An Introduction to Wavelets Through Linear AlgebraMathematics majors at Michigan State University take a “Capstone” course near the end of their undergraduate careers. The content of this course varies with each offering. Its purpose is to bring together different topics from the undergraduate curriculum and introduce students to a developing area in mathematics. This text was originally written for a Capstone course. Basicwavelettheoryisanaturaltopicforsuchacourse. Byname, wavelets date back only to the 1980s. On the boundary between mathematics and engineering, wavelet theory shows students that mathematics research is still thriving, with important applications in areas such as image compression and the numerical solution of differential equations. The author believes that the essentials of wavelet theory are suf?ciently elementary to be taught successfully to advanced undergraduates. This text is intended for undergraduates, so only a basic background in linear algebra and analysis is assumed. We do not require familiarity with complex numbers and the roots of unity. These are introduced in the ?rst two sections of chapter 1. In the remainder of chapter 1 we review linear algebra. Students should be familiar with the basic de?nitions in sections 1. 3 and 1. 4. From our viewpoint, linear transformations are the primary object of study; v Preface vi a matrix arises as a realization of a linear transformation. Many students may have been exposed to the material on change of basis in section 1. 4, but may bene?t from seeing it again. In section 1. |
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IV | 7 |
VIII | 16 |
IX | 29 |
X | 40 |
XI | 56 |
XII | 79 |
XIII | 101 |
XVII | 128 |
XXIX | 298 |
XXX | 309 |
XXXI | 321 |
XXXII | 330 |
XXXIII | 349 |
XXXVI | 362 |
XXXVII | 380 |
XXXVIII | 398 |
XVIII | 151 |
XIX | 165 |
XXII | 196 |
XXIII | 225 |
XXIV | 265 |
XXVII | 271 |
XXVIII | 279 |
XXXIX | 429 |
XL | 451 |
XLI | 459 |
XLII | 470 |
XLIII | 484 |
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analysis apply approximation assume assumption bases bounded called change of basis chapter coefficients complex components compression compute condition consider construction continuous converges convolution corresponding define Definition diagonal dimensional eigenvalues eigenvectors element equation equivalent example Exercise exists fact Figure filter Find finite follows formula Fourier Fourier basis frequency function given gives graph Hence Hint holds identity implies independent inequality infinite inner product integral inversion Lemma linear transformation linearly localized LP(R matrix multiplications nonzero norm Note obtain operator orthogonal orthonormal basis orthonormal set period Proof Prove relation Relative Remark represents requires respect result satisfies scaling sense sequence shows side signal similar similarly solution Suppose Theorem theory translation unitary values vector space wavelet wavelet basis wavelet system write