Undergraduate Course: Sensor Networks and Data Analysis 2 (ELEE08021)
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 | Sensing and data analysis is fundamental to all Engineering disciplines. It relies on a key understanding of sensor networks and how they communicate, resource and computation constraints, and an understanding of how data is sampled and then analysed. Signals are the output of sensors which have measured data, and this course gives an introduction to key signal analysis concepts.
This course aims to introduce students to the fundamentals of Sensor Networks, Signal Processing, Communication, and Information Theory. The course aims to provide an insight into time domain and frequency domain analysis of continuous-time signals, and provide an insight into the sampling process and properties of the resulting discrete-time 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 how sensor networks and signal analysis can be used in a variety of disciplines. |
| Course description |
1. Course overview, introduction to sensor networks and their roles in Engineering disciplines. Introduction of sensor types, sensor outputs, sensor networks, sensor signals. Introduction to the role of communications in sensor networks.
2. An introduction to the broader topic of signal processing, machine learning, and the role of these disciplines within Data Science. Considers applications of machine learning for detection, classification, segmentation, regression on signals received from sensor networks.
3. 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.
4. Signal decompositions and concept of signal building blocks.
5. An overview of spectral analysis techniques in general. Discussion of the role of Fourier Analysis, including trigonometric and complex Fourier series, Fourier transforms, Parseval's theorem, physical interpretations, and plotting spectra.
6. The Discrete-time world: Developing Nyquist's Sampling Theorem and Discrete-Time Signals.
7. An overview of communication theory: how data from sensors can be transmitted from the source to a remote receiver/sink for further processing or use. This will include: AM/FM/PM, OOK, FSK, and PSK.
8. Multiplexing techniques: Methods of combining signals from multiple sensors for transmission over a common medium. This will include: Frequency Division Multiplexing and Time Division Multiplexing.
9. Basic Information theory.
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2025/26, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 8,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
63 )
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| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam %: 70
Practical Exam %: 0
Coursework %: 30
The School has a 40% rule for this course, whereby you must achieve a minimum of 40% in coursework and 40% in written exam components, as well as an overall mark of 40% to pass a course. If you fail a course you will be required to resit it. You are only required to resit components which have been failed. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S2 (April/May) | Sensor Networks and Data Analysis 2 | 90 | | | Resit Exam Diet (August) | | 30 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand the role of sensor networks for acquiring data in Engineering applications;
- Distinguish between continuous time and discrete time representations of real world signals and analyse them using the appropriate theory and techniques. They should be able to apply them to deterministic, random, periodic and aperiodic signals, and distinguish between energy and power signals, being able to perform the appropriate measure calculation for a given signal;
- Correctly apply the appropriate theoretical analysis and description to the signals. This includes: evaluation of trigonometric, complex Fourier Series, and Fourier transforms of simple waveforms; providing a physical interpretation for these transforms, and plotting phase, magnitude, and line spectra; the Nyquist sampling theorem and analysing the effect of sampling on the frequency content of a signal;
- Describe techniques for generating, transmitting and decoding real world data or information. These include: describing various analogue/digital modulation schemes and circuits for their generation and reception, including AM/FM/PM, OOK, FSK, and PSK; explaining frequency division and time-vision multiplexing, and analysing simple multiplexing communication systems; explaining how communication signals can be modelled as a random process, and performing simple statistical and probabilistic analysis of simple communication schemes;
- Demonstrate an ability in the use of computer simulations to analysis simple signals and communication systems that form the basis of sensor networks.
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Reading List
Essential:
Digital signal processing, John G. Proakis, 2014
E-book
ISBN: 9781292038162
Pearson, Fourth edition, Pearson new international edition
Digital communications, Ian Glover, 2010
E-book
ISBN: 9780273718307
Prentice Hall, Third edition
Recommended:
Bayesian reasoning and machine learning, David Barber, 2012
E-book
ISBN: 9780521518147
Cambridge University Press |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr James Hopgood
Tel: (0131 6)50 5571
Email: |
Course secretary | Miss Katie Murray
Tel:
Email: |
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