The demand of sustainability is continuously increasing. Therefore, thermoplastic composites became a focus of research due to their good weight to performance ratio. Nevertheless, the limiting factor of their usage for some processes is the loss of consolidation during re-melting (deconsolidation), which reduces the part quality. Several studies dealing with deconsolidation are available. These studies investigate a single material and process, which limit their usefulness in terms of general interpretations as well as their comparability to other studies. There are two main approaches. The first approach identifies the internal void pressure as the main cause of deconsolidation and the second approach identifies the fiber reinforcement network as the main cause. Due to of their controversial results and limited variety of materials and processes, there is a big need of a more comprehensive investigation on several materials and processes. This study investigates the deconsolidation behavior of 17 different materials and material configurations considering commodity, engineering, and performance polymers as well as a carbon and two glass fiber fabrics. Based on the first law of thermodynamics, a deconsolidation model is proposed and verified by experiments. Universal applicable input parameters are proposed for the prediction of deconsolidation to minimize the required input measurements. The study revealed that the fiber reinforcement network is the main cause of deconsolidation, especially for fiber volume fractions higher than 48 %. The internal void pressure can promote deconsolidation, when the specimen was recently manufactured. In other cases the internal void pressure as well as the surface tension prevents deconsolidation. During deconsolidation the polymer is displaced by the volume increase of the void. The polymer flow damps the progress of deconsolidation because of the internal friction of the polymer. The crystallinity and the thermal expansion lead to a reversible thickness increase during deconsolidation. Moisture can highly accelerate deconsolidation and can increase the thickness by several times because of the vaporization of water. The model is also capable to predict reconsolidation under the defined boundary condition of pressure, time, and specimen size. For high pressure matrix squeeze out occur, which falsifies the accuracy of the model.The proposed model was applied to thermoforming, induction welding, and thermoplastic tape placement. It is demonstrated that the load rate during thermoforming is the critical factor of achieving complete reconsolidation. The required load rate can be determined by the model and is dependent on the cooling rate, the forming length, the extent of deconsolidation, the processing temperature, and the final pressure. During induction welding deconsolidation can tremendously occur because of the left moisture in the polymer at the molten state. The moisture cannot fully diffuse out of the specimen during the faster heating. Therefore, additional pressure is needed for complete reconsolidation than it would be for a dry specimen. Deconsolidation is an issue for thermoplastic tape placement, too. It limits the placement velocity because of insufficient cooling after compaction. If the specimen after compaction is locally in a molten state, it deconsolidates and causes residual stresses in the bond line, which decreases the interlaminar shear strength. It can be concluded that the study gains new knowledge and helps to optimize these processes by means of the developed model without a high number of required measurements. Aufgrund seiner guten spezifischen Festigkeit und Steifigkeit ist der endlosfaserverstärkte Thermoplast ein hervorragender Leichtbauwerkstoff. Allerdings kann es während des Wiederaufschmelzens durch Dekonsolidierung zu einem Verlust der guten mechanischen Eigenschaften kommen, daher ist Dekonsolidierung unerwünscht. In vielen Studien wurde die Dekonsolidierung mit unterschiedlichen Ergebnissen untersucht. Dabei wurde meist ein Material und ein Prozess betrachtet. Eine allgemeine Interpretation und die Vergleichbarkeit unter den Studien sind dadurch nur begrenzt möglich. Aus der Literatur sind zwei Ansätze bekannt. Dem ersten Ansatz liegt der Druckunterschied zwischen Poreninnendruck und Umgebungsdruck als Hauptursache der Dekonsolidierung zu Grunde. Beim zweiten Ansatz wird die Faserverstärkung als Hauptursache identifiziert. Aufgrund der kontroversen Ergebnisse und der begrenzten Anzahl der Materialien und Verarbeitungsverfahren, besteht die Notwendigkeit einer umfassenden Untersuchung über mehrere Materialien und Prozesse. Diese Studie umfasst drei Polymere (Polypropylen, Polycarbonat und Polyphenylensulfid), drei Gewebe (Köper, Atlas und Unidirektional) und zwei Prozesse (Autoklav und Heißpressen) bei verschiedenen Faservolumengehalten. Es wurde der Einfluss des Porengehaltes auf die interlaminare Scherfestigkeit untersucht. Aus der Literatur ist bekannt, dass die interlaminare Scherfestigkeit mit der Zunahme des Porengehaltes linear sinkt. Dies konnte für die Dekonsolidierung bestätigt werden. Die Reduktion der interlaminaren Scherfestigkeit für thermoplastische Matrizes ist kleiner als für duroplastische Matrizes und liegt im Bereich zwischen 0,5 % bis 1,5 % pro Prozent Porengehalt. Außerdem ist die Abnahme signifikant vom Matrixpolymer abhängig. Im Falle der thermisch induzierten Dekonsolidierung nimmt der Porengehalt proportional zu der Dicke der Probe zu und ist ein Maß für die Dekonsolidierung. Die Pore expandiert aufgrund der thermischen Gasexpansion und kann durch äußere Kräfte zur Expansion gezwungen werden, was zu einem Unterdruck in der Pore führt. Die Faserverstärkung ist die Hauptursache der Dickenzunahme beziehungsweise der Dekonsolidierung. Die gespeicherte Energie, aufgebaut während der Kompaktierung, wird während der Dekonsolidierung abgegeben. Der Dekompaktierungsdruck reicht von 0,02 MPa bis 0,15 MPa für die untersuchten Gewebe und Faservolumengehalte. Die Oberflächenspannung behindert die Porenexpansion, weil die Oberfläche vergrößert werden muss, die zusätzliche Energie benötigt. Beim Kontakt von benachbarten Poren verursacht die Oberflächenspannung ein Verschmelzen der Poren. Durch das bessere Volumen- Oberfläche-Verhältnis wird Energie abgebaut. Der Polymerfluss bremst die Entwicklung der Dickenzunahme aufgrund der erforderlichen Energie (innere Reibung) der viskosen Strömung. Je höher die Temperatur ist, desto niedriger ist die Viskosität des Polymers, wodurch weniger Energie für ein weiteres Porenwachstum benötigt wird. Durch den reversiblen Einfluss der Kristallinität und der Wärmeausdehnung des Verbundes wird während der Erwärmung die Dicke erhöht und während der Abkühlung wieder verringert. Feuchtigkeit kann einen enormen Einfluss auf die Dekonsolidierung haben. Ist noch Feuchtigkeit über der Schmelztemperatur im Verbund vorhanden, verdampft diese und kann die Dicke um ein Vielfaches der ursprünglichen Dicke vergrößern. Das Dekonsolidierungsmodell ist in der Lage die Rekonsolidierung vorherzusagen. Allerdings muss der Rekonsolidierungsdruck unter einem Grenzwert liegen (0,15 MPa für 50x50 mm² und 1,5 MPa für 500x500 mm² große Proben), da es sonst bei der Probe zu einem Polymerfluss aus der Probe von mehr als 2 % kommt. Die Rekonsolidierung ist eine inverse Dekonsolidierung und weist die gleichen Mechanismen in der entgegengesetzten Richtung auf. Das entwickelte Modell basiert auf dem ersten Hauptsatz der Thermodynamik und kann die Dicke während der Dekonsolidierung und der Rekonsolidierung vorhersagen. Dabei wurden eine homogene Porenverteilung und eine einheitliche, kugelförmige Porengröße angenommen. Außerdem wurde die Massenerhaltung angenommen. Um den Aufwand für die Bestimmung der Eingangsgrößen zu reduzieren, wurden allgemein gültige Eingabeparameter bestimmt, die für eine Vielzahl von Konfigurationen gelten. Das simulierte Materialverhalten mit den allgemein gültigen Eingangsparametern erzielte unter den definierten Einschränkungen eine gute Übereinstimmung mit dem tatsächlichen Materialverhalten. Nur bei Konfigurationen mit einer Viskositätsdifferenz von mehr als 30 % zwischen der Schmelztemperatur und der Prozesstemperatur sind die allgemein gültigen Eingangsparameter nicht anwendbar. Um die Relevanz für die Industrie aufzuzeigen, wurden die Effekte der Dekonsolidierung für drei weitere Verfahren simuliert. Es wurde gezeigt, dass die Kraftzunahmegeschwindigkeit während des Thermoformens ein Schlüsselfaktor für eine vollständige Rekonsolidierung ist. Wenn die Kraft zu langsam appliziert wird oder die finale Kraft zu gering ist, ist die Probe bereits erstarrt, bevor eine vollständige Konsolidierung erreicht werden kann. Auch beim Induktionsschweißen kann Dekonsolidierung auftreten. Besonders die Feuchtigkeit kann zu einer starken Zunahme der Dekonsolidierung führen, verursacht durch die sehr schnellen Heizraten von mehr als 100 K/min. Die Feuchtigkeit kann während der kurzen Aufheizphase nicht vollständig aus dem Polymer ausdiffundieren, sodass die Feuchtigkeit beim Erreichen der Schmelztemperatur in der Probe verdampft. Beim Tapelegen wird die Ablegegeschwindigkeit durch die Dekonsolidierung begrenzt. Nach einer scheinbar vollständigen Konsolidierung unter der Walze kann die Probe lokal dekonsolidieren, wenn das Polymer unter der Oberfläche noch geschmolzen ist. Die daraus resultierenden Poren reduzieren die interlaminare Scherfestigkeit drastisch um 5,8 % pro Prozent Porengehalt für den untersuchten Fall. Ursache ist die Kristallisation in der Verbindungszone. Dadurch werden Eigenspannungen erzeugt, die in der gleichen Größenordnung wie die tatsächliche Scherfestigkeit sind.

Novel image processing techniques have been in development for decades, but most of these techniques are barely used in real world applications. This results in a gap between image processing research and real-world applications; this thesis aims to close this gap. In an initial study, the quantification, propagation, and communication of uncertainty were determined to be key features in gaining acceptance for new image processing techniques in applications. This thesis presents a holistic approach based on a novel image processing pipeline, capable of quantifying, propagating, and communicating image uncertainty. This work provides an improved image data transformation paradigm, extending image data using a flexible, high-dimensional uncertainty model. Based on this, a completely redesigned image processing pipeline is presented. In this pipeline, each step respects and preserves the underlying image uncertainty, allowing image uncertainty quantification, image pre-processing, image segmentation, and geometry extraction. This is communicated by utilizing meaningful visualization methodologies throughout each computational step. The presented methods are examined qualitatively by comparing to the Stateof- the-Art, in addition to user evaluation in different domains. To show the applicability of the presented approach to real world scenarios, this thesis demonstrates domain-specific problems and the successful implementation of the presented techniques in these domains.

The gas phase infrared and fragmentation spectra of a systematic group of trimetallic oxo-centered transition metal complexes are shown and discussed, with formate and acetate bridging ligands and pyridine and water as axial ligands. The stability of the complexes, as predicted by appropriate ab initio simulations, is demonstrated to agree with collision induced dissociation (CID) measurements. A broad range of DFT calculations are shown. They are used to simulate the geometry, the bonding situation, relative stability and flexibility of the discussed complexes, and to specify the observed trends. These simulations correctly predict the trends in the band splitting of the symmetric and asymmetric carboxylate stretch modes, but fail to account for anharmonic effects observed specifically in the mid IR range. The infrared spectra of the different ligands are introduced in a brief literature review. Their changes in different environments or different bonding situations are discussed and visualized, especially the interplay between fundamental-, overtone-, and combination bands, as well as Fermi resonances between them. A new variation on the infrared multi photon dissociation (IRMPD) spectroscopy method is proposed and evaluated. In addition to the commonly considered total fragment yield, the cumulative fragment yield can be used to plot the wavelength dependent relative abundance of different fragmentation products. This is shown to include valuable additional information on the excited chromophors, and their coupling to specific fragmentation channels. High quality homo- and heterometallic IRMPD spectra of oxo centered carboxylate complexes of chromium and iron show the impacts of the influencing factors: the metal centers, the bridging ligands, their carboxylate stretch modes and CH bend modes, and the terminal ligands. In all four formate spectra, anharmonic effects are necessary to explain the observed spectra: combination bands of both carboxylate stretch modes and a Fermi resonance of the fundamental of the CH stretch mode, and a combination band of the asymmetric carboxylate stretch mode with the CH bend mode of the formate bridging ligand. For the water adduct species, partial hydrolysis is proposed to account for the changes in the observed carboxylic stretch modes. Appropriate experiments are suggested to verify the mode assignments that are not directly explained by the ab initio calculations, the available experimental results or other means like deuteration experiments.

Numerical Godeaux surfaces are minimal surfaces of general type with the smallest possible numerical invariants. It is known that the torsion group of a numerical Godeaux surface is cyclic of order \(m\leq 5\). A full classification has been given for the cases \(m=3,4,5\) by the work of Reid and Miyaoka. In each case, the corresponding moduli space is 8-dimensional and irreducible. There exist explicit examples of numerical Godeaux surfaces for the orders \(m=1,2\), but a complete classification for these surfaces is still missing. In this thesis we present a construction method for numerical Godeaux surfaces which is based on homological algebra and computer algebra and which arises from an experimental approach by Schreyer. The main idea is to consider the canonical ring \(R(X)\) of a numerical Godeaux surface \(X\) as a module over some graded polynomial ring \(S\). The ring \(S\) is chosen so that \(R(X)\) is finitely generated as an \(S\)-module and a Gorenstein \(S\)-algebra of codimension 3. We prove that the canonical ring of any numerical Godeaux surface, considered as an \(S\)-module, admits a minimal free resolution whose middle map is alternating. Moreover, we show that a partial converse of this statement is true under some additional conditions. Afterwards we use these results to construct (canonical rings of) numerical Godeaux surfaces. Hereby, we restrict our study to surfaces whose bicanonical system has no fixed component but 4 distinct base points, in the following referred to as marked numerical Godeaux surfaces. The particular interest of this thesis lies on marked numerical Godeaux surfaces whose torsion group is trivial. For these surfaces we study the fibration of genus 4 over \(\mathbb{P}^1\) induced by the bicanonical system. Catanese and Pignatelli showed that the general fibre is non-hyperelliptic and that the number \(\tilde{h}\) of hyperelliptic fibres is bounded by 3. The two explicit constructions of numerical Godeaux surfaces with a trivial torsion group due to Barlow and Craighero-Gattazzo, respectively, satisfy \(\tilde{h} = 2\). With the method from this thesis, we construct an 8-dimensional family of numerical Godeaux surfaces with a trivial torsion group and whose general element satisfy \(\tilde{h}=0\). Furthermore, we establish a criterion for the existence of hyperelliptic fibres in terms of a minimal free resolution of \(R(X)\). Using this criterion, we verify experimentally the existence of a numerical Godeaux surface with \(\tilde{h}=1\).

The Symbol Grounding Problem (SGP) is one of the first attempts to proposed a hypothesis about mapping abstract concepts and the real world. For example, the concept "ball" can be represented by an object with a round shape (visual modality) and phonemes /b/ /a/ /l/ (audio modality). This thesis is inspired by the association learning presented in infant development. Newborns can associate visual and audio modalities of the same concept that are presented at the same time for vocabulary acquisition task. The goal of this thesis is to develop a novel framework that combines the constraints of the Symbol Grounding Problem and Neural Networks in a simplified scenario of association learning in infants. The first motivation is that the network output can be considered as numerical symbolic features because the attributes of input samples are already embedded. The second motivation is the association between two samples is predefined before training via the same vectorial representation. This thesis proposes to associate two samples and the vectorial representation during training. Two scenarios are considered: sample pair association and sequence pair association. Three main contributions are presented in this work. The first contribution is a novel Symbolic Association Model based on two parallel MLPs. The association task is defined by learning that two instances that represent one concept. Moreover, a novel training algorithm is defined by matching the output vectors of the MLPs with a statistical distribution for obtaining the relationship between concepts and vectorial representations. The second contribution is a novel Symbolic Association Model based on two parallel LSTM networks that are trained on weakly labeled sequences. The definition of association task is extended to learn that two sequences represent the same series of concepts. This model uses a training algorithm that is similar to MLP-based approach. The last contribution is a Classless Association. The association task is defined by learning based on the relationship of two samples that represents the same unknown concept. In summary, the contributions of this thesis are to extend Artificial Intelligence and Cognitive Computation research with a new constraint that is cognitive motivated. Moreover, two training algorithms with a new constraint are proposed for two cases: single and sequence associations. Besides, a new training rule with no-labels with promising results is proposed.

Though environmental inequality research has gained extensive interest in the United States, it has received far less attention in Europe and Germany. The main objective of this book is to extend the research on environmental inequality in Germany. This book aims to shed more light on the question of whether minorities in Germany are affected by a disproportionately high burden of environmental pollution, and to increase the general knowledge about the causal mechanisms, which contribute to the unequal distribution of environmental hazards across the population. To improve our knowledge about environmental inequality in Germany, this book extends previous research in several ways. First, to evaluate the extent of environmental inequality, this book relies on two different data sources. On the on hand, it uses household-level survey data and self-reports about the impairment through air pollution. On the other hand, it combines aggregated census data and objective register-based measures of industrial air pollution by using geographic information systems (GIS). Consequently, this book offers the first analysis of environmental inequality on the national level that uses objective measures of air pollution in Germany. Second, to evaluate the causes of environmental inequality, this book applies a panel data analysis on the household level, thereby offering the first longitudinal analysis of selective migration processes outside the United States. Third, it compares the level of environmental inequality between German metropolitan areas and evaluates to which extent the theoretical arguments of environmental inequality can explain differing levels of environmental inequality across the country. By doing so, this book not only investigates the impact of indicators derived by the standard strand of theoretical reasoning but also includes structural characteristics of the urban space. All studies presented in this book confirm the disproportionate exposure of minorities to environmental pollution. Minorities live in more polluted areas in Germany but also in more polluted parts of the communities, and this disadvantage is most severe in metropolitan regions. Though this book finds evidence for selective migration processes contributing to the disproportionate exposure of minorities to environmental pollution, it also stresses the importance of urban conditions. Especially cities with centrally located industrial facilities yield a high level of environmental inequality. This poses the question of whether environmental inequality might be the result of two independent processes: 1) urban infrastructure confines residential choices of minorities to the urban core, and 2) urban infrastructure facilitates centrally located industries. In combination, both processes lead to a disproportionate burden of minority households.

Computational problems that involve dynamic data, such as physics simulations and program development environments, have been an important subject of study in programming languages. Recent advances in self-adjusting computation made progress towards achieving efficient incremental computation by providing algorithmic language abstractions to express computations that respond automatically to dynamic changes in their inputs. Selfadjusting programs have been shown to be efficient for a broad range of problems via an explicit programming style, where the programmer uses specific primitives to identify, create and operate on data that can change over time. This dissertation presents implicit self-adjusting computation, a type directed technique for translating purely functional programs into self-adjusting programs. In this implicit approach, the programmer annotates the (toplevel) input types of the programs to be translated. Type inference finds all other types, and a type-directed translation rewrites the source program into an explicitly self-adjusting target program. The type system is related to information-flow type systems and enjoys decidable type inference via constraint solving. We prove that the translation outputs well-typed self-adjusting programs and preserves the source program’s input-output behavior, guaranteeing that translated programs respond correctly to all changes to their data. Using a cost semantics, we also prove that the translation preserves the asymptotic complexity of the source program. As a second contribution, we present two techniques to facilitate the processing of large and dynamic data in self-adjusting computation. First, we present a type system for precise dependency tracking that minimizes the time and space for storing dependency metadata. The type system improves the scalability of self-adjusting computation by eliminating an important assumption of prior work that can lead to recording spurious dependencies. We present a type-directed translation algorithm that generates correct selfadjusting programs without relying on this assumption. Second, we show a probabilistic-chunking technique to further decrease space usage by controlling the fundamental space-time tradeoff in self-adjusting computation. We implement implicit self-adjusting computation as an extension to Standard ML with compiler and runtime support. Using the compiler, we are able to incrementalize an interesting set of applications, including standard list and matrix benchmarks, ray tracer, PageRank, sparse graph connectivity, and social circle counts. Our experiments show that our compiler incrementalizes existing code with only trivial amounts of annotation, and the resulting programs bring asymptotic improvements to large datasets from real-world applications, leading to orders of magnitude speedups in practice.

This thesis consists of five chapters. Chapter one elaborates on the principle of cognitive consistency and provides an overview of what extant research refers to as cognitive consistency theories (e.g., Abelson et al., 1968; Harmon-Jones & Harmon-Jones, 2007; Simon, Stenstrom, & Read, 2015). Moreover, it describes the most prominent theoretical representatives in this context, namely balance theory (Heider, 1946, 1958), congruity theory (Osgood & Tannenbaum, 1955), and cognitive dissonance theory (Festinger, 1957). Chapter one further outlines the role of individuals’ preference for cognitive consistency in the context of financial resource acquisition, the recruitment of employees and the acquisition of customers in the entrepreneurial context. Chapter two is co-authored by Prof. Dr. Matthias Baum and presents two separate studies in which we empirically investigate the hypothesis that social entrepreneurs face a systematic disadvantage, compared to for-profit entrepreneurs, when seeking to acquire financial resources. Further, our work goes beyond existing research by introducing biased perceptions as a factor that may constrain social enterprise resource acquisition and therefore possibly stall the process of social value creation. On the foundation of role congruity theory (Eagly & Karau, 2002), we emphasize on the question whether social entrepreneurs provide signals which are less congruent with the stereotype of successful entrepreneurs and, in such, are perceived as less competent. We further test whether such biased competency perceptions feed forward into a lower probability to receive funding. Chapter three is also co-authored by Prof. Dr. Matthias Baum as well as by Eva Henrich. The aim of this chapter is to further our understanding of the early recruitment phase and to contribute to the current debate about how firms should orchestrate their recruitment channels in order to enhance the creation of employer knowledge. We introduce the concept of integrated marketing communication into the recruitment field and examine how the level of consistency regarding job or organization information affects the recall and the recognition of that information. We additionally test whether information consistency among multiple recruitment channels influences information recognition failure quota. Answering this question is important as by failing to remember the source of recruitment information, job seekers may attribute job information to the wrong firm and thus create an incorrect employer knowledge. Chapter four, which is co-authored by Prof. Dr. Matthias Baum, introduces customer congruity perceptions between a brand and a reward in the context of customer referral programs as an essential driver of the effectiveness of such programs. More precisely, we posit and empirically test a model according to which the decision-making process of the customer recommending a firm involves multiple mental steps and assumes reward perceptions to be an immediate antecedent of brand evaluation, which then, ultimately shapes the likelihood of recommendation. The level of congruity/incongruity is set up as an antecedent state and affects the perceived attractiveness of the reward. Our work contributes to the discussion on the optimal level of congruity between a prevailing schema in the mind of the customer and a stimulus presented. In addition, chapter four introduces customer referral programs as a strategic tool for brand managers. Chapter four is further published in Psychology & Marketing. Chapter five first proposes that marketing strategies specifically designed to induce word-of-mouth (WOM) behavior are particular relevant for new ventures. Against the background that previous research suggests that customer perceptions of young firm age may influence customer behavior and the degree to which customers support new ventures (e.g., Choi & Shepherd, 2005; Stinchcombe, 1965), we secondly conduct an experiment to examine the causal mechanisms linking firm age and customer WOM. Chapter five, too, is co-authored by Prof. Dr. Matthias Baum.

Road accidents remain as one of the major causes of death and injuries globally. Several million people die every year due to road accidents all over the world. Although the number of accidents in European region have reduced in the past years, road safety still remains a major challenge. Especially in case of commercial trucks, due to the size and load of the vehicle, even minor collisions with other road users would lead to serious injuries or death. In order to reduce number of accidents, automotive industry is rapidly developing advanced driver assistance systems (ADAS) and automated driving technologies. Efficient and reliable solutions are required for these systems to sense, perceive and react to different environmental conditions. For vehicle safety applications such as collision avoidance with vulnerable road users (VRUs), it is not only important for the system to efficiently detect and track the objects in the vicinity of the vehicle but should also function robustly. An environment perception solution for application in commercial truck safety systems and for future automated driving is developed in this work. Thereby a method for integrated tracking and classification of road users in the near vicinity of the vehicle is formulated. The drawbacks in conventional multi-object tracking algorithms with respect to state, measurement and data association uncertainties have been addressed with the recent advancements in the field of unified multi-object tracking solutions based on random finite sets (RFS). Gaussian mixture implementation of the recently developed labeled multi-Bernoulli (LMB) filter [RSD15] is used as the basis for multi-object tracking in this work. Measurement from an high-resolution radar sensor is used as the main input for detecting and tracking objects. On one side, the focus of this work is on tracking VRUs in the near vicinity of the truck. As it is beneficial for most of the vehicle safety systems to also know the category that the object belongs to, the focus on the other side is also to classify the road users. All the radar detections believed to originate from a single object are clustered together with help of density based spatial clustering for application with noise (DBSCAN) algorithm. Each cluster of detections would have different properties based on the respective object characteristics. Sixteen distinct features based on radar detections, that are suitable for separating pedestrians, bicyclists and passenger car categories are selected and extracted for each of the cluster. A machine learning based classifier is constructed, trained and parameterised for distinguishing the road users based on the extracted features. The class information derived from the radar detections can further be used by the tracking algorithm, to adapt the model parameters used for precisely predicting the object motion according to the category of the object. Multiple model labeled multi-Bernoulli filter (MMLMB) is used for modelling different object motions. Apart from the detection level, the estimated state of an object on the tracking level also provides information about the object class. Both these informations are fused using Dempster-Shafer theory (DST) of evidence, based on respective class probabilities Thereby, the output of the integrated tracking and classification with MMLMB filter are classified tracks that can be used by truck safety applications with better reliability. The developed environment perception method is further implemented as a real-time prototypical system on a commercial truck. The performance of the tracking and classification approaches are evaluated with the help of simulation and multiple test scenarios. A comparison of the developed approaches to a conventional converted measurements Kalman filter with global nearest neighbour association (CMKF-GNN) shows significant advantages in the overall accuracy and performance.

This research explores the development of web based reference software for characterisation of surface roughness for two-dimensional surface data. The reference software used for verification of surface characteristics makes the evaluation methods easier for clients. The algorithms used in this software are based on International ISO standards. Most software used in industrial measuring instruments may give variations in the parameters calculated due to numerical changes in calculation. Such variations can be verified using the proposed reference software. The evaluation of surface roughness is carried out in four major steps: data capture, data align, data filtering and parameter calculation. This work walks through each of these steps explaining how surface profiles are evaluated by pre-processing steps called fitting and filtering. The analysis process is then followed by parameter evaluation according to DIN EN ISO 4287 and DIN EN ISO 13565-2 standards to extract important information from the profile to characterise surface roughness.