Ingeniero Civil Eléctrico / El presente trabajo de título tiene por objetivo caracterizar la respuesta en la actividad neuronal de sujetos que han sido expuestos a estímulos visuales con contenido emocional mediante el análisis de series de tiempo de los registros electroencefalográficos (EEG). En particular, se comparan tres estados emocionales en base a sus diferencias en los valores de valencia y excitación emocional. La hipótesis de este trabajo es que la respuesta emocional ante estímulos visuales puede ser caracterizada en registros EEG en las dimensiones de tiempo, frecuencia y topografía en el cuero cabelludo. Para esto se introduce un enfoque metodológico en el que se analizan canales individuales de EEG descompuestos en bandas de frecuencia. La base de datos utilizada consiste en nueve sujetos, cuyos registros fueron pre-procesados para eliminar el ruido y artefactos oculares. La metodología propuesta consiste en extracción de características, y la construcción de modelos predictivos de emociones basados en Máquinas de Soporte Vectorial y Bosques Aleatorios. De los nueve sujetos, seis fueron utilizados como conjunto de entrenamiento para construir los modelos predictivos y los tres sujetos restantes fueron usados como conjunto de prueba. Los resultados obtenidos fueron una completa discriminación entre emociones positivas y negativas. Para la distinción entre las tres emociones a la vez se obtuvo una precisión de 2/3. Las 20 características utilizadas para la clasificación incluyen canales de distintos lóbulos del cerebro y frecuencias que van desde la banda delta hasta la gamma. Se observó además una alta influencia de la actividad de la banda alfa en los estados emocionales. Los resultados sugieren que el registro de la actividad neuronal a través de EEG permite obtener signos del estado emocional en respuesta a estímulos visuales, pero para obtener una mayor precisión se deben combinar características de múltiples canales y frecuencias.

This study focuses on the ten most valued groups on the Nasdaq Stockholm exchange and their reported results for the period of 2009-2015. The purpose of the study was to investigate correlation between reported results on different levels and the progress of the share prices. Using Pearson’s correlation coefficient I was able to compare operating profit, net result and other comprehensive income to one another. I found that operating profit and net result came very close to each other, whilst other comprehensive income fell behind.  As it seems, share prices are affected by a numerous of variables, not only by reported results and earnings.

In this dissertation, I focus on source coding techniques as well as channel coding techniques. I addressed the challenges in WSN by developing (1) a new source coding strategy for erasure channels that has better distortion performance compared to MDC; (2) a new cooperative channel coding strategy for multiple access channels that has better channel outage performances compared to MIMO; (3) a new source-channel cooperation strategy to accomplish source-to-fusion center communication that reduces system distortion and improves outage performance. First, I draw a parallel between the 2x2 MDC scheme and the Alamouti's space time block coding (STBC) scheme and observe the commonality in their mathematical models. This commonality allows us to observe the duality between the two diversity techniques. Making use of this duality, I develop an MDC scheme with pairwise complex correlating transform. Theoretically, I show that MDC scheme results in: 1) complete elimination of the estimation error when only one descriptor is received; 2) greater efficiency in recovering the stronger descriptor (with larger variance) from the weaker descriptor; and 3) improved performance in terms of minimized distortion as the quantization error gets reduced. Experiments are also performed on real images to demonstrate these benefits. Second, I present a two-phase cooperative communication strategy and an optimal power allocation strategy to transmit sensor observations to a fusion center in a large-scale sensor network. Outage probability is used to evaluate the performance of the proposed system. Simulation results demonstrate that: 1) when signal-to-noise ratio is low, the performance of the proposed system is better than that of the MIMO system over uncorrelated slow fading Rayleigh channels; 2) given the transmission rate and the total transmission SNR, there exists an optimal power allocation that minimizes the outage probability; 3) on correlated slow fading Rayleigh channels, channel correlation will degrade the system performance in linear proportion to the correlation level. Third, I combine the statistical ranking of sensor observations with cooperative communication strategy in a cluster-based wireless sensor network. This strategy involves two steps: 1) ranking the sensor observations based on their test statistics; 2) building a two-phase cooperative communication model with an optimal power allocation strategy. The result is an optimal system performance that considers both sources and channels. I optimize the proposed model through analyses of the system distortion, and show that the cooperating nodes achieve maximum channel capacity. I also simulate the system distortion and outage to show the benefits of the proposed strategies.

Under certain loading conditions, surfaces topography coupled with materials degree of brittleness can significantly compromise the mechanical performance of structures. The foregoing remains valid even if roughness is intentionally introduced for engineering reasons. In either case, stress can concentrate. The case of the stress concentration in surfaces having randomly distributed pits is a problem that, although being very practical, yet it remains unsolved. The complexity of a random configuration renders difficult the problem of analytically finding relationships between surface parameters and markers indicative of mechanical failure. Another difficulty is the reproducibility of replicates of specimens possessing random rough surfaces, for destructive testing followed by statistical analysis. An experimental technique to produce highly controlled replicates of random rough surfaces (including modeling of degradation growth) was developed. This method was used to experimentally and statistically study the effects on fracture of early randomly degraded surfaces of poly methyl methacrylate (PMMA) versus topographical parameters. Growth of degradation was assumed to go from an engineering surface to one whose heights are normally distributed. (Early stage of degradation is meant to be that level of roughness which is in the neighborhood of the critical flaw size for a given material). Among other findings, it was found that neither stress nor strain alone can be used to predict fracture at this early stage of degradation. However, fracture location was found to be strongly correlated to the ratio of the root-mean square roughness (RMS) to auto correlation length (ACL), above some RMS threshold. This correlation decreases as the material becomes less brittle (i.e., decrease of Young’s modulus or increase of percent of elongation). Simultaneously, a boundary value problem involving traction-free random rough surfaces was solved using a perturbation method, assuming elastic and isotropic conditions. For small RMS/ACL ratio, the solution for the RMS stress concentration factor, kt was found to be: kt = 1 + 2*SQRT(2)*(RMS/ACL), which agrees very well with the experimental work. Finally, a generalization of stress concentration factor formulas for several geometrical configurations and loading conditions into the Modified Inglis Formula was proposed. Finite element analysis was carried out and comparison was made with both experimental and analytical results. Applications of these results are broad. In surface engineering, for example, our analytical solution can be coupled with Fick’s Law to find critical conditions under which a film could become unstable to random roughness. Additionally, in design and maintenance of surfaces in service, it can be used to preliminarily assess how stress concentrates in surfaces where well defined notches cannot be used as an approximation.

Natural weathering and wear of structural materials in service nearly always generate surface roughness, and follow the Central Limit Theorem prediction for surface topology. This study couples experimental and statistical theory, and FEM to extend knowledge of life of materials from initial service surface conditions through surface damage accumulation. Statistical moments and other parameters were correlated with fracture locations probability (H/N), versus auto correlation length, and depth. As the surface grows to a full Gaussian, H/N increases its dependence on profile’s Average and RMS Roughness, and derived parameters. This dependence shows an asymptotic limit behavior that approaches agreeably Griffith’s crack criterion, though with multiple pit locations. Importantly, a Transitional Region was observed, below which the probable location of fracture is uncorrelated to the parameters studied. This is because introduced roughness is insufficient to compete with impurities, internal and external manufacturing flaws, and scratches, due to handling and machining, on the samples.

Many physical, biological, ecological and behavioral events occur at times and rates that are exponentially distributed. Modeling these systems requires simulators that can accurately generate a large quantity of exponentially distributed random numbers, which is a computationally intensive task. To improve the performance of these simulators, one approach is to move portions of the computationally inefficient simulation tasks from software to custom hardware implemented in Field Programmable Gate Arrays (FPGAs). In this work, we study efficient FPGA implementations of exponentially distributed random number generators to improve simulator performance. Our approach is to generate uniformly distributed random numbers using standard techniques and scale them using the inverse cumulative distribution function (CDF). Scaling is implemented by curve fitting piecewise linear, quadratic, cubic, and higher order functions to solve for the inverse CDF. As the complexity of the scaling function increases (in terms of order and the number of pieces), number accuracy increases and additional FPGA resources (logic cells and block RAMs) are consumed. We analyze these tradeoffs and show how a designer with particular accuracy requirements and FPGA resource constraints can implement an accurate and efficient exponentially distributed random number generator.

Clustered data often feature nested structures and repeated measures. If coupled with binary outcomes and large samples (>10,000), this complexity can lead to non-convergence problems for the desired model especially if random effects are used to account for the clustering. One way to bypass the convergence problem is to split the dataset into small enough sub-samples for which the desired model convergences, and then recombine results from those sub-samples through meta-analysis. We consider two ways to generate sub-samples: the K independent samples approach where the data are split into k mutually-exclusive sub-samples, and the cluster-based approach where naturally existing clusters serve as sub-samples. Estimates or test statistics from either of these sub-sampling approaches can then be recombined using a univariate or multivariate meta-analytic approach. We also provide an innovative approach for simulating clustered and dependent binary data by simulating parameter templates that yield the desired cluster behavior. This approach is used to conduct simulation studies comparing the performance of the K independent samples and cluster-based approaches to generating sub-samples, the results from which are combined either with univariate and multivariate meta-analytic techniques. These studies show that using natural clusters leaded to lower biased test statistics when the number of clusters and treatment effect were large, as compared to the K independent samples approach for both the univariate and multivariate meta-analytic approaches. And the independent samples approach was preferred when the number of clusters and treatment effect were small. We also apply these methods to data on cancer screening behaviors obtained from electronic health records of n=15,652 individuals and showed that these estimated results support the conclusions from the simulation studies.

This thesis endeavours to model a trip demand function for recreationalangling in Sweden, including the individual expected per-trip utility of regionalattributes. The analysis is conducted with the use of a Random Utility Model (RUM)for the estimation of ‘site-specific’ utility, and a negative binomial logit model fortrip demand. The site quality variable is stated as expected catch-rates (CR) definedin terms of three different specifications: sample mean CR, individually perfectlyforesighted CR, and an econometrically predicted individual CR. Results indicatethat the econometrically estimated individual catch-rate specification performs wellas an explanatory variable both when modelling discrete site choice and trip demand,while the sample mean and perfectly foresighted CR specifications provide withunintuitive and insignificant parameter values. The inclusive value of the regionchosen by the angler, estimated with the RUM, was subsequently found to be asignificant predicting variable for the number of recreational angling trips conductedby Swedish anglers.

This June one of the biggest and most popular sports tournaments will take place in Austria and Switzerland, the European soccer championship 2008 (UEFA EURO 2008). Therefore millions of soccer fans in Europe and throughout the world are asking themselves: "Who is going to win the EURO 2008?" Many people, including sports experts and former players, give their guesses and expectations in the media, but there is also a group with financial incentives, like some economists who expect economical increases for the country of the winning team and bookmakers and their customers who directly make money with their beliefs. Some predictions are only guesses, but other predictions are based on quantitative methods, such as the studies of UBS Wealth Management Research Switzerland and the Raiffeisen Zentralbank. In this report we will introduce a new method for predicting the winner. Whereas other prediction methods are based on historical data, e.g., the Elo rating, or the FIFA/Coca Cola World rating, our method is based on current expectations, the bookmakers odds for winning the championship. In particular we use the odds for winning the championship for each of the 16 teams of 45 international bookmakers. By interpreting these odds as rating of the expected strength of the teams by the bookmakers, we derive a consensus rating by modelling the log-odds using a random-effects model with a team-specific random effect and a bookmaker-specific fixed effect. The consensus rating of a team can be used as an estimator for the unknown "true" strength of a team. Our method predicts team Germany with a probability of about 18.7% as the EURO 2008 winner. We predict also that the teams playing the final will be Germany and Spain with a probability of 13.9%, where Germany will win with a probability of 55%. In our study, Italy, the favorite according to the current FIFA/Coca Cola World ranking and Elo ranking, has a much lower probability than these teams to win the tournament: only 10.6%. The defending champion Greece has low chances to win the title again: about 3.4%. Furthermore, the expected performance of the host countries, Austria and Switzerland, is much better in the bookmakers consensus than in the retrospective Elo and FIFA/Coca Cola World ratings, i.e., indicating an (expected) home court advantage. Despite the associated increase in the winning probabilities, both teams have rather poor chances to win the tournament with probabilities of 1.3% and 4.0%, respectively. In a group effect study we investigate how much the classification into the four groups (A-D) affects the chance for a team to win the championship. / Series: Research Report Series / Department of Statistics and Mathematics

Two-periodic random walks are models for the one-dimensional motion of particles in which the jump probabilities depend on the parity of the currently occupied state. Such processes have interesting applications, for instance in chemical physics where they arise as embedded random walk of a special queueing problem. In this paper we discuss in some detail first passage time problems of two-periodic walks, the distribution of their maximum and the transition functions when the motion of the particle is restricted by one or two absorbing boundaries. As particular applications we show how our results can be used to derive the distribution of the busy period of a chemical queue and give an analysis of a somewhat weird coin tossing game. / Series: Research Report Series / Department of Statistics and Mathematics