Electrical and Electronic Engineering - Research Publications

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    HCV E1 influences the fitness landscape of E2 and may enhance escape from E2-specific antibodies
    Zhang, H ; Bull, RA ; Quadeer, AA ; McKay, MR (OXFORD UNIV PRESS, 2023-12-14)
    The Hepatitis C virus (HCV) envelope glycoprotein E1 forms a non-covalent heterodimer with E2, the main target of neutralizing antibodies. How E1-E2 interactions influence viral fitness and contribute to resistance to E2-specific antibodies remain largely unknown. We investigate this problem using a combination of fitness landscape and evolutionary modeling. Our analysis indicates that E1 and E2 proteins collectively mediate viral fitness and suggests that fitness-compensating E1 mutations may accelerate escape from E2-targeting antibodies. Our analysis also identifies a set of E2-specific human monoclonal antibodies that are predicted to be especially resilient to escape via genetic variation in both E1 and E2, providing directions for robust HCV vaccine development.
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    Hippocampal cognitive and relational map paradigms explored by multisensory encoding recording with wide-field calcium imaging
    Sun, D ; Shaik, NEK ; Unnithan, RR ; French, C (CELL PRESS, 2024-01-19)
    Two major theories have been proposed to explain hippocampal function: cognitive map and the relational theories. They differ in their views on whether hippocampal neurons can process non-spatial information independently. However, the explanatory power of these theories remains unresolved. Additionally, more complex aspects of hippocampal neural population responses to non-spatial stimuli have not been investigated. Here, we used miniaturized fluorescence microscopy to investigate mouse CA1 responses to spatial, visual, auditory modalities, and combinations. We found that while neuronal populations primarily processed spatial information, they also showed strong sensitivity to non-spatial modalities independent of spatial inputs, exhibiting distinct neuronal dynamics and coding patterns. These results provide strong support for the relational theories.
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    Reconfigurable optical crosshaul architecture for 6G radio access networks
    Tao, Y ; Ranaweera, C ; Edirisinghe, S ; Lim, C ; Nirmalathas, A ; Wosinska, L ; Song, T (Optica Publishing Group, 2023-12)
    The radio access network (RAN) architecture is undergoing a significant evolution to support the next-generation mobile networks and their emerging applications. To realize scalable and sustainable deployment and operations, RAN needs to consider the requirements of 6G and beyond wireless technologies such as ultra densification of cells, higher data rates, ubiquitous coverage, and new radio spectrum in the millimeter-wave band. This calls for a careful redesign of every aspect of RAN, including its crosshaul. The crosshaul is an important network segment in future RAN, capable of transporting diverse traffic types with varying stringent requirements within RAN. The crosshaul towards 6G is envisioned to be highly intelligent, reconfigurable, and adaptable to dynamic service requirements and network conditions. To this end, we propose a software defined network (SDN)-enabled reconfigurable optical crosshaul architecture (ROCA) that supports heterogeneous crosshaul transport technologies and dynamic functional splittings. ROCA enables efficient and intelligent control of the crosshaul data plane. The proposed architecture with a set of the next-generation RAN (NG-RAN) transport interfaces is evaluated using network models built on the ns-3 network simulator. Simulation results demonstrate the strengths and weaknesses of different crosshaul interfaces in agreement with the understanding of respective NG-RAN interfaces from the literature, which validates the modeling accuracy. We then demonstrate the reconfigurability of the architecture using a dynamic scenario with different reconfiguration strategies for meeting the user and network demands. The results indicate that ROCA serves as a scalable and flexible foundation for supporting high-capacity delay-stringent RAN that can be used in 6G and beyond wireless technologies.
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    A Geometry-Based Distributed Connectivity Maintenance Algorithm for Discrete-time Multi-Agent Systems with Visual Sensing Constraints
    Li, X ; Fu, J ; Liu, M ; Xu, Y ; Tan, Y ; Xin, Y ; Pu, Y ; Oetomo, D (WORLD SCIENTIFIC PUBL CO PTE LTD, 2024-03)
    This paper presents a novel approach to address the challenge of maintaining connectivity within a multi-agent system (MAS) when utilizing directional visual sensors. These sensors have become essential tools for enhancing communication and connectivity in MAS, but their geometric constraints pose unique challenges when designing controllers. Our approach, grounded in geometric principles, leverages a mathematical model of directional visual sensors and employs a gradient-descent optimization method to determine the position and orientation constraints for each sensor based on its geometric configuration. This methodology ensures network connectivity, provided that initial geometric constraints are met. Experimental results validate the efficacy of our approach, highlighting its practical applicability for a range of tasks within MAS.
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    On Distributed Nonconvex Optimisation via Modified ADMM
    Mafakheri, B ; Manton, JH ; Shames, I (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023)
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    Event-Triggered Boundary Control of 2 x 2 Semilinear Hyperbolic Systems
    Strecker, T ; Cantoni, M ; Aamo, OM (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024-01)
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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 (IEEE, 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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    Wearable Transmitter Coil Design for Inductive Wireless Power Transfer to Implantable Devices.
    Tai, Y-D ; Widdicombe, B ; Unnithan, RR ; Grayden, DB ; John, SE (IEEE, 2023-07)
    Wireless endovascular sensors and stimulators are emerging biomedical technologies for applications such as endovascular pressure monitoring, hyperthermia, and neural stimulations. Recently, coil-shaped stents have been proposed for inductive power transfer to endovascular devices using the stent as a receiver. However, less work has been done on the external transmitter components, so the maximum power transferable remains unknown. In this work, we design and evaluate a wearable transmitter coil that allows 50 mW power transfer in simulation.Clinical Relevance-This allows more accurate measurements and precise control of endovascular devices.
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