Recently, it has been demonstrated that a nonlinear spatial filter using second harmonic generation can implement a visible edge enhancement under invisible illumination, and it provides a promising application in biological imaging with light-sensitive specimens. But with this nonlinear spatial filter, all phase or intensity edges of a sample are highlighted isotropically, independent of their local directions. Here we propose a vectorial one to cover this shortage. Our vectorial nonlinear spatial filter uses two cascaded nonlinear crystals with orthogonal optical axes to produce superposed nonlinear vortex filtering. We show that with the control of the polarization of the invisible illumination, one can highlight the features of the samples in special directions visually. Moreover, we find the intensity of the sample arm can be weaker by two orders of magnitude than the filter arm. This striking feature may offer a practical application in biological imaging or microscopy, since the light field reflected from the sample is always weak. Our work offers an interesting way to see and emphasize the different directions of edges or contours of phase and intensity objects with the polarization control of the invisible illumination.
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http://dx.doi.org/10.1364/OE.404594 | DOI Listing |
Sensors (Basel)
January 2025
Department of Optical Engineering, Utsunomiya University, 7-2-1 Yoto, Utsunomiya 321-8585, Japan.
We describe the various steps of a gas imaging algorithm developed for detecting, identifying, and quantifying gas leaks using data from a snapshot infrared spectral imager. The spectral video stream delivered by the hardware allows the system to combine spatial, spectral, and temporal correlations into the gas detection algorithm, which significantly improves its measurement sensitivity in comparison to non-spectral video, and also in comparison to scanning spectral imaging. After describing the special calibration needs of the hardware, we show how to regularize the gas detection/identification for optimal performance, provide example SNR spectral images, and discuss the effects of humidity and absorption nonlinearity on detection and quantification.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, China.
Remaining useful life (RUL) prediction is a cornerstone of Prognostic and Health Management (PHM) for power machinery, playing a crucial role in ensuring the reliability and safety of these critical systems. In recent years, deep learning techniques have shown great promise in RUL prediction, providing more reliable and accurate outcomes. However, existing models often struggle with comprehensive feature extraction, especially in capturing the complex behavior of power machinery, where non-linear degradation patterns arise under varying operational conditions.
View Article and Find Full Text PDFBiosensors (Basel)
January 2025
Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.
Hyperspectral imaging (HSI) technology, which offers both spatial and spectral information, holds significant potential for enhancing diagnostic performance during endoscopy and other medical procedures. However, quantitative evaluation of HSI cameras is challenging due to various influencing factors (e.g.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
College of Information Science and Engineering, Hohai University, Nanjing 211100, China.
(1) Background: At present, the bio-inspired visual neural models have made significant achievements in detecting the motion direction of the translating object. Variable contrast in the figure-ground and environmental noise interference, however, have a strong influence on the existing model. The responses of the lobula plate tangential cell (LPTC) neurons of Drosophila are robust and stable in the face of variable contrast in the figure-ground and environmental noise interference, which provides an excellent paradigm for addressing these challenges.
View Article and Find Full Text PDFBrain Sci
December 2024
Aviation Psychology Research Office, Air Force Medical Center, Fourth Military Medical University, Beijing 100142, China.
Background: Spatial working memory is crucial for processing visual and spatial information, serving as a foundation for complex cognitive tasks. However, the effects of prolonged sleep deprivation on its dynamics and underlying neural mechanisms remain unclear. This study aims to investigate the specific trends and neural mechanisms underlying spatial working memory alterations during 36 h of acute sleep deprivation.
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