Regular physical activity is essential to maintain or even improve an individual’s health. There exist various guidelines on how much individuals should do. Therefore, it is important to monitor performed physical activities during people’s daily routine in order to tell how far they meet professional recommendations. This thesis follows the goal to develop a mobile, personalized physical activity monitoring system applicable for everyday life scenarios. From the mentioned recommendations, this thesis concentrates on monitoring aerobic physical activity. Two main objectives are defined in this context. On the one hand, the goal is to estimate the intensity of performed activities: To distinguish activities of light, moderate or vigorous effort. On the other hand, to give a more detailed description of an individual’s daily routine, the goal is to recognize basic aerobic activities (such as walk, run or cycle) and basic postures (lie, sit and stand).
With recent progress in wearable sensing and computing the technological tools largely exist nowadays to create the envisioned physical activity monitoring system. Therefore, the focus of this thesis is on the development of new approaches for physical activity recognition and intensity estimation, which extend the applicability of such systems. In order to make physical activity monitoring feasible in everyday life scenarios, the thesis deals with questions such as 1) how to handle a wide range of e.g.
everyday, household or sport activities and 2) how to handle various potential users. Moreover, this thesis deals with the realistic scenario where either the currently performed activity or the current user is unknown during the development and training
phase of activity monitoring applications. To answer these questions, this thesis proposes and developes novel algorithms, models and evaluation techniques, and performs thorough experiments to prove their validity.
The contributions of this thesis are both of theoretical and of practical value. Addressing the challenge of creating robust activity monitoring systems for everyday life the concept of other activities is introduced, various models are proposed and validated. Another key challenge is that complex activity recognition tasks exceed the potential of existing classification algorithms. Therefore, this thesis introduces a confidence-based extension of the well known AdaBoost.M1 algorithm, called ConfAdaBoost.M1. Thorough experiments show its significant performance improvement compared to commonly used boosting methods. A further major theoretical contribution is the introduction and validation of a new general concept for the personalization of physical activity recognition applications, and the development of a novel algorithm (called Dependent Experts) based on this concept. A major contribution of practical value is the introduction of a new evaluation technique (called leave-one-activity-out) to simulate when performing previously unknown activities in a physical activity monitoring system. Furthermore, the creation and benchmarking of publicly available physical activity monitoring datasets within this thesis are directly benefiting the research community. Finally, the thesis deals with issues related to the implementation of the proposed methods, in order to realize the envisioned mobile system and integrate it into a full healthcare application for aerobic activity monitoring and support in daily life.
The work presented in this thesis discusses the thermal and power management of multi-core processors (MCPs) with both two dimensional (2D) package and there dimensional (3D) package chips. The power and thermal management/balancing is of increasing concern and is a technological challenge to the MCP development and will be a main performance bottleneck for the development of MCPs. This thesis develops optimal thermal and power management policies for MCPs. The system thermal behavior for both 2D package and 3D package chips is analyzed and mathematical models are developed. Thereafter, the optimal thermal and power management methods are introduced.
Nowadays, the chips are generally packed in 2D technique, which means that there is only one layer of dies in the chip. The chip thermal behavior can be described by a 3D heat conduction partial differential equation (PDE). As the target is to balance the thermal behavior and power consumption among the cores, a group of one dimensional (1D) PDEs, which is derived from the developed 3D PDE heat conduction equation, is proposed to describe the thermal behavior of each core. Therefore, the thermal behavior of the MCP is described by a group of 1D PDEs. An optimal controller is designed to manage the power consumption and balance the temperature among the cores based on the proposed 1D model.
3D package is an advanced package technology, which contains at least 2 layers of dies stacked in one chip. Different from 2D package, the cooling system should be installed among the layers to reduce the internal temperature of the chip. In this thesis, the micro-channel liquid cooling system is considered, and the heat transfer character of the micro-channel is analyzed and modeled as an ordinary differential equation (ODE). The dies are discretized to blocks based on the chip layout with each block modeled as a thermal resistance and capacitance (R-C) circuit. Thereafter, the micro-channels are discretized. The thermal behavior of the whole system is modeled as an ODE system. The micro-channel liquid velocity is set according to the workload and the temperature of the dies. Under each velocity, the system can be described as a linear ODE model system and the whole system is a switched linear system. An H-infinity observer is designed to estimate the states. The model predictive control (MPC) method is employed to design the thermal and power management/balancing controller for each submodel.
The models and controllers developed in this thesis are verified by simulation experiments via MATLAB. The IBM cell 8 cores processor and water micro-channel cooling system developed by IBM Research in collaboration with EPFL and ETHZ are employed as the experiment objects.
A large class of estimators including maximum likelihood, least squares and M-estimators are based on estimating functions. In sequential change point detection related monitoring functions can be used to monitor new incoming observations based on an initial estimator, which is computationally efficient because possible numeric optimization is restricted to the initial estimation. In this work, we give general regularity conditions under which we derive the asymptotic null behavior of the corresponding tests in addition to their behavior under alternatives, where conditions become particularly simple for sufficiently smooth estimating and monitoring functions. These regularity conditions unify and even extend a large amount of existing procedures in the literature, while they also allow us to derive monitoring schemes in time series that have not yet been considered in the literature including non-linear autoregressive time series and certain count time series such as binary or Poisson autoregressive models. We do not assume that the estimating and monitoring function are equal or even of the same dimension, allowing for example to combine a non-robust but more precise initial estimator with a robust monitoring scheme. Some simulations and data examples illustrate the usefulness of the described procedures.
In this thesis, we develop a granular hydrodynamic model which covers the three principal regimes observed in granular systems, i.e. the dilute flow, the dense flow and the solid-like regime. We start from a kinetic model valid at low density and extend its validity to the granular solid-like behavior. Analytical and numerical results show that this model reproduces a lot of complex phenomena like for instance slow viscoplastic motion, critical states and the pressure dip in sand piles. Finally we formulate a 1D version of the full model and develop a numerical method to solve it. We present two numerical examples, a filling simulation and the flow on an inclined plane where the three regimes are included.
Die Klimaveränderung und die damit einhergehende verstärkte Fäulnisdynamik auf Trauben stellt eine Herausforderung für die nördlichen Weinbauregionen Europas dar. Daher wurde die vollautomatisch-optische Traubensortierung, eine innovative Verfahrenstechnik bei der Traubenverarbeitung, im zugrundeliegenden Projekt erstmals zur maschinellen Selektion fauler Beeren aus Hand- und Maschinenlesegut unter Praxisbedingungen in den Jahrgängen 2010 und 2011 eingesetzt. Hierzu wurden 15 Sortierversuche mit durchschnittlich zwei Tonnen Lesegut unterschiedlicher Rebsorten des Weinanbaugebietes Mosel mit unterschiedlichen Fäulnisarten und intensitäten durchgeführt, wobei Selektionsquoten bis zu 99 % erreicht wurden. Durch die Sortierung erfolgte eine Fraktionierung des Lesegutes in Saftvorlauf (aus geöffneten Beeren austretender Saft), Positivfraktion (gesunde Beeren) und Negativfraktion (faule Beeren), die im Vergleich zur unsortierten Kontrolle repräsentative Aussagen über den Fäulniseinfluss von Beeren auf die standardisiert verarbeiteten Moste und Weine lieferten.
Ziel dieser Arbeit war es, mittels vielseitiger analytischer und sensorischer Untersuchungen der einzelnen Fraktionen die vollautomatisch-optische Traubensortierung als Maßnahme zur Qualitätssicherung, -steigerung und -sicherheit der Moste und korrelierenden Weine zu bewerten. Im Rahmen der analytischen Untersuchungen wurden die Gehalte an Mykotoxinen, biogenen Aminen, Phenolen, Gluconsäure, Glycerin, organischen Säuren, Kationen, Schwefelbindungspartnern und Off-flavour-Verbindungen sowie das Mostgewicht bestimmt. Die notwendigen Methoden zur Bestimmung der Mykotoxine mittels HPLC-MS/MS sowie der phenolischen Verbindungen mittels HPLC-UV wurden entwickelt und validiert. Sensorisch wurden deskriptive sowie diskriminative Untersuchungen durchgeführt.
Die Ergebnisse der analytischen und sensorischen Untersuchungen aller Versuche zeigten, dass die Art und Entwicklung der auf Trauben auftretenden Fäulnis überwiegend zu qualitativ negativen Beeinflussungen der rebsortentypischen Weinstilistik führte. Die effiziente Selektion fäulnisbelasteter Beeren mittels vollautomatisch-optischer Traubensortierung und die hierdurch ermöglichte, individuelle Handhabung der einzelnen Sortierfraktionen tragen zur Sicherung, Steigerung und Sicherheit der Qualität von Weinen bei.
Es muss jedoch berücksichtigt werden, dass diese Ergebnisse nur den Zustand zweier Jahrgänge widerspiegeln. Zukünftige Untersuchungen über mehrere Jahre sollten daher angestrebt werden, um die beobachteten Trends zu bestätigen.
In the present work, the phase transitions in different Fe/FeC systems were studied by using the molecular dynamics simulation and the Meyer-Entel interaction potential (also the Johnson potential for Fe-C interaction). Fe-bicrystal, thin film, Fe-C bulk and Fe-C nanowire systems were investigated to study the behaviour of the phase transition, where the energetics, dynamics and transformations pathways were analysed.