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R 1,069.00
Introduction to Fuzzy Logic using MATLAB
This book provides a broad-ranging, but detailed overview of the basics of Fuzzy Logic. The fundamentals of Fuzzy Logic are discussed in detail, and illustrated with various solved examples. The book also deals with applications of Fuzzy Logic, to help readers more fully understand the concepts involved. Solutions to the problems are programmed using MATLAB 6.0, with simulated results. The MATLAB Fuzzy Logic toolbox is provided for easy reference.
R 5,485.70
R 214.00
R 1,628.00
Introduction to Modern Fortran for the Earth System Sciences
This work provides a short "getting started" guide to Fortran 90/95. The main target audience consists of newcomers to the field of numerical computation within Earth system sciences (students, researchers or scientific programmers). Furthermore, readers accustomed to other programming languages may also benefit from this work, by discovering how some programming techniques they are familiar with map to Fortran 95. The main goal is to enable readers to quickly start using Fortran 95 for writing useful programs. It also introduces a gradual discussion of Input/Output facilities relevant for Earth system sciences, from the simplest ones to the more advanced netCDF library (which has become a de facto standard for handling the massive datasets used within Earth system sciences). While related works already treat these disciplines separately (each often providing much more information than needed by the beginning practitioner), the reader finds in this book a shorter guide which links them. Compared to other books, this work provides a much more compact view of the language, while also placing the language-elements in a more applied setting, by providing examples related to numerical computing and more advanced Input/Output facilities for Earth system sciences. Naturally, the coverage of the programming language is relatively shallow, since many details are skipped. However, many of these details can be learned gradually by the practitioner, after getting an overview and some practice with the language through this book.
R 4,030.00
Introduction to Density Functional Theory
Density functional theory (DFT) is a quantum mechanical framework in which the ground state of interacting electronic systems is completely described in terms of the electron density. This leads to a practical approach for electronic structure calculations which is very widely used in (bio)chemistry, physics, and materials science due to its reasonable computational cost and useful accuracy. This textbook presents the basic concepts and features of DFT at an introductory level, suitable for undergraduate and beginning graduate students in science and engineering. Beginning with a brief review of elementary quantum mechanics, the book then introduces essential concepts such as many-body wave functions, orbitals, Slater determinants, functionals, and the variational principle. The fundamental theorems of DFT of Hohenberg and Kohn and Kohn and Sham are formulated and explained, followed by a case study of a one-dimensional model system to illustrate the self-consistent numerical solution of the Kohn-Sham equation. To use DFT in practice, the exchange-correlation energy functional must be approximated: the most important and widely used approximations are reviewed, including the local-density approximation, generalized gradient approximations, and hybrid functionals, explaining their origin and assessing their performance with many examples. Practical aspects of molecular and solid-state calculations, such as basis sets and pseudopotentials, are introduced, together with a brief review of chemical reactivity indices. The book ends with a chapter on time-dependent DFT, discussing real-time electron dynamics and excitation energies, and additional resources are provided in several appendices. Written in an accessible and student-friendly style, the book covers the essentials of DFT and electronic structure calculations in a fully self-contained manner and without overburdening readers with formal and technical details. The book also contains many end-of-chapter exercises, including hands-on computer simulations using Python, as well as many suggestions for further reading.
R 1,741.00
Practical Introduction to Computer Architecture
It is a great pleasure to write a preface to this book. In my view, the content is unique in that it blends traditional teaching approaches with the use of mathematics and a mainstream Hardware Design Language (HDL) as formalisms to describe key concepts. The book keeps the “machine” separate from the “application” by strictly following a bottom-up approach: it starts with transistors and logic gates and only introduces assembly language programs once their execution by a processor is clearly de ned. Using a HDL, Verilog in this case, rather than static circuit diagrams is a big deviation from traditional books on computer architecture. Static circuit diagrams cannot be explored in a hands-on way like the corresponding Verilog model can. In order to understand why I consider this shift so important, one must consider how computer architecture, a subject that has been studied for more than 50 years, has evolved. In the pioneering days computers were constructed by hand. An entire computer could (just about) be described by drawing a circuit diagram. Initially, such d- grams consisted mostly of analogue components before later moving toward d- ital logic gates. The advent of digital electronics led to more complex cells, such as half-adders, ip- ops, and decoders being recognised as useful building blocks.
R 1,842.34
Introduction to Project Management
This book presents the fundamentals of project management in simple language and an easy-to-understand format. It is targeted principally at those who are learning or desiring to learn project management as well as those who are already taking project management as a course of study or as a profession. It covers all the basic aspects of project management including the core areas prescribed by the Project Management Institute (PMI) in the Project Management Body of Knowledge (PMBOK) sixth edition. Although the PMBOK Guide seventh edition has significantly shifted focus from a process based standard to a principle based standard, it does not invalidate nor replace the detailed knowledge base contained in the sixth edition, which substantially emphasizes project management processes and knowledge areas. This is particularly apt for the traditional approach to project delivery, which is predictive in nature and has the bulk of the planning done upfront. The sections of the book are arranged in order of Project Management Processes as they fall within the respective Project Management Knowledge Areas. Experienced project manager, Davies Igberaese, presents all the basic content of traditional project management in a straightforward practical sequence as a typical project manager would go about the processes of initiating, planning, executing, monitoring, and closing a project without losing sight of the iterative nature of project management. The inclusion of Project Management Templates gives students and other users of the book the confidence required to effectively understand the basics of managing a wide variety of projects across disciplines including construction, building, industrial engineering, petroleum engineering, software engineering, information technology, business administration, and event management. Introduction to Project Management: A Source Book for Traditional PM Basics can serve as a core textbook for academic courses in project management, for preparing for PMP and CAPM Certification exams, as an excellent resource for new project managers, as well as a handy reference book for project sponsors.
R 9,949.94
Introduction to Computing with Fuzzy Sets
This book provides concise yet thorough coverage of the fundamentals and technology of fuzzy sets. Readers will find a lucid and systematic introduction to the essential concepts of fuzzy set-based information granules, their processing and detailed algorithms. Timely topics and recent advances in fuzzy modeling and its principles, neurocomputing, fuzzy set estimation, granulation–degranulation, and fuzzy sets of higher type and order are discussed. In turn, a wealth of examples, case studies, problems and motivating arguments, spread throughout the text and linked with various areas of artificial intelligence, will help readers acquire a solid working knowledge. Given the book’s well-balanced combination of the theory and applied facets of fuzzy sets, it will appeal to a broad readership in both academe and industry. It is also ideally suited as a textbook for graduate and undergraduate students in science, engineering, and operations research.
R 4,014.47
R 618.00
Introduction to Data Science
Introduction to Data Science: Statistics and Prediction Algorithms Through Case Studies teaches data science as a way of thinking statistically, not just as a collection of computational tools. Building on the topics covered in Introduction to Data Science: Data Wrangling and Visualization with R, this book is designed for students with some programming experience and basic mathematical maturity, this book builds the foundations of probability, statistical inference, regression, high-dimensional data analysis, and machine learning through real data examples and reproducible R code. It is suitable for one-semester course in advanced data science.The book shows how to reason about variability, uncertainty, prediction error, model assumptions, and validation. Through case studies involving polling, genetics, baseball, recommendation systems, image classification, and other modern datasets, readers learn how to connect probability models to data, summarize complex information, quantify uncertainty, fit and interpret models, evaluate prediction algorithms, and understand the statistical ideas behind machine learning. Each chapter is designed to support classroom teaching, self-study, and hands-on analysis, with exercises and companion web materials available through the book website.Key Features:Includes base R, data.table, and tidyverse code.Focuses on the statistical and probabilistic foundations of machine learning.Contains real-world case studies.Rafael A. Irizarry is Professor and Chair of the Department of Data Science at Dana-Farber Cancer Institute and Professor of Applied Statistics at Harvard. His research focuses on Genomics and he has taught several Data Science courses.
R 2,674.00
R 1,443.13
R 430.06
R 1,313.51
Introduction to Statistical Learning
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, deep learning, survival analysis, multiple testing, 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.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
R 3,663.00
Microsoft 365 Outlook For Dummies
Get up to speed on Microsoft's world-famous emailing, scheduling, collaborating, and organizing super-app Microsoft Outlook can do pretty much anything for you—short of cooking you a steak dinner. It can deliver and sort your email, filter out the junk, help you organize your life, send data to the cloud, sync up your various devices, and even integrate with iOS and Android. And in the latest edition of Microsoft Outlook For Dummies you'll learn how to do all of that, and more! Discover how to create automated mail-handling rules, translate messages into English or other languages, and share your calendar with other people. This latest edition even walks you through each of the four different versions of Outlook and their ideal use cases. You'll also find: Straightforward content that shows you how to draft and send emails, organize your calendars, and set up to-do lists, tasks, and remindersEasy explanations of hidden and advanced features that very few people know about or use, setting you up to impress your coworkers!Navigation tips for the Microsoft Outlook interface Perfect for novices who are brand-new to Outlook, Microsoft Outlook For Dummies is also a must-buy resource for email veterans looking to pick up the latest tips for the newest versions of Microsoft's world-famous email software.
R 684.00
Introduction to Project Management
This book presents the fundamentals of project management in simple language and an easy-to-understand format. It is targeted principally at those who are learning or desiring to learn project management as well as those who are already taking project management as a course of study or as a profession. It covers all the basic aspects of project management including the core areas prescribed by the Project Management Institute (PMI) in the Project Management Body of Knowledge (PMBOK) sixth edition. Although the PMBOK Guide seventh edition has significantly shifted focus from a process based standard to a principle based standard, it does not invalidate nor replace the detailed knowledge base contained in the sixth edition, which substantially emphasizes project management processes and knowledge areas. This is particularly apt for the traditional approach to project delivery, which is predictive in nature and has the bulk of the planning done upfront. The sections of the book are arranged in order of Project Management Processes as they fall within the respective Project Management Knowledge Areas. Experienced project manager, Davies Igberaese, presents all the basic content of traditional project management in a straightforward practical sequence as a typical project manager would go about the processes of initiating, planning, executing, monitoring, and closing a project without losing sight of the iterative nature of project management. The inclusion of Project Management Templates gives students and other users of the book the confidence required to effectively understand the basics of managing a wide variety of projects across disciplines including construction, building, industrial engineering, petroleum engineering, software engineering, information technology, business administration, and event management. Introduction to Project Management: A Source Book for Traditional PM Basics can serve as a core textbook for academic courses in project management, for preparing for PMP and CAPM Certification exams, as an excellent resource for new project managers, as well as a handy reference book for project sponsors.
R 2,167.02
R 1,459.53
Readable Introduction to Real Mathematics
Designed for an undergraduate course or for independent study, this text presents sophisticated mathematical ideas in an elementary and friendly fashion. The fundamental purpose of this book is to teach mathematical thinking while conveying the beauty and elegance of mathematics. The book contains a large number of exercises of varying difficulty, some of which are designed to help reinforce basic concepts and others of which will challenge virtually all readers. The sole prerequisite for reading this text is high school algebra. Topics covered include: * mathematical induction * modular arithmetic * the Fundamental Theorem of Arithmetic * Fermat's Little Theorem * RSA encryption * the Euclidean algorithm * rational and irrational numbers * complex numbers * cardinality * Euclidean plane geometry * constructibility (including a proof that an angle of 60 degrees cannot be trisected with a straightedge and compass)* infinite series * higher dimensional spaces.This textbook is suitable for a wide variety of courses and for a broad range of students of mathematics and other subjects. Mathematically inclined senior high school students will also be able to read this book.From the reviews of the first edition:“It is carefully written in a precise but readable and engaging style… I thoroughly enjoyed reading this recent addition to the Springer Undergraduate Texts in Mathematics series and commend this clear, well-organised, unfussy text to its target audiences.” (Nick Lord, The Mathematical Gazette, Vol. 100 (547), 2016) “The book is an introduction to real mathematics and is very readable. … The book is indeed a joy to read, and would be an excellent text for an ‘appreciation of mathematics’ course, among other possibilities.” (G.A. Heuer, Mathematical Reviews, February, 2015)“Many a benighted book misguidedly addresses the need [to teach mathematical thinking] by framing reasoning, or narrowly, proof, not as pervasive modality but somehow as itself an autonomous mathematical subject. Fortunately, the present book gets it right.... [presenting] well-chosen, basic, conceptual mathematics, suitably accessible after a K-12 education, in a detailed, self-conscious way that emphasizes methodology alongside content and crucially leads to an ultimate clear payoff. … Summing Up: Recommended. Lower-division undergraduates and two-year technical program students; general readers.” (D.V. Feldman, Choice, Vol. 52 (6), February, 2015)
R 1,546.39
Introduction to Error Analysis, third edition
This remarkable text by John R. Taylor has been a non-stop best-selling international hit since it was first published forty years ago. However, the two-plus decades since the second edition was released have seen two dramatic developments; the huge rise in popularity of Bayesian statistics, and the continued increase in the power and availability of computers and calculators. In response to the former, Taylor has added a full chapter dedicated to Bayesian thinking, introducing conditional probabilities and Bayes’ theorem. The several examples presented in the new third edition are intentionally very simple, designed to give readers a clear understanding of what Bayesian statistics is all about as their first step on a journey to become practicing Bayesians. In response to the second development, Taylor has added a number of chapter-ending problems that will encourage readers to learn how to solve problems using computers. While many of these can be solved using programs such as Matlab or Mathematica, almost all of them are stated to apply to commonly available spreadsheet programs like Microsoft Excel. These programs provide a convenient way to record and process data and to calculate quantities like standard deviations, correlation coefficients, and normal distributions; they also have the wonderful ability – if students construct their own spreadsheets and avoid the temptation to use built-in functions – to teach the meaning of these concepts.
R 2,325.00
Introduction to Reversible Computing
Few books comprehensively cover the software and programming aspects of reversible computing. Filling this gap, Introduction to Reversible Computing offers an expanded view of the field that includes the traditional energy-motivated hardware viewpoint as well as the emerging application-motivated software approach. Collecting scattered knowledge into one coherent account, the book provides a compendium of both classical and recently developed results on reversible computing. It explores up-and-coming theories, techniques, and tools for the application of reversible computing—the logical next step in the evolution of computing systems.The book covers theory, hardware and software aspects, fundamental limits, complexity analyses, practical algorithms, compilers, efficiency improvement techniques, and application areas. The topics span several areas of computer science, including high-performance computing, parallel/distributed systems, computational theory, compilers, power-aware computing, and supercomputing. The book presents sufficient material for newcomers to easily get started. It provides citations to original articles on seminal results so that readers can consult the corresponding publications in the literature. Pointers to additional resources are included for more advanced topics. For those already familiar with a certain topic within reversible computing, the book can serve as a one-stop reference to other topics in the field.
R 9,955.66
Introduction to Computation and Modeling for Differential Equations
Uses mathematical, numerical, and programming tools to solve differential equations for physical phenomena and engineering problems Introduction to Computation and Modeling for Differential Equations, Second Edition features the essential principles and applications of problem solving across disciplines such as engineering, physics, and chemistry. The Second Edition integrates the science of solving differential equations with mathematical, numerical, and programming tools, specifically with methods involving ordinary differential equations; numerical methods for initial value problems (IVPs); numerical methods for boundary value problems (BVPs); partial differential equations (PDEs); numerical methods for parabolic, elliptic, and hyperbolic PDEs; mathematical modeling with differential equations; numerical solutions; and finite difference and finite element methods. The author features a unique “Five-M” approach: Modeling, Mathematics, Methods, MATLAB®, and Multiphysics, which facilitates a thorough understanding of how models are created and preprocessed mathematically with scaling, classification, and approximation and also demonstrates how a problem is solved numerically using the appropriate mathematical methods. With numerous real-world examples to aid in the visualization of the solutions, Introduction to Computation and Modeling for Differential Equations, Second Edition includes: New sections on topics including variational formulation, the finite element method, examples of discretization, ansatz methods such as Galerkin’s method for BVPs, parabolic and elliptic PDEs, and finite volume methodsNumerous practical examples with applications in mechanics, fluid dynamics, solid mechanics, chemical engineering, heat conduction, electromagnetic field theory, and control theory, some of which are solved with computer programs MATLAB and COMSOL Multiphysics®Additional exercises that introduce new methods, projects, and problems to further illustrate possible applicationsA related website with select solutions to the exercises, as well as the MATLAB data sets for ordinary differential equations (ODEs) and PDEs Introduction to Computation and Modeling for Differential Equations, Second Edition is a useful textbook for upper-undergraduate and graduate-level courses in scientific computing, differential equations, ordinary differential equations, partial differential equations, and numerical methods. The book is also an excellent self-study guide for mathematics, science, computer science, physics, and engineering students, as well as an excellent reference for practitioners and consultants who use differential equations and numerical methods in everyday situations.
R 3,294.00
General Introduction to Data Analytics
A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniquesA unique illustrative example that extends throughout all the chaptersExercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.
R 3,353.00
Introduction to Probabilistic and Statistical Methods with Examples in R
This book strikes a healthy balance between theory and applications, ensuring that it doesn’t offer a set of tools with no mathematical roots. It is intended as a comprehensive and largely self-contained introduction to probability and statistics for university students from various faculties, with accompanying implementations of some rudimentary statistical techniques in the language R. The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis. Thanks to examples showing how to approach real-world problems using statistics, readers will acquire stronger analytical thinking skills, which are essential for analysts and data scientists alike.
R 3,294.06
R 639.00
Introduction to MultiAgent Systems
The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The book’s main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriateTo introduce the main issues surrounding the design of intelligent agentsTo introduce the main issues surrounding the design of a multi-agent societyTo introduce a number of typical applications for agent technology After reading the book the student should understand: The notion of an agent, how agents are distinct from other software paradigms (e.g. objects) and the characteristics of applications that lend themselves to agent-oriented softwareThe key issues associated with constructing agents capable of intelligent autonomous action and the main approaches taken to developing such agentsThe key issues in designing societies of agents that can effectively cooperate in order to solve problems, including an understanding of the key types of multi-agent interactions possible in such systemsThe main application areas of agent-based systems
R 2,176.00
R 794.00
R 553.74
Introduction to the Simulation of Dynamics Using Simulink
Designed for undergraduate students in the general science, engineering, and mathematics community, Introduction to the Simulation of Dynamics Using Simulink® shows how to use the powerful tool of Simulink to investigate and form intuitions about the behavior of dynamical systems. Requiring no prior programming experience, it clearly explains how to transition from physical models described by mathematical equations directly to executable Simulink simulations.Teaches students how to model and explore the dynamics of systemsStep by step, the author presents the basics of building a simulation in Simulink. He begins with finite difference equations and simple discrete models, such as annual population models, to introduce the concept of state. The text then covers ordinary differential equations, numerical integration algorithms, and time-step simulation. The final chapter offers overviews of some advanced topics, including the simulation of chaotic dynamics and partial differential equations.A one-semester undergraduate course on simulationWritten in an informal, accessible style, this guide includes many diagrams and graphics as well as exercises embedded within the text. It also draws on numerous examples from the science, engineering, and technology fields. The book deepens students’ understanding of simulated systems and prepares them for advanced and specialized studies in simulation. Ancillary materials are available at http://nw08.american.edu/~gray
R 4,249.00
R 367.00
R 762.00
Introduction to Syntax
This comprehensive introduction to syntax explains the basic concepts of syntax, and how the structures which are in place for describing the world can also be applied to a description of language structure. Edith Moravcsik presents a detailed introduction to syntactic description, including linear order, selection, categories, meaning, sound form, variation and change. The final selection provides a summary which looks at how we can explain syntax. The book includes student-friendly features, such as chapter summaries, suggestions for further reading, exercises, and a glossary of terms.
R 1,764.00
Introduction to Pragmatics
Introduction to Pragmatics guides students through traditional and new approaches in the field, focusing particularly on phenomena at the elusive semantics/pragmatics boundary to explore the role of context in linguistic communication. Offers students an accessible introduction and an up-to-date survey of the field, encompassing both established and new approaches to pragmaticsAddresses the traditional range of topics – such as implicature, reference, presupposition, and speech acts – as well as newer areas of research, including neo-Gricean theories, RelevanceTheory, information structure, inference, and dynamic approaches to meaningExplores the relationship and boundaries between semantics and pragmaticsIdeal for students coming to pragmatics for the first time
R 1,588.00
Introduction to Deep Learning Using R
Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You'll LearnUnderstand the intuition and mathematics that power deep learning modelsUtilize various algorithms using the R programming language and its packagesUse best practices for experimental design and variable selectionPractice the methodology to approach and effectively solve problems as a data scientistEvaluate the effectiveness of algorithmic solutions and enhance their predictive powerWho This Book Is ForStudents, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
R 1,682.99
R 594.31
Short Introduction to Intuitionistic Logic
Intuitionistic logic is presented here as part of familiar classical logic which allows mechanical extraction of programs from proofs. to make the material more accessible, basic techniques are presented first for propositional logic; Part II contains extensions to predicate logic. This material provides an introduction and a safe background for reading research literature in logic and computer science as well as advanced monographs. Readers are assumed to be familiar with basic notions of first order logic. One device for making this book short was inventing new proofs of several theorems. The presentation is based on natural deduction. The topics include programming interpretation of intuitionistic logic by simply typed lambda-calculus (Curry-Howard isomorphism), negative translation of classical into intuitionistic logic, normalization of natural deductions, applications to category theory, Kripke models, algebraic and topological semantics, proof-search methods, interpolation theorem. The text developed from materal for several courses taught at Stanford University in 1992-1999.
R 3,820.08
R 773.00
Introduction to Reconfigurable Supercomputing
This book covers technologies, applications, tools, languages, procedures, advantages, and disadvantages of reconfigurable supercomputing using Field Programmable Gate Arrays (FPGAs). The target audience is the community of users of High Performance Computers (HPC) who may benefit from porting their applications into a reconfigurable environment. As such, this book is intended to guide the HPC user through the many algorithmic considerations, hardware alternatives, usability issues, programming languages, and design tools that need to be understood before embarking on the creation of reconfigurable parallel codes. We hope to show that FPGA acceleration, based on the exploitation of the data parallelism, pipelining and concurrency remains promising in view of the diminishing improvements in traditional processor and system design.Table of Contents: FPGA Technology / Reconfigurable Supercomputing / Algorithmic Considerations / FPGA Programming Languages / Case Study: Sorting / Alternative Technologies and Concluding Remarks
R 1,382.08
Introduction to Software Design
This textbook provides a balanced coverage on software design, including design principles, methodologies, and techniques in contexts of designing modules of small and larger sizes with a multi-paradigm viewpoint emphasizing design trade-off. Commonly used design diagrams and patterns are also covered in terms of the design ideas behind and their effective use. The book also explores what (software) design thinking is, the role it plays in a design process, and ways to promote it. Despite object orientation being still the center of attention, there is a strong promotion throughout the book that software design should consider all appropriate design paradigms and methodologies.The book is organized in 10 chapters. Chapter 1 is dedicated to an exploration of what software design may be and entail. Next, chapters 2 and 3 are designed to help readers better understand object orientation, and the essentials of object-oriented design. Chapter 4 is then dedicated to the design of methods, while chapters 5, 6, and 8 respectively include topics about the design of objects, modeling with the Unified Modeling Language, and the use of design patterns. Larger software elements are often directly responsible for the formation of software architecture, thus chapter 7 covers four kinds of larger software elements: libraries, components, frameworks, and microservices, and their architectural implications. Chapter 9 finally brings the presentation of software design to an end with a coverage on software architecture focusing on software architectural views. Case studies are important in learning how to design software, hence chapter 10 then gathers some small design case studies that can be used in earlier chapters as appropriate. These case studies can be extended in different ways to provide additional design opportunities.This textbook book is intended for a junior level undergraduate course on software design, yet the depth of the book makes it also appropriate for a design course at beginning graduate level. Professionals may also find the book useful in their professional development.
R 2,834.54
