Signal Classification and the FFT.


Problem 1
Create a New Project called SigFourier (You must select: Multi-layer Network and Classification in the New Project Dialog) classify four signals in the frequency domain. Edit the BulidTrainSet.lab file to build an appropriate training set for the classification of the four signals. Your training set will include clean and noisy signals as described below using a random phase to build each case. The table indicates the number of training cases for each signal. (a) In the time domain, (b) in the frequency domain (using the Fourier Transform).

Signal    Clean    10% Noise    20% Noise    30% Noise  
Sine150150150150
Saw tooth150150150150
Triangular150150150150
Squared150150150150
Reject100100100100

ClassificationT

ClassificationF

Hint
To keep your files organized use the file names as described in the table below.

Test    Time Domain    Frequency Domain  
Build Train SetBuidTrainSetT.lab BuildTrainSetF.lab
Training SettrainSetInputT.csv, trainSetTarget.csvtrainSetInputF.csv, trainSetTarget.csv
Build Validation SetBuidValidSetT.labBuildValidSetF.lab
Validation SetvalidSetT.csv, validSetTarget.csvvalidSetF.csv, validSetTarget.csv
TrainingTrainT.labTrainF.lab
Check TrainingCheckTrainingT.labCheckTrainingF.lab
(Trainining) Confusion MatrixtrainConfT.emftrainConfF.emf
Validation ValidationT.lab ValidationF.lab
(Validation) Confusion MatrixvalidConfT.emfvalidConfF.emf

Problem 2
Edit the BuilValidSetT.lab file and the BuilValidSetF.lab file to build an appropriate validation set for problem 1 using random phase. The table below describes the number of validation cases for each shape.

Signal    Clean    10% Noise    20% Noise    30% Noise  
Sine100100100100
Saw tooth50505050
Triangular40404040
Squared70707070
Reject60606060

Problem 3
Edit the TrainT.lab file and the TrainF.lab file to design and train an ANN for the classification of the signals.

Problem 4
Edit the CheckTrainingT.lab file and the CheckTrainingF.lab file to check the training: (a) Compute the confusion matrix using the training set. (b) Plot the error for each network output. (c) Save the confusion matrix as a vector image (trainConfT.emf and trainConfF.emf).

Problem 5
Edit the ValidationT.lab file and the ValidationF.lab file to perform the validation of the ANN. (a) Compute the confusion matrix using the validation set. (b) Plot the error for each network output. (c) Save the confusion matrix as a vector image (validConfT.emf and validConfF.emf).

Problem 6
Generate a report in Microsoft Word. Write some conclusions in the report trying to compare your results using data in the time domain with the data in the frequency domain.

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