Andreas Argyriou's Books

Magnetic Small-Angle Neutron Scattering

A Probe for Mesoscale Magnetism Analysis

By: Andreas Michels

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Magnetic Small-Angle Neutron Scattering

A Probe for Mesoscale Magnetism Analysis

By: Andreas Michels

Magnetic Small-Angle Neutron Scattering provides the first extensive treatment of magnetic small-angle neutron scattering (SANS). The theoretical background required to compute magnetic SANS cross sections and correlation functions related to long-wavelength magnetization structures is laidout. The concepts are scrutinized based on the discussion of experimental neutron data. Regarding prior background knowledge, some familiarity with the basic magnetic interactions and phenomena as well as scattering theory is desired.Besides exposing the different origins of magnetic SANS, and furnishing the basics of the magnetic SANS technique in early chapters, a large part of the book is devoted to a comprehensive treatment of the continuum theory of micromagnetics, as it is relevant for the study of the elastic magneticSANS cross section. Analytical expressions for the magnetization Fourier components allow to highlight the essential features of magnetic SANS and to analyze experimental data both in reciprocal, as well as in real space. Later chapters provide an overview on the magnetic SANS of nanoparticles andso-called complex systems (e.g., ferrofluids, magnetic steels, spin glasses and amorphous magnets). It is this subfield where major progress is expected to be made in the coming years, mainly via the increased usage of numerical micromagnetic simulations (Chapter 7), which is a very promisingapproach for the understanding of the magnetic SANS from systems exhibiting nanoscale spin inhomogeneity.

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Page Count

374

Publisher

Oxford University Press

Published Date

ISBN 10

0198855176

ISBN 13

9780198855170

On-Chip Interconnect with aelite

Composable and Predictable Systems

By: Andreas Hansson,Kees Goossens

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On-Chip Interconnect with aelite

Composable and Predictable Systems

By: Andreas Hansson,Kees Goossens

The book provides a comprehensive description and implementation methodology for the Philips/NXP Aethereal/aelite Network-on-Chip (NoC). The presentation offers a systems perspective, starting from the system requirements and deriving and describing the resulting hardware architectures, embedded software, and accompanying design flow. Readers get an in depth view of the interconnect requirements, not centered only on performance and scalability, but also the multi-faceted, application-driven requirements, in particular composability and predictability. The book shows how these qualitative requirements are implemented in a state-of-the-art on-chip interconnect, and presents the realistic, quantitative costs.

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Page Count

212

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1441968652

ISBN 13

9781441968654

Regularization, Optimization, Kernels, and Support Vector Machines

By: Johan A. K. Suykens,Marco Signoretto,Andreas Argyriou

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Regularization, Optimization, Kernels, and Support Vector Machines

By: Johan A. K. Suykens,Marco Signoretto,Andreas Argyriou

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

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Page Count

525

Publisher

CRC Press

Published Date

ISBN 10

0367658984

ISBN 13

9780367658984

Book's by Andreas Argyriou

Magnetic Small-Angle Neutron Scattering

Magnetic Small-Angle Neutron Scattering

On-Chip Interconnect with aelite

On-Chip Interconnect with aelite

Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines

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