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

By Charles K. Chui

This textbook, except introducing the elemental features of utilized arithmetic, makes a speciality of contemporary themes similar to info information manipulation, details coding, info approximation, information dimensionality aid, facts compression, time-frequency and time scale bases, photograph manipulation, and photograph noise removing. The tools handled in additional element comprise spectral illustration and “frequency” of the knowledge, supplying useful info for, e.g. info compression and noise removing. moreover, a different emphasis is additionally wear the concept that of “wavelets” in reference to the “multi-scale” constitution of data-sets. The presentation of the e-book is trouble-free and simply available, requiring just some wisdom of undemanding linear algebra and calculus. All very important techniques are illustrated with examples, and every part includes among 10 an 25 routines. A instructing advisor, counting on the extent and self-discipline of directions is integrated for school room instructing and self-study.

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Extra info for Applied Mathematics: Data Compression, Spectral Methods, Fourier Analysis, Wavelets, and Applications

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8) with a = f (x) and b = g(x), and simply by integrating both sides of | f (x) + g(x)| p ≤ | f (x)| p + |g(x)| p over the interval J . 20) is called Minkowski’s inequality. 20) is trivial for p = 1, we will only consider 1 < p < ∞ in the following discussions. 20) is a consequence of Hölder’s inequality, in the following theorem. 10). 2 Sequence and Function Spaces 23 their product fg is in L 1 (J ), and 1/ p | f g| ≤ 1/q | f |p J |g|q J . 21), where p = q = 2, is called the Cauchy-Schwarz inequality, which is an example of the Cauchy-Schwarz inequality for the general inner product setting to be derived in the next section.

20) is trivial for p = 1, we will only consider 1 < p < ∞ in the following discussions. 20) is a consequence of Hölder’s inequality, in the following theorem. 10). 2 Sequence and Function Spaces 23 their product fg is in L 1 (J ), and 1/ p | f g| ≤ 1/q | f |p J |g|q J . 21), where p = q = 2, is called the Cauchy-Schwarz inequality, which is an example of the Cauchy-Schwarz inequality for the general inner product setting to be derived in the next section. 21) is provided in the following. | f |p First, dividing f and g by 1 p J assume that | f |q and 1 q respectively, we may J | f |p = J |g|q = 1.

Cn ∈ F . ⎭ It is easy to show that span{x j } is a subspace of V (see Exercise 15). Thus, alternatively, the set {x j } is said to span the vector space: span{x j }. Example 6 Let x1 = (1, 1, 3, −5), x2 = (2, −1, 2, 4) ∈ R4 and W = span{x1 , x2 }. Find W⊥ . Solution A vector c = (c1 , c2 , c3 , c4 ) ∈ R4 is in W⊥ if and only if c, x1 = 0 and c, x2 = 0. That is, c1 + c2 + 3c3 − 5c4 = 0, 2c1 − c2 + 2c3 + 4c4 = 0. Solving the above linear system, we have 5 1 4 14 c1 = − c3 + c4 , c2 = − c3 + c4 .

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