Description
Fourier transform, convolution. Sampling theorem. Applications to imaging: descriptors of spatial resolution, filtering. Correlation, noise power. Discrete Fourier transform, FFT. Filtering of noisy signals. Image reconstruction in computed tomography and magnetic resonance. Laplace transform. Integral transforms, application to boundary value problems.
Prerequisites
- Prerequisite(s): MATH 3705 , or permission of the Department.
Conditions and arrangements
- Prerequisite(s): MATH 3705 , or permission of the Department.
- Also offered at the graduate level, with different requirements, as PHYS 5313 , for which additional credit is precluded.
- Lectures three hours a week.
Reference text in its original language
Fourier transform, convolution. Sampling theorem. Applications to imaging: descriptors of spatial resolution, filtering. Correlation, noise power. Discrete Fourier transform, FFT. Filtering of noisy signals. Image reconstruction in computed tomography and magnetic resonance. Laplace transform. Integral transforms, application to boundary value problems.
- Prerequisite(s): MATH 3705 , or permission of the Department.
- Also offered at the graduate level, with different requirements, as PHYS 5313 , for which additional credit is precluded.
- Lectures three hours a week.
Sources and references
Dates and sources are retained to help you verify the information. Translations are provided to facilitate reading; the official source governs conditions and requirements.
Source reference : https://calendar.carleton.ca/undergrad/courses/PHYS/