We experimentally implement an optical algorithm for integration of a real-valued bivariate function. A user-defined function is encoded in the position-dependent phase of one of the polarization components of an optical beam. The integral of this function is retrieved by measuring a Stokes parameter of the polarization. We analyze the performance of the system as an integration device.
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http://dx.doi.org/10.1364/JOSAA.31.000704 | DOI Listing |
Sci Rep
December 2024
Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Mathematics, Faculty of Science, Jazan University, Jazan, 45142, Saudi Arabia.
It is widely recognized that fuzzy number theory relies on the characteristic function. However, within the fuzzy realm, the characteristic function transforms into a membership function contingent upon the interval [0,1]. This implies that real numbers and intervals represent exceptional cases of fuzzy numbers.
View Article and Find Full Text PDFBMC Health Serv Res
November 2024
Computing Science, University of Alberta, 116 St & 85 Ave, Edmonton, AB, T6G 2R3, Canada.
Background: The rate of 30-day all-cause hospital readmissions can affect the funding a hospital receives. An accurate and reliable readmission prediction model could save money and increase quality-of-care. Few projects have explored formulating this task as a survival prediction problem, where models can exploit a real-valued time-to-readmission target.
View Article and Find Full Text PDFBiomimetics (Basel)
October 2024
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, China.
Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. The dissected spike-coding process may result in information loss, leading to degenerated performance.
View Article and Find Full Text PDFComput Methods Programs Biomed
November 2024
Department of Biomedical Engineering & Physics, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands.
Background And Objectives: Artificial intelligence (AI) is revolutionizing Magnetic Resonance Imaging (MRI) along the acquisition and processing chain. Advanced AI frameworks have been applied in various successive tasks, such as image reconstruction, quantitative parameter map estimation, and image segmentation. However, existing frameworks are often designed to perform tasks independently of each other or are focused on specific models or single datasets, limiting generalization.
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