الصفحة 5
الصفحة 5
Computer Vision – ACCV 2007 ; 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part II
Contains sections on shape and texture, fitting, calbration, detection, image and video processing, applications, face and gesture, tracking, camera networks, and face/gesture/action detection and recognition. This book also covers learning, motion and tracking, retrival and search, and human pose estimation.
Computer Vision – ACCV 2007 ; 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part I
Contains sections on shape and texture, fitting, calbration, detection, image and video processing, applications, face and gesture, tracking, camera networks, and face/gesture/action detection and recognition. This book also covers learning, motion and tracking, retrival and search, and human pose estimation.
Computational Methods in Financial Engineering : Essays in Honour of Manfred Gilli
The focus of this book is the development of computational methods and analytical models in financial engineering that rely on computation. The book contains eighteen chapters written by leading researchers in the area on portfolio optimization and option pricing; estimation and classification; banking; risk and macroeconomic modelling. It explores and brings together current research tools and will be of interest to researchers, analysts and practitioners in policy and investment decisions in economics and finance. "This book collects frontier work by researchers in computational economics in a tribute to Manfred Gilli, a leading member of this community. Contributions cover many of the topics researched by Gilli during his career: portfolio optimization and option pricing, estimation and classification, as well as banking, risk and macroeconomic modeling. The editors have put together a remarkable panorama of the rapidly growing and diversifying field of computational economics and finance
Cognitive Supervision for Robot-Assisted Minimally Invasive Laser Surgery
This thesis lays the groundwork for the automatic supervision of the laser incision process, which aims to complement surgeons’ perception of the state of tissues and enhance their control over laser incisions. The research problem is formulated as the estimation of variables that are representative of the state of tissues during laser cutting. Prior research in this area leveraged numerical computation methods that bear a high computational cost and are not straightforward to use in a surgical setting. This book proposes a novel solution to this problem, using models inspired by the ability of experienced surgeons to perform precise and clean laser cutting. It shows that these new models, which were extracted from experimental data using statistical learning techniques, are straightforward to use in a surgical setup, allowing greater precision in laser-based surgical procedures.
Marine resource damage assessment : Liability and compensation for environmental damage
MARE-DASM research focused on: (i) the estimation and distribution of marine contaminants in order to assess their long term effects (ecotoxicology); (ii) the integration of these result into a Biological Effects SubModel and a mathematical model assessing the risks associated with accidental spillage of oil at sea and the damage this can cause (modelling); (iii) the assessment of the willingness to pay for ecological damage, based on the Contingent Valuation Method (economics); (iv) the development and evaluation of measures to be taken in order to guarantee a sustainable use of the Belgian part of the North Sea, taking into account the economic and social interests and values (social economics); (v) the potential to develop technical and legal procedures that allow ecological damage to the marine environment to be evaluated and compensated, taking into account constraints in national and international liability legislation (legal).
Machine learning for biomedical application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
Low-Power High-Level Synthesis for Nanoscale CMOS Circuits
Low-Power High-Level Synthesis for Nanoscale CMOS Circuits addresses the need for analysis, characterization, estimation, and optimization of the various forms of power dissipation in the presence of process variations of nano-CMOS technologies. The authors show very large-scale integration (VLSI) researchers and engineers how to minimize the different types of power consumption of digital circuits.
Linear Estimation and Detection in Krylov Subspaces
Focuses on the foundations of linear estimation theory which is essential for effective signal processing. In its first part, it gives a comprehensive overview of several key methods like reduced-rank signal processing and Krylov subspace methods of numerical mathematics. Based on the derivation of the multistage Wiener filter in its most general form, the relationship between statistical signal processing and numerical mathematics is presented. In the second part, the theory is applied to iterative multiuser detection receivers (Turbo equalization) which are typically desired in wireless communication systems.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
Lifetime Estimation of Welded Joints
In the paper the author attempts to assess the fatigue life of chosen welded joints. It focuses especially on chosen problems that accompany deter- nation of the fatigue life of welded joints, taking into consideration the strain energy density parameter.
Level Crossing Methods in Stochastic Models
Since its inception in 1974, the level crossing approach for analyzing a large class of stochastic models has become increasingly popular among researchers. This volume traces the evolution of level crossing theory for obtaining probability distributions of state variables and demonstrates solution methods in a variety of stochastic models including: queues, inventories, dams, renewal models, counter models, pharmacokinetics, and the natural sciences. Results for both steady-state and transient distributions are given, and numerous examples help the reader apply the method to solve problems faster, more easily, and more intuitively.
Learning in Economic Systems with Expectations Feedback
Recently economists have more and more focussed on scenarios in which agents' views of the world may be erroneous. These notes introduce the concept of perfect forecasting rules which provide best least-squares predictions along the evolution of an economic system.
Complex Motion ; 1st International Workshop, IWCM 2004, Günzburg, Germany, October 12-14, 2004, Revised Papers
The world we live in is a dynamic one: we explore it by moving through it, and many of the objects which we are interested in are also moving. Trafic, for instance, is an example of a domain where detecting and processing visual motion is of vital interest, both in a metaphoric as well as in a purely literal sense. Visual communication is another important example of an area of science which is dominated by the need to measure, understand, and represent visual motion in an eficient way. Visual motion is a subject of research which forces the investigator to deal with complexity; complexity in the sense of facing efiects of motion in a very large diversity of forms, starting from analyzing simple motion in a changing envir- ment (illumination, shadows, . . . ), under adverse observation conditions, such as bad signal-to-noiseratio (low illumination, small-scaleprocesses, low-dosex-ray, etc. ), covering also multiple motions of independent objects, occlusions, and - ing as far as dealing with objects which are complex in themselves (articulated objects such as bodies of living beings). The spectrum of problems includes, but does not end at, objects which are not ‘bodies’ at all, e. g. , when anal- ing fiuid motion, cloud motion, and so on. Analyzing the motion of a crowd in a shopping mall or in an airport is a further example that implies the need to struggle against the problems induced by complexity.
Blind smart helmet
The Smart Helmet for the Blind is a project aimed at providing solutions for the challenges faced by blind individuals in their daily lives. The problem of detecting objects, identifying obstacles and distances, knowing the current location, and using a mobile application is a common issue for blind people. To address these problems, the Smart Helmet project was created, utilizing advanced technology and artificial intelligence to provide real-time assistance to the wearer. The helmet is connected to a Raspberry Pi 4, which processes information from the helmet's cameras and AI algorithms to analyze and predict the surrounding environment for a blind person.
Bayesian Methods in the Search for MH370
This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was used to define the search zone in the southern Indian Ocean. The book describes particle-filter based numerical calculation of the aircraft flight-path probability distribution and validates the method using data from several of the involved aircraft’s previous flights. Finally it is shown how the Reunion Island flaperon debris find affects the search probability distribution.
Bandwidth Extension of Speech Signals
Bandwidth Extension of Speech Signals provides discussion on different approaches for efficient and robust bandwidth extension of speech signals while acknowledging the influence of noise corrupted real-world signals. The book describes the theory and methods for quality enhancement of clean speech signals and distorted speech signals.
Autonomous Navigation in Dynamic Environments
The purpose of this book is to address the challenging problem of Autonomous Navigation in Dynamic Environments, and to present new ideas and approaches in this newly emerging technical domain. The book surveys the state-of-the-art, discusses in detail various related challenging technical aspects, and addresses upcoming technologies in this field. The aim of the book is to establish a foundation for a broad class of mobile robot mapping and navigation methodologies for indoor, outdoor, and exploratory missions.Three main topics located on the cutting edge of the state of the art are addressed, from both the theoretical and technological point of views: Dynamic world understanding and modelling for safe navigation, Obstacle avoidance and motion planning in dynamic environments, and Human-robot physical interactions. Several models and approaches are proposed for solving problems such as Simultaneous Localization and Mapping (SLAM) in dynamic environments, Mobile obstacle detection and tracking, World state estimation and motion prediction, Safe navigation in dynamic environments, Motion planning in dynamic environments, Robust decision making under uncertainty, and Human-Robot physical interactions.
Automotive Control Systems : For Engine, Driveline, and Vehicle
Reflecting the trend to optimization through integrative approaches for engine, driveline and vehicle control, this book enables control engineers to understand engine and vehicle models necessary for controller design and also introduces mechanical engineers to vehicle-specific signal processing and automatic control. The emphasis on measurement, comparisons between performance and modelling, and realistic examples derive from the authors’ industrial experience at Bosch and interactions within IFAC and SAE. The second edition offers new or expanded topics such as diesel-engine modelling, diagnosis and anti-jerking control, and vehicle modelling and parameter estimation. The book addresses professional engineers as well as students.
Artificial neural networks : Recent advances, new perspectives and applications
This book explores the potential of ANNs for applications in different fields. Itincludes eight chapters that discuss deep learning, ANN tools, and other cutting-edgetechnologies. It also suggests avenues for further research into ANN techniques formedical imaging to detect breast tumors, classification of COVID-19 surveillancedatasets, health management, estimation of materials processing parameters, solarenergy management, and control of a petrochemical unit.
Artificial neural networks – ICANN 2007 ; 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I
This book contains learning theory, advances in neural network learning methods, ensemble learning, spiking neural networks, advances in neural network architectures neural network technologies, neural dynamics and complex systems, data analysis, estimation, spatial and spatio-temporal learning, evolutionary computing, meta learning, agents learning, complex-valued neural networks, as well as temporal synchronization and nonlinear dynamics in neural networks.
