Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableThe Effect of Fetal Heart Rate Segment Selection on Deep Learning Models for Fetal Compromise Detection(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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ItemAn efficient deep neural model for detecting crowd anomalies in videos(Springer, 2023-06-01)Identifying unusual crowd events is highly challenging, laborious, and prone to errors in video surveillance applications. We propose a novel end-to-end deep learning architecture called Stacked Denoising Auto-Encoder (DeepSDAE) to address these challenges, comprising SDAE, VGG16 and Plane-based one-class Support Vector Machine (SVM), abbreviated as PSVM, to detect anomalies such as stationary people in an active scene or loitering activities in a crowded scene. The DeepSDAE framework is a hybrid deep learning architecture. It consists of a four-layered SDAE and an enhanced convolutional neural network (CNN) model. Our framework employs Reinforcement Learning to optimise the learning parameters to detect crowd anomalies. We use the Markov Decision Process (MDP) with Deep Q-learning to find the optimal Q value. We also present a late fusion procedure to combine individual decisions from the intermediate and final layers of the SDAE and VGG16 networks to detect different anomalies. Our experiments on four real-world datasets reveal the superior performance of our proposed framework in detecting (frame-level and pixel-level) anomalies.
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ItemNo Preview AvailableComputerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review(MDPI, 2023-09)The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them.
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ItemNetwork Resource Allocation for Industry 4.0 with Delay and Safety Constraints(Institute of Electrical and Electronics Engineers, 2023)In this paper, we model a futuristic factory floor equipped with Automated Guided Vehicles (AGVs), cameras, and a Virtual Reality (VR) surveillance system; and connected to a 5G network for communication purposes. Motion planning of AGVs and VR applications is offloaded to an edge server for computational flexibility and reduced hardware on the factory floor. Decisions on the edge server are made using the video feed provided by the cameras in a controlled manner. Our objectives are to ensure factory floor safety and provide smooth VR experience in the surveillance room. Providing proper and timely allocation of network resources is of utmost importance to maintain the end-to-end delay necessary to achieve these objectives. We provide a statistical analysis to estimate the bandwidth required by a factory to satisfy the delay requirements 99.999 percent of the time. We formulate a nonconvex integer nonlinear problem aiming to minimize the safety and delay violations. To solve it, we propose a real-time network resource allocation algorithm that has linear time complexity in terms of the number of components connected to the wireless network. Our algorithm significantly outperforms existing solvers (genetic algorithm, surrogate optimizer) and meets the objectives using less bandwidth compared to existing methods.
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ItemNo Preview AvailableA Real-Time Tunable ECG Noise-Aware System for IoT-Enabled Devices(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-12-01)
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ItemDetection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling(IOP Publishing Ltd, 2022-02-28)Objective.Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings.Approach.Our method consists of extracting a fetal heart rate time series from each NI-FECG recording and computing an entropy profile using a data-driven range of the entropy tolerance parameterr. To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings.Main Results.We demonstrate that our method (TotalSampEn) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such asSampEn(AUC of 0.68) andFuzzyEn(AUC of 0.72). We also find that NI-FECG recordings incorrectly classified using the investigated entropy measures have significantly lower signal quality, and that excluding recordings of low signal quality (13.5% of recordings) increases the classification performance ofTotalSampEn(AUC of 0.90).Significance.The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.
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ItemEffect of Pd-Sensitization on Poisonous Chlorine Gas Detection Ability of TiO2: Green Synthesis and Low-Temperature Operation.(MDPI AG, 2022-05-31)Ganoderma lucidum mushroom-mediated green synthesis of nanocrystalline titanium dioxide (TiO2) is explored via a low-temperature (≤70 °C) wet chemical method. The role of Ganoderma lucidum mushroom extract in the reaction is to release the ganoderic acid molecules that tend to bind to the Ti4+ metal ions to form a titanium-ganoderic acid intermediate complex for obtaining TiO2 nanocrystallites (NCs), which is quite novel, considering the recent advances in fabricated gas sensing materials. The X-ray powder diffraction, field emission scanning electron microscopy, Raman spectroscopy, and Brunauer-Emmett-Teller measurements etc., are used to characterize the crystal structure, surface morphology, and surface area of as-synthesized TiO2 and Pd-TiO2 sensors, respectively. The chlorine (Cl2) gas sensing properties are investigated from a lower range of 5 ppm to a higher range of 400 ppm. In addition to excellent response-recovery time, good selectivity, constant repeatability, as well as chemical stability, the gas sensor efficiency of the as-synthesized Pd-TiO2 NC sensor is better (136% response at 150 °C operating temperature) than the TiO2 NC sensor (57% at 250 °C operating temperature) measured at 100 ppm (Cl2) gas concentration, suggesting that the green synthesized Pd-TiO2 sensor demonstrates efficient Cl2 gas sensing properties at low operating temperatures over pristine ones.
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ItemAchieving QoS for Real-Time Bursty Applications over Passive Optical Networks( 2021-09-05)Emerging real-time applications such as those classified under ultra-reliable low latency (uRLLC) generate bursty traffic and have strict Quality of Service (QoS) requirements. Passive Optical Network (PON) is a popular access network technology, which is envisioned to handle such applications at the access segment of the network. However, the existing standards cannot handle strict QoS constraints. The available solutions rely on instantaneous heuristic decisions and maintain QoS constraints (mostly bandwidth) in an average sense. Existing works with optimal strategies are computationally complex and are not suitable for uRLLC applications. This paper presents a novel computationally-efficient, far-sighted bandwidth allocation policy design for facilitating bursty traffic in a PON framework while satisfying strict QoS (age of information/delay and bandwidth) requirements of modern applications. To this purpose, first we design a delay-tracking mechanism which allows us to model the resource allocation problem from a control-theoretic viewpoint as a Model Predictive Control (MPC). MPC helps in taking far-sighted decisions regarding resource allocations and captures the time-varying dynamics of the network. We provide computationally efficient polynomial-time solutions and show its implementation in the PON framework. Compared to existing approaches, MPC reduces delay violations by approximately 15% for a delay-constrained application of 1ms target. Our approach is also robust to varying traffic arrivals.
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ItemAchieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks( 2022-01-13)Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E) quality of experience (QoE) for these applications depends on the synchronous allocation of networking and computing resources. However, the relationship between the resources and the E2E QoE outcomes is typically stochastic and non-linear. In order to make efficient resource allocation decisions, it is essential to model these relationships. This article presents a novel machine-learning based approach to learn these relationships and concurrently orchestrate both resources for this purpose. The machine learning models further help make robust allocation decisions regarding stochastic variations and simplify robust optimization to a conventional constrained optimization. When resources are insufficient to accommodate all application requirements, our framework supports executing some of the applications with minimal degradation (graceful degradation) of E2E QoE. We also show how we can implement the learning and optimization methods in a distributed fashion by the Software-Defined Network (SDN) and Kubernetes technologies. Our results show that deep learning-based modelling achieves E2E QoE with approximately 99.8\% accuracy, and our robust joint-optimization technique allocates resources efficiently when compared to existing differential services alternatives.
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