Electrical and Electronic Engineering - Research Publications

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End date: 2024 × Start date: 2020 × Type: Conference Paper ×

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    Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes
    Chen, L ; Zhu, J ; Evans, J (Institute of Electrical and Electronics Engineers, 2023)
    Due to mutual interference between users, power allocation problems in wireless networks are often non-convex and computationally challenging. Graph neural networks (GNNs) have recently emerged as a promising approach to tackling these problems and an approach that exploits the underlying topology of wireless networks. In this paper, we propose a novel graph representation method for wireless networks that include full-duplex (FD) nodes. We then design a corresponding FD Graph Neural Network (F-GNN) with the aim of allocating transmit powers to maximise the network throughput. Our results show that our F-GNN achieves state-of-art performance with significantly less computation time. Besides, F-GNN offers an excellent trade-off between performance and complexity compared to classical approaches. We further refine this trade-off by introducing a distance-based threshold for inclusion or exclusion of edges in the network. We show that an appropriately chosen threshold reduces required training time by roughly 20% with a relatively minor loss in performance.
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    The Effect of Fetal Heart Rate Segment Selection on Deep Learning Models for Fetal Compromise Detection
    Mendis, L ; Palaniswami, M ; Brownfoot, F ; Keenan, E (Institute of Electrical and Electronics Engineers, 2023)
    Monitoring the fetal heart rate (FHR) is common practice in obstetric care to assess the risk of fetal compromise. Unfortunately, human interpretation of FHR recordings is subject to inter-observer variability with high false positive rates. To improve the performance of fetal compromise detection, deep learning methods have been proposed to automatically interpret FHR recordings. However, existing deep learning methods typically analyse a fixed-length segment of the FHR recording after removing signal gaps, where the influence of this segment selection process has not been comprehensively assessed. In this work, we develop a novel input length invariant deep learning model to determine the effect of FHR segment selection for detecting fetal compromise. Using this model, we perform five times repeated five-fold cross-validation on an open-access database of 552 FHR recordings and assess model performance for FHR segment lengths between 15 and 60 minutes. We show that the performance after removing signal gaps improves with increasing segment length from 15 minutes (AUC = 0.50) to 60 minutes (AUC = 0.74). Additionally, we demonstrate that using FHR segments without removing signal gaps achieves superior performance across signal lengths from 15 minutes (AUC = 0.68) to 60 minutes (AUC = 0.76). These results show that future works should carefully consider FHR segment selection and that removing signal gaps might contribute to the loss of valuable information.
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    On Receding-Horizon Approximation in Time-Varying Optimal Control
    Sun, J ; Cantoni, M (IEEE, 2023-01-01)
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    Economic Model Predictive Control of Water Distribution Systems with Solar Energy and Batteries
    Zheng, X ; Wang, Y ; Weyer, E ; Manzie, C (IEEE, 2023-01-01)
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    Stability of Nonlinear Systems with Two Time Scales Over a Single Communication Channel
    Wang, W ; Maass, AI ; Nešić, D ; Tan, Y ; Postoyan, R ; Heemels, WPMH (IEEE, 2023-01-01)
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    A Group Formation Game for Local Anomaly Detection
    Ye, Z ; Alpcan, T ; Leckie, C (IEEE, 2023-01-01)
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    Feasibility detection for nested codesign of hypersonic vehicles
    van der Heide, C ; Cudmore, P ; Jahn, I ; Bone, V ; Dower, PM ; Manzie, C (IEEE, 2023)
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    Second-Order Properties of Noisy Distributed Gradient Descent
    Qin, L ; Cantoni, M ; Pu, Y (IEEE, 2023-01-01)