Course Name: Instrumentations and Measurements
Course Code: ERA215
Description: This module aims to provide students the opportunity to learn and use various types instruments used for engineering measurements. The gained knowledge will be based upon the principles and concepts of electrical measurement theory and practice. The main objective is to provide an understanding of measurement capabilities and limitations. Topics covered will include analogue and digital instrumentation, signal conditioning, sensors and transducers.
wenxun.lian@xmu.edu.my
Course Name: Random Signals and Noise
Course Code: ERA204
Description: The course will cover the probability, random variables, and stochastic processes in physical systems required for electrical engineering systems.
wenxun.lian@xmu.edu.my
The course develops advanced engineering mathematics concepts, covering multiple integrals, vector calculus, Fourier series, and statistics. It focuses on multivariable analysis, spatial modelling, statistical analysis, and signal representation, providing essential mathematical foundations for robotics, control, and engineering system analysis.
This course follows up on ERA115 Engineering Mathematics 1 by extending students’ knowledge of calculus through partial differentiation and introducing differential equations. Many physical laws are expressed mathematically as differential equations, and scientists and engineers must be able to solve these equations and interpret their solutions. This course focuses on ordinary differential equations and the essential concepts and analytical tools required to understand and solve them, including integral transforms. It also develops students’ understanding and application of partial differentiation and emphasises essential concepts and problem-solving skills in probability.
This course provides the necessary grounding in algebra, calculus, vectors, and complex numbers to students pursuing engineering degrees. Most of the content would be a review or extension of mathematics topics encountered since high school and pre-university. The intention is to ensure that students acquire a common core of mathematical competencies essential for subsequent technical courses.
This course introduces the fundamental concepts and operating principles of electrical machines, as well as the basics of electrical power systems and their protection.
This course provides fundamental knowledge of the principles, concepts and application of mechanics for solving engineering problems. Topics to be covered include equivalent systems of forces, resultants and distributed forces, equilibrium of rigid bodies, centroids, centers of gravity, fluid statics, moments of inertia, and friction.
This is an introductory course in computer-aided design. Students will be taught basic CAD commands, tools, multi-view drawing and dimensioning techniques.
Course Summary: This course provides a thorough introduction to the Python programming language. It will cover the basic syntax and grammar and expose students to practical programming techniques. Besides, this course offers the students about programming and application of NumPy and Tensorflow, which are the necessary tools for intelligence systems. More importantly, the students should be prepared to work in Linux environment.
Lecturer Name: Tony Hii Chang Soon
Course timeline: Mon (4:00pm - 6:00pm), Tue (11:00am - 2:00pm)
Course Summary: This course introduces the fundamental principles and techniques of artificial intelligence. Topics include problem solving and search, heuristic and adversarial search, computational intelligence, machine learning, neural networks, and data mining. Students will apply appropriate AI algorithms and programming tools to solve practical problems.
Lecturer Name: Tony Hii Chang Soon
Course timeline: Tue (3:00pm - 5:00pm), Wed (9:00am - 10:00am)
Course Summary: This course introduces fundamental machine learning methods and their applications to real-world problems. Topics include machine learning workflow, data preparation and model evaluation, instance-based learning, decision trees and ensemble methods, artificial neural networks, Bayesian learning, support vector machines, and probabilistic graphical models. Students will apply the knowledge and skills acquired in the course through an individual project.
Lecturer Name: Tony Hii Chang Soon
Course timeline: Mon (8:00am - 10:00am). Wed (11:00am - 1.00pm)
This course covers fundamentals of signals and system analysis with applications drawn from filtering, audio and image processing, communications, and automatic control. Topics covered in this course include convolution, Fourier series and trnasforms, sampling and discrete-time processing of continuous-time signals, modulation, Laplace and Z-transform, and feedback systems.
To provide basic understanding in discrete -time signals and systems, spectral analysis, and design of digital filters.
This course introduces fundamental electronic components essential for robotics applications, including diodes, transistors, and their practical uses in circuits. Students will develop a solid understanding of these devices, their operating principles, and their role in robotic systems, sensors, and control circuits.
This course provides a foundation for students to understand modern computer system architecture with a focus on its applications in robotics. It equips students with essential knowledge, fundamental concepts, and design techniques relevant to robotics computing, including performance trade-offs, hardware-software interactions, and real-time processing. Key topics include machine structures, embedded system architectures, parallel processing, and multicore computing, all within the context of robotic systems. The course is structured around the three primary components of computing in robotics: processors for control and computation, memory systems for efficient data handling, and networks for communication between robotic subsystems.
To study various types of transistor amplifiers, analysis concepts and their applications.
To introduce techniques of analysis and design of feedback control systems based mainly on classical theory.
To understand the basics of logic circuits and their applications in digital systems
To introduce laws of circuit theory and the behaviour of circuits during steady-state and transient conditions.