الصفحة 72
الصفحة 72
Adaptive Spatial Filters for Electromagnetic Brain Imaging
Adaptive spatial filters are powerful algorithms for electromagnetic brain imaging that enable high-fidelity reconstruction of neuronal activity. This book describes the technical advances of adaptive spatial filters for electromagnetic brain imaging by integrating and synthesizing available information and describes various factors that affect its performance.
Adaptive Nonlinear System Identification : The Volterra and Wiener Model Approaches
Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches introduces engineers and researchers to the field of nonlinear adaptive system identification. The book includes recent research results in the area of adaptive nonlinear system identification and presents simple, concise, easy-to-understand methods for identifying nonlinear systems. These methods use adaptive filter algorithms that are well known for linear systems identification. They are applicable for nonlinear systems that can be efficiently modeled by polynomials.After a brief introduction to nonlinear systems and to adaptive system identification, the author presents the discrete Volterra model approach. This is followed by an explanation of the Wiener model approach. Adaptive algorithms using both models are developed. The performance of the two methods are then compared to determine which model performs better for system identification applications.
Adaptive Mesh Refinement - Theory and Applications; Proceedings of the Chicago Workshop on Adaptive Mesh Refinement Methods, Sept. 3-5, 2003
Advanced numerical simulations that use adaptive mesh refinement (AMR) methods have now become routine in engineering and science. Originally developed for computational fluid dynamics applications these methods have propagated to fields as diverse as astrophysics, climate modeling, combustion, biophysics and many others. The underlying physical models and equations used in these disciplines are rather different, yet algorithmic and implementation issues facing practitioners are often remarkably similar. Unfortunately, there has been little effort to review the advances and outstanding issues of adaptive mesh refinement methods across such a variety of fields. This book attempts to bridge this gap. The book presents a collection of papers by experts in the field of AMR who analyze past advances in the field and evaluate the current state of adaptive mesh refinement methods in scientific computing.
Adaptive Machine Learning Algorithms with Python : Solve Data Analytics and Machine Learing Problems on Edge Devices
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. You will: Apply adaptive algorithms to practical applications and examples / Understand the relevant data representation features and computational models for time-varying multi-dimensional data / Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data / Speed up your algorithms and put them to use on real-world stationary and non-stationary data / Master the applications of adaptive algorithms on critical edge device computation applications
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
Adaptive Filtering : Algorithms and Practical Implementation
The book presents basic concepts of adaptive signal processing and filtering in a concise and straightforward manner. It concentrates on on-line algorithms whose adaptation occurs whenever a new sample of each environment signal is available. The material also illustrates block algorithms using a sub-band filtering framework whose adaptation occurs when a new block of data is available.
Adaptive and Personalized Semantic Web
Web Personalization can be defined as any set of actions that can tailor the Web experience to a particular user or set of users. To achieve effective personalization, organizations must rely on all available data, including the usage and click-stream data (reflecting user behaviour), the site content, the site structure, domain knowledge, as well as user demographics and profiles. In addition, efficient and intelligent techniques are needed to mine this data for actionable knowledge, and to effectively use the discovered knowledge to enhance the users' Web experience.
Adaptive and natural computing algorithms ; Proceedings of the International Conference in Coimbra, Portugal, 2005
The ICANNGA series of Conferences has been organised since 1993 and has a long history of promoting the principles and understanding of computational intelligence paradigms within the scientific community and is a reference for established workers in this area.and this book about Proceedings of the International Conference in Coimbra, Portugal, 2005 including Topics Artificial Intelligence Simulation and Modeling / Mathematics of Computing / Computer Applications
Adaptive and Natural Computing Algorithms ; 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II
The ICANNGA series of conferences has been organized since 1993 and has a long history of promoting the principles and understanding of computational intelligence paradigms within the scientifc community. the ICANNGA series has established itself as a reference for scientists and practitioners in this area. The series has also been of value to young researchers wishing both to extend their knowledge and experience and to meet experienced professionals in their ?elds. In a rapidly advancing world, where technology and engineering change d- matically, new challenges in computer science compel us to broaden the c- ference scope in order to take into account new developments.
Adaptive and Natural Computing Algorithms ; 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part I
Constitutes the refereed proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007. The 178 revised full papers presented were carefully reviewed and selected from a total of 474 submissions. The 94 papers of the first volume are organized in topical sections on evolutionary computation, genetic algorithms, particle swarm optimization, learning, optimization and games, fuzzy and rough systems, just as classification and clustering. The second volume contains 84 contributions related to neural networks, support vector machines, biomedical signal and image processing, biometrics, computer vision, as well as to control and robotics.
Adaptive and Multilevel Metaheuristics
Presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multilevel metaheuristics, with application to both combinatorial and continuous optimization.
Active Sensor Planning for Multiview Vision Tasks
Describes some effective strategies to generate a sequence of viewing poses and sensor settings for optimally completing a perception task. Several methods are proposed to solve the problems in both model-based and nonmodel-based vision tasks. For model-based applications, the method involves determination of the optimal sensor placements and a shortest path through these viewpoints for automatic generation of a perception plan.
Active mining ; 2nd International workshop, AM 2003, Maebashi, Japan, October 28, 2003, revised selected papers
"This volume contains the papers selected for presentation at the 2nd Inter- tional Workshop on Active Mining (AM 2003) which was organized in conju- tion with the 14th International Symposium on Methodologies for Intelligent Systems (ISMIS 2003), The workshop was organized by the Maebashi Institute of Technology for shed light on the future development of active mining. "This volume contains : Topics Database Management / Artificial Intelligence / Algorithm Analysis and Problem Complexity / Health Informatics / Bioinformatics
Acquiring card payments
Covers: Payment cards and protocols / EMV contact chip and contactless transactions / Disputes, arbitration, and compliance / Data security standards in the payment card industry / Validation algorithms / Code tables / Basic cryptography / Pin block formats and algorithms
Acoustic MIMO Signal Processing
Telecommunication systems and human-machine interfaces start employing multiple microphones and loudspeakers in order to make conversations and interactions more lifelike, hence more efficient. This development gives rise to a variety of acoustic signal processing problems under multiple-input multiple-output (MIMO) scenarios, encompassing distant speech acquisition, sound source localization and tracking, echo and noise control, source separation and speech dereverberation, and many others. The last decade has witnessed a growing interest in exploring these problems, but there has been little effort to develop a theory to have all these problems investigated in a unified framework. This unique book attempts to fill the gap.
Acoustic Emission Testing : Basics for Research - Applications in Civil Engineering
Covers all levels from the description of AE basics for AE beginners (level of a student) to sophisticated AE algorithms and applications to real large-scale structures as well as the observation of the cracking process in laboratory specimen to study fracture processes.
Accelerator Programming Using Directives ; 6th International Workshop, WACCPD 2019, Denver, CO, USA, November 18, 2019, Revised Selected Papers
This book constitutes the refereed post-conference proceedings of the 6th International Workshop on Accelerator Programming Using Directives, WACCPD 2019, held in Denver, CO, USA, in November 2019. The 7 full papers presented have been carefully reviewed and selected from 13 submissions. The papers share knowledge and experiences to program emerging complex parallel computing systems. They are organized in the following three sections: porting scientific applications to heterogeneous architectures using directives; directive-based programming for math libraries; and performance portability for heterogeneous architectures.
Abstraction, refinement and proof for probabilistic systems
Probabilistic techniques are increasingly being employed in computer programs and systems because they can increase efficiency in sequential algorithms, enable otherwise nonfunctional distribution applications, and allow quantification of risk and safety in general. This makes operational models of how they work, and logics for reasoning about them, extremely important. Abstraction, Refinement and Proof for Probabilistic Systems presents a rigorous approach to modeling and reasoning about computer systems that incorporate probability. Its foundations lie in traditional Boolean sequential-program logic—but its extension to numeric rather than merely true-or-false judgments takes it much further, into areas such as randomized algorithms, fault tolerance, and, in distributed systems, almost-certain symmetry breaking. The presentation begins with the familiar "assertional" style of program development and continues with increasing specialization: Part I treats probabilistic program logic, including many examples and case studies; Part II sets out the detailed semantics; and Part III applies the approach to advanced material on temporal calculi and two-player games.
Abstraction Refinement for Large Scale Model Checking
This book describes recent research developments in automatic abstraction refinement techniques. The authors address the main challenge in abstraction refinement, i.e., the ability to efficiently reach or come close to the optimum abstraction (the smallest abstract model that proves or refutes the given property). A suite of fully automatic abstraction techniques are proposed to improve the overall computation efficiency. The suite of algorithms presented in this book has demonstrated significant improvement over the prior art; some of them have already been adopted by the EDA companies in their commercial/in-house verification tools.
Abstract Computing Machines : A Lambda Calculus Perspective
The book addresses ways and means of organizing computations, highlighting the relationship between algorithms and the basic mechanisms and runtime structures necessary to execute them using machines. It completely abstracts from concrete programming languages and machine architectures, taking instead the lambda calculus as the basic programming and program execution model to design various abstract machines for its correct implementation. The emphasis is on fully normalizing machines based on full-fledged beta-reductions as essential prerequisites for symbolic computations that treat functions and variables truly as first-class objects. Their weakly normalizing counterparts are shown to be functional abstract machines that sacrifice the flavors of full beta-reductions for decidedly simpler runtime structures and improved runtime efficiency. Further downgrading of the lambda calculus leads to classical imperative machines that permit side-effecting operations on the runtime environment.
