Articles by "Computer Engineering"

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Showing posts with label Computer Engineering. Show all posts

Walter Savitch, Kenrick Mock ... 984 pages - Publisher: Pearson; 5th edition (March, 2012) ... Language: English - ISBN-10: 013283071X - ISBN-13: 978-0132830713

Praised for providing an engaging balance of thoughtful examples and explanatory discussion, best-selling author Walter Savitch explains concepts and techniques in a straightforward style using understandable language and code enhanced by a suite of pedagogical tools. Absolute C++ is appropriate for both introductory and intermediate C++ programmers. This edition is available with MyProgrammingLab, an innovative online homework and assessment tool. Through the power of practice and immediate personalized feedback, MyProgrammingLab helps students fully grasp the logic, semantics, and syntax of programming.

Marco Dorigo, Thomas Stützle ... 319 pages - Publisher: Bradford Book; (June, 2004) ... Language: English - ISBN-10: 0262042193 - ISBN-13: 978-0262042192

The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

R. Venkata Rao ... 334 pages - Publisher: Springer; (June, 2018) ... Language: English - ISBN-10: 331978921X - ISBN-13: 978-3319789217

This book introduces readers to the “Jaya” algorithm, an advanced optimization technique that can be applied to many physical and engineering systems. It describes the algorithm, discusses its differences with other advanced optimization techniques, and examines the applications of versions of the algorithm in mechanical, thermal, manufacturing, electrical, computer, civil and structural engineering. In real complex optimization problems, the number of parameters to be optimized can be very large and their influence on the goal function can be very complicated and nonlinear in character. Such problems cannot be solved using classical methods and advanced optimization methods need to be applied. The Jaya algorithm is an algorithm-specific parameter-less algorithm that builds on other advanced optimization techniques. The application of Jaya in several engineering disciplines is critically assessed and its success compared with other complex optimization techniques such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and other recently developed algorithms.

Ke-Lin Du, M. N. S. Swamy ... 489 pages - Publisher: Springer; (December, 2016) ... Language: English - ISBN-10: 3319454013 - ISBN-13: 978-3319454016

This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing.  Over 100 different types of these methods are discussed in detail.  The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material.  Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others.  General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described.  Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics.  Introduced in the appendix are some benchmarks for the evaluation of metaheuristics. Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science.  It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.

July 06, 2019 , ,
William Bober ... 274 pages - Publisher: CRC Press; 1st edition (August, 2017) ... Language: English - ISBN-10: 1138032379 - ISBN-13: 978-1138032378

All disciplines of science and engineering use numerical methods for complex problem analysis, due to the highly mathematical nature of the field. Analytical methods alone are unable to solve many complex problems engineering students and professionals confront. Introduction to MATLAB® Programming for Engineers and Scientists examines the basic elements of code writing, and describes MATLAB® methods for solving common engineering problems and applications across the range of engineering disciplines. The text uses a class-tested learning approach and accessible two-color page design to guide students from basic programming to the skills needed for future coursework and engineering practice. Features: Presents clear and detailed explanations of the basic steps in learning MATLAB * Includes real-world examples from all major engineering disciplines * Prepares students for Numerical numerical Methods methods and other courses that involve MATLAB * Incudes two-color graphics to highlight MATLAB screen shots and step-by-step procedures * Supported by a range of instructor supplements and optional MATLAB code

Stephen J. Chapman ... 1056 pages - Publisher: McGraw-Hill; 4th edition (March 2017) ... Language: English - ISBN-10: 0073385891 - ISBN-13: 978-0073385891

Fortran for Scientists and Engineers teaches simutaneously both the fundamentals of the Fortran language and a programming style that results in good, maintainable programs. In addition, it serves as a reference for Professionals working in the industry. Among its strengths are its concise, clear explanations of Fortran Syntax and Programming Procedures, the inclusion of a wealth of examples and exercises to help students grasp difficult concepts, and its explanations about how to understand code written for older versions of Fortran.

Ian Chivers, Jane Sleightholme ... 956 pages - Publisher: Springer; 4th edition (August, 2018) ... Language: English - ISBN-10: 3319755013 - ISBN-13: 978-3319755014

This fourth Edition presents new examples on submodules, derived type i/o, object oriented programming, abstract interfaces and procedure pointers, C interop, sorting and searching, statistics and converting to more modern versions of Fortran. Key Features: Highlights the core language features of modern Fortran including data typing, array processing, control structures, functions, subroutines, modules and submodules, user defined types, pointers, operator overloading, generic programming, parallel programming, abstract interfaces, procedure pointers. Pinpoints common problems that occur when programming. Illustrates the use of several compilers. Introduction to Programming with Fortran has been written for the complete beginner with little or no programming background as well as existing Fortran programmers and those with programming experience in other languages.

Ian Chivers, Jane Sleightholme, Balakumar Balachandran, James Duncan, Keith Herold, Gregory Walsh ... 674 pages - Publisher: Springer; 3rd edition (August, 2015) ... Language: English - ISBN-10: 3319177001 - ISBN-13: 978-3319177007

In response to feedback from course delegates this third edition has been revised throughout. It expands on the second edition with new and updated examples in the chapters on arithmetic, i/o, character data, modules, data structuring and generic programming with minor updates to the rest of the chapters. Key Features: Lots of clear, simple examples highlighting the core language features  of modern Fortran including data typing, array processing,  control structures, functions, subroutines, modules,  user defined types, pointers, operator overloading, generic programming, object oriented programming and parallel programming. + Pinpoints common problems that occur when programming + Illustrates the use of several compilers + With better standards conformance in compilers  there are new examples illustrating the following major features: C Interop + IEEE arithmetic + parameterised derived types. Introduction to Programming with Fortran will appeal to the complete beginner, existing. Fortran programmers wishing to update their code and those with programming experience in other languages.

May 24, 2019 , ,
Norman S. Clerman, Walter Spector ... 352 pages - Publisher: Cambridge Univ. Press; 1st edition (December, 2011) ... Language: English - ISBN-10: 052173052X - ISBN-13: 978-0521730525

Fortran is one of the oldest high-level languages and remains the premier language for writing code for science and engineering applications. Modern Fortran: Style and Usage is a book for anyone who uses Fortran, from the novice learner to the advanced expert. It describes best practices for programmers, scientists, engineers, computer scientists, and researchers who want to apply good style and incorporate rigorous usage in their own Fortran code or to establish guidelines for a team project. The presentation concentrates primarily on the characteristics of Fortran 2003, while also describing methods in Fortran 90/95 and valuable new features in Fortran 2008. The authors draw on more than a half century of experience writing production Fortran code to present clear succinct guidelines on formatting, naming, documenting, programming, and packaging conventions and various programming paradigms such as parallel processing (including OpenMP, MPI, and coarrays), OOP, generic programming, and C language interoperability. Programmers working with legacy code will especially appreciate the section on updating old programs.

April 04, 2019 , ,
Silas Toms ... 210 pages - Publisher: Packt Publishing; (February, 2015) ... Language: English - ISBN-10: 1783988665 - ISBN-13: 978-1783988662 ...

Use the ArcPy module to automate the analysis and mapping of geospatial data in ArcGIS: Perform GIS analysis faster by automating tasks, such as selecting data or buffering data, by accessing GIS tools using scripting. Access the spatial data contained within shapefiles and geodatabases, for updates, analysis and even transformation between spatial reference systems. Produce map books and automate the mapping of geospatial analyses, reducing the time needed to produce and display the results. Who This Book Is For: If you are a GIS student or professional who needs an understanding of how to use ArcPy to reduce repetitive tasks and perform analysis faster, this book is for you. It is also a valuable book for Python programmers who want to understand how to automate geospatial analyses. What You Will Learn: Understand how to integrate Python into ArcGIS and make GIS analysis faster and easier + Model an analysis and export it to Python for further improvement + Create Python functions from exported scripts using ArcToolbox tools to avoid repetitive code + Update the records of interest in your existing geospatial data automatically using data cursors + Add new geospatial data to existing datasets automatically from field-collected data or data produced during analysis + Export formatted analysis results to spreadsheets automatically + Update map documents with analysis-generated data and export maps to PDF or image formats + Create geometric networks and analyze routes using scripts.

Benjamin Baka ... 312 pages - Publisher: Packt Publishing; (May, 2017) ... Language: English - ASIN: B01IF7NLM8 by Amazon ...

Data structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this book, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The book will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the book, you will learn how to build components that are easy to understand, debug, and use in different applications.

March 12, 2019 , ,
Kent D. Lee, Steve Hubbard ... 363 pages - Publisher: Springer; (January, 2015) ... Language: English- ISBN-10: 3319130714 - ISBN-13: 978-3319130712 ...

This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background.

Peter Sestoft ... 341 pages - Publisher: Springer; 2nd edition (September, 2017) ... Language: English - ISBN-10: 331960788X - ISBN-13: 978-3319607887 ...

This book uses a functional programming language (F#) as a metalanguage to present all concepts and examples, and thus has an operational flavour, enabling practical experiments and exercises. It includes basic concepts such as abstract syntax, interpretation, stack machines, compilation, type checking, garbage collection, and real machine code. Also included are more advanced topics on polymorphic types, type inference using unification, co- and contravariant types, continuations, and backwards code generation with on-the-fly peephole optimization. This second edition includes two new chapters. One describes compilation and type checking of a full functional language, tying together the previous chapters. The other describes how to compile a C subset to real (x86) hardware, as a smooth extension of the previously presented compilers.The examples present several interpreters and compilers for toy languages, including compilers for a small but usable subset of C, abstract machines, a garbage collector, and ML-style polymorphic type inference. Each chapter has exercises. Programming Language Concepts covers practical construction of lexers and parsers, but not regular expressions, automata and grammars, which are well covered already. It discusses the design and technology of Java and C# to strengthen students’ understanding of these widely used languages.

September 24, 2018 , ,
Walter Savitch, Kenrick Mock ... 1272 pages - Publisher: Pearson; 5th edition (March, 2012) ... Language: English - ISBN-10: 0132830310 - ISBN-13: 978-0132830317 ...

Praised for providing an engaging balance of thoughtful examples and explanatory discussion, best-selling author Walter Savitch explains concepts and techniques in a straightforward style using understandable language and code enhanced by a suite of pedagogical tools. Absolute Java is appropriate for both introductory and intermediate programming courses introducing Java.

Narasimha Karumanchi ... 434 pages - Publisher: CareerMonk Plublications; 5th edition (August, 2016) ... Language: English - ISBN-10: 819324527X - ISBN-13: 978-8193245279 ...

A handy guide of sorts for any computer science professional, Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles is a solution bank for various complex problems related to data structures and algorithms. It can be used as a reference manual by those readers in the computer science industry. The book has around 21 chapters and covers Recursion and Backtracking, Linked Lists, Stacks, Queues,Trees, Priority Queue and Heaps, Disjoint Sets ADT, Graph Algorithms, Sorting, Searching, Selection Algorithms [Medians], Symbol Tables, Hashing, String Algorithms, Algorithms Design Techniques, Greedy Algorithms, Divide and Conquer Algorithms, Dynamic Programming, Complexity Classes, and other Miscellaneous Concepts. Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles by Narasimha Karumanchi was published in March, and it is coded in C/C++ language. This book serves as guide to prepare for interviews, exams, and campus work. It is also available in Java. In short, this book offers solutions to various complex data structures and algorithmic problems.

Jared P. Lander ... 560 pages - Publisher: Addison-Wesley Professional; 2nd edition (June, 2017) ... Language: English - ISBN-10: 013454692X - ISBN-13: 978-0134546926 ...

Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes: Explore R, RStudio, and R packages * Use R for math: variable types, vectors, calling functions, and more * Exploit data structures, including data.frames, matrices, and lists * Read many different types of data * Create attractive, intuitive statistical graphics * Write user-defined functions * Control program flow with if, ifelse, and complex checks * Improve program efficiency with group manipulations * Combine and reshape multiple datasets * Manipulate strings using R’s facilities and regular expressions * Create normal, binomial, and Poisson probability distributions * Build linear, generalized linear, and nonlinear models *Program basic statistics: mean, standard deviation, and t-tests * Train machine learning models * Assess the quality of models and variable selection * Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods * Analyze univariate and multivariate time series data * Group data via K-means and hierarchical clustering * Prepare reports, slideshows, and web pages with knitr * Display interactive data with RMarkdown and htmlwidgets * Implement dashboards with Shiny * Build reusable R packages with devtools and Rcpp.

Wes McKinney ... 544 pages - Publisher: O'Reilly Media; 2nd edition (October, 2017) ... Language: English - ISBN-10: 1491957662 - ISBN-13: 978-1491957660 ... 

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing * Learn basic and advanced features in NumPy (Numerical Python) * Get started with data analysis tools in the pandas library * Use flexible tools to load, clean, transform, merge, and reshape data * Create informative visualizations with matplotlib * Apply the pandas groupby facility to slice, dice, and summarize datasets * Analyze and manipulate regular and irregular time series data * Learn how to solve real-world data analysis problems with thorough, detailed examples.

R. Lyman Ott, Micheal T. Longnecker ... 1297 pages - Publisher: Brooks Cole; 6th edition (December, 2008) ... Language: English - ISBN-10: 0495017582 - ISBN-13: 978-0495017585 ...

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani ... 426 pages - Publisher: Springer; (September, 2017) ... Language: English - ISBN-10: 1461471370 - ISBN-13: 978-1461471370 ...

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

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