Courses
The following courses are relevant to our research directions:
Optical Sciences
OPTI 501: Electromagnetic Waves – Vector fields, Maxwell’s equations, electromagnetic field energy, wave equation, polarized light, time average measurement, Fresnel equations, scalar and vector potentials, gauge transformations, dispersion, metal optics, crystal optics, dipole radiation, mathematical formalism of polarized light, guided waves.
OPTI 502: Optical Design and Instrumentation I – Rays and wavefronts, Snell’s Law, mirror and prism systems, Gaussian imagery and cardinal points, paraxial ray tracing, stops and pupils, illumination systems, elementary optical systems, optical materials, dispersion, systems of thin prisms, system analysis using ray trace code, chromatic aberrations and achromatization, monochromatic aberrations, ray fans, spot diagrams, balancing of aberrations, aspheric systems.
OPTI 505R: Diffraction and Interferometry – Interference and interferometry, concepts of coherence, holography, diffraction theory, Fraunhofer and Fresnel diffraction, volume diffraction, Gaussian beam propagation, optical transfer function, speckle.
OPTI 508: Probability and Statistics in Optics – Probability theory, stochastic processes, noise, statistical optics, information theory, hypothesis testing, estimation, restoration.
OPTI 511R: Laboratory – Optical Physics and Lasers – Fundamental concepts of quantum mechanics; application to model quantum systems; interaction of light with atoms; perturbation theory; two-level atom approximation; nonlinear optics; pulsed and CW laser operation; thermal sources; optical detectors.
OPTI 512R: Linear Systems and Fourier Transforms – Mathematical background, convolution, the Fourier transform, linear filtering and sampling, two-dimensional operations, diffraction, image formation.
OPTI 636: Noise in Imaging Systems – Development of mathematical tools for describing stochastic processes in single optical detectors and complex imaging systems; understanding the effect of image processing and reconstruction algorithms on image noise; development of a quantitative approach to assessing and optimizing image quality.
OPTI 637: Principles of Image Science – Mathematical description of imaging systems and noise; introduction to inverse problems; introduction to statistical decision theory; prior information; image reconstruction and radon transform; image quality; applications in medical imaging; other imaging systems.