This thesis focuses on the simulation of the gyroscopic effect using the software MSC Adams. A simple shaft-disk system was created and parameter of the sys-tem were changed in order to study the influence of the gyroscopic effect. It was shown that an increasing bearing stiffness reduces the precession motion. Fur-thermore, it was shown that the gyroscopic effect vanishes if the disk of system is placed symmetrically on the shaft, which reduces the system to a Jeffcott-Ro-tor. The second objective of this study was to analyze different defects in a simple fixed axis gear set. In particular, a cracked shaft, a cracked pinion and a chipped pinion as well as a healthy gear system were created and tested in Adams. The contact force between the two gears was monitored and the 2D and 3D frequency spectrum, as well as the Wavelet Transform, were plotted in order to compare the individual defects. It was shown that the Wavelet Transform is a powerful tool, capable of identifying a cracked gear with a non-constant speed. The last part of this study included fault detection with statistical methods as well as with the Sideband Energy Ratio (SER). The time domain signal of the individual faults were used to compare the mean, the standard deviation and the root mean square. Furthermore, the noise profile in the frequency spectrum was tracked with statistical methods using the mean and the standard deviation. It was demonstrated that it is possible to identify a cracked gear, as well as a chipped gear, with statistical methods. However, a cracked shaft could not be identified. The results also show that SER was only capable to identify major defects in a gear system such as a chipped tooth.

There is a sharp increase in the number of old people living alone throughout the world. More often than not, such people require continuous and immediate care and attention in their everyday lives, hence the need for round the clock monitoring, albeit in a respectful, dignified and non-intrusive way. For example, continuous care is required when they become frail and less active, and immediate attention is required when they fall or remain in the same position for a long time. To this extent, various monitoring technologies have been developed, yet there are major improvements still to be realised. Current technologies include indoor positioning systems (IPSs) and health monitoring systems. The former relies on defined configurations of various sensors to capture a person's position within a given space in real-time. The functionality of the sensors varies depending on receiving appropriate data using WiFi, radio frequency identification (RFIO), ultrawide band (UWB), dead reckoning (OR), infrared indoor (IR), Bluetooth (BLE), acoustic signal, visible light detection, and sound signal monitoring. The systems use various algorithms to capture proximity, location detection, time of arrival, time difference of arrival angle, and received signal strength data. Health monitoring technologies capture important health data using accelerometers and gyroscope sensors. In some studies, audio fingerprinting has been used to detect indoor environment sound variation and have largely been based on recognising TV sound and songs. This has been achieved using various staging methods, including pre-processing, framing, windowing, time/frequency domain feature extraction, and post-processing. Time/frequency domain feature extraction tools used include Fourier Transforms (FTs}, Modified Discrete Cosine Transform (MDCT}, Principal Component Analysis (PCA), Mel-Frequency Cepstrum Coefficients (MFCCs), Constant Q Transform (CQT}, Local Energy centroid (LEC), and Wavelet transform. Artificial intelligence (Al) and probabilistic algorithms have also been used in IPSs to classify and predict different activities, with interesting applications in healthcare monitoring. Several tools have been applied in IPSs and audio fingerprinting. They include Radial Basis Kernel (RBF), Support Vector Machine (SVM), Decision Trees (DTs), Hidden Markov Models (HMMs), Na'ive Bayes (NB), Gaussian Mixture Modelling (GMM), Clustering algorithms, Artificial Neural Networks (ANNs), and Deep Learning (DL). Despite all these attempts, there is still a major gap for a completely non-intrusive system capable of monitoring what an elderly person living alone is doing, where and for how long, and providing a quick traffic-like risk score prompting, therefore immediate action or otherwise. In this thesis, a cost-effective and completely non-intrusive indoor positioning and activity-monitoring system for elderly people living alone has been developed, tested and validated in a typical residential living space. The proposed system works based on five phases: (1)Set-up phase that defines the typical activities of daily living (TADLs). (2)Configuration phase that optimises the implementation of the required sensors in exemplar flat No.1. (3)Learning phase whereby sounds and position data of the TADLs are collected and stored in a fingerprint reference data set. (4)Listening phase whereby real-time data is collected and compared against the reference data set to provide information as to what a person is doing, when, and for how long. (5)Alert phase whereby a health frailty score varying between O unwell to 10 healthy is generated in real-time. Two typical but different residential flats (referred to here are Flats No.1 and 2) are used in the study. The system is implemented in the bathroom, living room, and bedroom of flat No.1, which includes various floor types (carpet, tiles, laminate) to distinguish between various sounds generated upon walking on such floors. The data captured during the Learning Phase yields the reference data set and includes position and sound fingerprints. The latter is generated from tests of recording a specific TADL, thus providing time and frequency-based extracted features, frequency peak magnitude (FPM), Zero Crossing Rate (ZCR), and Root Mean Square Error (RMSE). The former is generated from distance measurement. The sampling rate of the recorded sound is 44.1kHz. Fast Fourier Transform (FFT) is applied on 0.1 seconds intervals of the recorded sound with minimisation of the spectral leakage using the Hamming window. The frequency peaks are detected from the spectrogram matrices to get the most appropriate FPM between the reference and sample data. The position detection of the monitored person is based on the distance between that captured from the learning and listening phases of the system in real-time. A typical furnished one-bedroom flat (flat No.2) is used to validate the system. The topologies and floorings of flats No.1 and No.2 are different. The validation is applied based on "happy" and "unusual" but typical behaviours. Happy ones include typical TADLs of a healthy elderly person living alone with a risk metric higher than 8. Unusual one's mimic acute or chronic activities (or lack thereof), for example, falling and remaining on the floor, or staying in bed for long periods, i.e., scenarios when an elderly person may be in a compromised situation which is detected by a sudden drop of the risk metric (lower than 4) in real-time. Machine learning classification algorithms are used to identify the location, activity, and time interval in real-time, with a promising early performance of 94% in detecting the right activity and the right room at the right time.

Grease is among the most widely used lubricants in rolling element bearings. Proper understanding of the effect of lubrication due to grease aging can provide a significant increase in the life of the engineering systems. However, at present, there is no sufficient understanding of the grease aging effect in rolling elements bearing. This restricts the optimal usage of the bearing and timely monitoring of the grease. The current research work tries to address this issue with an experimental investigation. This project studies the behavior of 4 types of greases in rolling elements bearings for various operating conditions by recording the temperature and acoustic emission data. The aged samples were prepared to keep in the oven at 150 °C for a series of time duration letting it go through the chemical changes and thermal degradation. Tests were carried out in a test rig with the different levels of oxidized greases for 5 hrs time. And the effects in bearing temperature, acoustic emission were recorded. This is an investigation to analyze the effects of grease composition and aging in rolling elements lubrication by means of acoustic emission and bearing temperature. The IR spectroscopy was carried from the samples collected from the oven in order to understand the change in lubricant composition. The results show that the grease with di-urea thickener and base oil of synthetic ether and polyolester gives the best bearing temperature and acoustic emission behavior compared to the other grease type. The possibility of using the acoustic emission and temperature data to monitor the grease aging is also presented. Along with this, the possibility of using the AE statistical methods, AE count method, and energy plot were also explored to relate with the degree of aging.

This work concentrates on the application of diversity techniques and space time block coding for future high speed mobile wireless communications on multicarrier systems. At first, alternative multicarrier kernels robust for high speed doubly-selective fading channel are sought. They include the comparisons of discrete Fourier transform (DFT), fractional Fourier transform (FrFT) and wavelet transform (WT) multicarrier kernels. Different wavelet types, including the raised-cosine spectrum wavelets are implemented, evaluated and compared. From different wavelet families, orthogonal wavelets are isolated from detailed evaluations and comparisons as suitable for multicarrier applications. The three transforms are compared over a doubly-selective channel with the WT significantly outperforming all for high speed conditions up to 300 km/hr. Then, a new wavelet is constructed from an ideal filter approximation using established wavelet design algorithms to match any signal of interest; in this case under bandlimited criteria. The new wavelet showed better performance than other traditional orthogonal wavelets. To achieve MIMO communication, orthogonal space-time block coding, OSTBC, is evaluated next. First, the OSTBC is extended to assess the performance of the scheme over extended receiver diversity order. Again, with the extended diversity conditions, the OSTBC is implemented for a multicarrier system over a doubly-selective fading channel. The MIMO-OFDM systems (implemented using DFT and WT kernels) are evaluated for different operating frequencies, typical of LTE standard, with Doppler effects. It was found that, during high mobile speed, it is better to transmit OFDM signals using lower operating frequencies. The information theory for the 2-transmit antenna OSTBC does not support higher order implementation of multi-antenna systems, which is required for the future generation wireless communications systems. Instead of the OSTBC, the QO-STBC is usually deployed to support the design of higher order multi-antenna systems other than the 2-transmit antenna scheme. The performances of traditional QO-STBC methods are diminished by some off-diagonal (interference) terms such that the resulting system does not attain full diversity. Some methods for eliminating the interference terms have earlier been discussed. This work follows the construction of cyclic matrices with Hadamard matrix to derive QO-STBC codes construction which are N-times better than interference free QO-STBC, where N is the number of transmit antenna branches.

The well-known fast Fourier transform (FFT) is one of the most important and widely used algorithms in a multitude of disciplines including engineering, natural sciences, scientific computing, and signal processing. Nevertheless, its restriction to equispaced data represents a significant limitation in practice. Consequently, this has led to the development of the nonequispaced fast Fourier transform (NFFT), which permits the use of arbitrary nodes in the spatial domain. In a variety of applications, such as magnetic resonance imaging (MRI), solution of partial differential equations (PDEs), etc., however, there is a need for the inverse transform, i.e., computing Fourier data from given nonequispaced function evaluations of trigonometric polynomials, or even of bandlimited functions. For this reason, this thesis focuses on the presentation of new efficient inversion methods for the NFFT, which can be realized with the complexity of a single NFFT, and on the generalization of these methods to the setting of bandlimited functions. Additionally, the evaluation problem for bandlimited functions is addressed as well. In particular, the present thesis provides the first comprehensive overview of the so-called regularized Shannon sampling formulas.:1 Introduction 2 Nonequispaced fast Fourier transforms 3 Direct inversion methods for the NFFT 4 Regularized Shannon sampling formulas 5 Fast sinc methods 6 Reconstruction of the Fourier transform of bandlimited functions from nonequispaced spatial data

The present thesis provides a modularized view on the structure of fast numerical methods for computing Coulomb interactions between charged particles in three-dimensional space. Thereby, the common structure is given in terms of three self-contained algorithmic frameworks that are built on top of each other, namely fast Fourier transform (FFT), nonequispaced fast Fourier transform (NFFT) and NFFT based particle-mesh methods (P²NFFT). For each of these frameworks algorithmic enhancement and parallel implementations are presented with special emphasis on scalability up to hundreds of thousands of parallel processes. In the context of FFT massively parallel algorithms are composed from hardware adaptive low level modules provided by the FFTW software library. The new algorithmic NFFT concepts include pruned NFFT, interlacing, analytic differentiation, and optimized deconvolution in Fourier space with respect to a mean square aliasing error. Enabled by these generalized concepts it is shown that NFFT provides a unified access to particle-mesh methods. Especially, mixed-periodic boundary conditions are handled in a consistent way and interlacing can be incorporated more efficiently. Heuristic approaches for parameter tuning are presented on the basis of thorough error estimates. / Die vorliegende Dissertation beschreibt einen modularisierten Blick auf die Struktur schneller numerischer Methoden für die Berechnung der Coulomb-Wechselwirkungen zwischen Ladungen im dreidimensionalen Raum. Die gemeinsame Struktur ist geprägt durch drei selbstständige und auf einander aufbauenden Algorithmen, nämlich der schnellen Fourier-Transformation (FFT), der nicht äquidistanten schnellen Fourier-Transformation (NFFT) und der NFFT-basierten Teilchen-Gitter-Methode (P²NFFT). Für jeden dieser Algorithmen werden Verbesserungen und parallele Implementierungen vorgestellt mit besonderem Augenmerk auf massiv paralleler Skalierbarkeit. Im Kontext der FFT werden parallele Algorithmen aus den Hardware adaptiven Modulen der FFTW Softwarebibliothek zusammengesetzt. Die neuen NFFT-Konzepte beinhalten abgeschnittene NFFT, Versatz, analytische Differentiation und optimierte Entfaltung im Fourier-Raum bezüglich des mittleren quadratischen Aliasfehlers. Mit Hilfe dieser Verallgemeinerungen bietet die NFFT einen vereinheitlichten Zugang zu Teilchen-Gitter-Methoden. Insbesondere gemischt periodische Randbedingungen werden einheitlich behandelt und Versatz wird effizienter umgesetzt. Heuristiken für die Parameterwahl werden auf Basis sorgfältiger Fehlerabschätzungen angegeben.

Approved for public release, distribution is unlimited / Orthogonal frequency division multiplexing (OFDM) is being successfully used in numerous applications. It was chosen for IEEE 802.11a wireless local area network (WLAN) standard, and it is being considered for the fourthgeneration mobile communication systems. Along with its many attractive features, OFDM has some principal drawbacks. Sensitivity to frequency errors is the most dominant of these drawbacks. In this thesis, the frequency offset and phase noise effects on OFDM based communication systems are investigated under a variety of channel conditions covering both indoor and outdoor environments. The simulation performance results of the OFDM system for these channels are presented. / Lieutenant Junior Grade, Turkish Navy

The computation of the Coulomb potentials and forces in charged particle systems under 3d-periodic boundary conditions is possible in an efficient way by utilizing the Ewald summation formulas and applying the fast Fourier transform (FFT). In this paper we consider the particle-particle NFFT (P2NFFT) approach, which is based on the fast Fourier transform for nonequispaced data (NFFT) and compare the error behaviors regarding different window functions, which are used in order to approximate the given continuous charge distribution by a mesh based charge density. Typically B-splines are applied in the scope of particle mesh methods, as for instance within the well-known particle-particle particle-mesh (P3M) algorithm. The publicly available P2NFFT algorithm allows the application of an oversampled FFT as well as the usage of different window functions. We consider for the first time also an approximation by Bessel functions and show how the resulting root mean square errors in the forces can be predicted precisely and efficiently. The results show that, if the parameters are tuned appropriately, the Bessel window function is in many cases even the better choice in terms of computational costs. Moreover, the results indicate that it is often advantageous in terms of efficiency to spend some oversampling within the NFFT while using a window function with a smaller support.

Este trabalho tem seu foco na monitoração dos processos de usinagem das máquinas ferramentas sem a utilização de sensores dedicados à medição direta das grandezas mecânicas envolvidas no processo de usinagem em si. Assim sendo, optou-se por um método de medição indireta utilizando um estimador de potência e torque baseado nas grandezas elétricas na entrada de energia do motor de indução da máquina ferramenta. O núcleo central do trabalho consiste na medição de processos de usinagem utilizando um dinamômetro de alta precisão próprio para máquina ferramenta para obter os valores médios e os modos de vibração do processo máquina ferramenta-usinagem e fazer a confrontação com os mesmos dados obtidos pelo estimador de torque. A pesquisa realizada utilizou ferramentas clássicas de processamento de sinais, como é o caso da FFT, assim como duas poderosas ferramentas atuais, a transformada de wavelet (WT) e a transformada de Hilbert-Huang (HHT). Em adição foi utilizado um processo de otimização para a determinação dos parâmetros do motor de indução on-line baseado em uma metaheurística denominada busca de harmonia (Harmony Search HS) / This work focuses on monitoring the machining processes in machine tools without the use of dedicated transducers to directly measure mechanicals quantities of the machining process. The approached method used the option of measuring the power and torque through a estimator based in the electrical quantities in the input of the induction motor used by machine tool. The core of the work consists in the measuring the mechanical processes of machining using a high precision dynamometer specified to machine tool to get the average values of mechanical quantities and the vibration modes of the set machine tool-machining process, the objective is to compare the same data group got from dynamometer and estimator. The research data was based on classical analysis tool such as Fourier transform (FFT) and on modern powerful tools such as wavelet transforms (WT) and Hilbert-Huang transform (HHT). To refine the measuring process of average values of torque and power using the induction motor a method to estimates the motor parameters was added based on an optimization method named harmony search (HS).

[pt] O objetivo desta tese é apresentar novas alternativas para modelagem de variáveis aleatórias no setor de seguros, utilizando o arcabouço dos modelos orientados por score com parâmetros variantes no tempo. No primeiro artigo, propomos um modelo dinâmico para a distribuição do sinistro agregado, que corresponde à soma aleatória dos valores de sinistros (severidade) em determinado período de tempo. A obtenção da distribuição do sinistro agregado é um problema clássico na teoria do risco e fundamental para precificação de seguros, cálculo de provisões e de probabilidade de ruína. No entanto, a obtenção da expressão analítica para essa distribuição de probabilidade é uma tarefa difícil. Neste trabalho, especificamos distribuições não-Gaussianas, tanto para o número de sinistros como para severidade, sob o arcabouço GAS (Generalized Autoregressive Score), e, por meio do uso da Transformada Rápida de Fourier obtemos, numericamente, a distribuição do sinistro agregado. O segundo artigo trata da incorporação do efeito de variáveis macroeconômicas na modelagem de variáveis relevantes no setor de seguros, em linha com requisito internacional de avaliação de provisões de forma consistente com mercado, a qual leva em consideração as informações disponíveis nos mercados financeiros e de capital relevantes, utilizando metodologias e parâmetros consistentes com esses mercados. Modelamos uma série bivariada de número de sinistros (duas linhas de negócios) de seguros financeiros com modelos autorregressivos e utilizamos cópulas para modelar a estrutura de dependência das séries temporais condicionado aos modelos ajustados nas marginais. Com esta abordagem, é possível simular números de sinistros futuros de mais de uma carteira, podendo esse resultado ser utilizado em uma avaliação consistente de provisões e da saúde financeira da seguradora. / [en] The objective of this thesis is to present new alternatives for modeling random variables in the insurance industry, using the framework of the score driven models with time-varying parameters. In the first paper, we propose a dynamic model for the aggregate claims distribution, which corresponds to a random sum of claims severity in a certain period of time. Obtaining the aggregate claims distribution is a classic problem in the Risk Theory and fundamental for premium estimation, measurement of obligations and ruin probability valuation. However, obtaining the analytic expression for this probability distribution is a hard task. In this work, we specify nonGaussian distributions for both the number of claims and for the claims severity, under the GAS framework, and, through the use of the fast Fourier transform, we obtain, numerically, the aggregate claims distribution. The second paper deals with the incorporation of macroeconomic variables on the modeling of relevant variables in the insurance sector, in line with the international requirements for market consistent valuation of insurance liabilities, which means that one should take into account the available information in relevant financial and capital markets, using methodologies and parameters consistent with these markets. We model a bivariate time series (two lines of business) of financial insurance with autoregressive models and use copulas models to consider the dependency structure of the time series conditioned to the fitted models for the marginals. Within this approach, it is possible to simulate the numbers of claims from more than one portfolio, and this result can be used in a consistent valuation of liabilities and of the financial health of an insurer.