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# Application of fourier series

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• 1. Applicat ion of fourier series inSAMPLINGPresented by: GIRISH DHARESHWAR
• 2. WHAT IS SAMPLING ?• It is the process of taking the samples of the signal at intervals Aliasing cannot distinguish between higher and lower frequencies Sampling theorem:  to avoid aliasing, sampling rate must be at least twice the maximum frequency component (`bandwidth’) of the signal
• 3. • Sampling theorem says there is enough information to reconstruct the signal, which means sampled signal looks like original one
• 4. Why ??????????• Most signals are analog in nature, and have to be sampled loss of information• Eg :Touch-Tone system of telephone dialling, when button is pushed two sinusoid signals are generated (tones) and transmitted, a digital system speech signal determines the frequences and uniquely identifies the button – digital
• 5. Where ???IN COMMUNICATIONA AO NL G D ITA IG L D ITA IG L SML G A P IN DP SS NL IG A S NL IG A S NL IG A• Convert analog signals into the digital information-sampling & involves analog-to-digital conversionD ITA IG L D ITA IG L A AO NL G DP S S NL IG A R C N TR C N E O S U TIOS NL IG A S NL IG A convert the digital information, after being processed back to an analog signal• involves digital-to-analog conversion & reconstruction e.g. text-to-speech signal (characters are used to generate artificial sound)
• 6. AA GN LO D ITA IG L AA G N LO D ITA IG L S MP G A LIN S NL IG A DP S S NL IG A R C N TR C N E O S U TIO S NL IG AS NL IG A perform both A/D and D/A conversions e.g. digital recording and playback of music (signal is sensed by microphones, amplified, converted to digital, processed, and converted back to analog to be played
• 7. Sampling rate :8 5*sin (2 4t)64 Amplitude = 52 Frequency = 4 Hz0-2-4-6 We take an-8 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 ideal sine wave to discuss effects of A sine wave sampling
• 8. A sine wave signal and correct sampling 8 5*sin(2 4t) 6 Amplitude = 5 4 2 Frequency = 4 Hz 0 Sampling rate = 256 samples/second -2 Sampling duration = -4 1 second -6 We do sampling of 4Hz -8 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 with 256 Hz so sampling seconds is much higher rate than the base frequency, good Thus after sampling we can reconstruct the original signal
• 9. Here sampling rate is 8.5 Hzand the frequency is 8 Hz An undersampled signal Sampling rate Red dots 2 sin(2 8t), SR = 8.5 Hz represent the sampled data 1.5 1 0.5 0 Undersampling -0.5 can be confusing -1 Here it suggests a different -1.5 frequency of -2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 sampled signal  Loss of information
• 10. The Discrete Time Fourier Transform(DTFT) and its Inverse :• The Fourier transform is an equation to calculate the frequency, amplitude and phase of each sampled signal needed to make up any given signal f(t): F ( ) f (t ) e x p ( i t ) dt 1 f (t ) F ( ) ex p (i t) d 2
• 11. (t)function Properties t (t ) d t 1 (t a ) f (t ) d t (t a ) f (a ) dt f (a )ex p ( i t ) d t 2 (ex p [ i ( ) t ] d t 2 (
• 12. The Fourier Transform of (t) is 1. ( t ) exp( i t ) dt exp( i [0]) 1 (t) tAnd the Fourier Transform of 1 is ( ): 1 exp( i t ) dt 2 ( ( ) t
• 13. The Fourier transform of exp(i 0 t) F exp( i 0 t) exp( i 0 t ) exp( i t ) dt exp( i [ 0 ] t ) dt 2 ( 0 ) exp(i 0t) F {exp(i 0t)} Im t Re tThe function exp(i 0t) is the essential component of Fourier analysis. It isa pure frequency.
• 14. The Fourier transform of cos( t) F cos( 0 t) cos( 0 t ) exp( i t ) dt 1 exp( i 0 t) exp( i 0 t ) exp( i t ) dt 2 1 1 exp( i [ 0 ] t ) dt exp( i [ 0 ] t ) dt 2 2 ( 0 ) ( 0 ) cos( 0t) F {cos( t )} 0 t
• 15. The Modulation Theorem: The FourierTransform of E(t) cos( 0 t)F E ( t ) cos( 0t ) E ( t ) cos( 0t ) exp( i t ) dt 1 E ( t ) exp( i 0t ) exp( i 0t ) exp( i t ) dt 2 1 1 E ( t ) exp( i [ 0 ] t ) dt E ( t ) exp( i [ 0 ]t) dt 2 2 1  1  F E ( t ) cos( 0t ) E( 0) E( 0) 2 2 F E ( t ) cos( 0t ) If E(t) = (t), then: - 0 0
• 16. The Fourier transform and its inverse are symmetrical:f(t) -> F( ) and F(t) -> f( ) (almost).If f(t) Fourier transforms to F( ), then F(t) Fourier transforms to: F (t ) ex p ( i t ) dtRearranging: 2 f( ) 1 2 F ( t ) e x p ( i[ ] t) dt 2Relabeling the integration variable from t to ’, we can see that we have aninverse Fourier transform: 1 2 F( ) exp( i[ ] )d 2 2 f( )This is why it is often said that f and F are a “Fourier Transform Pair.”
• 17. Summary• Fourier analysis for periodic functions focuses on the study of Fourier series• The Fourier Transform (FT) is a way of transforming a continuous signal into the frequency domain• The Discrete Time Fourier Transform (DTFT) is a Fourier Transform of a sampled signal• The Discrete Fourier Transform (DFT) is a discrete numerical equivalent using sums instead of integrals that can be computed on a digital computer• As one of the applications DFT and then Inverse DFT (IDFT) can be used to compute standard convolution product and thus to perform linear filtering