Code Directory
 Visual Basic & VB.NET
New Code
Uber for Massage 2.0
Job Board Software 1.1
RentALL - Airbnb Clone | Built with ReactJS 2.6
VB.Net PDF 2020.6.0
Flowrigami 1.0.0
SentiVeillance SDK Trial 7.3.2020.03.02
Entity Developer 6.8
dbExpress driver for SQLite 4.2
dbForge SQL Complete 6.5
Advanced Amazon Clone App 2.0
Appjetty Delivery Date Manager 1.0.0
The C# Barcode Library 2020.5.0
Chowtro - Online Restaurant Food Ordering Software 1.0
Alvas.Audio.Core 2019.0
Sainsburys Script 1.3.2
Top Code
dbForge SQL Complete 4.8
dbExpress Driver for SQLite 3.9
Availability Booking Calendar PHP 1.0
ATN Site Builder 3.0
ATN Resume Finder 2.0
Aglowsoft SQL Query Tools 8.2
Classified Ad Lister 1.0
Solid File System OS edition 5.1
Deals and Discounts Website Script 1.0.2
SentiVeillance SDK Trial 7.3.2020.03.02
PHP Review Script 1.0
ICPennyBid Penny Auction Script 4.0
Invoice Manager by PHPJabbers 3.0
PHP Taxi Booking Script 1.0.4
Top Rated
phpEnter 5.1.
Single Leg MLM 1.2.1
Azizi search engine script PHP 4.1.10
Paste phpSoftPro 1.4.1
Extreme Injector 3.7
Deals and Discounts Website Script 1.0.2
Solid File System OS edition 5.1
Classified Ad Lister 1.0
Aglowsoft SQL Query Tools 8.2
Invoice Manager by PHPJabbers 3.0
ICPennyBid Penny Auction Script 4.0
PHP Review Script 1.0
ATN Resume Finder 2.0
ATN Site Builder 3.0
Availability Booking Calendar PHP 1.0
Screenshot Fast root-mean-square (RMS) power 1.0
File ID: 78293

Screenshot Fast root-mean-square (RMS) power 1.0
Download Screenshot Fast root-mean-square (RMS) power 1.0http://www.mathworks.comReport Error Link
License: Freeware
File Size: 10.0 KB
Downloads: 13
Submit Rating:
Screenshot Fast root-mean-square (RMS) power 1.0 Description
Description: FASTRMS Instantaneous root-mean-square (RMS) power via convolution.

FASTRMS(X), when X is a vector, is the time-varying RMS power of X, computed using a 5-point rectangular window centered at each point in the signal. The output is the same size as X and contains, for each point in X, an estimate of the instantaneous power expressed in the signal.

FASTRMS(X), when X is a matrix, is the time-varying RMS power of the columns of X.

FASTRMS(X,WINDOW), if WINDOW is a vector, computes the moving quadratic mean using the weights specified in WINDOW. If WINDOW is
%an integer, a LENGTH(WINDOW)-point rectangular window is used. When FASTRMS is being used to estimate the instantaneous amplitude of an oscillatory, zero-mean signal X (see below), WINDOW should be chosen based on the frequency content of X. Lower frequency signals require longer windows, whereas higher frequency signals allow shorter windows. As a rule of thumb, the window should be at least as long as one period of the signal.

FASTRMS(X,WINDOW,DIM), when X is a matrix, computes the RMS power along the dimension DIM. (DIM specifies the "time" axis for a matrix of
many trials.)

FASTRMS(X,WINDOW,DIM,AMP), if AMP is nonzero, applies a correction so that the output RMS reflects the equivalent amplitude of a sinusoidal input signal. That is, FASTRMS mutliplies the output by SQRT(2) to account for the fact that the integral of sin^2(t) over one period, t ~ [0,2*pi], equals (1/SQRT(2)).

The speed of FASTRMS is achieved by using convolution to compute the moving average of the squared signal. For this reason, FASTRMS also achieves maximal resolution, as the output is exactly the same size as X. However, the tradeoff is that some "edge effects" are incurred on the first and last approximately LENGTH(WINDOW)/2 samples. That is, since the convolution is computed using a zeropadded version of X, the RMS power will appear diminished near the beginning and end of the signal. Therefore, FASTRMS is best used on large input signals X.


Fs = 200; T = 5; N = T*Fs; t = linspace(0,T,N);
noise = randn(N,1);
[a,b] = butter(5, [9 12]/(Fs/2));
x = filtfilt(a,b,noise);
window = gausswin(0.25*Fs);
rms = fastrms(x,window,[],1);
plot(t,x,t,rms*[1 -1],'LineWidth',2);
xlabel('Time (sec)'); ylabel('Signal')
title('Instantaneous amplitude via RMS')

Created by Scott McKinney, January 2011

License: Freeware

Related: maximal, Resolution, achieves, reason, average, squared, approximately, incurred, effectsquot, tradeoff, quotedge, Compute, convolution, Input, mutliplies, sinusoidal, equivalent

O/S:BSD, Linux, Solaris, Mac OS X

File Size: 10.0 KB

Downloads: 13

More Similar Code

Plot a Taylor diagram from statistics values given by STDs (standard deviations), RMSs (centered root mean square difference) and CORs (correlation)

Ref: K. Taylor
Summarizing multiple aspects of model performance in a single diagram. JGR 2001

Compare two images using the root mean squared analysis. A result close to 0 means a good match.

The LMSE package contains two subroutines. LMSE computes the minimum mean square error (MSE) possible if one image is allowed to be linearly scaled in intensity. LMSEDIFF computes the difference image after the target image is scaled according to...

I was trying out modifications of the LMS algorithm so that it will converge faster and the mean square error will also be smaller. Getting to one of the drawbacks of LMS, that it has only one controllable parameter "mu", the selection...

Dew Lab Studio contains: MtxVec library with Stats Master, DSP Master and Data Miner add ons.MtxVec v2.0 feature list:object oriented numerical library for Delphi and C++ Builder users and .NETComplete support for complex numbers for all functions...

A generalized mean, also known as power mean, Holder mean or Kolmogorov-Negumo function of the mean, is an abstraction of the Pythagorean means included harmonic, geometric, and arithmetic mean.

It is defined as,

Mk =...

Delphi/C++ Builder VCL and FireMonkey (FMX) components library for fast audio processing.
Allows audio capture, processing, playback, and broadcastiong with zero lines of program code.
AudioLab supports Wave Win32 API, Audio ACM, and...

AudioLab is a set of .NET 2.0-4.5 components for fast audio processing.
Allows audio capture, processing, playback and br with zero lines of program code.
AudioLab supports Wave Win32 API, Audio ACM, and the latest DirectX Media Objects...

This toolbox estimates the following volatility loss functions:
1. Mean Square Error, MSE
2. Mean Absolute Deviation, MAD
3. Mean Logarithm of Absolute Errors, MLAE
4. Heteroskedasticity-adjusted Mean Square Error, HMSE

Model for Soft Independent Modeling of Class Analogy (SIMCA)


x (samples x descriptors) for calibration
classes (samples x 1) classes numbers (must be >0)


User Review for Screenshot Fast root-mean-square (RMS) power
- required fields

Please enter text on the image