الصفحة 1
الصفحة 1
Theoretical Computer Science ; Vol. 3701 ; 9th Italian Conference, ICTCS 2005, Siena, Italy, October 12-14, 2005, Proceedings
This book constitutes the refereed proceedings of the 9th International Conference on Theoretical Computer Science, ICTCS 2005, held at the Certosa di Pontignano, Siena, Italy, in October 2005. The 29 revised full papers presented together with an invited paper and abstracts of 2 invited talks were carefully reviewed and selected from 83 submissions. The papers address all current issues in theoretical computer science and focus especially on analysis and design of algorithms, computability, computational complexity, cryptography, formal languages and automata, foundations of programming languages and program analysis, natural computing paradigms (quantum computing, bioinformatics), program specification and verification, term rewriting, theory of logical design and layout, type theory, security, and symbolic and algebraic computation.
Problems on algorithms : A comprehensive exercise book for students in software engineering
Provides a comprehensive collection of practical problems on the design, analysis and verification of algorithms / Includes approximately 1500 designed problems / Presents algorithms which are supported by figures, hints, solutions, and comments / Provides a collection of practical problems on the basic and advanced data structures, design, and analysis of algorithms. To make this book suitable for self-instruction, about one-third of the algorithms are supported by solutions, and some others are supported by hints and comments.
Foundations of Trusted Autonomy
This book establishes the foundations needed to realize the ultimate goals for artificial intelligence, such as autonomy and trustworthiness. Aimed at scientists, researchers, technologists, practitioners, and students, it brings together contributions offering the basics, the challenges and the state-of-the-art on trusted autonomous systems in a single volume. The book is structured in three parts, with chapters written by eminent researchers and outstanding practitioners and users in the field. The first part covers foundational artificial intelligence technologies, while the second part covers philosophical, practical and technological perspectives on trust. Lastly, the third part presents advanced topics necessary to create future trusted autonomous systems. The book augments theory with real-world applications including cyber security, defence and space.
A Guide to Graph Algorithms
Offers high-quality content in the research area of graph algorithms and explores the latest developments in graph algorithmics. The reader will gain a comprehensive understanding of how to use algorithms to explore graphs. It is a collection of texts that have proved to be trend setters and good examples of that. The book aims at providing the reader with a deep understanding of the structural properties of graphs that are useful for the design of efficient algorithms. These algorithms have applications in finite state machine modelling, social network theory, biology, and mathematics. The book contains many exercises, some up at present-day research-level. The exercises encourage the reader to discover new techniques by putting things in a clear perspective.
3-D Shape Estimation and Image Restoration : Exploiting Defocus and Motion-Blur
Images contain information about the spatial properties of the scene they depict. When coupled with suitable assumptions, images can be used to infer three-dimensional information. This useful volume concentrates on motion blur and defocus, which can be exploited to infer the 3-D structure of a scene—as well as its radiance properties—and which in turn can be used to generate novel images with better quality. 3-D Shape Estimation and Image Restoration presents a coherent framework for the analysis and design of algorithms to estimate 3-D shape from defocused and motion blurred images, and to eliminate defocus and motion blur to yield "restored" images. It provides a collection of algorithms that are optimal with respect to the chosen model and estimation criterion.
