Steven b damelin (56 resultados)

Paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Paperback. Condición: Very Good. First Paperback Edition. Very good paperback copy (NOT ex-library). Spine is uncreased, binding tight and sturdy; text also very good. Light shelfwear to corners, rubbing, but nothing serious; exterior looks nice (see our photo). 1st paperback printing, 2012. xii, 449pp. Ships same or next business day from Dinkytown in Minneapolis, Minnesota. …

Hardcover. Condición: Very Good. Estado de la sobrecubierta: No Dust Jacket Issued. First Edition. NOT ex-lib. 1st printing. Hardcover in very good condition. Interior appears free of markings. Exterior looks nice, shelfwear is very minor. Binding is tight, sturdy, and square. 1st edition with "first published" statement on CP. Ships same or next business day from Dinkytown in Minneapolis, Minnesota.…

Condición: Very Good.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Condición: New.

Condición: New. Develops mathematical and probabilistic tools needed to give rigorous derivations and applications of fundamental results in signal processing theory. Series: Cambridge Texts in Applied Mathematics. Num Pages: 462 pages, 50 b/w illus. 265 exercises. BIC Classification: TJK; UYAM; UYS. Category: (P) Professional & Vocational. Dimension: 152 x 228 x 23. Weight in Grams: 694. . 2011. Illustrated. paperback. . . . . …

Condición: New. In English.

Condición: As New. Unread book in perfect condition.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: new.

Condición: As New. Unread book in perfect condition.

Condición: New. Develops mathematical and probabilistic tools needed to give rigorous derivations and applications of fundamental results in signal processing theory. Series: Cambridge Texts in Applied Mathematics. Num Pages: 462 pages, 50 b/w illus. 265 exercises. BIC Classification: TJK; UYAM; UYS. Category: (P) Professional & Vocational. Dimension: 152 x 228 x 23. Weight in Grams: 694. . 2011. Illustrated. paperback. . . . . Books ship from the US and Ireland. …

Condición: New.

Condición: New.

Hardback. Condición: New. Near Extensions and Alignment of Data in Rn Comprehensive resource illustrating the mathematical richness of Whitney Extension Problems, enabling readers to develop new insights, tools, and mathematical techniques Near Extensions and Alignment of Data in Rn demonstrates a range of hitherto unknown connections between current research problems in engineering, mathematics, and data science, exploring the mathematical richness of near Whitney Extension Problems, and presenting a new nexus of applied, pure and computational harmonic analysis, approximation theory, data science, and real algebraic geometry. For example, the book uncovers connections between near Whitney Extension Problems and the problem of alignment of data in Euclidean space, an area of considerable interest in computer vision. Written by a highly qualified author, Near Extensions and Alignment of Data in Rn includes information on: Areas of mathematics and statistics, such as harmonic analysis, functional analysis, and approximation theory, that have driven significant advances in the fieldDevelopment of algorithms to enable the processing and analysis of huge amounts of data and data setsWhy and how the mathematical underpinning of many current data science tools needs to be better developed to be usefulNew insights, potential tools, and mathematical techniques to solve problems in Whitney extensions, signal processing, shortest paths, clustering, computer vision, optimal transport, manifold learning, minimal energy, and equidistribution Providing comprehensive coverage of several subjects, Near Extensions and Alignment of Data in Rn is an essential resource for mathematicians, applied mathematicians, and engineers working on problems related to data science, signal processing, computer vision, manifold learning, and optimal transport.…

Condición: As New. Unread book in perfect condition.

Condición: New. In English.

Paperback. Condición: Brand New. 449 pages. 9.00x6.00x1.00 inches. In Stock.

Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New.

Condición: New. 2023. 1st Edition. hardcover. . . . . .

Condición: New.

Condición: New.

Condición: New. Develops mathematical and probabilistic tools needed to give rigorous derivations and applications of fundamental results in signal processing theory. Series: Cambridge Texts in Applied Mathematics. Num Pages: 462 pages, 50 b/w illus. 265 exercises. BIC Classification: TJK; UYAM; UYS. Category: (P) Professional & Vocational. Dimension: 247 x 174 x 25. Weight in Grams: 800. . 2011. Illustrated. hardcover. . . . . …

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Arising from courses taught by the authors, this largely self-contained treatment is ideal for mathematicians who are interested in applications or for students from applied fields who want to understand the mathematics behind their subject. Early chapters cover Fourier analysis, functional analysis, probability and linear algebra, all of which have been chosen to prepare the reader for the applications to come. The book includes rigorous proofs of core results in compressive sensing and wavelet convergence. Fundamental is the treatment of the linear system y= x in both finite and infinite dimensions. There are three possibilities: the system is determined, overdetermined or underdetermined, each with different aspects. The authors assume only basic familiarity with advanced calculus, linear algebra and matrix theory and modest familiarity with signal processing, so the book is accessible to students from the advanced undergraduate level. Many exercises are also included.…

Condición: New. In English.