How To Reduce Quantization Error In Adc
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Methods To Reduce Quantization Error In Pcm
Reducing ADC Quantization Noise Reducing ADC Quantization Noise Two techniques, oversampling and dithering, have gained wide acceptance in improving the noise performance of commercial analog-to-digital converters. Jun 17, 2005 Richard Lyons and Randy Yates | Microwaves and RF EMAIL Tweet Comments 0 Download this article in .PDF format This file type includes high resolution graphics and schematics quantization noise can be reduced by increasing when applicable. Analog-to-digital converters (ADCs) provide the vital transformation of analog signals into digital code in many systems. They perform amplitude quantization of an analog input signal into binary output words of finite length, normally in the range of 6 to 18 b, an inherently nonlinear process. This nonlinearity manifests itself as wideband noise in the ADC's binary output, called quantization noise, limiting an ADC's dynamic range. This article describes the two most popular methods for improving the quantization noise performance in practical ADC applications: oversampling and dithering. Related Finding Ways To Reduce Filter Size Matching An ADC To A Transformer Tunable Oscillators Aim At Reduced Phase Noise Large-Signal Approach Yields Low-Noise VHF/UHF Oscillators To understand quantization noise reduction methods, first recall that the signal-to-quantization-noise ratio, in dB, of an ideal N-bit ADC is SNRQ = 6.02N + 4.77 + 20log10 (LF) dB, where: LF = the loading factor measure of the ADC's input analog voltage level. (A derivation of SNRQ is provided in ref. 1.) Parameter LF i
Product Showcase DIGI-KEY CONTINUING EDUCATION CENTER Earn IEEE Professional Development Hours | Next class: October 24 - Day 1: An Introduction to Serial ... the quantization error in an analog-to-digital converter can be reduced by MORE > Blogs Mechatronics Zone Oversampling Reduces Quantization Errors BioEmail ThisPrintComment Jon Titus, Contributing
Quantization Error In Analog To Digital Conversion
Technical Editor7/27/2012 18 comments NO RATINGSLogin to Rate Tweet No matter how many bits an analog-to-digital converter (ADC) provides, the
What Is Quantization
digital output can only approximate the original signal. This approximation gives rise to quantization errors, or quantization "noise." The error values fall between ±1/2 the voltage represented by the ADC's least-significant bit (LSB) and http://mwrf.com/components/reducing-adc-quantization-noise they have a fairly uniform distribution within this range. Although ADCs with higher resolution reduce the quantization errors, they always remain within ±1/2 LSB. You can think of the quantization error as adding white noise to the digitized information. By definition, white noise has a "flat" power spectrum over the fsample/2 bandwidth. So how can a data-acquisition system reduce quantization errors? Because these errors depend only on an ADCs http://www.designnews.com/author.asp?doc_id=246011 resolution, sampling at a much higher rate than you would normally spreads the quantization noise over a larger bandwidth. And thus the power density for a fixed bandwidth decreases as fsample increases. In practice, a higher sample rate decreases the quantization noise superimposed on the digital data for the signal you want to measure. But the reduction of the noise comes at a price -- more data to process and the need to digitally filter the data. Doubling the sample rate increases the ADC resolution by 1/2 LSB. This sigma-delta modulator converts an analog-input signal to a stream of logic-1 bits proportional to the signal voltage.
This type of oversampling becomes impractical, though, unless you also use a technique called noise shaping. This technique decreases noise in the bandwidth of interest by "shifting" it to higher frequencies where it has less effect on your signals of interest. Sigma-delta, also called delta-sigma, ADCs provide this function and produce high resolutions for relatively low-frequency signals. A sound card, for example, relies on a sigma-delta converter that oversamples at up to 192 ksamples/sec. Some converters operate at 256 times the Nyquist frequency and provide resolutions of 20 or more bits. The simplest sigma-delta ADC uses a difference amplitour help Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring http://electronics.stackexchange.com/questions/61596/quantization-noise-and-quantization-error developers or posting ads with us Electrical Engineering Questions Tags Users Badges Unanswered Ask Question _ Electrical Engineering Stack Exchange is a question and answer site for electronics and electrical engineering professionals, students, and enthusiasts. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Quantization noise and Quantization error up vote 6 down vote favorite quantization error 1 What is the difference between the quantization noise and quantization error in ADC? I understood that the quantization error you get when you convert analog to digital and quantization noise when you convert from digital to analog. adc conversion share|improve this question asked Mar 20 '13 at 10:08 Sam 13314 add a comment| 4 Answers 4 active oldest votes up vote 4 down vote accepted The quantization noise is an abstraction, meant to represent the quantization error as quantization error in a signal (so it can be compared to other forms of noise. You consider the quantization noise as the difference between the (real) quantized signal and the (ideal) sampled one. Because of the loss of information due to quantization, a signal that is A/D and then D/A converted will show an additional noise due to quantization. A situation in which using quantization noise is useful is when determining the quantization depth (number of levels/bits) of a signal. By comparing the quantization noise to the other noise sources, it's possible to determine the maximum reasonable number of levels for the quantization, because additional bits would be absorbed by noise. This of course happens if the sampling rule is respected. share|improve this answer edited Mar 20 '13 at 10:24 answered Mar 20 '13 at 10:17 clabacchio♦ 11k42061 grazie per la risposta. –Sam Mar 20 '13 at 10:27 (English mode OFF) Prego :) (English mode ON) –clabacchio♦ Mar 20 '13 at 10:33 add a comment| up vote 3 down vote An very important aspect of quantization noise that has not yet been mentioned is that unlike some types of noise, it cannot in general be removed by filtering, but adding the right sort of noise to a signal before it is sampled can cause change the character of the quantization noise in such a way that much of it can be removed. Fo
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