Near-Field Physical-Layer Authentication Under Impersonation Attacks
本文研究了近场条件下物理层认证在模仿攻击下的问题，通过最小化合法发送者与攻击者信号之间的均方误差来分析，并提出了单天线和多天线攻击者的最优预编码方案。
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Human-robot conversation with multiple participants in noisy public spaces
研究提出了一种音频系统，使用单个多通道麦克风阵列增强嘈杂环境中的多人对话信号，提高语音识别和远程操作的沉浸感。
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GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
该研究提出GAP-Prompt方法，通过实例级自适应提示解决持续学习中的灾难性遗忘问题，实现在多个基准测试中达到最先进的性能。
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CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction
This study addresses the ill-posed inverse problem in sparse-view CT reconstruction and the poor convergence of existing deep learning methods by proposing a compact deep unfolding framework inspired by second-order optimization. By constructing a structured Hessian proxy with a conjugate gradient solver and designing a global-local regularization module that integrates convolutional features with Nyström attention, the method effectively models image priors. Experiments on AAPM and DeepLesion datasets demonstrate stable convergence, significant noise power reduction, and enhanced visual fidelity. Achieving superior quantitative metrics compared to state-of-the-art approaches, this work provides an efficient and reliable solution for sparse-view CT reconstruction.
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Study of Multiuser Scheduling Based on User Satisfaction for MU-MIMO Systems
This study addresses the challenges of high computational complexity and fairness trade-offs in MU-MIMO scheduling by proposing a User Satisfaction-based Scheduling Algorithm (US-SA). The method transforms high-dimensional combinatorial optimization into efficient sub-problems through the construction of low-dimensional subgrouping matrices and a satisfied-user elimination mechanism. Experimental results demonstrate that US-SA achieves performance comparable to optimal exhaustive search while significantly reducing computational overhead. Furthermore, it outperforms existing mainstream schemes in throughput, spectral efficiency, and fairness, effectively balancing system performance with user experience.
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Near-Field Physical-Layer Authentication Under Impersonation Attacks
本文研究了近场条件下物理层认证在模仿攻击下的问题，通过最小化合法发送者与攻击者信号之间的均方误差来分析，并提出了单天线和多天线攻击者的最优预编码方案。
0 citationsRead paper
Human-robot conversation with multiple participants in noisy public spaces
研究提出了一种音频系统，使用单个多通道麦克风阵列增强嘈杂环境中的多人对话信号，提高语音识别和远程操作的沉浸感。
0 citationsRead paper
GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
该研究提出GAP-Prompt方法，通过实例级自适应提示解决持续学习中的灾难性遗忘问题，实现在多个基准测试中达到最先进的性能。
0 citationsRead paper
CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction
This study addresses the ill-posed inverse problem in sparse-view CT reconstruction and the poor convergence of existing deep learning methods by proposing a compact deep unfolding framework inspired by second-order optimization. By constructing a structured Hessian proxy with a conjugate gradient solver and designing a global-local regularization module that integrates convolutional features with Nyström attention, the method effectively models image priors. Experiments on AAPM and DeepLesion datasets demonstrate stable convergence, significant noise power reduction, and enhanced visual fidelity. Achieving superior quantitative metrics compared to state-of-the-art approaches, this work provides an efficient and reliable solution for sparse-view CT reconstruction.
0 citationsRead paper
Study of Multiuser Scheduling Based on User Satisfaction for MU-MIMO Systems
This study addresses the challenges of high computational complexity and fairness trade-offs in MU-MIMO scheduling by proposing a User Satisfaction-based Scheduling Algorithm (US-SA). The method transforms high-dimensional combinatorial optimization into efficient sub-problems through the construction of low-dimensional subgrouping matrices and a satisfied-user elimination mechanism. Experimental results demonstrate that US-SA achieves performance comparable to optimal exhaustive search while significantly reducing computational overhead. Furthermore, it outperforms existing mainstream schemes in throughput, spectral efficiency, and fairness, effectively balancing system performance with user experience.
0 citationsRead paper