How can fluctuations in one-dimensional time series data be characterized and how can detected effects be decomposed into their dynamical origins or causes? In the context of these questions, a variety of problems are discussed and solutions are introduced. The first issue concerns the causes of anomalous diffusion. A previously proposed framework decomposes the Hurst exponent into the Joseph, Noah, and Moses effects. They represent violations of the three premises of the central limit theorem. Here the framework is applied to an intermittent deterministic system, which exhibits a rich combination of all three effects. Nevertheless, the results provide an intuitive interpretation of the dynamics. In addition, the framework is theoretically discussed and connected to a calculation that proves its validity for a large class of systems. Once the type of anomalous statistical behavior is classified, one might ask what the dynamical origin of the effects is. Especially the property of long range temporal correlations (the Joseph effect) is discussed in detail. In measurements, they might arise from different dynamical origins or can be explained as an emerging phenomenon. A collection of different routes to the observed behavior is established here. A popular tool for detecting long range correlations is detrended fluctuation analysis. Its advantages over traditional methods are stability and smoothness for timescales up to one fourth of the measurement time and the ability to neglect the slow dynamics and trends. Recently, a theory for an analytical understanding of this method was introduced. In this thesis, the method is further analyzed and developed. An approach is presented that enables scientists to use this method for short range correlated data, even if the dynamics is very complex. Fluctuations can be decomposed into a superposition of linear models that explain its features. Therefore, on the one hand, this thesis is about understanding the effects of anomalous diffusion. On the other hand, it is about widening the applicability of one of its detection methods such that it becomes useful for understanding normal or complex statistical behavior. A good example of a complex system, where the proposed stochastic methods are useful, is the atmosphere. Here it is shown how detrended fluctuation analysis can be used to uncover oscillatory modes and determine their periods. One of them is the El Ni\~no southern oscillation. A less well known and more challenging application is a 7--8 year mode in European temperature fluctuations. A power grid is a very different type of complex system. However, using the new method, it is possible to generate a data model that incorporates the important features of the grid frequency.

Dagens smarta enheter bygger nuförtiden allt mer på att ständigt hålla sig uppkopplade till allt inom dess omgivning. Industrier och hem innehåller alltmer små batteridrivna sensorer samt enheter som kommunicerar med varandra, dock är detta en begränsning när det gäller räckvidden av en enhet. Målet med denna uppsatsen är att undersöka användarbarhet av nya funktioner inom Bluetooth, samt belysa fördelar och nackdelar vilket kan uppstå med respektive teknologi när det gäller förlängd räckvidd. Vidare utfördes en jämförelsestudie, med målet att framföra skillnader för hur Bluetooth Mesh skiljer sig gentemot de andra Mesh teknologierna.Resultatet av denna uppsatsen visar att Bluetooth Mesh och Long-range har diverse svagheter och styrkor när det gäller olika användningsområden. Överföring av data med en högre hastighet och ett måttligt avstånd skulle vara tillräckligt för Long-range, medan Bluetooth Mesh anpassar sig mer för en större täckning och lättare dataöverföringar. / The smart devices of today are more and more dependent on being constantly connected to everything in its surrounding. Industries and homes contain more and more small battery powered sensors and devices, communicating with each other. However, there is a limitation when it comes to the range coverage of a device. The purpose of this thesis is to investigate the usefulness of the new features mesh networking and extended range for Bluetooth, as well as highlight the pros and cons that may exist with respective extended range technologies. Furthermore, a theoretical comparative study was conducted, with the aim of presenting some of the differences between Bluetooth Mesh and other common Mesh technologies. The results show that both Bluetooth Mesh and Long-range have strengths and weaknesses when it comes to different use cases. Transferring data with a bit higher throughput and a moderate distance would be suitable for a Long-range purpose, while Bluetooth Mesh is more suitable for a larger coverage and lighter data transfer.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第17924号 / 情博第506号 / 新制||情||89(附属図書館) / 30744 / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 梅野 健, 教授 中村 佳正, 教授 船越 満明 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

In modern world sensors play important role where they help to acquire information about the procecess, such as temperature, velocity,distance, etc. Based on this information acquired from the sensorsdecisions can be made, for example to increase heating in the buildingor accelerate the car.In many cases, a single sensor type cannot provide enough information for complex decision making, for example, when the physicalproperties of the process are outside of the measurement range of thesensor. As a result, in order to achieve desired performance levels, acombination of sensors should be used in an integrated manner.Sensor generated data need to be processed into information throughthe use of appropriate decision making models in order to improveoverall performance. Here we compare two sensors which are shortrange and long-range sensor. We use a short-range and long-rangesensor, and calculates the distance from both sensors to the same object by using Arduino UNO microcontroller. The sensors that we usein our work have overlapping or common interval in their measurementranges. Therefore we investigated how we can make a decision aboutthe distance to an object when the acquired data from both sensors isin that common range.

Scanning transmission electron microscopy (STEM) has been widely used for characterization of materials; to identify micro- and nano-structures within a sample and to analyze crystal and defect structures. High-angle annular dark field (HAADF) STEM imaging using atomic number (Z) contrast has proven capable of resolving atomic structures with better than 2 A lateral resolution. In this work, the HAADF STEM imaging mode is used in combination with multislice simulations. This combination is applied to the investigation of the temperature dependence of the intensity collected by the HAADF detector in silicon, and to convergent beam electron diffraction (CBED) to measure the degree of chemical order in intermetallic nanoparticles. The experimental and simulation results on the high–angle scattering of 300 keV electrons in crystalline silicon provide a new contribution to the understanding of the temperature dependence of the HAADF intensity. In the case of 300 keV, the average high-angle scattered intensity slightly decreases as the temperature increases from 100 K to 300 K, and this is different from the temperature dependence at 100 keV and 200 keV where HAADF intensity increases with temperature, as had been previously reported by other workers. The L10 class of hard magnetic materials has attracted continuous attention as a candidate for high-density magnetic recording media, as this phase is known to have large magnetocrystalline anisotropy, with magnetocrystalline anisotropy constant, Ku, strongly dependent on the long-range chemical order parameter, S. A new method is developed to assess the degree of chemical order in small FePt L10 nanoparticles by implementing a CBED diffraction technique. Unexpectedly, the degree of order of individual particles is highly variable and not a simple function of particle size or sample composition. The particle-to-particle variability observed is an important new aspect to the understanding of phase transformations in nanoparticle systems.

This work presents the Czochralski growth dynamics as time-varying delay based model, applied to the growth of La3Ga5.5Ta0.5O14 (LGT) piezoelectric crystals. The growth of high-quality large-diameter oxides by Czochralski technique requires the theoretical understanding and optimization of all relevant process parameters, growth conditions, and melts chemistry. Presently, proportional-integral- derivative (PID) type controllers are widely accepted for constant-diameter crystal growth by Czochralski. Such control systems, however, do not account for aspects such as the transportation delay of the heat from crucible wall to the crystal solidification front, heat radiated from the crucible wall above the melt surface, and varying melt level. During crystal growth, these time delays play a dominant role, and pose a significant challenge to the control design. In this study, a time varying linear delay model was applied to the identification of nonlinearities of the growth dynamics. Initial results reveled the benefits of this model with actual growth results. These results were used to develop a long-range model predictive control system design. Two different control techniques using long range prediction are studied for the comparative study. Development and testing of the new control system on real time growth system are discussed in detail. The results are promising and suggest future work in this direction. Other discussion about the problems during the crystal growth, optimization of crystal growth parameters are also studied along with the control system design.

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We consider a gas of indistinguishable bosons, confined to a ring of radius R, and interacting via a pair-wise cosine potential. This may be thought of as the quantized Hamiltonian Mean Field (HMF) model for bosons originally introduced by Chavanis as a generalization of Antoni and Ruffo’s classical model. This thesis contains three parts: In part one, the dynamics of a Bose-condensate are considered by studying a generalized Gross-Pitaevskii equation (GGPE). Quantum effects due to the quantum pressure are found to substantially alter the system’s dynamics, and can serve to inhibit a pathological instability for repulsive interactions. The non-commutativity of the large-N , long-time, and classical limits is discussed. In part two, we consider the GGPE studied above and seek static solutions. Exact solutions are identified by solving a non-linear eigenvalue problem which is closely related to the Mathieu equation. Stationary solutions are identified as solitary waves (or solitons) due to their small spatial extent and the system’s underlying Galilean invariance. Asymptotic series are developed to give an analytic solution to the non- linear eigenvalue problem, and these are then used to study the stability of the solitary wave mentioned above. In part three, the exact solutions outlined above are used to study quantum fluctuations of gapless excitations in the HMF model’s symmetry broken phase. It is found that this phase is destroyed at zero temperature by large quantum fluctuations. This demonstrates that mean-field theory is not exact, and can in fact be qualitatively wrong, for long-range interacting quantum systems, in contrast to conventional wisdom. / Thesis / Doctor of Philosophy (PhD) / The Hamiltonian Mean Field (HMF) model was initially proposed as a simplified description of self-gravitating systems. Its simplicity shortens calculations and makes the underlying physics more transparent. This has made the HMF model a key tool in the study of systems with long-range interactions. In this thesis we study a quantum extension of the HMF model. The goal is to understand how quantum effects can modify the behaviour of a system with long-range interactions. We focus on how the model relaxes to equilibrium, the existence of special “solitary waves”, and whether quantum fluctuations can prevent a second order (quantum) phase transition from occurring at zero temperature.

Analogue quantum simulators have proven to be an extremely versatile tool for the study of strongly-correlated condensed matter systems both near and far from equilibrium. An enticing prospect is the quantum simulation of non- Fermi liquids which lack a quasiparticle description and feature prominently in the study of strange metals, fast scrambling of quantum information, as well as holographic quantum matter. Yet, large-scale laboratory realisations of such systems remain outstanding. In this thesis, we present a proposal for the analogue quantum simulation of one such system, the Sachdev–Ye–Kitaev (SYK) model, using cavity quantum electrodynamics (cQED). We discuss recent experimental advances in this pursuit, and perform analysis of this and related models. Through a combination of analytic calculations and numeric simulations, we show how driving a cloud of fermionic atoms trapped in a multi- mode optical cavity, and subjecting it to a spatially disordered AC-Stark shift, can realise an effective model which retrieves the physics of the SYK model, with random all-to-all interactions and fast scrambling. Working towards the SYK model, we present results from a recent proof-of-principle cQED experiment which implemented the disordered light-shift technique to quantum simulate all- to-all interacting spin models with quenched disorder. In this context, we show analytically how disorder-driven localisation can be extracted from spectroscopic probes employed in cQED experiments, despite their lack of spatially resolved information. Further, we numerically investigate the post-quench dynamics of the SYK model, finding a universal, super-exponential equilibration in the disorder-averaged far-from-equilibrium dynamics. These are reproduced analytically through an effective master equation. Our work demonstrates the increasing capabilities of cQED quantum simulators, highlighting how these may be used to study the fascinating physics of holographic quantum matter and other disorder models in the lab.