Achromatic CMOS-Integrated Four-Bit Orbital Angular Momentum Mode Detector at Three Wavelengths.

The simultaneous detection of the orbital angular momentum (OAM) and wavelength offers new opportunities for optical multiplexing. However, because of the dispersion of lens functions for Fourier transformation, the mode conversions at distinct wavelengths cannot be achieved in the same plane. Here we propose an ultracompact achromatic complementary metal oxide semiconductor (CMOS)-integrated OAM mode detector. Specifically, a spatial multiplexed scheme, randomly interleaving the phase distributions for distributing the superposed OAM modes into preset positions at distinct wavelengths, is presented. In addition, such a nanoprinted achromatic OAM detector featuring a microscale size and a short focal length can be integrated onto a CMOS chip. Consequently, the four-bit incident light beams at three discrete wavelengths (633, 532, and 488 nm) can be distinguished with a high degree of accuracy evaluated by the average standardized Euclidean distance of ∼0.75 between the analytical and target results. Our results showcase a miniaturized platform for achieving high-capacity information processing.

Hotspots and trends in health-oriented physical literacy research: a visual analysis based on the WOS database.

BACKGROUND: The World Health Organization has proposed that physical activity is a meaningful way to improve the quality of human life and reduce the probability of chronic non-communicable diseases and that humans should change their mindset from the actual effectiveness of physical activity in promoting health to the new view that "physical activity makes life more meaningful." The introduction and development of physical literacy reveal the critical role of physical activity in improving human health and the importance of human initiative in physical activity for healthy development. Therefore, the objectives of this paper are (1) to conduct a bibliometric analysis of the literature on physical literacy, assessing the scope, frequency, and geographical distribution of research publications from various countries and institutions from 2015 to 2023; (2) to visualize keywords in articles on the topic of Physical literacy to analyze whether there is a link between physical literacy and health, and (3) based on the results of the visual analysis, we propose that proper health is built on the sense of physical literacy and further construct the circular path of physical literacy, physical activity, and physical health improvement. METHODS: Using VOSviewer software v.1.6.18, this study searched the core collection of the Web of Science database from 2015 to April 15, 2023, using "physical literacy" as a keyword to explore the current international research on physical literacy. RESULTS: A total of 3,446 articles were included, and a correlation map was derived based on the co-occurrence frequency of keywords, which showed that physical literacy was highly correlated with six concepts: health literacy, physical activity, health, children, adolescents, and prevention. CONCLUSION: Based on the analysis of literature visualization techniques, there is a high correlation between physical literacy and health, and international physical literacy research is in a trend of multi-point amplification, with research hotspots gradually shifting from the field of sports to the field of health and closely related to the field of health, indicating that physical literacy aims to promote the achievement of individual health by driving humans to increase physical activity.

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding.

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed, ultrawide bandwidths and low energy consumption because the optical dimensions (time, space, wavelength, and polarization) could be utilized to increase the degree of freedom. However, due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain, the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here, we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses. The proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction, and deep-learning diffractive layers as a classifier. The resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding, leading to an accuracy as high as 97.2% for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes, as well as a resistance to eavesdropping in point-to-point free-space transmission. Moreover, through extending the target encoded modes into multiplexed OAM states, we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%. Our work provides a deep insight to the mechanism of machine learning with spatial modes basis, which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.