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DIGITAL SIGNAL PROCESSING

Course Code: EA3203 • Study year: III • Academic Year: 2019-2020
Domain: Electronic engineering and telecommunications • Field of study: Applied Electronics
Type of course: Compulsory
Language of instruction: English
Erasmus Language of instruction: English
Name of lecturer: Remus Dobra
Seminar tutor: Remus Dobra
Form of education Full-time
Form of instruction: Class
Number of teaching hours per semester: 56
Number of teaching hours per week: 4
Semester: Summer
Form of receiving a credit for a course: Grade
Number of ECTS credits allocated 4

Course aims:

Principles of signal processing
Getting on the types of signals and basic schemes
Getting on Digital Signal Processing
Theory of mathematical transformations applied signals
Getting the signal filtering and aliasing phenomenon.

Course Entry Requirements:

• Fundamental knowledge in signals.

Course contents:

The course covers the following main topics: Lecture 1 - Introduction. Classification of signals. Communication signals used in audio / video / data. Course 2 - Channels of communication. Parameters communications environments. Course 3 - Sampling signals. Dithering signals. The phenomenon of aliasing. Course 4 - Signal processing audio / video. Separation and synchronizing transmission. Course 5 - correlation function, autocorrelation, amplitude-frequency spectra, power spectra. Course 6 - transmission and data processing. Modulation and demodulation parameters. Lecture 7 - modulation amplitude, phase, frequency, pulse. Lecture 8 - demodulating modulated signals. Lecture 9 - signal conversion. ADC. Lecture 10 - signal conversion. Digital-analog converter. Lecture 11 - dedicated digital information processing circuits. Lecture 12 - Types of filters. Passive filters. Lecture 13 - Procedures for filtering signals. Active filters. Lecture 14 - Applications of fuzzy logic and neural networks in signal processing.

Teaching methods:

Lecture, conversation, exemplification, exercises.

Learning outcomes:

Application of basic methods for acquisition and signal processing.

Learning outcomes verification and assessment criteria:

Projects/Assignments –60%; continuous assessment – 40%.

Recommended reading:

• http://ocw.mit.edu/resources/res-6-008-digital-signal-processing-spring-2011/
• http://www.analog.com/en/design-center/landing-pages/001/beginners-guide-to-dsp.html
• S Salivahanan, A Vallavaraj, C Gnanapriya, Digital Signal Processing, 2000