EE 230 Lecture 42 Spring 2010

EE 230 Lecture 42 - EE 230 Lecture 42 Data Converters Nonideal Effects Review from Last Time Characterization of Quantization Noise Saw tooth

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EE 230 Lecture 42 Data Converters Nonideal Effects
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Characterization of Quantization Noise Saw tooth excitation 12 lSB QRMS X X = SIG-RMS NOISE-RMS X SNR= X What is X SIG-RMS ? X IN t X REF 12 REF SIG RMS X X = Review from Last Time:
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Characterization of Quantization Noise X IN t X REF X OUT t X REF /8 0 X REF /4 3X REF /8 X REF /2 5X REF /8 3X REF /4 7X REF /8 X QE t X LSB Sinusoidal excitation Consider an ADC Quantization noise is difficult to analytically characterize Still need RMS value of X QE (t) Will consider error in interpreted output Review from Last Time:
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Characterization of Quantization Noise Sinusoidal excitation Consider an ADC Will consider error in interpreted output QE LSB Review from Last Time:
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Characterization of Quantization Noise Sinusoidal excitation Consider an ADC (clocked) Theorem: If n(t) is a random process, then provided that the RMS value is measured over a large interval where the parameters σ and μ are the standard deviation and the mean of <n(kT)> 22 RMS V σ μ ≅+ () 1 1 2 1 RMS V L tT t L nt d t T + ≅≅ + where T is the sampling interval and T L is a large interval This theorem can thus be represented as Review from Last Time:
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Characterization of Quantization Noise Sinusoidal excitation Consider an ADC The quantization noise samples of the ADC output are approximately uniformly distributed between in the interval [-X LSB /2 , X LSB /2] <n(kT)> ~ U [-X LSB /2 , X LSB /2]
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This note was uploaded on 02/01/2012 for the course EE 230 taught by Professor Mina during the Fall '08 term at Iowa State.

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EE 230 Lecture 42 - EE 230 Lecture 42 Data Converters Nonideal Effects Review from Last Time Characterization of Quantization Noise Saw tooth

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