Difference between revisions of "2S2122 Activity 3.2"
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Ryan Antonio (talk | contribs) (Created page with "== Instructions == '''Submission guidelines:''' * For every programming exercise, you are to submit your .ipynb file into your respective submission bin. * To download your ....") |
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* To download your .ipynb file, first in your Google Colab page go to '''File > Download > Download .ipynb file'''. | * To download your .ipynb file, first in your Google Colab page go to '''File > Download > Download .ipynb file'''. | ||
* Don't forget to rename your .ipynb file with the following format '''"class_lastname_firstname_studentnumber.ipynb"'''. | * Don't forget to rename your .ipynb file with the following format '''"class_lastname_firstname_studentnumber.ipynb"'''. | ||
| + | |||
| + | |||
| + | == Noisy Images== | ||
| + | |||
| + | For this exercise, we will simulate a binary symmetric channel BSC using images. This exercise follows the discussion about Noisy Images in our Wiki page. All you have to do is create a simulator for this. The goal of this exercise is just to give you a feel of how noise affects our data and how can we correlate it with our theory of information. | ||
| + | |||
| + | The objectives are: | ||
| + | * Task 1 - Create a function that extracts the probability of the binary data. | ||
| + | * Task 2 - Adding noise to images. | ||
| + | * Task 3 - Creating functions for information measures. | ||
| + | * Task 4 - Answer the following questions. | ||
| + | |||
| + | You will be provided with images from our Git dump. They're simple binary images anyway. We pre-processed these images already. | ||
| + | |||
| + | You can checkout the exercise in this [https://colab.research.google.com/drive/1mQDCBlwx92cGyiyn41qCrLqcWhtvTrjA?usp=sharing link]. | ||
Latest revision as of 00:08, 3 March 2022
Instructions
Submission guidelines:
- For every programming exercise, you are to submit your .ipynb file into your respective submission bin.
- To download your .ipynb file, first in your Google Colab page go to File > Download > Download .ipynb file.
- Don't forget to rename your .ipynb file with the following format "class_lastname_firstname_studentnumber.ipynb".
Noisy Images
For this exercise, we will simulate a binary symmetric channel BSC using images. This exercise follows the discussion about Noisy Images in our Wiki page. All you have to do is create a simulator for this. The goal of this exercise is just to give you a feel of how noise affects our data and how can we correlate it with our theory of information.
The objectives are:
- Task 1 - Create a function that extracts the probability of the binary data.
- Task 2 - Adding noise to images.
- Task 3 - Creating functions for information measures.
- Task 4 - Answer the following questions.
You will be provided with images from our Git dump. They're simple binary images anyway. We pre-processed these images already.
You can checkout the exercise in this link.
