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Antonios K. Alexandridis Wavelet Neural Networks


A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification. The authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification also includes: • Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence • Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction • An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks • Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics.

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Группа авторов Statistical Modeling by Wavelets


A comprehensive, step-by-step introduction to wavelets in statistics. What are wavelets? What makes them increasingly indispensable in statistical nonparametrics? Why are they suitable for «time-scale» applications? How are they used to solve such problems as denoising, regression, or density estimation? Where can one find up-to-date information on these newly «discovered» mathematical objects? These are some of the questions Brani Vidakovic answers in Statistical Modeling by Wavelets. Providing a much-needed introduction to the latest tools afforded statisticians by wavelet theory, Vidakovic compiles, organizes, and explains in depth research data previously available only in disparate journal articles. He carefully balances both statistical and mathematical techniques, supplementing the material with a wealth of examples, more than 100 illustrations, and extensive references-with data sets and S-Plus wavelet overviews made available for downloading over the Internet. Both introductory and data-oriented modeling topics are featured, including: * Continuous and discrete wavelet transformations. * Statistical optimality properties of wavelet shrinkage. * Theoretical aspects of wavelet density estimation. * Bayesian modeling in the wavelet domain. * Properties of wavelet-based random functions and densities. * Several novel and important wavelet applications in statistics. * Wavelet methods in time series. Accessible to anyone with a background in advanced calculus and algebra, Statistical Modeling by Wavelets promises to become the standard reference for statisticians and engineers seeking a comprehensive introduction to an emerging field.

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Patrick J. Van Fleet Discrete Wavelet Transformations

Группа авторов Handbook of Neural Engineering


An important new work establishing a foundation for future developments in neural engineering The Handbook of Neural Engineering provides theoretical foundations in computational neural science and engineering and current applications in wearable and implantable neural sensors/probes. Inside, leading experts from diverse disciplinary groups representing academia, industry, and private and government organizations present peer-reviewed contributions on the brain-computer interface, nano-neural engineering, neural prostheses, imaging the brain, neural signal processing, the brain, and neurons. The Handbook of Neural Engineering covers: Neural signal and image processing–the analysis and modeling of neural activity and EEG-related activities using the nonlinear and nonstationary analysis methods, including the chaos, fractal, and time-frequency and time-scale analysis methods–and how to measure functional, physiological, and metabolic activities in the human brain using current and emerging medical imaging technologies Neuro-nanotechnology, artificial implants, and neural prosthesis–the design of multi-electrode arrays to study how the neurons of human and animals encode stimuli, the evaluation of functional changes in neural networks after stroke and spinal cord injuries, and improvements in therapeutic applications using neural prostheses Neurorobotics and neural rehabilitation engineering–the recent developments in the areas of biorobotic system, biosonar head, limb kinematics, and robot-assisted activity to improve the treatment of elderly subjects at the hospital and home, as well as the interactions of the neuron chip, neural information processing, perception and neural dynamics, learning memory and behavior, biological neural networks, and neural control

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Patrick J. Van Fleet Discrete Wavelet Transformations


An «applications first» approach to discrete wavelet transformations Discrete Wavelet Transformations provides readers with a broad elementary introduction to discrete wavelet transformations and their applications. With extensive graphical displays, this self-contained book integrates concepts from calculus and linear algebra into the construction of wavelet transformations and their various applications, including data compression, edge detection in images, and signal and image denoising. The book begins with a cursory look at wavelet transformation development and illustrates its allure in digital signal and image applications. Next, a chapter on digital image basics, quantitative and qualitative measures, and Huffman coding equips readers with the tools necessary to develop a comprehensive understanding of the applications. Subsequent chapters discuss the Fourier series, convolution, and filtering, as well as the Haar wavelet transform to introduce image compression and image edge detection. The development of Daubechies filtersis presented in addition to coverage of wavelet shrinkage in the area of image and signal denoising. The book concludes with the construction of biorthogonal filters and also describes their incorporation in the JPEG2000 image compression standard. The author's «applications first» approach promotes a hands-on treatment of wavelet transforma-tion construction, and over 400 exercises are presented in a multi-part format that guide readers through the solution to each problem. Over sixty computer labs and software development projects provide opportunities for readers to write modules and experiment with the ideas discussed throughout the text. The author's software package, DiscreteWavelets, is used to perform various imaging and audio tasks, compute wavelet transformations and inverses, and visualize the output of the computations. Supplementary material is also available via the book's related Web site, which includes an audio and video repository, final project modules, and softwarefor reproducing examples from the book. All software, including the DiscreteWavelets package, is available for use with Mathematica®, MATLAB®, and Maple. Discrete Wavelet Transformations strongly reinforces the use of mathematics in digital data applications, sharpens programming skills, and provides a foundation for further study of more advanced topics, such as real analysis. This book is ideal for courses on discrete wavelet transforms and their applications at the undergraduate level and also serves as an excellent reference for mathematicians, engineers, and scientists who wish to learn about discrete wavelet transforms at an elementary level.

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William Pearlman Wavelet Image Compression

Patrick Fleet J.Van Wavelet Theory


A self-contained, elementary introduction to wavelet theory and applications Exploring the growing relevance of wavelets in the field of mathematics, Wavelet Theory: An Elementary Approach with Applications provides an introduction to the topic, detailing the fundamental concepts and presenting its major impacts in the world beyond academia. Drawing on concepts from calculus and linear algebra, this book helps readers sharpen their mathematical proof writing and reading skills through interesting, real-world applications. The book begins with a brief introduction to the fundamentals of complex numbers and the space of square-integrable functions. Next, Fourier series and the Fourier transform are presented as tools for understanding wavelet analysis and the study of wavelets in the transform domain. Subsequent chapters provide a comprehensive treatment of various types of wavelets and their related concepts, such as Haar spaces, multiresolution analysis, Daubechies wavelets, and biorthogonal wavelets. In addition, the authors include two chapters that carefully detail the transition from wavelet theory to the discrete wavelet transformations. To illustrate the relevance of wavelet theory in the digital age, the book includes two in-depth sections on current applications: the FBI Wavelet Scalar Quantization Standard and image segmentation. In order to facilitate mastery of the content, the book features more than 400 exercises that range from theoretical to computational in nature and are structured in a multi-part format in order to assist readers with the correct proof or solution. These problems provide an opportunity for readers to further investigate various applications of wavelets. All problems are compatible with software packages and computer labs that are available on the book's related Web site, allowing readers to perform various imaging/audio tasks, explore computer wavelet transformations and their inverses, and visualize the applications discussed throughout the book. Requiring only a prerequisite knowledge of linear algebra and calculus, Wavelet Theory is an excellent book for courses in mathematics, engineering, and physics at the upper-undergraduate level. It is also a valuable resource for mathematicians, engineers, and scientists who wish to learn about wavelet theory on an elementary level.

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Zhengyou He Wavelet Analysis and Transient Signal Processing Applications for Power Systems


An original reference applying wavelet analysis to power systems engineering • Introduces a modern signal processing method called wavelet analysis, and more importantly, its applications to power system fault detection and protection • Concentrates on its application to the power system, offering great potential for fault detection and protection • Presents applications, examples, and case studies, together with the latest research findings • Provides a combination of the author’s tutorial notes from electrical engineering courses together with his own original research work, of interest to both industry and academia

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Rahman M. Azizur An Introduction to Wavelet Modulated Inverters


The introductory chapter briefly presents the fundamental topologies and operation of power inverters. The second chapter contains a description of wavelet basis functions and sampling theory with particular reference to the switching model of inverters. Chapter three outlines the connection between the non-uniform sampling theorem and wavelet functions to develop an ideal sampling-reconstruction process to operate an inverter for obtaining its optimal performances. The scale based linearly combined basis functions are developed in chapter four in order to successfully operate single phase wavelet modulated inverters. Chapter four also contains the development of the non-dyadic type multiresolution analysis, that are responsible for sampling and recontruction of three continuous time reference modulating signals for three phase inverters. The performances of single phase wavelet modulated inverters for static, dynamic and non-linear loads are presented in chapter five, while chapter six contains the simulation and experimental performances of three phase wavelet modulated voltage source inverters for different loads at various operating conditions. This book presents the latest technology in the advancing power electronics field.

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‪Antonios K. Alexandridis‬ - ‪Google Scholar‬

Antonios K. Alexandridis. University of Macedonia, Verified email at uom.edu.gr - Homepage. Weather Derivatives Wavelet Networks Wavelet Analysis Finance Pricing. Articles Cited by Co-authors. Title. Sort. Sort by citations Sort by year Sort by title. Cited by. Cited by. Year; Wavelet Neural Networks: A Practical Guide. A Alexandridis, A Zapranis . Neural Networks 42, 1-27, 2013. 181: 2013 ...

Antonios ALEXANDRIDIS | Professor (Assistant) | PhD, MSc ...

Dr. Antonios Alexandridis is an Assistant Professor in Financial Derivatives at the University of Macedonia, Department of Accounting and Finance. His research interests have always been closely...

Wavelet Neural Networks: With Applications in Financial ...

Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification | Antonios K. Alexandridis, Achilleas D. Zapranis | ISBN: 9781118592526 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Antonios K. Alexandridis · OverDrive (Rakuten OverDrive ...

Antonios K. Alexandridis, PhD, is Lecturer of Finance in the School of Mathematics, Statistics, and Actuarial Science at the University of Kent. Dr. Alexandridis’ research interests include financial derivative modeling, pricing and forecasting, m...

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Antonios Alexandridis – The Conversation

Antonios Alexandridis is a Ph.D. Research student in Social Anthropology at the Vrije University Amsterdam. He is currently working on issues related to migration, mobility and social movements by ...

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Antonios K. Alexandridis; Achilleas D. Zapranis A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods.

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Antonios Alexandridis synes godt om dette. Erfaring. UNICEF. 2 år 7 måneder. Education Officer UNICEF. mar. 2020 – nu 8 måneder. Athens, Attiki, Greece. Education Consultant UNICEF. apr. 2018 – mar. 2020 2 år. Athens, Greece. Project Manager for the Akelius Digital Language Learning project in partnership with the Akelius Foundation. Vrije Universiteit Amsterdam (VU Amsterdam) 4 år 7 ...

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Antonios Alexandridis, University of Kent, Kent Business School, Faculty Member. Studies Weather Risk Management, Finance, and Weather Risks.

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Wavelet Neural Networks (eBook) by Antonios K ...

Wavelet Neural Networks (eBook) by Antonios K. Alexandridis, Achilleas D. Zapranis (Author), isbn:9781118596296, synopsis:A step-by-step introduction to modeling, traini...

Global financial crisis and multiscale systematic risk ...

Abstract In this paper, we have investigated the impact of the global financial crisis on the multihorizon nature of systematic risk and market risk using daily data of eight major European equity ...

Wavelet Neural Networks: With Applications in Financial ...

Antonios K. Alexandridis, Achilleas D. Zapranis. Through extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applications, specifically, providing the ...

Wavelet Neural Networks eBook by Antonios K. Alexandridis ...

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Antonios Alexandridis | Truthout

Antonios Alexandridis Antonios Alexandridis is a research student at the University of Sussex. Truthout Dec 10, 2017. Latest from Antonios Alexandridis. Op-Ed. Fortress Europe Continues to Treat Migrants as Criminals. All this emphasis on policing and military measures is based on the idea that being a refugee is a crime. by Dimitris Dalakoglou & Antonios Alexandridis, The Conversation October ...

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Series featuring Antonios Alexandridis. Education: needs, rights and access in displacement (FMR 60) # Episode Title Description People Date ; 1 : FMR 60 - Refugee education in Greece: integration or segregation? Although education policies have been devised to integrate these children into the Greek education system, these policies have actually led to some students being segregated. Giorgos ...

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Antonio ALEXANDRIDIS, Professor (Full) of University of Patras, Pátra (UP) | Read 159 publications | Contact Antonio ALEXANDRIDIS

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A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully...

Weather Derivatives: Modeling and Pricing Weather-Related ...

Weather Derivatives: Modeling and Pricing Weather-Related Risk by Antonios Alexandridis (2012-11-29) | Antonios Alexandridis;Achilleas Zapranis | ISBN: | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Antonios Alexandridis | Vrije Universiteit Amsterdam ...

Antonios Alexandridis, Vrije Universiteit Amsterdam, Department of Social and Cultural Anthropology, Faculty Member. Studies Global perspectives in teaching of Social studies, Modern Greece, and Global Financial Crisis.

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J. Peter Dosch Manual of Neural Therapy According to Huneke


More and more patients with chronic pain and other disorders are relying on neural therapy – a treatment concept based on employing the properties of local anesthetics to regulate disorders of the autonomic system – to alleviate their symptoms. Yet there are precious few spezialized, didactic resources for medical practitioners interested in learning about this highly effective therapeutic alternative.The <strong><i>Manual of Neural Therapy According to Huneke</i></strong> offers accessible, practical information on all aspects of neural therapy as it is practiced today. Designed for use in the classroom and in the clinic, this illustrated manual comprises three sections:– Theory and Practice of neural therapy according to Huneke – including detailed definitions of all terminology; discussions of experiments, successes, and failures of neural therapy; the scientific theory behind segmental therapy and interference fields; and practical applications.– Encyclopedia of neural therapy – featuring an encyclopedia of conditions and indications in every anatomic region, as well as numerous case studies. – Techniques of neural therapy – a comprehensive, substantially illustrated list of injection procedures, including detailed guidance on insertion points, direction, and depth. Distilling decades of clinical research and hands-on experience, this unique book is essential reading for practitioners of all disciplines interested in exploring how contemporary neural therapy can complement and enhance the way they practice medicine.

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Akira Hirose Complex-Valued Neural Networks. Advances and Applications


Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and superconducting waves. This fact is a critical advantage in practical applications in diverse fields of engineering, where signals are routinely analyzed and processed in time/space, frequency, and phase domains. Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely subject. Demonstrating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of: Conventional complex-valued neural networks Quaternionic neural networks Clifford-algebraic neural networks Presented by international experts in the field, Complex-Valued Neural Networks: Advances and Applications is ideal for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians interested in computational intelligence, artificial intelligence, machine learning theories, and algorithms.

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Yoav Goldberg Neural Network Methods in Natural Language Processing


Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

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Edward Aboufadel Discovering Wavelets


An accessible and practical introduction to wavelets With applications in image processing, audio restoration, seismology, and elsewhere, wavelets have been the subject of growing excitement and interest over the past several years. Unfortunately, most books on wavelets are accessible primarily to research mathematicians. Discovering Wavelets presents basic and advanced concepts of wavelets in a way that is accessible to anyone with only a fundamental knowledge of linear algebra. The basic concepts of wavelet theory are introduced in the context of an explanation of how the FBI uses wavelets to compress fingerprint images. Wavelet theory is further developed in the setting of function spaces. The book then moves on to present more advanced topics such as filters, multiresolution analysis, Daubechies' wavelets, and further applications. The book concludes with a series of projects and problems that introduce advanced topics and offer starting points for research. Sample projects that demonstrate real wavelet applications include image compression, a wavelet-based search engine, processing with Daubechies' wavelets, and more. Among the special features of Discovering Wavelets are: * Real-life, hands-on examples that involve actual wavelet applications * A companion Web site containing Pixel Images software and Maple files to be used with the projects in the book * Challenging problems that reinforce and expand on the ideas being developed * An appendix containing the linear algebra needed to understand wavelets as presented in the book

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Nan Zheng Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design


Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e. g. , deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.

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Thomas D. Coates, Jr. Neural Interfacing

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