Designed for professionals and advanced students, Pointers On C provides a comprehensive resource for those needing in-depth coverage of the C programming language. An extensive explanation of pointer basics and a thorough exploration of their advanced features allows programmers to incorporate the power of pointers into their C programs. Complete coverage, detailed explanations of C programming idioms, and thorough discussion of advanced topics makes Pointers On C a valuable tutorial and reference for students and professionals alike.

Graph grammars originated in the late 60s, motivated by considerations about pattern recognition and compiler construction. Since then the list of areas which have interacted with the development of graph grammars has grown quite impressively. Besides the aforementioned areas it includes software specification and development, VLSI layout schemes, database design, modeling of concurrent systems, massively parallel computer architectures, logic programming, computer animation, developmental biology, music composition, visual languages, and many others.The area of graph grammars and graph transformations generalizes formal language theory based on strings and the theory of term rewriting based on trees. As a matter of fact within the area of graph grammars, graph transformation is considered a fundamental programming paradigm where computation includes specification, programming, and implementation.Over the last 25-odd years graph grammars have developed at a steady pace into a theoretically attractive and well-motivated research field. In particular, they are now based on very solid foundations, which are presented in this volume. Volume 1 of the indispensable Handbook of Graph Grammars and Computing by Graph Transformations includes a state-of-the-art presentation of the foundations of all the basic approaches to rule-based graph specification and transformation: algebraic approach, logic approach, node-based rewriting, (hyper)edge-based rewriting, programmed graph rewriting, and 2-structures. The book has been written in a tutorial/survey style to enhance its usefulness.

As Cavalli and Sarma astutely remarked in the introduction to this volume, it is quite remarkable that SDL '97 may have the first participant younger than SDL itself. SDL '97 provides the opportunity to reflect the course SDL has taken and why it has been successful over two decades where other languages addressing the same market have failed.

SDL now also has a permanent companion in MSC (Message Sequence Charts). MSC today is a language in its own right and has its areas of application both in conjunction with SDL and independently or in combination with other techniques. MSC has strong structuring concepts to specify message sequences for large systems and can be used to develop scenarios, which is extremely useful for test and design environments. The SDL Forum today really is the SDL and MSC Forum.

"This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Universite Cote d'Azur, Nice, France Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist." Professor Charles Bouveyron INRIA Chair in Data Science Universite Cote d'Azur, Nice, France

Barry Kauler explains the exacting details of Windows programming at the systems level, revealing architectural details that Microsoft has never publicly documented. Ideal for software developers who are moving applications from 3.x to Win95, the book gives how-to information and systems programming tips and tricks. The companion disk contains all example programs, source code, and utilities in the book.

This is the second edition of the first introductory textbook written for the FORTRAN 90 standard. It remains suitable for the novice scientific programmer, drawing on a larger number of examples and exercises in this new edition.

For courses in introductory Computer Science courses using Java, and other introductory programming courses in Computer Science, Computer Engineering, CIS, MIS, IT, and Business. Ideal for a wide range of introductory computer science courses, Java: An Introduction to Problem Solving and Programming, 8th Edition introduces students to object-oriented programming and important concepts such as design, testing and debugging, programming style, interfaces and inheritance, and exception handling. A concise, accessible introduction to Java, the text covers key Java language features in a manner that resonates with introductory programmers. Objects are covered early and thoroughly in the text. The author's tried-and-true pedagogy incorporates numerous case studies, programming examples, and programming tips, while flexibility charts and optional graphics sections allow instructors to order chapters and sections based on their course needs. This 8th Edition incorporates new examples, updated material, and revisions.

Security is too important to be left in the hands of just one department or employee-it's a concern of an entire enterprise. Enterprise Security Architecture shows that having a comprehensive plan requires more than the purchase of security software-it requires a framework for developing and maintaining a system that is proactive. The book is based around the SABSA layered framework. It provides a structured approach to the steps and processes involved in developing security architectures. It also considers how some of the major business issues likely to be encountered can be resolved.

Developing a large-scale software system in C++ requires more than just a sound understanding of the logical design issues covered in most books on C++ programming. To be successful, you will also need a grasp of physical design concepts that, while closely tied to the technical aspects of development, include a dimension with which even expert software developers may have little or no experience.

This is the definitive book for all C++ software professionals involved in large development efforts such as databases, operating systems, compilers, and frameworks. It is the first C++ book that actually demonstrates how to design large systems, and one of the few books on object-oriented design specifically geared to practical aspects of the C++ programming language.

In this book, Lakos explains the process of decomposing large systems into physical (not inheritance) hierarchies of smaller, more manageable components. Such systems with their acyclic physical dependencies are fundamentally easier and more economical to maintain, test, and reuse than tightly interdependent systems. In addition to explaining the motivation for following good physical as well as logical design practices, Lakos provides you with a catalog of specific techniques designed to eliminate cyclic, compile-time, and link-time (physical) dependencies. He then extends these concepts from large to very large systems. The book concludes with a comprehensive top-down approach to the logical design of individual components. Appendices include a valuable design pattern "Protocol Hierarchy" designed to avoid fat interfaces while minimizing physical dependencies; the details of implementing an ANSI C compatible C++procedural interface; and a complete specification for a suite of UNIX-like tools to extract and analyze physical dependencies. Practical design rules, guidelines, and principles are also collected in an appendix and indexed for quick reference.
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This book can be used as an experiment and reference book for algorithm design courses, as well as a training manual for programming contests. It contains 247 problems selected from ACM-ICPC programming contests and other programming contests. There's detailed analysis for each problem. All problems, and test datum for most of problems will be provided online. The content will follow usual algorithms syllabus, and problem-solving strategies will be introduced in analyses and solutions to problem cases. For students in computer-related majors, contestants and programmers, this book can polish their programming and problem-solving skills with familarity of algorithms and mathematics.

Partial differential equations (PDEs) play an important role in the natural sciences and technology, because they describe the way systems (natural and other) behave. The inherent suitability of PDEs to characterizing the nature, motion, and evolution of systems, has led to their wide-ranging use in numerical models that are developed in order to analyze systems that are not otherwise easily studied. Numerical Solutions for Partial Differential Equations contains all the details necessary for the reader to understand the principles and applications of advanced numerical methods for solving PDEs. In addition, it shows how the modern computer system algebra MathematicaA(R) can be used for the analytic investigation of such numerical properties as stability, approximation, and dispersion.

Developed from the author's many years of teaching computing courses, Programming in C plus plus for Engineering and Science guides students in designing programs to solve real problems encountered in engineering and scientific applications. These problems include radioactive decay, pollution indexes, digital circuits, differential equations, Internet addresses, data analysis, simulation, quality control, electrical networks, data encryption, beam deflection, and many other areas. To make it easier for novices to develop programs, the author uses an object-centered design approach that helps students identify the objects in a problem and the operations needed; develop an algorithm for processing; implement the objects, operations, and algorithm in a program; and test, correct, and revise the program. He also revisits topics in greater detail as the text progresses. By the end of the book, students will have a solid understanding of how C plus plus can be used to process complex objects, including how classes can be built to model objects. Web ResourceThe book's website at http://cs.calvin.edu/books/c plus plus/engr-sci provides source code, expanded presentations, links to relevant sites, reference materials, lab exercises, and projects. For instructors, solutions to exercises and PowerPoint slides for classroom use are available upon qualifying course adoption.

The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks.
Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective. The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary.
C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method.
The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.

This book demonstrates how Processing is an excellent language for beginners to learn the fundamentals of computer programming. Originally designed to make it simpler for digital artists to learn to program, Processing is a wonderful first language for anyone to learn. Given its origins, Processing enables a multimodal approach to programming instruction, well suited to students with interests in computer science or in the arts and humanities. The book uses Processing's capabilities for graphics and interactivity in order to create examples that are simple, illustrative, interesting, and fun. It is designed to appeal to a broad range of readers, including those who want to learn to program to create digital art, as well as those who seek to learn to program to process numerical information or data. It can be used by students and instructors in a first course on programming, as well as by anyone eager to teach them self to program. Following a traditional sequence of topics for introducing programming, the book introduces key computer science concepts, without overwhelming readers with extensive detail. Additional exercises are available, as are other supplementary materials for instructors looking to introduce even more computer science concepts associated with the topics. Several online chapters are also provided that introduce slightly more advanced topics in Processing, such as two-dimensional arrays, manipulation of strings, and file input and output. The conversational style and pace of the book are based upon the authors' extensive experience with teaching programming to a wide variety of beginners in a classroom. No prior programming experience is expected.

For problems that require extensive computation, a C++ program can race through billions of examples faster than most other computing choices. C++ enables mathematicians of virtually any discipline to create programs to meet their needs quickly, and is available on most computer systems at no cost. C++ for Mathematicians: An Introduction for Students and Professionals accentuates C++ concepts that are most valuable for pure and applied mathematical research. This is the first book available on C++ programming that is written specifically for a mathematical audience; it omits the language's more obscure features in favor of the aspects of greatest utility for mathematical work. The author explains how to use C++ to formulate conjectures, create images and diagrams, verify proofs, build mathematical structures, and explore myriad examples. Emphasizing the essential role of practice as part of the learning process, the book is ideally designed for undergraduate coursework as well as self-study. Each chapter provides many problems and solutions which complement the text and enable you to learn quickly how to apply them to your own problems. Accompanying downloadable resources provide all numbered programs so that readers can easily use or adapt the code as needed. Presenting clear explanations and examples from the world of mathematics that develop concepts from the ground up, C++ for Mathematicians can be used again and again as a resource for applying C++ to problems that range from the basic to the complex.

Jumpstart your MFC programming without the tedious study of C plus plus! Now you can learn C plus plus and MFC together -- learning C plus plus principles on a need-to-know basis. Author Richard Raposa has refined this tutorial over years of teaching Windows programming in quick

Build complex embedded systems faster and with lower costs by: Knowing when and how much simulation testing is appropriate Applying engineering methods to simulation design and development Using the best tools available to develop simulations. Va

This extensive library of computer programs-written in C language-allows readers to solve numerical problems in areas of linear algebra, ordinary and partial differential equations, optimization, parameter estimation, and special functions of mathematical physics.
The library is based on NUMAL, the program assemblage developed and used at the Centre for Mathematics and Computer Science in Amsterdam, one of the world's leading research centers. The important characteristic of the library is its modular structure. Because it is highly compact, it is well-suited for use on personal computers.
The library offers the expert a prodigious collection of procedures for implementing numerical methods. The novice can experiment with the worked examples provided and use the more comprehensive procedures to perform mathematical computations. The library provides a powerful research tool for computer scientists, engineers, and applied mathematicians.

Designed as a self-study guide, the book describes the real-world tradeoffs encountered in building a production-quality, platform-retargetable compiler. The authors examine the implementation of lcc, a production-quality, research-oriented retargetable compiler, designed at AT&T Bell Laboratories for the ANSI C programming language. The authors' innovative approach-a "literate program" that intermingles the text with the source code-uses a line-by-line explanation of the code to demonstrate how lcc is built.

This second edition of Compact Numerical Methods for Computers presents reliable yet compact algorithms for computational problems. As in the previous edition, the author considers specific mathematical problems of wide applicability, develops approaches to a solution and the consequent algorithm, and provides the program steps. He emphasizes useful applicable methods from various scientific research fields, ranging from mathematical physics to commodity production modeling. While the ubiquitous personal computer is the particular focus, the methods have been implemented on computers as small as a programmable pocket calculator and as large as a highly parallel supercomputer. New to the Second Edition Presents program steps as Turbo Pascal code Includes more algorithmic examples Contains an extended bibliography The accompanying software (available by coupon at no charge) includes not only the algorithm source codes, but also driver programs, example data, and several utility codes to help in the software engineering of end-user programs. The codes are designed for rapid implementation and reliable use in a wide variety of computing environments. Scientists, statisticians, engineers, and economists who prepare/modify programs for use in their work will find this resource invaluable. Moreover, since little previous training in numerical analysis is required, the book can also be used as a supplementary text for courses on numerical methods and mathematical software.

This is the second edition of the first introductory textbook written for the FORTRAN 90 standard. It remains suitable for the novice scientific programmer, drawing on a larger number of examples and exercises in this new edition.

This book describes the concepts and application of model-based development (MBD), model transformations, and Agile MBD to a wide range of software systems. It covers systems requirements engineering, system specification and design, verification, reuse, and system composition in the context of Agile MBD. Examples of applications in finance, system migration, internet systems and software refactoring are given. An established open-source MBD technology, UML-RSDS, is used throughout to illustrate the concepts. The book is suitable for industrial practitioners who need training in Agile MBD, and those who need to understand the issues to be considered when introducing MBD in an industrial context. It is also suitable for academic researchers, and for use as text for undergraduate or postgraduate courses in MBD. Examples for educational use of UML-RSDS are included in the book.

Does it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Python is an easy language to learn and use, but that also means systems can quickly grow beyond comprehension. Thankfully, Python has features to help developers overcome maintainability woes. In this practical book, author Patrick Viafore shows you how to use Python's type system to the max. You'll look at user-defined types, such as classes and enums, and Python's type hinting system. You'll also learn how to make Python extensible and how to use a comprehensive testing strategy as a safety net. With these tips and techniques, you'll write clearer and more maintainable code. Learn why types are essential in modern development ecosystems Understand how type choices such as classes, dictionaries, and enums reflect specific intents Make Python extensible for the future without adding bloat Use popular Python tools to increase the safety and robustness of your codebase Evaluate current code to detect common maintainability gotchas Build a safety net around your codebase with linters and tests

Leverage Modern Language Constructs to Write High-Quality Code Faster The functional and concurrent programming language features supported by modern languages can be challenging, even for experienced developers. These features may appear intimidating to OOP programmers because of a misunderstanding of how they work. Programmers first need to become familiar with the abstract concepts that underlie these powerful features. In Functional and Concurrent Programming, Michel Charpentier introduces a core set of programming language constructs that will help you be productive in a variety of programming languages-now and in the future. Charpentier illustrates key concepts with numerous small, focused code examples, written in Scala, and with case studies that provide a thorough grounding in functional and concurrent programming skills. These skills will carry from language to language-including the most recent incarnations of Java. Using these features will enable developers and programmers to write high-quality code that is easier to understand, debug, optimize, and evolve. Key topics covered include: Recursion and tail recursion Pattern matching and algebraic datatypes Persistent structures and immutability Higher-order functions and lambda expressions Lazy evaluation and streams Threads and thread pools Atomicity and locking Synchronization and thread-safe objects Lock-free, non-blocking patterns Futures, promises, and functional-concurrent programming As a bonus, the book includes a discussion of common typing strategies used in modern programming languages, including type inference, subtyping, polymorphism, type classes, type bounds, and type variance. Most of the code examples are in Scala, which includes many of the standard features of functional and concurrent programming; however, no prior knowledge of Scala is assumed. You should be familiar with concepts such as classes, methods, objects, types, variables, loops, and conditionals and have enough programming experience to not be distracted by simple matters of syntax.

Data structures provide a means to managing large amounts of information such as large databases, using SEO effectively, and creating Internet/Web indexing services. This book is designed to present fundamentals of data structures for beginners using the Java programming language in a friendly, self-teaching format. Practical analogies using real world applications are integrated throughout the text to explain technical concepts. The book includes a variety of end-of-chapter practice exercises, e.g., programming, theoretical, and multiple-choice.