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This book explores how the concept or urban experimentation is being used to reshape practices of knowledge production in urban debates about resilience, climate change governance, and socio-technical transitions. With contributions from leading scholars, and case studies from the Global North and South, from small to large scale cities, this book suggests that urban experiments offer novel modes of engagement, governance, and politics that both challenge and complement conventional strategies. The book is organized around three cross-cutting themes. Part I explores the logics of urban experimentation, different approaches, and how and why they are deployed. Part II considers how experiments are being staged within cities, by whom, and with what effects? Part III examines how entire cities or groups of cities are constructed as experiments. This book seeks to contribute a deeper and more socially and politically nuanced understanding of how urban experiments shape cities and drive wider changes in society, providing a framework to examine the phenomenon of urban experimentation in conceptual and empirical detail.
The treatise supports understanding the phenomena of complexity in engineering, distinguishes complexity from other challenges and presents an overview of definitions and applied approaches.The historical background of complexity management is explained by highlighting the important epochs, their key actors and their discoveries, findings and developments. Knowing about the appearance of early system awareness in ancient Greece, the creation of mechanical philosophy in the 17th century and the discovery of classic physics enables the reader to better comprehend modern system sciences and management approaches.A classification of complexity management approaches by research fields indicates current focus areas and starting points for future discussions. In a comprehensive map, the classification points out mutual overlaps between engineering disciplines in terms of similar complexity management approaches.Finally, the treatise introduces a generic complexity management framework, which is based on structural management approaches.
As the automotive industry faces into the smart era through advances in sensing, computation, storage, communication, and actuation technologies, a larger number of more complex control applications with better performances are expected to be on board. This requires an implementation platform with abundant resources, which is a major challenge in the cost-sensitive automotive domain. The implementation platform, often embedded in an Electronic Control Unit (ECU) and shared by multiple applications to save cost, is mainly comprised of a processor for computation, memory for storing instructions and data, and bus for internal and external communication. Conventionally, automotive control systems are designed using model-based approaches, where the details of the implementation platform are ignored. Techniques that integrate the characteristics of implementation resources into control algorithms design are largely missing. Such a separate design paradigm is too conservative in resources dimensioning and utilization for modern vehicles. This monograph presents recently developed approaches in automotive control systems design that take implementation resources into consideration, aiming to improve the control performances for a given amount of resources, or equivalently, realize the required control performances with fewer resources. While communication resources have been extensively explored in the literature of networked embedded control systems, this book focuses on memory and computation resources, which have started to receive attention from the academic community and industry just recently. As Electric Vehicles (EVs) have become a new trend in the automotive industry, energy resources of EVs are also investigated. A number of real-world applications validate the resource-aware automotive systems design techniques presented in the monograph. This text will be of interest to researchers and engineers in the automotive, embedded system and control domains.
The main topics of this book include advanced control, cognitive data processing, high performance computing, functional safety, and comprehensive validation. These topics are seen as technological bricks to drive forward automated driving. The current state of the art of automated vehicle research, development and innovation is given. The book also addresses industry-driven roadmaps for major new technology advances as well as collaborative European initiatives supporting the evolvement of automated driving. Various examples highlight the state of development of automated driving as well as the way forward. The book will be of interest to academics and researchers within engineering, graduate students, automotive engineers at OEMs and suppliers, ICT and software engineers, managers, and other decision-makers.