ENEL420-23S2 (C) Semester Two 2023

Advanced Signals

15 points

Details:
Start Date: Monday, 17 July 2023
End Date: Sunday, 12 November 2023
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 30 July 2023
  • Without academic penalty (including no fee refund): Sunday, 1 October 2023

Description

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.

Learning Outcomes

  • 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)
    • University Graduate Attributes

      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.

Prerequisites

Restrictions

ENEL440

Course Coordinator

Le Yang

Lecturer

Richard Clare

Assessment

Assessment Due Date Percentage 
Assignment One 20%
Assignment Two 30%
Final Exam 50%

Textbooks / Resources

Recommended Reading

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.

Additional Course Outline Information

Mahi ā-Ākonga | Workload (expected distribution of student hours, note 15 points = 150 hours):

Contact Hours

Lectures: 36
Tutorials: 0
Workshops: 0
Laboratories: 0

Independent study

Review of lectures: 25
Test and exam preparation: 25
Assignments: 64
Tutorial preparation: 0
Laboratory calculations: 0

Total 150

Indicative Fees

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 .

All ENEL420 Occurrences

  • ENEL420-23S2 (C) Semester Two 2023