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Lesson 3. Discrete-time Fourier Analysis. Lesson 2 Recap. Basis Signals. Scaled and Shifted Unit Impulse:. Complex Exponentials:. Convolution is Matrix-Vector Multiplication. Vector x multiplies with a matrix A, whose rows are folded and shifted h.
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Lesson 3 Discrete-time Fourier Analysis
Basis Signals Scaled and Shifted Unit Impulse: Complex Exponentials:
Convolution is Matrix-Vector Multiplication. • Vector x multiplies with a matrix A, whose rows are folded and shifted h. • We can think of a LTI as a matrix, the input signal as a vector and the output signal as another vector.
Complex exponentials are eigenvectors of the convolution matrix. • What is an eigenvector of a matrix? A x = λ x x is an eigenvector of A λ is an eigenvalue corresponding to x
Represent an input signal as an superposition of the complex exponentials
How to calculate Ak? Discrete-time Fourier Transform (DTFT)
Two Important Properties • Periodicity • Symmetry Implication: We only need to consider
Properties of DTFT • Linearity
Properties of DTFT • Time Shifting
Properties of DTFT • Frequency shifting
Properties of DTFT • Conjugation
Properties of DTFT • Folding
Properties of DTFT • Convolution
Properties of DTFT • Multiplication
Properties of DTFT • Energy (Parseval’s Theorem)
Frequency-domain Representation of LTI • Complex exponentials are the eigenvectors of LTI. • The Fourier Transform of the impulse response gives the eigenvalues.
Frequency-domain representation of LTI Phase response Magnitude (gain) response
Sampling of Analog Signals Continuous-Time Fourier Transform (CTFT)
Sampling of Analog Signals Sampling Interval Sampling Frequency (sam / sec)
Poisson Summation Formula • Dirac Comb
Sampling of Analog Signals Aliasing Formula
Sampling of Analog Signals Signal Bandwidth Nyquist rate Harry Nyquist