations Each chapter includes a section on BiomedicalImportance and a summary of the topics covered Review questions follow each of the eleven sections Case studies in every chapter emphasize the clinical relevance to biochemistry NEW coverage of toxic naturally-occurring amino acids; extraterrestrial biomolecules; computer-aided drug design; the role of complement cascade in bacterial and viral infection; secreted mediators of cell-cell signaling between leukocytes; the role of mast cells, basophils, andeosinophils; and the hazard of antioxidants that down-regulate radical signaling for apoptosis and increase risk of cancer
Applauded by medical students for its current and engaging style, Harper's Illustrated Biochemistry is an essential for USMLE review and the single best reference for learning the clinical relevance of any biochemistry topic.
Printing in Plastic is aimed at creative people comfortable using power tools such as a table saw, circular saw, and drill press. Authors James Kelly and Patrick Hood-Daniel lead you through building a personal fabrication machine based upon a set of blueprints downloaded from their website. Example projects get you started in designing and fabricating your own parts. Bring your handyman skills, and apply patience during the build process. You too can be the proud owner of a personal fabricator—a three-dimensional printer.
Leads you through building a personal fabrication machine capable of creating small parts and objects from plastic Provides example projects to get you started on the road to designing and fabricating your own parts Provides an excellent parent/child, or small group project
Imagine a robotic stuffed animal that can read and respond to a child’s emotional state, a commercial that can recognize and change based on a customer’s facial expression, or a company that can actually create feelings as though a person were experiencing them naturally. Heart of the Machine explores the next giant step in the relationship between humans and technology: the ability of computers to recognize, respond to, and even replicate emotions. Computers have long been integral to our lives, and their advances continue at an exponential rate. Many believe that artificial intelligence equal or superior to human intelligence will happen in the not-too-distance future; some even think machine consciousness will follow. Futurist Richard Yonck argues that emotion, the first, most basic, and most natural form of communication, is at the heart of how we will soon work with and use computers.
Instilling emotions into computers is the next leap in our centuries-old obsession with creating machines that replicate humans. But for every benefit this progress may bring to our lives, there is a possible pitfall. Emotion recognition could lead to advanced surveillance, and the same technology that can manipulate our feelings could become a method of mass control. And, as shown in movies like Her and Ex Machina, our society already holds a deep-seated anxiety about what might happen if machines could actually feel and break free from our control. Heart of the Machine is an exploration of the new and inevitable ways in which mankind and technology will interact.
This Handbook is intended to serve as the basic reference point on image and video processing, in the field, in the research laboratory, and in the classroom. Each chapter has been written by carefully selected, distinguished experts specializing in that topic and carefully reviewed by the Editor, Al Bovik, ensuring that the greatest depth of understanding be communicated to the reader. Coverage includes introductory, intermediate and advanced topics and as such, this book serves equally well as classroom textbook as reference resource.
• Provides practicing engineers and students with a highly accessible resource for learning and using image/video processing theory and algorithms
• Includes a new chapter on image processing education, which should prove invaluable for those developing or modifying their curricula
• Covers the various image and video processing standards that exist and are emerging, driving today’s explosive industry
• Offers an understanding of what images are, how they are modeled, and gives an introduction to how they are perceived
• Introduces the necessary, practical background to allow engineering students to acquire and process their own digital image or video data
• Culminates with a diverse set of applications chapters, covered in sufficient depth to serve as extensible models to the reader’s own potential applications
About the Editor...
Al Bovik is the Cullen Trust for Higher Education Endowed Professor at The University of Texas at Austin, where he is the Director of the Laboratory for Image and Video Engineering (LIVE). He has published over 400 technical articles in the general area of image and video processing and holds two U.S. patents. Dr. Bovik was Distinguished Lecturer of the IEEE Signal Processing Society (2000), received the IEEE Signal Processing Society Meritorious Service Award (1998), the IEEE Third Millennium Medal (2000), and twice was a two-time Honorable Mention winner of the international Pattern Recognition Society Award. He is a Fellow of the IEEE, was Editor-in-Chief, of the IEEE Transactions on Image Processing (1996-2002), has served on and continues to serve on many other professional boards and panels, and was the Founding General Chairman of the IEEE International Conference on Image Processing which was held in Austin, Texas in 1994.
* No other resource for image and video processing contains the same breadth of up-to-date coverage
* Each chapter written by one or several of the top experts working in that area
* Includes all essential mathematics, techniques, and algorithms for every type of image and video processing used by electrical engineers, computer scientists, internet developers, bioengineers, and scientists in various,
Imaging Informatics Professionals (IIPs) have come to play an indispensable role in modern medicine, and the scope of this profession has grown far beyond the boundaries of the PACS. A successful IIP must not only understand the PACS itself, but also have knowledge of clinical workflow, a base in several medical specialties, and a solid IT capability regarding software interactions and networking. With the introduction of a certification test for the IIP position, a single source was needed to explain the fundamentals of imaging informatics and to demonstrate how those fundamentals are applied in everyday practice. Practical Imaging Informatics describes the foundations of information technology and clinical image management, details typical daily operations, and discusses rarer complications and issues.
Intended for novices to the world of OpenCV and computer vision, as well as OpenCV veterans that want to learn about what's new in OpenCV 3, this book is useful as a reference for experts and a training manual for beginners, or for anybody who wants to familiarize themselves with the concepts of object classification and detection in simple and understandable terms. Basic knowledge about Python and programming concepts is required, although the book has an easy learning curve both from a theoretical and coding point of view.What You Will LearnInstall and familiarize yourself with OpenCV 3's Python APIGrasp the basics of image processing and video analysisIdentify and recognize objects in images and videosDetect and recognize faces using OpenCVTrain and use your own object classifiersLearn about machine learning concepts in a computer vision contextWork with artificial neural networks using OpenCVDevelop your own computer vision real-life applicationIn Detail
OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance.
Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.Style and approach
This book is a comprehensive guide to the brand new OpenCV 3 with Python to develop real-life computer vision applications.
This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on.What You Will LearnApply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like imageDetect and track various body parts such as the face, nose, eyes, ears, and mouthStitch multiple images of a scene together to create a panoramic imageMake an object disappear from an imageIdentify different shapes, segment an image, and track an object in a live videoRecognize an object in an image and build a visual search engineReconstruct a 3D map from imagesBuild an augmented reality applicationIn Detail
Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel.
This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications.
This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner's level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples.
The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation.Style and approach
This is a conversational-style book filled with hands-on examples that are really easy to understand. Each topic is explained very clearly and is followed by a programmatic implementation so that the concept is solidified. Each topic contributes to something bigger in the following chapters, which helps you understand how to piece things together to build something big and complex.
A refresher for more experienced readers, the first part of the book presents an introduction to Python, Python modules, reading and writing images using Python, and an introduction to images. The second part discusses the basics of image processing, including pre/post processing using filters, segmentation, morphological operations, and measurements. The last part describes image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry.
Learn dozens of hacks for building interfaces that respond to body movements, gestures, and voice, using open source toolkits such as openFrameworks, the Processing IDE, and OpenKinect driver library. Whether you’re an artist, designer, researcher, or hobbyist, this book will give you a running start with Kinect.Set up a development environment in Windows 7, Mac OSX, or UbuntuBuild special effects apps with tools such as Synapse and CinderCreate gestural interfaces to integrate and control digital music componentsCapture the realistic motions of a 3D model with NI mate, Blender, and AnimataDesign gesture-based games with the ZigFu SDKRecreate the dimensions of any room in realtime, using RGBDemoUse gestures to navigate robots and control PC interfaces
Despite the focus on architectural support in current architectures, some historical perspective is necessary to appropriately frame the problem. The first half of the book provides the historical perspective of the theoretical framework developed four decades ago by Popek and Goldberg. It also describes earlier systems that enabled virtualization despite the lack of architectural support in hardware.
As is often the case, theory defines a necessary—but not sufficient—set of features, and modern architectures are the result of the combination of the theoretical framework with insights derived from practical systems. The second half of the book describes state-of-the-art support for virtualization in both x86-64 and ARM processors. This book includes an in-depth description of the CPU, memory, and I/O virtualization of these two processor architectures, as well as case studies on the Linux/KVM, VMware, and Xen hypervisors. It concludes with a performance comparison of virtualization on current-generation x86- and ARM-based systems across multiple hypervisors.
Knowledge flow — a mobile learning platform provides Apps and Books.
Knowledge flow provides learning book of Biochemistry. This book of biochemistry provides together biology and chemistry is for all medical and science students, teachers and professionals across the world. Biochemistry explores the chemical processes, biochemistry focuses on processes at chemical level.
1. Introduction to Biochemistry
3. Atom and chemical bonds
5. General Structure of Proteins
6. Classification of Proteins
8. Nucleic Acids
10. Concepts of Metabolism
To find more education books, visit here http://knowledgeflow.in/books.
Perfect for hobbyists, makers, artists, and gamers, Making Things See shows you how to build every project with inexpensive off-the-shelf components, including the open source Processing programming language and the Arduino microcontroller. You’ll learn basic skills that will enable you to pursue your own creative applications with Kinect.Create Kinect applications on Mac OS X, Windows, or LinuxTrack people with pose detection and skeletonization, and use blob tracking to detect objectsAnalyze and manipulate point cloudsMake models for design and fabrication, using 3D scanning technologyUse MakerBot, RepRap, or Shapeways to print 3D objectsDelve into motion tracking for animation and gamesBuild a simple robot arm that can imitate your arm movementsDiscover how skilled artists have used Kinect to build fascinating projects