Elastic optical network (EON) has been proposed recently as a spectrum-efficient optical layer to adapt to rapidly-increasing traffic demands instead of current deployed wavelength-division-multiplexing (WDM) optical network. In contrast with conventional WDM optical cross-connect (OXCs) based on wavelength selective switches (WSSs), the EON OXCs are based on spectrum selective switches (SSSs) which are much more expensive than WSSs, especially for large-scale switching architectures. So the transition cost from WDM OXCs to EON OXCs is a major obstacle to realizing EON. In this paper, we propose and experimentally demonstrate a transition OXC (TOXC) structure based on 2-stage cascading switching architectures, which make full use of available WSSs in current deployed WDM OXCs to reduce number and port count of required SSSs. Moreover, we propose a contention-aware spectrum allocation (CASA) scheme for EON built with the proposed TOXCs. We show by simulation that the TOXCs reduce the network capital expenditure transiting from WDM optical network to EON about 50%, with a minor traffic blocking performance degradation and about 10% accommodated traffic number detriment compared with all-SSS EON OXC architectures.
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Comput Med Imaging Graph
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CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
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Center for Advanced Biomolecular Recognition, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.
During the COVID-19 pandemic, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) has been recognized as the most reliable diagnostic tool. However, there is a need to develop multiplexed assays capable of analyzing multiple genes simultaneously to expand its application. To address this, a multiplexed RT-qPCR using a double emulsion (DE)-based carrier and a polymer microparticle reactor, termed primer-incorporated network tailored with Taqman probe (TaqPIN) is developed.
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Third Institute of Physics - Biophysics, Georg August University, Friedrich-Hund Platz 1, 37077, Göttingen, Germany.
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Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
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View Article and Find Full Text PDFInsect Sci
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