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 | Semester 1 (On campus) |
| Domestic Tuition Fees (NZD) | $685.39 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Limited to
- BSc(Hons), PGDipSci, MSc, MAppSc
- Contact
- Teaching staff
Course Co-ordinator: Dr Ashton Bradley
- Textbooks
Textbooks 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:
- 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
- Understand the convolution integral and its relation to the delta function and the superposition principle
- 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
- Find the Fourier transform of generalised functions from the definition
- 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
- Understand the effect of causality on a system transfer function, the Hilbert transform and the Kramers-Kronig relation
- 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
- Be introduced to stationary stochastic processes and be able to calculate the effect of a linear system on the power spectrum of a signal
- Be able to use matched filtering to optimally find signals in noise
