Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
5BBM477SAMPLED DATA SYSTEMS3+0+03606.09.2024

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The course aims to teach analysis of sampled data systems in time and frequency domain; to teach pulse transfer functions, sampling process, properties of finite and infinite impulse response systems; to teach fast Fourier transform and Fundamentals of Kalman filters.
Course Content Sampling of signals
Reconstruction from sampled signals
Nyquist sampling theorem
Z transform
Signal and system analysis in frequency domain
Finite impulse response (FIR) filters
Infinite impulse response (IIR) filters
Fourier transform, discrete time Fourier transform, discrete Fourier transform and fast Fourier transform
Kalman filters
Course Methods and Techniques Lecture, Problem Solving
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 )
Course Coordinator None
Name of Lecturers Prof. Dr. Mehmet Önder Efe
Asist Prof.Dr. Burak Kükçü
Asist Prof.Dr. Özgür Erkent
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources
Course Notes A.V. Oppenheim ve R.W. Schafer, Discrete-Time Signal Processing, Pearson, 2010.
V.K. Ingle ve J.G. Proakis, Digital Signal Processing Using MATLAB, Cengage Learning, 2012.


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
Midterm Exam 1 % 40
Assignment 2 % 20
GenelSınav 1 % 40
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 5 70
Assignments 2 5 10
Preparation for Midterm Exam 1 20 20
General Exam Preparation 1 38 38
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 After completing this course, the students will Be able to analyze systems represented in time and frequency domain
2 Be able to analyze and interpret filters
3 Be able to interpret the content of a signal via fast Fourier transform
4 Be able to design Kalman filters

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Sampling of continuous time signals
2 Reconstruction from sampled data signal and Nyquist sampling theorem
3 Z transform
4 Z transform
5 Signal and system analysis in frequency domain
6 Signal and system analysis in frequency domain
7 Finite impulse response (FIR) filters
8 Midterm
9 Infinite impulse response (FIR) filters
10 Fourier transform
11 Discrete time Fourier transform and discrete Fourier transform
12 Fast Fourier transform
13 State space representation, stability, structural properties
14 Kalman Filter
15 Preparation to final exam
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 2 1 1 2 2 1 1 1
C1
C2
C3
C4

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://bilsis.hacettepe.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2687597&lang=en