EEE2204 : Random Signals and Processes
- Offered for Year: 2017/18
- Module Leader(s): Dr Wai Lok Woo
- Owning School: Engineering
- Teaching Location: Singapore
Semesters
Semester 1 Credit Value: | 10 |
ECTS Credits: | 5.0 |
Aims
The aim of this module is to provide students with an appreciation of modern mathematical statistics and probability theory and their various engineering applications. The module will provide the mathematical foundation for these universal theoretical models as the basis for the justification of dealing with data sets that are influenced by chance effects.
Outline Of Syllabus
• Representation of data, mean, standard deviation, variance, random sampling, stratified random sampling, quota sampling.
• Experiments and outcomes, sets theory for probability theory, relational probability, joint probability, conditional probability, total probability, independence probability, Bayes theorem, permutations and combinations.
• Random variables, cumulative distribution function, probability density function (pdf), mean and variance of a distribution, Chebyshev’s inequality, Central Limit Theorem (CLT).
• Binomial distribution, Poisson distribution, Gaussian distribution, distribution of several random variables, unbiased and minimum variance estimator.
• Sampling theory, estimation of parameters, precision of estimates, confidence intervals for mean of distribution, difference of means, standard deviation, and variance ratio.
• Estimation theory, covariance, correlation coefficient, goodness-of-fit, contingency table of different random processes.
• Statistical curve fitting, least-square and minimum mean squared error (MMSE) estimate, standard error estimate, probability interpretation of regression, time-series process prediction.
• Detection theory, Type-I and Type-II error, maximum likelihood (ML) test, maximum a posteriori probability (MAP) test, Bayes’s test, Neyman-Pearson test.
Teaching Methods
Teaching Activities
Category | Activity | Number | Length | Student Hours | Comment |
---|---|---|---|---|---|
Guided Independent Study | Assessment preparation and completion | 1 | 5:00 | 5:00 | Case study 2. |
Guided Independent Study | Assessment preparation and completion | 1 | 5:00 | 5:00 | Case study 1. |
Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | Final examination. |
Guided Independent Study | Assessment preparation and completion | 15 | 1:00 | 15:00 | Revision for final examination. |
Scheduled Learning And Teaching Activities | Lecture | 6 | 1:00 | 6:00 | Tutorials |
Scheduled Learning And Teaching Activities | Lecture | 24 | 1:00 | 24:00 | N/A |
Guided Independent Study | Independent study | 43 | 1:00 | 43:00 | General reading; reviewing lecture notes; solving practice problems. |
Total | 100:00 |
Teaching Rationale And Relationship
Lectures provide core material and guidance for further reading. Case studies are explored and set within tutorials.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
Description | Length | Semester | When Set | Percentage | Comment |
---|---|---|---|---|---|
Written Examination | 120 | 1 | A | 70 | N/A |
Other Assessment
Description | Semester | When Set | Percentage | Comment |
---|---|---|---|---|
Case study | 1 | M | 15 | Case study 1. |
Case study | 1 | M | 15 | Case study 2. |
Assessment Rationale And Relationship
The examination provides the opportunity for the student to demonstrate their understanding of the course material. The case studies enable students to demonstrate that they are able to apply this understanding and their analysis and skills to novel situations.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- EEE2204's Timetable