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ELEC441 Linear Systems and Noise

An introduction to the "systems" approach to solving physical problems: generalised functions, the Fourier transform, sampling and the FFT, causality and the Kramers-Kronig relations, noise processes and matched filtering.

Paper title Linear Systems and Noise
Paper code ELEC441
Subject Electronics
EFTS 0.0833
Points 10 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $628.08
International Tuition Fees (NZD) $2,573.97

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Limited to
BSc(Hons), PGDipSci, MSc, MAppSc
Contact
jevon.longdell@otago.ac.nz
Teaching staff
Course Co-ordinator: Assoc Prof Jevon Longdell
Textbooks
Text books are not required for this paper.
Graduate Attributes Emphasised
Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy, Self-motivation, Teamwork.
View more information about Otago's graduate attributes.
Learning Outcomes
After completing this paper students are expected to:
  1. Have a good understanding of the delta function and generalised functions in general and be able to use the formal definition of generalised functions for doing calculus on generalised functions
  2. Understand the convolution integral and its relation to the delta function and the superposition principle
  3. Be familiar with the Fourier transform and its properties and be comfortable finding Fourier transforms using the properties of the Fourier transform and the Fourier transforms for a base set of functions
  4. Find the Fourier transform of generalised functions from the definition
  5. Understand sampling and its effects in the Fourier domain and be able to derive the sampling theorem and show the relationship between the discrete and continuous Fourier transforms
  6. Understand the effect of causality on a system transfer function, the Hilbert transform and the Kramers-Kronig relation
  7. Be able to solve problems related to the one dimensional propagation of a signal through a dispersive and for the narrow bandwidth approximation derive expressions for the group and phase velocities
  8. Be introduced to stationary stochastic processes and be able to calculate the effect of a linear system on the power spectrum of a signal
  9. Be able to use matched filtering to optimally find signals in noise

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Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
None

An introduction to the "systems" approach to solving physical problems: generalised functions, the Fourier transform, sampling and the FFT, causality and the Kramers-Kronig relations, noise processes and matched filtering.

Paper title Linear Systems and Noise
Paper code ELEC441
Subject Electronics
EFTS 0.0833
Points 10 points
Teaching period First Semester
Domestic Tuition Fees Tuition Fees for 2018 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Limited to
BSc(Hons), PGDipSci, MSc, MAppSc
Contact
jevon.longdell@otago.ac.nz
Teaching staff
Course Co-ordinator: Assoc Prof Jevon Longdell
Textbooks
Text books are not required for this paper.
Graduate Attributes Emphasised
Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy, Self-motivation, Teamwork.
View more information about Otago's graduate attributes.
Learning Outcomes
After completing this paper students are expected to:
  1. Have a good understanding of the delta function and generalised functions in general and be able to use the formal definition of generalised functions for doing calculus on generalised functions
  2. Understand the convolution integral and its relation to the delta function and the superposition principle
  3. Be familiar with the Fourier transform and its properties and be comfortable finding Fourier transforms using the properties of the Fourier transform and the Fourier transforms for a base set of functions
  4. Find the Fourier transform of generalised functions from the definition
  5. Understand sampling and its effects in the Fourier domain and be able to derive the sampling theorem and show the relationship between the discrete and continuous Fourier transforms
  6. Understand the effect of causality on a system transfer function, the Hilbert transform and the Kramers-Kronig relation
  7. Be able to solve problems related to the one dimensional propagation of a signal through a dispersive and for the narrow bandwidth approximation derive expressions for the group and phase velocities
  8. Be introduced to stationary stochastic processes and be able to calculate the effect of a linear system on the power spectrum of a signal
  9. Be able to use matched filtering to optimally find signals in noise

^ Top of page

Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
None