The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. The aim is for readers to be able to devise and implement automated techniques to extract information from spatial grids such as radar, satellite or high-resolution survey imagery.

For more than thirty years, the History of Cartography Project has charted the course for scholarship on cartography, bringing together research from a variety of disciplines on the creation, dissemination, and use of maps. Volume 6, Cartography in the Twentieth Century, continues this tradition with a groundbreaking survey of the century just ended and a new full-color, encyclopedic format. The twentieth century is a pivotal period in map history. The transition from paper to digital formats led to previously unimaginable dynamic and interactive maps. Geographic information systems radically altered cartographic institutions and reduced the skill required to create maps. Satellite positioning and mobile communications revolutionized wayfinding. Mapping evolved as an important tool for coping with complexity, organizing knowledge, and influencing public opinion in all parts of the globe and at all levels of society. Volume 6 covers these changes comprehensively, while thoroughly demonstrating the far-reaching effects of maps on science, technology, and society—and vice versa. The lavishly produced volume includes more than five hundred articles accompanied by more than a thousand images. Hundreds of expert contributors provide both original research, often based on their own participation in the developments they describe, and interpretations of larger trends in cartography. Designed for use by both scholars and the general public, this definitive volume is a reference work of first resort for all who study and love maps.

This book is aimed at serving researchers, engineers, scientists, and engineering graduate and PhD students of engineering and physical science together with individuals interested in engineering and science. This book focuses on the application of engineering methods to complex systems including transportation, building, and manufacturing, with approaches representing a wide variety of disciplines of engineering and science. Throughout the book, great emphases are placed on engineering applications of complex systems, as well as the methodologies of automation, including artificial intelligence, automated and intelligent control, energy analysis, energy modelling, energy management, and optimised energy efficiency. The significant impact of recent studies that have been selected for presentation are of high interest in engineering complex systems. An attempt has been made to expose the reading audience of engineers and researchers to a broad range of theoretical and practical topics. The topics contained in the present book are of specific interest to engineers who are seeking expertise in transportation, building, and manufacturing technologies as well as mathematical modelling of complex systems, engineering approaches to engineering complex problems, automation via artificial intelligence methods, automated and intelligent control, and energy systems. The primary audience of this book are researchers, graduate students, and engineers in mechanical engineering, control engineering, computer engineering, electrical engineering, and science disciplines. In particular, the book can be used for training graduate and PhD students as well as senior undergraduate students to enhance their knowledge by taking a graduate or advanced undergraduate course in the areas of complex systems, control systems, energy systems, and engineering applications. The covered research topics are also of interest to engineers and academia who are seeking to expand their expertise in these areas.

Over the last decade, the role of computational simulations in all aspects of aerospace design has steadily increased. However, despite the many advances, the time required for computations is far too long. This book examines new ideas and methodologies that may, in the next twenty years, revolutionize scientific computing. The book specifically looks at trends in algorithm research, human computer interface, network-based computing, surface modeling and grid generation and computer hardware and architecture. The book provides a good overview of the current state-of-the-art and provides guidelines for future research directions. The book is intended for computational scientists active in the field and program managers making strategic research decisions.

The advancement of sustainable energy is becoming an important concern for many countries. The traditional electrical grid supports only one-way interaction of power being delivered to the consumers. The emergence of improved sensors, actuators, and automation technologies has consequently improved the control, monitoring and communication techniques within the energy sector, including the Smart Grid system. With the support of the aforementioned modern technologies, the information flows in two-ways between the consumer and supplier. This data communication helps the supplier in overcoming challenges like integration of renewable technologies, management of energy demand, load automation and control. Renewable energy (RE) is intermittent in nature and therefore difficult to predict. The accurate RE forecasting is very essential to improve the power system operations. The forecasting models are based on complex function combinations that include seasonality, fluctuation, and dynamic nonlinearity. The advanced intelligent computing algorithms for forecasting should consider the proper parameter determinations for achieving optimization. For this we need, new generation research areas like Machine learning (ML), and Artificial Intelligence (AI) to enable the efficient integration of distributed and renewable generation at large scale and at all voltage levels. The modern research in the above areas will improve the efficiency, reliability and sustainability in the Smart grid.