Training Bayesian networks for image segmentation

We are concerned with the problem of image segmentation in which each pixel is assigned to one of a predefined finite number of classes. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of segmentations. Markov Random Fields (MRFs) have been used to incorporate some of this prior knowledge, but this not entirely satisfactory as inference in MRFs is NP-hard. The multiscale quadtree model of Bouman and Shapiro (1994) is an attractive alternative, as this is a tree-structured belief network in which inference can be carried out in linear time (Pearl 1988). It is an hierarchical model where the bottom-level nodes are pixels, and higher levels correspond to downsampled versions of the image. The conditional-probability tables (CPTs) in the belief network encode the knowledge of how the levels interact. In this paper we discuss two methods of learning the CPTs given training data, using (a) maximum likelihood and the EM algorithm and (b) emphconditional maximum likelihood (CML). Segmentations obtained using networks trained by CML show a statistically-significant improvement in performance on synthetic images. We also demonstrate the methods on a real-world outdoor-scene segmentation task.

Fast response static var generator for improving the power system performance of an electric arc furnace (EAF)

Cascaded multilevel inverters-based Static Var Generators (SVGs) are FACTS equipment introduced for active and reactive power flow control. They eliminate the need for zigzag transformers and give a fast response. However, with regard to their application for flicker reduction in using Electric Arc Furnace (EAF), the existing multilevel inverter-based SVGs suffer from the following disadvantages. (1) To control the reactive power, an off-line calculation of Modulation Index (MI) is required to adjust the SVG output voltage. This slows down the transient response to the changes of reactive power; and (2) Random active power exchange may cause unbalance to the voltage of the d.c. link (HBI) capacitor when the reactive power control is done by adjusting the power angle d alone. To resolve these problems, a mathematical model of 11-level cascaded SVG, was developed. A new control strategy involving both MI (modulation index) and power angle (d) is proposed. A selected harmonics elimination method (SHEM) is taken for switching pattern calculations. To shorten the response time and simplify the controls system, feed forward neural networks are used for on-line computation of the switching patterns instead of using look-up tables. The proposed controller updates the MI and switching patterns once each line-cycle according to the sampled reactive power Qs. Meanwhile, the remainder reactive power (compensated by the MI) and the reactive power variations during the line-cycle will be continuously compensated by adjusting the power angles, d. The scheme senses both variables MI and d, and takes action through the inverter switching angle, qi. As a result, the proposed SVG is expected to give a faster and more accurate response than present designs allow. In support of the proposal there is a mathematical model for reactive powered distribution and a sensitivity matrix for voltage regulation assessment, MATLAB simulation results are provided to validate the proposed schemes. The performance with non-linear time varying loads is analysed and refers to a general review of flicker, of methods for measuring flickers due to arc furnace and means for mitigation.

A generative model for online depth fusion

We present a probabilistic, online, depth map fusion framework, whose generative model for the sensor measurement process accurately incorporates both long-range visibility constraints and a spatially varying, probabilistic outlier model. In addition, we propose an inference algorithm that updates the state variables of this model in linear time each frame. Our detailed evaluation compares our approach against several others, demonstrating and explaining the improvements that this model offers, as well as highlighting a problem with all current methods: systemic bias. © 2012 Springer-Verlag.

Algorithm of Decision Support in Emergency

Constant increase of human population result in more and more people living in emergency dangerous regions. In order to protect them from possible emergencies we need effective solution for decision taking in case of emergencies, because lack of time for taking decision and possible lack of data. One among possible methods of taking such decisions is shown in this article.

Generalized correlated equilibrium for two-person games in extensive form with perfect information

A correlation scheme (leading to a special equilibrium called “soft” correlated equilibrium) is applied for two-person finite games in extensive form with perfect information. Randomization by an umpire takes place over the leaves of the game tree. At every decision point players have the choice either to follow the recommendation of the umpire blindly or freely choose any other action except the one suggested. This scheme can lead to Pareto-improved outcomes of other correlated equilibria. Computational issues of maximizing a linear function over the set of soft correlated equilibria are considered and a linear-time algorithm in terms of the number of edges in the game tree is given for a special procedure called “subgame perfect optimization”.

A fresh look at decision making in international investment choices: Firm international coherence and home-host country relatedness

Understanding how decisions for international investments are made and how this affects the overall pattern of investments and firm’s performance is of particular importance both in strategy and international business research. This dissertation introduced first home-host country relatedness (HHCR) as the degree to which countries are efficiently combined within the investment portfolios of firms. It theorized and demonstrated that HHCR will vary with the motivation for investments along at least two key dimensions: the nature of foreign investments and the connectedness of potential host countries to the rest of the world. Drawing on cognitive psychology and decision-making research, it developed a theory of strategic decision making proposing that strategic solutions are chosen close to a convenient anchor. Building on research on memory imprinting, it also proposed that managers tend to rely on older knowledge representation. In the context of international investment decisions, managers use their home countries as an anchor and are more likely to choose as a site for foreign investments host countries that are ‘close’ to the home country. These decisions are also likely to rely more strongly on closeness to time invariant country factors of historic and geographic nature rather than time-variant institutions. Empirical tests using comprehensive investments data by all public multinational companies (MNC) worldwide, or over 15,000 MNCs with over half a million subsidiaries, support the claims. Finally, the dissertation introduced the concept of International Coherence (IC) defined as the degree to which an MNE’s network comprises countries that are related. It was hypothesized that maintaining a high level of coherence is important for firm performance and will enhance it. Also, the presence of international coherence mitigates some of the negative effects of unrelated product diversification. Empirical tests using data on foreign investments of over 20,000 public firms, while also developing a home-host country relatedness index for up to 24,300 home-host pairs, provided support for the theory advanced.

Micropartículas contendo nanopartículas de quitosana usando sulfato e genipina para liberação modificada da triancinolona: obtenção, caracterização e estudo em células tumorais

Chitosan is a polymer biocompatibility and biodegradability widely used in drug delivery systems. The co-crosslinking of chitosan with sodium sulfate and genipin, to form particulate systems is related of making them more resistant to acidic pH and to modulate the release kinetics for the oral route. Triamcinolone is a glucocorticoid with anti-inflammatory and immunosuppressive actions. The nanoparticles were prepared by co-crosslinking and characterized for particle size, PDI, zeta potential, crosslinking degree, encapsulation rate, morphology, infrared spectroscopy, thermal analysis, release kinetics and cells studies. The nanoparticles were prepared initially without genipin with sodium sulphate and the particles parameters were monitored in function of different ratio of drug / polymer, different concentrations of sodium sulfate and polysorbate 80 and the drip mode of crosslinkers on polymers. After optimizing conditions, the chosen system parameters without genipin included mean diameter of 312.20 ± 5.70 nm, PDI 0.342 ± 0.013 and zeta potential of 20.18 ± 2.28 mV. The genipin was introduced into the system analyzing different concentrations (0.5, 1.0 and 2.0 mM) and crosslinking times (3, 6, 12 and 24 h). Evaluating crosslinking time with genipin (0.5 mM) it was showed that varying the genipin reaction time the systems size ranged from 235.1 to 334.4 nm, the PDI from 0.321 to 0.392 and zeta potential 20.92 to 30.39 mV. The crosslinking degree that coud vary from 14 to 30 %. Nanoparticles without genipina, 6 h and 24 h crosslinking time were dried by spray-drying method. Analysis by scanning electron micrograph (SEM) revealed that the microparticles showed spherical morphology. The encapsulation rate was 75 ± 2.3 % using validated HPLC methodology. The infrared analysis showed chemical interactions between the components of the formulation. Thermal analysis showed that systems with a higher degree of crosslinking had a higher thermal stability. On release kinetics, increasing the degree of crosslinking was able to decrease the concentration and rate of release of triamcinolone. In studies with liver cancer cells (HepG2) and colon (HT-29), the microparticulate prepared with triamcinolone and 24 h of crosslinking with genipin showed a potential for antitumor activity in hepatic cell line HepG2. Therefore, a new delivery system for triamcinolone on polymeric nanoparticles of chitosan cocrosslinked with genipin and sodium sulfate was obtained with hepatic antitumor potential.

Proposta de um processador multithreading com características de previsibilidade

The real-time embedded systems design requires precise control of the passage of time in the computation performed by the modules and communication between them. Generally, these systems consist of several modules, each designed for a specific task and restricted communication with other modules in order to obtain the required timing. This strategy, called federated architecture, is already becoming unviable in front of the current demands of cost, required performance and quality of embedded system. To address this problem, it has been proposed the use of integrated architectures that consist of one or few circuits performing multiple tasks in parallel in a more efficient manner and with reduced costs. However, one has to ensure that the integrated architecture has temporal composability, ie the ability to design each task temporally isolated from the others in order to maintain the individual characteristics of each task. The Precision Timed Machines are an integrated architecture approach that makes use of multithreaded processors to ensure temporal composability. Thus, this work presents the implementation of a Precision Machine Timed named Hivek-RT. This processor which is a VLIW supporting Simultaneous Multithreading is capable of efficiently execute real-time tasks when compared to a traditional processor. In addition to the efficient implementation, the proposed architecture facilitates the implementation real-time tasks from a programming point of view.

Dispersion and energy relations in periodic chains and grids

In this study wave propagation, dispersion relations, and energy relations for linear elastic periodic systems are analyzed. In particular, the dispersion relations for monoatomic chain of infinite dimension are obtained analytically by writing the Block-type wave equation for a unit cell in order to capture the dynamic behavior for chains under prescribed vibration. By comparing the discretized model (mass-spring chain) with the solid bar system, the nonlinearity of the dispersion relation for chain indicates that the periodic lattice is dispersive in contrast to the continuous rod, which is non dispersive. Further investigations have been performed considering one-dimensional diatomic linear elastic mass-spring chain. The dispersion relations, energy velocity, and group velocity have been derived. At certain range of frequencies harmonic plane waves do not propagate in contrast with monoatomic chain. Also, since the diatomic chain considered is a linear elastic chain, both of the energy velocity and the group velocity are identical. As long as the linear elastic condition is considered the results show zero flux condition without residual energy. In addition, this paper shows that the diatomic chain dispersion relations are independent on the unit cell scheme. Finally, an extension for the study covers the dispersion and energy relations for 2D- grid system. The 2x2 grid system show a periodicity of the dispersion surface in the wavenumber domain. In addition, the symmetry of the surface can be exploited to identify an Irreducible Brillouin Zone (IBZ). Compact representations of the dispersion properties of multidimensional periodic systems are obtained by plotting frequency as the wave vector’s components vary along the boundary of the IBZ, which leads to a widely accepted and effective visualization of bandgaps and overall dispersion properties.

A Study of How Economic Attitudes Are Shaped by Environmental Shocks and Life Experiences

Social attitudes, attitudes toward financial risk and attitudes toward deferred gratification are thought to influence many important economic decisions over the life-course. In economic theory, these attitudes are key components in diverse models of behavior, including collective action, saving and investment decisions and occupational choice. The relevance of these attitudes have been confirmed empirically. Yet, the factors that influence them are not well understood. This research evaluates how these attitudes are affected by large disruptive events, namely, a natural disaster and a civil conflict, and also by an individual-specific life event, namely, having children.

By implementing rigorous empirical strategies drawing on rich longitudinal datasets, this research project advances our understanding of how life experiences shape these attitudes. Moreover, compelling evidence is provided that the observed changes in attitudes are likely to reflect changes in preferences given that they are not driven just by changes in financial circumstances. Therefore the findings of this research project also contribute to the discussion of whether preferences are really fixed, a usual assumption in economics.

In the first chapter, I study how altruistic and trusting attitudes are affected by exposure to the 2004 Indian Ocean tsunami as long as ten years after the disaster occurred. Establishing a causal relationship between natural disasters and attitudes presents several challenges as endogenous exposure and sample selection can confound the analysis. I take on these challenges by exploiting plausibly exogenous variation in exposure to the tsunami and by relying on a longitudinal dataset representative of the pre-tsunami population in two districts of Aceh, Indonesia. The sample is drawn from the Study of the Tsunami Aftermath and Recovery (STAR), a survey with data collected both before and after the disaster and especially designed to identify the impact of the tsunami. The altruistic and trusting attitudes of the respondents are measured by their behavior in the dictator and trust games. I find that witnessing closely the damage caused by the tsunami but without suffering severe economic damage oneself increases altruistic and trusting behavior, particularly towards individuals from tsunami affected communities. Having suffered severe economic damage has no impact on altruistic behavior but may have increased trusting behavior. These effects do not seem to be caused by the consequences of the tsunami on people’s financial situation. Instead they are consistent with how experiences of loss and solidarity may have shaped social attitudes by affecting empathy and perceptions of who is deserving of aid and trust.

In the second chapter, co-authored with Ryan Brown, Duncan Thomas and Andrea Velasquez, we investigate how attitudes toward financial risk are affected by elevated levels of insecurity and uncertainty brought on by the Mexican Drug War. To conduct our analysis, we pair the Mexican Family Life Survey (MxFLS), a rich longitudinal dataset ideally suited for our purposes, with a dataset on homicide rates at the month and municipality-level. The homicide rates capture well the overall crime environment created by the drug war. The MxFLS elicits risk attitudes by asking respondents to choose between hypothetical gambles with different payoffs. Our strategy to identify a causal effect has two key components. First, we implement an individual fixed effects strategy which allows us to control for all time-invariant heterogeneity. The remaining time variant heterogeneity is unlikely to be correlated with changes in the local crime environment given the well-documented political origins of the Mexican Drug War. We also show supporting evidence in this regard. The second component of our identification strategy is to use an intent-to-treat approach to shield our estimates from endogenous migration. Our findings indicate that exposure to greater local-area violent crime results in increased risk aversion. This effect is not driven by changes in financial circumstances, but may be explained instead by heightened fear of victimization. Nonetheless, we find that having greater economic resources mitigate the impact. This may be due to individuals with greater economic resources being able to avoid crime by affording better transportation or security at work.

The third chapter, co-authored with Duncan Thomas, evaluates whether attitudes toward deferred gratification change after having children. For this study we also exploit the MxFLS, which elicits attitudes toward deferred gratification (commonly known as time discounting) by asking individuals to choose between hypothetical payments at different points in time. We implement a difference-in-difference estimator to control for all time-invariant heterogeneity and show that our results are robust to the inclusion of time varying characteristics likely correlated with child birth. We find that becoming a mother increases time discounting especially in the first two years after childbirth and in particular for those women without a spouse at home. Having additional children does not have an effect and the effect for men seems to go in the opposite direction. These heterogeneous effects suggest that child rearing may affect time discounting due to generated stress or not fully anticipated spending needs.

Sensor Planning for Bayesian Nonparametric Target Modeling

Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including environmental monitoring, traffic planning, endangered species tracking, dynamic scene analysis, autonomous robot navigation, and human motion modeling. As shown by these successful applications, Bayesian nonparametric models are able to adjust their complexities adaptively from data as necessary, and are resistant to overfitting or underfitting. However, most existing works assume that the sensor measurements used to learn the Bayesian nonparametric target kinematics models are obtained a priori or that the target kinematics can be measured by the sensor at any given time throughout the task. Little work has been done for controlling the sensor with bounded field of view to obtain measurements of mobile targets that are most informative for reducing the uncertainty of the Bayesian nonparametric models. To present the systematic sensor planning approach to leaning Bayesian nonparametric models, the Gaussian process target kinematics model is introduced at first, which is capable of describing time-invariant spatial phenomena, such as ocean currents, temperature distributions and wind velocity fields. The Dirichlet process-Gaussian process target kinematics model is subsequently discussed for modeling mixture of mobile targets, such as pedestrian motion patterns.

Novel information theoretic functions are developed for these introduced Bayesian nonparametric target kinematics models to represent the expected utility of measurements as a function of sensor control inputs and random environmental variables. A Gaussian process expected Kullback Leibler divergence is developed as the expectation of the KL divergence between the current (prior) and posterior Gaussian process target kinematics models with respect to the future measurements. Then, this approach is extended to develop a new information value function that can be used to estimate target kinematics described by a Dirichlet process-Gaussian process mixture model. A theorem is proposed that shows the novel information theoretic functions are bounded. Based on this theorem, efficient estimators of the new information theoretic functions are designed, which are proved to be unbiased with the variance of the resultant approximation error decreasing linearly as the number of samples increases. Computational complexities for optimizing the novel information theoretic functions under sensor dynamics constraints are studied, and are proved to be NP-hard. A cumulative lower bound is then proposed to reduce the computational complexity to polynomial time.

Three sensor planning algorithms are developed according to the assumptions on the target kinematics and the sensor dynamics. For problems where the control space of the sensor is discrete, a greedy algorithm is proposed. The efficiency of the greedy algorithm is demonstrated by a numerical experiment with data of ocean currents obtained by moored buoys. A sweep line algorithm is developed for applications where the sensor control space is continuous and unconstrained. Synthetic simulations as well as physical experiments with ground robots and a surveillance camera are conducted to evaluate the performance of the sweep line algorithm. Moreover, a lexicographic algorithm is designed based on the cumulative lower bound of the novel information theoretic functions, for the scenario where the sensor dynamics are constrained. Numerical experiments with real data collected from indoor pedestrians by a commercial pan-tilt camera are performed to examine the lexicographic algorithm. Results from both the numerical simulations and the physical experiments show that the three sensor planning algorithms proposed in this dissertation based on the novel information theoretic functions are superior at learning the target kinematics with

little or no prior knowledge

Algorithms for Geometric Matching, Clustering, and Covering

With the popularization of GPS-enabled devices such as mobile phones, location data are becoming available at an unprecedented scale. The locations may be collected from many different sources such as vehicles moving around a city, user check-ins in social networks, and geo-tagged micro-blogging photos or messages. Besides the longitude and latitude, each location record may also have a timestamp and additional information such as the name of the location. Time-ordered sequences of these locations form trajectories, which together contain useful high-level information about people's movement patterns.

The first part of this thesis focuses on a few geometric problems motivated by the matching and clustering of trajectories. We first give a new algorithm for computing a matching between a pair of curves under existing models such as dynamic time warping (DTW). The algorithm is more efficient than standard dynamic programming algorithms both theoretically and practically. We then propose a new matching model for trajectories that avoids the drawbacks of existing models. For trajectory clustering, we present an algorithm that computes clusters of subtrajectories, which correspond to common movement patterns. We also consider trajectories of check-ins, and propose a statistical generative model, which identifies check-in clusters as well as the transition patterns between the clusters.

The second part of the thesis considers the problem of covering shortest paths in a road network, motivated by an EV charging station placement problem. More specifically, a subset of vertices in the road network are selected to place charging stations so that every shortest path contains enough charging stations and can be traveled by an EV without draining the battery. We first introduce a general technique for the geometric set cover problem. This technique leads to near-linear-time approximation algorithms, which are the state-of-the-art algorithms for this problem in either running time or approximation ratio. We then use this technique to develop a near-linear-time algorithm for this

shortest-path cover problem.

Distributed Extremum-Seeking Control with Applications to High-Altitude Balloons

The real-time optimization of large-scale systems is a difficult problem due to the need for complex models involving uncertain parameters and the high computational cost of solving such problems by a decentralized approach. Extremum-seeking control (ESC) is a model-free real-time optimization technique which can estimate unknown parameters and can optimize nonlinear time-varying systems using only a measurement of the cost function to be minimized. In this thesis, we develop a distributed version of extremum-seeking control which allows large-scale systems to be optimized without models and with minimal computing power. First, we develop a continuous-time distributed extremum-seeking controller. It has three main components: consensus, parameter estimation, and optimization. The consensus provides each local controller with an estimate of the cost to be minimized, allowing them to coordinate their actions. Using this cost estimate, parameters for a local input-output model are estimated, and the cost is minimized by following a gradient descent based on the estimate of the gradient. Next, a similar distributed extremum-seeking controller is developed in discrete-time. Finally, we consider an interesting application of distributed ESC: formation control of high-altitude balloons for high-speed wireless internet. These balloons must be steered into a favourable formation where they are spread out over the Earth and provide coverage to the entire planet. Distributed ESC is applied to this problem, and is shown to be effective for a system of 1200 ballons subjected to realistic wind currents. The approach does not require a wind model and uses a cost function based on a Voronoi partition of the sphere. Distributed ESC is able to steer balloons from a few initial launch sites into a formation which provides coverage to the entire Earth and can maintain a similar formation as the balloons move with the wind around the Earth.

Entwicklung eines Lineardirektantriebs für Transportbänder

Transportbandsyteme werden im Bereich der Intralogistik zur Förderung von Stück- sowie leichten Schüttgütern eingesetzt. Der Antrieb der Transportbänder erfolgt dabei üblicherweise kraftschlüssig über die Antriebstrommel. Der Einsatz von Lineardirektantrieben ermöglicht als Antriebsalternative die Erzeugung der Vorschubkräfte und die Krafteinleitung in das Transportband über der Länge des Lineardirektantriebs verteilt. Dadurch lässt sich die Transportbandvorspannung erheblich reduzieren und damit auch ein leichteres und kostengünstigeres Transportband im Vergleich zu konventionellen Antriebskonzepten verwenden. Basierend auf vorherigen Untersuchungen wurde ein Lineardirektantrieb auf Basis eines Hybridmotors entwickelt, welcher die Vorteile permanentmagneterregter Maschinen mit denen der Reluktanzmotorprinzipien vereint. Auf Grundlage der durch Simulation ermittelten Kraftentfaltkungscharakteristik des Linearmotors auf die Läuferelemente wurden verschiedene Konzepte zur Befestigung dieser Läuferelemente entwickelt, untersucht und erprobt. Aufgrund der Elastizität von Transportbändern sind zudem alternative Führungskonzepte erforderlich, da geometrische Abweichungen der am Transportband befestigten Läuferelemente zu hohen Verlusten durch Reibung führen. Unterschiedliche Führungskonzepte, belegt durch Messungen, geben einen Ausblick auf den linearmotorischen Antrieb.

Generic wind estimation and compensation based on residual generators and higher-order sliding mode schemes

This master thesis proposes a solution to the approach problem in case of unknown severe microburst wind shear for a fixed-wing aircraft, accounting for both longitudinal and lateral dynamics. The adaptive controller design for wind rejection is also addressed, exploiting the wind estimation provided by suitable estimators. It is able to successfully complete the final approach phase even in presence of wind shear, and at the same time aerodynamic envelope protection is retained. The adaptive controller for wind compensation has been designed by a backstepping approach and feedback linearization for time-varying systems. The wind shear components have been estimated by higher-order sliding mode schemes. At the end of this work the results are provided, an autonomous final approach in presence of microburst is discussed, performances are analyzed, and estimation of the microburst characteristics from telemetry data is examined.