MatlabScript for adding Gaussian Noise

Question

john amoo otoo 2022-11-30

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1867138-matlabscript-for-adding-gaussian-noise

评论： Walter Roberson 2022-12-1

Please I have been able to fix my script but ran into an error. Attached is the script for adding a Gaussian Noise to a signal and extracting the features

%% clear
load('John_3PhaseFault0ohm.mat')
%% does this signals has any NaNs, if so remove
SteadyStateNoneFaultState = rmmissing(SteadyStateNoneFaultState);
Data3Phase0hmsFaultData = rmmissing(Data3Phase0hmsFaultData);
SSTime = SteadyStateNoneFaultState.Time;
SSNFS = SteadyStateNoneFaultState.SteadyStateNoneFaultState;
DPTime = Data3Phase0hmsFaultData.Time;
DPFD = Data3Phase0hmsFaultData.Data3Phase0hmsFaultData;
%% Feature extraction section  
for k=1:level+1
    signals = Data3Phase0hmsFaultData;
    reqSNR = [15];    %noise in dB
    sigEner = norm(signals(:,k))^2;                % energy of the signals
    noiseEner = sigEner/(10^(reqSNR/10));        % energy of noise to be added
    noiseVar = noiseEner/(length(signals));     % variance of noise to be added
    ST = sqrt(noiseVar);                   % std. deviation of noise to be added
    noise = ST*randn(size(signals));        % noise
    noisySig = signals+noise;               % noisy signals
end
% Plot & Observe the data
subplot(2,1,1)
plot(noisySig);
title('Noise Signal')
subplot(2,1,2)
plot(noise);
title('Noise')
%% Let's observe the FFT power spectrum for differences
feat_fault = getmswtfeat(noisySig,32,16,100000);

Error

>> Noisysig

Unrecognized function or variable 'level'.

Error in Noisysig (line 11)

for k=1:level+1

>>

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john amoo otoo 2022-11-30

Walt,

I have updated the script and I now have an error at feat_fault.The error says I need to add an end to a function. I have added the end and I still have an error

%% clear

load('John_3PhaseFault0ohm.mat')

%% does this signals has any NaNs, if so remove

SteadyStateNoneFaultState = rmmissing(SteadyStateNoneFaultState);

Data3Phase0hmsFaultData = rmmissing(Data3Phase0hmsFaultData);

SSTime = SteadyStateNoneFaultState.Time;

SSNFS = SteadyStateNoneFaultState.SteadyStateNoneFaultState;

DPTime = Data3Phase0hmsFaultData.Time;

DPFD = Data3Phase0hmsFaultData.Data3Phase0hmsFaultData;

%% Feature extraction section

%% Chop the signal according to a sliding window approach

% allocate memory

function feature_out = getmswtfeat(signals,winsize,wininc,SF)

if nargin < 4

if nargin < 3

if nargin < 2

error('A sliding window approach requires the window size (winsize) as input')

end

error('A sliding window approach requires the window increment (wininc) as input')

end

error('Please provide the sampling frequency of this signal')

end

%% The number of decomposition levels

decomOption = 1;

if decomOption==1

J=8; % Number of decomposition levels set manually here

elseif decomOption==2

J=wmaxlev(winsize,'db8'); % Number of decomposition levels set based on window size and wavelet family

else

J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)

end

%% make sure you have some parameters pre-defined

% specify the number of samples

datasize = size(signals,1);

% based on the number of samples, winsize, and wininc, how many windows we

% will have? this is "numwin"

numwin = floor((datasize - winsize)/wininc)+1;

% how many signals (electrodes) are we processing

Nsignals = size(signals,2);

% how many features we plan to extract

%%

NF = 8;

% predefine zeros matrix to allocate memory for output features

%feature_out = zeros(numwin,(J+1)*NF*Nsignals);

feature_out = zeros(numwin,(J+1)*Nsignals);

for dims =1:Nsignals

signals = Data3Phase0hmsFaultData;

for dims =1:Nsignals

x=signals(:,dims);

winsize = 32;

numwin = 16;

feat = zeros(winsize,numwin);

st = 1;

en = winsize;

for i = 1:numwin

feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));

st = st + wininc;

en = en + wininc;

end

%% Multisignal one-dimensional wavelet transform decomposition

dec = mdwtdec('col',feat,J,'db8');

% Proceed with Multisignal 1-D decomposition energy distribution

if isequal(dec.dirDec,'c')

dim = 1;

end

[cfs,longs] = wdec2cl(dec,'all');

level = length(longs)-2;

if dim==1

cfs = cfs';

longs = longs';

end

for k=1:level+1

signals = Data3Phase0hmsFaultData;

reqSNR = [15]; %noise in dB

sigEner = norm(signals(:,k))^2; % energy of the signals

noiseEner = sigEner/(10^(reqSNR/10)); % energy of noise to be added

noiseVar = noiseEner/(length(signals)); % variance of noise to be added

ST = sqrt(noiseVar); % std. deviation of noise to be added

noise = ST*randn(size(signals)); % noise

noisySig = signals+noise; % noisy signals

end

% Plot & Observe the data

subplot(2,1,1)

plot(noisySig);

title('Noise Signal')

subplot(2,1,2)

plot(noise);

title('Noise')

%% Let's observe the FFT power spectrum for differences

feat_fault = getmswtfeat(noisySig,32,16,100000);

end

>> Noisysig

Error: File: Noisysig.m Line: 101 Column: 1

All functions in a script must be closed with an 'end'.

>>

john amoo otoo 2022-12-1

Walt. thanks this script seems to work without any errors but I do not get any output results in the work spave and plots

%% clear

load('John_3PhaseFault0ohm.mat')

%% does this signals has any NaNs, if so remove

SteadyStateNoneFaultState = rmmissing(SteadyStateNoneFaultState);

Data3Phase0hmsFaultData = rmmissing(Data3Phase0hmsFaultData);

SSTime = SteadyStateNoneFaultState.Time;

SSNFS = SteadyStateNoneFaultState.SteadyStateNoneFaultState;

DPTime = Data3Phase0hmsFaultData.Time;

DPFD = Data3Phase0hmsFaultData.Data3Phase0hmsFaultData;

%% Chop the signal according to a sliding window approach

% allocate memory

function feature_out = getmswtfeat(signals,winsize,wininc,SF)

if nargin < 4

if nargin < 3

if nargin < 2

error('A sliding window approach requires the window size (winsize) as input')

end

error('A sliding window approach requires the window increment (wininc) as input')

end

error('Please provide the sampling frequency of this signal')

end

%% The number of decomposition levels

decomOption = 1;

if decomOption==1

J=8; % Number of decomposition levels set manually here

elseif decomOption==2

J=wmaxlev(winsize,'db2'); % Number of decomposition levels set based on window size and wavelet family

else

J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)

end

%% make sure you have some parameters pre-defined

% specify the number of samples

datasize = size(signals,1);

% based on the number of samples, winsize, and wininc, how many windows we

% will have? this is "numwin"

numwin = floor((datasize - winsize)/wininc)+1;

% how many signals (electrodes) are we processing

Nsignals = size(signals,2);

% how many features we plan to extract

%%

NF = 8;

% predefine zeros matrix to allocate memory for output features

%feature_out = zeros(numwin,(J+1)*NF*Nsignals);

feature_out = zeros(numwin,(J+1)*Nsignals);

signals = Data3Phase0hmsFaultData;

for dims =1:Nsignals

x=signals(:,dims);

end

winsize = 32;

numwin = 16;

feat = zeros(winsize,numwin);

st = 1;

en = winsize;

for i = 1:numwin

feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));

st = st + wininc;

en = en + wininc;

end

%% Multisignal one-dimensional wavelet transform decomposition

dec = mdwtdec('col',feat,J,'db8');

% Proceed with Multisignal 1-D decomposition energy distribution

if isequal(dec.dirDec,'c')

dim = 1;

end

[cfs,longs] = wdec2cl(dec,'all');

level = length(longs)-2;

if dim==1

cfs = cfs';

longs = longs';

end

for k=1:level+1

signals = Data3Phase0hmsFaultData;

reqSNR = [15]; %noise in dB

sigEner = norm(signals(:,k))^2; % energy of the signals

noiseEner = sigEner/(10^(reqSNR/10)); % energy of noise to be added

noiseVar = noiseEner/(length(signals)); % variance of noise to be added

ST = sqrt(noiseVar); % std. deviation of noise to be added

noise = ST*randn(size(signals)); % noise

noisySig = signals+noise; % noisy signals

end

%% Let's observe the FFT power spectrum for differences

feat_fault = getmswtfeat(noisySig,32,16,100000);

% Plot & Observe the data

subplot(2,1,1)

plot(noisySig)

title('noisySignal')

end

john amoo otoo 2022-12-1

编辑：Walter Roberson 2022-12-1

在 MATLAB Online 中打开

Walt

Walter Roberson,please could you look into this. I want to add a scipt to tabulate standard deviation, mean square error and variance.Anyhelp on this

% GETMSWTFEAT Gets the Multiscale Wavelet Transform features, these
% include: Energy, Variance, Standard Deviation, and Waveform Length
% feat = getmswtfeat(x,winsize,wininc,SF)
% ------------------------------------------------------------------
% The signals in x are divided into multiple windows of size
% "winsize" and the windows are spaced "wininc" apart.
% Inputs
% ------
%    signals:	columns of signals
%    winsize:	window size (length of x)
%    wininc:	spacing of the windows (winsize)
%    SF:        sampling frequency
%
% Outputs
% -------
% =========================================================================
% REFERENCE: MATLAB CODE: Multi Scale Wavelet Decomposition: Dr. Rami Khushaba
% Email: Rami.Khushaba@sydney.edu.au
% URL: www.rami-khushaba.com (Matlab Code Section)
function feature_out = getmswtfeat(signals,winsize,wininc,SF)
if nargin < 4
    if nargin < 3
        if nargin < 2
            error('A sliding window approach requires the window size (winsize) as input')
        end
        error('A sliding window approach requires the window increment (wininc) as input')
    end
    error('Please provide the sampling frequency of this signal')
end
%% The number of decomposition levels 
decomOption = 1;
if decomOption==1
    J=8; % Number of decomposition levels set manually here
elseif decomOption==2
    J=wmaxlev(winsize,'sym2'); % Number of decomposition levels set based on window size and wavelet family
else
    J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)
end
%% make sure you have some parameters pre-defined
% specify the number of samples
datasize = size(signals,1);
% based on the number of samples, winsize, and wininc, how many windows we
% will have? this is "numwin"
numwin = floor((datasize - winsize)/wininc)+1;
% how many signals (electrodes) are we processing
Nsignals = size(signals,2);
% how many features we plan to extract
%% 
NF = 8;
% predefine zeros matrix to allocate memory for output features
%feature_out = zeros(numwin,(J+1)*NF*Nsignals);
feature_out = zeros(numwin,(J+1)*Nsignals);
for dims =1:Nsignals
    x=signals(:,dims);
    %% Chop the signal according to a sliding window approach
    % allocate memory
    feat = zeros(winsize,numwin);
    st = 1;
    en = winsize;
    for i = 1:numwin
        feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));
        st = st + wininc;
        en = en + wininc;
    end
    
    %% Multisignal one-dimensional wavelet transform decomposition
    dec = mdwtdec('col',feat,J,'sym2');
    % Proceed with Multisignal 1-D decomposition energy distribution
    
    if isequal(dec.dirDec,'c')
        dim = 1;
    end
    [cfs,longs] = wdec2cl(dec,'all');
    level = length(longs)-2;
    
    if dim==1
        cfs = cfs';
        longs = longs';
    end
    numOfSIGs             = size(cfs,1);
    num_CFS_TOT           = size(cfs,2);
    absCFS                = abs(cfs);
    absCFS0               = (cfs);
    cfs_POW2              = absCFS.^2;
    Energy                = sum(cfs_POW2,2);
    percentENER           = zeros(size(cfs_POW2));
    notZER                = (Energy>0);
    percentENER(notZER,:) = cfs_POW2(notZER,:);%./Energy(notZER,ones(1,num_CFS_TOT));
    
    %% or try this version below and tell us which  one is the best on your data
    % percentENER(notZER,:) = cfs_POW2(notZER,:);
    
    %% Pre-define and allocate memory
    tab_ENER    = zeros(numOfSIGs,level+1);
    tab_VAR     = zeros(numOfSIGs,level+1);
    tab_STD     = zeros(numOfSIGs,level+1);
    tab_WL      = zeros(numOfSIGs,level+1);
    tab_entropy = zeros(numOfSIGs,level+1);
    
    %% Feature extraction section
    st = 1;
    for k=1:level+1
        nbCFS                = longs(k);
        en                   = st+nbCFS-1;
        %tab_ENER(:,k)        = sum(percentENER(:,st:en),2);% energy per waveform
        %tab_VAR(:,k)         = var(percentENER(:,st:en),0,2); % variance of coefficients
        %tab_STD(:,k)         = std(percentENER(:,st:en),[],2); % standard deviation of coefficients
        %tab_WL(:,k)          = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
        %percentENER(:,st:en) = percentENER(:,st:en)./repmat(sum(percentENER(:,st:en),2),1,size(percentENER(:,st:en),2));
        prob                 = percentENER(:,st:en);%./repmat(sum(percentENER(:,st:en),2),1,longs(k)) + eps;
        tab_entropy(:,k)     = -sum(prob.*log(prob),2);%./size(percentENER(:,st:en),2);
        st = en + 1;
    end
    %feature_out(:,(1:(NF*(J+1)))+(dims-1)*((J+1)*NF)) =log([tab_ENER tab_VAR tab_STD tab_WL tab_entropy]);
    feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_entropy;
    feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_STD;
    feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_VAR;
    feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_WL;
    
end

Walter Roberson 2022-12-1

What difficulty are you encountering with the code you have that you commented out?

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MatlabScript for adding Gaussian Noise

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