The analysis of superpositions of Orbital Angular Momentum (OAM) modes is a challenging problem, particularly when atmospheric turbulence is present or when the phase structure of the wavefront is not available. In such conditions it is not possible to correct the distortions and reconstruct the vorticial phase structure: the rings and petals that characterize the intensity profiles of such beams become deformed and may even lose integrity. These artifacts may compromise the possibility of establishing free-space optical links based on OAM superpositions. We propose using a particular selection of Laguerre-Gauss modes and convolutional neural networks for a reliable classification of superpositions of two modes. The network (based on a pre-trained network AlexNet that combines convolutional and fully-connected layers) is trained as a classifier based on 2-d intensity profiles that can be obtained from a digital camera. For illustrating the proposed method, we used simulations of light beams propagated through L = 1 km with three levels of turbulence: C2n ∈ {2×10-15, 9.24×10-15, 2.9×10-14} m-2/3. The emitted beams are made up of 2 different Laguerre-Gauss modes with OAM between -15 and +15, and radial indices between 0 and 3. Classification results show that the radial index can be used effectively to enlarge the set of information symbols.
Significance: Collagen is the most abundant protein in vertebrates and is found in tissues that regularly experience tension, compression, and shear forces. However, the underlying mechanism of collagen fibril formation and remodeling is poorly understood.
Aim: We explore how a collagen monomer is visualized using fluorescence microscopy and how its spatial orientation is determined. Defining the orientation of collagen monomers is not a trivial problem, as the monomer has a weak contrast and is relatively small. It is possible to attach fluorescence tags for contrast, but the size is still a problem for detecting orientation using fluorescence microscopy.
Approach: We present two methods for detecting a monomer and classifying its orientation. A modified Gabor filter set and an automatic classifier trained by convolutional neural network based on a synthetic dataset were used.
Results: By evaluating the performance of these two approaches with synthetic and experimental data, our results show that it is possible to determine the location and orientation with an error of ∼37 deg of a single monomer with fluorescence microscopy.
Conclusions: These findings can contribute to our understanding of collagen monomers interaction with collagen fibrils surface during fibril formation and remodeling.
Collagen is one of the most important proteins in mammals, conforming most animal tissues. This work explores how a basic collagen monomer unit is visualized using fluorescence microscopy and how its spatial orientation is determined. Defining the orientation of collagen monomers is not a trivial problem, as the particle has a weak contrast and is relatively small. Possible attach fluorescence tags for contrast, but the size is still a problem for detecting orientation using fluorescence microscopy. This document presents a simulation of the visualization of collagen monomers and two methods for detecting monomer and classifying its orientation. A modify Gabor filter set, and an automatic classifier, trained by convolutional neuronal network (CNN), were used. By evaluating the performance of these two approaches compare to human observation, our results show that it is possible to determine the location and orientation of a single monomer with fluorescence microscopy. These findings can contribute to understanding collagen elements as collagen fibril.
Free-space optical communications are highly sensitive to distortions induced by atmospheric turbulence. This is particularly relevant when using orbital angular momentum (OAM) to send information. As current machine learning techniques for computer vision allow for accurate classification of general images, we have studied the use of a convolutional neural network for recognition of intensity patterns of OAM states after propagation experiments in a laboratory. The effect of changes in magnification and level of turbulence were explored. An error as low as 2.39% was obtained for a low level of turbulence when the training and testing data came from the same optical setup. Finally, in this article we suggest data augmentation procedures to face the problem of training before the final calibration of a communication system, with no access to data for the actual magnification and level of turbulence of real application conditions.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.