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http://dx.doi.org/10.1016/j.xinn.2025.100868 | DOI Listing |
ACS Sens
September 2025
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, 999077, China.
Detecting multicomponent gases over extensive concentration ranges with laser spectroscopy faces challenges of complex configurations, intricate spectral analysis, and reduced accuracy. Neural networks offer transformative potential for advancing laser spectroscopy by facilitating real-time optimization and automation of experimental processes. Here, we report a frequency-modulated continuous-wave (FMCW) spectroscopic system enhanced by a feedforward neural network (FNN) algorithm.
View Article and Find Full Text PDFSensors (Basel)
August 2025
Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China.
With the advancement of modern technologies, the digitization of metering data has significantly improved the efficiency and accuracy of data collection, analysis, and management. However, the growing prevalence of data tampering techniques has raised serious concerns regarding the trustworthiness and integrity of such data. To address this challenge, this study proposes an improved SM2 digital signature algorithm enhanced with high-precision time information to strengthen the reliability of metering data.
View Article and Find Full Text PDFDaytime star detection represents a significant advancement over traditional methods, with applications in astronomical navigation, atmospheric inversion, and satellite-ground communication. However, daylight conditions impose challenges such as limited exposure time, elevated background noise, and pronounced atmospheric turbulence. These factors reduce the accuracy, success rates, and adaptability of traditional star point extraction algorithms, directly affecting the performance of attitude and orientation systems.
View Article and Find Full Text PDFHigh precision extrasolar planet detection based on the radial velocity (RV) method has important scientific significance for studying planet formation, galaxy evolution, and exploring the origin of life and extraterrestrial civilizations. The asymmetric common-path coherent-dispersion spectrometer (CODES) has great potential in the field of exoplanet detection due to its high stability and high throughput. However, the non-ideal characteristics of the telescope and limitation of the detector resolution will cause the problem of uneven distribution of received starlight intensity and the sub-pixel Doppler shift.
View Article and Find Full Text PDFEstimation of primary productivity (PP) and particulate organic carbon (POC) in inland waters is of significant importance in understanding the global climate change and carbon cycle. In this study, we developed an enhanced PP inversion model based on a large number of lidar (light detection and ranging) profiles, in-situ observations, and satellite remote sensing data, which enabled us to obtain the vertical profile distribution of PP in water bodies along the lidar tracks of 400 km. Concurrently, we established a novel model for retrieving the POC applicable to inland waters based on a considerable number of lidar profiles, in-situ observations, and organic element analyzer measurements, which allowed us to obtain the vertical profile distribution of POC along the lidar tracks of 400 km.
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