Signal Processing in Smart Systems

Unit code:NEE3001Study level:Undergraduate

Credit points

12
(Generally, 1 credit = 10 hours of classes and independent study.)

Location

Footscray Park

Prerequisites

NEE2002 - Analogue and Digital Signals and Systems
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)

Overview

In this unit, you will explore how information is captured, transformed, and transmitted within the smart systems that underpin contemporary life, from autonomous vehicles and wearable devices to connected sensors and communication networks. You will apply mathematical tools such as Fourier, Laplace, and Z-transforms to represent and analyse signals in both time and frequency domains, developing insight into how engineers clean, compress, and optimise information.

Through programming and simulation, you will design and test signal-processing algorithms that enhance accuracy and reliability while balancing computational efficiency and resource use. You will evaluate how these techniques support intelligent control, communication, and biomedical applications, linking analytical thinking with real-world impact. By the end of this unit, you will be able to analyse, design, and implement signal-processing solutions and digital filters that improve the performance, safety, and sustainability of modern engineering systems.

Learning Outcomes

On successful completion of this unit, students will be able to:

Evaluate mathematical representations of signals using Fourier, Laplace, and Z-domain analysis;
Design and synthesise Digital Signal Processing (DSP) algorithms for filtering, sampling, and real-time applications;
Assess the influence of noise, distortion, and quantisation on accuracy and system integrity;
Implement simulations both independently and collaboratively using MATLAB or Python, documenting results with clarity and professionalism; and
Integrate DSP methods into adaptive smart technologies that improve reliability and sustainability.

Assessment

For Melbourne campuses

Assessment type: Exercise | Grade: 15%

In-class analytical DSP activity using MATLAB or Python (Individual) (30 mins)

Assessment type: Project | Grade: 35%

Application mini-project with verified datasets (Group) (2500 words)

Assessment type: Test | Grade: 50%

Applied in-class problem-solving and code interpretation (Individual) (120 mins)

Exercise: This secure assessment ensures accountability by requiring students to perform hands-on analytical DSP tasks under supervision. Real-time observation and process-focused questioning verify understanding and application of analytical skills.

Project: Structured project documenting individual contributions, DSP design, analysis, and application. Explicit evaluation of individual contributions further supports the assessment and assurance of each student’s achievement of learning outcomes.

Test: This secure invigilated assessment ensures authenticity by requiring students to demonstrate their problem-solving and design processes in real time.