Signal-To-Noise Ratio (SNR)

Total Page:16

File Type:pdf, Size:1020Kb

Signal-To-Noise Ratio (SNR) 6.02 Fall 2014! Lecture #8! • Noise • PDF’s, means, variances, Gaussian noise • Bit error rate for bipolar signaling 6.02 Fall 2014 Lecture 8, Slide #1 Single Link Communication Model! End-host Original source computers Receiving app/user Digitize Render/display, (if needed) etc. Source binary digits (“message bits”) Source coding Source decoding Bit stream Bit stream Channel Channel Mapper Recv Decoding Coding Bits + samples Bits Signals (reducing or (bit error Xmit (Voltages) + removing correction) samples over Demapper physical link bit errors) 6.02 Fall 2014 Lecture 8, Slide #2 From Baseband to Modulated Signal, and Back! codeword ! bits in! generate! x[n] 1001110101 digitized ! modulate! DAC! symbols! NOISY & DISTORTING ANALOG CHANNEL! demodulate! sample & ADC! 1001110101 & filter! y[n] threshold! codeword ! bits out! 6.02 Fall 2014 Lecture 8, Slide #3 Mapping Bits to Samples at Transmitter! codeword ! bits in! generate! x[n] 1001110101 digitized ! L=16 samples per bit! symbols! On-Off Signaling ‘0’à0 volts ‘1’àV volts 1 0 0 1 1 1 0 1 0 1 6.02 Fall 2014 Lecture 8, Slide #4 Samples after Processing at Receiver! codeword bits in generate 1001110101 digitized modulate DAC symbols x[n] NOISY & DISTORTING ANALOG CHANNEL y[n] Assuming no noise, ! ADC demodulate filter only end-to-end distortion! y[n] 6.02 Fall 2014 Lecture 8, Slide #5 Mapping Samples to Bits at Receiver! codeword bits in generate 1001110101 digitized modulate DAC symbols n = sample index! x[n] j = bit index! NOISY & DISTORTING ANALOG CHANNEL y[n] b[j] demodulate sample & 1001110101 ADC & filter threshold codeword bits out 16 samples per bit! b[j] 1 0 0 1 1 1 0 1 0 1 Threshold 6.02 Fall 2014 Lecture 8, Slide #6 For this lecture and next, ! assume no distortion, only ! additive zero-mean noise! • Received signal:! ! y[n] = x[n] + w[n]! ! i.e., received sample y[n] = the transmitted sample x[n] + ! zero-mean noise w[n] on that sample! ! Assume noise is iid (independent and identically distributed at each n)! ! • Signal-to-Noise Ratio (SNR)! – usually denotes the ratio of ! (time-averaged or peak) signal power, i.e., squared amplitude of x[n]! to! noise variance, i.e., expected squared amplitude of w[n]! 6.02 Fall 2014 Lecture 8, Slide #7 Noise Characterization:! From Histogram to PDF! Experiment: create histograms of sample values from independent trials of increasing lengths.! ! Histogram typically “converges” to a shape that is known – after normalization to unit area – as a ! probability density function (PDF)! 6.02 Fall 2014 Lecture 8, Slide #8 Using the PDF in Probability Calculations! We say that W is a continuous random variable governed by the PDF ≥ fW(w) 0 if W takes on a numerical value in the range of w1 to w2 with a probability calculated from the PDF of W as:! w p(w < W < w ) = 2 f (w)dw 1 2 ∫ w W 1 fW(w) w! w1 w2 A PDF is not a probability – its associated integrals are.! Note that the PDF is nonnegative, and the area under it is 1. ! 6.02 Fall 2014 Lecture 8, Slide #9 Mean & Variance of Continuous r.v. W! fW(w) σw W! μ The mean or expected value W is defined and computed as:! ∞ µ = w f (w)dw W ∫ −∞ W 2 The variance σW is the expected squared variation or deviation of the random variable around the mean, and is thus computed as:! ∞ E[(W − µ )22] ] = σ 2 = (w − µ )2 f (w)dw W W ∫ −∞ W W The square root of the variance is the standard deviation, σW! 6.02 Fall 2014 Lecture 8, Slide #10 Mean of a Function a(W) :! ∞ E[a(W )] = a(w) f (w)dw ∫ −∞ W Mean of a Sum b(V) + c(W) :! E[b(V)+ c(W )] = E[b(V)]+ E[c(W )] Mean of b(V) + c(W) + d(V)e(W)! when V, W are independent :! E[b(V)+ c(W )+ d(V)e(W )] = E[b(V)]+ E[c(W )]+ E[d(V)]E[e(W )] 6.02 Fall 2014 Lecture 8, Slide #11 The Gaussian Distribution! A Gaussian random variable W with ! mean μ and ! variance σ2 has a PDF described by! 2 −(w−µ) 1 2σ 2 fW (w) = e 2πσ 2 6.02 Fall 2014 Lecture 8, Slide #12 The Q(.) Function --- ! Area in the tail of a ! zero-mean unit-variance Gaussian! 1 1 v2/2 Q(t)= e− dv p2⇡ Zt Q( t)=1 Q(t) − − 2 2 t e t /2 1 e t /2 − <Q(t) < − ,t>0 (1 + t2) p2⇡ t p2⇡ 6.02 Fall 2014 Lecture 8, Slide #13 Tail probability of a general Gaussian ! in terms of the Q(.) function! 1 1 (v µ)2/(2σ2) e− − dv 2 p2⇡ t Z t µ = Q − σ ⇣ ⌘ 6.02 Fall 2014 Lecture 8, Slide #14 The Ubiquity of Gaussian Noise! The net noise observed at the receiver is often the sum of many small, independent random contributions from many factors. Under fairly mild conditions, the Central Limit Theorem says their sum will be a Gaussian.! ! The figure below shows the histograms of the results of 10,000 trials of summing 100 random samples drawn from [-1,1] using two different distributions.! 1 Triangular Uniform 0.5 PDF PDF -1 1 -1 1 6.02 Fall 2014 Lecture 8, Slide #15 Distinguishing “1” from “0”! • Assume bipolar signaling: ! Transmit L samples x[.] at +Vp (=V1) to signal a “1”! Transmit L samples x[.] at –Vp (=V0) to signal a “0”! • Simple-minded receiver: take a single sample value y[nj] at an appropriately chosen instant nj in the j-th bit interval. Decide between the following two hypotheses: ! y[n] = +Vp + w[n] (==> “1”)! or ! y[n] = –Vp + w[n] (==> “0”)! ! where w[n] is Gaussian, zero-mean, variance σ2! 6.02 Fall 2014 Lecture 8, Slide #16 Connecting the SNR and BER! P(“0”)=0.5 P(“1”)=0.5 µ=V V E µ=-Vp p p = S σ= σ σ= σnoise noise 2 2σ = N0 -Vp +Vp 2E BER = P(error) = Q( S ) N0 6.02 Fall 2014 Lecture 8, Slide #17 In terms of erfc (as in notes, kind of ugly!)! P(“0”)=0.5 P(“1”)=0.5 µ=V µ=-Vp p Vp = ES σ= σ σ= σnoise noise 2 2σ = N0 -Vp +Vp 1 E 1 V BER = P(error) = erfc( S ) = erfc( p ) 2 N0 2 σ 2 6.02 Fall 2014 Lecture 8, Slide #18 SNR Example! Changing the amplification factor (gain) A leads to different SNR values:! • Lower A → lower SNR! • Signal quality degrades with lower SNR! 6.02 Fall 2014 Lecture 8, Slide #19 10logX X Signal-to-Noise Ratio (SNR)! 100 10000000000 90 1000000000 The Signal-to-Noise ratio (SNR) is useful in 80 100000000 judging the impact of noise on system 70 10000000 performance:! 60 1000000 P 50 100000 SNR = signal 40 10000 30 1000 Pnoise 20 100 SNR for power is often measured in ! 10 10 decibels (dB):! 0 1 ! P $ -10 0.1 signal -20 0.01 SNR (dB) =10log10 # & # & -30 0.001 " Pnoise % -40 0.0001 -50 0.000001 Caution: For measuring ratios of ! amplitudes rather than powers, take! -60 0.0000001 -70 0.00000001 20 log10 (ratio).! -80 0.000000001 -90 0.0000000001 3.01db is a factor of 2! -100 0.00000000001 6.02 Fall 2014 in power ratio! Lecture 8, Slide #20 .
Recommended publications
  • Blind Denoising Autoencoder
    1 Blind Denoising Autoencoder Angshul Majumdar In recent times a handful of papers have been published on Abstract—The term ‘blind denoising’ refers to the fact that the autoencoders used for signal denoising [10-13]. These basis used for denoising is learnt from the noisy sample itself techniques learn the autoencoder model on a training set and during denoising. Dictionary learning and transform learning uses the trained model to denoise new test samples. Unlike the based formulations for blind denoising are well known. But there has been no autoencoder based solution for the said blind dictionary learning and transform learning based approaches, denoising approach. So far autoencoder based denoising the autoencoder based methods were not blind; they were not formulations have learnt the model on a separate training data learning the model from the signal at hand. and have used the learnt model to denoise test samples. Such a The main disadvantage of such an approach is that, one methodology fails when the test image (to denoise) is not of the never knows how good the learned autoencoder will same kind as the models learnt with. This will be first work, generalize on unseen data. In the aforesaid studies [10-13] where we learn the autoencoder from the noisy sample while denoising. Experimental results show that our proposed method training was performed on standard dataset of natural images performs better than dictionary learning (K-SVD), transform (image-net / CIFAR) and used to denoise natural images. Can learning, sparse stacked denoising autoencoder and the gold the learnt model be used to recover images from other standard BM3D algorithm.
    [Show full text]
  • A Comparison of Delayed Self-Heterodyne Interference Measurement of Laser Linewidth Using Mach-Zehnder and Michelson Interferometers
    Sensors 2011, 11, 9233-9241; doi:10.3390/s111009233 OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors Article A Comparison of Delayed Self-Heterodyne Interference Measurement of Laser Linewidth Using Mach-Zehnder and Michelson Interferometers Albert Canagasabey 1,2,*, Andrew Michie 1,2, John Canning 1, John Holdsworth 3, Simon Fleming 2, Hsiao-Chuan Wang 1,2 and Mattias L. Åslund 1 1 Interdisciplinary Photonics Laboratories (iPL), School of Chemistry, University of Sydney, 2006, NSW, Australia; E-Mails: andrew.michie@sydney.edu.au (A.M.); john.canning@sydney.edu.au (J.C.); soongking@gmail.com (H.-C.W.); mattias.aslund@gmail.com (M.L.Å.) 2 Institute of Photonics and Optical Science (IPOS), School of Physics, University of Sydney, 2006, NSW, Australia; E-Mail: simon.fleming@sydney.edu.au (S.F.) 3 SMAPS, University of Newcastle, Callaghan, NSW 2308, Australia; E-Mail: john.holdsworth@newcastle.edu.au (J.H.) * Author to whom correspondence should be addressed; E-Mail: albert.canagasabey@sydney.edu.au; Tel.: +61-2-9351-1984. Received: 17 August 2011; in revised form: 13 September 2011 / Accepted: 23 September 2011 / Published: 27 September 2011 Abstract: Linewidth measurements of a distributed feedback (DFB) fibre laser are made using delayed self heterodyne interferometry (DHSI) with both Mach-Zehnder and Michelson interferometer configurations. Voigt fitting is used to extract and compare the Lorentzian and Gaussian linewidths and associated sources of noise. The respective measurements are wL (MZI) = (1.6 ± 0.2) kHz and wL (MI) = (1.4 ± 0.1) kHz. The Michelson with Faraday rotator mirrors gives a slightly narrower linewidth with significantly reduced error.
    [Show full text]
  • Estimation from Quantized Gaussian Measurements: When and How to Use Dither Joshua Rapp, Robin M
    1 Estimation from Quantized Gaussian Measurements: When and How to Use Dither Joshua Rapp, Robin M. A. Dawson, and Vivek K Goyal Abstract—Subtractive dither is a powerful method for remov- be arbitrarily far from minimizing the mean-squared error ing the signal dependence of quantization noise for coarsely- (MSE). For example, when the population variance vanishes, quantized signals. However, estimation from dithered measure- the sample mean estimator has MSE inversely proportional ments often naively applies the sample mean or midrange, even to the number of samples, whereas the MSE achieved by the when the total noise is not well described with a Gaussian or midrange estimator is inversely proportional to the square of uniform distribution. We show that the generalized Gaussian dis- the number of samples [2]. tribution approximately describes subtractively-dithered, quan- tized samples of a Gaussian signal. Furthermore, a generalized In this paper, we develop estimators for cases where the Gaussian fit leads to simple estimators based on order statistics quantization is neither extremely fine nor extremely coarse. that match the performance of more complicated maximum like- The motivation for this work stemmed from a series of lihood estimators requiring iterative solvers. The order statistics- experiments performed by the authors and colleagues with based estimators outperform both the sample mean and midrange single-photon lidar. In [3], temporally spreading a narrow for nontrivial sums of Gaussian and uniform noise. Additional laser pulse, equivalent to adding non-subtractive Gaussian analysis of the generalized Gaussian approximation yields rules dither, was found to reduce the effects of the detector’s coarse of thumb for determining when and how to apply dither to temporal resolution on ranging accuracy.
    [Show full text]
  • Image Denoising by Autoencoder: Learning Core Representations
    Image Denoising by AutoEncoder: Learning Core Representations Zhenyu Zhao College of Engineering and Computer Science, The Australian National University, Australia, caesarsoddy@gmail.com Abstract. In this paper, we implement an image denoising method which can be generally used in all kinds of noisy images. We achieve denoising process by adding Gaussian noise to raw images and then feed them into AutoEncoder to learn its core representations(raw images itself or high-level representations).We use pre- trained classifier to test the quality of the representations with the classification accuracy. Our result shows that in task-specific classification neuron networks, the performance of the network with noisy input images is far below the preprocessing images that using denoising AutoEncoder. In the meanwhile, our experiments also show that the preprocessed images can achieve compatible result with the noiseless input images. Keywords: Image Denoising, Image Representations, Neuron Networks, Deep Learning, AutoEncoder. 1 Introduction 1.1 Image Denoising Image is the object that stores and reflects visual perception. Images are also important information carriers today. Acquisition channel and artificial editing are the two main ways that corrupt observed images. The goal of image restoration techniques [1] is to restore the original image from a noisy observation of it. Image denoising is common image restoration problems that are useful by to many industrial and scientific applications. Image denoising prob- lems arise when an image is corrupted by additive white Gaussian noise which is common result of many acquisition channels. The white Gaussian noise can be harmful to many image applications. Hence, it is of great importance to remove Gaussian noise from images.
    [Show full text]
  • ECE 417 Lecture 3: 1-D Gaussians Mark Hasegawa-Johnson 9/5/2017 Contents
    ECE 417 Lecture 3: 1-D Gaussians Mark Hasegawa-Johnson 9/5/2017 Contents • Probability and Probability Density • Gaussian pdf • Central Limit Theorem • Brownian Motion • White Noise • Vector with independent Gaussian elements Cumulative Distribution Function (CDF) A “cumulative distribution function” (CDF) specifies the probability that random variable X takes a value less than : Probability Density Function (pdf) A “probability density function” (pdf) is the derivative of the CDF: That means, for example, that the probability of getting an X in any interval is: Example: Uniform pdf The rand() function in most programming languages simulates a number uniformly distributed between 0 and 1, that is, Suppose you generated 100 random numbers using the rand() function. • How many of the numbers would be between 0.5 and 0.6? • How many would you expect to be between 0.5 and 0.6? • How many would you expect to be between 0.95 and 1.05? Gaussian (Normal) pdf Gauss considered this problem: under what circumstances does it make sense to estimate the mean of a distribution, , by taking the average of the experimental values, ? He demonstrated that is the maximum likelihood estimate of if Gaussian pdf Attribution: jhguch, https://commons. wikimedia.org/wik i/File:Boxplot_vs_P DF.svg Unit Normal pdf Suppose that X is normal with mean and standard deviation (variance ): Then is normal with mean 0 and standard deviation 1: Central Limit Theorem The Gaussian pdf is important because of the Central Limit Theorem. Suppose are i.i.d. (independent and identically distributed), each having mean and variance . Then Example: the sum of uniform random variables Suppose that are i.i.d.
    [Show full text]
  • Topic 5: Noise in Images
    NOISE IN IMAGES Session: 2007-2008 -1 Topic 5: Noise in Images 5.1 Introduction One of the most important consideration in digital processing of images is noise, in fact it is usually the factor that determines the success or failure of any of the enhancement or recon- struction scheme, most of which fail in the present of significant noise. In all processing systems we must consider how much of the detected signal can be regarded as true and how much is associated with random background events resulting from either the detection or transmission process. These random events are classified under the general topic of noise. This noise can result from a vast variety of sources, including the discrete nature of radiation, variation in detector sensitivity, photo-graphic grain effects, data transmission errors, properties of imaging systems such as air turbulence or water droplets and image quantsiation errors. In each case the properties of the noise are different, as are the image processing opera- tions that can be applied to reduce their effects. 5.2 Fixed Pattern Noise As image sensor consists of many detectors, the most obvious example being a CCD array which is a two-dimensional array of detectors, one per pixel of the detected image. If indi- vidual detector do not have identical response, then this fixed pattern detector response will be combined with the detected image. If this fixed pattern is purely additive, then the detected image is just, f (i; j) = s(i; j) + b(i; j) where s(i; j) is the true image and b(i; j) the fixed pattern noise.
    [Show full text]
  • Adaptive Noise Suppression of Pediatric Lung Auscultations with Real Applications to Noisy Clinical Settings in Developing Countries Dimitra Emmanouilidou, Eric D
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 9, SEPTEMBER 2015 2279 Adaptive Noise Suppression of Pediatric Lung Auscultations With Real Applications to Noisy Clinical Settings in Developing Countries Dimitra Emmanouilidou, Eric D. McCollum, Daniel E. Park, and Mounya Elhilali∗, Member, IEEE Abstract— Goal: Chest auscultation constitutes a portable low- phy or other imaging techniques, as well as chest percussion cost tool widely used for respiratory disease detection. Though it and palpation, the stethoscope remains a key diagnostic de- offers a powerful means of pulmonary examination, it remains vice due to its portability, low cost, and its noninvasive nature. riddled with a number of issues that limit its diagnostic capabil- ity. Particularly, patient agitation (especially in children), back- Chest auscultation with standard acoustic stethoscopes is not ground chatter, and other environmental noises often contaminate limited to resource-rich industrialized settings. In low-resource the auscultation, hence affecting the clarity of the lung sound itself. high-mortality countries with weak health care systems, there is This paper proposes an automated multiband denoising scheme for limited access to diagnostic tools like chest radiographs or ba- improving the quality of auscultation signals against heavy back- sic laboratories. As a result, health care providers with variable ground contaminations. Methods: The algorithm works on a sim- ple two-microphone setup, dynamically adapts to the background training and supervision
    [Show full text]
  • Lecture 18: Gaussian Channel, Parallel Channels and Rate-Distortion Theory 18.1 Joint Source-Channel Coding (Cont'd) 18.2 Cont
    10-704: Information Processing and Learning Spring 2012 Lecture 18: Gaussian channel, Parallel channels and Rate-distortion theory Lecturer: Aarti Singh Scribe: Danai Koutra Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the permission of the Instructor. 18.1 Joint Source-Channel Coding (cont'd) Clarification: Last time we mentioned that we should be able to transfer the source over the channel only if H(src) < Capacity(ch). Why not design a very low rate code by repeating some bits of the source (which would of course overcome any errors in the long run)? This design is not valid, because we cannot accumulate bits from the source into a buffer; we have to transmit them immediately as they are generated. 18.2 Continuous Alphabet (discrete-time, memoryless) Channel The most important continuous alphabet channel is the Gaussian channel. We assume that we have a signal 2 Xi with gaussian noise Zi ∼ N (0; σ ) (which is independent from Xi). Then, Yi = Xi + Zi. Without any Figure 18.1: Gaussian channel further constraints, the capacity of the channel can be infinite as there exists an inifinte subset of the inputs which can be perfectly recovery (as discussed in last lecture). Therefore, we are interested in the case where we have a power constraint on the input. For every transmitted codeword (x1; x2; : : : ; xn) the following inequality should hold: n n 1 X 1 X x2 ≤ P (deterministic) or E[x2] = E[X2] ≤ P (randomly generated codebits) n i n i i=1 i=1 Last time we computed the information capacity of a Gaussian channel with power constraint.
    [Show full text]
  • Link Budgets 1
    Link Budgets 1 Intuitive Guide to Principles of Communications www.complextoreal.com Link Budgets You are planning a vacation. You estimate that you will need $1000 dollars to pay for the hotels, restaurants, food etc.. You start your vacation and watch the money get spent at each stop. When you get home, you pat yourself on the back for a job well done because you still have $50 left in your wallet. We do something similar with communication links, called creating a link budget. The traveler is the signal and instead of dollars it starts out with “power”. It spends its power (or attenuates, in engineering terminology) as it travels, be it wired or wireless. Just as you can use a credit card along the way for extra money infusion, the signal can get extra power infusion along the way from intermediate amplifiers such as microwave repeaters for telephone links or from satellite transponders for satellite links. The designer hopes that the signal will complete its trip with just enough power to be decoded at the receiver with the desired signal quality. In our example, we started our trip with $1000 because we wanted a budget vacation. But what if our goal was a first-class vacation with stays at five-star hotels, best shows and travel by QE2? A $1000 budget would not be enough and possibly we will need instead $5000. The quality of the trip desired determines how much money we need to take along. With signals, the quality is measured by the Bit Error Rate (BER).
    [Show full text]
  • Robust Signal-To-Noise Ratio Estimation Based on Waveform Amplitude Distribution Analysis
    Robust Signal-to-Noise Ratio Estimation Based on Waveform Amplitude Distribution Analysis Chanwoo Kim, Richard M. Stern Department of Electrical and Computer Engineering and Language Technologies Institute Carnegie Mellon University, Pittsburgh, PA 15213 {chanwook, rms}@cs.cmu.edu speech and background noise are independent, (2) clean Abstract speech follows a gamma distribution with a fixed shaping In this paper, we introduce a new algorithm for estimating the parameter, and (3) the background noise has a Gaussian signal-to-noise ratio (SNR) of speech signals, called WADA- distribution. Based on these assumptions, it can be seen that if SNR (Waveform Amplitude Distribution Analysis). In this we model noise-corrupted speech at an unknown SNR using algorithm we assume that the amplitude distribution of clean the Gamma distribution, the value of the shaping parameter speech can be approximated by the Gamma distribution with obtained using maximum likelihood (ML) estimation depends a shaping parameter of 0.4, and that an additive noise signal is uniquely on the SNR. Gaussian. Based on this assumption, we can estimate the SNR While we assume that the background noise can be by examining the amplitude distribution of the noise- assumed to be Gaussian, we will demonstrate that this corrupted speech. We evaluate the performance of the algorithm still provides better results than the NIST STNR WADA-SNR algorithm on databases corrupted by white algorithm, even in the presence of other types of maskers such noise, background music, and interfering speech. The as background music or interfering speech, where the WADA-SNR algorithm shows significantly less bias and less corrupting signal is clearly not Gaussian.
    [Show full text]
  • Discrimination of Time Intervals Marked by Brief Acoustic Pulses of Various Intensities and Spectra
    Perception & Psychophysics 1977, Vol. 21 (2),125-142 Discrimination of time intervals marked by brief acoustic pulses of various intensities and spectra PIERRE L. DIVENYI and WILLIAM F. DANNER Central Institute/or the Deaf, St. Louis, Missouri 63110 Experienced observers were asked to identify, in a four-level 2AFC situation, the longer of two unfilled time intervals, each of which was marked by a pair of 20-msec acoustic pulses. When all the markers were identical, high-level (86-dB SPL) bursts of coherently gated sinusoids or bursts of band-limited Gaussian noise, a change in the spectrum of the markers generally did not affect performance. On the other hand, for I-kHz tone-burst markers, intensity decreases below 25 dB SL were accompanied by sizable deterioration of the discrimination performance, especially at short (25-msec) base intervals. Similarly large changes in performance were observed also when the two tonal markers of each interval were made very dissimilar from each other, either in frequency (frequency difference larger than 1 octave) or in intensity (level of the first marker at least 45 dB below the level of the second marker). Time-difference thresholds in these two latter cases were found to be nonmonotonically related to the base interval, the minima occurring between 40- and 80-msec onset separations. For the past decade, readers of the psychophysics acoustic complexity of speech, the answer to this and speech literature have witnessed a growing question may be very difficult. To appreciate this interest in the perception of temporal aspects of difficulty, one should remember that phonetic seg­ speech (for a review, see Studdert-Kennedy, 1975).
    [Show full text]
  • The Power Spectral Density of Phase Noise and Jitter: Theory, Data Analysis, and Experimental Results by Gil Engel
    AN-1067 APPLICATION NOTE One Technology Way • P. O. Box 9106 • Norwood, MA 02062-9106, U.S.A. • Tel: 781.329.4700 • Fax: 781.461.3113 • www.analog.com The Power Spectral Density of Phase Noise and Jitter: Theory, Data Analysis, and Experimental Results by Gil Engel INTRODUCTION GENERAL DESCRIPTION Jitter on analog-to-digital and digital-to-analog converter sam- There are numerous techniques for generating clocks used in pling clocks presents a limit to the maximum signal-to-noise electronic equipment. Circuits include R-C feedback circuits, ratio that can be achieved (see Integrated Analog-to-Digital and timers, oscillators, and crystals and crystal oscillators. Depend- Digital-to-Analog Converters by van de Plassche in the References ing on circuit requirements, less expensive sources with higher section). In this application note, phase noise and jitter are defined. phase noise (jitter) may be acceptable. However, recent devices The power spectral density of phase noise and jitter is developed, demand better clock performance and, consequently, more time domain and frequency domain measurement techniques costly clock sources. Similar demands are placed on the spectral are described, limitations of laboratory equipment are explained, purity of signals sampled by converters, especially frequency and correction factors to these techniques are provided. The synthesizers used as sources in the testing of current higher theory presented is supported with experimental results applied performance converters. In the following section, definitions to a real world problem. of phase noise and jitter are presented. Then a mathematical derivation is developed relating phase noise and jitter to their frequency representation.
    [Show full text]