Course Catalog 2007-2008

MIT-3016 ANALYSIS OF MEASUREMENT DATA 1, 7 cr
Analysis of Measurement Data 1

Courses persons responsible
Risto Ritala

Lecturers
Risto Ritala

Lecturetimes and places
Per I: Monday 12 - 15, SH108
Per II: Monday 14 - 16, SH108

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

Objectives
Develops capability to assess properties of systems by analyzing and computing characteristics of stochastic measurement signals and pairs of signals.

Content
Content Core content Complementary knowledge Specialist knowledge
1. Measurement as reflecting reality, probabilistic view. Measurement uncertainty.  Principles of Bayesian statistics and Bayesian measurement information theory.    
2. One and two variable normal distributions and their use in abnormality detection and state recognition.       
3. Identification of statistical models by maximum likelihood or least mean squares. Motivation for identification methods.   General maximum likelihood / maximum a posteriori identification.    
4. Covariance function, spectrum and their non-parametric estimation. Effect of sampling on estimates.  Introduction to parametric spectrum estimation. Introduction to time series analysis.  Relationship between cross-covariance/spectrum and joint probability density function of time series. 
5. Spectral analysis of linear dynamic and stochastic systems.       

Requirements for completing the course
Examination and computer exercises.

Evaluation criteria for the course

  • Exam 0-30 points. By doing homework exercises, up to 5 bonus points for exam. Particpation in 4/5 computer exercises mandatory.

  • Used assessment scale is numeric (1-5)

    • Prerequisites
    Prequisite relations (Sign up to TUT Intranet required)

    Additional information about prerequisites
    Background in probability, statistics and Fourier transforms.

    Distance learning

  • ITC utilized during the course

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

  • Estimate as a percentage of the implementation of the course

    • - Contact teaching: 70 %
    - Distance learning: 0 %
    - Proportion of a student's independent study: 30 %

    Scaling
    Methods of instructionHours
    Lectures 45
    Exercises 15
    Assignments 27

    Study materials Hours
    Course overheads 50

    Other scaledHours
    New tools and study methods 5
    Preparation for exam 50
    Total sum 192

    Course homepage

    Last modified 05.03.2007
    Modified byRisto Ritala