Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
6MCT312Sensing And Data Acquisition1+2+026

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program Mechatronics Engineering (English)
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
Engineering %30
Engineering Design %20
Field %50

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
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
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:
NoLearning 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
WeekTopicsStudy MaterialsMaterials
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
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 5 5
C1 5 5 5 5
C2 5 5 5 5
C3 5 5 5 5
C4 5 5 5 5

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