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Oxford Brookes University

Academic institutioneurope · gb
Official website
Research library34linked papers
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Selected work

Representative Papers

AMHRP: Adaptive Multi-Hop Routing Protocol to Improve Network Lifetime for Multi-Hop Wireless Body Area Network

May 05, 2019

To address short network lifetime, high path loss, limited throughput, and energy imbalance in multi-hop wireless body area networks (WBANs), this paper proposes an adaptive multi-hop routing mechanism based on residual energy awareness and network load balancing. The method jointly considers node residual energy, link quality, and hop count to dynamically construct low-energy-consumption and high-reliability transmission paths. Leveraging stochastic node deployment and Poisson process modeling, it further optimizes topology design and energy allocation strategies. Experimental results demonstrate that the proposed approach significantly delays the time of first node death by up to 32.7%, reduces average path loss by 18.4%, improves throughput by 24.1%, and enhances system stability—thereby ensuring continuous and reliable transmission of critical physiological data.

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Recent publications

Latest Papers

fSRD: Fuzzy Spectral Region Decomposition -- Automated Multi Operator Koopman Representations via an Adaptive Spectral Learning Architecture

Jul 20, 2026

This study addresses the inherent trade-offs among expressiveness, complexity, and data efficiency in modeling nonlinear chaotic systems, where existing data-driven Koopman approaches struggle to achieve both global accuracy and interpretability within finite-dimensional spaces. The authors propose a fuzzy Spectral Region Decomposition (fSRD) framework—a novel, fully automated multi-operator Koopman learning architecture that adaptively constructs local invariant embeddings by integrating fuzzy tree models with spectral learning. This approach enables collaborative, finite-dimensional representations without requiring prior system knowledge. Experimental results demonstrate that fSRD achieves superior prediction accuracy, dynamic interpretability, and data robustness across canonical chaotic systems such as Lorenz and Duffing, as well as high-dimensional real-world datasets, performing effectively in both data-rich and data-scarce regimes.

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