This study focus on trying to understand what factors regarding bumper materials and coatings affect the radar transparency at 77 GHz. Dielectric spectroscopy was done at 25 unique samples, consisting of various plastic substrates, primers, basecoats and clearcoats with the Free-spaced method in the 60-90 GHz region. The plastic substrate consisted of Polypropylene-blend with different fillers such as talc, carbon black and metal flakes. The basecoats analyzed were 2 solid factory coatings (one black and one white), 5 factory coating containing different effect pigments such as metal flakes, Xiralic, and Mica, 1 factory PVD coating and 2 aftermarket basecoats. All samples were provided by a Volvo Cars’ supplier, however, some samples were repainted with the aftermarket coatings. The complex permittivites were calculated for each PP-blend and coating from the curve fitting of the measured S-parameters from the Free-spaced method. Material analysis such as thickness measurements of the plastic substrate and coatings, ATR-FTIR spectroscopy, TGA and DSC were done at all plastic substrates. The plastic substrates and the coating were also observed in optical microscope and in SEM. The calculated permittivities were compared with the results from the material analysis and correlations between increased metal content in the basecoat and higher real permittivity were found. Some relationship between the size of the metal flakes and the radar transparency could also be observed. Further, correlations between higher real permittivity with higher concentration of talc and carbon black were detected. MATLAB was used to provide an example for optimization of the plastic thickness for a given basecoat in order to have minimal reflection at 77 GHz.

The work of this thesis is concerned with the aggregation and advancement of modelling practise as used within modern day product development and optimisation environments making use of Model Based Design (¿MBD¿) and similar procedures. A review of model development and use forms the foundation of the work, with the findings being aggregated into two unique approaches for rapid model development and reconfiguration; the Plug-and-Simulate (¿PaS¿) approach and the Paradigm for Large Model Creation (¿PLMC¿); each shown to posses its own advantages. To support the MBD process a model optimisation algorithm that seeks to eliminate parameters that are of little or no significance to a simulation is developed. Eliminations are made on the basis of an energy analysis which determines the activity of a number of energy elements. Low activity elements are said to be of less significance to the global dynamics of a model and thus become targets for elimination. A model configuration tool is presented that brings together the PLMC and parameter elimination algorithm. The tool is shown to be useful for rapid configuration and reconfiguration of models and is capable of automatically running the optimisation algorithms thus producing a simulation model that is parametrically and computationally optimised. The response of the plug-and-simulate drivetrain submodels, assembled to represent a front wheel drive drivetrain, is examined. The resulting model is subjected to a torque step-input and an empirically obtained torque curve that characterises the input to a drivetrain undergoing steady acceleration. The model displays the expected response in both its full parameter and parameter reduced versions with simulation efficiency gains observed in the parameter reduced version. / EPSRC

The tibia is a frequent site of injury in frontal automotive collisions. The bulk of experimental cadaveric studies on injury tolerance assume load is applied in line with the leg’s long axis, leaving non-standard postures largely uninvestigated. The purpose of this work was to study the effects of non-standard postures on the tibia’s injury tolerance. A pneumatic system was designed to facilitate impact testing. This system allows the user to fire a projectile of variable mass towards a specimen at a range of velocities by varying the supplied air pressure. Impact tests were performed using pairs of isolated cadaveric tibias. Within each pair of specimens, two postures were compared by varying the angle of the bone’s long axis relative to the direction of impact, representing knee extension and corresponding plantarflexion. It was found that the specimens held further from the axial posture sustained injury at lower forces. Two commonly-used Anthropomorphic Test Device legforms were impacted in these non-standard postures. New load limits were proposed for the use of these devices in off-axis impact testing. In order to compare directly with the loads measured by the legforms, it was necessary to measure forces and moments internal to the bone’s long axis. A non-invasive load estimation method was developed and tested using strain measured from the surface of four specimens. The method performed poorly under impact conditions, but may be refined in the future. Quantifying the effect of posture on injury risk in the tibia allows for the refinement of existing injury criteria. Ultimately, this can be used to enhance the design of protective devices to reduce the incidence of tibia fractures in automotive collisions. / Thesis / Master of Applied Science (MASc) / Fractures of the tibia (the shin bone) are common in automotive collisions, and often lead to long-term impairment. Experimental studies on these kinds of injuries are usually performed with the lower leg aligned with the direction of impact, which does not reflect the range of postures an occupant may assume during a crash. Cadaveric tibias were subjected to impact loading in two different postures. It was found that the specimens held further from an axial posture sustained fractures at lower forces. Two commonly-used crash test dummy legs were also impacted in these non-standard postures to test their performance. Suggestions were made for new load limits to be used with these devices in non-standard postures. The finding that leg posture has an effect on injury risk in the tibia can be used in the future to design and evaluate better protective devices and ultimately reduce the incidence of these injuries.

The Internet of Things (IoT) is spearheading a significant revolution in the realm of computing systems for the next generation. IoT has swiftly permeated various domains, including healthcare, manufacturing, military, and transportation, becoming an essential component of numerous smart devices and applications. However, as the number of IoT devices proliferates, security concerns have surged, resulting in severe attacks in recent years. Consequently, it is imperative to conduct a comprehensive investigation into IoT networks to identify and address vulnerabilities in order to preempt potential adversarial activities. The aim of this research is to examine different IoT-based systems and comprehend their security weaknesses. Additionally, the objective is to develop effective strategies to mitigate vulnerabilities and explore the security loopholes inherent in IoT-based systems, along with a plan to rectify them. IoT-based systems present unique challenges due to the expanding adoption of IoT technology across diverse applications, accompanied by a wide array of IoT devices. Each IoT network has its own limitations, further compounding the challenge. For instance, IoT devices used in sensor networks often face constraints in terms of resources, possessing limited power and computational capabilities. Moreover, integration of IoT with existing systems introduces security issues. A prime example of this integration is found in connected cars, where traditional in-vehicle networks, designed to connect internal car components, must be highly robust to meet stringent requirements. However, modern cars are now connected to a wide range of IoT nodes through various interfaces, thus creating new security challenges for professionals to address. This work offers a comprehensive investigation plan for different types of IoT-based systems with varying constraints to identify security vulnerabilities. We also propose security measures to mitigate the vulnerabilities identified in our investigation, thereby preventing adversarial activities. To facilitate the exploration and investigation of vulnerabilities, our work is divided into two parts: resource-constrained IoT-based systems (sensor networks, smart homes) and robustness-constrained IoT-based systems (connected cars). In our investigation of resource-constrained IoT networks, we focus on two widely used service-oriented IoT protocols, namely Universal Plug and Play (UPnP) and Message Queue Telemetry Transport (MQTT). Through a structured phase-by-phase analysis of these protocols, we establish a comprehensive threat model that explains the existing security gaps in communications. The threat models present security vulnerabilities of service-oriented resource-constrained IoT networks and the corresponding security attacks that exploit these vulnerabilities. We propose security solutions to mitigate the identified vulnerabilities and defend against potential security breaches. Our security analysis demonstrates that the proposed measures successfully thwart adversarial activities, and our experimental data supports the feasibility of the proposed models. For robustness-constrained IoT-based systems, we investigate the in-vehicle networks of modern cars, specifically focusing on the Controller Area Network (CAN) bus system, which is widely adopted for connecting Electronic Control Units (ECUs) in vehicles. To uncover vulnerabilities in these in-vehicle networks, we leverage fuzz testing, a method that involves testing with random data. Fuzz testing over the CAN bus is a well-established technique for detecting security vulnerabilities in in-vehicle networks. Furthermore, the automatic execution of test cases and assessment of robustness make CAN bus fuzzing a popular choice in the automotive testing community. However, a major drawback of fuzz testing is the generation of a large volume of execution reports, often containing false positives. Consequently, all execution reports must be manually reviewed, which is time-consuming and prone to human errors. To address this issue, we propose an automatic investigation mechanism to identify security vulnerabilities from fuzzing logs, considering the class, relative severity, and robustness of failures. Our proposed schema utilizes artificial intelligence (AI) to identify genuine security-critical vulnerabilities from fuzz testing execution logs. Additionally, we provide mechanisms to gauge the relative severity and robustness of a failure, thereby determining the criticality of a vulnerability. Moreover, we propose an AI-assisted vulnerability scoring system that indicates the criticality of a vulnerability, offering invaluable assistance in prioritizing the mitigation of critical issues in in-vehicle networks. / Computer and Information Science

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