Semiconductor Multimaterial Optical Fibers for Biomedical Applications.

Integrated sensors and transmitters of a wide variety of human physiological indicators have recently emerged in the form of multimaterial optical fibers. The methods utilized in the manufacture of optical fibers facilitate the use of a wide range of functional elements in microscale optical fibers with an extensive variety of structures. This article presents an overview and review of semiconductor multimaterial optical fibers, their fabrication and postprocessing techniques, different geometries, and integration in devices that can be further utilized in biomedical applications. Semiconductor optical fiber sensors and fiber lasers for body temperature regulation, in vivo detection, volatile organic compound detection, and medical surgery will be discussed.

Convolutional neural network for apple bruise detection based on hyperspectral.

The timely detection of apple bruises caused by collision and squeeze is of great significance to reduce the economic losses of the apple industry. This study proposed a spectral analysis model (SpectralCNN) based on a one-dimensional convolutional neural network to detect apple bruises. The influences of six spectral preprocessing methods on the SpectralCNN model were firstly analyzed in this paper. Compared with traditional chemometric models, the SpectralCNN model had a better accuracy, which was demonstrated not depend on the spectral preprocessing method by experiment results. Then, 20 characteristic wavelengths could be extracted by successive projection algorithm. The SpectralCNN model could achieve an accuracy of 95.79% on the test set of characteristic wavelengths, indicating that the extracted characteristic wavelengths contain most of the features of bruised and healthy pixels.

Intelligent detection of hard seeds of snap bean based on hyperspectral imaging.

As a common problem in snap beans, hard seed has seriously affected the large-scale industrial planting and yield of snap bean. To realize accurate, quick and non-destructive identifying the hard seeds of snap bean is of great significance to avoiding the effects of hard seeds on germination and growth. This research was based on hyperspectral imaging (HSI) to achieve accurate detection of hard seeds of snap bean. This study obtained the characteristic spectra from the hyperspectral image of a single seed, and then combined the synthetic minority over-sampling technique (SMOTE) and Tomek links to balance the numbers of hard and non-hard seed samples. The characteristic wavelengths were extracted from the average spectrum. Then the average spectrum was processed by first derivative (1D). After that, the characteristic wavelengths could be extracted using successive projections algorithm (SPA). Finally, a radial basis function-support vector machine (RBF-SVM) model was established to realize the intelligent detection of hard seeds, and the detection accuracy rate reached 89.32%. The research results showed that HSI technology could achieved accurate, fast and non-destructive testing of the hard seeds of snap bean, which is of great significance to the large-scale and standardized planting of snap bean and increase the yield per unit area.

Biocompatible and Biodegradable Polymer Optical Fiber for Biomedical Application: A Review.

This article discusses recent advances in biocompatible and biodegradable polymer optical fiber (POF) for medical applications. First, the POF material and its optical properties are summarized. Then, several common optical fiber fabrication methods are thoroughly discussed. Following that, clinical applications of biocompatible and biodegradable POFs are discussed, including optogenetics, biosensing, drug delivery, and neural recording. Following that, biomedical applications expanded the specific functionalization of the material or fiber design. Different research or clinical applications necessitate the use of different equipment to achieve the desired results. Finally, the difficulty of implanting flexible fiber varies with its flexibility. We present our article in a clear and logical manner that will be useful to researchers seeking a broad perspective on the proposed topic. Overall, the content provides a comprehensive overview of biocompatible and biodegradable POFs, including previous breakthroughs, as well as recent advancements. Biodegradable optical fibers have numerous applications, opening up new avenues in biomedicine.

Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM.

How to quickly and accurately select sugarbeet seeds with reliable germination is very important to sugarbeet planting. In this study, the hyperspectral images of 3072 sugarbeet seeds of the same variety were collected, and were successively processed by binarization, morphology, contour extraction and so on. The average spectrum of the single seed image was obtained by image segmentation. Comprehensive analysis of the evaluation parameters of the five spectral preprocessing methods revealed that the second derivative (2D) processing was optimal. Successive projections algorithm (SPA) was used to extract 16 characteristic wavelengths. Support vector machine radial basis function (SVM-RBF), k-nearest neighbor (KNN) and random forest (RF) models were established at the full wavelength and characteristic wavelength respectively to predict the germination of sugarbeet seeds. By analyzing the prediction accuracy of the three models, it was found that the SVM-RBF model provided the highest prediction accuracy in the test set (the prediction accuracy of the full wavelength was 95.5%, and the prediction accuracy of the characteristic wavelength was 92.32%). The research results showed that the hyperspectral image processing technology could accurately predict the germination rate of sugarbeet seeds, and realize the rapid and non-destructive prediction of the germination status of sugarbeet seeds.

Parallel-quadrature on-axis phase-shifting common-path interferometer using a polarizing beam splitter.

A common-path parallel-quadrature on-axis phase-shifting interferometry using a modified Michelson configuration with a polarizing cube beam splitter is proposed for quantitative phase measurement. The frequency spectrum of the circularly polarized object beam is split into two beams using a beam splitter. One beam is converted to a 45° linearly polarized beam to act as the object beam, and the other beam is low-filtered by a pinhole mirror to act as the reference beam. Two interferograms with quadrature phase shift can be simultaneously captured by combining the 45° linearly polarized object beam with the circularly polarized reference beam through a 45° tilted polarizing cube beam splitter, and the phase of a specimen can be then retrieved through a two-step phase-shifting algorithm. Experiments are carried out to demonstrate the validity and stability of the proposed method.