Intelligente Bauteile sind zunehmend in der Forschung und Industrie von Interesse, aufgrund ihrer vielfältigen Einsatzmöglichkeiten. Ein Beispiel dafür ist ein aktuelles Projekt des Bundesexzellenzclusters MERGE, welches sich mit der Entwicklung einer Mittelkonsole befasst, die als Bedienelement in einem Kraftfahrzeug dienen und durch Berührungen Aktionen ausführen soll. Um diese Funktionalität zu ermöglichen, ist es notwendig, die mittels piezokeramischer Sensoren erzeugten elektrischen Signale hinsichtlich der Lokalisation des Einschlags auszuwerten. Dies bezüglich werden verschiedene Signaleigenschaften auf ihre Eignung unter Verwendung einer support vector machine untersucht. Die Ergebnisse zeigen, dass durch die energetische Betrachtung der Signale eine Einschlagslokalisation realisierbar ist, aber Einschränkungen in der praktischen Verwendbarkeit aufweist.

In vielen Forschungsbereichen wie Medizin, Natur- oder Ingenieurwissenschaften spielt die wissenschaftliche Visualisierung eine wichtige Rolle und hilft Wissenschaftlern neue Erkenntnisse zu gewinnen. Der Hauptgrund hierfür ist, dass Visualisierungen das Unsichtbare sichtbar machen können. So können Visualisierungen beispielsweise den Verlauf von Nervenfasern im Gehirn von Probanden oder den Luftstrom um Hindernisse herum darstellen. Diese Arbeit trägt insbesondere zum Teilgebiet der Strömungsvisualisierung bei, welche sich mit der Untersuchung von Prozessen in Flüssigkeiten und Gasen beschäftigt. Eine beliebte Methode, um Einblicke in komplexe Datensätze zu erhalten, besteht darin, einfache und bekannte Strukturen innerhalb eines Datensatzes aufzuspüren. In der Strömungsvisualisierung führt dies zum Konzept der lokalen Linearisierung und Linearität im Allgemeinen. Dies liegt daran, dass lineare Vektorfelder die einfachste Form von nicht-trivialen Feldern darstellen und diese sehr gut verstanden sind. In der Regel werden simulierte Datensätze in einzelne Zellen diskretisiert, welche auf linearer Interpolation basieren. Beispielsweise können auch stationäre Punkte in der Vektorfeldtopologie mittels linearen Strömungsverhaltens charakterisiert werden. Daher ist Linearität allgegenwärtig. Durch das Verständnis von lokalen linearen Strömungsverhalten in Vektorfeldern konnten verschiedene Visualisierungsmethoden erheblich verbessert werden. Ähnliche Erfolge sind auch für andere Methoden zu erwarten. In dieser Arbeit wird das Konzept der Linearität in der Visualisierung weiterentwickelt. Zunächst wird eine bestehende Definition von linearen Nachbarschaften hin zu affin-linearen Nachbarschaften erweitert. Affin-lineare Nachbarschaften sind Regionen mit einem überwiegend linearem Strömungsverhalten. Es wird eine detaillierte Diskussion über die Definition sowie die gewählten Fehlermaße durchgeführt. Weiterhin wird ein Region Growing-Verfahren vorgestellt, welches affin-lineare Nachbarschaften um beliebige Positionen bis zu einem bestimmten, benutzerdefinierten Fehlerschwellwert extrahiert. Um die lokale Linearität in Vektorfeldern zu messen, wird ein komplementärer Ansatz, welcher die Qualität der bestmöglichen linearen Näherung für eine gegebene n-Ring-Nachbarschaft berechnet, diskutiert. In einer ersten Anwendung werden affin-lineare Nachbarschaften an stationären Punkten verwendet, um deren Einflussbereich sowie ihre Wechselwirkung mit der sie umgebenden, nichtlinearen Strömung, aber auch mit sehr nah benachbarten stationären Punkten zu visualisieren. Insbesondere bei sehr großen Datensätzen kann die analytische Beschreibung der Strömung innerhalb eines linearisierten Bereichs verwendet werden, um Vektorfelder zu komprimieren und vorhandene Visualisierungsansätze zu beschleunigen. Insbesondere sollen eine Reihe von Komprimierungsalgorithmen für gitterbasierte Vektorfelder verbessert werden, welche auf der sukzessiven Entfernung einzelner Gitterkanten basieren. Im Gegensatz zu vorherigen Arbeiten sollen affin-lineare Nachbarschaften als Grundlage für eine Segmentierung verwendet werden, um eine obere Fehlergrenze bereitzustellen und somit eine hohe Qualität der Komprimierungsergebnisse zu gewährleisten. Um verschiedene Komprimierungsansätze zu bewerten, werden die Auswirkungen ihrer jeweiligen Approximationsfehler auf die Stromlinienintegration sowie auf integrationsbasierte Visualisierungsmethoden am Beispiel der numerischen Berechnung von Lyapunov-Exponenten diskutiert. Zum Abschluss dieser Arbeit wird eine mögliche Erweiterung des Linearitätbegriffs für Vektorfelder auf zweidimensionalen Mannigfaltigkeiten vorgestellt, welche auf einer adaptiven, atlasbasierten Vektorfeldzerlegung basiert. / In many research areas, such as medicine, natural sciences or engineering, scientific visualization plays an important role and helps scientists to gain new insights. This is because visualizations can make the invisible visible. For example, visualizations can reveal the course of nerve fibers in the brain of test persons or the air flow around obstacles. This thesis in particular contributes to the subfield of flow visualization, which targets the investigation of processes in fluids and gases. A popular way to gain insights into complex datasets is to identify simple and known structures within a dataset. In case of flow visualization, this leads to the concept of local linearizations and linearity in general. This is because linear vector fields represent the most simple class of non-trivial fields and they are extremely well understood. Typically, simulated datasets are discretized into individual cells that are based on linear interpolation. Also, in vector field topology, stationary points can be characterized by considering the local linear flow behavior in their vicinity. Therefore, linearity is ubiquitous. Through the understanding of local linear flow behavior in vector fields by applying the concept of local linearity, some visualization methods have been improved significantly. Similar successes can be expected for other methods. In this thesis, the use of linearity in visualization is investigated. First, an existing definition of linear neighborhoods is extended towards the affine linear neighborhoods. Affine linear neighborhoods are regions of mostly linear flow behavior. A detailed discussion of the definition and of the chosen error measures is provided. Also a region growing algorithm that extracts affine linear neighborhoods around arbitrary positions up to a certain user-defined approximation error threshold is introduced. To measure the local linearity in vector fields, a complementary approach that computes the quality of the best possible linear approximation for a given n-ring neighborhood is discussed. As a first application, the affine linear neighborhoods around stationary points are used to visualize their region of influence, their interaction with the non-linear flow around them as well as their interaction with closely neighbored stationary points. The analytic description of the flow within a linearized region can be used to compress vector fields and accelerate existing visualization approaches, especially in case of very large datasets. In particular, the presented method aims at improving over a series of compression algorithms for grid-based vector fields that are based on edge collapse. In contrast to previous approaches, affine linear neighborhoods serve as the basis for a segmentation in order to provide an upper error bound and also to ensure a high quality of the compression results. To evaluate different compression approaches, the impact of their particular approximation errors on streamline integration as well as on integration-based visualization methods is discussed on the example of Finite-Time Lyapunov Exponent computations. To conclude the thesis, a first possible extension of linearity to fields on two-dimensional manifolds, based on an adaptive atlas-based vector field decomposition, is given.

<p>  </p> <p>Vector-borne diseases (VBDs) represent a significant health burden worldwide, threatening approximately 80% of the global population. Insecticide-based vector control is the most effective method to manage many VBDs, but its efficacy has been declining due to high levels of resistance in vector populations to the main insecticide classes which operate via limited modes of action. Therefore, the discovery of new chemistries from non-conventional chemical classes and with novel modes of action is a priority for the control of vectors and VBDs. Natural products (NPs) are diverse in chemical structures and, potentially, modes of action. They have been used as insecticides for many decades and have inspired the development of multiple synthetic insecticides, suggesting the discovery of novel NPs could lead to the development of highly effective insecticides. </p> <p><br></p> <p>In this thesis, I report two studies with a main goal to identify novel mosquito-active insecticide leads that operate via modes of action distinct from existing insecticides. First, I tested the hypothesis that new mosquito-active insecticide leads with novel chemical structures, possibly operating via novel modes of action, can be identified by high-content larval phenotypic screening against a natural product collection and using novel phenotypic endpoints in addition to mortality endpoints. Here, I performed a high-content larval phenotypic screen using first instar (L1) larvae of <em>Aedes aegypti</em> (Linnaeus, 1762) against 3,680 compounds from the AnalytiCon MEGx Natural Product Libraries and a screening platform developed by Murgia et al., (2022). Compounds were screened in a 384-well plate format using the Perkin Elmer Opera Phenix and larvae were scored for lethal and novel phenotypic endpoints. Screening revealed five chemistries that caused larval mortality, including rotenone and a new NP chemistry, NP-4. The identification of rotenone confirmed the ability of the screen to detect mosquito-active NP chemistries. NP-4 caused high levels of larval mortality in the screen, and toxicity was confirmed in a subsequent concentration-response assay against third instar (L3) larvae of <em>Ae. aegypti</em>. 140 chemistries that caused atypical larval phenotypes, including cuticular pigmentation and morphometric changes relative to negative controls, were also identified by the screen. Some of these chemistries may operate by disruption of pathways regulating melanization, growth and development, and novel targets in the insect nervous systems, thus representing potential leads for further insecticide toxicity and mode of action studies. To facilitate quantitative analyses of atypical phenotypes, an attempt was made to assess the morphometrics of the thorax in larvae exposed to test chemistry, relative to control larvae. However, assessment was limited by the number of larvae images of suitable quality for measurements. </p> <p><br></p> <p>In the second study, I tested the hypothesis that metergoline (Murgia et al., 2022) and NP-4 (this study), two chemistries identified by the HTP phenotypic screen described in this project, operate via disruption of targets in the insect nervous systems that are distinct from the current insecticidal modes of products used in mosquito control programs. Specifically, I explored the hypothesis that metergoline operates via one or more insect orthologs of the mammalian G protein-coupled serotonin and dopamine receptors. An electrophysiology study was performed using the suction electrode technique and ganglia of the German cockroach, <em>Blattella germanica </em>(Linnaeus, 1767). To facilitate the investigation of metergoline agonism/antagonism and disruption of invertebrate GPCR signaling, 5-hydroxytryptamine (5-HT; serotonin) was included as a chemical probe. Electrophysiological recordings showed 5-HT (10µM and 1mM) and metergoline (10µM) caused no significant neurological activity at the tested concentrations in comparison to the saline control. However, a consistent neuro-inhibitory trend was observed, suggesting possible agonism of a 5-HT1-like receptor ortholog and antagonism of a putative 5-HT7-like receptor ortholog in the cockroach, respectively.  NP-4 caused significant neuro-inhibition at the tested concentration of 20µM, in comparison to the negative saline control. Given the demonstration of rapid contact toxicity to <em>Ae. aegypti</em> larvae and neurological inhibition in <em>B. germanica</em>, we propose NP-4 may act at one or more conserved targets in the insect nervous systems, which remain to be elucidated. </p> <p><br></p> <p>The significance of the present study is three-fold. First, this study reports the first high-content phenotypic screen of mosquito larvae against a NP collection and identification of 145 mosquito-active chemistries associated with lethal and phenotypic endpoints. These data confirm that the screening platform provided an innovative and effective system to rapidly identify mosquito-active small molecules with potential novel modes of action. Second, metergoline and NP-4 represent potential novel chemical leads for the development of new insecticides that can be incorporated into vector control programs targeting insecticide-resistant populations. Lastly, the study describes the first electrophysiology study of 5-HT, metergoline, and NP-4 via the suction electrode technique in an insect system and contributes new knowledge to the study of the insect serotonergic system, which represents an expanding area of vector biology research given its roles in feeding regulation.  </p> <p><br></p> <p>Future studies resulting from this thesis might include: (1) development of a set of morphometric criteria for quantitative analyses of atypical larval phenotypes, (2) incorporation of new phenotypic endpoints to expand the capacity of the screen to identify novel mode of action chemistries for insecticide discovery, and (3) identification of chemistry candidates suitable for further development from the 140 chemistries associated with atypical larval phenotypes in the primary screen using chemo-informatic and toxicological studies. In addition, studies using reverse transcription-polymerase chain reaction (RT-PCR), cell-based expression systems, mutant/insecticide resistant strains, and patch clamp electrophysiology could be pursued to further investigate the molecular mode of action of metergoline and NP-4, and potential for vector control.</p>

This thesis treats the problem of source coding in situations where the encoded data is subject to errors. The typical scenario is a communication system, where source data such as speech or images should be transmitted from one point to another. A problem is that most communication systems introduce some sort of error in the transmission. A wireless communication link is prone to introduce individual bit errors, while in a packet based network, such as the Internet, packet losses are the main source of error. The traditional approach to this problem is to add error correcting codes on top of the encoded source data, or to employ some scheme for retransmission of lost or corrupted data. The source coding problem is then treated under the assumption that all data that is transmitted from the source encoder reaches the source decoder on the receiving end without any errors. This thesis takes another approach to the problem and treats source and channel coding jointly under the assumption that there is some knowledge about the channel that will be used for transmission. Such joint source--channel coding schemes have potential benefits over the traditional separated approach. More specifically, joint source--channel coding can typically achieve better performance using shorter codes than the separated approach. This is useful in scenarios with constraints on the delay of the system. Two different flavors of joint source--channel coding are treated in this thesis; multiple description coding and channel optimized vector quantization. Channel optimized vector quantization is a technique to directly incorporate knowledge about the channel into the source coder. This thesis contributes to the field by using channel optimized vector quantization in a couple of new scenarios. Multiple description coding is the concept of encoding a source using several different descriptions in order to provide robustness in systems with losses in the transmission. One contribution of this thesis is an improvement to an existing multiple description coding scheme and another contribution is to put multiple description coding in the context of channel optimized vector quantization. The thesis also presents a simple image coder which is used to evaluate some of the results on channel optimized vector quantization. / QC 20101108

Product similarity matching for food retail is studied in this thesis. The goal is to find products that are similar but not necessarily of the same brand which can be used as a replacement product for a product that is out of stock or does not exist in a specific store. The aim of the thesis is to examine which machine learning model that is best suited to perform the product similarity matching. The product data used for training the models were name, description, nutrients, weight and filters (labels, for example organic). Product similarity matching was performed pairwise and the similarity between the products was measured by jaccard distance for text attributes and relative difference for numeric values. Random Forest, Logistic Regression and Support Vector Machines were tested and compared to a baseline. The baseline computed the jaccard distance for the product names and did the classification based on a threshold value of the jaccard distance. The result was measured by accuracy, F-measure and AUC score. Random Forest performed best in terms of all evaluation metrics and Logistic Regression, Random Forest and Support Vector Machines all performed better than the baseline. / I den här rapporten studeras produktliknande matchning för livsmedel. Målet är att hitta produkter som är liknande men inte nödvändigtvis har samma märke som kan vara en ersättningsprodukt till en produkt som är slutsåld eller inte säljs i en specifik affär. Syftet med den här rapporten är att undersöka vilken maskininlärningsmodel som är bäst lämpad för att göra produktliknande matchning. Produktdatan som användes för att träna modellerna var namn, beskrivning, näringsvärden, vikt och märkning (exempelvis ekologisk). Produktmatchningen gjordes parvis och likhet mellan produkterna beräknades genom jaccard index för textattribut och relativ differens för numeriska värden. Random Forest, logistisk regression och Support Vector Machines testades och jämfördes mot en baslinje. I baslinjen räknades jaccard index ut enbart för produkternas namn och klassificeringen gjordes genom att använda ett tröskelvärde för jaccard indexet. Resultatet mättes genom noggrannhet, F-measure och AUC. Random Forest presterade bäst sett till alla prestationsmått och logistisk regression, Random Forest och Support Vector Machines gav alla bättre resultat än baslinjen.

Denna studie har syftet att undersöka sambandet mellan luftföroreningar och olika fordonsvariabler, såsom årsmodell, bränsletyp och fordonstyp, på Hornsgatan i Stockholm. Studien avser att besvara vilka faktorer som har störst inverkan på luftkvaliteten. Utförandet baseras på maskininlärningsalgoritmerna Random Forest och Support Vector Regression, vilka jämförs utifrån R² och RMSE. Modellerna skapade med Random Forest överträffar Support Vector Regression för de olika luftföroreningarna. Den modell som presterade bäst var modellen för kolmonoxid vilken hade ett R²-värde på 99.7%. Den modell som gav prediktioner med lägst R²-värde, 68.4%, var modellen för kvävedioxid. Överlag var resultaten goda i relation till tidigare studier. Utifrån modellerna diskuteras variablers inverkan och olika åtgärder som kan införas i Stockholm Stad och på Hornsgatan för att förbättra luftkvaliteten. / This study aims to investigate the relationship between multiple air pollution and different vehicle variables, such as vehicle year, fuel type and vehicle type, on Hornsgatan in Stockholm. The study intends to answer which factors have the greatest impact on air quality. The implementation is based on the two machine learning algorithms Random Forest and Support Vector Regression, which are compared based on R² and RMSE. The models created with Random Forest outperform Support Vector Regression for the various air pollutants. The best performing model was the carbon monoxide model which had an R²-value of 99.7%. The model that gave predictions with the lowest R²-value, 68.4%, was the model for nitrogen dioxide. Overall, the results were good in relation to previous studies. With regards to these models, the impact of variables and different measures that can be introduced in the City of Stockholm and on Hornsgatan to improve air quality are discussed.

Denna studie har syftet att undersöka sambandet mellan luftföroreningar och olika fordonsvariabler, såsom årsmodell, bränsletyp och fordonstyp, på Hornsgatan i Stockholm. Studien avser att besvara vilka faktorer som har störst inverkan på luftkvaliteten. Utförandet baseras på maskininlärningsalgoritmerna Random Forest och Support Vector Regression, vilka jämförs utifrån R^2 och RMSE. Modellerna skapade med Random Forest överträffar Support Vector Regression för de olika luftföroreningarna. Den modell som presterade bäst var modellen för kolmonoxid vilken hade ett R^2-värde på 99.7%. Den modell som gav prediktioner med lägst R^2-värde, 68.4%, var modellen för kvävedioxid. Överlag var resultaten goda i relation till tidigare studier. Utifrån modellerna diskuteras variablers inverkan och olika åtgärder som kan införas i Stockholm Stad och på Hornsgatan för att förbättra luftkvaliteten. / This study aims to investigate the relationship between multiple air pollution and different vehicle variables, such as vehicle year, fuel type and vehicle type, on Hornsgatan in Stockholm. The study intends to answer which factors have the greatest impact on air quality. The implementation is based on the two machine learning algorithms Random Forest and Support Vector Regression, which are compared based on R^2 and RMSE. The models created with Random Forest outperform Support Vector Regression for the various air pollutants. The best performing model was the carbon monoxide model which had an R^2-value of 99.7%. The model that gave predictions with the lowest R^2-value, 68.4%, was the model for nitrogen dioxide. Overall, the results were good in relation to previous studies. With regards to these models, the impact of variables and different measures that can be introduced in the City of Stockholm and on Hornsgatan to improve air quality are discussed.

Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties create bias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics.

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