Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
6	MKT312	Detection And Data Collection	1+2+0	2	6

Course Details

Language of Instruction	Turkish
Level of Course Unit	Bachelor's Degree
Department / Program	Mechatronics Engineering
Mode of Delivery	Face to Face
Type of Course Unit	Compulsory
Objectives of the Course	To give knowledge about sampling, resolution, precision. To give the ability to generate experitmental setups, and the ability for data acquisation and modern signal processing.
Course Content	Introduction to data acqusition and signal processing. Types of signals. Components in the measurement chain. Sampling theorem. Resolution and accuracy. Time Domain Processing: Statistical analysis, correlation, convolution and digital filtering. Frequency Domain Processing: Fourier series, frequency resolution and windowing, Digital fourier analysis, aliasing, autospectrum, cross spectrum, Coherence, Transfer (Frequency Response) Function.
Course Methods and Techniques
Prerequisites and co-requisities	None
Course Coordinator	Associate Prof.Dr. Savaş DİLİBAL
Name of Lecturers	Associate Prof.Dr. Kadir ERKAN
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	Lynn, P. A. Introductory Digital Signal processing With Computer Applications. John Wiley & Sons, 1994. Stearns D. D. and David, R. A., Signal Processing Algorithms in Matlab, Prentice-Hall Inc, 1996 Ifeachor E.C. and Jervis B.W. Digital Signal Processing: A Practical Approach, Addison-Wesley, 1997
	Ders anlatımı


	Ara sınav, final sınavı

Course Category

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Course Duration	14	2	28
Hours for off-the-c.r.stud	14	3	42
Mid-terms	1	20	20
Practice	14	2	28
Final examination	1	25	25
Total Work Load			Number of ECTS Credits 5 143

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	To make an experiment setup for a measurement task
2	Ability of proper hardware chose and integration for digital measurements
3	To obtain an optimal sampling time for a digital measurement and acquire reliable data in an optimal size
4	Abitility to comment on digital data and to form an opinion about original data

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to data acqusition and signal processing
2	Types of signals
3	Components in the measurement chain
4	Sampling theorem. Resolution and accuracy.
5	Time Domain Processing
6	Statistical analysis, correlation
7	Midterm exam
8	Convolution and digital filtering
9	Frequency Domain Processing
10	Fourier series, frequency resolution and windowing
11	Digital fourier analysis
12	Aliasing, autospectrum, cross spectrum
13	Coherence, Transfer (Frequency Response) Function.
14	General revision and problem solving
15	General revision and problem solving
16	Final exam

Contribution of Learning Outcomes to Programme Outcomes

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https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=205063&curProgID=62&lang=en