Deep Learning for Optimization of Spatial Light Modulators Databases and Information Systems


Spatial Light Modulators can be used to modulate the effective shape of light, e.g., a laser beam. They are thus useful in industrial applications like laser cutting where beam shapes need to be adapted quickly. However, generating complex shapes is time-consuming and error-prone using current algorithms based on Fourier transformations. In this work, a deep learning approach that automatically generates the correct modulations to obtain the desired shape should be explored. The Thesis is conducted in cooperation with the company LIDROTEC(

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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