Undergraduate Course: Signals and Communication Systems 2 (SCEE08007)
Course Outline
School  School of Engineering 
College  College of Science and Engineering 
Credit level (Normal year taken)  SCQF Level 8 (Year 2 Undergraduate) 
Availability  Available to all students 
SCQF Credits  10 
ECTS Credits  5 
Summary  This course aims to introduce students to the fundamentals of Signal Processing, Communication, and Information Theory. The course aims to provide an insight into time domain and frequency domain analysis of continuoustime signals, and provide an insight into the sampling process and properties of the resulting discretetime signals. The course then introduces the students to basic communication modulation techniques, as well as probability theory for analysing random signals. At the end of the module students will have acquired sufficient expertise in these concepts to appreciate and analyse physicallayer communication signals. 
Course description 
1. Course overview, and introduction to signals, systems, communications and the broader topic of signal processing (1 hour).
2. Nature of, and types of signals; definitions of continuous time, discrete time, periodic, aperiodic, deterministic and random. Introduction to phasors and concept of frequency of single tone, typical signals and signal classification, power and energy (2 hours).
3. Signal decompositions and concept of signal building blocks (1 hour)
4. Fourier Analysis, including trigonometric and complex Fourier series, Fourier transforms, Parseval's theorem, physical interpretations, and plotting spectra (3 hours).
5. Convolution, including the concept of an impulse and the impulse response of a linear system; the concept and application of convolution, and evaluating the convolution integral using graphical methods (3 hours)
6. Nyquist's Sampling Theorem and DiscreteTime Signals (including discretetime convolution) (3 hours)
7. Introduction to communication theory and modulation techniques, including OOK, FSK, and PSK (2 hours)
8. Multiplexing techniques, including Frequency Division Multiplexing and Time Division Multiplexing (2 hours)
9. Basic Information theory and probability (3 hours).

Information for Visiting Students
Prerequisites  None 
High Demand Course? 
Yes 
Course Delivery Information
Not being delivered 
Learning Outcomes
On completion of this course, the student will be able to:
 A student should be able to distinguish between, and give examples of, deterministic and random, periodic and aperiodic, continuoustime and discretetime signals. For these signals, students should be able distinguish between energy and power signals, be able to perform the appropriate measure calculation for a given signal.
 The student should be able to evaluate the trigonometric, complex Fourier Series, and Fourier transforms of simple waveforms, provide a physical interpretation for these transforms, and plot phase, magnitude, and line spectra. The student should also be able to apply Parseval's theorem for each transform.
 The student should be able recall the Nyquist sampling theorem and analyse the effect of sampling on the frequency content of a signal.
 The student should be able to describe various pulse modulation schemes and circuits for their generation and reception, including OOK, FSK, and PSK; explain frequency division and timevision multiplexing, and analyse simple multiplexing communication systems; explain how communication signals can be modelled as a random process, and perform simple statistical and probabilistic analysis of simple communication schemes.
 The student should be able to demonstrate an ability of use MATLAB to analyse simple signals and communication systems.

Reading List
See lecture notes for full reading list. 
Additional Information
Graduate Attributes and Skills 
Not entered 
Keywords  Continuous and discretetime signal,Fourier analysis,Nyquist sampling theory,communication system 
Contacts
Course organiser  Dr James Hopgood
Tel: (0131 6)50 5571
Email: James.Hopgood@ed.ac.uk 
Course secretary  Mrs Megan InchKellingray
Tel: (0131 6)51 7079
Email: M.Inch@ed.ac.uk 

