Noise (electronics) | Time series models

Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. The modifiers denote specific characteristics: * Additive because it is added to any noise that might be intrinsic to the information system. * White refers to the idea that it has uniform power across the frequency band for the information system. It is an analogy to the color white which has uniform emissions at all frequencies in the visible spectrum. * Gaussian because it has a normal distribution in the time domain with an average time domain value of zero. Wideband noise comes from many natural noise sources, such as the thermal vibrations of atoms in conductors (referred to as thermal noise or Johnson–Nyquist noise), shot noise, black-body radiation from the earth and other warm objects, and from celestial sources such as the Sun. The central limit theorem of probability theory indicates that the summation of many random processes will tend to have distribution called Gaussian or Normal. AWGN is often used as a channel model in which the only impairment to communication is a linear addition of wideband or white noise with a constant spectral density (expressed as watts per hertz of bandwidth) and a Gaussian distribution of amplitude. The model does not account for fading, frequency selectivity, interference, nonlinearity or dispersion. However, it produces simple and tractable mathematical models which are useful for gaining insight into the underlying behavior of a system before these other phenomena are considered. The AWGN channel is a good model for many satellite and deep space communication links. It is not a good model for most terrestrial links because of multipath, terrain blocking, interference, etc. However, for terrestrial path modeling, AWGN is commonly used to simulate background noise of the channel under study, in addition to multipath, terrain blocking, interference, ground clutter and self interference that modern radio systems encounter in terrestrial operation. (Wikipedia).

Jonathan defines what white noise actually is and how it's used to mask other annoying sounds. Learn more at HowStuffWorks.com: http://science.howstuffworks.com/question47.htm Share on Facebook: http://goo.gl/n7YNrZ Share on Twitter: http://goo.gl/Fq9InS Subscribe: http://goo.gl/ZYI7Gt V

From playlist Episodes hosted by Jonathan

Spinodal decomposition in the Allen-Cahn equation without and with noise

This simulation compares solutions of the Allen-Cahn equation without and with noise. The left half of the display shows the case without noise, while the right half shows the case with an additional space-time white noise, meaning here that independent Gaussian random variables are added

From playlist Reaction-diffusion equations

What if there was a way to create white noise for your sense of smell? Trace is here to explain how scientists were able to successfully mask odors using “white smell.” Read More: White smell: the olfactory equivalent of white noise http://www.newscientist.com/article/dn22514-white-sm

From playlist DNews Favorites

Time Series Talk : White Noise

Intro to white noise in time series analysis

From playlist Time Series Analysis

Mixture Models 4: multivariate Gaussians

Full lecture: http://bit.ly/EM-alg We generalise the equations for the case of a multivariate Gaussians. The main difference from the previous video (part 2) is that instead of a scalar variance we now estimate a covariance matrix, using the same posteriors as before.

From playlist Mixture Models

Analysis of Quantization Error

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Modeling quantization error as uncorrelated noise. Signal to quantization noise ratio as a function of the number of bits used to represent the sign

From playlist Sampling and Reconstruction of Signals

I discuss causal and non-causal noise filters: the moving average filter and the exponentially weighted moving average. I show how to do this filtering in Excel and Python

From playlist Discrete

Lec 1 | MIT 6.451 Principles of Digital Communication II

Introduction; Sampling Theorem and Orthonormal PAM/QAM; Capacity of AWGN Channels View the complete course: http://ocw.mit.edu/6-451S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.451 Principles of Digital Communication II

Markov processes and applications-3 by Hugo Touchette

PROGRAM : BANGALORE SCHOOL ON STATISTICAL PHYSICS - XII (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, Bengaluru) and Sanjib Sabhapandit (RRI, Bengaluru) DATE : 28 June 2021 to 09 July 2021 VENUE : Online Due to the ongoing COVID-19 pandemic, the school will be conducted through online

From playlist Bangalore School on Statistical Physics - XII (ONLINE) 2021

Carsten Chong (Columbia) -- Asymptotic behavior of the stochastic heat equation with Lévy noise

We discuss some recent results about the macroscopic behavior of the solution to the stochastic heat equation with Lévy noise. For a fixed spatial point, we show that the solution develops unusually large peaks as time tends to infinity. As this already occurs under additive noise, we refe

From playlist Northeastern Probability Seminar 2020

Hubert Lacoin (IMPA) -- The continuum directed polymer in Lévy Noise as a scaling limit

Directed polymer in a random environment is one of the simplest and most studied disordered models in statistical mechanics. The aim of the talk is to introduce a continuum version of the directed polymer model which appears as a scaling limit when considering an "intermediate disorder re

From playlist Columbia SPDE Seminar

Alejandro Torres-Forné - Variational models and algorithms for GW denoising and reconstruction

Recorded 29 November 2021. Alejandro Torres-Forné of the University of Valencia presents "Variational models and algorithms for GW denoising and reconstruction: applications" at IPAM's Workshop IV: Big Data in Multi-Messenger Astrophysics. Abstract: In this talk, we will show the applicati

From playlist Workshop: Big Data in Multi-Messenger Astrophysics

(PP 6.2) Multivariate Gaussian - examples and independence

Degenerate multivariate Gaussians. Some sketches of examples and non-examples of Gaussians. The components of a Gaussian are independent if and only if they are uncorrelated.

From playlist Probability Theory

Constructing a solution of the 2D Kardar-Parisi-Zhang equation (Lecture - 03) by Sourav Chatterjee

INFOSYS-ICTS RAMANUJAN LECTURES SOME OPEN QUESTIONS ABOUT SCALING LIMITS IN PROBABILITY THEORY SPEAKER Sourav Chatterjee (Stanford University, California, USA) DATE & TIME 14 January 2019 to 18 January 2019 VENUE Madhava Lecture Hall, ICTS campus GALLERY Lecture 1: Yang-Mills for mathemat

From playlist Infosys-ICTS Ramanujan Lectures

Motion Estimation | Student Competition: Computer Vision Training

In this video, you will learn how to estimate motion between video frames using Optical Flow. Get files: https://bit.ly/2ZBy0q2 Explore the MATLAB and Simulink Robotics Arena: https://bit.ly/2yIgwfS -------------------------------------------------------------------------------------------

From playlist Student Competition: Computer Vision Training

Nicolas Perkowski (FU Berlin) -- Mass asymptotics for parabolic Anderson model with WN potential

We study the long time behavior of the total mass of the 2d parabolic Anderson model (PAM) with white noise potential, which is the universal scaling limit of 2d branching random walks in small random environments. There are several known results on the long time behavior of the PAM for mo

From playlist Columbia Probability Seminar

Davar Khoshnevisan (Utah) -- Ergodicity and CLT for SPDEs

I will summarize some of the recent collaborative work with Le Chen, David Nualart, and Fei Pu in which we characterize when the solution to a large family of parabolic stochastic PDE is ergodic in its spatial variable. We also identify when there are Gaussian fluctuations associated to th

From playlist Columbia SPDE Seminar

Teach Astronomy - Doppler Effect

http://www.teachastronomy.com/ The Doppler Effect is the shift of wavelength or frequency of a source of waves due to the motion of that source of waves. Doppler Effect is most familiar in terms of sound waves. As a source of sound, such as a siren, approaches you, the pitch or frequency

From playlist 06. Optics and Quantum Theory