For semester, masters, and internship projects visit the Project Offers section.
2022 Spring
‘Imaging Optics (Micro-421)’
Instructor
Prof. Demetri Psaltis
Teaching assistant
Nyazi Ulas Dinç
Objective
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.
Contents
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.
Ray Optics
Waveguides, endoscopy
Coherence, interferometry, OCT
Holography
Imaging 3D objects, tomography
Required prior knowledge
Fundamentals of optics and electromagnetism
Teaching
Ex cathedra, exercises and simulations using MATLAB or Python according to student’s preference
2022 Spring
‘Deep Learning for Optical Imaging (Micro-723)’
Instructor
Prof. Demetri Psaltis
Teaching assistant
Amirhossein Saba and Ilker Oguz
Summary
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.
Contents
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.
Keywords
Deep learning, TensorFlow, Artificial neural networks, Imaging
Learning Prerequisites
Required courses
Proficiency in Python, basic optics
Recommended courses
MICRO-567 Optical Wave Proagation
Important concepts to start the course
Python familiarity, linear systems, basic optics
Learning Outcomes
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.
Teaching methods
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
Assessment methods
Homeworks
Project report
Resources
Bibliography
Tensor flow
Notes/Handbook
Class notes will be posted on Moodle