Electrical and Electronic Engineering - Research Publications

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    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.
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    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.
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    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.
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    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)
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    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.
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    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.
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    Real-time monitoring of construction sites: Sensors, methods, and applications
    Rao, AS ; Radanovic, M ; Liu, Y ; Hu, S ; Fang, Y ; Khoshelham, K ; Palaniswami, M ; Tuan, N (ELSEVIER, 2022-04)
    The construction industry is one of the world's largest industries, with an annual budget of $10 trillion globally. Despite its size, the efficiency and growth in labour productivity in the construction industry have been relatively low compared to other sectors, such as manufacturing and agriculture. To this extent, many studies have recognised the role of automation in improving the efficiency and safety of construction projects. In particular, automated monitoring of construction sites is a significant research challenge. This paper provides a comprehensive review of recent research on the real-time monitoring of construction projects. The review focuses on sensor technologies and methodologies for real-time mapping, scene understanding, positioning, and tracking of construction activities in indoor and outdoor environments. The review also covers various case studies of applying these technologies and methodologies for real-time hazard identification, monitoring workers’ behaviour, workers’ health, and monitoring static and dynamic construction environments.
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    Achieving AI-Enabled Robust End-to-End Quality of Experience Over Backhaul Radio Access Networks
    Roy, D ; Rao, AS ; Alpcan, T ; Das, G ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-09)
    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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    Achieving QoS for bursty uRLLC applications over passive optical networks
    Roy, D ; Rao, AS ; Alpcan, T ; Das, G ; Palaniswami, M (Optica Publishing Group, 2022-05)
    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. The 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 for such applications. The available solutions rely on instantaneous heuristic decisions and maintain QoS constraints (mostly bandwidth) in an average sense. Existing proposals in generic networks with optimal strategies are computationally complex and are, therefore, not suitable for uRLLC applications. This paper presents a novel computationally efficient, far-sighted bandwidth allocation policy design for facilitating bursty uRLLC traffic in a PON framework while satisfying strict QoS (age of information/delay and bandwidth) requirements. 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) problem. MPC helps in making far-sighted decisions regarding resource allocations and captures the time-varying dynamics of the network. We provide computationally efficient polynomial time solutions and show their implementation in the PON framework. Compared to existing approaches, MPC can improve delay violations by 15% and 45% at loads of 0.8 and 0.9, respectively, for delay-constrained applications of 1 ms and 4 ms. Our approach is also robust to varying traffic arrivals.
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    Novel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors
    Datta, S ; Karmakar, CK ; Yan, B ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-06)
    Stroke survivors are often characterized by hemiparesis, i.e., paralysis in one half of the body, severely affecting upper limb movements. Monitoring the progression of hemiparesis requires manual observation of limb movements at regular intervals, and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparesis in acute stroke. We propose novel measures of similarity and asymmetry in hand activities through bivariate Poincaré analysis between two-hand accelerometer data for quantifying hemiparetic severity. The proposed descriptors characterize the distribution of activity surrogates derived from acceleration of the two hands, on a 2D bivariate Poincaré Plot. Experiments show that while the descriptors CSD1 and CSD2 can identify hemiparetic patients from control subjects, their normalized difference CSDR and the descriptors Complex Cross-Correlation Measure ( C3M) and Activity Asymmetry Index ( AAI) can distinguish between mild, moderate and severe hemiparesis. These measures are compared with traditional measures of cross-correlation and evaluated against the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard for hemiparetic severity estimation. This study, undertaken on 40 acute stroke patients with varying levels of hemiparesis and 15 healthy controls, validates the use of short length ( 5 minutes) wearable accelerometry data for identifying hemiparesis with greater clinical sensitivity. Results show that the proposed descriptors with a hierarchical classification model outperform state-of-the-art methods with overall accuracy of 0.78 and 0.85 for 4-class and 3-class hemiparesis identification respectively.