Introduction to Computer Science



Fall 2012, Room 4263, 9:10~12:00 Thursday

Instructor: Kun-chan Lan

(this course is offered in English)


    * Know what is "Computer science"?
    * Familiarize yourself with the basic
          o Terminologies
          o Principles
          o Theories
    * Also, a strong hands-on focus
          o Homework
          o Project


    * Data storage
    * Computer architecture
    * Operating system
    * Networking
    * Algorithm
    * Programming language
    * Artificial intelligence


•week 1 (9/20) Out for conference
•week 2 (9/27) Administration issue
•week 3 (10/4) tutorial on GPS, iRobotsand XWave(John, Steve and Cosmo)


•week 4 (10/11) History and Hardware


•week 5 (10/19) Data Storage
•week 6 (10/26) Operating System(I)
•week 7 (11/1) Operating System(II)
•week 8 (11/8) Networking (II)
•week 9 (11/15) ) Networking (II)
•week 10 (11/22) Midterm exam, Homework II due
•week 11 (11/29) Algorithms (I)

Advanced topic

•week 12 (12/6) Algorithms (II)
•Week 13 (12/13) Programming language
•week 14 (12/20) Artificial Intelligence
•week 15 (12/27) Demo for homework II
•week 16 (1/3)  Final project demo
•week 17  (1/10) Final exam 

Lecture Slides

week 1 (9/20) Out for conference
week 2 (9/27) Administration issue [video
week 3 (10/4) tutorial on GPS, iRobotsand and XWave [video1] [video2]
week 4 (10/11) History[1][2][3] and Hardware [video1] [video2] [video3] [video4] [video5]
week 5 (10/19) Data Storage [video1] [video2] [video3] [video4] [video5]
week 6 (10/26) Operating System(I) [video1][video2][video3][video4][video5]
week 7 (11/1) Operating System(II) [video1][video2][video3][video4][video5]
week 8 (11/8) Networking(I) [video1][video2][video3][video4][video5]
week 9 (11/15) ) Networking (II)[video1][video2][video3][video4]
week 11 (11/29) Algorithms (I)[video1][video2]
week 12 (12/6) Algorithms (II)[video1][video2]
Week 13 (12/13) Programming language [video1][video2]
week 14 (12/20) Artificial Intelligence [video1][video2]

Text Book

    * J. Glenn Brookshear, Computer Science -- An Overview, 11th edition, Addison-Wesley
          o ISBN-10: 0132569035 | ISBN-13: 978-0132569033


Prof. Kun-chan Lan
  Office: Room 309 (East Block, Yun-Ping building, Kuang-Fu Campus)
  Office hours: 14-15pm on Wednesday and Friday or by appointment via e-mail


  Email:  This email address is being protected from spambots. You need JavaScript enabled to view it.
  TEL: +886 6 2757575 ext 62550





•Working as a team (55%) – hands-on exercise
–Creative project (25%)
–Homework Exercise (30%)
   •Two, each one accounts for 15%
•Working as an individual (45% + 10% bonus)
–Exam (45%)
  •Midterm 20%
  •Final 25%
–In-class quiz and class participation (10%)


Homework I(Peeking your brain):
 •How your brain wave look like on a computer?
1.Record your brain wave for 30 second
  1.calculate the average frequency of your brain wave
  2.Calculate the average magnitude (i.e. voltage) of your brain wave
2.Generate  theta wave or alpha wave (either by yourself or with a tool somehinglike 
Homework II
•In this exercise, we want to answer this question
–Is it easier to meet your friends on-line or to meet them in real world?
    *Every time when you are on-line
        -Go to this website
        -Enter your student ID and click on the “Start” button on the page
        -Before you go offline, click on the “End” button on the page
    *TA will announce an address let you download data.   
    *Compare your online times with your teammates’
    *Draw the ‘overlay’ time when you and your teammate are both on-line with Microsoft         Excel
    *Record your location with GPS
    *A GPS logger will be loaned to you, and you should carry it all the time
    *If you are indoor, find out your GPS location via Google Map and record it manually
    *Download your GPS log everyday
        –See instructions at
            •Remember to recharge the battery!!
    *Compare your mobility data with your teammates’
    *Draw the ‘overlay’ time when you and your teammate are “close” to each other with           Microsoft Excel
        –“close” is defined as your GPS location is less than 10m from your teammates’
Important dates
    *Trace collection period (we only have around 100 GPS loggers)
        –甲班(and 外系/轉系): 10/1-10/31
        –乙班:  11/1-11/30
    *Results due
        –11/12 midnight (甲班) 12/10 midnight (乙班)
        –Submit your results (submission instruction will be announced later)
        –NO late submission
    *The more detailed raw data you collected, the higher grade
        –GPS logger collect location every second
            •So you should have a maximum of 86400 data entries every day
            •GPS can only work outdoor. Estimate your GPS location using Google Earth if                     you are indoor
            •Your analysis of results should be sensible
            •An example is at
Homework II(Auto-parking):   
    * Learn simple programming via iRobot
    * Use the programmable robot (iRobot) to simulate auto-parking
    * TA will give you a tutorial on how to use and program iRobot (and
      how to use GPS for your homework)
What to do?
    * You will be given two locations A, and B (somewhere around our
    * You need to write a program to move iRobot from A to B
How would I evaluate the performance of your program?
    * How much time it will take for your program to move iRobot from A
      to B (the shorter, the better!)?
    * Could your program park the iRobot exactly at location B? (in this
      homework, we use location B to simulate the 停車格)
Loaning Equipment
    * We only have 17 iRobot ($15K each) but we have more than 100 students
    * The equipment needs to be SHARED
    * The loaning time of any equipment from TA (including iRobot,
       sensor, GPS, etc) is up to 3 days
    * First come, first serve!
        -Make a reservation when all the iRobot have been checked out

Term project

    * Design a car navigation system
    * Car navigation
          o Provide a route from A to B for the driver
          o A good navigation system
                + should provide a route that has the shortest travel
                  time from A to B
                + relies on accurate road information
Road information
    * Traffic light cycle (how long you need to wait for the red light)
    * Road length
    * Number of lanes
    * Traffic density, i.e. how many cars moving on the same road (e.g.

      rush-hours vs. off-peak time)

How to design your navigation system?

     1. Collect the road info from the real-world
     2. Design an algorithm that use those road traffic parameters we previously discuss
        (I only listed 4 parameters, you are strongly welcome to add more if you wish) to
        predict the driving time from A to B

Collect Road information

    * We will assign different teams to collect traffic info for different areas (抽籤)
          o 東區
          o 北區

Example of Traces

路 口 紅 燈 綠 燈
長榮路/大學路 30 seconds 45 seconds
長榮路/裕農路 20 seconds 30 seconds

路 名 路 段 Number of lanes Road length
長榮路三段 小東路-大學路 2 720m

路 名 路 段 Time Car density
長榮路三段 小東路-大學路 7am-8am 1239
長榮路三段 小東路-大學路 8am-9am 1102
長榮路三段 大學路-小東路 7am-8am 1011
長榮路三段 大學路-小東路 8am-9am 1259
    * Car density: In a given duration, the number of cars entering that
      particular road
    * Duration for collection of car density
          o 7-9am
          o 11-1pm
          o 5-7pm
Example of the algorithm
    * Should look like an equation or a function
    * For example
          o Travel time =  red light duration + car speed/road distance
            * number of lanes
          o PS. I did not use ?car density? in the example above?but you
            should try to make use of EVERY possible parameter in your
    •You will need to present your algorithm in mid-semester (3-5 minutes) and explain             how you design your algorithm
Demo your project demo
•In the end of semester, you will demo your project as the following
•I will ask you, for example, how long it takes to drive at a speed of 30km/hrfrom 成功校區 to Costcoat 1pm
•You should use the collected road information traces and your algorithm to predict the travel time
Score for the final project demo
    * Your score = 25 x ( 1 - D / real travel time)
          -here D = | your predicted travel time ? real travel time |
Project Evaluation
    * Mid-term
         –A 3-5 minute presentation for your navigation algorithm (10%)
          •And one-page, 11-pt-font, double-spaced report that describes/explain/justify                 your algorithm    
    * Final
         - Project demo (15%)


•In Q&A form
–Questions mostly are from the textbook exercise and lecture slides•2 Exams
•2 Exams    
  –Midterm (20%)
  –Final (25%)


•In class
•Problems related to the topics of the week

Fall 2013, Room 4263, 9:10~12:00 Thursday

Instructor: Kun-chan Lan

(this course is offered in English)

To motivate

•The best project (the one that have the most accurate prediction of driving time)
–will be given a 王品餐券
•The 2ndbest project
–will be given 2 movie tickets