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Autonomes fahren : Technische, rechtliche und gesellschaftliche aspekte = Autonomous driving : Technical, legal and social aspects
This book provides answers to a wide range of these and other questions. Experts from Germany and the USA describe central topics related to the automation of vehicles on public roads from an engineering and social science perspective. They show which "decisions" are required of an autonomous vehicle or which "ethics" must be programmed. The authors discuss expectations and concerns that characterize the individual and societal acceptance of autonomous driving. An increased safety potential through autonomous vehicles is compared to the challenges and solution approaches that play a role in securing the safety concept. In addition, they explain what possibilities for change and opportunities arise for our mobility and the reorganization of traffic, not least for freight traffic. The book thus offers an up-to-date, comprehensive and scientifically sound examination of the topic of "autonomous driving".
AutoCAD 2006 VBA : A programmer's reference
This book will help you take full advantage of the Visual Basic for Applications programming environment within AutoCAD 2006. Whether you want to automate routine tasks, or create sophisticated applications or design that can be generated programmatically, this book will empower you to make AutoCAD work for you. This book is also suitable if you're a power user who wants to make use of the advanced features of AutoCAD 2006 VBA, and you need a fast reference for the full AutoCAD object model. You'll learn to run Autodesk design tasks in the embedded VBA environment. This book is loaded with source code examples, and gives you no matter your skill level the necessary tools to accomplish your AutoCAD 2006 automation tasks.
Artificial intelligence techniques in hydrology and water resources management
The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices.
Artificial intelligence in the design process : The impact on creativity and team collaboration
Discusses how to include artificial intelligence (AI) systems in the early stages of the design process. Today designers need new tools capable of supporting them in dealing with the increasing project's complexity and empowering their performances and capabilities. AI systems appear to be powerful means to enhance designers' creativity. This assumption was tested in a workshop where sixteen participants collaborated with three AI systems throughout the creative phases of research, sketching, and color selection. Results show that designers can access a broader level of variance and inspiration while reducing the risk of fossilization by triggering lateral thinking through AI-generated data. Therefore, AI could significantly impact the creative phases of the design process if applied consciously. Being AI systems intelligent agents, the book treats the Human-AI collaboration as a collaboration between human agents, proposing a set of guidelines helpful to achieving an efficient partnership with the machine.
Artificial intelligence hardware design : Challenges and solutions
Learn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in the field. A thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models Explorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement Discussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU An examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition
Applied and computational mathematics for digital environments
Contains the 11 papers that were accepted and published in the Special Issue “Applied and Computational Mathematics for Digital Environments” of the MDPI Mathematics journal. The topics of interest include, among others, scientific research, applied tasks, and problems in the following areas: The construction of mathematical and information models of intelligent computer systems for monitoring and controlling the parameters of digital environments; The development of intelligent optimization algorithms that search for optimal parameter values of mathematical and information models in digital environments; Software and mathematical technologies in the implementation of intelligent monitoring and computer control of the parameters of digital environments; The development and application of mathematical and information models, machine learning methods, and artificial intelligence for the analysis and processing of big data in digital environments.
Application of computational electromagnetics techniques and artificial intelligence in the engineering
Introduces the latest developments in electromagnetic computing and artificial intelligence technology. Artificial intelligence technology can be applied to the modeling, analysis, and optimization design of microwave equipment, solving the routing problem of self-organizing networks in small unmanned aerial vehicle systems, calculating the radiation characteristics of antenna arrays on large electrical platforms, analyzing the impact of electromagnetic wave coupling on electronic devices, simulating the field distribution characteristics of electronic devices, and so on. With the help of artificial intelligence, designers can more conveniently, quickly, and accurately solve engineering problems.
Anti-fragile ICT Systems
Introduces a novel approach to the design and operation of large ICT systems. It views the technical solutions and their stakeholders as complex adaptive systems and argues that traditional risk analyses cannot predict all future incidents with major impacts. To avoid unacceptable events, it is necessary to establish and operate anti-fragile ICT systems that limit the impact of all incidents, and which learn from small-impact incidents how to function increasingly well in changing environments. The book applies four design principles and one operational principle to achieve anti-fragility for different classes of incidents. It discusses how systems can achieve high availability, prevent malware epidemics, and detect anomalies. Analyses of Netflix’s media streaming solution, Norwegian telecom infrastructures, e-government platforms, and Numenta’s anomaly detection software show that cloud computing is essential to achieving anti-fragility for classes of events with negative impacts.
Analyzing computer system performance with Perl::PDQ
Analyzing computer system performance is often regarded by most system administrators, IT professionals and software engineers as a black art that is too time consuming to learn and apply. Finally, this book by acclaimed performance analyst Dr. Neil Gunther makes this subject understandable and applicable through programmatic examples. The means to this end is the open-source performance analyzer Pretty Damn Quick (PDQ) written in Perl As the epigraph in this book points out, Common sense is the pitfall of performance analysis. The performance analysis framework that replaces common sense is revealed in the first few chapters of Part I. The important queueing concepts embedded in PDQ are explained in a very simple style that does not require any knowledge of formal probability theory. Part II begins with a full specification of how to set up and use PDQ replete with examples written in Perl. Subsequent chapters present applications of PDQ to the performance analysis of multicomputer architectures, benchmark results, client/server scalability, and Web-based applications.
An Introduction to Quantum and Vassiliev Knot Invariants
Provides an accessible introduction to knot theory, focussing on Vassiliev invariants, quantum knot invariants constructed via representations of quantum groups, and how these two apparently distinct theories come together through the Kontsevich invariant. Consisting of four parts, the book opens with an introduction to the fundamentals of knot theory, and to knot invariants such as the Jones polynomial. The second part introduces quantum invariants of knots, working constructively from first principles towards the construction of Reshetikhin-Turaev invariants and a description of how these arise through Drinfeld and Jimbo's quantum groups. Its third part offers an introduction to Vassiliev invariants, providing a careful account of how chord diagrams and Jacobi diagrams arise in the theory, and the role that Lie algebras play. The final part of the book introduces the Konstevich invariant. This is a universal quantum invariant and a universal Vassiliev invariant, and brings together these two seemingly different families of knot invariants. The book provides a detailed account of the construction of the Jones polynomial via the quantum groups attached to sl(2), the Vassiliev weight system arising from sl(2), and how these invariants come together through the Kontsevich invariant.
An Introduction to Optical Wireless Mobile Communications
Discusses existing conventional radio frequency (RF)-based wireless access technology and presents the challenges that can impact the requirements of the future wave of new wireless services in the context of artificial intelligence (AI) driven autonomous systems and machine-type communications. The relationship between visible light communications (VLC) and light fidelity (LiFi), is explored, and the major advantages of VLC and LiFi such as security and data density, and discuss existing research challenges are also introduced. Channel modeling techniques are provided for mobile multiuser scenarios, and will introduce key building blocks to achieve LiFi cellular networks achieving orders of magnitude improvements of area spectral efficiency compared to state-of-the-art. Challenges that arise from moving from a static point-to-point visible light link to a LiFi network that is capable of serving hundreds of mobile and fixed nodes are discussed. An overview of recent standardization activities and the commercialization challenges of this disruptive technology is also provided.
An introduction to ontology engineering
Provides the reader with a comprehensive introductory overview of ontology engineering. A secondary aim is to provide hands-on experience in ontology development that illustrate the theory. The book is divided into three blocks: Block I: logic foundations for ontologies both regarding the languages (mainly First Order predicate Logic, Description Logics, and OWL) and automated reasoning. Block II: developing good ontologies with methods and methodologies, the top-down approach with foundational ontologies, and the bottom-up approach to extract as much useful content as possible from legacy material. Block III: advanced topics with a selection of areas of specialisation, including Ontology-Based Data Access, the interaction between ontologies and natural languages (multilingual ontologies, controlled natural language), and advanced modelling with additional language features (fuzzy and temporal ontologies)
Ambient Intelligence ; European Conference, AmI 2008, Nuremberg, Germany, November 19-22, 2008. Proceedings
This book constitutes the refereed proceedings of the Second European Conference on Ambient Intelligence, AmI 2008, held in Nuremberg, Germany, in November 2008.
Algorithms for Decision Making
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.
Algorithms and data structures for massive datasets
Learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You'll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects--and there's no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you'll find the sweet spot of saving space without sacrificing your data's accuracy. About the Technology Standard algorithms and data structures may become slow--or fail altogether--when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost.
Algorithmic Learning in a Random World
This new monograph integrates mathematical theory and revealing experimental work. It demonstrates mathematically the validity of the reliability claimed by conformal predictors when they are applied to independent and identically distributed data, and it confirms experimentally that the accuracy is sufficient for many practical problems. Later chapters generalize these results to models called repetitive structures, which originate in the algorithmic theory of randomness and statistical physics. The approach is flexible enough to incorporate most existing methods of machine learning, including newer methods such as boosting and support vector machines and older methods such as nearest neighbors and the bootstrap.
Algorithmes dapproximation
Le champ des algorithmes d'approximation est aujourd'hui l'un des domaines de recherche les plus actifs en informatique. Il allie la profondeur de la théorie mathématique aux promesses d'applications pratiques d'un intérêt considérable. La plupart des problèmes issus d'applications relevant de domaines aussi différents que la conception de circuits VLSI, la conception et la planification de réseaux, l'ordonnancement, la théorie des jeux, la biologie ou la théorie des nombres, sont des problèmes NP-difficiles. Leur résolution exacte demanderait des ressources informatiques inaccessibles et ne peut donc être envisagée. Pour faire face à cette situation, un grand nombre d'algorithmes proposant des solutions approchées à ces problèmes ont été développés.
AI in learning : Designing the future
AI (artificial intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning.
Agent Technology from a Formal Perspective
The field of agent & multi-agent systems is experiencing tremendous growth. At the same time the field of formal methods is blossoming and has proven its importance in industrial and government applications. The FAABS (Formal Approaches to Agent-Based Systems) workshops, merging the concerns of the two fields, provided a timely and compelling platform on which the growing concerns and requirement of agent-based systems users that systems should be accompanied by behavioral assurances, could be discussed. This book has arisen from the overwhelming response to FAABS ’00, ’02 & ’04 and all chapters are updated or represent new research, and are designed to provide a more in-depth treatment of the topic. Examples of how others have applied formal methods to agent-based systems are included, plus formal method tools & techniques that readers can apply to their own systems.
Affect and Emotion in Human-Computer Interaction : From Theory to Applications
The present book provides an account of the latest work on a variety of aspects related to affect and emotion in human-technology interaction. It covers theoretical issues, user experience and design aspects as well as sensing issues, and reports on a number of affective applications that have been developed in recent years.
