# Shape Classification and the FFT.

 Problem 1 Build an appropriate training set for the classification of the four shapes in the time domain and in the frequency domain (using the Fourier Transform). Your training set will include clean and noisy shapes as described below using a random phase to build each case. The table indicates the number of training cases for each shape.

 Shape Clean 10% Noise 20% Noise 30% Noise Cardioid 250 250 250 250 Rose 150 150 150 150 Irish 200 200 200 200 Heart 150 150 150 150

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

 Test Time Domain Frequency Domain Build Train Set BuidTrainSetT.lab BuildTrainSetF.lab Training Set TrainSetT.csv TrainSetF.csv Build Validation Set BuidValidSetT.csv BuildValidSetF.csv Validation Set ValidSetT.csv ValidSetF.csv Training TrainT.lab TrainF.lab Check Training CheckTrainingT.lab CheckTrainingF.lab (Trainining) Confusion Matrix trainConfT.emf trainConfF.emf Validation ValidationT.lab ValidationF.lab (Validation) Confusion Matrix validConfT.emf validConfF.emf

 Problem 2 Build an appropriate validation set for problem 1 using random phase. The table below describes the number of validation cases for each shape.

 Shape Clean 10% Noise 20% Noise 30% Noise Cardioid 100 100 100 100 Rose 50 50 50 50 Irish 40 40 40 40 Heart 90 90 90 90

 Problem 3 Design and train an ANN for the classification of the shapes.

 Problem 4 Check the training: (a) Compute the confusion matrix using the training set. (b) Save the confusion matrix as vector image.

 Problem 5 Perform the validation of the ANN. (a) Compute the confusion matrix using the validation set. (b) Save the confusion matrix as vector image.

 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.