Module Details

Module Code: INTR8040
Title: Signal and Data Processing
Long Title: Signal and Data Processing
NFQ Level: Advanced
Valid From: Semester 2 - 2021/22 ( January 2022 )
Duration: 1 Semester
Credits: 5
Field of Study: 5213 - Interdisciplinary Engineering
Module Delivered in: 1 programme(s)
Module Description: This module equips the student with a suite of algorithmic tools that typically form the building blocks of modern signal and data processing-based engineering applications. The objective is to offer a high-level overview of the techniques studied, supply real-world context and demonstrate how to apply the techniques in practice. Sample software tools in one, or more, programming languages will be examined and their characteristics studied, with emphasis being placed on their application and appropriate use.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Estimate signal characteristics using time and frequency domain-based techniques in a suitable programming language.
LO2 Compare and apply classification and dimensionality reduction techniques.
LO3 Evaluate typical signal modelling techniques for engineering applications.
LO4 Determine feasible model parameters using optimization algorithms.
LO5 Analyse a real-world, engineering problem and formulate a solution, using the algorithmic tools studied in a suitable programming language.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Unavoidable Errors in Computing
Digital representation in numbers, finite-precision arithmetic, implications for calculations.
Time-domain System Analysis
Impulse Response, Continuous/discrete time convolution, common time-based filtering.
Interpolation
Polynomial Curve, Piece-wise linear, etc.
Frequency-based Analysis
Discrete-Time Fourier Transform (FFT), Convolution and cross-correlation techniques, frequency-based filtering.
Statistical Methods
Generalised linear models e.g. Least Squares regression. Time-series methods e.g. ARIMA, SSA. Markov chains/models.
Machine Learning
Regression/classification Trees, K-means clustering and Dimensionality Reduction techniques e.g. PCA.
Optimization Algorithms for Parameter Tuning
e.g. Genetic algorithms, Simulated annealing, Particle-swarm optimization.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 30
Timing Every Second Week Learning Outcomes 1,2,3,4
Assessment Description
Class exercises to reinforce the theory studied.
Assessment Type Open-book Examination % of Total Mark 30
Timing Week 9 Learning Outcomes 1,2,3
Assessment Description
Solving problems based on the material studied in class to date.
Assessment Type Project % of Total Mark 40
Timing Week 13 Learning Outcomes 1,2,3,4,5
Assessment Description
At the semester midpoint, the student will be tasked to solve a real-world engineering problem. The student will investigate the problem and formulate alternative approaches to a solution. The student will select the most appropriate approach, based on preliminary testing. A prototype solution will then be constructed and evaluated on actual data. The breakdown of the project marks are as follows: (i) 5 % Document outlining proposed, potential solutions to the problem (ii) 5 % Presentation outlining adopted solution, demonstrating results to justify choice, (iii) 10 % Final presentation demonstrating degree of success of prototype solution, (iv) 20 % Final project submission material.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lab Contact The theory and practice will be delivered in a lab setting. Every Week 4.00 4
Independent & Directed Learning (Non-contact) Non Contact Review and practice of material studied Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lab Contact The theory and practice will be delivered in a lab setting. Every Week 3.00 3
Independent & Directed Learning (Non-contact) Non Contact Review and practice of material studied Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Lindfield, George, Penny, John. (2019), Numerical Methods: Using MATLAB, 4th. Academic Press, [ISBN: 012-8-12-2560].
  • Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier. (2020), Machine Learning: Algorithms and Applications, 1st. CRC Press, p.226, [ISBN: 036-7-57-4675].
Supplementary Book Resources
  • Kiusalaas, Jaan. (2013), Numerical Methods in Engineering with Python 3, Cambridge University Press, p.438, [ISBN: 110-7-03-3853].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_ESMPR_8 Bachelor of Engineering (Honours) in Smart Product Engineering 7 Mandatory