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.

Shape Classification and the FFT.

© Copyright 2000-2021 Wintempla selo. All Rights Reserved. Jul 22 2021. Home