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Sparse Phased Array Antennas: Theory and Applications
This ground-breaking resource gives you the background theories and know-how you need to effectively design active phased array antennas with wider bandwidth and scan volume utilizing sparse array technology.The book shows you how to incorporate aperiodic arrays and sparse arrays as a solution for overcoming the restrictions faced in conventional phased antenna designs - such as blind spots, limited scan volume, large power and cooling requirements, RF path losses, and increased complexity - while adhering to the maintenance of SWAP-C resources widely used in aerospace and defence. Packed with step-by-step information and research results unavailable in any other single source to date, the book presents new concepts and techniques that potentially can be applied to many critical defense and commercial requirements such as: radars, satcom on move, sonars, weather monitoring, 5G and 6G for mobile communication, fault and crack detection in buildings and underground pipelines, automotive anti-collisions mechanism in automobiles, mine detection, through wall imaging, and more. The book helps you to understand the fundamental antenna technology being deployed in modern systems and equips you to design problem-solving sparse array models proven by electromagnetic simulations that can reduce the cost and overall complexity of the existing systems.Numerous design studies are documented to validate the theories presented.The book takes into account the functional constraints in designing commercial and military systems while demonstrating provable techniques that are practical and achievable. This is an important resource for phased array antenna designers interested in utilizing sparse array technology with wider bandwidth and scan volume.The book's straightforward approach and easy-to-follow language also make it accessible to students and those new to the field.
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Sparse Solutions of Underdetermined Linear Systems
This textbook presents a special solution of underdetermined linear systems where the number of nonzero entries in the solution is very small compared to the total number of entries.This is called sparse solution. As underdetermined linear systems can be very different, the authors explain how to compute a sparse solution by many approaches. Sparse Solutions of Underdetermined Linear Systems:Contains 72 algorithms for finding sparse solutions of underdetermined linear systems and their applications for matrix completion, graph clustering, and phase retrieval. Provides a detailed explanation of these algorithms including derivations and convergence analysis. Includes exercises for each chapter to help the reader understand the material. This textbook is appropriate for graduate students in math and applied math, computer science, statistics, data science, and engineering.Advisors and postdocs will also find the book of interest. It is appropriate for the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory.
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Sparse Polynomial Approximation of High-Dimensional Functions
Over seventy years ago, Richard Bellman coined the term "the curse of dimensionality" to describe phenomena and computational challenges that arise in high dimensions.These challenges, in tandem with the ubiquity of high-dimensional functions in real-world applications, have led to a lengthy, focused research effort on high-dimensional approximation—that is, the development of methods for approximating functions of many variables accurately and efficiently from data.This book provides an in-depth treatment of one of the latest installments in this long and ongoing story: sparse polynomial approximation methods.These methods have emerged as useful tools for various high-dimensional approximation tasks arising in a range of applications in computational science and engineering.It begins with a comprehensive overview of best s-term polynomial approximation theory for holomorphic, high-dimensional functions, as well as a detailed survey of applications to parametric differential equations.It then describes methods for computing sparse polynomial approximations, focusing on least squares and compressed sensing techniques. Sparse Polynomial Approximation of High-Dimensional Functions presents the first comprehensive and unified treatment of polynomial approximation techniques that can mitigate the curse of dimensionality in high-dimensional approximation, including least squares and compressed sensing.It develops main concepts in a mathematically rigorous manner, with full proofs given wherever possible, and it contains many numerical examples, each accompanied by downloadable code.The authors provide an extensive bibliography of over 350 relevant references, with an additional annotated bibliography available on the book's companion website (www.sparse-hd-book.com). This text is aimed at graduate students, postdoctoral fellows, and researchers in mathematics, computer science, and engineering who are interested in high-dimensional polynomial approximation techniques.
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Petrarch’s Lyric Poems : The Rime Sparse and Other Lyrics
For teachers and students of Petrarch, Robert M. Durling’s edition of the poems has become the standard one.Readers have praised the translation as both graceful and accurate, conveying a real understanding of what this difficult poet is saying. The literalness of the prose translation makes this beautiful book especially useful to students who lack a full command of Italian. And students reading the verse in the original will find here an authoritative text.
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Sparse Estimation with Math and R : 100 Exercises for Building Logic
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience.This textbook approaches the essence of sparse estimation by considering math problems and building R programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution.To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages.The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each).Written in an easy-to-follow and self-contained style, this book will also be perfectmaterial for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis. This book is one of a series of textbooks in machine learning by the same author.Other titles are: - Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679)- Statistical Learning with Math and Python (https://www.springer.com/gp/book/9789811578762)- Sparse Estimation with Math and Python
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Pier Paolo Scattolin: Suoni E Rime Sparse: Opere Corali, Vocali E Strumentali
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