Provides guidance on performance enhancement and reliability of IC chips. Provides a detailed hybrid optimization strategy for the optimal arrangement of IC chips on a board. The MATLAB program for the hybrid optimization strategy along with its stability analysis is carried out in a detailed manner.

The Unified Modeling Language, better known as UML, has become the de facto standard modeling language for analyzing and designing software applications and systems. Software analysis and design is just as much an art as it is a science. UML Diagramming: A Catalog of Cases shows the art and the science behind successful software analysis and design with more than 35 case studies of applications of a variety of industries, including: Transportation Healthcare Supply chain management Education Agriculture Manufacturing The book explains UML diagramming through case studies to help systems and software developers specify, visualize, construct, and document the artifacts of software systems. The cases demonstrate how UML embodies software engineering best practices for modeling large and complex systems. They show how UML is an intuitive diagramming language that can be easily understood by end-users and business professionals. These cases studies also demonstrate how UML is a powerful language for communicating software designs to help developers and end users validate application scope, requirements, and features. Case studies highlighted in the book included: WEBMED healthcare service system services Inventory management system Business process outsourcing (BPO) management system Weather monitoring system Product recommendation system Textile management system Smart traffic management system Online pharmacy management system Placement automation system Farm management system Art gallery management system Website development This catalog of UML case studies is an invaluable reference for students studying software engineering, programmers starting out their careers, and seasoned systems developers needing a reference guide.

Data science and machine learning-two of the world's hottest fields-are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more-all created with Colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Book Features: Provides a unified view of the most popular metaheuristic methods currently in use Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems Covers design aspects and implementation in MATLAB (R) Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

The Unified Modeling Language, better known as UML, has become the de facto standard modeling language for analyzing and designing software applications and systems. Software analysis and design is just as much an art as it is a science. UML Diagramming: A Catalog of Cases shows the art and the science behind successful software analysis and design with more than 35 case studies of applications of a variety of industries, including: Transportation Healthcare Supply chain management Education Agriculture Manufacturing The book explains UML diagramming through case studies to help systems and software developers specify, visualize, construct, and document the artifacts of software systems. The cases demonstrate how UML embodies software engineering best practices for modeling large and complex systems. They show how UML is an intuitive diagramming language that can be easily understood by end-users and business professionals. These cases studies also demonstrate how UML is a powerful language for communicating software designs to help developers and end users validate application scope, requirements, and features. Case studies highlighted in the book included: WEBMED healthcare service system services Inventory management system Business process outsourcing (BPO) management system Weather monitoring system Product recommendation system Textile management system Smart traffic management system Online pharmacy management system Placement automation system Farm management system Art gallery management system Website development This catalog of UML case studies is an invaluable reference for students studying software engineering, programmers starting out their careers, and seasoned systems developers needing a reference guide.

Combining theory and practice, this updated new edition provides a complete overview of how to create deep and meaningful quests for games. It uses the Unity game engine in conjunction with Fungus and other free plugins to provide an accessible entry into quest design. The book begins with an introduction to the theory and history of quests in games, before covering four theoretical components of quests: their spaces, objects, actors, and challenges. Each chapter also includes a practical section, with accompanying exercises and suggestions for the use of specific technologies for four crucial aspects of quest design: * level design * quest item creation * NPC and dialogue construction * scripting This book will be of great interest to all game designers looking to create new, innovative quests in their games. It will also appeal to new media researchers, as well as humanities scholars in the fields of mythology and depth-psychology that want to bring computer-assisted instruction into their classroom in an innovative way. The companion website includes lecture and workshop slides, and can be accessed at: www.designingquests.com

With the introduction of Ferret, Ruby users now have one of the fastest and most flexible search libraries available. And it's surprisingly easy to use.

This book will show you how to quickly get up and running with Ferret. You'll learn how to index different document types such as PDF, Microsoft Word, and HTML, as well as how to deal with foreign languages and different character encodings. "Ferret" describes the Ferret Query Language in detail along with the object-oriented approach to building queries.

You will also be introduced to sorting, filtering, and highlighting your search results, with an explanation of exactly how you need to set up your index to perform these tasks. You will also learn how to optimize a Ferret index for lightning fast indexing and split-second query results.

Learning by Doing with National Instruments Development Boards starts with a brief introduction to LabVIEW programming, which is required to explore the National Instrument platform, an introduction that includes detailed installation and licensing setup. Further, it gives the features and configuration setup of NI SPEEDY-33, NI ELVIS and myRIO boards. The focus of the book is on worked-out case studies for students working in different areas of electronics such as basic digital design, biomedical instrumentation, sensors and measurement. Data acquisition using SPEEDY-33, NI -ELVIS and myRIO kits is also odiscussed. The book also examines the myRIO platform.

1) The book provides a basic understanding to readers who want to start their career in web application development. The book develops an understanding of the basics concepts and then takes a broader turn to enlighten the readers about the horizons which can be accessed using JavaScript. 2) JavaScript is exhaustively used in almost all kinds of application development, be it the web applications, cross platform mobile applications, server backend applications. JavaScript is now being used for full stack development and is thus used to write independent applications. Since the need for JavaScript web developers is high, the numbers of students should be high as well. 3) As compared to other books in the market, the proposed book would cover more topics (or, better, levels) in a single volume.

Do you have creative ideas that you wish you could transform into code? Do you want to boost your problem solving and logic skills? Do you want to enhance your career by adopting an algorithmic mindset? In our increasingly digital world, coding is an essential skill. Communicating an algorithm to a machine to perform a set of tasks is vital. Beginner's Guide to Code Algorithms: Experiments to Enhance Productivity and Solve Problems written by Deepankar Maitra teaches you how to think like a programmer. The author unravels the secret behind writing code - building a good algorithm. Algorithmic thinking leads to asking the right question and enables a shift from issue resolution to value creation. Having this mindset will make you more marketable to employers. This book takes you on a problem-solving journey to expand your mind and increase your willingness to experiment with code. You will: Learn the art of building an algorithm through hands-on exercises Understand how to develop code for inspiring productivity concepts Build a mentality of developing algorithms to solve problems Develop, test, review, and improve code through guided experimentation This book is designed to develop a culture of logical thinking through intellectual stimulation. It will benefit students and teachers of programming, business professionals, as well as experienced users of Microsoft Excel who wish to become proficient with macros.

Resolves linear and non-linear data structures in C language using the algorithm, diagrammatically and its time and space complexity analysis. Covers interview questions and MCQs on all topics of campus readiness Identifies possible solutions to each problem. Includes real life and computational applications of linear and non-linear data structures

Discusses core concepts and principles of deep learning in gaming and animation with applications. Covers application of neural networks for intelligent video game character. Discusses automated video generation and real time animation. Covers technological advancements in virtual reality (VR), and augmented reality (AR).

This book covers theoretical aspects of the physical processes, derivation of the governing equations and their solutions. It focusses on hydraulics, hydrology, and contaminant transport, including implementation of computer codes with practical examples. Python-based computer codes for all the solution approaches are provided for better understanding and easy implementation. The mathematical models are demonstrated through applications and the results are analyzed through data tables, plots, and comparison with analytical and experimental data. The concepts are used to solve practical applications like surface and ground water flow, flood routing, crop water requirement and irrigation scheduling. Combines the area of computational hydraulics, hydrology, and water resources engineering with Python Gives deep description of the basic equations and the numerical solutions of both 1D and 2D problems including the numerical codes Includes step-by-step translation of numerical algorithms in computer codes with focus on learners and practitioners Demonstration of theory, mathematical models through practical applications Analysis of each example through data tables, plots, and correlation with reality This book is aimed at senior undergraduates and graduate students in Civil Engineering, Coastal Engineering, Hydrology, and Water Resources Engineering.

Software development has been a troubling since it first started. There are seven chronic problems that have plagued it from the beginning: Incomplete and ambiguous user requirements that grow by >2% per month. Major cost and schedule overruns for large applications > 35% higher than planned. Low defect removal efficiency (DRE) < 85% on large systems. Cancelled projects that are not completed: > 30% above 10,000 function points. Poor quality and low reliability after the software is delivered: > 5 bugs per FP. Breach of contract litigation against software outsource vendors. Expensive maintenance and enhancement costs after delivery. These are endemic problems for software executives, software engineers and software customers but they are not insurmountable. In Software Development Patterns and Antipatterns, software engineering and metrics pioneer Capers Jones presents technical solutions for all seven. The solutions involve moving from harmful patterns of software development to effective patterns of software development. The first section of the book examines common software development problems that have been observed in many companies and government agencies. The data on the problems comes from consulting studies, breach of contract lawsuits, and the literature on major software failures. This section considers the factors involved with cost overruns, schedule delays, canceled projects, poor quality, and expensive maintenance after deployment. The second section shows patterns that lead to software success. The data comes from actual companies. The section's first chapter on Corporate Software Risk Reduction in a Fortune 500 company was based on a major telecom company whose CEO was troubled by repeated software failures. The other chapters in this section deal with methods of achieving excellence, as well as measures that can prove excellence to C-level executives, and with continuing excellence through the maintenance cycle as well as for software development.

Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists. Readers only need a basic understanding of Python includes over a hundred exercises to allow readers to practice their skills

Start Writing Production-Ready Go Code Fast Thousands of developers and teams want to start taking advantage of Go, the powerful language used in projects ranging from Kubernetes to Docker and Vault. Go Fundamentals is specifically designed to get you up-to-speed fast, to leverage your existing knowledge of other languages, and to help you avoid common mistakes made by Go newcomers. Based on author Mark Bates's and Cory LaNou's pioneering Gopher Guides training curricula, this guide will allow you to quickly understand and use Go syntax, core features, and idioms. Reflecting Go through version 1.18--which includes Go's exciting new support for generics--this guide prepares you to write robust, reliable, well-performing production code right from the outset. Learn how Go manages packages, modules, and dependencies Apply Go basics, such as variable declaration, types, and control flow Work effectively with collection types, iteration, functions, structs, and pointers Understand Go Slices and use them properly Write idiomatic Go, using principles such as embedding and composition Expertly use concurrency to improve code performance Create proper tests to quickly identify and fix problems Write simpler, better code with generics and interfaces Take advantage of channels, context, sync primatives, and other advanced features Go is lightweight, simple, and perfect for modern cloud-native and microservices development, which is why Go developers are in such high demand. With this guide and six months' experience with any modern programming language, you'll have what you need to leap into Go programming. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists. Readers only need a basic understanding of Python includes over a hundred exercises to allow readers to practice their skills

Software Engineering: A Methodical Approach (Second Edition) provides a comprehensive, but concise introduction to software engineering. It adopts a methodical approach to solving software engineering problems, proven over several years of teaching, with outstanding results. The book covers concepts, principles, design, construction, implementation, and management issues of software engineering. Each chapter is organized systematically into brief, reader-friendly sections, with itemization of the important points to be remembered. Diagrams and illustrations also sum up the salient points to enhance learning. Additionally, the book includes the author's original methodologies that add clarity and creativity to the software engineering experience. New in the Second Edition are chapters on software engineering projects, management support systems, software engineering frameworks and patterns as a significant building block for the design and construction of contemporary software systems, and emerging software engineering frontiers. The text starts with an introduction of software engineering and the role of the software engineer. The following chapters examine in-depth software analysis, design, development, implementation, and management. Covering object-oriented methodologies and the principles of object-oriented information engineering, the book reinforces an object-oriented approach to the early phases of the software development life cycle. It covers various diagramming techniques and emphasizes object classification and object behavior. The text features comprehensive treatments of: Project management aids that are commonly used in software engineering An overview of the software design phase, including a discussion of the software design process, design strategies, architectural design, interface design, database design, and design and development standards User interface design Operations design Design considerations including system catalog, product documentation, user message management, design for real-time software, design for reuse, system security, and the agile effect Human resource management from a software engineering perspective Software economics Software implementation issues that range from operating environments to the marketing of software Software maintenance, legacy systems, and re-engineering This textbook can be used as a one-semester or two-semester course in software engineering, augmented with an appropriate CASE or RAD tool. It emphasizes a practical, methodical approach to software engineering, avoiding an overkill of theoretical calculations where possible. The primary objective is to help students gain a solid grasp of the activities in the software development life cycle to be confident about taking on new software engineering projects.

Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds. You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results.

Microservices architecture (MSA) is increasingly popular with software architects and engineers as it accelerates software solution design, development, and deployment in a risk-free manner. Placing a software system into a production environment is elegantly simplified and sped up with the use of MSA development platforms, runtime environments, acceleration engines, design patterns, integrated frameworks, and related tools. The MSA ecosystem is expanding with third-party products that automate as many tasks as possible. MSA is being positioned as the enterprise-grade and agile-application design method. This book covers in-depth the features and facilities that make up the MSA ecosystem. Beginning with an overview of Service-Oriented Architecture (SOA) that covers the Common Object Request Broker Architecture (CORBA), Distributed Component Object Model (DCOM), and Remote Method Invocation (RMI), the book explains the basic essentials of MSA and the continuous delivery of applications to customers. The book gives software developers insight into: Current and emerging communication models Key architectural elements of MSA-based applications Designing efficient APIs for microservices MSA middleware platforms such as REST, SOAP, Apache Thrift, and gRPC Microservice discovery and the API gateway Service orchestration and choreography for composing individual services to achieve a useful business process Database transactions in MSA-centric applications Design, composition, security, and deployment patterns MSA security Modernizing legacy applications The book concludes with a chapter on composing and building powerful microservices. With the exponential growth of IoT devices, microservices are being developed and deployed on resource-constrained but resource-intensive devices in order to provide people-centric applications. The book discusses the challenges of these applications. Finally, the book looks at the role of microservices in smart environments and upcoming trends including ubiquitous yet disappearing microservices.

Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This success, however, is conditioned on availability of huge annotated datasets to training AI models. Data annotation is a time-consuming and expensive task which still is being performed by human workers. Learning efficiently from less data is a next step for making AI more similar to natural intelligence. Transfer learning has been suggested a remedy to relax the need for data annotation. The core idea in transfer learning is to transfer knowledge across similar tasks and use similarities and previously learned knowledge to learn more efficiently. In this book, we provide a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities. We cover various machine learning scenarios and demonstrate that this idea can be used to overcome challenges of zero-shot learning, few-shot learning, domain adaptation, continual learning, lifelong learning, and collaborative learning.

Software development has been a troubling since it first started. There are seven chronic problems that have plagued it from the beginning: Incomplete and ambiguous user requirements that grow by >2% per month. Major cost and schedule overruns for large applications > 35% higher than planned. Low defect removal efficiency (DRE) < 85% on large systems. Cancelled projects that are not completed: > 30% above 10,000 function points. Poor quality and low reliability after the software is delivered: > 5 bugs per FP. Breach of contract litigation against software outsource vendors. Expensive maintenance and enhancement costs after delivery. These are endemic problems for software executives, software engineers and software customers but they are not insurmountable. In Software Development Patterns and Antipatterns, software engineering and metrics pioneer Capers Jones presents technical solutions for all seven. The solutions involve moving from harmful patterns of software development to effective patterns of software development. The first section of the book examines common software development problems that have been observed in many companies and government agencies. The data on the problems comes from consulting studies, breach of contract lawsuits, and the literature on major software failures. This section considers the factors involved with cost overruns, schedule delays, canceled projects, poor quality, and expensive maintenance after deployment. The second section shows patterns that lead to software success. The data comes from actual companies. The section's first chapter on Corporate Software Risk Reduction in a Fortune 500 company was based on a major telecom company whose CEO was troubled by repeated software failures. The other chapters in this section deal with methods of achieving excellence, as well as measures that can prove excellence to C-level executives, and with continuing excellence through the maintenance cycle as well as for software development.

Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts introduces advanced software engineering to software engineers, scientists, postdoctoral researchers, academicians, software consultants, management executives, doctoral students, and advanced level postgraduate computer science students. This book contains research articles addressing numerous software engineering research challenges associated with various software development-related activities, including programming, testing, measurements, human factors (social software engineering), specification, quality, program analysis, software project management, and more. It provides relevant theoretical frameworks, empirical research findings, and evaluated solutions addressing the research challenges associated with the above-mentioned software engineering activities. To foster collaboration among the software engineering research community, this book also reports datasets acquired systematically through scientific methods and related to various software engineering aspects that are valuable to the research community. These datasets will allow other researchers to use them in their research, thus improving the quality of overall research. The knowledge disseminated by the research studies contained in the book will hopefully motivate other researchers to further innovation in the way software development happens in real practice.

This is the first book to explain Ripple-Down Rules, an approach to building knowledge-based systems which is more similar to machine learning methods than other rule-based systems but which depends on using an expert rather than applying statistics to data The book provides detailed worked examples and uses publicly available software to demonstrate Ripple-Down Rules The examples enable users to build their own RDR tools

This is the first book to explain Ripple-Down Rules, an approach to building knowledge-based systems which is more similar to machine learning methods than other rule-based systems but which depends on using an expert rather than applying statistics to data The book provides detailed worked examples and uses publicly available software to demonstrate Ripple-Down Rules The examples enable users to build their own RDR tools