We report the implementation of a liquid crystal-on-silicon, three-dimensional (3-D) diffractive display based on the partial pixel architecture. The display generates multiple stereoscopic images that are perceived as a static 3-D scene with one-dimensional motion parallax in a manner that is functionally equivalent to a holographic stereogram. The images are created with diffraction gratings formed in a thin liquid crystal layer by fringing electric fields from transparent indium tin oxide interdigitated electrodes. The electrodes are controlled by an external drive signal that permits the 3-D scene to be turned on and off. The display has a contrast ratio of 5.8, which is limited principally by optical scatter caused by extraneous fringing fields. These scatter sources can be readily eliminated. The display reported herein is the first step toward a real-time partial pixel architecture display in which large numbers of dynamic gratings are independently controlled by underlying silicon drive circuitry.
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http://dx.doi.org/10.1364/AO.34.003756 | DOI Listing |
Struct Dyn
January 2025
Department of Physics, University of Wisconsin-Milwaukee, 3135 N. Maryland Ave, Milwaukee, Wisconsin 53211, USA.
There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations.
View Article and Find Full Text PDFMeat Sci
January 2025
Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea. Electronic address:
This study evaluated the performance of a deep-learning-based model that predicted cooking loss in the semispinalis capitis (SC) muscle of pork butts using hyperspectral images captured 24 h postmortem. To overcome low-scale samples, 70 pork butts were used with pixel-based data augmentation. Principal component regression (PCR) and partial least squares regression (PLSR) models for predicting cooking loss in SC muscle showed higher R values with multiplicative signal correction, while the first derivative resulted in a lower root mean square error (RMSE).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China.
The Chang'e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole-Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed.
View Article and Find Full Text PDFInt J Biomed Imaging
December 2024
Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE) 576104, Manipal, Karnataka, India.
Generative models, especially diffusion models, have gained traction in image generation for their high-quality image synthesis, surpassing generative adversarial networks (GANs). They have shown to excel in anomaly detection by modeling healthy reference data for scoring anomalies. However, one major disadvantage of these models is its sampling speed, which so far has made it unsuitable for use in time-sensitive scenarios.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Computer Science and Technology, Xinzhou Normal University, Xinzhou, China.
In semantic image segmentation tasks, most methods fail to fully use the characteristics of different scales and levels but rather directly perform upsampling. This may cause some effective information to be mistaken for redundant information and discarded, which in turn causes object segmentation confusion. As a convolutional layer deepens, the loss of spatial detail information makes the segmentation effect achieved at the object boundary insufficiently accurate.
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