Orbital angular momentum (OAM) mode division multiplexing (MDM) has emerged as a new multiplexing technology that can significantly increase transmission capacity. In addition, probabilistic shaping (PS) is a well-established technique that can increase the transmission capacity of an optical fiber to close to the Shannon limit. However, both the mode coupling and the nonlinear impairment lead to a considerable gap between the OAM-MDM channel and the conventional additive white Gaussian noise (AWGN) channel, meaning that existing PS technology is not suitable for an OAM-MDM intensity-modulation direct-detection (IM-DD) system. In this paper, we propose a Bayesian generative adversarial network (BGAN) emulator based on an end-to-end (E2E) learning strategy with probabilistic shaping (PS) for an OAM-MDM IM/DD transmission with two modes. The weights and biases of the BGAN emulator are treated as a probability distribution, which can be accurately matched to the stochastic nonlinear model of OAM-MDM. Furthermore, a BGAN emulator based on an E2E learning strategy is proposed to find the optimal probability distribution of PS for an OAM-MDM IM/DD system. An experiment was conducted on a 200 Gbit/s two OAM modes carrierless amplitude phase-32(CAP-32) signal over a 5 km ring-core fiber transmission, and the results showed that the proposed BGAN emulator outperformed a conventional CGAN emulator, with improvements in modelling accuracy of 29.3% and 26.3% for the two OAM modes, respectively. Moreover, the generalized mutual information (GMI) of the proposed E2E learning strategy outperformed the conventional MB distribution and the CGAN emulator by 0.31 and 0.33 bits/symbol and 0.16 and 0.2 bits/symbol for the two OAM modes, respectively. Our experimental results demonstrate that the proposed E2E learning strategy with the BGAN emulator is a promising candidate for OAM-MDM IM/DD optic fiber communication.
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http://dx.doi.org/10.1364/OE.502563 | DOI Listing |
J Eval Clin Pract
February 2025
Akşehir Kadir Yallagöz Health School, Selcuk University, Konya, Türkiye.
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Methods: Seventy third-year nursing students from a state university in Türkiye participated in the study. They were split into two groups: the experimental group, which received care plans based on AI, and the control group, which received traditional instruction.
Sci Rep
December 2024
School of Basic Medicine, Dali University, Dali, 671003, Yunnan, China.
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December 2024
School of Business, Shanghai Dianji University, Shanghai, China.
Rural Revitalization (RR) is a key national strategy in China aimed at sustainable rural development and has gained significant attention. Given the unique characteristics of different villages, understanding differentiated paths to achieve RR is essential. This study introduces a new "5I Framework" (INDUS-INHAB-INDOC-INFRA-INCOM) to assess RR's overall development status (ODS) and differentiated paths.
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December 2024
College of Electronic Engineering, National University of Defense Technology, Hefei, 230000, China.
Spectrum sensing is a key technology and prerequisite for Transform Domain Communication Systems (TDCS). The traditional approach typically involves selecting a working sub-band and maintaining it without further changes, with spectrum sensing being conducted periodically. However, this approach presents two main issues: on the one hand, if the selected working band has few idle channels, TDCS devices are unable to flexibly switch sub-bands, leading to reduced performance; on the other hand, periodic sensing consumes time and energy, limiting TDCS's transmission efficiency.
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December 2024
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition.
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