Federated learning-based wavelength demodulation system for multi-point distributed multi-peak FBG sensors.

Machine learning-based demodulation of multi-peak fiber Bragg grating (FBG) sensors has been extensively studied, demonstrating superior performance compared to conventional algorithms because it can neglect potential physical constraints. As the number of real-world monitoring points increases, the volume of fiber-optic sensing data grows exponentially. This necessitates aggregating data from various regions (e.

Ensemble learning for retinal disease recognition under limited resources.

Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approaches has gained extensive traction for executing these analysis tasks, demonstrating remarkable performance compared to labor-intensive manual analyses.

PDMS diaphragm based miniature fiber-optic Fabry-Perot dynamic pressure sensor for turbomachinery application.

Small-sized, highly sensitive dynamic pressure sensors are crucial in the field of turbomachinery application. In this paper, a fiber-tip structure dynamic pressure sensor utilizing a small piece of glass tube as the air cavity and PDMS material as the diaphragm was fabricated. It has the advantage of being small in size with the diameter of 125 µm while having high sensitivity of 26.

Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia From Chest X-Ray Images.

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice.

High-efficiency FBG array sensor interrogation system via a neural network working with sparse data.

FBG array sensors have been widely used in the multi-point monitoring of large structures due to their excellent optical multiplexing capability. This paper proposes a cost-effective demodulation system for FBG array sensors based on a Neural Network (NN). The stress variations applied to the FBG array sensor are encoded by the array waveguide grating (AWG) as transmitted intensities under different channels and fed to an end-to-end NN model, which receives them and simultaneously establishes a complex nonlinear relationship between the transmitted intensity and the actual wavelength to achieve absolute interrogation of the peak wavelength.

Efficient calculation of optical properties of suspended-core fiber via a machine learning algorithm.

Growing nonlinearity demands in mid-infrared applications place more outstanding requirements on fiber structure design. Chalcogenide suspended-core fibers (SCFs) are considered excellent candidates for mid-infrared applications due to their significant advantages in nonlinearity and dispersion management. However, traditional numerical methods for accurate modeling and optimization of SCFs often rely on the performance of computing devices and have many limitations when dealing with complex models.

Fabry-Perot interferometric sensor demodulation system utilizing multi-peak wavelength tracking and neural network algorithm.

For FPI sensor demodulation systems to be used in actual engineering measurement, they must have high performance, low cost, stability, and scalability. Excellent performance, however, necessitates expensive equipment and advanced algorithms. This research provides a new absolute demodulation system for FPI sensors that is high-performance and cost-effective.

Semi-Supervised Deep Learning Model for Efficient Computation of Optical Properties of Suspended-Core Fibers.

Suspended-core fibers (SCFs) are considered the best candidates for enhancing fiber nonlinearity in mid-infrared applications. Accurate modeling and optimization of its structure is a key part of the SCF structure design process. Due to the drawbacks of traditional numerical simulation methods, such as low speed and large errors, the deep learning-based inverse design of SCFs has become mainstream.

Cost-effective improvement of the performance of AWG-based FBG wavelength interrogation via a cascaded neural network.

Fiber Bragg grating (FBG) sensors have been widely applied in various applications, especially for structural health monitoring. Low cost, wide range, and low error are necessary for an excellent performance FBG sensor signal demodulation system. Yet the improvement of performance is commonly accompanied by costly and complex systems.

Medical Internet of Things to Realize Elderly Stroke Prevention and Nursing Management.

Stroke is a major disease that seriously endangers the lives and health of middle-aged and elderly people in our country, but its implementation of secondary prevention needs to be improved urgently. The application of IoT technology in home health monitoring and telemedicine, as well as the popularization of cloud computing, contributes to the early identification of ischemic stroke and provides intelligent, humanized, and preventive medical and health services for patients at high risk of stroke. This article clarifies the networking structure and networking objects of the rehabilitation system Internet of Things, clarifies the functions of each part, and establishes an overall system architecture based on smart medical care; the design and optimization of the mechanical part of the stroke rehabilitation robot are carried out, as well as kinematics and dynamic analysis.

Variation Patterns of the Volatiles during Germination of the Foxtail Millet (: The Relationship between the Volatiles and Fatty Acids in Model Experiments.

Functional and nutritional compounds are increased during foxtail millet germination while bad smell is produced due to the fatty acid oxidation. To eliminate the unpleasant aroma, the origins of the volatiles must be known. A comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry showed forty-nine volatiles containing 8 ketones, 10 aldehydes, 20 alkanes, 4 alcohols, 5 alkenes, and 2 furans were tentatively identified, and they increased during the germination of the foxtail millet.

15-oxoeicosatetraenoic acid mediates monocyte adhesion to endothelial cell.

Background: A great number of studies reported that 12/15-lipoxygenase (12/15-LO) played an important role in atherosclerosis. And its arachidonic acid(AA) metabolite, 15(S)-hydroperoxy-5,8,11,13-(Z,Z,Z,E)-eicosatetraenoic acid (15(S)-HETE), is demonstrated to mediate endothelial dysfunction. 15-oxo-5,8,11,13-(Z,Z,Z,E)-eicosatetraenoic acid (15-oxo-ETE) was formed from 15-hydroxyprostaglandin dehydrogenase (PGDH)-mediated oxidation of 15(S)-HETE.