Electrical and Electronic Engineering - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 117
  • Item
    No Preview Available
    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.
  • Item
    Thumbnail Image
    An efficient deep neural model for detecting crowd anomalies in videos
    Yang, M ; Tian, S ; Rao, AS ; Rajasegarar, S ; Palaniswami, M ; Zhou, Z (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.
  • Item
    No Preview Available
    Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review
    Mendis, L ; Palaniswami, M ; Brownfoot, F ; Keenan, E (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.
  • Item
    Thumbnail Image
    Network Resource Allocation for Industry 4.0 with Delay and Safety Constraints
    Sardar, AA ; Rao, AS ; Alpcan, T ; Das, G ; Palaniswami, M (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.
  • Item
    No Preview Available
    A Real-Time Tunable ECG Noise-Aware System for IoT-Enabled Devices
    Rahman, S ; Karmakar, C ; Yearwood, J ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-12-01)
  • Item
    Thumbnail Image
    Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling
    Keenan, E ; Karmakar, C ; Udhayakumar, RK ; Brownfoot, FC ; Lakhno, I ; Shulgin, V ; Behar, JA ; Palaniswami, M (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.
  • Item
    Thumbnail Image
    Effect of Pd-Sensitization on Poisonous Chlorine Gas Detection Ability of TiO2: Green Synthesis and Low-Temperature Operation.
    Ekar, S ; Nakate, UT ; Khollam, YB ; Shaikh, SF ; Mane, RS ; Rana, AUHS ; Palaniswami, M (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.
  • Item
    Thumbnail Image
    Energy Efficient Time Synchronization in WSN for Critical Infrastructure Monitoring
    Rao, AS ; Gubbi, J ; Tuan, N ; Nguyen, J ; Palaniswami, M ; Wyld, DC ; Wozniak, M ; Chaki, N ; Meghanathan, N ; Nagamalai, D (SPRINGER-VERLAG BERLIN, 2011-01-01)
    Wireless Sensor Networks (WSN) based Structural Health Monitoring (SHM) is becoming popular in analyzing the life of critical infrastructure such as bridges on a continuous basis. For most of the applications, data aggregation requires high sampling rate. A need for accurate time synchronization in the order of 0.6 − 9 μs every few minutes is necessary for data collection and analysis. Two-stage energy-efficient time synchronization is proposed in this paper. Firstly, the network is divided into clusters and a head node is elected using Low-Energy Adaptive Clustering Hierarchy based algorithm. Later, multiple packets of different lengths are used to estimate the delay between the elected head and the entire network hierarchically at different levels. Algorithmic scheme limits error to 3-hop worst case synchronization error. Unlike earlier energy-efficient time synchronization schemes, the achieved results increase the lifetime of the network.
  • Item
    Thumbnail Image
    A robust algorithm for foreground extraction in crowded scenes
    Rao, AS ; Gubbi, J ; Marusic, S ; Palaniswami, M (IEEE, 2012-12-01)
    The widespread availability of surveillance cameras and digital technology has improved video based security measures in public places. Surveillance systems have been assisting officials both in civil and military applications. It is helping to identify unlawful activities by means of uninterrupted transmission of surveillance videos. By this, the system is adding extraneous onus on to the already existing workload of security officers. Instead, if the surveillance system is intelligent and efficient enough to identify the events of interest and alert the officers, it alleviates the burden of continuous monitoring. In other words, our existing surveillance systems are lacking to identify the objects that are dissimilar in shape, size, and color especially in identifying human beings (nonrigid motions). Global illumination changes, frequent occurrences of shadows, insufficient lighting conditions, unique properties of slow and fast moving objects, unforeseen appearance of objects and its behavior, availability of system memory, etc., may be ascribed to the limitations of existing systems. In this paper, we present a filtering technique to extract foreground information, which uses RGB component and chrominance channels to neutralize the effects of nonuniform illumination, remove shadows, and detect both slow-moving and distant objects.
  • Item
    Thumbnail Image
    A Pilot Study of Urban Noise Monitoring Architecture Using Wireless Sensor Networks
    Gubbi, J ; Marusic, S ; Rao, AS ; Law, YW ; Palaniswami, M (IEEE, 2013-01-01)
    Internet of Things (IoT) is denned as interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications. As the world urban population is set to cross unprecedented levels, adequate provision of services and infrastructure poses huge challenges. The emerging IoT that offers ubiquitous sensing and actuation can be utilized effectively for managing urban environments. In this paper, a new architecture for noise monitoring in urban environments is proposed. The architecture is scalable and applicable to other sensors required for city management. In addition to the architecture, a new noise monitoring hardware platform is reported and visualization of the data is presented. An emerging citizen centric participatory sensing is discussed in the context of noise monitoring.