The course aims to improve practical usage of knowledge experienced in Signals and Systems and DSP courses. Python is used as the main simulation tool for signal generation and analysis.
Prerequisite(s)
Preferred: Signals & Systems, Digital Signal Processing
Corequisite(s)
-
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Lecturer Basri Erdoğan
Course Assistant(s)
Schedule
Thursday, 09:00 - 12:45, ELKLAB 2
Office Hour(s)
2D-13, 13:00 - 14:00
Teaching Methods and Techniques
1) Script Files in Python Programming Language 2.7.10 (IDLE or Spyder 2 Platforms)
2) Powerpoint Slides in Lectures (in HTML5 file format)
3) OpenBoard Lectıre Notes (in "pdf" file format)
4) Lecture notes in Jupyter Notebook (in "ipynb" file format)
Principle Sources
-
Other Sources
-
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to course and Python usage. Synthesis of simple discrete time sinusoidal signals via Python.
2. Week
Discrete Fourier Transform, Part 1: Basis Functions of DFT and mutual projections.
3. Week
Discrete Fourier Transform, Part 2: Calculation by projections. fft() command, resolution.
4. Week
Time-varying Phase Functions
5. Week
Analysis of Time-varying frequency content: Short time Fourier transform
6. Week
Midterm 1
7. Week
Above Nyquist rate sampling: Aliasing
8. Week
Time domain filters: Single Echo filter
9. Week
Time domain filters: Multiple Echo Filter
10. Week
Frequency Domain filters: Effect of zeroes and poles on frequency response
11. Week
Midterm 2
12. Week
Frequency Domain filters: Digital FIR filter design using Python
13. Week
Frequency Domain filters: Digital IIR filter design using Python
14. Week
Mean, variance, histograms. Effects on autocorreleation vector.
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
50
Homework / Term Projects / Presentations
10
0
Attendance
14
0
Final Exam
1
50
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
Can convert a continuous time signal to its discrete time form with a given sampling frequency.
LO-2
Can Interpret, generate and plot a discrete time sinusoidal signal with given amplitude and frequency using Python
LO-3
Can plot and analyze FFT of a discrete time signal.
LO-4
Can generate a digital echo filter with given delay and attenuation
LO-5
Can interpret the effect of zeros and poles on a discrete time filter.
LO-6
Can design digital FIR and IIR filters using Python modules.
