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This highly accessible textbook teaches programming from first principles. In common with many programming courses, it uses Python as the introductory programming language before going on to use Java as the vehicle for more advanced programming concepts.The first part, which teaches Python, covers fundamental programming concepts, such as data types and control structures and functions. It introduces more complex data types such as lists and dictionaries and also deals with file handling. It introduces object-oriented concepts and ends with a case study bringing together all the topics of the first semester.  The second part uses Java to teach advanced concepts and centres around object-oriented programming, teaching key object-oriented concepts such as inheritance and polymorphism.  The semester again ends with an advanced case study bringing together all the topics of the second semester.Topics and features: Assumes no prior knowledge, and makes the transition from Python to Java a smooth process Features numerous exercises and also an illustrative case study for each language Examines procedural and object-oriented methodologies, as well as design principles Covers such advanced topics as interfaces and lambda expressions, exceptions and Collections Includes a chapter on graphics programming in Python using Tkinter  Introduces the latest Java technology for graphical interfaces, JavaFX Explains design concepts using UML notation Offering a gentle introduction to the field and assuming no prerequisite background, Programming in Two Semesters is the ideal companion to undergraduate modules in software development or programming.  In addition, it will serve as a strong primer for professionals looking to strengthen their knowledge of programming with these languages.

This textbook presents a systematic methodology for program development by using design recipes, i.e. a series of steps, each with a specific outcome, that takes a problem solver from a problem statement to a working and tested programmed solution. It introduces the reader to generative recursion, heuristic searching, accumulative recursion, tail recursion, iteration, mutation, loops, program correctness, and vectors. It uses video game development to make the content fun while at the same time teaching problem-solving techniques.The book is divided into four parts. Part I presents introductory material on basic problem solving and program design. It starts by reviewing the basic steps of a design recipe using structural recursion on a list. It then proceeds to review code refactoring-a common technique used to refine programs when a better or more elegant way is found to solve a problem-and introduces the reader to randomness. Next, Part II explores a new type of recursion called generative recursion. It navigates the reader through examples involving fractal image generation, efficient sorting, and efficient searching techniques such as binary, depth-first, and breadth-first search. Part III then explores a new type of recursion called accumulative (or accumulator) recursion. Examples used include finding a path in a graph, improving insertion sorting, and list-folding operations. Finally, Part IV explores mutation. To aid the reader in properly sequencing mutations it presents Hoare Logic and program correctness. In addition, it introduces vectors, vector processing, in-place operations, and circular data. Throughout the whole book complexity analysis and empirical experimentation is used to evaluate solutions.This textbook targets undergraduates at all levels as well as graduate students wishing to learn about program design. It details advanced types of recursion, a disciplined approach to the use of mutation, and illustrates the design process by developing a video game exploiting iterative refinement.

This modern, self-contained textbook provides an accessible introduction to the field from the perspective of a practicing programmer, supporting a detailed presentation of the fundamental concepts and techniques with practical exercises and fully worked out implementation examples. This much-anticipated 3rd edition of the definitive textbook on Digital Image Processing has been completely revised and expanded with new content, improved illustrations and teaching material.Topics and features:Contains new chapters on fitting of geometric primitives, randomized feature detection (RANSAC), and maximally stable extremal regions (MSER).Includes exercises for most chapters and provides additional supplementarymaterials and software implementations at an associated website.Uses ImageJ for all examples, a widely used open source imaging environment thatcan run on all major platforms.Describes each solution in a stepwise manner in mathematical form, as abstract pseudocode algorithms, and as complete Java programs that can be easily ported to other programming languages.Presents suggested outlines for a one- or two-semester course in the preface.Advanced undergraduate and graduate students will find this comprehensive and example-rich textbook will serve as the ideal introduction to digital image processing. It will also prove invaluable to researchers and professionals seeking a practically focused self-study primer.

The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.

Computation theory is a discipline that uses mathematical concepts and tools to expose the nature of "e;computation"e; and to explain a broad range of computational phenomena: Why is it harder to perform some computations than others?  Are the differences in difficulty that we observe inherent, or are they artifacts of the way we try to perform the computations?  How does one reason about such questions?This unique textbook strives to endow students with conceptual and manipulative tools necessary to make computation theory part of their professional lives. The work achieves this goal by means of three stratagems that set its approach apart from most other texts on the subject.For starters, it develops the necessary mathematical concepts and tools from the concepts' simplest instances, thereby helping students gain operational control over the required mathematics. Secondly, it organizes development of theory around four "e;pillars,"e; enabling students to see computational topics that have the same intellectual origins in physical proximity to one another. Finally, the text illustrates the "e;big ideas"e; that computation theory is built upon with applications of these ideas within "e;practical"e; domains in mathematics, computer science, computer engineering, and even further afield.Suitable for advanced undergraduate students and beginning graduates, this textbook augments the "e;classical"e; models that traditionally support courses on computation theory with novel models inspired by "e;real, modern"e; computational topics,such as  crowd-sourced computing, mobile computing, robotic path planning, and volunteer computing.Arnold L. Rosenberg is Distinguished Univ. Professor Emeritus at University of Massachusetts, Amherst, USA. Lenwood S. Heath is Professor at Virgina Tech, Blacksburg, USA.            

In its revised fifth edition, this book covers ethical, social and policy challenges arising from the convergence of computing and telecommunication and the spread of mobile information devices. Asks important questions about the impact of new technologies.

This textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success. Topics and features:Focuses on approaches supported by mathematical arguments, rather than sole computing experiencesInvestigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from themConsiders key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithmsExamines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problemAddresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrizationInvestigates the mathematical principles involves with natural language processing and computer visionKeeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire bookAlthough this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations ¿beyond¿ the sole computing experience.

This stimulating textbook presents a broad and accessible guide to the fundamentals of discrete mathematics, highlighting how the techniques may be applied to various exciting areas in computing. The text is designed to motivate and inspire the reader, encouraging further study in this important skill. Features: provides an introduction to the building blocks of discrete mathematics, including sets, relations and functions; describes the basics of number theory, the techniques of induction and recursion, and the applications of mathematical sequences, series, permutations, and combinations; presents the essentials of algebra; explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability; reviews the history of logic, discussing propositional and predicate logic, as well as advanced topics; examines the field of software engineering, describing formal methods; investigates probability and statistics.

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.

Differing from other books on the subject, this one uses the framework of constraint databases to provide a natural and powerful generalization of relational databases. An important theme running through the text is showing how relational databases can smoothly develop into constraint databases, without sacrificing any of the benefits of relational databases whilst gaining new advantages. Peter Revesz begins by discussing data models and how queries may be addressed to them. From here, he develops the theory of relational and constraint databases, including Datalog and the relational calculus, concluding with three sample constraint database systems -- DISCO, DINGO, and RATHER. Advanced undergraduates and graduates in computer science will find this a clear introduction to the subject, while professionals and researchers will appreciate this novel perspective on their subject.

Assembly language continues to hold a core position in the programming world because of its similar structure to machine language and its very close links to underlying computer-processor architecture and design. These features allow for high processing speed, low memory demands, and the capacity to act directly on the system's hardware. This completely revised second edition of the highly successful Introduction to Assembly Language Programming introduces the reader to assembly language programming and its role in computer programming and design. The focus is on providing readers with a firm grasp of the main features of assembly programming, and how it can be used to improve a computer's performance. The revised edition covers a broad scope of subjects and adds valuable material on protected-mode Pentium programming, MIPS assembly language programming, and use of the NASM and SPIM assemblers for a Linux orientation. All of the language's main features are covered in depth. The book requires only some basic experience with a structured, high-level language.Topics and Features: Introduces assembly language so that readers can benefit from learning its utility with both CISC and RISC processors [ NEW ].- Employs the freely available NASM assembler, which works with both Microsoft Windows and Linux operating systems [ NEW ].- Contains a revised chapter on "e;Basic Computer Organization"e; [ NEW].- Uses numerous examples, hands-on exercises, programming code analyses and challenges, and chapter summaries.- Incorporates full new chapters on recursion, protected-mode interrupt processing, and floating-point instructions [ NEW ].Assembly language programming is part of several undergraduate curricula in computer science, computer engineering, and electrical engineering. In addition, this newly revised text/reference can be used as an ideal companion resource in a computer organization course or as a resource for professional courses.

This advanced textbook provides a comprehensive survey of hardware and software architectural principles and methods of computer systems organization and design. It covers both CISC and RISC processors in detail, presenting Pentium, PowerPC, MIPS, SPARC and Itanium. In addition, assembly language programming for both CISC (Pentium) and RISC (MIPS) processors is covered in detail. Numerous assembly language code examples are included to give hands-on experience to students. These examples are not code fragments, but completely working programs that the students can run when they download the free assemblers. Topics and features: * Detailed discussion of digital logic and memory design * Concepts are related to practical designs and implementations * Presentation of material is suitable for self-study * Extensive examples and figures are used to help students grasp the concepts * Large number of end-of-chapter exercises will reinforce the concepts discussed in the chapter * Free assembler information for students to download from book website * Each chapter begins with an overview and ends with a summary.

Concurrent computing is gaining ground in interest as it becomes increasingly feasible to implement distributed computing across networks of workstations. This book, by one of the subject's leading figures, provides a comprehensive survey of the subject beginning with proposotional logic and concluding with concurrent programming. It is based on graduate courses taught at Cornell University and is designed to be used as a graduate text. There are exercises at the end of each chapter to extend and illustrate the main themes covered. Professor Schneier emphasizes the use of formal methods and assertional reasoning using notation and paradigms drawn from programming to drive the exposition. As a result, all those interested in studying concurrent computing will find this to be an invaluable approach to the subject.

In writing this book, our goal was to produce a text suitable for a first course in mathematical logic more attuned than the traditional textbooks to the re- cent dramatic growth in the applications oflogic to computer science. Thus, our choice oftopics has been heavily influenced by such applications. Of course, we cover the basic traditional topics: syntax, semantics, soundnes5, completeness and compactness as well as a few more advanced results such as the theorems of Skolem-Lowenheim and Herbrand. Much ofour book, however, deals with other less traditional topics. Resolution theorem proving plays a major role in our treatment of logic especially in its application to Logic Programming and PRO- LOG. We deal extensively with the mathematical foundations ofall three ofthese subjects. In addition, we include two chapters on nonclassical logics - modal and intuitionistic - that are becoming increasingly important in computer sci- ence. We develop the basic material on the syntax and semantics (via Kripke frames) for each of these logics. In both cases, our approach to formal proofs, soundness and completeness uses modifications of the same tableau method in- troduced for classical logic. We indicate how it can easily be adapted to various other special types of modal logics. A number of more advanced topics (includ- ing nonmonotonic logic) are also briefly introduced both in the nonclassical logic chapters and in the material on Logic Programming and PROLOG.

A basic issue in computer science is the complexity of problems. Computational complexity measures how much time or memory is needed as a function of the input problem size. Descriptive complexity is concerned with problems which may be described in first-order logic. By virtue of the close relationship between logic and relational databses, it turns out that this subject has important applications to databases such as analysing the queries computable in polynomial time, analysing the parallel time needed to compute a query, and the analysis of nondeterministic classes. This book is written as a graduate text and so aims to provide a reasonably self-contained introduction to this subject. The author has provided numerous examples and exercises to further illustrate the ideas presented.

A new advanced textbook/reference providing a comprehensive survey of hardware and software architectural principles and methods of computer systems organization and design. The book is suitable for a first course in computer organization. The style is similar to that of the author's book on assembly language in that it strongly supports self-study by students. This organization facilitates compressed presentation of material. Emphasis is also placed on related concepts to practical designs/chips. Topics: material presentation suitable for self- study; concepts related to practical designs and implementations; extensive examples and figures; details provided on several digital logic simulation packages; free MASM download instructions provided; and end-of-chapter exercises.

Much current research in computer science is concerned with two questions: is a program correct? And how can we improve a correct program preserving correctness? This latter question is known as the refinement of programs and the purpose of this book is to consider these questions in a formal setting. In fact, correctness turns out to be a special case of refinement and so the focus is on refinement. Although a reasonable background knowledge is assumed from mathematics and CS, the book is a self-contained introduction suitable for graduate students and researchers coming to this subject for the first time. There are numerous exercises provided of varying degrees of challenge.

An introductory course in Software Engineering remains one of the hardest subjects to teach. Much of the difficulty stems from the fact that Software Engineering is a very wide field which includes a wide range of topics. Con­ sequently, what should be the focus of an introductory course remains a challenge with many possible viewpoints. This third edition of the book approaches the problem from the perspec­ tive of what skills a student should possess after the introductory course, particularly if it may be the only course on software engineering in the stu­ dent's program. The goal of this third edition is to impart to the student knowledge and skills that are needed to successfully execute a project of a few person-months by employing proper practices and techniques. In- dently, a vast majority of the projects executed in the industry today are of this scope¿executed by a small team over a few months. Another objective of the book is to lay the foundation for the student for advanced studies in Software Engineering. Executing any software project requires skills in two key dimensions¿ engineering and project management. While engineering deals with issues of architecture, design, coding, testing, etc., project management deals with planning, monitoring, risk management, etc. Consequently, this book fo­ cuses on these two dimensions, and for key tasks in each, discusses concepts and techniques that can be applied effectively on projects.