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The Stalingrad Cauldron
The encirclement of the German 6th Army at Stalingrad in mid-November 1942 and its final collapse in February 1943 was a signature defeat for Hitler, as more than 100,000 of his soldiers were marched off into captivity. Frank Ellis tackles this oft-told tale from the unique perspective of the German officers and men trapped inside the Red Army's ever-closing ring of forces. This approach makes palpable the growing desperation of an army that began its campaign confident of victory but that long before the end could see how hopeless their situation had become. Highlighting these pages are three previously unpublished German army division accounts, translated here for the first time by Ellis. ...
The Global Methamphetamine Problem: Approaches to Elucidate the Neurobiology, Epidemiology and Therapeutic Effectiveness
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Advanced Microsystems for Automotive Applications 2014
The automobile is going through the biggest transformation in its history. Automation and electrification of vehicles are expected to enable safer and cleaner mobility. The prospects and requirements of the future automobile affect innovations in major technology fields like driver assistance systems, vehicle networking and drivetrain development. Smart systems such as adaptive ICT components and MEMS devices, novel network architectures, integrated sensor systems, intelligent interfaces and functional materials form the basis of these features and permit their successful and synergetic integration. It has been the mission of the International Forum on Advanced Microsystems for Automotive Applications (AMAA) for more than fifteen years to detect novel trends and to discuss the technological implications from early on. Therefore, the topic of the AMAA 2014 will be “Smart Systems for Safe, Clean and Automated Vehicles”. This book contains peer-reviewed papers written by leading engineers and researchers which all address the ongoing research and novel developments in the field.
Intelligent Autonomous Systems 15
This book presents the latest advances and research achievements in the fields of autonomous robots and intelligent systems, presented at the IAS-15 conference, held in Baden-Baden, Germany, in June 2018. It brings together contributions from researchers, engineers and practitioners from all over the world on the main trends of robotics: navigation, path planning, robot vision, human detection, and robot design – as well as a wide range of applications. This installment of the conference reflects the rise of machine learning and deep learning in the robotics field, as employed in a variety of applications and systems. All contributions were selected using a rigorous peer-review process to ensure their scientific quality. The series of biennial IAS conferences was started in 1986: since then, it has become an essential venue for the robotics community.
Point Cloud Processing for Environmental Analysis in Autonomous Driving using Deep Learning
Autonomous self-driving cars need a very precise perception system of their environment, working for every conceivable scenario. Therefore, different kinds of sensor types, such as lidar scanners, are in use. This thesis contributes highly efficient algorithms for 3D object recognition to the scientific community. It provides a Deep Neural Network with specific layers and a novel loss to safely localize and estimate the orientation of objects from point clouds originating from lidar sensors. First, a single-shot 3D object detector is developed that outputs dense predictions in only one forward pass. Next, this detector is refined by fusing complementary semantic features from cameras and joint probabilistic tracking to stabilize predictions and filter outliers. The last part presents an evaluation of data from automotive-grade lidar scanners. A Generative Adversarial Network is also being developed as an alternative for target-specific artificial data generation.
