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An advanced course on methods for digitally processing signals. Practical methods of designing digital signal filters, especially those with finite impulse response, including implementation on devices with finite precision. Statistical signal processing and estimation. Multidimensional signals and signal processing. The multidimensional Fourier transform and applications. Time-frequency analysis and the wavelet transform.
This course helps the students achieve an understanding of the analysis of signals and their processing using digital, statistical and machine learning techniques.
At the conclusion of this course you should be able to:LO1: Implement and evaluate different filter design methods for noise and interference mitigation in real-world signals (WA1, WA2, WA3, WA5)LO2: Apply the 2-dimensional Fourier transform to image processing problems such as filtering, computer tomography and deconvolution (WA1, WA2, WA3)LO3: Apply dimensionality reduction methods and compressive sensing techniques for signal compression and pattern extraction (WA1, WA2, WA3)LO4: Design and implement different classifiers such as the support vector machine for real-world problems (WA1, WA2, WA3)LO5: Research an advanced signal processing topic, design an application for the topic, and communicate results orally and as a written report in a team environment (WA1, WA2, WA3, WA4, WA5, WA9, WA10)
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Critically competent in a core academic discipline of their award
Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
ENEL320 OR ENMT301
ENEL440
Le Yang
Richard Clare
Ambardar, Ashok; Analog and digital signal processing ; 2nd ed; Brooks/Cole Pub. Co., 1999.
Brunton, Steven L. , Kutz, Jose Nathan; Data-driven science and engineering : machine learning, dynamical systems, and control ; Cambridge University Press, 2019.
Burrus, C. S; Computer-based exercises for signal processing using MATLAB ; Prentice-Hall, 1994.
Chen, Wai-Kai; The circuits and filters handbook ; CRC Press ; IEEE Press, 1995.
Ifeachor, Emmanuel C. , Jervis, Barrie W; Digital signal processing : a practical approach ; 2nd ed; Prentice Hall, 2002.
Ingle, Vinay K. , Proakis, John G; Digital signal processing using MATLAB® : a problem solving companion ; Fourth edition; Cengage Learning, 2016.
Jianxin Wu; Essentials of Pattern Recognition, An Accessible Approach ; Cambridge University Press, 2020 (Digital Format).
Kay, Steven M; Fundamentals of statistical signal processing ; Prentice-Hall PTR, 1993.
Oppenheim, Alan V. , Schafer, Ronald W., Buck, John R; Discrete-time signal processing ; 2nd ed; Prentice Hall, 1999.
Proakis, John G. , Manolakis, Dimitris G; Digital signal processing : principles, algorithms, and applications ; 3rd ed; Prentice Hall, 1996.
Contact HoursLectures: 36Tutorials: 0Workshops: 0Laboratories: 0 Independent studyReview of lectures: 25Test and exam preparation: 25Assignments: 64Tutorial preparation: 0Laboratory calculations: 0 Total 150
Domestic fee $1,164.00
International fee $5,750.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Electrical and Computer Engineering .