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PART A For each of the following tables of values coming from a sensor, would it be best to use the values, the first derivative, or the second derivative to threshold noise? Explain. Apply the method you chose and identify the threshold rule (i.e. If x > y or x

PART A For each of the following tables of values coming from a sensor, would it be best to use the values, the first derivative, or the second derivative to threshold noise? Explain. Apply the method you chose and identify the threshold rule (i.e. If x > y or x

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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In many cases, data contain noise. This noise may be prohibitive to interpreting the data into useful, accurate information. Noise can come in several forms, an anomaly from the output of a sensor, noise with a normal distribution, salt and pepper noise, distortion at a particular frequency, and others. Filter can be used to remove or reduce the noise. Using filters, you can also remove or alter data that doesn't have noise and create ‘noise' or special effects. In this problem solving exercise we will use some basic methods to filter noise from data, including filtering an image. PART A For each of the following tables of values coming from a sensor, would it be best to use the values, the first derivative, or the second derivative to threshold noise? Explain. Apply the method you chose and identify the threshold rule (i.e. If x > y or x < y then noise) ii. i. Sensor First Second values Deriv. Deriv. 2 4 6 8 12 17 13 15 17 19 21 PART B 20 21 20 22 21 21 20 28 55 58 51 20 28 255 48 57 20 22 42 45 0 21 20 23 44 52 56 22 20 22 21 22 21 22 Sensor First values Deriv. 20 Filter F: 22 101 106 114 125 139 159 170 187 207 230 256 Given the following image, apply filter F to this image (do not use Matlab for this - do the calculations manually): Second Deriv. 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 The '0' and '255' represent black and white noise, respectively. Comment on what happened to the noise after this filter was applied.
Transcribed Image Text:In many cases, data contain noise. This noise may be prohibitive to interpreting the data into useful, accurate information. Noise can come in several forms, an anomaly from the output of a sensor, noise with a normal distribution, salt and pepper noise, distortion at a particular frequency, and others. Filter can be used to remove or reduce the noise. Using filters, you can also remove or alter data that doesn't have noise and create ‘noise' or special effects. In this problem solving exercise we will use some basic methods to filter noise from data, including filtering an image. PART A For each of the following tables of values coming from a sensor, would it be best to use the values, the first derivative, or the second derivative to threshold noise? Explain. Apply the method you chose and identify the threshold rule (i.e. If x > y or x < y then noise) ii. i. Sensor First Second values Deriv. Deriv. 2 4 6 8 12 17 13 15 17 19 21 PART B 20 21 20 22 21 21 20 28 55 58 51 20 28 255 48 57 20 22 42 45 0 21 20 23 44 52 56 22 20 22 21 22 21 22 Sensor First values Deriv. 20 Filter F: 22 101 106 114 125 139 159 170 187 207 230 256 Given the following image, apply filter F to this image (do not use Matlab for this - do the calculations manually): Second Deriv. 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 The '0' and '255' represent black and white noise, respectively. Comment on what happened to the noise after this filter was applied.
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