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The production of commodities such as cocoa, rubber, oil palm and cashew, is the main driver of deforestation in West Africa (WA). The practiced production systems correspond to a land managment approach referred to as agroforestry systems (AFS), which consist of managing trees and crops on the same unit of land.Because of the ubiquity of trees, AFS reported as viable solution for climate mitigation; the carbon sequestrated by the trees could be estimated with remote sensing (RS) data and methods and reported as emission reduction efforts. However, the diversity in AFS in relation to their composition, structure and spatial distribution makes it challenging for an accurate monitoring of carbon stocks using RS. Therefore, the aim of this research is to propose a RS-based approach for the estimation of carbon sequestration in AFS across the climatic regions of WA. The main objectives were to (i) provide an accurate classification map of AFS by modelling the spatial distribution of the classification error; (ii) estimate the carbon stock of AFS in the main climatic regions of WA using RS data; (iii) evaluate the dynamic of carbon stocks within AFS across WA. Three regions of interest (ROI) were defined in Cote d'Ivoire and Burkina Faso, one in each climatic region of WA namely the Guineo-Congolian, Guinean and Sudanian, and three field campaigns were carried out for data collection. The collected data consisted of reference points for image classification, biometric tree measurements (diameter, height, species) for biomass estimation. A total of 261 samples were collected in 12 AFS across WA. For the RS data, yearly composite images from Sentinel-1 and -2 (S1 and S2), ALOS-PALSAR and GEDI data were used. A supervised classification using random forest (RF) was implemented and the classification error was assessed using the Shannon entropy generated from the class probabilities. For carbon estimation, different RS data, machine learning algorithms and carbon reference sources were compared for the prediction of the aboveground biomass in AFS. The assessment of the carbon dynamic was carried between 2017 and 2021. An average carbon map was genrated and use as reference for the comparison of annual carbon estimations, using the standard deviation as threshold. As far as the results are concerned, the classification accuracy was higher than 0.9 in all the ROIs, and AFS were mainly represented by rubber (38.9%), cocoa (36.4%), palm (10.8%) in the ROI-1, mango (15.2%) and cashew (13.4%) in ROI-2, shea tree (55.7%) and African locust bean (28.1%) in ROI-3. However, evidence of misclassification was found in cocoa, mango, and shea butter. The assessment of the classification error suggested that the error level was higher in the ROI-3 and ROI-1. The error generated from the entropy was able to reduced the level of misclassification by 63% with 11% of loss of information. Moreover, the approach was able to accuretely detect encroachement in protected areas. On carbon estimation, the highest prediction accuracy (R²>0.8) was obtained for a RF model using the combination of S1 and S2 and AGB derived from field measurements. Predictions from GEDI could only be used as reference in the ROI-1 but resulted in a prediction error was higher in cashew, mango, rubber and cocoa plantations, and the carbon stock level was higher in African locust bean (43.9 t/ha), shea butter (15 t/ha), cashew (13.8 t/ha), mango (12.8 t/ha), cocoa (7.51 t/ha) and rubber (7.33 t/ha). The analysis showed that carbon stock is determined mainly by the diameter (R²=0.45) and height (R²=0.13) of trees. It was found that crop plantations had the lowest biodiversity level, and no significant relationship was found between the considered biodiversity indices and carbon stock levels. The assessment of the spatial distribution of carbon sources and sinks showed that cashew plantations are carbon emitters due to firewood collection, while cocoa plantations showed the highest potential for carbon sequestration. The study revealed that Sentinel data could be used to support a RS-based approach for modelling carbon sequestration in AFS. Entropy could be used to map crop plantations and to monitor encroachment in protected areas. Moreover, field measurements with appropriate allometric models could ensure an accurate estimation of carbon stocks in AFS. Even though AFS in the Sudanian region had the highest carbon stocks level, there is a high potential to increase the carbon level in cocoa plantations by integrating and/or maintaining forest trees.
Die Entstehung kollinearer und nicht-kollinearer Spinstrukturen wird auf verschiedene magnetische Wechselwirkungen zurückgeführt. Für Anwendungen in der Medizin und in der Datenspeicherung ist es notwendig zu verstehen, unter welchen Parametern Frustrationen auftreten, um diese entweder zu vermeiden oder zu nutzen. In dieser Arbeit werden kollineare und nicht-kollineare Spinstrukturen auf zwei verschiedenen Materialsystemen untersucht. Das erste Materialsystem besteht aus drei atomaren Lagen Mangan auf einer (001) Oberfläche eines Wolfram-Einkristalls und das zweite Materialsystem enthält Mangan, welches verbunden mit Sauerstoff kettenförmig auf einer (001) Oberfläche eines Iridium-Einkristalls vorliegt.
Spinpolarisierte Rastertunnelmikroskopie (SP-RTM)-Messungen und -Simulationen der
dreilagigen, pseudomorphen Manganoberfläche ergeben eine nicht-kollineare Spinstruktur. Dichtefunktionaltheorie (DFT)-Berechnungen legen eine kollineare ↑↓↓-
Spinkonstellation nahe. Unter Berücksichtigung der chiralen biquadratischen Paarwechselwirkung befinden sich konische Spinspiralen mit kleinem Öffnungswinkel nah an dem energetisch niedrigsten Zustand. Spinaufgelöste DFT-Berechnungen sind abhängig von der genäherten, geometrischen Relaxation der atomaren Struktur. Kombinierte SP-RTM-Methoden weisen auf einem dreilagigen Materialsystem Spinspiralen nach und zufolge der DFT ist der kollineare bzw. nicht-kollineare Zustand des Systems durch den Abstand seiner Lagen bedingt.
SP-RTM-Messungen auf den Manganoxidketten weisen je nach Präparation eine kollineare antiferromagnetische (AFM) oder eine nicht-kollineare Spinstruktur nach. Zudem wird präsentiert, dass sich diese Spinstrukturen durch zwei verschiedene Sauerstoffdrücke und die Zufuhr von Wärme während der Präparation ineinander umschalten lassen. Durch niederenergetische Elektronenbeugung mit variabler Spannung werden zwei atomare Strukturen bestimmt, welche sich durch ihren Oxidationsgrad unterscheiden. Die nicht-kollineare Spinstruktur ist bereits in der Fachliteratur als 120° chirale Spinspirale, verursacht durch die Dzyaloshinskii-Moriya-verstärkte Ruderman-Kittel-Kasuya-Yosida (RKKY)-Wechselwirkung, bekannt. Nach aktuellen, kollinearen DFT-Berechnungen ist die kollineare Spinstruktur als AFM entlang der Ketten und als ferromagnetische Kopplung zwischen den Ketten ermittelt. Aufgrund des Nachweises eines höheren Oxidationsgrades wird eine stärkere RKKY-Austauschwechselwirkung auf der Basis der Heisenberg-Austauschwechselwirkung vermutet. Hier korreliert die Entstehung kollinearer oder nicht-kollinearer Spinstrukturen mit dem Oxidationsgrad.
Verschiedene Konzepte der Röntgenmikroskopie haben sich mittlerweile im Labor etabliert und ermöglichen heute aufschlussreiche Einblicke in eine Vielzahl von Probensystemen. Der „Labormaßstab“ bezieht sich dabei auf Analysemethoden, die in Form von einem eigenständigen Gerät betrieben werden können. Insbesondere sind sie unabhängig von der Strahlerzeugung an einer Synchrotron-Großforschungseinrichtung und einem sonst kilometergroßen Elektronen-speicherring. Viele der technischen Innovationen im Labor sind dabei ein Transfer der am Synchrotron entwickelten Techniken. Andere wiederum basieren auf der konsequenten Weiterentwicklung etablierter Konzepte. Die Auflösung allein ist dabei nicht entscheidend für die spezifische Eignung eines Mikroskopiesystems im Ganzen. Ebenfalls sollte das zur Abbildung eingesetzte Energiespektrum auf das Probensystem abgestimmt sein. Zudem muss eine Tomographieanalage zusätzlich in der Lage sein, die Abbildungsleistung bei 3D-Aufnahmen zu konservieren.
Nach einem Überblick über verschiedene Techniken der Röntgenmikroskopie konzentriert sich die vorliegende Arbeit auf quellbasierte Nano-CT in Projektionsvergrößerung als vielversprechende Technologie zur Materialanalyse. Hier können höhere Photonenenergien als bei konkurrierenden Ansätzen genutzt werden, wie sie von stärker absorbierenden Proben, z. B. mit einem hohen Anteil von Metallen, zur Untersuchung benötigt werden. Das bei einem ansonsten idealen CT-Gerät auflösungs- und leistungsbegrenzende Bauteil ist die verwendete Röntgen-quelle. Durch konstruktive Innovationen sind hier die größten Leistungssprünge zu erwarten. In diesem Zuge wird erörtert, ob die Brillanz ein geeignetes Maß ist, um die Leistungsfähigkeit von Röntgenquellen zu evaluieren, welchen Schwierigkeiten die praktische Messung unterliegt und wie das die Vergleichbarkeit der Werte beeinflusst. Anhand von Monte-Carlo-Simulationen wird gezeigt, wie die Brillanz verschiedener Konstruktionen an Röntgenquellen theoretisch bestimmt und miteinander verglichen werden kann. Dies wird am Beispiel von drei modernen Konzepten von Röntgenquellen demonstriert, welche zur Mikroskopie eingesetzt werden können. Im Weiteren beschäftigt sich diese Arbeit mit den Grenzen der Leistungsfähigkeit von Transmissionsröntgenquellen. Anhand der verzahnten Simulation einer Nanofokus-Röntgenquelle auf Basis von Monte-Carlo und FEM-Methoden wird untersucht, ob etablierte Literatur¬modelle auf die modernen Quell-konstruktionen noch anwendbar sind. Aus den Simulationen wird dann ein neuer Weg abgeleitet, wie die Leistungsgrenzen für Nanofokus-Röntgenquellen bestimmt werden können und welchen Vorteil moderne strukturierte Targets dabei bieten.
Schließlich wird die Konstruktion eines neuen Nano-CT-Gerätes im Labor-maßstab auf Basis der zuvor theoretisch besprochenen Nanofokus-Röntgenquelle und Projektionsvergrößerung gezeigt, sowie auf ihre Leistungsfähigkeit validiert. Es ist spezifisch darauf konzipiert, hochauflösende Messungen an Materialsystemen in 3D zu ermöglichen, welche mit bisherigen Methoden limitiert durch mangelnde Auflösung oder Energie nicht umsetzbar waren. Daher wird die praktische Leistung des Gerätes an realen Proben und Fragestellungen aus der Material¬wissenschaft und Halbleiterprüfung validiert. Speziell die gezeigten Messungen von Fehlern in Mikrochips aus dem Automobilbereich waren in dieser Art zuvor nicht möglich.
Motivated by the perceived great potential of chiral polymers, the presented work aimed at the investigation of synthesis, solubility and optical activity of chiral poly(2,4-disubstituted-2-oxazoline)s. A novel polymeric carrier based on ABA-type triblock copolymers poly(2-oxazoline)s with chiral and racemic hydrophobic blocks was developed for the formulation of chiral and achiral drugs (Fig. 5.1). Poly(2-methyl-2-oxazoline) (pMeOx) was used as hydrophilic A block, and poly(2-ethyl-4-ethyl-2-oxazoline) (pEtEtOx) and poly(2-propyl-4-methyl-2-oxazoline) (pPrMeOx) were used as hydrophobic B blocks. Curcumin (CUR), paclitaxel (PTX) and chiral/racemic ibuprofen (R/S/RS-IBU) were applied as model drugs. Nanoformulations were prepared consisting of these triblock copolymers and model drugs. ...
There is great interest in affordable, precise and reliable metrology underwater:
Archaeologists want to document artifacts in situ with high detail.
In marine research, biologists require the tools to monitor coral growth and geologists need recordings to model sediment transport.
Furthermore, for offshore construction projects, maintenance and inspection millimeter-accurate measurements of defects and offshore structures are essential.
While the process of digitizing individual objects and complete sites on land is well understood and standard methods, such as Structure from Motion or terrestrial laser scanning, are regularly applied, precise underwater surveying with high resolution is still a complex and difficult task.
Applying optical scanning techniques in water is challenging due to reduced visibility caused by turbidity and light absorption.
However, optical underwater scanners provide significant advantages in terms of achievable resolution and accuracy compared to acoustic systems.
This thesis proposes an underwater laser scanning system and the algorithms for creating dense and accurate 3D scans in water.
It is based on laser triangulation and the main optical components are an underwater camera and a cross-line laser projector.
The prototype is configured with a motorized yaw axis for capturing scans from a tripod.
Alternatively, it is mounted to a moving platform for mobile mapping.
The main focus lies on the refractive calibration of the underwater camera and laser projector, the image processing and 3D reconstruction.
For highest accuracy, the refraction at the individual media interfaces must be taken into account.
This is addressed by an optimization-based calibration framework using a physical-geometric camera model derived from an analytical formulation of a ray-tracing projection model.
In addition to scanning underwater structures, this work presents the 3D acquisition of semi-submerged structures and the correction of refraction effects.
As in-situ calibration in water is complex and time-consuming, the challenge of transferring an in-air scanner calibration to water without re-calibration is investigated, as well as self-calibration techniques for structured light.
The system was successfully deployed in various configurations for both static scanning and mobile mapping.
An evaluation of the calibration and 3D reconstruction using reference objects and a comparison of free-form surfaces in clear water demonstrate the high accuracy potential in the range of one millimeter to less than one centimeter, depending on the measurement distance.
Mobile underwater mapping and motion compensation based on visual-inertial odometry is demonstrated using a new optical underwater scanner based on fringe projection.
Continuous registration of individual scans allows the acquisition of 3D models from an underwater vehicle.
RGB images captured in parallel are used to create 3D point clouds of underwater scenes in full color.
3D maps are useful to the operator during the remote control of underwater vehicles and provide the building blocks to enable offshore inspection and surveying tasks.
The advancing automation of the measurement technology will allow non-experts to use it, significantly reduce acquisition time and increase accuracy, making underwater metrology more cost-effective.
Accurate crop monitoring in response to climate change at a regional or field scale
plays a significant role in developing agricultural policies, improving food security,
forecasting, and analysing global trade trends. Climate change is expected to
significantly impact agriculture, with shifts in temperature, precipitation patterns, and
extreme weather events negatively affecting crop yields, soil fertility, water availability,
biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable
information combined with crop growth models (CGMs) for yield assessment by
monitoring crop development, detecting crop changes, and assessing the impact of
climate change on crop yields. This dissertation aims to investigate the potential of RS
data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR)
for the Free State of Bavaria (70,550 km2
), Germany. The first chapter of the dissertation
describes the reasons favouring the importance of accurate crop yield predictions for
achieving sustainability in agriculture. Chapter second explores the accuracy
assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data
(high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low
spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one
day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial
and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud
or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more
suitable for agricultural monitoring than the other synthetic products fused. Chapter
third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter
2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The
chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal
(8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light
use efficiency approach (LUE)) for accurate crop yield estimations of both crop types.
Chapter third observes that the observations of high temporal resolution (8-day)
products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately
measuring the yield of WW and OSR. The chapter investigates that the simple light use
efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more
precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher
input parameters. Chapter four researches the relationship of spatiotemporal fusion
modelling using STRAFM on crop yield prediction for WW and OSR using the LUE
model for Bavaria from 2001 to 2019. The chapter states the high positive correlation
coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and
modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter
analyses the impact of climate variables on crop yield predictions by observing an
increase in R2
(0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when
the climate effect is included in the model. The fifth chapter suggests that the coupling
of the LUE model to the random forest (RF) model can further reduce the relative root
mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by
14.3% (for both WW and OSR), compared to results just relying on LUE. The same
chapter concludes that satellite-based crop biomass, solar radiation, and temperature
are the most influential variables in the yield prediction of both crop types. Chapter six
attempts to discuss both pros and cons of RS technology while analysing the impact of
land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that
the modelled biomass of both crops is positively impacted by land use diversity to the
radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The
chapter also discusses the future implications by stating that including some dependent
factors (such as the management practices used, soil health, pest management, and
pollinators) could improve the relationship of RS-modelled crop yields with
biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as
unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving
accurate crop yield predictions for precision farming. In addition, the chapter highlights
the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher
crop yield accuracies.
Accurate crop monitoring in response to climate change at a regional or field scale plays a significant role in developing agricultural policies, improving food security, forecasting, and analysing global trade trends. Climate change is expected to significantly impact agriculture, with shifts in temperature, precipitation patterns, and extreme weather events negatively affecting crop yields, soil fertility, water availability, biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable information combined with crop growth models (CGMs) for yield assessment by monitoring crop development, detecting crop changes, and assessing the impact of climate change on crop yields. This dissertation aims to investigate the potential of RS data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR) for the Free State of Bavaria (70,550 km2), Germany. The first chapter of the dissertation describes the reasons favouring the importance of accurate crop yield predictions for achieving sustainability in agriculture. Chapter second explores the accuracy assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more suitable for agricultural monitoring than the other synthetic products fused. Chapter third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter 2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal (8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light use efficiency approach (LUE)) for accurate crop yield estimations of both crop types. Chapter third observes that the observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately measuring the yield of WW and OSR. The chapter investigates that the simple light use efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Chapter four researches the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for WW and OSR using the LUE model for Bavaria from 2001 to 2019. The chapter states the high positive correlation coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter analyses the impact of climate variables on crop yield predictions by observing an increase in R2 (0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when the climate effect is included in the model. The fifth chapter suggests that the coupling of the LUE model to the random forest (RF) model can further reduce the relative root mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. The same chapter concludes that satellite-based crop biomass, solar radiation, and temperature are the most influential variables in the yield prediction of both crop types. Chapter six attempts to discuss both pros and cons of RS technology while analysing the impact of land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that the modelled biomass of both crops is positively impacted by land use diversity to the radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The chapter also discusses the future implications by stating that including some dependent factors (such as the management practices used, soil health, pest management, and pollinators) could improve the relationship of RS-modelled crop yields with biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving accurate crop yield predictions for precision farming. In addition, the chapter highlights the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher crop yield accuracies.
In produzierenden Unternehmen werden verschiedene Vorgehensweisen zur Planung, Überwachung und Steuerung von Produktionsabläufen eingesetzt. Einer dieser Methoden wird als Vorgangsknotennetzplantechnik bezeichnet. Die einzelnen Produktionsschritte werden als Knoten definiert und durch Pfeile miteinander verbunden. Die Pfeile stellen die Beziehungen der jeweiligen Vorgänge zueinander und damit den Produktionsablauf dar. Diese Technik erlaubt den Anwendern einen umfassenden Überblick über die einzelnen Prozessrelationen. Zusätzlich können mit ihr Vorgangszeiten und Produktfertigstellungszeiten ermittelt werden, wodurch eine ausführliche Planung der Produktion ermöglicht wird. Ein Nachteil dieser Technik begründet sich in der alleinigen Darstellung einer ausführbaren Prozessabfolge. Im Falle eines Störungseintritts mit der Folge eines nicht durchführbaren Vorgangs muss von dem originären Prozess abgewichen werden. Aufgrund dessen wird eine Neuplanung erforderlich. Es werden Alternativen für den gestörten Vorgang benötigt, um eine Fortführung des Prozesses ungeachtet der Störung zu erreichen. Innerhalb dieser Arbeit wird daher eine Erweiterung der Vorgangsknotennetzplantechnik beschrieben, die es erlaubt, ergänzend zu dem geplanten Soll-Prozess Alternativvorgänge für einzelne Vorgänge darzulegen. Diese Methode wird als Maximalnetzplan bezeichnet. Die Alternativen werden im Falle eines Störungseintritts automatisch evaluiert und dem Anwender in priorisierter Reihenfolge präsentiert. Durch die Verwendung des Maximalnetzplans kann eine aufwendige Neuplanung vermieden werden. Als Anwendungsbeispiel dient ein Montageprozess, mithilfe dessen die Verwendbarkeit der Methode dargelegt wird. Weiterführend zeigt eine zeitliche Analyse zufallsbedingter Maximalnetzpläne eine Begründung zur Durchführung von Alternativen und damit den Nutzen des Maximalnetzplans auf. Zusätzlich sei angemerkt, dass innerhalb dieser Arbeit verwendete Begrifflichkeiten wie Anwender, Werker oder Mitarbeiter in maskuliner Schreibweise niedergeschrieben werden. Dieses ist ausschließlich der Einfachheit geschuldet und nicht dem Zweck der Diskriminierung anderer Geschlechter dienlich. Die verwendete Schreibweise soll alle Geschlechter ansprechen, ob männlich, weiblich oder divers.
Metallic nanostructures possess the ability to support resonances in the visible wavelength regime which are related to localized surface plasmons. These create highly enhanced electric fields in the immediate vicinity of metal surfaces. Nanoparticles with dipolar resonance also radiate efficiently into the far-field and hence serve as antennas for light. Such optical antennas have been explored during the last two decades, however, mainly as standalone units illuminated by external laser beams and more recently as electrically driven point sources, yet merely with basic antenna properties. This work advances the state of the art of locally driven optical antenna systems. As a first instance, the electric driving scheme including inelastic electron tunneling over a nanometer gap is merged with Yagi-Uda theory. The resulting antenna system consists of a suitably wired feed antenna, incorporating a tunnel junction, as well as several nearby parasitic elements whose geometry is optimized using analytical and numerical methods. Experimental evidence of unprecedented directionality of light emission from a nanoantenna is provided. Parallels in the performance between radiofrequency and optical Yagi-Uda arrays are drawn. Secondly, a pair of electrically connected antennas with dissimilar resonances is harnessed as electrodes in an organic light emitting nanodiode prototype. The organic material zinc phthalocyanine, exhibiting asymmetric injection barriers for electrons and holes, in conjunction with the electrode resonances, allows switching and controlling the emitted peak wavelength and directionality as the polarity of the applied voltage is inverted. In a final study, the near-field based transmission-line driving of rod antenna systems is thoroughly explored. Perfect impedance matching, corresponding to zero back-reflection, is achieved when the antenna acts as a generalized coherent perfect absorber at a specific frequency. It thus collects all guided, surface-plasmon mediated input power and transduces it to other nonradiative and radiative dissipation channels. The coherent interplay of losses and interference effects turns out to be of paramount importance for this delicate scenario, which is systematically obtained for various antenna resonances. By means of the here developed semi-analytical toolbox, even more complex nanorod chains, supporting topologically nontrivial localized edge states, are studied. The results presented in this work facilitate the design of complex locally driven antenna systems for optical wireless on-chip communication, subwavelength pixels, and loss-compensated integrated plasmonic nanocircuitry which extends to the realm of topological plasmonics.
Development, Simulation and Evaluation of Mobile Wireless Networks in Industrial Applications
(2023)
Manyindustrialautomationsolutionsusewirelesscommunicationandrelyontheavail-
ability and quality of the wireless channel. At the same time the wireless medium is
highly congested and guaranteeing the availability of wireless channels is becoming
increasingly difficult. In this work we show, that ad-hoc networking solutions can be
used to provide new communication channels and improve the performance of mobile
automation systems. These ad-hoc networking solutions describe different communi-
cation strategies, but avoid relying on network infrastructure by utilizing the Peer-to-
Peer (P2P) channel between communicating entities.
This work is a step towards the effective implementation of low-range communication
technologies(e.g. VisibleLightCommunication(VLC), radarcommunication, mmWave
communication) to the industrial application. Implementing infrastructure networks
with these technologies is unrealistic, since the low communication range would neces-
sitate a high number of Access Points (APs) to yield full coverage. However, ad-hoc
networks do not require any network infrastructure. In this work different ad-hoc net-
working solutions for the industrial use case are presented and tools and models for
their examination are proposed.
The main use case investigated in this work are Automated Guided Vehicles (AGVs)
for industrial applications. These mobile devices drive throughout the factory trans-
porting crates, goods or tools or assisting workers. In most implementations they must
exchange data with a Central Control Unit (CCU) and between one another. Predicting
if a certain communication technology is suitable for an application is very challenging
since the applications and the resulting requirements are very heterogeneous.
The proposed models and simulation tools enable the simulation of the complex inter-
action of mobile robotic clients and a wireless communication network. The goal is to
predict the characteristics of a networked AGV fleet.
Theproposedtoolswereusedtoimplement, testandexaminedifferentad-hocnetwork-
ing solutions for industrial applications using AGVs. These communication solutions
handle time-critical and delay-tolerant communication. Additionally a control method
for the AGVs is proposed, which optimizes the communication and in turn increases the
transport performance of the AGV fleet. Therefore, this work provides not only tools
for the further research of industrial ad-hoc system, but also first implementations of
ad-hoc systems which address many of the most pressing issues in industrial applica-
tions.