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Course Catalog 2010-2011
SGN-1157 Signal Processing Basics, 3-5 cr |
Person responsible
Aram Danielyan, Markku Mäkitalo, Karen Eguiazarian
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
For 3 Credits: In-class and homework exercises. 70% of lectures (2 hours of lectures every week) and 100% of exercises have mandatory attendance. All the homeworks have to be returned before the last lecture of the course. Grading will be based on in class and homework exercises. No exam. For 5 Credits (mandatory for further Signal Processing studies): Final exam and exercises, including small homeworks. 70% of lectures (4 hours of lectures every week) and 100% of exercises have mandatory attendance. All the homeworks have to be returned before taking the final exam.
Learning outcomes
For 3 Credits, students should know how to sample a signal, how to analyse a signal in time and frequency domains, able to be use convolution, correlation, discrete-time and digital Fourier transforms. For 5 Credits, students should know additionally to above, how to use z-transforms and to analyse signals and systems in z-domain, as well as know fast Fourier transform algorithms and DSP algorithms implementation
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | For 3 Credits: 1 Introduction of different signals, analog vs. digital 2 Audio & speech - sampling - ADC and DAC - aliasing examples - filtering - spectrum 3 Image and video processing - aliasing effects - image enhancement - edge detection, etc. 4 Basic tools: impulse response, convolution Fourier transform | ||
| 2. | For 5 Credits: Additionally to those for 3 Credits 1 Signal analysis 2 LTI systems inFourier and z-transform domains 3 DSP algorithms implementation |
Evaluation criteria for the course
For 3 Credits: Course is graded according to in-class and homework exercises. No exam. For 5 Credits: Course is graded according to exam results, and in-class and homework exercises
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Prerequisite relations (Requires logging in to POP)
Correspondence of content
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More precise information per implementation
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