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Model-Based Systems Engineering (MBSE), which tackles architecting and design of complex systems through the use of formal models, is emerging as the most critical component of systems engineering. This textbook specifies the two leading conceptual modeling languages, OPM—the new ISO 19450, composed primarily by the author of this book, and OMG SysML. It provides essential insights into a domain-independent,discipline-crossing methodology of developing or researching complex systems of any conceivable kind and size. Combining theory with a host of industrial, biological, and daily life examples, the book explains principles and provides guidelines for architecting complex, multidisciplinary systems, making it an indispensable resource for systems architects and designers, engineers of any discipline, executives at all levels, project managers, IT professionals, systems scientists, and engineering students. Professor Dov Dori is Harry Lebensfeld Chair in Industrial Engineering and Head of the Enterprise System Modeling Laboratory at the Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology. Since 2000 he has been intermittently Visiting Professor at MIT's Engineering Systems Division, where he is currently Lecturer. He received his PhD in Computer Science in 1988 from Weizmann Institute of Science, MSc in Operations Research from Tel Aviv University in 1981, and BSc in Industrial Engineering and Management from Technion in 1975. Professor Dov Dori invented and developed Object-Process Methodology (OPM), recently adopted as ISO 19450. He has authored over 300 publications, including journal and conference papers, books, and book chapters. Prof. Dori has mentored over 50 graduate students. He chaired or was co-chair of nine international conferences and workshops. Among his many editorial duties, Prof. Dori was Associate Editor of IEEE Transaction on Pattern Analysis and Machine Intelligence, and currently he is Associate Editor of Systems Engineering. He is Fellow of INCOSE – International Council on Systems Engineering, Fellow of IAPR – International Association for Pattern Recognition, Member of Omega Alpha Association – International Honor Society for Systems Engineering, and Senior Member of IEEE and of ACM. His research interests include model-based systems engineering, conceptual modeling of complex systems, systems architecture and design, software and systems engineering, and systems biology.

Computer science --- Programming --- patroonherkenning --- object recognition --- computers --- programmeertalen --- computerkunde

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Over the past 40 years, neurobiology and computational neuroscience has proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. One of the main difficulties that arises when designing automatic vision systems is developing a mechanism that can recognize - or simply find - an object when faced with all the possible variations that may occur in a natural scene, with the ease of the primate visual system. The area of the brain in primates that is dedicated at analyzing visual information is the visual cortex. The visual cortex performs a wide variety of complex tasks by means of simple operations. These seemingly simple operations are applied to several layers of neurons organized into a hierarchy, the layers representing increasingly complex, abstract intermediate processing stages. In this Research Topic we propose to bring together current efforts in neurophysiology and computer vision in order 1) To understand how the visual cortex encodes an object from a starting point where neurons respond to lines, bars or edges to the representation of an object at the top of the hierarchy that is invariant to illumination, size, location, viewpoint, rotation and robust to occlusions and clutter; and 2) How the design of automatic vision systems benefit from that knowledge to get closer to human accuracy, efficiency and robustness to variations.

object recognition --- Neuronal modeling --- shape --- Neuromorphic --- Computational neuroscence --- Attention --- Visual Cortex --- Computer Vision

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The articles in this Research Topic provide a state-of-the-art overview of the current progress in integrating computational and empirical research on visual object recognition. Developments in this exciting multidisciplinary field have recently gained momentum: High performance computing enabled breakthroughs in computer vision and computational neuroscience. In parallel, innovative machine learning applications have recently become available for datamining the large-scale, high resolution brain data acquired with (ultra-high field) fMRI and dense multi-unit recordings. Finally, new techniques to integrate such rich simulated and empirical datasets for direct model testing could aid the development of a comprehensive brain model. We hope that this Research Topic contributes to these encouraging advances and inspires future research avenues in computational and empirical neuroscience.

object recognition --- fMRI --- multimodal data integration --- neural networks --- invariance --- computational neuroscience --- Computer Vision --- Feature representation --- Neurophysiology --- ventral visual pathway

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The articles in this Research Topic provide a state-of-the-art overview of the current progress in integrating computational and empirical research on visual object recognition. Developments in this exciting multidisciplinary field have recently gained momentum: High performance computing enabled breakthroughs in computer vision and computational neuroscience. In parallel, innovative machine learning applications have recently become available for datamining the large-scale, high resolution brain data acquired with (ultra-high field) fMRI and dense multi-unit recordings. Finally, new techniques to integrate such rich simulated and empirical datasets for direct model testing could aid the development of a comprehensive brain model. We hope that this Research Topic contributes to these encouraging advances and inspires future research avenues in computational and empirical neuroscience.

object recognition --- fMRI --- multimodal data integration --- neural networks --- invariance --- computational neuroscience --- Computer Vision --- Feature representation --- Neurophysiology --- ventral visual pathway

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"Reading is at the interface between the vision and spoken language domains. An emergent bulk of research indicates that learning to read strongly impacts on non-linguistic visual object processing, both at the behavioral level (e.g., on mirror image processing-enantiomorphy-) and at the brain level (e.g., inducing top-down effects as well as neural competition effects). Yet, many questions regarding the exact nature, locus, and consequences of these effects remain hitherto unanswered. The current Special Topic aims at contributing to the understanding of how such a cultural activity as reading might modulate visual processing by providing a landmark forum in which researchers define the state of the art and future directions on this issue. We thus welcome reviews of current work, original research, and opinion articles that focus on the impact of literacy on the cognitive and/or brain visual processes. In addition to studies directly focusing on this topic, we will consider as highly relevant evidence on reading and visual processes in typical and atypical development, including in adult people differing in schooling and literacy, as well as in neuropsychological cases (e.g., developmental dyslexia). We also encourage researchers on nonhuman primate visual processing to consider the potential contribution of their studies to this Special Topic" -- page 2.

Reading, Psychology of. --- Visual perception --- Research. --- visual word recognition --- developmental dyslexia --- face recognition --- literacy acquisition --- Reading development --- visual object recognition --- Letter processing --- Visual Processing