In order to achieve online non-destructive testing of mold growth and detection on wooden cultural relics, this paper proposes a reflective fiber optic sensor composed of one transmitting fiber and six inclined receiving fibers. The sensor is used to conduct experimental research on the growth of Trichoderma longibrachiatum and Cladosporium cultivated on wooden samples, and the relationship between the spectral information, absorbance, and growth height of the two molds is obtained. The experimental results indicate that the sensor can identify and accurately measure mold on the surface of wooden cultural relics, and the proposed sensor has good application prospects in the field of cultural relic detection.
X-ray fluorescence computed tomography (XFCT), as a non-invasive imaging technique, has attracted much attention for its simulation and reconstruction. In this study, we built a simulation model of cone-beam X-ray fluorescence CT using Geant4 and simulated the propagation and interaction process of X-rays in the phantom by Monte Carlo simulation. Then, we acquired the projection data and used the FDK (Feldkamp-Davis-Kress) resolution algorithm to reconstruct the images in three dimensions. The results show that cone-beam X-ray fluorescence CT combined with the FDK algorithm can effectively reconstruct the images, which provides strong support for non-invasive imaging and trace element distribution analysis.
The microscopic evaluation of tissues is greatly aided by histological staining, which plays an important role in diagnostic pathology and life science research, but histological staining is often labor-intensive and expensive. Unlike conventional microscopy methods, multiphoton microimaging does not require any special labeling or staining of the sample prior to observation. Histologically stained pathology images can now be acquired by deep learning thanks to advancements in the field of image generation and image transformation. In this paper, we use deep learning network to perform virtual staining on the acquired multiphoton microscopy images, and the results show that the histological stained images generated by virtual staining can show most of the details in the original images, which is close to the original stained images, and it is feasible to perform virtual staining on multiphoton images using deep learning.
We report a Biosensor Based on Ex-TFG Coated with Molybdenum disulfide. From RI~156.342nm/RIU of bare grating to 167.739nm/RIU, then the antibody test of foot-and-mouth disease antigen detection was carried out. It was calculated that the dissociation constant KD of the sensor was about 1.35×10-9M, and the affinity constant KA was about 7.4×108M-1. The experimental results show that the sensor has good detection effect and good biocompatibility.
In this work, we introduced a sound source localization system based on fiber optic FP cavity microphones using time difference of arrival, and conducted experimental analysis on the positioning accuracy of localization system within a range of 1m×1m. We applied cross correlation to estimate the time difference of arrival, and combined Chan localization algorithm and Taylor localization algorithm to estimate the coordinate position of the sound source. Experimental results indicated that the maximum deviation between the estimated coordinates and the actual sound source coordinates in the x-axis and y-axis directions was less than 2.35 cm.
KEYWORDS: Data modeling, Performance modeling, Evolutionary algorithms, Neural networks, Principal component analysis, Data processing, Spectral data processing, Education and training, Machine learning, Tunable filters
Paper cultural relics are important carriers of splendid history and culture, and have important historical research value. As paper is mainly rich in cellulose, starch and protein, paper cultural relics are prone to mould, insects and other microorganisms in the process of long-term preservation, leading to corrosion, deterioration and even destruction of cultural relics. Fumigation method is currently more widely used in a rapid means of control of cultural relics of mould and mildew, fumigant residue detection is the establishment of a set of scientific fumigation method in an indispensable part. In this paper, for the surface of paper cultural relics there are fumigant residues and no residues of spectral characteristics of the variability, based on the characteristics of spectral nondestructive testing, using BP neural network algorithm, SVM algorithm, KNN algorithm, 1D-CNN algorithm were established to establish discriminatory models, according to the different models of the discrimination accuracy of the model performance assessment, select the optimal modelling method.
The purpose of this study is to present a technique for enhancing multi-rotor spoke microphone array configurations through the utilisation of the Multiple Population Genetic Algorithm (MPGA) to meet the acoustic positioning demands of small UAVs. First, we analyse the pros and cons of both the Simple Genetic Algorithm (SGA) and the MPGA. Then, we construct the objective function using the main lobe width and peak side-lobe level as the optimisation parameters. To simplify the process, we determine the distance from the origin to each array element on a rotating arm and represent these coordinates as an array along the spoke of the rotating arm. This array serves as a sample individual based on the rotational symmetry of multiple rotating arms. The results of the simulation demonstrate that the method effectively enhances the resolution of the array in the UAV noise band by narrowing the main lobe width while maintaining the maximum sub-lobe level. Furthermore, this methodology showcases efficient convergence speed and resilience, emphasising the viability of utilising MPGA for the purpose of microphone array design. The current study introduces an effective optimisation approach for constructing microphone arrays used in acoustic positioning systems for small unmanned aerial vehicles (UAVs). The proposed method improves the performance and accuracy of acoustic localisation systems for UAVs and provides reliable technical support for monitoring and locating UAV missions.
In order to obtain the characteristics of orange penicillium growth on the surface of paper cultural relics, a reflective concave stepped oblique lens fiber optic sensor was developed. And in order to be more in line with the characteristics of paper artefacts, the paper artefacts were subjected to ink dyeing treatment. The sensor was used to monitor and analyse the growth process of Penicillium oryzae on the surface of ink-dyed cotton paper samples and burlap samples, and the structure and height of Penicillium oryzae biofilm were characterized by super depth-of-field microscope. The study shows that the sensor can accurately measure the biofilm height of the growth information of Penicillium oryzae on the surface of ink-dyed paper samples, and the output signal of the sensor has a linear relationship with the biofilm height of Penicillium oryzae.
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.