This contributed volume collects the scientific results of the DFG Priority Program 1180 Prediction and Manipulation of the Interactions between Structures and Processes. The research program has been conducted during the years 2005 and 2011, whereas the primary goal was the analysis of the interactions between processes and structures in modern production facilities. This book presents the findings of the 20 interdisciplinary subprojects, focusing on different manufacturing processes such as high performance milling, tool grinding or metal forming. It contains experimental investigations as well as mathematical modeling of production processes and machine interactions. New experimental advancements and novel simulation approaches are also included.

Man-machine interaction is the interdisciplinary field, focused on a human and a machine in conjunction. It is the intersection of computer science, behavioural sciences, social psychology, ergonomics, security. It encompasses study, design, implementation, and evaluation of small- and large-scale, interacting, computing, hardware and software systems dedicated for human use. Man-machine interaction builds on supportive knowledge from both sides, the machine side providing techniques, methods and technologies relevant for computer graphics, visualisation, programming environments, the human side bringing elements of communication theory, linguistics, social sciences, models of behaviour. The discipline aims to improve ways in which machines and their users interact, making hardware and software systems better adapted to user's needs, more usable, more receptive, and optimised for desired properties. This monograph is the second edition in the series, providing the reader with a selection of high-quality papers dedicated to current progress, new developments and research trends in man-machine interactions area. In particular, the topical subdivisions of this volume include human-computer interfaces, robot control and navigation systems, bio-data analysis and mining, pattern recognition for medical applications, sound, text and image processing, design and decision support, rough and fuzzy systems, crisp and fuzzy clustering, prediction and regression, algorithms and optimisation, and data management systems.

Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modalities of communication in both Artificial Intelligence and Human-Machine Interaction. The book not only provides cross disciplinary research in the fields of multimodal signal acquisition and sensing, analysis, IoTs (Internet of Things), Artificial Intelligence, and system architectures, it also evaluates the role of Artificial Intelligence I in relation to the realization of contemporary Human Machine Interaction (HMI) systems.Readers are introduced to the multimodal signals and their role in the identification of the intended subjects, mental state and the realization of HMI systems are explored, and the applications of signal processing and machine/ensemble/deep learning for HMIs are assessed. A description of proposed methodologies is provided, and related works are also presented. This is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of HMIs, Brain-Computer Interface (BCI), Prosthesis, Computer vision, and Mental state estimation, and all those who wish to broaden their knowledge in the allied field. - Covers advances in the multimodal signal processing and artificial intelligence assistive HMIs - Presents theories, algorithms, realizations, applications, approaches, and challenges that will have their impact and contribution in the design and development of modern and effective HMI (Human Machine Interaction) system - Presents different aspects of the multimodal signals, from the sensing to analysis using hardware/software, and making use of machine/ensemble/deep learning in the intended problem-solving

The automotive industry is experiencing a significant technological shift, driven by electrification, connectivity, intelligence, sharing, and the trend towards “software-defined cars.” With this transformation, the traditional car cabin is evolving into a digital, intelligent, and mobile smart terminal that prioritizes passenger-centric design and data fusion. The design of in-car interaction has become essential in creating engaging, emotional, and humanized user experiences. Future success for traditional car manufacturers lies in developing effective communication methods and context for the intelligent car cabin while addressing relevant issues.

Human-Machine Interaction for Automated Vehicles: Driver Status Monitoring and the Takeover Process explains how to design an intelligent human-machine interface by characterizing driver behavior before and during the takeover process. Multiple solutions are presented to accommodate different sensing technologies, driving environments and driving styles. Depending on the availability and location of the camera, the recognition of driving and non-driving tasks can be based on eye gaze, head movement, hand gesture or a combination. Technical solutions to recognize drivers various behaviors in adaptive automated driving are described with associated implications to the driving quality. Finally, cutting-edge insights to improve the human-machine-interface design for safety and driving efficiency are also provided, based on the use of this sensing capability to measure drivers' cognition capability. - Covers everything needed to design an effective driver monitoring system, including sensors, areas to monitor, computing devices, and data analysis algorithms - Explores aspects of driver behavior that should be considered when designing an intelligent HMI - Examines the L3 take-over process in detail

Describes the implementation of modern features of man-machine interfaces and offers design guidelines, case studies and discusses algorithms for the implementation. Offers access to extensive public domain software for computer vision, classification and virtual reality.