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Download Applied Mathematics: Data Compression, Spectral Methods, by Charles K. Chui PDF

By Charles K. Chui

This textbook, except introducing the fundamental elements of utilized arithmetic, makes a speciality of contemporary themes reminiscent of info facts manipulation, info coding, information approximation, facts dimensionality aid, information compression, time-frequency and time scale bases, photograph manipulation, and snapshot noise removing. The tools handled in additional aspect comprise spectral illustration and “frequency” of the knowledge, supplying worthy details for, e.g. facts compression and noise elimination. in addition, a different emphasis is additionally wear the idea that of “wavelets” in reference to the “multi-scale” constitution of data-sets. The presentation of the publication is undemanding and simply available, requiring just some wisdom of uncomplicated linear algebra and calculus. All very important techniques are illustrated with examples, and every part comprises among 10 an 25 workouts. A educating advisor, counting on the extent and self-discipline of directions is incorporated for lecture room educating and self-study.

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Additional resources for Applied Mathematics: Data Compression, Spectral Methods, Fourier Analysis, Wavelets, and Applications

Example text

3 3 3 3 Thus, 5 1 4 14 c = − c3 + c4 , − c3 + c4 , c3 , c4 3 3 3 3 4 5 1 14 = c3 − , − , 1, 0 + c4 , , 0, 1 3 3 3 3 c4 c3 = (−5, −4, 3, 0) + (1, 14, 0, 3). 3 3 Therefore W⊥ = span{(−5, −4, 3, 0), (1, 14, 0, 3)}. Exercises Exercise 1 Let ·, · be the inner product as introduced in Definition 1. Show that x, ay = a¯ x, y , where a¯ is the complex conjugate of a ∈ C. Exercise 2 Provide a proof of Theorem 1. Exercise 3 Provide a proof of Theorem 2. 3 Inner-Product Spaces 33 Exercise 4 In each of the following, compute x, y , x , and y .

Yn ) ∈ Cn , to satisfy the conjugate symmetry condition in the above definition of the inner product. 2) are called the standard (or ordinary) inner products for Rn and Cn . 2). Example 1 Let V = Cn and F = C. 2) is an inner product for Cn . Solution We verify (a)–(c) of Definition 1, as follows: (a) For x, y ∈ Cn , 26 1 Linear Spaces x, y = x1 y 1 + · · · + xn y n = y 1 x1 + · · · + y n xn = y1 x 1 + · · · + yn x n = y, x . (b) For all a, b ∈ C and x, y, z ∈ Cn , ax + by, z = (ax1 + by1 )z 1 + · · · + (axn + byn )z n = (ax1 z 1 + · · · + axn z n ) + (by1 z 1 + · · · + byn z n ) = a x, z + b y, z .

C) For 0 < p < 1, ∞ x p = |x j | p . 3) j=−∞ (d) For p = 0, x 0 = # of non-zero terms in x. 4) By using the above measurements for infinite sequences, we now introduce the following subsets of the set of infinite sequences of complex numbers: p = x = {x j } : x p < ∞, x j ∈ C . When there is no ambiguity, the same notation p will be used for the subset of infinite sequences x of real numbers for each p, where 0 ≤ p ≤ ∞. 2 Sequence and Function Spaces 15 It follows from the above definition of measurements that ∞ is precisely the set of all bounded infinite sequences, and 0 is the set of all infinite sequences, each of which has at most finitely many non-zero terms.

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