Notice for EIE2108 (+EIE2106) FIMT students



LAST UPDATED TIME: Nov 27, 10:00:00 HKT 2020



What's new:

 

Notes

Textbook & References

Tutorial

Quiz result

Lab material

Useful info

Supplementary materials

Links

Self-learning

Q and A

Pls revisit this homepage frequently to update your information.

·        Weighting of different components:

o   Exam: 0%

o   Continuous Assessment.: 100%:

§  lab activities: 24%

§  projects: 16%

§  test: 40%

§  quizzes and assignments: 20%


 

·        Solution of Tutorial set 5 are updated. It’s presented in a better way and some typo mistakes are corrected. You are suggested to use the updated version to study the example of watermarking. However, it is not covered in the final assessment. (2020/11/27)

·        Tutorial set 5 solution is uploaded. It is not covered in the final assessment. (2020/11/27)

·        As mentioned in the class, do your revision based on the tutorial question sets, quizzes, exercises and lab tasks. There will be 8 questions. Answer any questions to take 80 out of 85 marks. Only one of them is Python-related because we have assigned a weighting of 40% to lab activities and projects already.  (2020/11/26)

·        Tutorial 5 solutions are updated. (2020/11/27)

·        Tutorial 4 solutions are updated. (2020/11/20)

·        A dataset for you to verify your code for realizing task 3 is available here. You can also get a supplementary note here. It provides you some extra information and assistance to start task 3. (2020/11/17)

·        In case you need some reference data to verify your code for lab task 1, you can get it from  http://www.eie.polyu.edu.hk/~enyhchan/EIE2108%20-%20lab%20task%201%20-%20reference.pdf. (2020/11/11)

·        A FAQ page for lab activities is added. (2020/11/5) It was updated @ (2020/11/10) and  (2020/11/17)  

·        lab data set are uploaded. Typo mistakes in lab sheet are corrected. (2020/10/31)

·        New sets of notes were uploaded. (2020/10/28)

·        Extra exercise (+ solution) for complex numbers is here. (2020/10/24)

·        Tutorial 3 solution is updated. The typo errors that were highlighted in the tutorial discussion were corrected in this updated version. (2020/10/24)   

·        Lab assignment (activities + project) are ready for download. (2020/10/16) 

·        New arrangements of quizzes: (2020/9/26) 

To use our lecture time more efficiently, we will

o   reduce the number of quizzes (either by turning some of them into class assignments or just cancelling them) and

o   conduct quizzes in tutorials instead of lectures unless specified otherwise.

According to the original arrangement, there will be 10 class activities (quizzes + class assignments). In case the total number of quizzes and class assignments are fewer than 10 at the end, the weighting of each activity will be adjusted. Suppose we have N quizzes / class assignments at the end. I will count your best N-1 towards the final grade.  The 20% will be equally shared by the N-1 activities. 

·        Tutorial solution set 1 was uploaded. (2020/9/26) 

·        Python programming notes was uploaded. (2020/9/26) 

·        The schedule is updated based on the survey result here. (2020/9/14) 

·        Tutorial question sets were uploaded. (2020/9/12)   


 

Tentative schedule is as follows:

Tentative schedule    (updated @ 2020/9/14 based on the survey result here) 



Notes

!! My ppt version is subject to change as I may update it for better presentation.
!! You may download the newest version of handouts here.

  • Matrix theory                                              [1in1 version]  updated @ 2020/9/7 - 9:30pm
  • Calculus                                                        [1in1 version]
  • Complex number                                       [1in1 version]
  • Python programming                               [1in1 version] (2020/9/26) 
  • Python programming - tasks                   [1in1 version] (2020/10/28) 
  • Digital rights management                      [1in1 version] (2020/10/28) 


Textbook and references:
 

·         S. Banerjee, Elements of Multimedia, Chapman and Hall/CRC. 2019  (available in Polyu library)

·         S. Nagar, Introduction to Python for Engineers and Scientists, Cambridge University Press 2014.: Open Source Solutions for Numerical Computation, Apress, 2018 (A link  for downloading the e-version is here)

·         K.R. Rao, D. N. Kim, J. J. Hwang, Video coding standards - AVS China, H.264/MPEG-4 PART 10, HEVC, VP6, DIRAC and VC-1. Springer, 2014 (A link  for downloading the e-version is here)



Tutorial arrangement

·        There is no tutorial session in week 1. 

Schedule: 

Week

Venue

Time

2-13

On-line

Fri, 1:30pm-2:20pm

 



Tutorial problems:

 (Note: Tutorial 1 means it is the 1st tutorial question set in a sequence of question sets. It does not imply that it has to be discussed in tutorial session 1.) 



Revision guidelines:

  • Do revision based on the tutorial question sets, quizzes, exercises and lab tasks.


Quiz and test results:

There will be quizzes or classwork every week from week 3 to 13.

·        2 marks each.

·        Total ceiling is 20% 

 You can always seek for help from me and the tutor (Mr. Felix Yu @DE503).

·         Classwork 1 result: (2020/10/9) 

You can get the feedback from blackboard and use your score to locate your position in the class. Full mark is 4. The mean and the standard deviation are, respectively, 3.16 and 1.32. Please seek help from tutor or me in case your score is lower than 2.



Laboratory materials:

  • Lab schedule: 
    • Date:    Wednesdays of weeks 9, 10 and 11   
    • Time:    12:30 pm-15:20 am
    • Venue:  On-line 
  • Lab activities:
    • Lab assignments (include lab activities + project)  (2020/10/16)
    • 5 datasets for activity 1 is here. Use data set No. n in your test, where n = 1 + ((sum of all digits  of your student numbers) mod 5). For example, if your student number is 19435207, then use data set 2 (because 1 + ((1+9+4+3+5+2+0+7) mod 5) = 2). (2020/10/31)
    • A dataset for you to verify your code for realizing task 3 is available here. You can get a supplementary note here. It provides some information and assistance to start task 3. (2020/11/17)

·         FAQ (2020/11/4) updated @ (2020/11/10) updated @ (2020/11/17)



Some useful information:

 

·        Cheat sheets for python programming are available in  https://sinxloud.com/python-cheat-sheet-beginner-advanced/

·        Cheat sheets for mathematics  are available in https://doubleroot.in/cheat-sheets/ 

Before you use them, make sure that you know how to use them. Somebody else's medicine could be poison to you. 



Useful links:

·         A good website for you to learn Python from ground 0:   https://www.w3schools.com/python/

·         A place where you can get an easy-to-start Python distribution:     https://www.anaconda.com/products/individual  (Look for Anaconda Installers)

Its user guide is available in https://docs.anaconda.com/anaconda/user-guide/

·         Door to Matplotlib: https://matplotlib.org/3.2.1/contents.html

·         Door to Numpy: https://numpy.org/

Some clips available in Youtube

·         How to install Anaconda Python, Jupyter Notebook And Spyder on Windows 10  :  https://www.youtube.com/watch?v=5mDYijMfSzs  (It shows you how to download and install Anaconda and provides you some information about Jupitor notebook + Spyder.)

·         Introduction to the Spyder IDE for Python : https://www.youtube.com/watch?v=zYNRqVimU3Q

·         Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping) https://www.youtube.com/watch?v=GB9ByFAIAH4

    • @1:35: It explains why processing Numpy array is much faster than processing lists.
    • It’s well organized with a detailed video timeline.

 

Software:

·         Download anaconda from https://www.anaconda.com/products/individual  (Look for Anaconda Installers) and install it in your computer. We will use it to do tutorial and lab activities in class. Get it ready before week 4.

·         Anaconda is bundled with some commonly used packages such as Numpy and MatPlotlib. You don’t need to separately install them. After installing Anaconda, you only need to import them to your program when using them.

·         Anaconda is bundled with some development tools such as Spyder. Spyder is a useful integrated development environment (IDE) for you to develop Python code. We will use it in the class.

·         You need them to do assignments and laboratory activities, so better get familiar with them as soon as possible.