VPS-QMSD configuration in CV-QKD yields the lowest accepted QBER under Erlang-modeled underwater turbulence compared to VPS-QMLD and VPS-HD, with analytical expressions validated by Monte Carlo simulations.
Mffaloc: Csi-based multifeatures fusion adaptive device-free passive indoor fingerprinting localization.IEEE Internet of Things Journal, 11(8):14100–14114
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UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.
citing papers explorer
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CV-QKD over Turbulence Channels with Virtual Photon Subtraction and Quantum Multiple-Symbol Detection for Underwater Quantum Communications
VPS-QMSD configuration in CV-QKD yields the lowest accepted QBER under Erlang-modeled underwater turbulence compared to VPS-QMLD and VPS-HD, with analytical expressions validated by Monte Carlo simulations.
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UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.