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Photonics, Volume 8, Issue 9 (September 2021) – 61 articles

Cover Story (view full-size image): Knowledge of the scattering and absorption parameters of biological tissue in a broad wavelength range is important for a variety of light-based diagnostic and therapeutical applications, such as for treatment planning in photodynamic therapy, for understanding microscopical images, and for non-invasive measurements of vital parameters. In this paper, we present spectra of the effective scattering coefficient µs’ and absorption coefficient µa of different porcine sample types for wavelengths between 400 nm and 1400 nm obtained with integrating sphere measurements and solutions of the radiative transport equation. The derived spectra were applied to quantify the concentration of relevant tissue chromophores, for example, oxy- and deoxyhemoglobin, collagen, water, and fat. View this paper
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12 pages, 1173 KiB  
Article
Characterization of Collagen I Fiber Thickness, Density, and Orientation in the Human Skin In Vivo Using Second-Harmonic Generation Imaging
by Marius Kröger, Johannes Schleusener, Sora Jung and Maxim E. Darvin
Photonics 2021, 8(9), 404; https://doi.org/10.3390/photonics8090404 - 21 Sep 2021
Cited by 11 | Viewed by 3076
Abstract
The assessment of dermal alterations is necessary to monitor skin aging, cancer, and other skin diseases and alterations. The gold standard of morphologic diagnostics is still histopathology. Here, we proposed parameters to distinguish morphologically different collagen I structures in the extracellular matrix and [...] Read more.
The assessment of dermal alterations is necessary to monitor skin aging, cancer, and other skin diseases and alterations. The gold standard of morphologic diagnostics is still histopathology. Here, we proposed parameters to distinguish morphologically different collagen I structures in the extracellular matrix and to characterize varying collagen I structures in the skin with similar SAAID (SHG-to-AF Aging Index of Dermis, SHG—second-harmonic generation; AF—autofluorescence) values. Test datasets for the papillary and reticular extracellular matrix from images in 24 female subjects, 36 to 50 years of age, were generated. Parameters for SAAID, edge detection, and fast Fourier transformation directionality were determined. Additionally, textural analyses based on the grey level co-occurrence matrix (GLCM) were conducted. At first, changes in the GLCM parameters were determined in the native greyscale images and, furthermore, in the Hilbert-transformed images. Our results demonstrate a robust set of parameters for noninvasive in vivo classification for morphologically different collagen I structures in the skin, with similar and different SAAID values. We anticipate our method to enable an automated prevention and monitoring system with an age- and gender-specific algorithm. Full article
(This article belongs to the Special Issue Tissue Optics)
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12 pages, 859 KiB  
Article
Variant-Coherence Gaussian Sources
by Franco Gori and Massimo Santarsiero
Photonics 2021, 8(9), 403; https://doi.org/10.3390/photonics8090403 - 21 Sep 2021
Cited by 4 | Viewed by 1471
Abstract
The celebrated Gaussian Schell model source with its shift-invariant degree of coherence may be the basis for devising sources with space-variant properties in the spirit of structured coherence. Starting from superpositions of Gaussian Schell model sources, we present two classes of genuine cross-spectral [...] Read more.
The celebrated Gaussian Schell model source with its shift-invariant degree of coherence may be the basis for devising sources with space-variant properties in the spirit of structured coherence. Starting from superpositions of Gaussian Schell model sources, we present two classes of genuine cross-spectral densities whose degree of coherence varies across the source area. The first class is based on the use of the Laplace transform while the second deals with cross-spectral densities that are shape-invariant upon paraxial propagation. For the latter, we present a set of shape-invariant cross-spectral densities for which the modal expansion can be explicitly found. We finally solve the problem of ascertain whether an assigned cross-spectral density is shape-invariant by checking if it satisfies a simple differential constraint. Full article
(This article belongs to the Special Issue Structured Light Coherence)
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16 pages, 3390 KiB  
Article
Going Deeper into OSNR Estimation with CNN
by Fangqi Shen, Jing Zhou, Zhiping Huang and Longqing Li
Photonics 2021, 8(9), 402; https://doi.org/10.3390/photonics8090402 - 20 Sep 2021
Cited by 7 | Viewed by 2050
Abstract
As optical performance monitoring (OPM) requires accurate and robust solutions to tackle the increasing dynamic and complicated optical network architectures, we experimentally demonstrate an end-to-end optical signal-to-noise (OSNR) estimation method based on the convolutional neural network (CNN), named OptInception. The design principles of [...] Read more.
As optical performance monitoring (OPM) requires accurate and robust solutions to tackle the increasing dynamic and complicated optical network architectures, we experimentally demonstrate an end-to-end optical signal-to-noise (OSNR) estimation method based on the convolutional neural network (CNN), named OptInception. The design principles of the proposed scheme are specified. The idea behind the combination of the Inception module and finite impulse response (FIR) filter is elaborated as well. We experimentally evaluate the mean absolute error (MAE) and root-mean-squared error (RMSE) of the OSNR monitored in PDM-QPSK and PDM-16QAM signals under various symbol rates. The results suggest that the MAE reaches as low as 0.125 dB and RMSE is 0.246 dB in general. OptInception is also proved to be insensitive to the symbol rate, modulation format, and chromatic dispersion. The investigation of kernels in CNN indicates that the proposed scheme helps convolutional layers learn much more than a lowpass filter or bandpass filter. Finally, a comparison in performance and complexity presents the advantages of OptInception. Full article
(This article belongs to the Section Optical Communication and Network)
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