Year 2 Modules Taken

EE2027 – Electronic Circuits

Building on the basic circuit concepts introduced through EE2111A, this module introduces the operating principles of transistors and how they are used in amplifier circuits. It discusses the foundational concepts of transistor amplifiers and analyses their performance. It also introduces operational amplifiers as a circuit component and describes how functional analog circuits, which can be applied to solving complex engineering problems, can be designed and analysed using operational amplifiers. LTSpice will be introduced as a circuit analysis tool. To augment learning, two laboratory sessions will be included focusing on the topics of single transistor amplifiers and Op-Amp circuits, respectively.

Grade obtained: A+

EE2028 – Microcontroller Programming and Interfacing

This module teaches students how to program microcontrollers and achieve computer interfacing using C programming and industry standard protocols. The course extends the C programming students have learnt earlier, covers microprocessor instruction sets and how to program microcontrollers to interface with other devices in order to build an embedded system. The course culminates in an assignment in which students design and build an embedded system that meets requirements and specifications.

Grade obtained: A+

EE2029 – Introduction to Electrical Energy Systems

This module covers the fundamental principles of modern electrical energy systems; including three-phase analysis, electric generations, electric loads, and power electronic converters. Students will learn how to analyse, model, and predict the performance of energy systems and devices including single-phase and three-phase systems, transformers, and various types of generators. Students will develop a broad systems perspective and an understanding of the principal elements of electrical energy systems. This will serve as the foundation of higher-level topics in power engineering. Furthermore, lectured materials are relevant to PE exams for preparing students to work effectively in complex electrical engineering problems.

Grade obtained: A

EE2033 – Integrated System Lab

This module serves as the hands-on counterpart for EE2027 and EE2023. Students will practice and strengthen the knowledge learnt in electromagnetics, devices and circuits, and signals and systems through a series of experiments with the aim of integrating these knowledge to build an integrated digital communication system. The experiment will touch on important concepts, such as opamp characterization, circuit design specifications and component choice, frequency domain signal analysis, OOK modulation, frequency spectrum, and wireless communication system. Towards the end, the students will form an integrated view on these topics through a mini-project that encompass all these fields.

Grade obtained: A

EE2211 – Introduction to Machine Learning

This module introduces students to various machine learning concepts and applications, and the mathematical tools needed to understand them. Topics include supervised and unsupervised machine learning techniques, optimization, overfitting, regularization, crossvalidation and evaluation metrics. The mathematical tools include basic topics in probability and statistics, linear algebra, and optimization. These concepts will be illustrated through various machine learning techniques and examples, such as forecasting population growth, classifying E-mail as spam or non-spam and predicting heart disease.

Grade obtained: A+

EE3731C – Signal Analytics

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.

Grade obtained: A+

EE4704 – Image Processing and Analysis

The goal of this module is to introduce students to the fundamental concepts underlying digital image processing and techniques for manipulating and analysing image data. This course will provide students with a good foundation in computer vision and image processing, which is important for those intending to proceed to biomedical engineering, intelligent systems and multimedia signal processing. The following topics are taught: elements of a vision system, image acquisition, 2-D discrete Fourier transform, image enhancement techniques, theoretical basis and techniques for image compression, segmentation methods including edge detection, feature extraction including texture measurement, and object recognition.

Grade obtained: A+

EG2101 – Pathways to Engineering Leadership

Recognizing that each professional leadership journey to comprises an individual’s internalised learning and experiences, this module provides a platform for students to explore different means and take active steps towards honing their professional and leadership skills based on their needs and experiences. Students will meet with mentors to discuss talks, lectures, workshops and other initiatives that can help them in their professional journey and be guided in reflecting on this journey for deeper impact. Despite the individual nature of each leadership journey, ethical values are recognized as indispensable for every engineering professional and will be part of this module.

Grade obtained: CS

EG2701A – Aspirational Project I

This is the first of a series of two 2-semesters long modules intended to allow students to pursue a project of their interest under the supervision of a faculty mentor. The idea is to allow students to follow their aspirations and work towards an impactful goal. The project may be carried out within or outside NUS and may last more than one semester. It may or may not be confined to engineering disciplines, but it should have a clear articulation of possible impact on society or community life. This module can only be read by students of the EScholars Programme.

Grade obtained: CS

UTW2001Z – The Semiotics of Colour

Colour is key in visual communication. In this module, students will engage with the topic from a social semiotics and multimodal perspective to explore how colour meanings, for example ideational, interpersonal and textual, are created and interpreted. Students will develop a research paper around an artefact of their choice from fields such as marketing, design, visual and performing arts or their discipline, to examine how colour conveys meanings and/or how these colour meanings are perceived in the community. Through their project, students may explore a range of social issues related to, for example, gender and race.

Grade obtained: A- (exercised S/U option)

ACC1701X – Accounting for Decision Makers

The course provides an introduction to accounting from a user perspective. Financial reporting is covered from the viewpoint of an external investor. The focus is on how accounting can help investors make better decisions. Book-keeping and preparation of financial statements are also covered at an introductory level, as investors need to be aware of how the financial statements are derived.

Grade obtained: A+

DAO1701X – Decision Analytics using Spreadsheets

This module prepares students with theory and skills to capture business insights from data for decision making using spreadsheets. Practical examples and cases with rich data are used to stimulate students’ interest and foster understanding of the use of Business Analytics in management.

Grade obtained: A+

EE4002R – Research Capstone

This research capstone project provides students with an opportunity to work on a complex engineering problem with a strong element of investigative and exploratory research. It requires a confluence of knowledge, skills and capabilities in project management and communications. The project will involve a varied blend of research, design and development activities and is carried out over two semesters. The project proposal can come from a faculty member or student. It may arise during the student’s industrial attachment or as part of an on-going research project and may involve direct industrial and research institutes’ participation. Students will be assessed individually.

Grade obtained: IP (In-Progress)

EE5904 – Neural Networks

In this module students will learn various neural network models and develop all the essential background needed to apply these models to solve practical pattern recognition and regression problems. The main topics that will be covered are: single and multilayer perceptrons, support vector machines, radial basis function networks, Kohonen networks, principal component analysis, and recurrent networks. There is a compulsory computer project for this module. This module is intended for graduate students and engineers interested in learning about neural networks and using them to solve real world problems.

Grade obtained: A+

The following modules were taken on audit / ungraded basis:

The goal of this module is to introduce the students to the problems and solutions of modern computer vision, with the main emphasis on recovering properties of the 3D world from image and video sequence. After this module, students are expected to be able to understand and compute the basic geometric and photometric properties of the 3D world (such as point depth and surface orientation), and to apply various methods for video manipulation such as segmentation, matting, and composition. Main topics covered include: Singular value decomposition, projective geometry, Marr’s paradigm, calibration problems, correspondence and flow, epipolar geometry, motion estimation, reflectance models, shape from shading, photometric stereo, color processing, texture analysis and synthesis, advanced segmentation, matting and composition techniques.

EE4218 – Embedded Hardware System Design

The goal of this module is to enable students to understand and be able to practise the principles of designing complex embedded systems. After completing this module, students must be able to translate system specifications into executable computation models using a high level specification language and map these formal specifications into a register-transfer level hardware description language (HDL) that can be implemented on an FPGA. Main topics covered include: Methodology for designing embedded systems; specification and modelling of systems; architectures of embedded systems; mapping specifications into architectures; rapid prototyping on FPGA platforms. Students are required to implement an embedded system by going though the complete design flow with state-of-the-art Electronic Design Automation (EDA) tools.

EE4603 – Biomedical Imaging Systems

The purpose of this course is to present an overview of biomedical imaging systems. The course will examine various imaging modalities including X-ray, ultrasound, nuclear, and MRI. How these images are formed and what types of information they provide will be presented. Image analysis techniques will also be discussed. Specific analysis techniques will include the analysis of cardiac ultrasound, mammography, and MRI functional imagery.

EE5934 – Deep Learning

Deep learning refers to machine learning methods based on deep neural networks to learn data representation and perform learning tasks. This course provides an introduction to deep learning. Students taking this course will learn the basic theories, models, algorithms, and recent progress of deep learning, and obtain empirical experience. The course starts with machine learning basics and classical neural network models, followed by deep convolutional neural networks, recurrent neural networks, reinforcement learning and applications to computer vision and speech recognition. The students are expected to have good knowledge of calculus, linear algebra, probability and statistics as a prerequisite.