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In this study, vestibular-based motion perception was investigated. The vestibular system, located in the inner ear, is responsible for sensing translational and rotational motion. A previous study carried out at the University of Toronto found that motion discriminability degraded with increasing motion intensity. Namely, difference thresholds were observed to grow when the amplitude of stimuli increased, suggesting internal noise effects. The current work served as a follow-up to this study and developed a simulation framework to reproduce the results. Analysis of the experiment confirmed that trends in thresholds did not arise due to external motion base noise. Signal detection theory was used as a framework to model the discrimination task and a particle filter perception model was modified to permit the estimation of noise in the otolith, the organ responsible for detecting specific force. Given the limited dataset, simulations successfully reproduced the experimental findings while quantifying the contributions of the internal noise source.
This book presents studies of self-motion by an international group of basic and applied researchers including biologists, psychologists, comparative physiologists, kinesiologists, aerospace and control engineers, physicians, and physicists. Academia is well represented and accounts for most of the applied research offered. Basic theoretical research is further represented by private research companies and also by government laboratories on both sides of the Atlantic. Researchers and students of biology, psychology, physiology, kinesiology, engineering, and physics who have an interest in self-motion -- whether it be underwater, in space, or on solid ground -- will find this volume of interest. This book presents studies of self-motion by an international group of basic and applied researchers including biologists, psychologists, comparative physiologists, kinesiologists, aerospace and control engineers, physicians, and physicists. Academia is well represented and accounts for most of the applied research offered. Basic theoretical research is further represented by private research companies and also by government laboratories on both sides of the Atlantic. Researchers and students of biology, psychology, physiology, kinesiology, engineering, and physics who have an interest in self-motion -- whether it be underwater, in space, or on solid ground -- will find this volume of interest.
II. Sensation, Perception & Attention: John Serences (Volume Editor) (Topics covered include taste; visual object recognition; touch; depth perception; motor control; perceptual learning; the interface theory of perception; vestibular, proprioceptive, and haptic contributions to spatial orientation; olfaction; audition; time perception; attention; perception and interactive technology; music perception; multisensory integration; motion perception; vision; perceptual rhythms; perceptual organization; color vision; perception for action; visual search; visual cognition/working memory.)
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 (available at fs.unm.edu/DSmT-book4.pdf or www.onera.fr/sites/default/files/297/2015-DSmT-Book4.pdf) in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well. We want to thank all the contributors of this fifth volume for their research works and their interests in the development of DSmT, and the belief functions. We are grateful as well to other colleagues for encouraging us to edit this fifth volume, and for sharing with us several ideas and for their questions and comments on DSmT through the years. We thank the International Society of Information Fusion (www.isif.org) for diffusing main research works related to information fusion (including DSmT) in the international fusion conferences series over the years. Florentin Smarandache is grateful to The University of New Mexico, U.S.A., that many times partially sponsored him to attend international conferences, workshops and seminars on Information Fusion. Jean Dezert is grateful to the Department of Information Processing and Systems (DTIS) of the French Aerospace Lab (Office National d’E´tudes et de Recherches Ae´rospatiales), Palaiseau, France, for encouraging him to carry on this research and for its financial support. Albena Tchamova is first of all grateful to Dr. Jean Dezert for the opportunity to be involved during more than 20 years to follow and share his smart and beautiful visions and ideas in the development of the powerful Dezert-Smarandache Theory for data fusion. She is also grateful to the Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, for sponsoring her to attend international conferences on Information Fusion.
Although it is now possible to integrate many millions of transistors on a single chip, traditional digital circuit technology is now reaching its limits, facing problems of cost and technical efficiency when scaled down to ever-smaller feature sizes. The analysis of biological neural systems, especially for visual processing, has allowed engineers to better understand how complex networks can effectively process large amounts of information, whilst dealing with difficult computational challenges. Analog and parallel processing are key characteristics of biological neural networks. Analog VLSI circuits using the same features can therefore be developed to emulate brain-style processing. Using standard CMOS technology, they can be cheaply manufactured, permitting efficient industrial and consumer applications in robotics and mobile electronics. This book explores the theory, design and implementation of analog VLSI circuits, inspired by visual motion processing in biological neural networks. Using a novel approach pioneered by the author himself, Stocker explains in detail the construction of a series of electronic chips, providing the reader with a valuable practical insight into the technology. Analog VLSI Circuits for the Perception of Visual Motion: analyses the computational problems in visual motion perception; examines the issue of optimization in analog networks through high level processes such as motion segmentation and selective attention; demonstrates network implementation in analog VLSI CMOS technology to provide computationally efficient devices; sets out measurements of final hardware implementation; illustrates the similarities of the presented circuits with the human visual motion perception system; includes an accompanying website with video clips of circuits under real-time visual conditions and additional supplementary material. With a complete review of all existing neuromorphic analog VLSI systems for visual motion sensing, Analog VLSI Circuits for the Perception of Visual Motion is a unique reference for advanced students in electrical engineering, artificial intelligence, robotics and computational neuroscience. It will also be useful for researchers, professionals, and electronics engineers working in the field.