Wavefront shaping and deep learning in fiber endoscopy
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Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...
Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...
For the EU DEMO conductor testing, a temperature sensor based on Fiber Bragg Grating (FBG) optical fiber is studied at the EPFL Swiss Plasma Center. The SULTAN test facility has been upgraded to use fiber optic sensor for temperature monitoring and quench ...
To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...
The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...
Aerodynamic shape optimization (ASO) is a key technique in aerodynamic designs, aimed at enhancing an object’s physical performance while adhering to specific constraints. Traditional parameterization methods for ASO often require substantial manual tuning ...
Soft actuators with a function of variable stiffness are beneficial to the improvement of the adaptability of robots, expanding the application areas and environments. We propose a tendon-driven soft bending actuator that can change its stiffness using fib ...
In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performa ...
Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...
Previously, space-time wave packets (STWPs) have been gen-erated in free space with reduced diffraction and a tunable group velocity by combining multiple frequency comb lines each carrying a single Bessel mode with a unique wave num-ber. It might be poten ...