Postgraduate Course: Discrete-time Signal Analysis (MSc) (PGEE10018)
|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
|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 in-depth 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.
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 wide-sense 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.
Entry Requirements (not applicable to Visiting Students)
|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 2020/21, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Seminar/Tutorial Hours 12,
Formative Assessment Hours 1,
Summative Assessment Hours 2.5,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
Coursework is based on on-line tests.
||Each section in the course has an on-line test associated with it. On taking a test, students will receive immediate feedback on their progress, with additional guidance on the reasoning behind the correct answer, or answers. The same format of questioning is used in the online summative assessment.
|No Exam Information
On completion of this course, the student will be able to:
- An in-depth 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
|Digital Signal Processing: Principles, Algorithms and Applications, New International Edition, Proakis & Manolakis - £56.99 from Blackwells or Amazon|
|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 Python code provided during the course, giving them an understanding of how the techniques can be transferred to their working life.
|Additional Class Delivery Information
||2 lectures, 1 examples/tutorial per week
|Keywords||Fourier transform,random process,spectral density,digital filters,signal processing,correlation
|Course organiser||Dr David Laurenson
Tel: (0131 6)50 5579
|Course secretary||Miss Jo Aitkenhead
Tel: (0131 6)50 5532