Master of Science in ​Computer Control & Automation

Master (Coursework)

Provided by

Programme Type

Full-time, Part-time

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The MSc (Computer Control & Automation) programme provides practising engineers with advanced practical tools in the development, integration, and operation of computer-based control and automation systems.

 

 

Have a good relevant bachelor's degree

Relevant working experience is an advantage

For applicants whose native language is not English, TOEFL/IELTS score is to be submitted with the application for admission:

         TOEFL Score (Test dates must be within 2 years or less from the date of application):

          ≥ 563 (paper-based)

          ≥ 223 (computer-based)

          ≥ 85 (internet-based)

         IELTS Score (Test date must be within 2 years or less from the date of application):

          ≥ 6.0

Applicants without TOEFL/IELTS would still be eligible to apply, but they may be subjected to an interview/test if deemed necessary by the School.

Programme Structure

There are two options of study, one with coursework and dissertation, and the other with coursework only. Each course is of 3 AUs and consists of 39 hours of lectures. Candidates who undertake a project of 6 AUs must submit a dissertation on it.

Option 1
Coursework + Dissertation

Option 2
Coursework
8 courses + dissertation project
(30 AUs in total) 		10 courses
(30 AUs in total)
4 specialized electives (≥ 12 AUs) 4 specialized electives ((≥ 12 AUs)
4 general electives (≤ 12 AUs)
  		6 general electives (≤ 18 AUs)
 
Dissertation (6 AUs)  

Full-time students are strongly recommended to select the dissertation option, however this option is recommended only for students with a high level of English proficiency. Students taking the dissertation option may take a longer time to complete the programme.

Note:  The programme structure will be subject to change without prior notice.

 

Duration

Both full-time and part-time programmes are offered (unless stated). Part-time candidates are expected to obtain permission from their employer before admission to the programme. All classes are conducted in the evenings, while ex aminations are conducted during office hours.

Type of Coursework Programme

Minimum Candidature

Maximum Candidature
Master of Science
(Full-Time) 		1 year 3 year
Master of Science
(Part-Time) 		2 year 4 year

 

Programme Calendar

Semester 1 August to December
Semester 2 January to May
Week 1 to 14 Lecture (Inclusive of 1-week recess)
Week 15 to 17 Examinations
Other Vacation

 

Graduate courses offered by Master of Science (MSc) Computer Control & Automation:

Specialized Elective Courses (Students are required to take a min of 4 out of all the 5 specialized elective courses)

Course Code Course Title Course Content AUs
EE6203  COMPUTER CONTROL SYSTEMS  Discrete-time system modelling and analysis. Cascade compensation. State-space design methods. Optimal control. Design and implementation of digital controllers. 3
EE6204  SYSTEMS ANALYSIS  Linear, Dynamic and Integer Programming. Optimization Techniques. Random Processes. Queuing Models. Markov Decision Process. 3
EE6221  ROBOTICS & INTELLIGENT SENSORS  Overview of robotics. Motion planning and control. Mobile robots . Controller hardware/software systems. Sensor systems and integration. 3
EE6222  MACHINE VISION  Fundamentals of image processing & analysis. Feature Extraction Techniques. Pattern / Object Recognition and Interpretation. Three- Dimensional Computer Vision. Three-Dimensional Recognition Techniques. Biometrics. 3
EE6225  MULTIVARIABLE CONTROL SYSTEMS ANALYSIS & DESIGN  Basic control algorithms. Model Predictive Control. Multivariable control. Plant parameter estimation. Case studies in process control. 3

 

General Elective Courses

Course Code Course Title Course Content AUs
EE6010  PROJECT MANAGEMENT & TECHNOPRENEURSHIP  Project Initiation and Planning. Project Scheduling and Implementation. Project Monitoring, Control and Evaluation. Innovation and Entrepreneurship. 3
EE6102  CYBER SECURITY & BLOCKCHAIN TECHNOLOGY  Cyber Security Threat Landscape, Industry 4.0 and Cyber Security, Cyber Security Education, Awareness and Compliance, Cyber Security Planning, Policies and Compliance, Cyber Security Risk Assessments and Biometric-based Security approaches, Public key Infrastructure (PKI), Web Security and role of firewalls and Intrusion Detection, Online Payment, and Cryptocurrencies. Basics of Blockchain technology, Types of blockchain Technology, Blockchain Technology Applications for Industry 4.0, use cases and real-world case studies 3
EE6223  COMPUTER CONTROL NETWORKS  Data Networks in Control and Automation. Local Area Network Concepts and Fieldbus. Application Layer of Fieldbus and MAP. Internetworking and Protocols. Real-time Operating Systems and Distributed Control. Network Performance and Planning. Multimedia in Advanced Control and Instrumentation. 3
EE6227  GENETIC ALGORITHMS & MACHINE LEARNING  Review of Combinatorics and Probability. Introduction of Genetic Algorithms. Differential Evolution. Particle Swarm Optimization. Advanced Techniques. Principles of Machine Learning. Paradigms of Machine Learning. Kernel Methods. 3
EE6228  PROCESS MODELING & SCHEDULING  Introduction to Operation Process Modeling – Mathematical Programs, Event-Driven Models (MDP/HMM).
Introduction to Scheduling Theory – Flow Shop Problems, Job Shop Problems, AGV Fleet Management in Material Transportation.
Introduction to Optimization – Linear and Quadratic Programming, Integer Programming (Branch-and-Bound, Branch-and-Cut), Lagrangian relaxation and decomposition,Metaheuristics, Reinforcement Learning.
Introduction to Command Execution – Command Execution System (MES),Closed-loop Resilient Operation Replanning		 3
EE6285  COMPUTATIONAL INTELLIGENCE  Introduction to Fuzzy Logic, Introduction to Fuzzy Sets, Introduction to Fuzzy Inference Systems, Fuzzy Logic Applications, Introduction to Genetic Algorithm, Fundamental Concepts of Artificial Neural Networks and Neural Network Architectures, Neural Network Applications 3
EE6301  SMART BIOSENSORS & SYSTEMS FOR HEALTHCARE  Introduction to biosensors and healthcare; Optical biosensors-fundamentals; Optical biosensors-applications; Biomedical imaging with optical technologies; Introduction to electrical biosensors- fundamentals; Introduction to electrical biosensors- fabrications; Applications of electrical biosensor; Introduction to bio-intelligent systems; Artificial intelligence in medical sensing and imaging 3
EE6341  ADVANCED ANALOG CIRCUITS  Low Noise Circuits, Wide-Bandwidth Amplifiers, Power Amplifiers, Active Filters, DC-DC Converters 3
EE6401  ADVANCED DIGITAL SIGNAL PROCESSING  Discrete-time signal analysis and filter design. Multi-rate digital signal processing. Linear prediction and optimal linear filters. Power spectrum estimation.  3
EE6405  NATURAL LANGUAGE PROCESSING  Traditional: Bag-of-words, Preprocessing, Term weighting scheme, Feature extraction,. Topic modeling , ML classifiers and clustering methods, Evaluation Metrics, Word Embeddings
Deep Neural Networks: Graph convolutional network, Seq2Seq, Attention mechanism, Transformers and self-attention, Pretrained Language Models, Fine-tuning (hyper-parameter tuning), Applications (chatbot, machine translation, sentiment analysis, summarisation, classification, generation, auto-complete)		 3
EE6427  VIDEO SIGNAL PROCESSING  Image and Video Basics. Image and Video Transform Coding.
Filtering and Error Resilience for Image and Video. Image and Video Coding Principles and Standards. Recent and Emerging Topics in Image and Video Processing.		 3
EE6483  ARTIFICIAL INTELLIGENCE & DATA MINING  Structures and Strategies for State Space Representation & Search. Heuristic Search. Data Mining Concepts and Algorithms. Classification and Prediction methods. Unsupervised Learning and Clustering Analysis. 3
EE6497  PATTERN RECOGNITION & DEEP LEARNING  Introduction, probability review, Bayesian Inference, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning (CNN, RNN), Training Deep Networks, Deep Network Architectures, Applications, Generative Models, Self-supervised Learning. 3
EE6503  MODERN ELECTRICAL DRIVES  Introduction. DC Motor Drives. Induction Motor Drives. Synchronous Motor Drives. Servo-Motor Drives. 3
EE6506  POWER SEMICONDUCTOR BASED CONVERTER IN RENEWABLE ENERGY SYSTEMS  Module 1: Overview of power electronic circuits and semiconductor devices, Module 2: Power diodes and thyristors as switching devices, Module 3: Power transistors as switching devices 2, Module 4: Protection of devices from overheating di/dt, dv/dt, Module 5: Passive components and magnetics, Module 6: Renewable energy systems 3
EE6509  RENEWABLE ENERGY SYSTEMS IN SMART GRIDS  Introduction to Power Systems with Distributed Generation. Distributed Generation. Energy Storage. Smart Grids.  3
EE6511  POWER SYSTEM MODELLING & CONTROL  Steady-state Power System Networks. Network Components. Stability Analysis. Power System Control.  3
EE6534  MODERN DISTRIBUTION SYSTEM WITH RENEWABLE RESOURCES  Operation of distribution systems. Power quality. Solar power systems. Wind power systems. 3
EE7204  LINEAR SYSTEMS  Input/Output System Models. State Space Representation. Norms of Signals and Systems. Decomposition of Linear Time-Invariant Systems. Linear Feedback Design. Convex Optimization for Linear System Analysis and Design. 3
EE7205  RESEARCH METHODS  Research Preparation and Planning. Research Sources and Review. Quantitative Methods for Data Analysis. Experimental research methods. Academic Writing & Presentation 3
EE7207  NEURAL NETWORKS AND DEEP LEARNING The key topics to be covered in the context of deep neural networks
and deep learning will encompass convolutional neural networks (CNN), modern recurrent neural
networks (RNN), the attention mechanism and the transformer, self-supervised learning, graph
neural networks, all of which represent the cutting-edge methods in the realm of deep learning. In
addition, some typical applications and advanced topics of deep learning will be introduced.		 3
EE7401  PROBABILITY & RANDOM PROCESSES  Probability concepts. Random variables. Multiple random variables. Sum of random variables and multidimensional distributions. Random Sequences. Probability density function estimation. Random variable simulation. Random processes. Correlation functions. Spectral density. Random processes in linear systems. Optimum linear systems. Nonlinear systems.  3
EE7403  IMAGE ANALYSIS & PATTERN RECOGNITION  Image Fundamentals. Image Enhancement and Restoration. Image Analysis. Decision Theory and Statistical Estimation. Classification and Clustering. Dimensionality Reduction. 3
EE6008  COLLABORATIVE RESEARCH & DEVELOPMENT PROJECT  Project Charter, Design and prototype development, Project implementation, Testing and instrumentation, Project report, Oral presentation, Demonstration 3

 


Note: the above curriculum is subject to change.

 

Tuition Fees

Five MSc programmes (Communications Engineering, Computer Control & Automation, Electronics, Power Engineering and Signal Processing) are self-financed programmes. 

Students of these programmes are not eligible for Service Obligation/ MOE Subsidies. 

The tuition fees per module (3 AUs) and per dissertation (6 AUs) for admission from AY2020 onwards are shown in the table as follows:

Singaporeans (SC) Singapore PRs (SPR) International Students (IS)
Per Module Per Dissertation^ Per Module Per Dissertation^ Per Module Per Dissertation^
S$3,270* S$6,540* S$3,815* S$7,630* S$4,360* S$8,720*
Minimum Total Programme Fee Minimum Total Programme Fee Minimum Total Programme Fee
S$32,700* S$38,150* S$43,600*

*Inclusive of 9% GST from 1 Jan 2024.

^The tuition fee for the Dissertation (6 AUs) will be twice of each module fee.

All fees listed above are in Singapore dollars (S$) and subject to annual revision by the school. The tuition fee is exclusive of living expenses and miscellaneous student fees.

The deposit fee of S$2,000 is payable upon acceptance of the offer and is non-refundable. It will be deducted from the full tuition fee.

Important Updates:

Below table for intakes starting from August 2024.

Per Course: S$4,883.20* Per Dissertation^: S$9,766.40*
Minimum Total Tuition Fees: S$48,832*
SC/SPR Incentive
All Singapore Citizens (SC) and Singapore Permanent Residents (SPR) will be eligible to receive a S$5,000 subsidy, and S$10,000 for needy SC/SPR students.
 
NTU Alumni Incentive
All NTU Alumni will receive 10% study incentives in the form of reduction in fees.

*Inclusive of 9% GST from 1 Jan 2024.
A Goods and Services Tax (GST) of 9% is levied on the import of goods, as well as nearly all supplies of goods and services in Singapore starting 1 Jan 2024.

A deposit fee of S$5,000 (from August 2024's intake onward) is payable upon acceptance of the offer and is non-refundable. It will be deducted from the full tuition fee.


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