SEE712 - Applied Signal Processing
| Year: | 2026 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Waurn Ponds (Geelong) |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | For students enrolled in S460, S461, S462, S463, S465, S466, S467: completion of 18 credits points including units SEE216, SEE222 and SEE307 or Unit Chair approval. For students enrolled in S550, S751, S756, S757, S787: Nil. For all other students: Unit Chair approval. |
| Corequisite: | Nil |
| Incompatible with: | Nil |
| Study commitment: | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. |
| Scheduled learning activities - campus: | 1 x 1 hour practical experience (laboratory) per week, 1 x 1 hour seminar per week |
| Note: | Assumed Knowledge This unit assumes a foundational understanding of signals and systems, including key concepts such as convolution, frequency response, and basic spectral analysis. Students should also be comfortable with introductory probability and statistics, particularly random variables, expectation, variance, and correlation, as these are critical for understanding stochastic signal behaviour and estimation theory. Basic proficiency in MATLAB is required, including scripting, and plotting. Prior exposure to digital signal processing techniques such as sampling, filtering, and spectral estimation will be beneficial. Students who need a refresher are encouraged to complete the free Signal Processing related MATLAB onramp courses before the first lab session. Equipment Requirements Learning experiences and assessment activities in this unit require that students have access to MATLAB for all laboratory and assessment activities. Access to MATLAB is provided through Deakin University’s Software Library for enrolled students. Datasets required for signal modelling, estimation, and system design activities will be provided during the practical classes. A computer capable of running MATLAB and associated toolboxes is essential for completing the practicals and assessments. Students interested in extending their project work to real-time FPGA implementation would have access to Intel FPGA Board in the laboratory. |
Content
This unit allows students to develop advanced expertise in statistical signal processing, with emphasis on stochastic models, Bayesian and optimal filtering, and parametric and non-parametric modelling. Students review probability and random processes before applying estimation theory to real-world signals. They design and implement robust algorithms, exploring the role of correlation structures, spectral analysis, and adaptive filtering. Through hands-on MATLAB and DSP projects focused on biomedical and audio signals, students build critical skills for research and emerging engineering applications.
Unit fee information
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
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