PPT-5.0 Discrete-time Fourier Transform

51 Discretetime Fourier Transform Representation for discretetime signals Chapters 3 4 5 Chap 3 Periodic Fourier Series Chap 4 Aperiodic Fourier Transform Chap 5 Aperiodic . Fourier Series Vs. Fourier Transform. We use Fourier Series to represent periodic signals. We will use Fourier Transform to represent non-period signal.. Increase T. o. . to. infinity. (periodic). aperiodic. beeldverwerking. (8D040). dr. Andrea Fuster. Prof.dr. . Bart . ter. . Haar. . Romeny. dr. Anna . Vilanova. Prof.dr.ir. . Marcel . Breeuwer. The Fourier Transform II. Contents. Fourier Transform of sine and cosine. 5.1 Discrete-time Fourier Transform . Representation for discrete-time signals. Chapters 3, 4, 5. Chap. 3 . Periodic. Fourier Series. Chap. 4 . Aperiodic . Fourier Transform . Chap. 5 . Aperiodic . Patrick Freer. Honours. Project Presentation. Today’s questions. The Five . Whats. :. What is the Project?. What has been Done?. What Transforms are used?. What is the Time Frame?. What Next?. 1. What is the Project. MatLab. Lecture 11:. Lessons Learned from the Fourier Transform. . Lecture 01. . Using . MatLab. Lecture 02 Looking At Data. Lecture 03. . Probability and Measurement Error. . Lecture 04 Multivariate Distributions. Continues Fourier Transform - 2D. Fourier Properties. Convolution . Theorem. Image Processing. Fourier Transform 2D. The 2D Discrete Fourier Transform. For an image. f(x,y) x=0..N-1, y=0..M-1, . there are two-indices basis functions. 4.1 DFT . . In practice the Fourier components of data are obtained by digital computation rather than by . analog. processing. . The . analog. values have to be sampled at regular intervals and the sample values are converted to a digital binary representation by using ADC. . Data Compression. By Joseph . Gehring. What is a Fourier Transform?. From Simple Wikipedia:. “A.  . Fourier transform.  is a . math function.  that makes a sometimes less useful function into another more useful function. Announcements:. HW . 4. . posted, . due Tues May 8 at 4:30pm. . No late HWs as solutions will be available immediately.. Midterm details on next page. HW . 5 will . be posted . Fri May 11. , . due . MatLab. Lecture 11:. Lessons Learned from the Fourier Transform. . Lecture 01. . Using . MatLab. Lecture 02 Looking At Data. Lecture 03. . Probability and Measurement Error. . Lecture 04 Multivariate Distributions. 23 March 2011. Lowe. 1. Announcements. Lectures on both Monday, March 28. th. , and Wednesday, March 30. th. .. Fracture Testing. Aerodynamic Testing. Prepare for the Spectral Analysis sessions for next week: http://www.aoe.vt.edu/~aborgolt/aoe3054/manual/inst4/index.html. MatLab. 2. nd. Edition. Lecture 11:. Lessons Learned from the Fourier Transform. . Lecture 01. . Using . MatLab. Lecture 02 Looking At Data. Lecture 03 Probability and Measurement Error. Lecture 04 Multivariate Distributions. Chapter-2 : Signals & Systems . Review. Marc Moonen & . Toon. van . Waterschoot. Dept. E.E./ESAT-STADIUS, KU Leuven. marc.moonen@kuleuven.be. www.esat.kuleuven.be. /. stadius. /. Chapter-2 : Signals & Systems Review. Department of Biological Sciences. National University of Singapore. http://. www.cs.ucdavis.edu. /~. koehl. /Teaching/BL5229. koehl. @. cs.ucdavis.edu. Fourier analysis: the dial tone phone. We use Fourier analysis everyday…without knowing it! A dial tone.