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Provides a link between the theory & applications of automatic control, emphasizing the latest developments & practical applications. Of interest to control & industrial engineers, operations researchers, & systems scientists.
What is Articulated Body Pose Estimation In the field of computer vision, the study of techniques and systems that recover the pose of an articulated body, which is comprised of joints and rigid parts, through the use of image-based observations is referred to as the articulated body pose estimation. It is one of the longest-lasting challenges in computer vision because of the complexity of the models that relate observation with position, and because of the range of scenarios in which it would be useful. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Articulated body pose estimation Chapter 2: Image segmentation Chapter 3: Simultaneous localization and mapping Chapter 4: Gesture recognition Chapter 5: Video tracking Chapter 6: Fundamental matrix (computer vision) Chapter 7: Structure from motion Chapter 8: Bag-of-words model in computer vision Chapter 9: Point-set registration Chapter 10: Michael J. Black (II) Answering the public top questions about articulated body pose estimation. (III) Real world examples for the usage of articulated body pose estimation in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Articulated Body Pose Estimation.
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A Machine-Learning Approach to Parameter Estimation, the sixth volume of the CAS Monograph Series, is now available for download. In this monograph, CAS Fellows Jim Kunce and Som Chatterjee address the use of machine-learning techniques to solve insurance problems. Their model can use any regression-based machine-learning algorithm to analyze the nonlinear relationships between the parameters of statistical distributions and features that relate to a specific problem. Unlike traditional stratification and segmentation, the authors' machine-learning approach to parameter estimation (MLAPE) learns the underlying parameter groups from the data and uses validation to ensure appropriate predictive powe
What Is Robotics The study of robotics draws from a variety of fields, including computer science and engineering. The study of robotics encompasses not only the creation of robots but also their operation, programming, and utilization. The objective of robotics is to create devices that can be of service to and aid human beings. Robotics is an interdisciplinary field that merges many subfields of engineering, including mechanical engineering, electrical engineering, information engineering, mechatronics engineering, electronics, biomedical engineering, computer engineering, control systems engineering, software engineering, and more. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Robotics Chapter 2: Robot Chapter 3: Humanoid robot Chapter 4: Subsumption architecture Chapter 5: Automation Chapter 6: Actuator Chapter 7: Simultaneous localization and mapping Chapter 8: Swarm robotics Chapter 9: Robotic sensing Chapter 10: Soft robotics (II) Answering the public top questions about robotics. (III) Real world examples for the usage of robotics in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of robotics' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of robotics.
Nonlinear Structures & Systems, Volume 1: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the first volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Nonlinear Dynamics, including papers on: Experimental Nonlinear Dynamics Jointed Structures: Identification, Mechanics, Dynamics Nonlinear Damping Nonlinear Modeling and Simulation Nonlinear Reduced-Order Modeling Nonlinearity and System Identification