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Item Open Access A comparative analysis of hybrid sensor fusion schemes for visual–inertial navigation(Institute of Electrical and Electronics Engineers (IEEE), 2025-12-31) Tabassum, Tarafder Elmi; Petrunin, Ivan; Rana, Zeeshan A.Visual Inertial Odometry (VIO) has been extensively studied for navigation in GNSS-denied environments, but its performance can be heavily impacted by the complexity of the navigation environments such as weather conditions, illumination variation, flight dynamics, and environmental structure. Hybrid fusion approaches integrating Neural Networks (NN), especially Gated Recurrent units (GRU) with the Kalman filters (KF), such as Error-State Kalman Filter (ESKF) have shown promising results mitigating system nonlinearities due to challenging environmental conditions data issues, there is a lack of systematic studies quantitively analysing and comparing performance differences unhand. To address this gap and enable robust navigation in complex conditions, this study proposes and systematically analyses the performance of three hybrid fusion schemes for VIO-based navigation of Unmanned Aerial Vehicles (UAV). These three hybrid VIO schemes include Visual Odometry (VO) error compensation using NN, KF error compensation using NN, and prediction of Kalman gain using NN. The comparative analysis is performed using data generated in MATLAB incorporating the Unreal Engine involving diverse challenging environmental conditions: fog, rain, illumination level variability and variability in the number of features available for extraction during the UAV flight in the urban environment. The results demonstrate the performance improvement achieved by hybrid VIO fusion schemes compared to ESKF-based traditional fusion methods in the presence of multiple visual failure modes. Comparative analysis reveals notable improvement achieved by method 1 with enhancements of 93% in sunny, 91% in foggy and 90% in rainy conditions than the other two hybrid VIO architectures.Item Open Access Dual-Chamber microbial fuel cell for Azo-Dye degradation and electricity generation in Textile wastewater treatment(Elsevier, 2025-09) Ndive, Julius Nnamdi; Eze, Simeon Okechukwu; Nnabuife, Somtochukwu Godfrey; Kuang, Boyu; Rana, Zeeshan A.Textile wastewater, particularly azo dyes, poses significant environmental challenges due to its poor biodegradability and toxicity. This study explores a dual-chamber microbial fuel cell (MFC) for simultaneous wastewater treatment and electricity generation. The MFC consists of an anaerobic anode chamber and an aerobic cathode chamber, separated by a proton exchange membrane (PEM). Electroactive microorganisms in the anode chamber metabolize organic substrates, including azo dye contaminants, breaking them down into simpler by-products. Electrons released during this process flow through an external circuit to generate current, while protons migrate across the PEM to the cathode chamber for oxygen reduction. Electrochemically active microbes were isolated from azo-dye-contaminated soil, and their degradation abilities validated through assays. Optimized carbon-based electrodes and a Nafion 117 PEM were used to enhance conductivity and microbial activity. UV–Vis spectroscopy tracked dye degradation, with the absorbance peak of reactive yellow dye at 410 nm decreasing from 2.9 to 0.4, indicating effective azo-bond cleavage. The MFC achieved peak voltage and current outputs of 0.20 mV and 0.16 mA, respectively, demonstrating its dual functionality. Adding NaCl as a supporting electrolyte further improved ionic conductivity and performance. This study demonstrates MFC technology as a sustainable solution for industrial wastewater challenges, integrating microbial degradation with bioelectricity generation. Future work should address scalability, operational stability, and advanced electrode designs to enhance its practical applications.Item Open Access Resilient time dissemination fusion framework for UAVs for smart cities(MDPI, 2025-03-17) Negru, Sorin Andrei; Arora, Triyan Pal; Petrunin, Ivan; Guo, Weisi; Tsourdos, Antonios; Sweet, David; Dunlop, GeorgeFuture smart cities will consist of a heterogeneous environment, including UGVs (Unmanned Ground Vehicles) and UAVs (Unmanned Aerial Vehicles), used for different applications such as last mile delivery. Considering the vulnerabilities of GNSS (Global Navigation System Satellite) in urban environments, a resilient PNT (Position, Navigation, Timing) solution is needed. A key research question within the PNT community is the capability to deliver a robust and resilient time solution to multiple devices simultaneously. The paper is proposing an innovative time dissemination framework, based on IQuila’s SDN (Software Defined Network) and quantum random key encryption from Quantum Dice to multiple users. The time signal is disseminated using a wireless IEEE 802.11ax, through a wireless AP (Access point) which is received by each user, where a KF (Kalman Filter) is used to enhance the timing resilience of each client into the framework. Each user is equipped with a Jetson Nano board as CC (Companion Computer), a GNSS receiver, an IEEE 802.11ax wireless card, an embedded RTC (Real Time clock) system, and a Pixhawk 2.1 as FCU (Flight Control Unit). The paper is presenting the performance of the fusion framework using the MUEAVI (Multi-user Environment for Autonomous Vehicle Innovation) Cranfield’s University facility. Results showed that an alternative timing source can securely be delivered fulfilling last mile delivery requirements for aerial platforms achieving sub millisecond offset.Item Open Access Designing and testing of HDPE–N2O hybrid rocket engine(MDPI, 2025-03-13) Arora, Triyan Pal; Buttrey, Noah; Kirman, Peter; Khadtare, Sanmukh; Kamath, Eeshaan; del Gatto, Dario; Isoldi, AdrianoHybrid Rocket Engines (HREs) combine the advantages of solid and liquid propellants, offering thrust control, simplicity, safety, and cost efficiency. Part of the research on this rocket architecture focuses on optimising combustion chamber design to enhance performance, a process traditionally reliant on time-consuming experimental adjustments to chamber lengths. In this study, two configurations of HREs were designed and tested. The tests aimed to study the impact of post-chamber lengths on rocket engine performance by experimental firings on a laid-back test engine. This study focused on designing, manufacturing, and testing a laid-back hybrid engine with two chamber configurations. The engine features a small combustion chamber, an L-shaped mount, a spark ignition, and nitrogen purging. Data acquisition includes thermocouples, pressure transducers, and a load cell for thrust measurement. Our experimental findings provide insights into thrust, temperature gradients, pressure, and plume characteristics. A non-linear regression model derived from the experimental data established an empirical relationship between performance and chamber lengths, offering a foundation for further combustion flow studies. The post-chamber length positively impacted the engine thrust performance by 2.7%. Conversely, the pre-chamber length negatively impacted the performance by 1.3%. Further data collection could assist in refining the empirical relation and identifying key threshold values.Item Open Access Signal-to-interference-noise-ratio density distribution for UAV-carried IRS-to-6G ground communication(IEEE, 2025) Nnamani, Christantus Obinna; Anioke, Chidera Linda; Al-Rubaye, Saba; Tsourdos, AntoniosThis paper investigates the probability distribution of the signal-to-interference noise ratio (SINR) for a 6G communication system comprising a multi-antenna transmitter, an intelligent reflecting surface (IRS) and a remote receiver station. A common assumption in the literature is that the density distribution function for SINR and signal-to-noise ratio (SNR) of an IRS-to-ground communication follows a Rayleigh and Rician distribution. This assumption is essential as it influences the derivation of the properties of the communication system such as the physical layer security models and the designs of IRS controller units. Therefore, in this paper, we present an analytical derivation for the density distribution functions of the SINR for an IRS-to-6G ground communication ameliorating the typical assumptions in the literature. We demonstrated that the SINR density function of an IRS-to-6G ground communication contains a hypergeometric function. We further applied the derived density distribution function to determine the average secrecy rate for passive eavesdropping.Item Open Access How to find opinion leader on the online social network?(Springer, 2025-05-01) Jin, Bailu; Zou, Mengbang; Wei, Zhuangkun; Guo, WeisiOnline social networks (OSNs) provide a platform for individuals to share information, exchange ideas, and build social connections beyond in-person interactions. For a specific topic or community, opinion leaders are individuals who have a significant influence on others’ opinions. Detecting opinion leaders and modeling influence dynamics is crucial as they play a vital role in shaping public opinion and driving conversations. Existing research have extensively explored various graph-based and psychology-based methods for detecting opinion leaders, but there is a lack of cross-disciplinary consensus between definitions and methods. For example, node centrality in graph theory does not necessarily align with the opinion leader concepts in social psychology. This review paper aims to address this multi-disciplinary research area by introducing and connecting the diverse methodologies for identifying influential nodes. The key novelty is to review connections and cross-compare different multi-disciplinary approaches that have origins in: social theory, graph theory, compressed sensing theory, and control theory. Our first contribution is to develop cross-disciplinary discussion on how they tell a different tale of networked influence. Our second contribution is to propose trans-disciplinary research method on embedding socio-physical influence models into graph signal analysis. We showcase inter- and trans-disciplinary methods through a Twitter case study to compare their performance and elucidate the research progression with relation to psychology theory. We hope the comparative analysis can inspire further research in this cross-disciplinary area.Item Open Access Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization(IEEE, 2024-12-08) Li, Jiawei; Wang, Dawei; Zhang, Jiankang; Alfarraj, Osama; He, Yixin; Al-Rubaye, Saba; Yu, Keping; Mumtaz, ShahidThis paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.Item Open Access Understanding the relevance of parallelising machine learning algorithms using CUDA for sentiment analysis(Association for Computing Machinery (ACM), 2024-10-17) Chai, Dakun Mang; Moulitsas, Irene; Bisandu, Desmond B.Sentiment classification is essential in natural language processing, leveraging machine learning algorithms to understand the sentiment expressed in textual data. Over the years, advancements in machine learning, particularly with Naive Bayes (NB) and Support Vector Machines (SVM), have tremendously improved sentiment classification. These models benefit from word embedding techniques such as Word2Vec and GloVe, which provide dense vector representations of words, capturing their semantic and syntactic relationships. This paper explores the parallelisation of NB and SVM models using CUDA on GPUs to enhance computational efficiency and performance. Despite the computational power offered by GPUs, the literature on parallelising machine learning methods, especially for sentiment classification, remains limited. Our work aims to fill this gap by comparing the performance of NB and SVM on CPU and GPU platforms, focusing on execution time and model accuracy. Our experiments demonstrate that NB outperforms SVM in execution time and overall efficiency, mainly when using GPU acceleration. The NB model consistently achieves higher accuracy, precision, and F1 scores with Word2Vec and GloVe embeddings. The results show the importance of leveraging GPU acceleration using varying numbers of threads per block for large-scale sentiment analysis and laying the foundation for parallelising sentiment classification tasks.Item Open Access Blockchain and distributed digital watermarking effort on federated learning: innovating intellectual property protection(IEEE, 2024-12-02) Chao, Kailin; Li, JunJie; Jiang, Yirui; Xiao, Jianmao; Cao, YuanlongFederated Learning with Digital Watermarks (FLDW) have been recognized as a promising solution for property protection. However, the existing FLDW-related technologies neglect the requirements of decentralized settings, leading to recurrent issues such as discrepancies in distributed client data. This paper introduces a Blockchain Federated Learning Intellectual Property Protection Framework (BFLIPR), to address the data security and model validation challenges in decentralized federated learning environments. BFLIPR merges blockchain, digital watermarking, and federated learning technologies. By harnessing the blockchain’s tamper-proof properties, digital watermarking’s concealment capabilities, and federated learning’s distributed feature, the framework offers a solution that aligns with intellectual property protection mechanism, to bolster data security and property safeguarding. Experimental findings demonstrate its high feasibility and robust for data privacy and model security in the federated learning.Item Open Access Enhancing performance and interpretability of multivariate time-series model through sparse saliency(IEEE, 2024-09-24) Kong, Xiangqi; Xing, Yang; Liu, Zeyu; Tsourdos, Antonios; Wikander, AndreasExplainable time-series modelling is an essential task for modern intelligent transportation systems (ITS). How-ever, balancing accuracy and interpretability in multivariate time series forecasting presents significant challenges. These challenges arise from the necessity to understand the significance of features and their temporal variations. Factors such as autocorrelation in time series and data processing techniques like sliding windows expand feature sets, thereby complicating pattern recognition using traditional post-hoc explanation methods and making the issue even more complex. To overcome these challenges, in this study, we propose a flexible post-process approach which generates sparse and normalized saliency values based on existing saliency generation methods such as GradientSHAP. Additionally, an optional window aggregation and alignment strategy is introduced to align with the original time series dataset, enhancing the intuitive understanding of feature importance. Furthermore, the potential use of sparse saliency for data augmentation to improve the model is explored. Lastly, we utilize naturalistic data from San Francisco airport to demonstrate our approach for ITS time-series prediction and explanation. The evaluation results indicate that integrating sparse saliency from high-performing models not only boosts the performance of XGBoost models by 10.92% but also simplifies model complexity, facilitating easier interpretation.Item Open Access Mitigating no fault found phenomena through ensemble learning: a mixture of experts approach(IEEE, 2024-09-24) Liu, Zeyu; Kong, Xiangqi; Chen, Yang; Wang, Ziyue; Jia, Huamin; Al-Rubaye, SabaIn the aviation industry, the reliance on precise fault diagnostic decision-making is critical for equipment maintenance. A significant challenge encountered is the erroneous categorization of components under 'No Fault Found' (NFF), which subjects these components to unwarranted repairs or further testing. Such misclassifications not only trap on airlines through costly cycles of unnecessary maintenance but also exacerbate degeneration and potential safety hazards. Consequently, there is a heightened demand for the development of effective fault diagnosis models that are adapting to the aircraft complex systems and adeptly addressing issues related to the NFF phenomenon. In this study, we draw inspiration from ensemble learning and propose a multiple Naive Bayes experts (MNBMoEs) approach based on a mixture of experts (MoEs) model. This method leverages the predictive advantages of each sub-model on specific features, allowing the hybrid expert decision to outperform any single expert. It also includes a quantitative analysis method for the NFF issue, derived from the confusion matrix according to the industrial definition of NFF. Experiments evaluated on public datasets results show that the ensemble learning approach, based on Mixture of Multiple Naive-Bayes expert models, can effectively utilize the strengths of different models, improving fault diagnosis accuracy to 96.96%, with a maximum reduction in NFF occurrence rates of up to 94.17% and 84.2% model performance improvement.Item Open Access Cascade network stability of synchronized traffic load balancing with heterogeneous energy efficiency policies(IEEE, 2024-12-08) Zou, Mengbang; Guo, WeisiCascade stability of load balancing is critical for ensuring high efficiency service delivery and preventing undesirable handovers. In energy efficient networks that employ diverse sleep mode operations, handing over traffic to neighbouring cells' expanded coverage must be done with minimal side effects. Current research is largely concerned with designing distributed and centralized efficient load balancing policies that are locally stable. There is a major research gap in identifying largescale cascade stability for networks with heterogeneous load balancing policies arising from diverse plug-and-play sleep mode policies in ORAN, which will cause heterogeneity in the network stability behaviour. Here, we investigate whether cells arbitrarily connected for load balancing and having an arbitrary number undergoing sleep mode can: (i) synchronize to a desirable load-balancing state, and (ii) maintain stability. For the first time, we establish the criterion for stability and prove its validity for any general load dynamics and random network topology. Whilst its general form allows all load balancing and sleep mode dynamics to be incorporated, we propose an ORAN architecture where the network service management and orchestration (SMO) must monitor new load balancing policies to ensure overall network cascade stability.Item Open Access A comprehensive review and future challenges of energy-aware path planning for small unmanned aerial vehicles with hydrogen-powered hybrid propulsion(Cambridge University Press (CUP), 2025) Çinar, Hasan; Ignatyev, Dmitry; Zolotas, ArgyriosUnmanned aerial vehicles (UAVs) with fully electric propulsion systems mainly use lithium-based batteries. However, using fuel cells, hybrid propulsion systems are created to improve the flight time and payload capacity of the UAVs. Energy management and energy-aware path planning are important aspects to be explored in hybrid-propulsion powered UAV configurations. These facilitate optimal power distribution among energy sources and motion planning considering energy consumption, respectively. In the literature, although there are many studies on the energy management of hybrid-powered UAVs and path planning of only battery-powered UAVs, there are research gaps in energy-aware path planning of hybrid-powered UAVs. Additionally, the energy management of hybrid-powered UAVs is usually considered independent of path planning in the literature. This paper thoroughly reviews recent energy-aware path planning for small UAVs to address the listed critical challenges above, providing a new perspective and recommendations for further research. Firstly, this study evaluates the recent status of path planning, hydrogen-based UAVs, and energy management algorithms and identifies some challenges. Later, the applications of hydrogen-powered UAVs are summarised. In addition, hydrogen-based hybrid power system topologies are defined for small UAVs. Then, the path-planning algorithms are classified, and existing studies are discussed. Finally, this paper provides a comprehensive and critical assessment of the status of energy-aware path planning of UAVs, as well as detailed future work recommendations for researchers.