000			03622nam a2200337 i 4500
008			980126s1999 nyua b 001 0 eng
010			_a98004682
020			_a0387984852 _qhardcover : acid-free paper
040			_aDLC _cDLC _dC#P _dOHX _dCIN _dBAUN
041	1		_aeng _hfre
049			_aBAUN_MERKEZ
050	0	4	_aQA403.5 _b.G37 1999
082	0	0	_221
100	1		_aGasquet, Claude
245	1	0	_aFourier analysis and applications : _bfiltering, numerical computation, wavelets / _cC. Gasquet, P. Witomski ; translated by R. Ryan
264		1	_aNew York : _bSpringer, _c[1999]
264		4	_c©1999
300			_axviii, 442 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aTexts in applied mathematics ; _v30
504			_aIncludes bibliographical references (pages [433]-436) and index
505	0	0	_tTranslator's Preface. _tPreface to the French Edition. _tCh. I. Signals and Systems. _tLesson 1. Signals and Systems. _tLesson 2. Filters and Transfer Functions. _tCh. II. Periodic Signals. _tLesson 3. Trigonometric Signals. _tLesson 4. Periodic Signals and Fourier Series. _tLesson 5. Pointwise Representation. _tLesson 6. Expanding a Function in an Orthogonal Basis. _tLesson 7. Frequencies, Spectra, and Scales. _tCh. III. The Discrete Fourier Transform and Numerical Computations. _tLesson 8. The Discrete Fourier Transform. _tLesson 9. A Famous, Lightning-Fast Algorithm. _tLesson 10. Using the FFT for Numerical Computations. _tCh. IV. The Lebesgue Integral. _tLesson 11. From Riemann to Lebesgue. _tLesson 12. Measuring Sets. _tLesson 13. Integrating Measurable Functions. _tLesson 14. Integral Calculus. _tCh. V. Spaces. _tLesson 15. Function Spaces. _tLesson 16. Hilbert Spaces. _tCh. VI. Convolution and the Fourier Transform of Functions. _tLesson 17. The Fourier Transform of Integrable Functions. _tLesson 18. The Inverse Fourier Transform. _tLesson 19. The Space [actual symbol not reproducible] (R). _tLesson 20. The Convolution of Functions. _tLesson 21. Convolution, Derivation, and Regularization. _tLesson 22. The Fourier Transform on L[superscript 2](R). _tLesson 23. Convolution and the Fourier Transform. _tCh. VII. Analog Filters. _tLesson 24. Analog Filters Governed by a Differential Equation. _tLesson 25. Examples of Analog Filters. _tCh. VIII. Distributions. _tLesson 26. Where Functions Prove to Be Inadequate. _tLesson 27. What Is a Distribution?. _tLesson 28. Elementary Operations on Distributions. _tLesson 29. Convergence of a Sequence of Distributions. _tLesson 30. Primitives of a Distribution. _tCh. IX. Convolution and the Fourier Transform of Distributions. _tLesson 31. The Fourier Transform of Distributions. _tLesson 32. Convolution of Distributions. _tLesson 33. Convolution and the Fourier Transform of Distributions. _tCh. X. Filters and Distributions. _tLesson 34. Filters, Differential Equations, and Distributions. _tLesson 35. Realizable Filters and Differential Equations. _tCh. XI. Sampling and Discrete Filters. _tLesson 36. Periodic Distributions. _tLesson 37. Sampling Signals and Poisson's Formula. _tLesson 38. The Sampling Theorem and Shannon's Formula. _tLesson 39. Discrete Filters and Convolution. _tLesson 40. The z-Transform and Discrete Filters. _tCh. XII. Current Trends: Time-Frequency Analysis. _tLesson 41. The Windowed Fourier Transform. _tLesson 42. Wavelet Analysis. _tReferences. _tIndex.
650		0	_aFourier analysis
700	1		_aWitomski, Patrick
830		0	_917005 _aTexts in applied mathematics ; _v30
900			_a20594
900			_bSatın
942			_2lcc _cKT
999			_c16870 _d16870