ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE

Syllabus ( GEOD 512 )


   Basic information
Course title: Time Series Analysis and Filter of Geodetic Observations
Course code: GEOD 512
Lecturer: Prof. Dr. Cemal Özer YİĞİT
ECTS credits: 7.5
GTU credits: 3 (3+0+0)
Year, Semester: 1/2, Fall and Spring
Level of course: Second Cycle (Master's)
Type of course: Departmental Elective
Language of instruction: Turkish
Mode of delivery: Face to face
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: With this course, students will be able to learn analysis and filter of time series obtained from geodetic observation in time and frequency domain, especially contributing to discipline such as geosciences and evaluating of structural health, etc., and interpretation of information obtained. They will have the ability to perform information extraction from geodetic observation using Signal processing algorithms in Matlab.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Make analysis and filtering of Time Series in time and frequency domain

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
    2. Formulate and solve advanced engineering problems
    3. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
    4. Design and conduct research projects independently
    5. Work effectively in multi-disciplinary research teams
    6. Develop an awareness of continuous learning in relation with modern technology
    7. Find out new methods to improve his/her knowledge.
    8. Effectively express his/her research ideas and findings both orally and in writing

    Method of assessment

    1. Written exam
    2. Homework assignment
  2. Extract information from geodetic observation with time stamp and interpret its results

    Contribution to Program Outcomes

    1. Formulate and solve advanced engineering problems
    2. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
    3. Design and conduct research projects independently
    4. Develop an awareness of continuous learning in relation with modern technology
    5. Find out new methods to improve his/her knowledge.

    Method of assessment

    1. Written exam
  3. Work together with other engineering discipline and conduct projects

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
    2. Formulate and solve advanced engineering problems
    3. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
    4. Operate modern equipments and hardwares, and use related technical skills in the field of Geodesy and Photogrammetry Engineering.
    5. Work effectively in multi-disciplinary research teams
    6. Develop an awareness of continuous learning in relation with modern technology
    7. Find out new methods to improve his/her knowledge.

    Method of assessment

    1. Homework assignment
   Contents Up
Week 1: Introduction to time series, fundamental components of time series, Signal to Noise concept
Week 2: Importance of time series analysis in geodesy, contribution to project in geosciences and structural health monitoring, and literature reviews.
Week 3: Analysis of a geodetic time series in the time domain,
Week 4: Fast Fourier Transform(FFT), and its geodetic applications
Week 5: Filtering of Geodetic Time series in the time domain
Week 6: Filtering of Geodetic Time series in the frequency domain
Week 7: Matlab implementations with regards to filtering
Week 8: Midterm Exam
Week 9: Features and analysis of low-frequency GNSS time series and velocity estimation
Week 10: Features and analysis of high-frequency GNSS time series, and its FFT analysis
Week 11: Investigation of ionosphere with time series analysis.
Week 12: Analysis of sea-level changes with time series
Week 13: Adaptive filter and its geodetic applications
Week 14: Matlab implementations with regards to the adaptive filter
Week 15*: General review
Week 16*: Final Exam
Textbooks and materials:
Recommended readings: Allen, R., L., and Mills, D., W., 2004, Signal Analysis, Time, Frequency, Scale and Structure, IEEE Press, USA
Chatfield, C.: The Analysis of Time Series, Chapman & Hall/CRC, 1996.
Sevütekin S., Nargeleçekenler M. 2005. Zaman Serileri Analizi, Nobel yayıncılık, 341 sf., Ankara
Proakis, J.G., Manolakis, D.G., Çeviri Editörü:Salor, Ö., Digital Signal Processing: Principles, Algorithms, and Applications, (Sayısal Sinyal İşleme), 4. Baskıdan çeviri, Nobel yayıncılık, 1024 sf., Ankara
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 8 40
Other in-term studies: 0
Project: 0
Homework: 6,14 20
Quiz: 0
Final exam: 16 40
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 3 15
Practice, Recitation: 0 0
Homework: 6 12
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 5 2
Mid-term: 1 3
Personal studies for final exam: 6 2
Final exam: 1 2
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
-->