2,708 research outputs found
MECHANICAL ENERGY HARVESTER FOR POWERING RFID SYSTEMS COMPONENTS: MODELING, ANALYSIS, OPTIMIZATION AND DESIGN
Finding alternative power sources has been an important topic of study worldwide. It is vital to find substitutes for finite fossil fuels. Such substitutes may be termed renewable energy sources and infinite supplies. Such limitless sources are derived from ambient energy like wind energy, solar energy, sea waves energy; on the other hand, smart cities megaprojects have been receiving enormous amounts of funding to transition our lives into smart lives. Smart cities heavily rely on smart devices and electronics, which utilize small amounts of energy to run. Using batteries as the power source for such smart devices imposes environmental and labor cost issues. Moreover, in many cases, smart devices are in hard-to-access places, making accessibility for disposal and replacement difficult. Finally, battery waste harms the environment.
To overcome these issues, vibration-based energy harvesters have been proposed and implemented. Vibration-based energy harvesters convert the dynamic or kinetic energy which is generated due to the motion of an object into electric energy. Energy transduction mechanisms can be delivered based on piezoelectric, electromagnetic, or electrostatic methods; the piezoelectric method is generally preferred to the other methods, particularly if the frequency fluctuations are considerable. In response, piezoelectric vibration-based energy harvesters (PVEHs), have been modeled and analyzed widely. However, there are two challenges with PVEH: the maximum amount of extractable voltage and the effective (operational) frequency bandwidth are often insufficient. In this dissertation, a new type of integrated multiple system comprised of a cantilever and spring-oscillator is proposed to improve and develop the performance of the energy harvester in terms of extractable voltage and effective frequency bandwidth. The new energy harvester model is proposed to supply sufficient energy to power low-power electronic devices like RFID components. Due to the temperature fluctuations, the thermal effect over the performance of the harvester is initially studied. To alter the resonance frequency of the harvester structure, a rotating element system is considered and analyzed. In the analytical-numerical analysis, Hamiltonâs principle along with Galerkinâs decomposition approach are adopted to derive the governing equations of the harvester motion and corresponding electric circuit. It is observed that integration of the spring-oscillator subsystem alters the boundary condition of the cantilever and subsequently reforms the resulting characteristic equation into a more complicated nonlinear transcendental equation. To find the resonance frequencies, this equation is solved numerically in MATLAB. It is observed that the inertial effects of the oscillator rendered to the cantilever via the restoring force effects of the spring significantly alter vibrational features of the harvester. Finally, the voltage frequency response function is analytically and numerically derived in a closed-from expression. Variations in parameter values enable the designer to mutate resonance frequencies and mode shape functions as desired. This is particularly important, since the generated energy from a PVEH is significant only if the excitation frequency coming from an external source matches the resonance (natural) frequency of the harvester structure. In subsequent sections of this work, the oscillator mass and spring stiffness are considered as the design parameters to maximize the harvestable voltage and effective frequency bandwidth, respectively. For the optimization, a genetic algorithm is adopted to find the optimal values. Since the voltage frequency response function cannot be implemented in a computer algorithm script, a suitable function approximator (regressor) is designed using fuzzy logic and neural networks. The voltage function requires manual assistance to find the resonance frequency and cannot be done automatically using computer algorithms. Specifically, to apply the numerical root-solver, one needs to manually provide the solver with an initial guess. Such an estimation is accomplished using a plot of the characteristic equation along with human visual inference. Thus, the entire process cannot be automated. Moreover, the voltage function encompasses several coefficients making the process computationally expensive. Thus, training a supervised machine learning regressor is essential. The trained regressor using adaptive-neuro-fuzzy-inference-system (ANFIS) is utilized in the genetic optimization procedure. The optimization problem is implemented, first to find the maximum voltage and second to find the maximum widened effective frequency bandwidth, which yields the optimal oscillator mass value along with the optimal spring stiffness value. As there is often no control over the external excitation frequency, it is helpful to design an adaptive energy harvester. This means that, considering a specific given value of the excitation frequency, energy harvester system parameters (oscillator mass and spring stiffness) need to be adjusted so that the resulting natural (resonance) frequency of the system aligns with the given excitation frequency. To do so, the given excitation frequency value is considered as the input and the system parameters are assumed as outputs which are estimated via the neural network fuzzy logic regressor. Finally, an experimental setup is implemented for a simple pure cantilever energy harvester triggered by impact excitations. Unlike the theoretical section, the experimental excitation is considered to be an impact excitation, which is a random process. The rationale for this is that, in the real world, the external source is a random trigger. Harmonic base excitations used in the theoretical chapters are to assess the performance of the energy harvester per standard criteria. To evaluate the performance of a proposed energy harvester model, the input excitation type consists of harmonic base triggers. In summary, this dissertation discusses several case studies and addresses key issues in the design of optimized piezoelectric vibration-based energy harvesters (PVEHs). First, an advanced model of the integrated systems is presented with equation derivations. Second, the proposed model is decomposed and analyzed in terms of mechanical and electrical frequency response functions. To do so, analytic-numeric methods are adopted. Later, influential parameters of the integrated system are detected. Then the proposed model is optimized with respect to the two vital criteria of maximum amount of extractable voltage and widened effective (operational) frequency bandwidth. Corresponding design (influential) parameters are found using neural network fuzzy logic along with genetic optimization algorithms, i.e., a soft computing method. The accuracy of the trained integrated algorithms is verified using the analytical-numerical closed-form expression of the voltage function. Then, an adaptive piezoelectric vibration-based energy harvester (PVEH) is designed. This final design pertains to the cases where the excitation (driving) frequency is given and constant, so the desired goal is to match the natural frequency of the system with the given driving frequency. In this response, a regressor using neural network fuzzy logic is designed to find the proper design parameters. Finally, the experimental setup is implemented and tested to report the maximum voltage harvested in each test execution
The Development of Microdosimetric Instrumentation for Quality Assurance in Heavy Ion Therapy, Boron Neutron Capture Therapy and Fast Neutron Therapy
This thesis presents research for the development of new microdosimetric instrumentation for use with solid-state microdosimeters in order to improve their portability for radioprotection purposes and for QA in various hadron therapy modalities. Monte Carlo simulation applications are developed and benchmarked, pertaining to the context of the relevant therapies considered. The simulation and experimental findings provide optimisation recommendations relating to microdosimeter performance and possible radioprotection risks by activated materials.
The first part of this thesis is continuing research into the development of novel Silicon-on-Insulator (SOI) microdosimeters in the application of hadron therapy QA. This relates specifically to the optimisation of current microdosimeters, development of Monte Carlo applications for experimental validation, assessment of radioprotection risks during experiments and advanced Monte Carlo modelling of various accelerator beamlines.
Geant4 and MCNP6 Monte Carlo codes are used extensively in this thesis, with rigorous benchmarking completed in the context of experimental verification, and evaluation of the similarities and differences when simulating relevant hadron therapy facilities.
The second part of this thesis focuses on the development of a novel wireless microdosimetry system - the Radiodosimeter, to improve the operation efficiency and minimise any radioprotection risks. The successful implementation of the wireless Radiodosimeter is considered as an important milestone in the development of a microdosimetry system that can be operated by an end-user with no prior knowledge
Accurate quantum transport modelling and epitaxial structure design of high-speed and high-power In0.53Ga0.47As/AlAs double-barrier resonant tunnelling diodes for 300-GHz oscillator sources
Terahertz (THz) wave technology is envisioned as an appealing and conceivable solution in the context of several potential high-impact applications, including sixth generation (6G) and beyond consumer-oriented ultra-broadband multi-gigabit wireless data-links, as well as highresolution imaging, radar, and spectroscopy apparatuses employable in biomedicine, industrial processes, security/defence, and material science. Despite the technological challenges posed by the THz gap, recent scientific advancements suggest the practical viability of THz systems. However, the development of transmitters (Tx) and receivers (Rx) based on compact semiconductor devices operating at THz frequencies is urgently demanded to meet the performance requirements calling from emerging THz applications.
Although several are the promising candidates, including high-speed III-V transistors and photo-diodes, resonant tunnelling diode (RTD) technology offers a compact and high performance option in many practical scenarios. However, the main weakness of the technology is currently represented by the low output power capability of RTD THz Tx, which is mainly caused by the underdeveloped and non-optimal device, as well as circuit, design implementation approaches. Indeed, indium phosphide (InP) RTD devices can nowadays deliver only up to around 1 mW of radio-frequency (RF) power at around 300 GHz. In the context of THz wireless data-links, this severely impacts the Tx performance, limiting communication distance and data transfer capabilities which, at the current time, are of the order of few tens of gigabit per second below around 1 m.
However, recent research studies suggest that several milliwatt of output power are required to achieve bit-rate capabilities of several tens of gigabits per second and beyond, and to reach several metres of communication distance in common operating conditions. Currently, the shortterm target is set to 5â10 mW of output power at around 300 GHz carrier waves, which would allow bit-rates in excess of 100 Gb/s, as well as wireless communications well above 5 m distance, in first-stage short-range scenarios. In order to reach it, maximisation of the RTD highfrequency RF power capability is of utmost importance. Despite that, reliable epitaxial structure design approaches, as well as accurate physical-based numerical simulation tools, aimed at RF power maximisation in the 300 GHz-band are lacking at the current time.
This work aims at proposing practical solutions to address the aforementioned issues. First, a physical-based simulation methodology was developed to accurately and reliably simulate the static current-voltage (IV ) characteristic of indium gallium arsenide/aluminium arsenide (In-GaAs/AlAs) double-barrier RTD devices. The approach relies on the non-equilibrium Greenâs function (NEGF) formalism implemented in Silvaco Atlas technology computer-aided design (TCAD) simulation package, requires low computational budget, and allows to correctly model In0.53Ga0.47As/AlAs RTD devices, which are pseudomorphically-grown on lattice-matched to InP substrates, and are commonly employed in oscillators working at around 300 GHz. By selecting the appropriate physical models, and by retrieving the correct materials parameters, together with a suitable discretisation of the associated heterostructure spatial domain through finite-elements, it is shown, by comparing simulation data with experimental results, that the developed numerical approach can reliably compute several quantities of interest that characterise the DC IV curve negative differential resistance (NDR) region, including peak current, peak voltage, and voltage swing, all of which are key parameters in RTD oscillator design.
The demonstrated simulation approach was then used to study the impact of epitaxial structure design parameters, including those characterising the double-barrier quantum well, as well as emitter and collector regions, on the electrical properties of the RTD device. In particular, a comprehensive simulation analysis was conducted, and the retrieved output trends discussed based on the heterostructure band diagram, transmission coefficient energy spectrum, charge distribution, and DC current-density voltage (JV) curve. General design guidelines aimed at enhancing the RTD device maximum RF power gain capability are then deduced and discussed.
To validate the proposed epitaxial design approach, an In0.53Ga0.47As/AlAs double-barrier RTD epitaxial structure providing several milliwatt of RF power was designed by employing the developed simulation methodology, and experimentally-investigated through the microfabrication of RTD devices and subsequent high-frequency characterisation up to 110 GHz. The analysis, which included fabrication optimisation, reveals an expected RF power performance of up to around 5 mW and 10 mW at 300 GHz for 25 ÎŒm2 and 49 ÎŒm2-large RTD devices, respectively, which is up to five times higher compared to the current state-of-the-art. Finally, in order to prove the practical employability of the proposed RTDs in oscillator circuits realised employing low-cost photo-lithography, both coplanar waveguide and microstrip inductive stubs are designed through a full three-dimensional electromagnetic simulation analysis.
In summary, this work makes and important contribution to the rapidly evolving field of THz RTD technology, and demonstrates the practical feasibility of 300-GHz high-power RTD devices realisation, which will underpin the future development of Tx systems capable of the power levels required in the forthcoming THz applications
Adaptive vehicular networking with Deep Learning
Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networksâ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities.
In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects.
The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup.
The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios.
The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the âidealâ optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark
Erfassung und Evaluierung von Teilentladungen in Leistungstransformatoren mit speziellen Sensoren und Diagnoseverfahren
Transformers are key elements of the power grid. Due to their importance and high initial cost, asset managers utilize monitoring and diagnostic tools to optimize their operation and extend their service life. The main objective of this thesis is to develop new methods in the field of monitoring and diagnosis of transformers in order to reduce maintenance costs and decrease the frequency of forced outages. For this purpose, two concepts are proposed.
Small generator step-up transformers are essential in wind and photovoltaic parks. The first presented concept entails an online fault gas monitoring system for these transformers, specially hermetically-sealed transformers. The developed compact, maintenance-free and cost-effective monitoring system continuously tracks the level of the key leading indicators of transformer faults in the gas cushion.
The second presented concept revolves around partial discharge (PD) assessment by the UHF measurement technique, which is based on capturing the electromagnetic (EM) waves emitted in case of PD in the insulation of a transformer. In this context, the complex EM system established when probes are introduced into the tank of a transformer and with PD as the excitation source is analyzed. Drawing on this foundation, a practical approach to the detection and classification of PD with the focus on the selection of the optimal frequency range for performing UHF measurements depending on the device under test is presented. The UHF measurement technique also offers the possibility of PD localization. Here, the determined arrival time (AT) of the captured signals is critical. A PD localization algorithm, based on a multi-data-set approach with a novel AT determination method, is proposed. The methods and algorithms proposed for the detection, classification and localization of PD are validated by means of practical experiments
Mobility classification of cattle with micro-Doppler radar
Lameness in dairy cattle is a welfare concern that negatively impacts animal productivity and farmer profitability. Micro-Doppler radar sensing has been previously suggested as a potential system for automating lameness detection in ruminants. This thesis investigates the refinement of the proposed automated system by analysing and enhancing the repeatability and accuracy of the existing scoring method in cattle mobility scoring, used to provide labels in machine learning. The main aims of the thesis were (1) to quantify the performance of the micro-Doppler radar sensing method for the assessment of mobility, (2) to characterise and validate micro-Doppler radar signatures of dairy cattle with varying degrees of gait impairment, and (3) to develop machine learning algorithms that can infer the mobility status of the animals under test from their radar signatures and support automatic contactless classification.
The first study investigated inter-assessor agreement using a 4-level system and modifications to it, as well as the impact of factors such as mobility scoring experience, confidence in scoring decisions, and video characteristics. The results revealed low levels of agreement between assessors' scores, with kappa values ranging from 0.16 to 0.53. However, after transforming and reducing the mobility scoring system levels, an improvement was observed, with kappa values ranging from 0.2 to 0.67. Subsequently, a longitudinal study was conducted using good-agreement scores as ground truth labels in supervised machine-learning models. However, the accuracy of the algorithmic models was found to be insufficient, ranging from 0.57 to 0.63. To address this issue, different labelling systems and data pre-processing techniques were explored in a cross-sectional study. Nonetheless, the inter-assessor agreement remained challenging, with an average kappa value of 0.37 (SD = 0.16), and high-accuracy algorithmic predictions remained elusive, with an average accuracy of 56.1 (SD =16.58). Finally, the algorithms' performance was tested with high-confidence labels, which consisted of only scores 0 and 3 of the AHDB system. This testing resulted in good classification accuracy (0.82), specificity (0.79), and sensitivity (0.85). This led to the proposal of a new approach to producing labels, testing vantage point changes, and improving the performance of machine learning models (average accuracy = 0.7 & SD = 0.17, average sensitivity = 0.68 & SD = 0.27, average specificity = 0.75 & SD = 0.17).
The research identified a challenge in creating high-confidence diagnostic labels for supervised machine learning-based algorithms to automate the detection and classification of lameness in dairy cows. As a result, the original goals were partially overridden, with the focus shifted to creating reliable labels that would perform well with radar data and machine learning. This point was considered necessary for smooth system development and process automation. Nevertheless, we managed to quantify the performance of the micro-Doppler radar system, partially develop the supervised machine learning algorithms, compare levels of agreement among multiple assessors, evaluate the assessment tools, assess the mobility evaluation process and gather a valuable data set which can be used as a foundation for subsequent studies. Finally, the thesis suggests changes in the assessment process to improve the prediction accuracy of algorithms based on supervised machine learning with radar data
Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 â Your Brain on Art)
[Italiano]: âGrafonomia e cervello su arte, creativitĂ e innovazioneâ.
Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunitĂ , e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualitĂ e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creativitĂ e innovazione; neuro-ingegneria e arte ispirata dal cervello, creativitĂ e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: âGraphonomics and your brain on art, creativity and innovationâ.
A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine.
The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art.
The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics
Compute-proximal Energy Harvesting for Mobile Environments: Fundamentals, Applications, and Tools
Over the past two decades, we have witnessed remarkable achievements in computing, sensing, actuating, and communications capabilities of ubiquitous computing applications. However, due to the limitations in stable energy supply, it is difficult to make the applications ubiquitous. Batteries have been considered a promising technology for this problem, but their low energy density and sluggish innovation have constrained the utility and expansion of ubiquitous computing. Two key techniquesâenergy harvesting and power managementâhave been studied as alternatives to overcome the battery limitations.
Compared to static environments such as homes or buildings, there are more energy harvesting opportunities in mobile environments since ubiquitous systems can generate various forms of energy as they move. Most of the previous studies in this regard have been focused on human movements for wearable computing, while other mobile environments (e.g., cars, motorcycles, and bikes) have received limited attention.
In this thesis, I present a class of energy harvesting approaches called compute-proximal energy harvesting, which allows us to develop energy harvesting technology where computing, sensing, and actuating are needed in vehicles. Computing includes sensing phenomena, executing instructions, actuating components, storing information, and communication. Proximal considers the harvesting of energy available around the specific location where computation is needed, reducing the need for excessive wiring.
A primary goal of this new approach is to mitigate the effort associated with the installation and field deployment of self-sustained computing and lower the entry barriers to developing self-sustainable systems for vehicles. In this thesis, I first select an automobile as a promising case study and discuss the opportunities, challenges, and design guidelines of compute-proximal energy harvesting with practical yet advanced examples in the automotive domain.
Second, I present research in the design of small-scale wind energy harvesters and the implementation and evaluation of two advanced safety sensing systemsâa blind spot monitoring system and a lane detection systemâwith the harvested power from wind. Finally, I conduct a study to democratize the lessons learned from the automotive case studies for makers and people with no prior experience in energy harvesting technology. In this study, I seek to understand what problems they have encountered and what possible solutions they have considered while dealing with energy harvesting technology. Based on the findings, I develop a comprehensive energy harvesting toolkit and examine its utility, usability, and creativity through a series of workshops.Ph.D
Spintronic Operations Driven by Terahertz Electromagnetic Pulses
Spintronic devices, supplementing and surpassing charge-based electronics by including the electron spin, have recently begun to reach the market. Information carriers
such as electrons (in field-effect transistors) and photons (in optical fibers) have already
reached the terahertz range (THz, 10^12 Hz). To make the electron spin compatible and
competitive, spintronic operations need to be pushed to THz frequencies. So far, is is
unclear whether fundamental spintronic effects such as spin accumulation or spin-orbit
torque can be transferred to THz frequencies. In this respect, it is also important to note
that the THz range coincides with many fundamental excitations, for instance phonons,
magnons, and the relaxation of electronic currents. Strong THz electromagnetic pulses
can be used to study such fundamental excitations, making use of both the electric and
magnetic fields of the electromagnetic pulse.
In this thesis, strong THz electromagnetic pulses are applied to spintronic thinfilm stacks to drive charge and spin currents, apply torque and manipulate magnetic
order. A short optical probe pulse or a resistance probe interrogate the transient magnetic
response.
First, a measurement strategy is developed to simultaneously detect all components
of the vector magnetization of thin film magnets in optical transmission probe experiments
at normal incidence, requiring only a variation in the initial probe polarization. To this
end, the magnetic circular and linear birefringence (MCB, MLB) effects are measured
simultaneously and a calibration strategy for the often neglected MLB effect is presented.
Second, using this detection scheme, we study the THz frequency operation of
spintronic effects in ferromagnetic(FM)/non-magnetic (NM) heavy metal stacks. We find
signatures of THz spin accumulation at the FM/NM interface. The spins injected into a
ferromagnet relax within ⌠100 fs, in line with electron-spin equilibration times measured
by ultrafast optically induced demagnetization. Indications of the field-like spin-orbit
torque (FL-SOT) are found.
Third, an effective method to modulate the relative THz electric and magnetic field
amplitudes in thin film samples is presented, enabling one to disentangle effects driven
by the electric or the magnetic component of the THz electromagnetic pulse. A nearperfect conductor (THz mirror) quenches the THz electric field in a region close to the
mirror, while doubling the THz magnetic field. Measurements with a ferromagnetic thin
film confirmed a THz magnetic field increase of 1.97 ± 0.06 and a suppression of the THz
electric field in the sample.
Finally, we utilize the electric-field suppression effect close to metals to optically gate
the THz electric field driven resistance modulation of an antiferromagnet (AFM) grown on
a semiconducting substrate. An optically induced transient substrate conductance depletes
the THz electric field in the AFM layer, while not perturbing the AFM magnetic order
directly. A simple model of parallel conductances is presented, confirming the experimental
observations.
In conclusion, this thesis is an important contribution to push fundamental spintronic effects such as spin accumulation and spin-orbit torque to the THz range. The
developed methodologies are helpful to advance nonlinear THz spectroscopy of magnetic
materials.Da die ersten auf spintronischen Prinzipien erbauten Speicher den Markt erreichen
und gleichzeitig InformationstrÀger wie Elektronen (in Feldeffekttransistoren) und Photonen (in Glasfaserkabeln) in den Terahertz-Frequenzbereich (THz, 10^12 Hz) vordringen,
stellt sich die Frage, ob die Spintronik, welche die Elektronik um den Elektronenspin erweitert, mit solch hohen Frequenzen kompatibel ist. Gleichzeitig ist der THz-Frequenzbereich,
welcher elementare Anregungen wie Phononen und Magnonen enthÀlt, auch fur die Grundlagenforschung interessant. Um diese Anregungen zu untersuchen bieten sich elektromagnetische THz-Pulse mit hohen FeldstÀrken an, denn sie können direkt an elektrische und
magnetische Resonanzen koppeln. Diese Arbeit untersucht mit THz-Lichtpulsen, die in
spintronischen DĂŒnnfilmproben Spin- und Ladungsströme induzieren, ob elementare spintronische Effekte, wie die Spin-Akkumulation oder das Spin-Bahn-Drehmoment, auch bei
THz-Frequenzen aktiv sind. Die magnetische Antwort wird mit kurzen optischen Pulsen
oder mittels elektrischer Messungen zeitaufgelöst abgefragt.
Die spintronischen Effekte werden in ferromagnetischen (FM)/nichtmagnetischen
(NM) Dunnfilm-Metallmultilagen untersucht, wobei zuerst eine Messmethode erarbeitet š
wird, um alle rÀumlichen Anteile der Probenmagnetisierung gleichzeitig zu bestimmen.
Hierzu werden die magnetische zirkulÀre Doppelbrechung (MCB) und die, oft vernachlÀssigte, magnetische lineare Doppelbrechung (MLB), welche der Abfragepuls beim Durchdringen der Probe entlang der Probennormale erfÀhrt, gleichzeitig bestimmt. Ein besonderes Augenmerk liegt auf der Normierung des MLB-Signals. Mithilfe dieser neuartigen
Messmethode werden Indizien fur eine THz Spin-Akkumulation und das feldartige Spin- š
Bahn-Drehmoment (FL-SOT) an der FM/NM GrenzflÀche gefunden, welche auf einen
Spinaustausch zwischen dem nichtmagnetischen Schwermetall und dem FM zuruckgefĂŒhrt š
werden. Die in den FM eindringenden Spins relaxieren auf einer Zeitskala von ⌠100 fs, was
mit Ergebnissen aus ultraschnellen optischen Demagnetisierungsstudien ubereinstimmt. š
ZusÀtzlich wird die nichtlineare THz-Spektroskopie dahingehend erweitert, vom
elektrischen oder magnetischen THz-Feld getriebene Signale unterscheiden zu können, indem die relativen StÀrken der elektromagnetischen Felder im Inneren einer Dunnfilmprobe
beeinflusst werden. Hierbei unterdruckt ein elektrisch leitender THz Spiegel das THz elektrische Feld in der Probe, wÀhrend das THz magnetische Feld um einen Faktor 1.97±0.06
verstÀrkt wird. Diese Unterdruckung des THz elektrischen Feldes in der NÀhe eines Leiters
wird genutzt, um die vom THz elektrischen Feld getriebene Widerstandsmodulation in
einem, auf einem (optisch angeregten) halbleitenden Substrat gewachsenen, Antiferromagneten (AFM) zu steuern. Dabei wird die Wirkung des THz elektrischen Feldes im AFM
unterdruckt ohne den magnetischen Zustand des AFM zu stören. Ein einfaches Modell
stutzt die Interpretation der Beobachtungen.
Zusammenfassend leistet diese Arbeit einen wichtigen Beitrag, um spintronische Effekte wie die Spin-Akkumulation und das Spin-Bahn-Drehmoment im THz-Frequenzbereich
zu etablieren und erweitert zusÀtzlich die Möglichkeiten der nichtlinearen THz-Spektroskopie
an Magneten
Design and analysis of metamaterial based microstrip patch antennas for wireless applications.
Doctoral Degree. University of KwaZulu-Natal, Durban.Due to the tremendous growth of wireless communication applications, there is an enormous demand for more compact antennas with high speed, wider coverage, high gain, and multi-band properties. The microstrip patch antennas (MPAs) and multiple-input multiple-output (MIMO) antennas with high gain and multi-band properties are suitable to fulfil these requirements. MPAs have been found to possess unique qualities such as light weight, low profile, easy fabrication, and integration. However, the low gain, narrow bandwidth, and mutual coupling in the MIMO antennas limit the performance of MIMO systems. Several techniques have been studied and implemented over the years, but they are not without limitations. The utilization of artificial materials such as metamaterials has proven to be efficient in overcoming the limitations of MPAs.
Due to the advancement in modern technology, it is necessary to study and use recently developed metamaterial structures. Metamaterials (MeTMs) are artificially engineered materials with electromagnetic properties that are not found in nature. MeTMs are used due to their electric and magnetic properties. The goal of this thesis is to design and investigate a novel metamaterial structure which can be integrated into the microstrip patch antennas for improving their performance. The design, simulation, and measurement of the metamaterial is carried out on the Computer Simulation Technology (CST) studio suite, Advance Design Systems (ADS) software, MATLAB, and the Rohde and Schwarz network analyzer etc.
In this thesis, a novel I-shaped metamaterial (ISMeTM) structure is proposed, designed, and investigated. The proposed novel ISMeTM unit cell structure in this work has a characteristic shape that distinguishes it from earlier multi-band MeTMs in the literature. The structure's unit cell is designed to have an overall compact size of 10 mm Ă 10 mm. The structure generates transmission coefficients at 6.31 GHz, 7.79 GHz, 9.98 GHz, 10.82 GHz, 11.86 GHz, 13.36 GHz, and 15. 5 GHz. These frequency bands are ideal for multi-band satellite communication systems, C, X, and Ku-bands, and radar applications etc.
The performance of the MPA is improved in this work, by integrating a novel square split ring resonator (SSRR) metamaterial. The performance of the proposed antenna is investigated and analyzed. The SSRR is designed to have a dimension of 25 x 21.4 x 1.6 mm2 which is the same dimension as the radiating patch of the MPA. The SSRR is etched over the antenna, and it operates at single operating frequency of 5.8 GHz with improved gain from 4.04 to 5.3 dBi.
Further, the MPA with improved parameters for multiband wireless systems is designed, analyzed, fabricated, and measured. The proposed design utilizes the ISMeTM array as superstrate with the area of 70 x 70 mm2. The superstrate is etched over a rectangular MPA exhibiting multi-band properties. This antenna resonates at 6.31, 9.65, 11.45 GHz with increased bandwidth at 240 MHz, 850 MHz, and 1010 MHz. The overall gain of the antenna increases by 74.18%. The antenna is fabricated and measured. The simulated results and the measured results are found to be in good agreement.
The mutual coupling and low gain problems in MIMO patch antennas is also addressed in this thesis. A 3 x 5-unit cell array of the ISMeTM is used as a superstrate over a two port MIMO patch antenna. The two port MIMO antenna with the superstrate provides triple-band operation and operates over three resonance frequencies at 6.31, 9.09, and 11.41 GHz. A mutual coupling reduction of 26 dB, 33 dB, and 22 dB for the first band, second band and third band, respectively is attained.
In this thesis, a novel I-shaped metamaterial structure is introduced, which produces multiband operation. The presented metamaterial is suitable for various multiband wireless communication applications. The integration of a square split ring resonator metamaterial enhances the performance of the antenna. Using the I-shaped metamaterial a high gain multiband microstrip antenna is designed. The I-shaped metamaterial array is utilized to improve the performance of the MIMO antenna. Various antenna parameters confirm that the presented MIMO antenna is suitable for multiband wireless communications
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