Category: NUS Electrical Engineering

Our BEng (Electrical Engineering) programme is designed to graduate versatile engineers for immediate employment and to prepare them for challenges ahead. Our programmes have strong emphasis on scientific and engineering fundamentals and a high degree of flexibility which can provide a wide diversity of educational experiences. It provides a mix of education, business and research opportunities which is unique in its diversity and richness, allowing the students to plan their individual educational experience in accordance with their career aspirations.

There are 3 differentiated Engineering Pathways for engineering students, they are:

Practising Professional Pathway (PPP): This is for graduates who will be engaged in professional practices such as civic services, infrastructural design, deployment and operation of systems and devices.

Innovation & Design Centric Pathway (iDP): This is for careers in engineering design and innovation, with emphasis on entrepreneurship. Students will be awarded a Second Major in Innovation and Design when you successfully complete all the requirements of iDP

Research-focused Pathway (RfP): This prepares suitable students for graduate studies for careers in R&D and academia.

These pathways give students the flexibility to explore engineering with different emphases. All students in a discipline read common technical core modules, which provide fundamental knowledge & skills. The pathways will be differentiated by the type of electives, type of internships and project work. The pathway requirements are summarized as below.

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This module provides the students with a good overview of renewable energy generation techniques for promoting the advancement and use of economically and environmentally sustainable energy systems. Renewable energy sources including solar, wind, hydro and geothermal are studied in detail. The module will cover the integration of these sources into the smart grid, and strategies for demand side management for efficient resource utilisation. Issues related to environmental impact of renewable energy generation, as well as, their economics will be discussed. Models of smart distribution systems with embedded generation and microgrids will be introduced.

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Photonics technology is everywhere around us, and disruptive advances in photonics have impacted our everyday lives, e.g., LED lighting, flexible OLED displays in mobile phones, ultra-thin and curved television displays. This course will introduce the underlying photonic principles underlying these recent photonic applications, i.e., the generation, modulation and detection of light, and their application. Emphasis is placed on the fundamentals of device operation and their use in current photonic devices and applications. The aim is to equip students to meet the demand of the expanding optoelectronic industry and to prepare them for advanced study and research in photonic technology. Topics include introduction to photometry, and electro-optical properties of semiconductors and lowdimensional semiconductor structures, as well as applications such as light emitting devices, lasers, detectors, modulators and displays. Recent advances e.g. quantum devices, and organic LEDs and photonic crystals will also be introduced.

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This 100% CA module introduces students to the various stages in the design cycle of a closed-loop control system, namely modeling, identification, simulation, controller design and implementation. Students will appreciate the concepts of models and model structures, the ways to obtain them and their applications. Two modeling approaches will be covered; physical modeling which includes the principles and phases ofmodeling using basic physical relationships, and identification approaches covering both non-parametric and parametric identification. Practical issues in modeling, including instrument calibration, model structure selection, data collection configuration, selection of test signals and model validation will also be duly covered. Via project work, students will consolidate the topics covered in class with hands-on experience in modeling, simulating and controlling real systems. They will be equipped with useful practical skills at the end of this course.

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The module introduces the concepts behind making mobile ground robots and unmanned aerial vehicles autonomous. Basic methods underlying robot path planning, sensor fusion, obstacle avoidance and mapping in robotic systems will be taught. The most recent advances in these systems will also be discussed. Students will use the Robot Operating System (ROS) to simulate and implement these methods to enhance their learning experience.

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This module introduces fuzzy logic and neural networks, two tools used in robotics, and their application. It examines the principles of fuzzy sets and fuzzy logic, which leads to fuzzy inference and control. It also covers the structures and learning process of a neural network including genetic algorithm and classification. Topics covered include: fuzzy set theory, fuzzy systems and control of robots, basic concepts of neural networks, single-layer and multilayer perceptions, self-organizing maps, neural network training and neural network modelling of robots. Applications to Robotics will be specifically elaborated throughout the course.

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This module will cover sensors, instrumentation and control systems commonly used in the industry. The sensor and instrumentation part includes topics such as signal processing and conversion, transducers and actuators, instrumentation amplifiers, non-linear amplifiers, issues pertaining to grounds, shields and power supplies. The control portion covers the evolution and types of control systems, centralized control, direct digital control (DDC), distributed control systems (DCS), fieldbuses, PID control: tuning methods and refinements, auto-tuning principles and implementation, available industrial PID controllers and their operation. It will include other common control systems such as feed-forward, cascade, ratio, selective, split range, time-delay compensation, sequence control and PLC.

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This module provides the foundation for a more advanced level control systems course. Topics include system description, controllability, observability, selection of pole locations for good design, observer design, full-order and reduced-order observers, combined control law and observer. It is also a first course in nonlinear systems and control. Topics include non-linearities in control systems, use of root-locus in analysis of non-linear systems, describing function and its use in analysis and design of control systems, non-linear ordinary differential equations, singular points, and phase-plane analysis.

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In this module, students will do a research project over two semesters on a topic of current interest in Electrical and Computer Engineering. Students learn how to apply skills acquired in the classroom and also think of innovative ways of solving problems. Apart from intrinsic rewards such as the pleasure of problem solving, students are able to acquire skills for independent and lifelong learning. The objective of this module is to teach skills, such as questioning, forming hypotheses and gathering evidence. Students learn to work in a research environment.

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This module provides an introduction to signal processing methods. It is aimed at preparing students for high-level technical electives and graduate modules in signal analysis and machine intelligence. The topics covered include: digital filtering, multirate digital signal processing, introduction to wavelet transform, probability and random signals, stochastic processes, singular value decomposition, principle component analysis and multimedia applications.

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