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[prashantgujjar]

What would be the equation of the following surface?

 I have the raw data of X, Y, Z, where X and Y are inputs and Z is the output. Plotting the surface gives the red curve in the below picture:

The surface seems to be a simple function involving trigonometric functions. For example, plotting the equation cos(X)^2 + cos(Y)^2 gives the blue surface and it looks similar to the red graph (but the RMS error is very large).

I have tried introducing variables (a1*cos(X)^2 + a2*cos(Y)^2), where a1 and a2 vary from 0 to 1) but the RMS error is still large.

I also tried the surface fitting functions (MATLAB's fit function) but it's models are limited (unlike the curve fitting models which include Fourier series, sin series, etc).

How can I find the equation which gives the red surface?

Edit for clarity: I am able to use the lsqcurvefit (or any other curve fitting toolbox functions) to find which coefficients to use, but I am not sure what the equation should be. cos(X)^2 + cos(Y)^2, for example, is not the right equation but it does have a similar shape.

If this seems improbabilistic, how can I vary cos(X)^2 + cos(Y)^2 to align the end curves of the surface (where there seems to be highest misalignment)?

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Update 3: Added some surface plots.[X, Y] = meshgrid(-1:0.05:1); %% Polynomial function c = 1; b = 1/2; a = b; Z = c - b*Y.^2 - a*X.^2; % can try higher-order polynomial functions surf(X, Y, Z), grid on title('Quadratic function'), xlabel x, ylabel y, zlabel f(x,y)

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What would be the equation of the following surface?

[prashantgujjar]

How do I get the max value within a specified range on my plot?

I am writing a program that can read in audio files of recorded guitar notes in standard tuning (e,B,G,D,A,E), plot the signal and the frequency response, then compare the frequency value from the plot with the needed frequency to be in tune. The way I was doing it, was finding the max value or highest peak from the frequency response plot, then comparing it to a specified value needed for it to be in tune. I hit a problem when I got to the G string, because as you can see from the plot, the highest peak is not the first peak, and that is where the frequency I need lies (since a G3 note has to have a frequency of 196 to be in tune), so my method doesn’t work. How can I get the max y value between the 100 & 300 frequency range, then get the x value related to that max value?%Tuning for G String gG = 196.00; %Fourier Transform of G3 On Guitar [y,Fs] = audioread('3rd_String_G_vbr.mp3'); Nsamps = length(y); t = (1/Fs)*(1:Nsamps); %Prepare time data for plot %Do Fourier Transform y_fft = abs(fft(y)); %Retain Magnitude y_fft = y_fft(1:Nsamps/2); %Discard Half of Points f = Fs*(0:Nsamps/2-1)/Nsamps; %Prepare freq data for plot %Find max y value of the highest peak in the range, then find corresponding x %value [maxYValue, indexAtMaxY] = max(y_fft); xValueAtMaxYValue = f(indexAtMaxY(1));

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Use the sampling frequency to find the locations with which to bound the search — HiLo=[100 300]; % frequency bounds nHiLo=round(HiLo/f); % bounding locations [maxYValue, indexAtMaxY] = max(y_fft(nHiLo(1):nHiLo(2)); indexAtMaxY=indexAtMaxY+nHiLo(1); % offset computation

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How do I get the max value within a specified range on my plot?

[prashantgujjar]

How to pass a audio signal through a filter?

How to pass a audio signal through a filter ? It should work based on spectral energy i.e if its between 0–4 kHz it falls on speech band and If it falls between 4 kHz-8 kHz then it comes under noise band.Now the VOS(voice operated switch) should be activated if the signal is in speech band and it should be deactivated when it is in noise band. Can some one help me on this problem.

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The first step is to design a filter to obtain the A and B vector coefficients.Then use the Butter function, for instance to obtain your signal(type Butter in your Matlab command window and you will find many other type of filters). Once you have obtained the B and A vector coefficients you can filter your signal using the function filter.

y=filter(x,A,B) x(is your input signal) and y your filtered signal.

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How to pass a audio signal through a filter

[prashantgujjar]

Stretch the dynamic range of the given 8-bit grayscale image using MATL...

[prashantgujjar]

i get an error when creating newff please help me ..

the excel file has the dataset. it has 24 columns. first 12 columns has the target value 1. the last 12 colums have the target value 0. i wanted to design a NN for classification into 1 or 0. the data was copied to dat file. i selected all the 198 rowsand 24 colums to input.dat . single rown with 24 colums as target.dat and i get the following error . please rectify.

Error:

Error using newff (line 107) Input ranges is not a two column matrix. Error in signature1 (line 17) net=newff(input_n,target_n,13);

close all ; clc; clf; clear all; load input.dat; load target.dat; input1=input(1:198,1:24); target1=target(1:198,1); [input_n,input_n_struct]=mapminmax(input1'); [target_n,target_n_struct]=mapminmax(target1'); net=newff(input_n,target_n,13); net.divideFcn='divideblock'; net.divideParam.trainRatio=80; net.divideParam.valRatio=10; net.divideParam.testRatio=10; net.trainFcn='trainlm';    [net tr Y E]=train(net,input_n,target_n);         % Simulation disp('Simulation Output for the given input'); simoutput=sim(net,input_n); [input_n ; simoutput]; %Output Normalized Version disp('Normalized Simulation Output'); Norm_input=mapminmax('reverse',input_n,input_n_struct); Norm_simoutput=mapminmax('reverse',simoutput',target_n_struct); %[Norm_input Norm_simoutput]; [Norm_input]; [Norm_simoutput] [target Norm_simoutput] % Calculating Mean Square Error perfmse=mse(E); fprintf('Mean Square Error:%d \n',perfmse);

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It looks like you are using an obsolete version of NEWFF where the first input is minmax(input_n) instead of input_n.

However, even the latest version of NEWFF is obsolete.

What version of MATLAB do you have? Type ver into the command lineThe current versions of MLP are

Regression/curve-fitting: FITNET(calls FEEDFORWARDNET) instead of NEWFIT(calls NEWFF) Classification/pattern-recognition: PATTERNNET(calls FEEDFORWARDNET)instead of NEWPR(calls FEEDFORWARDNET)

[prashantgujjar]

Back Propagation Neural Network

I need a workable Back Propagation NN code. My Inputs are 100X3 dimension and outputs are 100X2 dimension.Sample size is 100.

For example 1st 5 samples are inputs [-46 -69 -82; -46 -69 -82; -46 -69 -82; -46 -69 -82; -46 -69 -82;... ] and outputs are [0 0;2 1;5 5;4 3; 3 5;...].

Please suggest me if BP is suitable for my problem and what learning technique and activation function will be better to solve this problem? Do I need to apply generalization? Kindly help me with the matlab code if possible. Thank you very much.

ANSWER

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Convert to matrices and transpose  

[I N ] = size(inputs) [ O N ] = size(targets)

Use fitnet for regression and curve-fitting

help fitnet doc fitnet

Use patternnet for classification and pattern-recognitionFor examples beyond the help/doc documentation try searching with

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[prashantgujjar]

Issue with batch normalization layer of saved CNN

When loading a previously trained CNN, I got an issue with the batch normalization layers. When looking into the loaded CNN model the trainable mean and variance are empty.

Name: 'batchnorm_1'

TrainedMean: [] TrainedVariance: [] So the checkpoint doesn't seem to save these parameters. Are there any workarounds for this issue? I am using Matlab R2018b. ANSWER

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We were able to reproduce the issue. We will inform you once the issue is fixed.Since TrainedMean and TrainedVariance are calculated after the training is finished, therefore as a workaround you can use the below mentioned codes to explicitly save and load the Model.

%To save model with name "demoModel", assuming your network is in "net" save('demoModel','net')

%To load model to variable net1 net1=load('demoModel.mat','net'); net1.net.Layers(n).TrainedMean %where n is the batch normalization layer

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[prashantgujjar]

Data Segmentation by proportion

Hi. I want to segmentate an ECG signal recorded for 10 h from a database. Every signal have about 9 milion sample with a sample rate of 250 Hz, so about 3000 seconds. I want to segmentate this signal in time domain so I can have 5 beats in every signal. Every beat is 1.2 s so every signal segmentated will be 6 s and about 1500 samples. I need this for feature extraction and a cnn clasification. How can i extract every signal. I first throught i need to find every QRS complex, but in some cases the P wave will be higher in amplitude than R and findpeaks will get that as a peak. I tried getting every signal in an matrice but it exceeds maximum array size and it can be difficult for further processing.

[signal,Fs,tm]=rdsamp('08455'); %recording name have 2 leads sig = signal(:,1); %we will wok on the first one s= 9205760;   %samples fs = 250;     %sample rate t = floor(s/ts);     %time for the recording t_i = 0.8;      %initial time for the inteval 2/3 of first 1.2 s i = floor(t/6);    %nr of signals for j =1:i            t_f = t_i +6;   %final time for the interval    m(s,j) = sig((t_i*fs):(t_f*fs));   %I wanted to get every signal in a matrice in which every column will be a signal    t_i = (t_f*ts +1)/ts;         %initial time for further intervals end

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you can use the function to detect positive and negative slope threshold crossing points this will give you the time index of the signal individual blocks I could try it inside your code if you could share some signal data

% demo code n=100; x=(1:n)/n; y= 0.5 + sin(20*x)+ 0.02*randn(1,n); threshold = 1; % [ind_pos,t0_pos,s0_pos,ind_neg,t0_neg,s0_neg]= crossing_V7(y,x,threshold,'linear'); % positive slope zero crossing points plot(x,y,'b',t0_pos,s0_pos,'+r',t0_neg,s0_neg,'+g','linewidth',2,'markersize',12);grid legend('signal','positive slope crossing points','negative slope crossing points');