For semester, masters, and internship projects visit the Project Offers section.
‘Imaging Optics (Micro-421)’
Prof. Demetri Psaltis
Nyazi Ulas Dinç
Give a tool for the treatment of electromagnetic wave propagation in linear media for imaging purposes. The student will be able to implement the Beam Propagation Method in MATLAB and simulate the contents of the course.
From Maxwell’s equation to beam propagation methods (BPM)
Near field. Propagation of plane waves, Gaussian beams, periodic structures and non-diffracting beams
Relationship to classical diffraction integrals
Thin transparencies, lenses, imaging
Imaging systems, Point Spread Function (PSF)
Optical resolution, confocal and super resolution microscopy techniques.
Coherence, interferometry, OCT
Imaging 3D objects, tomography
Required prior knowledge
Fundamentals of optics and electromagnetism
Ex cathedra, exercises and simulations using MATLAB or Python according to student’s preference
‘Deep Learning for Optical Imaging (Micro-723)’
Prof. Demetri Psaltis
Amirhossein Saba and Ilker Oguz
This course will focus on the practical implementation of artificial neural networks (ANN) using the open-source
TensorFlow machine learning library developed by Google for Python.
This course will focus on the practical implementation of artificial neural networks (ANN) using the open-source TensorFlow machine learning library developed by Google for Python. After a brief introduction to deep neural networks, the course will focus on the use and functionality of TensorFlow, and how it can be used to build models of different complexity for different types of optical imaging applications. Models will range from simple linear regression to convolutional neural networks (CNN) for image classification and mapping. The course will be assessed through coursework and group projects where the students will apply TensorFlow to specific machine learning applications.
Deep learning, TensorFlow, Artificial neural networks, Imaging
Proficiency in Python, basic optics
MICRO-567 Optical Wave Proagation
Important concepts to start the course
Python familiarity, linear systems, basic optics
By the end of the course, the student must be able to:
- Choose A computational imaging model
- Structure The database for training artificial neural networks
- Implement Artifical neural networks using the TensorFlow machine learning library.
1 hour/week lecture
1 hour/week interactive artificial neural network develoment for selected problems
Expected student activities
Attend lectures weekly
Attend exercise sessions
Participate in a class project
Turn in homework every two weeks
Class notes will be posted on Moodle