Experimental Robotics


Book Description

Incorporating papers from the 12th International Symposium on Experimental Robotics (ISER), December 2010, this book examines the latest advances across the various fields of robotics. Offers insights on both theoretical concepts and experimental results.




Informatics in Control, Automation and Robotics


Book Description

The present book includes a set of selected extended papers from the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2014), held in Vienna, Austria, from 1 to 3 September 2014. The conference brought together researchers, engineers and practitioners interested in the application of informatics to Control, Automation and Robotics. Four simultaneous tracks will be held, covering Intelligent Control Systems, Optimization, Robotics, Automation, Signal Processing, Sensors, Systems Modelling and Control, and Industrial Engineering, Production and Management. Informatics applications are pervasive in many areas of Control, Automation and Robotics. ICINCO 2014 received 301 submissions, from 49 countries, in all continents. After a double blind paper review performed by the Program Committee, 20% were accepted as full papers and thus selected for oral presentation. Additional papers were accepted as short papers and posters. A further selection was made after the Conference, based also on the assessment of presentation quality and audience interest, so that this book includes the extended and revised versions of the very best papers of ICINCO 2014. Commitment to high quality standards is a major concern of ICINCO that will be maintained in the next editions, considering not only the stringent paper acceptance ratios but also the quality of the program committee, keynote lectures, participation level and logistics.




Robotics, Computer Vision and Intelligent Systems


Book Description

This volume constitutes the papers of two workshops which were held in conjunctionwith the First International Conference on Robotics, Computer Vision and Intelligent Systems,ROBOVIS 2020, Virtual Event, in November 4-6, 2020 and Second International Conference on Robotics, Computer Vision and Intelligent Systems,ROBOVIS 2021, Virtual Event, in October 25-27, 2021. The 11 revised full papers presented in this book were carefully reviewed and selectedfrom 53 submissions.




Multi-Level Bayesian Models for Environment Perception


Book Description

This book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks. Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models. Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.




Advances in Depth Images Analysis and Applications


Book Description

This book constitutes the refereed proceedings of the International Workshop on Depth Image Analysis, held in conjunction with ICPR 2012 in Japan in November 2012. The 16 revised full papers presented at the workshop were carefully reviewed and selected from 27 submissions and are complemented with 3 invited papers that were also peer-reviewed. The papers are organized in topical sections on acquisition and modeling of depth data, processing and analysis of depth data, applications, and ICPR contest.




Bayesian Forecasting and Dynamic Models


Book Description

In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.




Introduction to WinBUGS for Ecologists


Book Description

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance. - Introduction to the essential theories of key models used by ecologists - Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGS - Provides every detail of R and WinBUGS code required to conduct all analyses - Companion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)




Workshop Proceedings of the 10th International Conference on Intelligent Environments


Book Description

Advances in the engineering of sensing and acting capabilities, distributed in a wide range of specialized devices nowadays, provide an opportunity for the fundamental advances in computer science made in the past few decades to impact our daily lives. Sensors/actuators deployed in a physical space – a house, an office, a classroom, a car, a street – facilitate a link between an automated decision-making system and a technologically-enriched space. The Intelligent Environment, a digital environment that supports people in their daily lives, is a very active area of research which is attracting an increasing number of professionals (both in academia and industry) worldwide. The prestigious 10th International Conference on Intelligent Environments (IE’14) is focused on the development of advanced Intelligent Environments and stimulates the discussion on several specific topics that are crucial to the future of the area. This volume is the combined proceedings of the workshops co-located with IE’14: 9th Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI’14); 2nd International Workshop on Applications of Affective Computing in Intelligent Environments (ACIE’14); 3rd edition of the Workshop on Future Intelligent Educational Environments (WOFIEE’14); 2nd Workshop on Cloud-of-Things 2014 (CoT’14); 3rd International Workshop on the Reliability of Intelligent Environments (WoRIE 2014); 4th Workshop on Creative Science 2014 (CS’14); and 1st Workshop on Hyperrealistic Intelligent Environments 2014 (HyperRealitIE’14). This book offers an overview of the latest developments in key areas of the development of Intelligent Environments.




Advances in Neural Information Processing Systems 9


Book Description

The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes neural networks and genetic algorithms, cognitive science, neuroscience and biology, computer science, AI, applied mathematics, physics, and many branches of engineering. Only about 30% of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. All of the papers presented appear in these proceedings.




Advances in Neural Information Processing Systems 19


Book Description

The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.