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The targeted design of monodisperse, mesoporous silica microspheres (MPSMs) as HPLC separation phases is still a challenge. The MPSMs can be generated via a multi-step template-assisted method. However, this method and the factors affecting the individual process steps and resulting material properties are scarcely understood, and specific control of the complex multi-step process has been hardly discussed. In this work, the key synthesis steps were systematically investigated by means of statistical Design of Experiment (DoE). In particular, three steps were considered in detail: 1) the synthesis of porous poly(glycidyl methacrylate-co-ethylene glycol dimethacrylate) (p(GMA-co-EDMA)) particles, which as template particles, determine the structure for the final MPSMs. In this context, functional models were generated, which allow the control of the template properties pore volume, pore size and specific surface area. 2) In the presence of amino-functionalized template particles, the sol-gel process was carried out under Stöber process conditions. The water to tetraethyl orthosilicate (TEOS) ratio, as well as the concentration of ammonia as basic catalyst were varied according to a face-centered central composite design (FCD). The incorporation of silica nanoparticles (SNPs) into the pore network of the porous polymers was investigated by scanning electron microscopy (SEM), evaluation of the pore properties assessed by nitrogen sorption measurements and determination of the inorganic content by thermogravimetric analysis (TGA). Here, the material properties, such as the amount of attached silica, can be specifically controlled in the resulting organic/silica hybrid material (hybrid beads, HBs). Furthermore, depending on the sol-gel conditions three, potentially four, reaction regimes were identified, leading to different HBs. These range from porous polymer particles coated with a thin protective silica layer, to interpenetrating networks of polymer and silica, to potential particles consisting of a porous polymer core coated with a silica shell. Also, the effects of the use of different precursors and solvents on silica incorporation were investigated. 3) To obtain MPSMs from the HBs, the organic polymer template was removed by calcination. The effects of sol-gel process conditions on the resulting MPSMs were evaluated and relationships between process conditions and material properties were shown in predictive models. Fully porous, spherical, monodisperse silica particles with sizes ranging from 0.5 µm to 7.8 µm and pore sizes from 3.5 nm to 72.4 nm can be prepared specifically. Subsequent to organo-functionalization, prepared MPSMs were applied as reversed-phase HPLC column materials. Here, the columns were successfully applied for the separation of proteins and amino acids. The separation performance of the materials depends largely on the property profile of the MPSMs, which is predetermined during the preparation of the HBs.
Ever since the 1980s, researchers in computer science and robotics have been working on making autonomous cars. Due to recent breakthroughs in research and devel- opment, such as the Bertha Benz Project [ZBS+14], the goal of fully autonomous vehicles seems closer than ever before. Yet a lot of questions remain unanswered. Especially now that the automotive industry moves towards autonomous systems in series production vehicles, the task of precise localization has to be solved with automotive grade sensors and keep memory and processing consumption at a mini- mum. This thesis investigates the Simultaneous Localization and Mapping (SLAM) prob- lem for autonomous driving scenarios on a parking lot using low cost automotive sensors. The main focus is herby devoted to the RAdio Detection And Ranging (RADAR) sensor, which has not been widely analyzed in an autonomous driving scenario so far, even though they are abundant in the automotive industry for ap- plications such as Adaptive Cruise Control (ACC). Due to the high noise floor, the radar sensor has widely been disregarded in the Intelligent Transportation Systems and Robotics communities with regards to SLAM applications. However in this thesis, it is shown that the RADAR sensor proves to be an affordable, robust and precise sensor, when modeling its physical properties correctly. In this regard, a GraphSLAM based framework is introduced, which extracts features from the RADAR sensor and generates an optimized map of the surroundings using the RADAR sensor alone. This framework is used to enable crowd based localization, which is not limited to the RADAR sensor alone. By integrating an automotive Light Detection and Ranging (LiDAR) and stereo camera sensor, a robust and precise localization system can be built that that is suitable for autonomous driving even in complex parking lot scenarios. It it is thereby shown that the RADAR sensor is strongly contributing to obtaining good results in a sensor fusion setup. These results were obtained on an extensive dataset on a parking lot, which has been recorded over the course of several months. It contains different weather conditions, different configurations of parked cars and a multitude of different trajectories to validate the approaches described in this thesis and to come to the conclusion that the RADAR sensor is a reliable sensor in series autonomous driving systems, both in a multi sensor framework and as a single component for localization.
The extracellular matrix (ECM) is the non-cellular part of tissues and represents the natural environment of the cells. Next to structural stability, it provides various physical, chemical, and mechanical cues that strongly regulate and influence cellular behavior and are required for tissue morphogenesis, differentiation, and homeostasis. Due to its promising characteristics, ECM is used in a wide range of tissue engineering and regenerative medicine approaches as a biomaterial for coatings and scaffolds. To date, there are two sources for ECM material. First, native ECM is generated by the removal of the residing cells of a tissue or organ (decellularized ECM; dECM). Secondly, cell-derived ECM (cdECM) can be generated by and isolated from in vitro cultured cells. Although both types of ECM were intensively used for tissue engineering and regenerative medicine approaches, studies directly characterizing and comparing them are rare. Hence, in the first part of this thesis, dECM from adipose tissue and cdECM from stem cells and adipogenic differentiated stem cells from adipose tissue (ASCs) were characterized towards their macromolecular composition, structural features, and biological purity. The dECM was found to exhibit higher levels of collagens and lower levels of sulfated glycosaminoglycans compared to cdECMs. Structural characteristics revealed an immature state of collagen fibers in cdECM samples. The obtained results revealed differences between the two ECMs that can relevantly impact cellular behavior and subsequently experimental outcome and should therefore be considered when choosing a biomaterial for a specific application. The establishment of a functional vascular system in tissue constructs to realize an adequate nutrient supply remains challenging. In the second part, the supporting effect of cdECM on the self‐assembled formation of prevascular‐like structures by microvascular endothelial cells (mvECs) was investigated. It could be observed that cdECM, especially adipogenic differentiated cdECM, enhanced the formation of prevascular-like structures. An increased concentration of proangiogenic factors was found in cdECM substrates. The demonstration of cdECMs capability to induce the spontaneous formation of prevascular‐like structures by mvECs highlights cdECM as a promising biomaterial for adipose tissue engineering. Depending on the purpose of the ECM material chemical modification might be necessary. In the third and last part, the chemical functionalization of cdECM with dienophiles (terminal alkenes, cyclopropene) by metabolic glycoengineering (MGE) was demonstrated. MGE allows the chemical functionalization of cdECM via the natural metabolism of the cells and without affecting the chemical integrity of the cdECM. The incorporated dienophile chemical groups can be specifically addressed via catalysts-free, cell-friendly inverse electron-demand Diels‐Alder reaction. Using this system, the successful modification of cdECM from ASCs with an active enzyme could be shown. The possibility to modify cdECM via a cell-friendly chemical reaction opens up a wide range of possibilities to improve cdECM depending on the purpose of the material. Altogether, this thesis highlighted the differences between adipose dECM and cdECM from ASCs and demonstrated cdECM as a promising alternative to native dECM for application in tissue engineering and regenerative medicine approaches.
In modern collaborative production environments where industrial robots and humans are supposed to work hand in hand, it is mandatory to observe the robot’s workspace at all times. Such observation is even more crucial when the robot’s main position is also dynamic e.g. because the system is mounted on a movable platform. As current solutions like physically secured areas in which a robot can perform actions potentially dangerous for humans, become unfeasible in such scenarios, novel, more dynamic, and situation aware safety solutions need to be developed and deployed.
This thesis mainly contributes to the bigger picture of such a collaborative scenario by presenting a data-driven convolutional neural network-based approach to estimate the two-dimensional kinematic-chain configuration of industrial robot-arms within raw camera images. This thesis also provides the information needed to generate and organize the mandatory data basis and presents frameworks that were used to realize all involved subsystems. The robot-arm’s extracted kinematic-chain can also be used to estimate the extrinsic camera parameters relative to the robot’s three-dimensional origin. Further a tracking system, based on a two-dimensional kinematic chain descriptor is presented to allow for an accumulation of a proper movement history which enables the prediction of future target positions within the given image plane. The combination of the extracted robot’s pose with a simultaneous human pose estimation system delivers a consistent data flow that can be used in higher-level applications.
This thesis also provides a detailed evaluation of all involved subsystems and provides a broad overview of their particular performance, based on novel generated, semi automatically annotated, real datasets.
Gegenstand dieser Arbeit ist die Darstellung und Charakterisierung einheitlicher, mesoporöser Silica-Partikel (MPSM) im Mikrometerbereich mit maßgeschneiderten Partikel- und Porendesign für die Hochleistungsflüssigkeitschromatographie. Die Synthese umfasst die Einlagerung von Silica-Nanopartikeln (SNP) in poröse organische Template, welche anschließend bei 600°C zersetzt werden. Die Impfsuspensionspolymerisation von Polystyrol-Partikeln, unter Verwendung von Glycidylmethacrylat, Ethylenglycoldimethacrylat und Porogenen, ermöglicht die Herstellung hochgradig einheitlicher, poröser p(GMA-co-EDMA)-Template. Der Einfluss wesentlicher Faktoren, einschließlich des Monomer-Porogen-Verhältnisses, des Monomerverhältnisses und der Porogenzusammensetzung, werden systematisch untersucht sowie ihre Auswirkungen auf die Porengröße, das Porenvolumen und die spezifische Oberfläche erläutert. Die Anbindung aminofunktionalisierter Substanzen erfolgt durch die Ringöffnung der Epoxidgruppe. Im anschließenden basischen Sol-Gel-Prozess werden die Silica-Nanopartikel aufgrund der Ladungsunterschiede in die funktionalisierten p(GMA-co-EDMA)-Template eingebaut. Die Partikelgröße der SNP beeinflusst wesentlich die Poreneigenschaften der MPSM und hängt von drei Faktoren ab: (i) der Wachstumsgeschwindigkeit in der kontinuierlichen Phase, die durch die Einstellungen des Sol-Gel-Prozesses gesteuert wird, (ii) der Diffusionsrate, die durch elektrostatische Anziehung reguliert wird und vom Grad der Funktionalisierung abhängt und (iii) der Porosität des Polymer-Templats. Die gezielte Anpassung der Poreneigenschaften durch die Prozesseinstellungen erlaubt die präzise Herstellung von MPSM, die auf spezifische Trennherausforderungen zugeschnitten werden und somit die Qualität der HPLC verbessern. Die vorgestellte Synthesestrategie ermöglicht, aufgrund des stufenweisen molekularen Aufbaus, eine bessere Adaption der stationären Phase an spezifische Trennherausforderungen.
Human recognition is an important part of perception systems, such as those used in autonomous vehicles or robots. These systems often use deep neural networks for this purpose, which rely on large amounts of data that ideally cover various situations, movements, visual appearances, and interactions. However, obtaining such data is typically complex and expensive. In addition to raw data, labels are required to create training data for supervised learning. Thus, manual annotation of bounding boxes, keypoints, orientations, or actions performed is frequently necessary. This work addresses whether the laborious acquisition and creation of data can be simplified through targeted simulation. If data are generated in a simulation, information such as positions, dimensions, orientations, surfaces, and occlusions are already known, and appropriate labels can be generated automatically. A key question is whether deep neural networks, trained with simulated data, can be applied to real data. This work explores the use of simulated training data using examples from the field of pedestrian detection for autonomous vehicles. On the one hand, it is shown how existing systems can be improved by targeted retraining with simulation data, for example to better recognize corner cases. On the other hand, the work focuses on the generation of data that hardly or not occur at all in real standard datasets. It will be demonstrated how training data can be generated by targeted acquisition and combination of motion data and 3D models, which contain finely graded action labels to recognize even complex pedestrian situations. Through the diverse annotation data that simulations provide, it becomes possible to train deep neural networks for a wide variety of tasks with one dataset. In this work, such simulated data is used to train a novel deep multitask network that brings together diverse, previously mostly independently considered but related, tasks such as 2D and 3D human pose recognition and body and orientation estimation.