This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to "experiment and learn" as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remain unchanged into the foreseeable future. For those looking to migrate their signal processing codes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts.

Software testing can be regarded as an art, a craft, and a science. The practical, step-by-step approach presented in this book provides a bridge between these different viewpoints. A single worked example runs throughout, with consistent use of test automation. Each testing technique is introduced in the context of this example, helping students see its strengths and weaknesses. The technique is then explained in more detail, providing a deeper understanding of underlying principles. Finally the limitations of each technique are demonstrated by inserting faults, giving learners concrete examples of when each technique succeeds or fails in finding faults. Coverage includes black-box testing, white-box testing, random testing, unit testing, object-oriented testing, and application testing. The authors also emphasise the process of applying the techniques, covering the steps of analysis, test design, test implementation, and interpretation of results. The book's web site has programming exercises and Java source code for all examples.

This book provides guidelines for practicing design science in the fields of information systems and software engineering research. A design process usually iterates over two activities: first designing an artifact that improves something for stakeholders and subsequently empirically investigating the performance of that artifact in its context. This "validation in context" is a key feature of the book - since an artifact is designed for a context, it should also be validated in this context. The book is divided into five parts. Part I discusses the fundamental nature of design science and its artifacts, as well as related design research questions and goals. Part II deals with the design cycle, i.e. the creation, design and validation of artifacts based on requirements and stakeholder goals. To elaborate this further, Part III presents the role of conceptual frameworks and theories in design science. Part IV continues with the empirical cycle to investigate artifacts in context, and presents the different elements of research problem analysis, research setup and data analysis. Finally, Part V deals with the practical application of the empirical cycle by presenting in detail various research methods, including observational case studies, case-based and sample-based experiments and technical action research. These main sections are complemented by two generic checklists, one for the design cycle and one for the empirical cycle. The book is written for students as well as academic and industrial researchers in software engineering or information systems. It provides guidelines on how to effectively structure research goals, how to analyze research problems concerning design goals and knowledge questions, how to validate artifact designs and how to empirically investigate artifacts in context - and finally how to present the results of the design cycle as a whole.

This book helps participants in agile software development environments learn to become leaders. Facilitative leaders should be at every level of the organization, from individual contributor to informal team leader to managers of all stripes -- it takes much focus and intentionality from senior organizational leaders, who have special obligations in creating successful lean and agile development environments. But, beyond the principles of facilitative leadership for agility, People over Process provides tips and demonstrative scenes for the more important and common software meetings: architecture simulations, project planning, team configurations, retrospectives, and more. The author fully illustrates the principles and shares proven techniques for the most important leadership events in agile projects. While this book focuses on facilitating extraordinarily well-prepared meetings, it serves as a metaphor for leadership more broadly. The leader's obligation to help their team make rigorous fact-based decisions; to gain broad input and have participants aligned on the outcomes and next steps; and to do so in an efficient way that respects the time of the participants is as relevant to every-day leadership activity as it is to conducting meetings. The author mixes background and explanation with demonstration -- in this case, the story of an agile project at the fictitious Pacifica Bank. The scenario constructed at Pacifica illustrates the concepts of effective leadership and productive workplace environments. The book concentrates on the flow of software from understanding what is needed through design, development, testing, and deployment. Essentially, the author provides a simple and powerful model of leadership, examples, and tips. This is not a cookbook on how to lead -- It is a set of principles and examples. All leaders must find their own way for their team, their organization, and their unique challenges.

This book is for everyone who needs to test the web. As a tester, you'll automate your tests. As a developer, you'll build more robust solutions. And as a team, you'll gain a vocabulary and a means to coordinate how to write and organize automated tests for the web. Follow the testing pyramid and level up your skills in user interface testing, integration testing, and unit testing. Your new skills will free you up to do other, more important things while letting the computer do the one thing it's really good at: quickly running thousands of repetitive tasks. This book shows you how to do three things: * How to write really good automated tests for the web. * How to pick and choose the right ones. * How to explain, coordinate, and share your efforts with others. If you're a traditional software tester who has never written an automated test before, this is the perfect book for getting started. Together, we'll go through everything you'll need to start writing your own tests. If you're a developer, but haven't thought much about testing, this book will show you how to move fast without breaking stuff. You'll test RESTful web services and legacy systems, and see how to organize your tests. And if you're a team lead, this is the Rosetta Stone you've been looking for. This book will help you bridge that testing gap between your developers and your testers by giving your team a model to discuss automated testing, and most importantly, to coordinate their efforts.The Way of the Web Tester is packed with cartoons, graphics, best practices, war stories, plenty of humor, and hands-on tutorial exercises that will get you doing the right things, the right way.

Python for Scientific Computation and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI). This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling. Features: No prior experience of programming is required. Online GitHub repository available with codes for readers to practice. Covers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computing. Full solutions to exercises are available as Jupyter notebooks on the Web.

Learn data science with Python by building five real-world projects! In Data Science Bookcamp you'll test and build your knowledge of Python and learn to handle the kind of open-ended problems that professional data scientists work on daily. Downloadable data sets and thoroughly-explained solutions help you lock in what you've learned, building your confidence and making you ready for an exciting new data science career. about the technologyIn real-world practice, data scientists create innovative solutions to novel open ended problems. Easy to learn and use, the Python language has become the de facto language for data science amongst researchers, developers, and business users. But knowing a few basic algorithms is not enough to tackle a vague and thorny problem. It takes relentless practice at cracking difficult data tasks to achieve mastery in the field. That's just what this book delivers. about the book Data Science Bookcamp is a comprehensive set of challenging projects carefully designed to grow your data science skills from novice to master. Veteran data scientist Leonard Apeltsin sets five increasingly difficult exercises that test your abilities against the kind of problems you'd encounter in the real world. As you solve each challenge, you'll acquire and expand the data science and Python skills you'll use as a professional data scientist. Ranging from text processing to machine learning, each project comes complete with a unique downloadable data set and a fully-explained step-by-step solution. Because these projects come from Dr. Apeltsin's vast experience, each solution highlights the most likely failure points along with practical advice for getting past unexpected pitfalls. When you wrap up these five awesome exercises, you'll have a diverse relevant skill set that's transferable to working in industry. what's inside Five in-depth Python exercises with full downloadable data sets Web scraping for text and images Organise datasets with clustering algorithms Visualize complex multi-variable datasets Train a decision tree machine learning algorithm about the readerFor readers who know the basics of Python. No prior data science or machine learning skills required. about the author Leonard Apeltsin is a senior data scientist and engineering lead at Primer AI, a startup that specializes in using advanced Natural Language Processing techniques to extract insight from terabytes of unstructured text data. His PhD research focused on bioinformatics that required analyzing millions of sequenced DNA patterns to uncover genetic links in deadly diseases.

This book describes the research of the authors over more than a decade on an end-to-end methodology for the design and development of Web Information Systems (WIS). It covers syntactics, semantics and pragmatics of WIS, introduces sophisticated concepts for conceptual modelling, provides integrated foundations for all these concepts and integrates them into the co-design method for systematic WIS development. WIS, i.e. data-intensive information systems that are realized in a way that arbitrary users can access them via web browsers, constitute a prominent class of information systems, for which acceptance by its a priori unknown users in varying contexts with respect to the presented content, the ease of functionality provided and the attraction of the layout adds novel challenges for modelling, design and development. This book is structured into four parts. Part I, Web Information Systems - General Aspects, gives a general introduction to WIS describing the challenges for their development, and provides a characterization by six decisive aspects: intention, usage, content, functionality, context and presentation. Part II, High-Level WIS Design - Strategic Analysis and Usage Modelling with Storyboarding, introduces methods for high-level design of WIS covering strategic aspects and the storyboarding method, which is discussed from syntactic, semantic and pragmatic perspectives. Part III, Conceptual WIS Design - Rigorous Modelling of Web Information Systems and their Layout with Web Interaction Types and Screenography, continues with conceptual design of WIS including layout and playout. This introduces the decisive web interaction types, the screenography method and adaptation aspects. The final Part IV, Rationale of the Co-Design Methodology and Systematic Development of Web Information Systems, describes the co-design method for WIS development and its application for the systematic engineering of systems. The book addresses the research community, and at the same time can be used for education of graduate students and as methodological support for professional WIS developers. For the WIS research community it provides methods for WIS modelling on all levels of abstraction including theoretical foundations and inference mechanisms as well as a sophisticated end-to-end methodology for systematic WIS engineering from requirements elicitation over conceptual modelling to aspects of implementation, layout and playout. For students and professional developers the book can be used as a whole for educational courses on WIS design and development, as well as for more specific courses on conceptual modelling of WIS, WIS foundations and reasoning, co-design and WIS engineering or WIS layout and playout development.

Does your Rails code suffer from bloat, brittleness, or inaccuracy? Cure these problems with the regular application of test-driven development. You'll use Rails 5.1, Minitest 5, and RSpec 3.6, as well as popular testing libraries such as factory_girl and Cucumber. Updates include Rails 5.1 system tests and Webpack integration. Do what the doctor ordered to make your applications feel all better. Side effects may include better code, fewer bugs, and happier developers. Your Ruby on Rails application is sick. Deadlines are looming, but every time you make the slightest change to the code, something else breaks. Nobody remembers what that tricky piece of code was supposed to do, and nobody can tell what it actually does. Plus, it has bugs. You need test-driven development: a process for improving the design, maintainability, and long-term viability of software. With both practical code examples and discussion of why testing works, this book starts with the most basic features delivered as part of core Ruby on Rails. Once you've integrated those features into your coding practice, work with popular third-party testing tools such as RSpec, Jasmine, Cucumber, and factory_girl. Test the component parts of a Rails application, including the back-end model logic and the front-end display logic. With Rails examples, use testing to enable your code to respond better to future change. Plus, see how to handle real-world testing situations. This new edition has been updated to Rails 5.1 and RSpec 3.6 and contains full coverage of new Rails features, including system tests and the Webpack-based JavaScript setup. What You Need: Ruby 2.4, Rails 5.1

Here, the authors propose a method for the formal development of parallel programs - or multiprograms as they prefer to call them. They accomplish this with a minimum of formal gear, i.e. with the predicate calculus and the well- established theory of Owicki and Gries. They show that the Owicki/Gries theory can be effectively put to work for the formal development of multiprograms, regardless of whether these algorithms are distributed or not.

Validation of computer systems is the process that assures the formal assessment and report of quality and performance measures for all the life-cycle stages of software and system development, its implementation, qualification and acceptance, operation, modification, requalification, maintenance and retirement (PICS CSV PI 011-3). It is a process that demonstrates the compliance of computer systems functional and non-functional requirements, data integrity, regulated company procedures and safety requirements, industry standards, and applicable regulatory authority's requirements. Compliance is a state of being in adherence to application-related standards or conventions or regulations in laws and similar prescriptions. This book, which is relevant to the pharmaceutical and medical devices regulated operations, provides practical information to assist in the computer validation to production systems, while highlighting and efficiently integrating worldwide regulation into the subject. A practical approach is presented to increase efficiency and to ensure that the validation of computer systems is correctly achieved.

The use of Python as a powerful computational tool is expanding with great strides. Python is a language which is easy to use, and the libraries of tools provides it with efficient versatility. As the tools continue to expand, users can create insightful models and simulations. While the tools offer an easy method to create a pipeline, such constructions are not guaranteed to provide correct results. A lot of things can go wrong when building a simulation - deviously so. Users need to understand more than just how to build a process pipeline. Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments, and includes a number of interesting examples, including Shakespeare, movie databases, virus spread, and Chess. Key Features: Several theories and applications are provided, each with working Python scripts. All Python functions written for this book are archived on GitHub. Readers do not have to be Python experts, but a working knowledge of the language is required. Students who want to know more about the foundations of modeling and simulation will find this an educational and foundational resource.

In today's business world, project managers not only have to be diligent in project planning and execution, but also need to have skills in persuasion, communication, and relationship management. Reflecting the changing landscape of projects today, Essential Project Management Skills empowers project managers to master the skills necessary to succeed in the current environment. Real case studies clarify concepts Divided into four sections by chapter and enhanced with tables and diagrams, the book begins by introducing the concepts related to the changing project landscape and their impact on projects. It then delves into the details of several trends within the project landscape, describes the challenges created by these trends for project managers, and provides illuminating case studies that elucidate each trend and its impact. Next, the book outlines the skills required to be successful in this new environment and offers techniques that can be applied to fully master them. Each of these skills is aligned with a case study in which the skills and techniques are used and the outcomes discussed. Finally, the book ties together the evolution of the project landscape, the challenges posed, and the new skills required to manage projects successfully in today's environment. The book also provides a checklist of the skills and techniques in the context of different project scenarios. Written by an expert in project management Written by an expert with a career of hands-on experience in the project management field, this volume is invaluable for project managers who have a fundamental background in project management principles and want to augment their toolbox of skills and techniques around the delivery of projects in today's complicated business environment.

Want to kill it at your job interview in the tech industry? Want to win that coding competition? Learn all the algorithmic techniques and programming skills you need from two experienced coaches, problem setters, and jurors for coding competitions. The authors highlight the versatility of each algorithm by considering a variety of problems and show how to implement algorithms in simple and efficient code. Readers can expect to master 128 algorithms in Python and discover the right way to tackle a problem and quickly implement a solution of low complexity. Classic problems like Dijkstra's shortest path algorithm and Knuth-Morris-Pratt's string matching algorithm are featured alongside lesser known data structures like Fenwick trees and Knuth's dancing links. The book provides a framework to tackle algorithmic problem solving, including: Definition, Complexity, Applications, Algorithm, Key Information, Implementation, Variants, In Practice, and Problems. Python code included in the book and on the companion website.

Although the use of cloud computing platforms and applications has expanded rapidly, most books on the subject focus on high-level concepts. There has long been a need for a book that provides detailed guidance on how to develop secure clouds.
Filling this void, Developing and Securing the Cloud provides a comprehensive overview of cloud computing technology. Supplying step-by-step instruction on how to develop and secure cloud computing platforms and web services, it includes an easy-to-understand, basic-level overview of cloud computing and its supporting technologies.
Presenting a framework for secure cloud computing development, the book describes supporting technologies for the cloud such as web services and security. It details the various layers of the cloud computing framework, including the virtual machine monitor and hypervisor, cloud data storage, cloud data management, and virtual network monitor. It also provides several examples of cloud products and prototypes, including private, public, and U.S. government clouds.
Reviewing recent developments in cloud computing, the book illustrates the essential concepts, issues, and challenges in developing and securing today s cloud computing platforms and applications. It also examines prototypes built on experimental cloud computing systems that the author and her team have developed at the University of Texas at Dallas.
This diverse reference is suitable for those in industry, government, and academia. Technologists will develop the understanding required to select the appropriate tools for particular cloud applications. Developers will discover alternative designs for cloud development, and managers will understand if it s best to build their own clouds or contract them out.

Bring your ideas to life with the latest Arduino hardware and software Arduino is an affordable and readily available hardware development platform based around an open source, programmable circuit board. You can combine this programmable chip with a variety of sensors and actuators to sense your environment around you and control lights, motors, and sound. This flexible and easy-to-use combination of hardware and software can be used to create interactive robots, product prototypes and electronic artwork, whether you're an artist, designer or tinkerer. Arduino For Dummies is a great place to start if you want to find out about Arduino and make the most of its incredible capabilities. It helps you become familiar with Arduino and what it involves, and offers inspiration for completing new and exciting projects. - Covers the latest software and hardware currently on the market - Includes updated examples and circuit board diagrams in addition to new resource chapters - Offers simple examples to teach fundamentals needed to move onto more advanced topics - Helps you grasp what's possible with this fantastic little board Whether you're a teacher, student, programmer, hobbyist, hacker, engineer, designer, or scientist, get ready to learn the latest this new technology has to offer!

With the rapid growth of technology in society, communication networks have become a heavily researched topic. Implementing these advanced systems is a challenge, however, due to the abundance of optimization problems within these networks. The use of meta-heuristic algorithms and nature-inspired computing has become a prevalent technique among researchers for solving these complex problems within communication networks. Despite its popularity, this specific computing technique lacks the appropriate amount of research that is needed for professionals to grasp a definite understanding. Nature-Inspired Computing Applications in Advanced Communication Networks is a collection of innovative research on the methods and applications of natural computation techniques and algorithms within communication systems such as wireless sensor networks, vehicular adhoc networks, and internet of things. While highlighting topics including mobile sensor deployment, routing optimization, and sleep scheduling, this book is ideally designed for researchers, network professionals, computer scientists, mathematicians, developers, scholars, educators, and students seeking to enhance their understanding of nature-inspired computing and its solutions within various advanced communication networks.

Computer systems have become an important element of the world economy, with billions of dollars spent each year on development, manufacture, operation, and maintenance. Combining coverage of computer system reliability, safety, usability, and other related topics into a single volume, Computer System Reliability: Safety and Usability eliminates the need to consult many different and diverse sources in the hunt for the information required to design better computer systems. After presenting introductory aspects of computer system reliability such as safety, usability-related facts and figures, terms and definitions, and sources for obtaining useful information on computer system reliability, safety, and usability, the book: Reviews mathematical concepts considered useful to understanding subsequent chapters Presents various introductory aspects of reliability, safety, and usability and computer system reliability basics Covers software reliability assessment and improvement methods Discusses important aspects of software quality and human error and software bugs in computer systems Highlights software safety and Internet reliability Details important aspects of software usability including the need for considering usability during the software development phase, software usability engineering process, software usability inspection methods, software usability test methods, and guidelines for conducting software usability testing Elucidates web usability facts and figures, common design errors, web page design, tools for evaluating web usability, and questions to evaluate website message communication effectiveness Examines important aspects of computer system life cycle costing Written by systems reliability expert B.S. Dhillon, the book is accessible to all levels of readership, making it useful to beginners and seasoned professiona

Database technology is an important subject in Computer Science. Every large company and nation needs a database to store information. The technology has evolved from file systems in the 60's, to Hierarchical and Network databases in the 70's, to relational databases in the 80's, object-oriented databases in the 90's, and to XML documents and NoSQL today. As a result, there is a need to reengineer and update old databases into new databases. This book presents solutions for this task. In this fourth edition, Chapter 9 - Heterogeneous Database Connectivity (HDBC) offers a database gateway platform for companies to communicate with each other not only with their data, but also via their database. The ability of sharing a database can contribute to the applications of Big Data and surveys for decision support systems. The HDBC gateway solution collects input from the database, transfers the data into its middleware storage, converts it into a common data format such as XML documents, and then distributes them to the users. HDBC transforms the common data into the target database to meet the user's requirements, acting like a voltage transformer hub. The voltage transformer converts the voltage to a voltage required by the users. Similarly, HDBC transforms the database to the target database required by the users. This book covers reengineering for data conversion, integration for combining databases and merging databases and expert system rules, normalization for eliminating duplicate data from the database, and above all, HDBC connects all legacy databases to one target database for the users. The authors provide a forum for readers to ask questions and the answers are given by the authors and the other readers on the Internet.

Engineering a Compiler, Third Edition covers the latest developments in compiler technology, with new chapters focusing on semantic elaboration (the problems that arise in generating code from the ad-hoc syntax-directed translation schemes in a generated parser), on runtime support for naming and addressability, and on code shape for expressions, assignments and control-structures. Leading educators and researchers, Keith Cooper and Linda Torczon, have revised this popular text with a fresh approach to learning important techniques for constructing a modern compiler, combining basic principles with pragmatic insights from their own experience building state-of-the-art compilers.

Figure 1.1. An outdoor scene "A bus is passing three cars which are parking between trees at the side of the road. Houses having two storeys are lined up at the street. 3 4 Introduction Figure 1.2. An assembly scene There seems to be a small open place between the group of houses in the foreground and the store in the background." In such or a similar way the content of the natural scene shown above can be described. It is quite easy to give such a short description. The problem is somewhat more complex for the second image. First of all, it can be stated that the image does not show an everyday scene. It appears as a kind of man made surrounding. But everyone can accept the following statements about this image: 1. The image shows a snapshot of an assembly line. 2. The robot in front is screwing. 3. There is no person in the working area of the robots. 4. All objects on the conveyor belt are worked on by robots. There are no free objects on the belt.

Fast Python for Data Science is a hands-on guide to writing Python code that can process more data, faster, and with less resources. It takes a holistic approach to Python performance, showing you how your code, libraries, and computing architecture interact and can be optimized together. Written for experienced practitioners, Fast Python for Data Science dives right into practical solutions for improving computation and storage efficiency. You'll experiment with fun and interesting examples such as rewriting games in lower-level Cython and implementing a MapReduce framework from scratch. Finally, you'll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working. About the technology Fast, accurate systems are vital for handling the huge datasets and complex analytical algorithms that are common in modern data science. Python programmers need to boost performance by writing faster pure-Python programs, optimizing the use of libraries, and utilizing modern multi-processor hardware; Fast Python for Data Science shows you how.

* Targests readers with a background in programming, interested in an introduction/refresher in statistical hypothesis testing * Uses Python throughout * Provides the reader with the opportunity of using the book whenever needed rather than following a sequential path.

The C programming language is a popular language in industries as well as academics. Since its invention and standardized as ANSI C, several other standards known as C99, C11, and C17 were published with new features in subsequent years. This book covers all the traits of ANSI C and includes new features present in other standards. The content of this book helps a beginner to learn the fundamental concept of the C language. The book contains a step-by-step explanation of every program that allows a learner to understand the syntax and builds a foundation to write similar programs. The explanation clarity, exercises, and illustrations present in this book make it a complete textbook in all aspects. Features: Other than ANSI C, the book explains the new C standards like C99, C11, and C17. Most basic and easy-to-follow programs are chosen to explain the concepts and their syntax. More emphasis is given to the topics like Functions, Pointers, and Structures. Recursion is emphasized with numerous programming examples and diagrams. A separate chapter on the command-line argument and preprocessors is included that concisely explains their usage. Several real-life figures are taken to explain the concepts of dynamic memory allocation, file handling, and the difference between structure and union. The book contains more than 260 illustrations, more than 200 programs, and exercises at the end of each chapter. This book serves as a textbook for UG/PG courses in science and engineering. The researcher, postgraduate engineers, and embedded software developers can also keep this book as reference material for their fundamental learning.