RESUMEN
Computational micro-spectrometers comprised of detector arrays and encoding structure arrays, such as on-chip Fabry-Perot (FP) cavity filters, have great potential in many in-situ applications owing to their compact size and snapshot imaging ability. Given manufacturing deviation and environmental influence are inevitable, easy and effective calibration for spectrometer is necessary, especially for in-situ applications. Currently calibration strategies based on iterative algorithms or neural networks require accurate measurements of pixel-level (spectral) encoding functions through monochromator or large amounts of standard samples. These procedures are time-consuming and expensive, thereby impeding in-situ applications. Meta-learning algorithms with few-shot learning ability can address this challenge by incorporating the prior knowledge in the simulated dataset. In this work, we propose a meta-learning algorithm free of measuring encoding function or large amounts of standard samples to calibrate a micro-spectrometer with manufacturing deviation effectively. Our micro-spectrometer comprises 16 types of FP filters covering a wavelength range of 550-720â nm. The center wavelength of each filter type deviates from the design up to 6â nm. After calibration with 15 different color data, the average reconstruction error on the test dataset decreased from 7.2 × 10-3 to 1.2 × 10-3, and further decreased to 9.4 × 10-4 when the calibration data increased to 24. The performance is comparable to algorithms trained with measured encoding function both in reconstruction error and generalization ability. We estimated that the cost of in-situ calibration through reflectance measurements of color chart decreased to one percent of the cost through monochromator measurements. By exploiting prior deviation information in simulation data with meta-learning, the efficiency and cost of calibration are significantly improved, thereby facilitating the large-scale production and in-situ application of micro-spectrometers.
RESUMEN
High-precision temperature control of large-area blackbodies has a pivotal role in temperature calibration and thermal imaging correction. Meanwhile, it is necessary to correct the temperature difference between the radiating (surface of use) and back surfaces (where the temperature sensor is installed) of the blackbody during the testing phase. Moreover, large-area blackbodies are usually composed of multiple temperature control channels, and manual correction in this scenario is error-prone and inefficient. At present, there is no method that can achieve temperature-automated calibration for a large-area blackbody radiation source. Therefore, this article is dedicated to achieving temperature-automated calibration for a large-area blackbody radiation source. First, utilizing two calibrated infrared thermometers, the optimal temperature measurement location was determined using a focusing algorithm. Then, a three-axis movement system was used to obtain the true temperature at the same measurement location on a large-area blackbody surface from different channels. This temperature was subtracted from the blackbody's back surface. The temperature difference was calculated employing a weighted algorithm to derive the parameters for calibration. Finally, regarding experimental verification, the consistency error of the temperature measurement point was reduced by 85.4%, the temperature uniformity of the surface source was improved by 40.4%, and the average temperature measurement deviation decreased by 43.8%. In addition, this system demonstrated the characteristics of strong environmental adaptability that was able to perform temperature calibration under the working conditions of a blackbody surface temperature from 100 K to 573 K, which decreased the calibration time by 9.82 times.
RESUMEN
The blood-brain barrier (BBB) is the main obstacle to hydrophilic and large molecules to enter the brain, maintaining the stability of the central nervous system (CNS). But many environmental factors may affect the permeability and structure of the BBB. Electromagnetic pulses (EMP) irradiation has been proven to enhance the permeability of the BBB, but the specific mechanism is still unclear. To explore the potential mechanism of EMP-induced BBB opening, this study investigated the permeability, fine structure and the proteins expression of the tight junction (TJ) of the BBB in the rats exposed to EMP. Using the leakage of fluorescein isothiocyanate-labeled dextran with different molecular mass under different field intensity of EMP exposure, we found that the tracer passing through the BBB is size-dependent in the rat exposed to EMP as field intensity increased. Transmission electron microscopy showed TJ of the endothelial cells in the EMP-exposed group was open, compared with the sham-irradiated group. But the levels of TJ proteins including ZO-1, claudin-5, or occludin were not changed as indicated by western blot. These data suggest that EMP induce BBB opening in a field intensity-dependent manner and probably through dysfunction of TJ proteins instead of their expression. Our findings increase the understanding of the mechanism for EMP working on the brain and are helpful for CNS protection against EMP.
