An international community of specialists reinvented the propeller during the Aeronautical Revolution, a vibrant period of innovation in North America and Europe from World War I to the end of World War II. They experienced both success and failure as they created competing designs that enabled increasingly sophisticated and 'modern' commercial and military aircraft to climb quicker and cruise faster using less power. Reinventing the Propeller nimbly moves from the minds of these inventors to their drawing boards, workshops, research and development facilities, and factories, and then shows us how their work performed in the air, both commercially and militarily. Reinventing the Propeller documents this story of a forgotten technology to reveal new perspectives on engineering, research and development, design, and the multi-layered social, cultural, financial, commercial, industrial, and military infrastructure of aviation.

This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.