Browsing by Author "Zolotas, Argyrios"
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Item Open Access Attack-detection architectural framework based on anomalous patterns of system performance and resource utilization - Part II(IEEE, 2021-06-11) Aloseel, Abdulmohsan; Al-Rubaye, Saba; Zolotas, Argyrios; Shaw, CarlThis paper presents a unique security approach for detecting cyber-attacks against embedded systems (ESs). The proposed approach has been shaped within an architectural framework called anomalous resource consumption detection (ARCD). The approach’s detection mechanism detects cyber-attacks by distinguishing anomalous performance and resource consumption patterns from a pre-determinable reference model. The defense mechanism of this approach acts as an additional layer of protection for ESs. This technique’s effectiveness was previously evaluated statistically, and in this paper, we tested this approach’s efficiency computationally by using the support-vector machine algorithm. The datasets were generated and collected based on a testbed model, where it was run repeatedly under different operation conditions (normal cases (Rs) versus attacked cases). The executed attack scenarios are 1) denial-of-service (DoS); 2) brute force (BF); and 3) remote code execution (RCE), and man-in-the-middle (MITM). A septenary tuple model, which consists of seven determinants that are analyzed based on seven statistical criteria, is the core of the detection mechanism. The prediction accuracy in terms of classifying anomalous patterns compared to normal patterns based on the confusion matrix revealed promising results, proving this approach’s effectiveness, where the final results confirmed very high prediction accuracies in terms of distinguishing anomalous patterns from the typical patterns. Integrating the ARCD concept into an operating system’s functionality could help software developers augment the existing security countermeasures of ESs. Adopting the ARCD approach will pave the way for software engineers to build more secure operating systems in line with the embedded system’s capabilities, without depleting its resources.Item Open Access Automated impact damage detection technique for composites based on thermographic image processing and machine learning classification(MDPI, 2022-11-22) Alhammad, Muflih; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Torbali, Muhammet E.; Genest, Marc; Zhang, Hai; Zolotas, Argyrios; Maldgue, Xavier P. V.Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique.Item Open Access A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights(Elsevier, 2023-09-29) Beyçimen, Semih; Ignatyev, Dmitry; Zolotas, ArgyriosThis article provides a detailed analysis of the assessment of unmanned ground vehicle terrain traversability. The analysis is categorized into terrain classification, terrain mapping, and cost-based traversability, with subcategories of appearance-based, geometry-based, and mixed-based methods. The article also explores the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) and other based end-to-end methods as crucial components for advanced terrain traversability analysis. The investigation indicates that a mixed approach, incorporating both exteroceptive and proprioceptive sensors, is more effective, optimized, and reliable for traversability analysis. Additionally, the article discusses the vehicle platforms and sensor technologies used in traversability analysis, making it a valuable resource for researchers in the field. Overall, this paper contributes significantly to the current understanding of traversability analysis in unstructured environments and provides insights for future sensor-based research on advanced traversability analysis.Item Open Access A comprehensive survey on Delaunay Triangulation: applications, algorithms, and implementations over CPUs, GPUs, and FPGAs(IEEE, 2024-01-15) Elshakhs, Yahia S.; Deliparaschos, Kyriakos M.; Charalambous, Themistoklis; Oliva, Gabriele; Zolotas, ArgyriosDelaunay triangulation is an effective way to build a triangulation of a cloud of points, i.e., a partitioning of the points into simplices (triangles in 2D, tetrahedra in 3D, and so on), such that no two simplices overlap and every point in the set is a vertex of at least one simplex. Such a triangulation has been shown to have several interesting properties in terms of the structure of the simplices it constructs (e.g., maximising the minimum angle of the triangles in the bi-dimensional case) and has several critical applications in the contexts of computer graphics, computational geometry, mobile robotics or indoor localisation, to name a few application domains. This review paper revolves around three main pillars: (I) algorithms, (II) implementations over central processing units (CPUs), graphics processing units (GPUs), and field programmable gate arrays (FPGAs), and (III) applications. Specifically, the paper provides a comprehensive review of the main state-of-the-art algorithmic approaches to compute the Delaunay Triangulation. Subsequently, it delivers a critical review of implementations of Delaunay triangulation over CPUs, GPUs, and FPGAs. Finally, the paper covers a broad and multi-disciplinary range of possible applications of this technique.Item Open Access Data supporting: 'Development of a thermal excitation source used in an active thermographic UAV platform'(Cranfield University, 2022-08-31 16:49) Deane, Shakeb; Tsourdos, Antonios; Avdelidis, Nico; Zolotas, Argyrios; P. V. Maldague, Xavier; Ibarra-Castanedo, Clemente; Genest, Marc; Pant, Shashank; Williamson, Alex; Withers, Stephen; Ahmadi, MohammadaliThis work aims to address the effectivenessand challenges of using active infrared thermography (IRT) on-board an unmannedaerial vehicle (UAV) platform. The work seeks to assess the performance ofsmall low powered forms of excitation which are suitable for activethermography and the ability to locate subsurface defects on composites. Anexcitation source in the form of multiple 250 W lamps are mounted onto a UAVand are solely battery powered with a remote trigger to power cycle them.Multiple experiments address the interference from the UAV whilst performing anactive IRT inspection. The optimal distances and time required for a UAV inspection using IRT is calculated. Multiple signal processing techniques areused to analyse the composites which helps locate the sub-surface defects. It was observedthat a UAV can successfully carry the required sensors and equipment for anActive thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for inspection of complexstructures is time-consuming. For example, a cherry picker would be required toinspect the tail of an aircraft. This solution looks to assist engineersinspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection.Item Open Access Development of a thermal excitation source used in an active thermographic UAV platform(Taylor & Francis, 2022-06-03) Deane, Shakeb; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Williamson, Alex A.; Withers, Stephen; Zolotas, Argyrios; Maldague, Xavier P. V.; Ahmadi, Mohammad; Pant, Shashank; Genest, Marc; Rabearivelo, Hobivola A.; Tsourdos, AntoniosThis work aims to address the effectiveness and challenges of using active infrared thermography (IRT) onboard an unmanned aerial vehicle (UAV) platform. The work seeks to assess the performance of small low-powered forms of excitation which are suitable for active thermography and the ability to locate subsurface defects on composites. An excitation source in multiple 250 W lamps is mounted onto a UAV and is solely battery powered with a remote trigger to power cycle them. Multiple experiments address the interference from the UAV whilst performing an active IRT inspection. The optimal distances and time required for a UAV inspection using IRT are calculated. Multiple signal processing techniques are used to analyse the composites which help locate the sub-surface defects. It was observed that a UAV can successfully carry the required sensors and equipment for an Active thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for the inspection of complex structures is time-consuming. For example, a cherry picker would be required to inspect the tail of an aircraft. This solution looks to assist engineers in inspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection.Item Open Access Diagnosis of composite materials in aircraft applications: towards a UAV active thermography inspection approach(Society of Photo-Optical Instrumentation Engineers (SPIE), 2021-04-12) Alhammad, Muflih; Avdelidis, Nicolas Peter; Deane, Shakeb; Ibarra-Castanedo, Clemente; Pant, Shashank; Nooralishahi, Parham; Ahmadi, Mohammad; Genest, Marc; Zolotas, Argyrios; Zanotti Fragonara, Luca; Valdes, Julio J.; Maldague, Xavier P. V.Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. This paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of active thermography techniques in the aerospace industry. Furthermore, as the use of unmanned aerial vehicles (UAVs) for the remote inspection of large and/or difficult access areas has significantly grown in the last few years thanks to their flexibility of flight and to the possibility to carry one or several measuring sensors, the aim to use a UAV active thermography system for the inspection of large composite aeronautical structures in a continuous dynamic mode is proposed.Item Open Access End-to-end one-shot path-planning algorithm for an autonomous vehicle based on a convolutional neural network considering traversability cost(MDPI, 2022-12-10) Bian, Tongfei; Xing, Yang; Zolotas, ArgyriosPath planning plays an important role in navigation and motion planning for robotics and automated driving applications. Most existing methods use iterative frameworks to calculate and plan the optimal path from the starting point to the endpoint. Iterative planning algorithms can be slow on large maps or long paths. This work introduces an end-to-end path-planning algorithm based on a fully convolutional neural network (FCNN) for grid maps with the concept of the traversability cost, and this trains a general path-planning model for 10 × 10 to 80 × 80 square and rectangular maps. The algorithm outputs the lowest-cost path while considering the cost and the shortest path without considering the cost. The FCNN model analyzes the grid map information and outputs two probability maps, which show the probability of each point in the lowest-cost path and the shortest path. Based on the probability maps, the actual optimal path is reconstructed by using the highest probability method. The proposed method has superior speed advantages over traditional algorithms. On test maps of different sizes and shapes, for the lowest-cost path and the shortest path, the average optimal rates were 72.7% and 78.2%, the average success rates were 95.1% and 92.5%, and the average length rates were 1.04 and 1.03, respectively.Item Open Access Fault detection in aircraft flight control actuators using support vector machines(MDPI, 2023-02-02) Grehan, Julianne; Ignatyev, Dmitry; Zolotas, ArgyriosFuture generations of flight control systems, such as those for unmanned autonomous vehicles (UAVs), are likely to be more adaptive and intelligent to cope with the extra safety and reliability requirements due to pilotless operations. An efficient fault detection and isolation (FDI) system is paramount and should be capable of monitoring the health status of an aircraft. Historically, hardware redundancy techniques have been used to detect faults. However, duplicating the actuators in an UAV is not ideal due to the high cost and large mass of additional components. Fortunately, aircraft actuator faults can also be detected using analytical redundancy techniques. In this study, a data-driven algorithm using Support Vector Machine (SVM) is designed. The aircraft actuator fault investigated is the loss-of-effectiveness (LOE) fault. The aim of the fault detection algorithm is to classify the feature vector data into a nominal or faulty class based on the health of the actuator. The results show that the SVM algorithm detects the LOE fault almost instantly, with an average accuracy of 99%.Item Open Access Feasible, robust and reliable automation and control for autonomous systems(MDPI, 2022-07-07) Hamid, Umar Zakir Abdul; Hu, Chuan; Zolotas, ArgyriosItem Open Access Fusion insights from ultrasonic and thermographic inspections for impact damage analysis(AIAA, 2023-06-08) Torbali, M. Ebubekir; Alhammad, Muflih; Zolotas, Argyrios; Avdelidis, Nicolas Peter; Ibarra-Castanedo, Clemente; Maldague, XavierLow energy impact damage in composite materials may be more concerning than it appears visually, often requiring a detailed examination for accurate assessment to ensure safe and sustainable operation. Non-destructive testing (NDT) methods provide such inspection techniques, and in this paper, NDT-based fusion is explored for enhanced identification of defect size and location compared to indepdently using individual NDT methods separately. Three Carbon Fiber Reinforced Polymer (CFRP) specimens are examined, each with an impact damage of a given energy level, using pulsed thermography (PT) and phased array (PA) ultrasonic methods. Following the extraction of binary defect shapes from source images, a decision-level fusion approach is performed. The results indicate that combining ultrasonic and infrared thermography (IRT) inspections for CFRP composite materials is promising to achieve enhanced and improved detection traceability.Item Open Access GNSS/INS/VO fusion using gated recurrent unit in GNSS denied environments(AIAA, 2023-01-19) Negru, Sorin A.; Geragersian, Patrick; Petrunin, Ivan; Zolotas, Argyrios; Grech, RaphaelUrban air mobility is a growing market, which will bring new ways to travel and to deliver items covering urban and suburban areas, at relatively low altitudes. To guarantee a safe and robust navigation, Unmanned Aerial Vehicles should be able to overcome all the navigational constraints. The paper is analyzing a novel sensor fusion framework with the aim to obtain a stable flight in a degraded GNSS environment. The sensor fusion framework is combining data coming from a GNSS receiver, an IMU and an optical camera under a loosely coupled scheme. A Federated Filter approach is implemented with the integration of two GRUs blocks. The first GRU is used to increase the accuracy in time of the INS, giving as output a more reliable position that it is fused, with the position information coming from, the GNSS receiver, and the developed Visual Odometry algorithm. Further, a master GRU block is used to select the best position information. The data is collected using a hardware in the loop setup, using AirSim, Pixhawk and Spirent GSS7000 hardware. As validation, the framework is tested, on a virtual UAV, performing a delivery mission on Cranfield university campus. Results showed that the developed fusion framework, can be used for short GNSS outages.Item Open Access Hybrid terrain traversability analysis in off-road environments(IEEE, 2022-03-22) Leung, Tiga Ho Yin; Ignatyev, Dmitry I.; Zolotas, ArgyriosThere is a significant growth in autonomy level in off-road ground vehicles. However, unknown off-road environments are often challenging due to their unstructured and rough nature. To find a path that the robot can move smoothly to its destination, it needs to analyse the surrounding terrain. In this paper, we present a hybrid terrain traversability analysis framework. Semantic segmentation is implemented to understand different types of the terrain surrounding the robot; meanwhile geometrical properties of the terrain are assessed with the aid of a probabilistic terrain estimation. The framework represents the traversability analysis on a robot-centric cost map, which is available to the path planners. We evaluated the proposed framework with synchronised sensor data captured while driving the robot in real off-road environments. This thorough terrain traversability analysis will be crucial for autonomous navigation systems in off-road environments.Item Open Access Model-based fully coupled propulsion-aerodynamics optimization for hybrid electric aircraft energy management strategy(Elsevier, 2022-01-20) Zhang, Jinning; Roumeliotis, Ioannis; Zolotas, ArgyriosHybrid electric aircraft concepts are high in future aviation agenda to enabling reduced fuel consumption and emissions. However, the additional weight of the introduced battery and electrical components and sizing cascading effects will impact aircraft mission analysis performance. Therefore, the potential benefit of adopting hybrid electric aircraft will highly depend on the applied energy management strategy (EMS). This paper presents a three-layer propulsion-mission analysis-EMS integrated multi-objective optimization scheme for hybrid electric aircraft identifying feasible design aspects considering fully coupled propulsion-aerodynamics effects. The ‘propulsion’ layer comprises the propulsion system modelling, integration approaches, and performance synthesis using an Artificial Neural Network (ANN)-based gas turbine surrogate model. The ‘mission-analysis’ layer is designed for multi-energy sources hybrid electric aircraft mission analysis considering fully coupled propulsion-aerodynamic effects. The ‘EMS’ layer utilizes non-dominated sorting genetic algorithm II (NSGA-II) addressing the design trade-off, i.e., block fuel burn, energy consumption, emissions. The proposed scheme is applied to a typical narrow-body aircraft, Boeing 737–800, equipped with mechanically integrated hybrid electric parallel propulsion configuration to explore flight electrification in civil aviation. Moreover, sensitivity analysis of battery technology level and flight mission definition is followed providing insights to hybrid electric aircraft application scenarios. The EMS optimization results indicate that for short/medium haul aircrafts which operate in high altitude with long flight durations, fuel as consumable energy source is prone to be used in initial stages to reduce aircraft weight and lift-dependent drag, while battery as non-consumable energy is optimally allocated in final flight stages of descent and landing. The design of hybrid electric aircraft is highly sensitive to both flight mission definition and battery specific energy projections. With battery specific energy projections of 1500 Wh/kg for the year 2035, optimal block fuel burn reduction by −44.62%, −31.47% and −21.86% can be obtained at the flight range design of 1000 nmi, 1250 nmi and 1500 nmi respectively.Item Open Access Multi-label classification algorithms for composite materials under infrared thermography testing(Taylor and Francis, 2022-10-14) Alhammad, Muflih; Avdelidis, Nicolas Peter; Ibarra Castanedo, Clemente; Maldague, Xavier; Zolotas, Argyrios; Torbali, M. Ebubekir; Genestc, MarcThe key idea in this paper is to propose multi-labels classification algorithms to handle benchmark thermal datasets that are practically associated with different data characteristics and have only one health condition (damaged composite materials). A suggested alternative approach for extracting the statistical contents from the thermal images, is also employed. This approach offers comparable advantages for classifying multi-labelled datasets over more complex methods. Overall scored accuracy of different methods utilised in this approach showed that Random Forest algorithm has a clear higher performance over the others. This investigation is very unique as there has been no similar work published so far. Finally, the results demonstrated in this work provide a new perspective on the inspection of composite materials using Infrared Pulsed Thermography.Item Open Access Non-linear control of a quadrotor with actuator delay(AIAA, 2024-01-04) Kartal, Muhammed R.; Ignatyev, Dmitry; Zolotas, ArgyriosDuring the last decade, Unmanned Aerial Vehicles (UAVs) gained significant interest for use in various application domains such as monitoring/surveillance, freight/cargo shipping, and agriculture spraying. Developing such vehicle platforms requires the utilization of robust control approaches to maintain stable and appropriate maneuvering capabilities, as well as to address system uncertainty such as payload variation or dynamic variations (e.g., spraying drones are affected by such uncertainty). Moreover, due to the rotor-based structure, rotorcraft UAVs are quite vulnerable to UAV systems control input signal delay. In terms of maintaining a robust approach for a rotorcraft UAV, it is essential to provide stability against UAV systems control input signal delay. This paper makes an analysis throughout to improve control efficiency against UAV systems control input signal delay. Through investigation and improved fault rejection, three controlling algorithms were designed and applied. The analysis focused on three different scenarios. Insights are discussed within the remit of command tracking performance with UAV systems control input signal delay.Item Open Access A novel approach for detecting cyberattacks in embedded systems based on anomalous patterns of resource utilization - Part I(IEEE, 2021-06-11) Aloseel, Abdulmohsan; Al-Rubaye, Saba; Zolotas, Argyrios; He, Hongmei; Shaw, CarlThis paper presents a novel security approach called Anomalous Resource Consumption Detection (ARCD), which acts as an additional layer of protection to detect cyberattacks in embedded systems (ESs). The ARCD approach is based on the differentiation between the predefined standard resource consumption pattern and the anomalous consumption pattern of system resource utilization. The effectiveness of the proposed approach is tested in a rigorous manner by simulating four types of cyberattacks: a denial-of-service attack, a brute-force attack, a remote code execution attack, and a man-in-the-middle attack, which are executed on a Smart PiCar (used as the testbed). A septenary tuple model consisting of seven parameters, representing the embedded system’s architecture, has been created as the core of the detection mechanism. The approach’s efficiency and effectiveness has been validated in terms of range and pattern by analyzing the collected data statistically in terms of mean, median, mode, standard deviation, range, minimum, and maximum values. The results demonstrated the potential for defining a standard pattern of resource utilization and performance of the embedded system due to a significant similarity of the parameters’ values at normal states. In contrast, the attacked cases showed a definite, observable, and detectable impact on resource consumption and performance of the embedded system, causing an anomalous pattern. Thus, by merging these two findings, the ARCD approach has been developed. ARCD facilitates building secure operating systems in line with the ES’s capabilities. Furthermore, the ARCD approach can work along with existing countermeasures to augment the security of the operating system layer.Item Open Access Predicting autonomous vehicle navigation parameters via image and image-and-point cloud fusion-based end-to-end methods(IEEE, 2022-10-13) Beycimen, Semih; Ignatyev, Dmitry; Zolotas, ArgyriosThis paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. Image-based and Image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained Image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed.Item Open Access Preface for feature topic on human driver behaviours for intelligent vehicles(Springer, 2024-01-19) Cao, Dongpu; Zolotas, Argyrios; Wang, Meng; Pirani, Mohammad; Li, WenboItem Open Access Self-supervised obstacle detection during autonomous UAS taxi operations(AIAA, 2023-01-19) Shaikh, Yousuf; Petrunin, Ivan; Zolotas, ArgyriosThis research explores the application of self-supervised learning techniques for obstacle detection and collision avoidance during UAS auto-taxi. Autoencoders were used to detect obstacles as anomalies by comparison of reconstruction errors. RGB cameras and millimetre wave radars covering conflict free zones (CFZs) around the own-ship were chosen to provide inputs to autoencoders. Results demonstrated that autoencoders were able to detect obstacles as anomalies within the CFZs but with certain limitations at lay the foundations of further work and investigation within the research area.