Postgraduate Course: Discretetime Signal Analysis (MSc) (PGEE10018)
Course Outline
School  School of Engineering 
College  College of Science and Engineering 
Credit level (Normal year taken)  SCQF Level 10 (Postgraduate) 
Availability  Not available to visiting students 
SCQF Credits  10 
ECTS Credits  5 
Summary  Students will study the theory, and the practical application, of statistical analysis to signals and systems described by random processes. The topic will be approached from both time and frequency domains with an emphasis on studying the effect that analysis tools have on the resulting analysis. The course provides indepth coverage of the discrete Fourier transform, and its role in spectrum estimation, as well as the design of finite impulse response filters, and their role in signal identification. In particular, issues such as resolution and dynamic range of an analysis system are dealt with, to give students an appreciation of how to apply the theory to engineering problems. 
Course description 
Students will explore the analysis of practical signals through time and frequency analysis techniques, and understand the effect of each step in the process. After successful completion of this course a student should be able to: explain the relationships between and be able to manipulate time domain and frequency domain representations of signals; apply correlation techniques to an analytic or numerical problem, and relate the outcome to the statistical properties of the signal source(s); correctly define probability density functions and cumulative distribution functions, and be able to manipulate them to find moments of random variables and their sums; define the distinctions between widesense stationary, stationary, and ergodic processes, and be able to reason to which category a random process belongs; derive the power spectrum of a signal; define techniques for calculating moments in spectral and temporal domains; explain the importance of linear phase filter design and apply time and frequency techniques to design a FIR filter; evaluate power spectral density at the output of a linear filter given the PSD at the input; recognise the effect of resolution and windowing functions upon the discrete Fourier transform; analyse the effects of downsampling and upsampling on a signal and recognise the importance of decimation and interpolation filtering; explain the basis of matched filtering and be able to determine an appropriate filter for a given problem; apply a Wiener filter to the detection of a signal corrupted by additive noise, and for signal prediction..

Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
Prohibited Combinations  Students MUST NOT also be taking
Digital Signal Analysis 4 (ELEE10010)

Other requirements  None 
Additional Costs  Purchase of course textbook (from £56.99) 
Course Delivery Information

Academic year 2016/17, Not available to visiting students (SS1)

Quota: None 
Course Start 
Semester 1 
Timetable 
Timetable 
Learning and Teaching activities (Further Info) 
Total Hours:
100
(
Lecture Hours 21,
Seminar/Tutorial Hours 10,
Formative Assessment Hours 1,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
64 )

Assessment (Further Info) 
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %

Additional Information (Assessment) 
Assessment will be based on a single examination paper of 2 hours duration. 
Feedback 
A formative class test is run in week 4 in the form of a single longform exam question, of a similar standard to the degree exam, covering material studied during the first three weeks. This is returned the following week, with a sample solution and comments on the student¿s attempt. Students are expected to consider this feedback, and reflect on their approach to study for this course. If students wish more detailed feedback, this can be discussed on an individual basis during the weekly Office Hour. 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S1 (December)   2:00  
Learning Outcomes
On completion of this course, the student will be able to:
 An indepth knowledge of the principal analysis techniques that can be applied to random processes
 The ability to produce a detailed specification of an appropriate analysis framework for a given problem scenario
 The skills to interpret the result of an analysis of a random process in view of the limitations of the applied analysis

Reading List
Digital Signal Processing: Principles, Algorithms and Applications, New International Edition, Proakis & Manolakis  £56.99 from Blackwells or Amazon 
Additional Information
Graduate Attributes and Skills 
Students will be able to apply the learned analytical techniques to practical problems throughout their career. Both the ability to apply theory, and the understanding of the effect of design choices on the resulting analysis output, will enable the student to gain a deep insight into the problem being explored. Students will have an appreciation of the effects of working with limited data, and be able to adjust their analysis accordingly. Students will also have the opportunity to experiment with applying the techniques through MATLAB code provided during the course, giving them an understanding of how the techniques can be transferred to their working life. 
Special Arrangements 
Exam must run concurrently with ELEE10010. 
Additional Class Delivery Information 
2 lectures, 1 examples class and 1 tutorial per week 
Keywords  Fourier transform,random process,spectral density,digital filters,signal processing,correlation 
Contacts
Course organiser  Dr David Laurenson
Tel: (0131 6)50 5579
Email: Dave.Laurenson@ed.ac.uk 
Course secretary  Miss Megan Inch
Tel: (0131 6)51 7079
Email: M.Inch@ed.ac.uk 

