Course Catalog 2007-2008

SGN-2056 DIGITAL LINEAR FILTERING II, 4 cr
Digital Linear Filtering II

Courses persons responsible
Tapio Saramäki

Lecturers
Tapio Saramäki

Lecturetimes and places
Per III: Tuesday 12 - 14, TB223
Per III: Thursday 12 - 14, TB223
Per III: Friday 12 - 14, TB223

Implementations
  Period 1 Period 2 Period 3 Period 4 Period 5 Summer
Lecture - - 4 h/week - - -
Exercise - - 2 h/week - - -
Assignment - - 18 h/per - - -
Exam  
(Timetable for academic year 2007-2008)

Objectives
Various traditional and more up-to-date approaches for synthesizing linear digital filters. Finite wordlength effects are considered in detail.

Content
Content Core content Complementary knowledge Specialist knowledge
1. Design and implementation of finite-impulse response digital filters using both the traditional approaches and approaches leading to efficient implementations.  The lecture notes review various alternatives of using the computationally-efficient structures inluded in the course.  During the lectures, some extra information not included in the lecture notes is given. 
2. Design and implementation of infinite-impulse response digital filters using both the traditional approaches and approaches leading to efficient implementations.
 		      
3. Finite wordlength effects are considered in more details compared to the course SGN-2016.       

Requirements for completing the course
Final examination and assignment.

Evaluation criteria for the course

  • Course is graded on the basis of answers to exam questions. Very good grade is obtained when exam questions are correctly answered and 2 homeworks out of 3 are accepted. Course acceptance threshold is approximately half the maximum exam points. The third homework is a volunteer work and is prized by increasing the exam result by one grade provided that the exam threshold is passed.

  • Used assessment scale is numeric (1-5)

    • Study material
    Type Name Auhor ISBN URL Edition, availability... Exam material Language
    Other literature "Digital Linear Filtering II" Tapio Saramäki       Yes  English 

    Prerequisites
    Code Course Credits M/R
    SGN-1250 SGN-1250 Signal Processing Applications 4 Recommendable
    SGN-2010 SGN-2010 Digital Linear Filtering I 5 Mandatory
    SGN-2016 SGN-2016 Digital Linear Filtering I 5 Mandatory

    Prequisite relations (Sign up to TUT Intranet required)

    Additional information about prerequisites
    Either SGN-2010 or SGN-2016 is required.

    Remarks

  • The course is suitable for postgraduate studies.

    • Distance learning

  • ITC utilized during the course

    • - In information distribution via homepage, newsgroups or mailing lists, e.g. current issues, timetables
    - In distributing and/or returning exercise work, material etc

  • Estimate as a percentage of the implementation of the course

    • - Contact teaching: 45 %
    - Distance learning: 0 %
    - Proportion of a student's independent study: 55 %

    Scaling
    Methods of instructionHours
    Lectures 48
    Exercises 18
    Assignments 18

    Other scaledHours
    Preparation for exam 20
    Exam/midterm exam 3
    Total sum 107

    Additional information related to course

    Correspondence of content
    8001102 Digital Linear Filtering II

    Course homepage

    Last modified 17.04.2007
    Modified bySari Peltonen