This volume aims to stimulate discussions on research involving the use of data and digital images as an understanding approach for analysis and visualization of phenomena and experiments. The emphasis is put not only on graphically representing data as a way of increasing its visual analysis, but also on the imaging systems which contribute greatly to the comprehension of real cases. Scientific Visualization and Imaging Systems encompass multidisciplinary areas, with applications in many knowledge fields such as Engineering, Medicine, Material Science, Physics, Geology, Geographic Information Systems, among others. This book is a selection of 13 revised and extended research papers presented in the International Conference on Advanced Computational Engineering and Experimenting -ACE-X conferences 2010 (Paris), 2011 (Algarve), 2012 (Istanbul) and 2013 (Madrid). The examples were particularly chosen from materials research, medical applications, general concepts applied in simulations and image analysis and other interesting related problems.
Learn the basics of serverless computing and how to develop event-driven architectures with the three major cloud platforms: Amazon Web Services, Microsoft Azure, and Google Cloud. This hands-on guide dives into the foundations of serverless computing, its use cases, and how to apply it using developer tools such as Node.js, Visual Studio Code, Postman, and Serverless Framework. You will apply the fundamentals of serverless technology from the ground up, and come away with a greater understanding of its power and how to make it work for you. This book teaches you how to quickly and securely develop applications without the hassle of configuring and maintaining infrastructure. You will learn how to harness serverless technology to rapidly reduce production time and minimize your costs, while still having the freedom to customize your code, without hindering functionality. Upon completion, you will have the knowledge and resources to build your own serverless application hosted in AWS, Azure, or Google Cloud and will have experienced the benefits of event-driven technology for yourself. What You´ll Learn Gain a deeper understanding of serverless computing and when to use it Use development tools such as Node.js, Postman, and VS code to quickly set up your serverless development environment and produce applications Apply triggers to your serverless functions that best suit the architecture for the problem the functions are solving Begin building applications across cloud providers that utilize the power of serverless technology Understand best development practices with serverless computing to maintain scalable and practical solutions Code with an agnostic approach to cloud providers to minimize provider dependency Who This Book Is For Any developer looking to expand current knowledge of serverless computing, its applications, and how to architect serverless solutions, or someone just beginning in these areas
Cloud Computing Native KMU - so lässt sich kurz und prägnant der Inhalt des vorliegenden Buches zusammenfassen. Dargestellt werden Grundlagen, Anwendungen, Migrationsstrategien, Sicherheitskonzepte, betriebliches Datenmanagement, technologisches Umfeld, Cloud-Initiativen und die vielen nützlichen Helfer aus dem Internet. Beschrieben, strukturiert und analysiert wird der Cloud-Markt in all seinen vielfältigen Formen und Verästlungen. Dem weiterführenden Interesse dienen sorgfältig recherchierte Hyperlinks. So bekommt der Leser ein systematisches und umfassendes Bild vom Cloud Computing unter KMU-Bedingungen.. All dies dient der optimalen Cloud-Nutzung in kleinen und mittleren Unternehmen respektive in Freiberufler-Büros, HomeOffices oder Start-up-Firmen. Eine solche Vorgehensweise ist ratsam, um diese moderne Technologie samt Umfeld besser zu verstehen und die vorhandenen Angebote am Markt optimal für den eigenen Betrieb nutzen zu können. In diesem Zusammenhang werden auch folgende Fragen thematisiert: Was heißt ´Cloud-Readiness´ für KMU? Ab wann und für wen lohnt sich die Cloud? Brauchen wir eine KMU-eigene Cloud-Policy?
This book would serve as an ideal guide for B.E., B.Tech., B.S., B.Sc, B.C.A., undergraduate students of Computer Science and Engineering, Information Technology, Electronics and Communication Engineering who wish to take up projects on applications of fog and edge computing. Students pursuing postgraduate course in Science and Engineering, M.E., M.Tech., M.S., M.Sc., M.C.A. students will find this book useful for their projects. Research Scholars working in the area of fog computing, edge computing and Internet of Things will find this book as a handy reference guide for their M.Phil., Ph.D. D.Sc., and other post-doctoral research works. Software and Hardware Engineers working in IT and ITES sector specifically on fog computing, edge computing and IoT domains, would find this book as a useful resource. As a word of conclusion, I believe that the reader will find this book as a helpful guide and a valuable source of information about principles of fog and edge computing.
Cloud Computing wird von nahezu allen führenden Analysten als einer der Top-5-IT-Trends gesehen, der gegenwärtig aus der Hype-Phase in den Status der praktischen betrieblichen Umsetzung übergeht. Inzwischen wird nicht mehr diskutiert, ob Cloud Computing überhaupt eine praktikable Möglichkeit des IT-Sourcing ist, sondern vielmehr, wie diese Möglichkeit sich sicher und mit hohem Nutzen für Firmen einsetzen lässt. Es wird aufgezeigt, wo die Vorteile aber auch die Stolpersteine liegen und welche prinzipiellen Lösungen es gibt, um die Chancen zu realisieren und Risiken möglichst zu umgehen.
High Performance Computing: Modern Systems and Practices is a fully comprehensive and easily accessible treatment of high performance computing, covering fundamental concepts and essential knowledge while also providing key skills training. With this book, domain scientists will learn how to use supercomputers as a key tool in their quest for new knowledge. In addition, practicing engineers will discover how supercomputers can employ HPC systems and methods to the design and simulation of innovative products, and students will begin their careers with an understanding of possible directions for future research and development in HPC. Those who maintain and administer commodity clusters will find this textbook provides essential coverage of not only what HPC systems do, but how they are used. Covers enabling technologies, system architectures and operating systems, parallel programming languages and algorithms, scientific visualization, correctness and performance debugging tools and methods, GPU accelerators and big data problems Provides numerous examples that explore the basics of supercomputing, while also providing practical training in the real use of high-end computers Helps users with informative and practical examples that build knowledge and skills through incremental steps Features sidebars of background and context to present a live history and culture of this unique field Includes online resources, such as recorded lectures from the authors´ HPC courses
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Problem solving in computing is referred to as computational thinking. The theory behind this concept is challenging in its technicalities, yet simple in its ideas. This book introduces the theory of computation from its inception to current form of complexity; from explanations of how the field of computer science was formed using classical ideas in mathematics by Gödel, to conceptualization of the Turing Machine, to its more recent innovations in quantum computation, hypercomputation, vague computing and natural computing. It describes the impact of these in relation to academia, business and wider society, providing a sound theoretical basis for its practical application. Written for accessibility, Demystifying Computation provides the basic knowledge needed for non-experts in the field, undergraduate computer scientists and students of information and communication technology and software development.
Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you´ll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python´s immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.
Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition , presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. W hat You´ll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.