A multimodal and adaptive real-time human -computer interface for peoples with severe motor disability

This dissertation introduces the design of a multimodal, adaptive real-time assistive system as an alternate human computer interface that can be used by individuals with severe motor disabilities. The proposed design is based on the integration of a remote eye-gaze tracking system, voice recognition software, and a virtual keyboard. The methodology relies on a user profile that customizes eye gaze tracking using neural networks. The user profiling feature facilitates the notion of universal access to computing resources for a wide range of applications such as web browsing, email, word processing and editing. ^ The study is significant in terms of the integration of key algorithms to yield an adaptable and multimodal interface. The contributions of this dissertation stem from the following accomplishments: (a) establishment of the data transport mechanism between the eye-gaze system and the host computer yielding to a significantly low failure rate of 0.9%; (b) accurate translation of eye data into cursor movement through congregate steps which conclude with calibrated cursor coordinates using an improved conversion function; resulting in an average reduction of 70% of the disparity between the point of gaze and the actual position of the mouse cursor, compared with initial findings; (c) use of both a moving average and a trained neural network in order to minimize the jitter of the mouse cursor, which yield an average jittering reduction of 35%; (d) introduction of a new mathematical methodology to measure the degree of jittering of the mouse trajectory; (e) embedding an onscreen keyboard to facilitate text entry, and a graphical interface that is used to generate user profiles for system adaptability. ^ The adaptability nature of the interface is achieved through the establishment of user profiles, which may contain the jittering and voice characteristics of a particular user as well as a customized list of the most commonly used words ordered according to the user's preferences: in alphabetical or statistical order. This allows the system to successfully provide the capability of interacting with a computer. Every time any of the sub-system is retrained, the accuracy of the interface response improves even more. ^

Altered Landscape or Arms Race? Making Sense of Military Spending in South America

In 2009, South American military spending reached a total of $51.8 billion, a fifty percent increased from 2000 expenditures. The five-year moving average of arms transfers to South America was 150 percent higher from 2005 to 2009 than figures for 2000 to 2004.[1] These figures and others have led some observers to conclude that Latin America is engaged in an arms race. Other reasons, however, account for Latin America’s large military expenditure. Among them: Several countries have undertaken long-prolonged modernization efforts, recently made possible by six years of consistent regional growth.[2] A generational shift is at hand. Armed Forces are beginning to shed the stigma and association with past dictatorial regimes.[3] Countries are pursuing specific individual strategies, rather than reacting to purchases made by neighbors. For example, Brazil wants to attain greater control of its Amazon rainforests and offshore territories, Colombia’s spending demonstrates a response to internal threats, and Chile is continuing a modernization process begun in the 1990s.[4] Concerns remain, however: Venezuela continues to demonstrate poor democratic governance and a lack of transparency; neighbor-state relations between Colombia and Venezuela, Peru and Chile, and Bolivia and Paraguay, must all continue to be monitored; and Brazil’s military purchases, although legitimate, will likely result in a large accumulation of equipment.[5] These concerns can be best addressed by strengthening and garnering greater participation in transparent procurement mechanism.[6] The United States can do its part by supporting Latin American efforts to embrace the transparency process. _________________ [1] Bromley, Mark, “An Arms Race in Our Hemisphere? Discussing the Trends and Implications of Military Expenditures in South America,” Brookings Institution Conference, Washington, D.C., June 3rd, 2010, Transcript Pgs. 12,13, and 16 [2] Robledo, Marcos, “The Rearmament Debate: A Chilean Perspective,” Power Point presentation, slide 18, 2010 Western Hemisphere Security Colloquium, Miami, Florida, May 25th-26th, 2010 [3] Yopo, Boris, “¿Carrera Armamentista en la Regiόn?” La Tercera, November 2nd, 2009, http://www.latercera.com/contenido/895_197084_9.shtml, accessed October 8th, 2010 [4] Walser, Ray, “An Arms Race in Our Hemisphere? Discussing the Trends and Implications of Military Expenditures in South America,” Brookings Institution Conference, Washington, D.C., June 3rd, 2010, Transcript Pgs. 49,50,53 and 54 [5] Ibid., Guevara, Iñigo, Pg. 22 [6] Ibid., Bromley, Mark, Pgs. 18 and 19

Classificador máquina de suporte vetorial com análise de Fourier aplicada em dados de EEG e EMG

The classifier support vector machine is used in several problems in various areas of knowledge. Basically the method used in this classier is to end the hyperplane that maximizes the distance between the groups, to increase the generalization of the classifier. In this work, we treated some problems of binary classification of data obtained by electroencephalography (EEG) and electromyography (EMG) using Support Vector Machine with some complementary techniques, such as: Principal Component Analysis to identify the active regions of the brain, the periodogram method which is obtained by Fourier analysis to help discriminate between groups and Simple Moving Average to eliminate some of the existing noise in the data. It was developed two functions in the software R, for the realization of training tasks and classification. Also, it was proposed two weights systems and a summarized measure to help on deciding in classification of groups. The application of these techniques, weights and the summarized measure in the classier, showed quite satisfactory results, where the best results were an average rate of 95.31% to visual stimuli data, 100% of correct classification for epilepsy data and rates of 91.22% and 96.89% to object motion data for two subjects.

Dynamic fault tree analysis with PEWMA modeling of event rates and Bayesian updating of material balance parameters using MCMC

This research explores Bayesian updating as a tool for estimating parameters probabilistically by dynamic analysis of data sequences. Two distinct Bayesian updating methodologies are assessed. The first approach focuses on Bayesian updating of failure rates for primary events in fault trees. A Poisson Exponentially Moving Average (PEWMA) model is implemnented to carry out Bayesian updating of failure rates for individual primary events in the fault tree. To provide a basis for testing of the PEWMA model, a fault tree is developed based on the Texas City Refinery incident which occurred in 2005. A qualitative fault tree analysis is then carried out to obtain a logical expression for the top event. A dynamic Fault Tree analysis is carried out by evaluating the top event probability at each Bayesian updating step by Monte Carlo sampling from posterior failure rate distributions. It is demonstrated that PEWMA modeling is advantageous over conventional conjugate Poisson-Gamma updating techniques when failure data is collected over long time spans. The second approach focuses on Bayesian updating of parameters in non-linear forward models. Specifically, the technique is applied to the hydrocarbon material balance equation. In order to test the accuracy of the implemented Bayesian updating models, a synthetic data set is developed using the Eclipse reservoir simulator. Both structured grid and MCMC sampling based solution techniques are implemented and are shown to model the synthetic data set with good accuracy. Furthermore, a graphical analysis shows that the implemented MCMC model displays good convergence properties. A case study demonstrates that Likelihood variance affects the rate at which the posterior assimilates information from the measured data sequence. Error in the measured data significantly affects the accuracy of the posterior parameter distributions. Increasing the likelihood variance mitigates random measurement errors, but casuses the overall variance of the posterior to increase. Bayesian updating is shown to be advantageous over deterministic regression techniques as it allows for incorporation of prior belief and full modeling uncertainty over the parameter ranges. As such, the Bayesian approach to estimation of parameters in the material balance equation shows utility for incorporation into reservoir engineering workflows.

Investigation of factors driving the costs of operating the 2020 Irish power system with large-scale wind generation

The main goal of this work is to determine the true cost incurred by the Republic of Ireland and Northern Ireland in order to meet their EU renewable electricity targets. The primary all-island of Ireland policy goal is that 40% of electricity will come from renewable sources in 2020. From this it is expected that wind generation on the Irish electricity system will be in the region of 32-37% of total generation. This leads to issues resulting from wind energy being a non-synchronous, unpredictable and variable source of energy use on a scale never seen before for a single synchronous system. If changes are not made to traditional operational practices, the efficient running of the electricity system will be directly affected by these issues in the coming years. Using models of the electricity system for the all-island grid of Ireland, the effects of high wind energy penetration expected to be present in 2020 are examined. These models were developed using a unit commitment, economic dispatch tool called PLEXOS which allows for a detailed representation of the electricity system to be achieved down to individual generator level. These models replicate the true running of the electricity system through use of day-ahead scheduling and semi-relaxed use of these schedules that reflects the Transmission System Operator's of real time decision making on dispatch. In addition, it carefully considers other non-wind priority dispatch generation technologies that have an effect on the overall system. In the models developed, three main issues associated with wind energy integration were selected to be examined in detail to determine the sensitivity of assumptions presented in other studies. These three issues include wind energy's non-synchronous nature, its variability and spatial correlation, and its unpredictability. This leads to an examination of the effects in three areas: the need for system operation constraints required for system security; different onshore to offshore ratios of installed wind energy; and the degrees of accuracy in wind energy forecasting. Each of these areas directly impact the way in which the electricity system is run as they address each of the three issues associated with wind energy stated above, respectively. It is shown that assumptions in these three areas have a large effect on the results in terms of total generation costs, wind curtailment and generator technology type dispatch. In particular accounting for these issues has resulted in wind curtailment being predicted in much larger quantities than had been previously reported. This would have a large effect on wind energy companies because it is already a very low profit margin industry. Results from this work have shown that the relaxation of system operation constraints is crucial to the economic running of the electricity system with large improvements shown in the reduction of wind curtailment and system generation costs. There are clear benefits in having a proportion of the wind installed offshore in Ireland which would help to reduce variability of wind energy generation on the system and therefore reduce wind curtailment. With envisaged future improvements in day-ahead wind forecasting from 8% to 4% mean absolute error, there are potential reductions in wind curtailment system costs and open cycle gas turbine usage. This work illustrates the consequences of assumptions in the areas of system operation constraints, onshore/offshore installed wind capacities and accuracy in wind forecasting to better inform the true costs associated with running Ireland's changing electricity system as it continues to decarbonise into the near future. This work also proposes to illustrate, through the use of Ireland as a case study, the effects that will become ever more prevalent in other synchronous systems as they pursue a path of increasing renewable energy generation.

Age determination and clay layer frequencies of sediments of the Black and Red Sea

Paleoenvironmental proxy data for ocean properties, eolian sediment input, and continental rainfall based on high-resolution analyses of sediment cores from the southwestern Black Sea and the northernmost Gulf of Aqaba were used to infer hydroclimatic changes in northern Anatolia and the northern Red Sea region during the last ~7500 years. Pronounced and coherent multicentennial variations in these records reveal patterns that strongly resemble modern temperature and rainfall anomalies related to the Arctic Oscillation/North Atlantic Oscillation (AO/NAO). These patterns suggest a prominent role of AO/NAO-like atmospheric variability during the Holocene beyond interannual to interdecadal timescales, most likely originating from solar output changes.

Effektiv lagerstyrning med AHP och tvådimensionell artikelklassificering : En fallstudie på Permobil AB, Timrå

Costs related to inventory are usually a significant amount of the company’s total assets. Despite this, companies in general don’t pay a lot of interest in it, even if the benefits from effective inventory are obvious when it comes to less tied up capital, increased customer satisfaction and better working environment. Permobil AB, Timrå is in an intense period when it comes to revenue and growth. The production unit is aiming for an increased output of 30 % in the next two years. To make this possible the company has to improve their way to distribute and handle material,The purpose of the study is to provide useful information and concrete proposals for action, so that the company can build a strategy for an effective and sustainable solution when it comes to inventory management. Alternative methods for making forecasts are suggested, in order to reach a more nuanced perception of different articles, and how they should be managed. Analytic Hierarchy Process (AHP) was used in order to give specially selected persons the chance to decide criteria for how the article should be valued. The criteria they agreed about were annual volume value, lead time, frequency rate and purchase price. The other method that was proposed was a two-dimensional model where annual volume value and frequency was the criteria that specified in which class an article should be placed. Both methods resulted in significant changes in comparison to the current solution. For the spare part inventory different forecast methods were tested and compared with the current solution. It turned out that the current forecast method performed worse than both moving average and exponential smoothing with trend. The small sample of ten random articles is not big enough to reject the current solution, but still the result is a reason enough, for the company to control the quality of the forecasts.

Determination of a Mean Solar Radiation Year and of a Typical Meteorological Year for the region of Funchal in the Madeira Island

This paper presents the determination of a mean solar radiation year and of a typical meteorological year for the region of Funchal in the Madeira Island, Portugal. The data set includes hourly mean and extreme values for air temperature, relative humidity and wind speed and hourly mean values for solar global and diffuse radiation for the period 2004-2014, with maximum data coverage of 99.7%. The determination of the mean solar radiation year consisted, in a first step, in the average of all values for each pair hour/day and, in a second step, in the application of a five days centred moving average of hourly values. The determination of the typical meteorological year was based on Finkelstein-Schafer statistics, which allows to obtain a complete year of real measurements through the selection and combination of typical months, preserving the long term averages while still allowing the analysis of short term events. The typical meteorological year validation was carried out through the comparison of the monthly averages for the typical year with the long term monthly averages. The values obtained were very close, so that the typical meteorological year can accurately represent the long term data series. The typical meteorological year can be used in the simulation of renewable energy systems, namely solar energy systems, and for predicting the energy performance of buildings.

Climatic fluctuations and homogenization of Northeast Brazil using precipitation data.

Before using the basic precipitation data in any agroclimatic study to assess the productivity it is important to check the data series for homogeneity. For this purpose data of 105 locations for the period 1912-1981 over northeast Brazil were used. The preliminary study indicate nonhomogeneity in the time series during 1940's at few locations. The amplitude of variation of time series when taken as 10-year moving average show quite different for different regions. It appears that this amplitude is related to time of onset of effective rains in some extent. There is also great diversity in the fluctuations. They present a great regional diversity. Some diversity. Some of the data in the low latitudes indicate presence of four cycles namely 52, 26, 13 & 6.5. years. The 52-year cycle is also evident in the case of onset of southwest Monsoon over a low latitude zone (Kerala Coast) in India. In the case of south Africa the prominent cycles are 60, 30, 15 & 10 similar situation appears to be present in the higher latitudes of northeast Brazil.

Some misspecfication problems in long-memory time series models

Doctor of Philosophy in Mathematics

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.

A multi-model approach to evaluate the role of environmental variability and fishing pressure in sardine fisheries

Understanding the fluctuations in population abundance is a central question in fisheries. Sardine fisheries is of great importance to Portugal and is data-rich and of primary concern to fisheries managers. In Portugal, sub-stocks of Sardina pilchardus (sardine) are found in different regions: the Northwest (IXaCN), Southwest (IXaCS) and the South coast (IXaS-Algarve). Each of these sardine sub-stocks is affected differently by a unique set of climate and ocean conditions, mainly during larval development and recruitment, which will consequently affect sardine fisheries in the short term. Taking this hypothesis into consideration we examined the effects of hydrographic (river discharge), sea surface temperature, wind driven phenomena, upwelling, climatic (North Atlantic Oscillation) and fisheries variables (fishing effort) on S. pilchardus catch rates (landings per unit effort, LPUE, as a proxy for sardine biomass). A 20-year time series (1989-2009) was used, for the different subdivisions of the Portuguese coast (sardine sub-stocks). For the purpose of this analysis a multi-model approach was used, applying different time series models for data fitting (Dynamic Factor Analysis, Generalised Least Squares), forecasting (Autoregressive Integrated Moving Average), as well as Surplus Production stock assessment models. The different models were evaluated, compared and the most important variables explaining changes in LPUE were identified. The type of relationship between catch rates of sardine and environmental variables varied across regional scales due to region-specific recruitment responses. Seasonality plays an important role in sardine variability within the three study regions. In IXaCN autumn (season with minimum spawning activity, larvae and egg concentrations) SST, northerly wind and wind magnitude were negatively related with LPUE. In IXaCS none of the explanatory variables tested was clearly related with LPUE. In IXaS-Algarve (South Portugal) both spring (period when large abundances of larvae are found) northerly wind and wind magnitude were negatively related with LPUE, revealing that environmental effects match with the regional peak in spawning time. Overall, results suggest that management of small, short-lived pelagic species, such as sardine quotas/sustainable yields, should be adapted to a regional scale because of regional environmental variability.

Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

Do Global CO2 Emissions from Fossil-Fuel Consumption Exhibit Long Memory? A Fractional Integration Analysis

In this article we use an autoregressive fractionally integrated moving average approach to measure the degree of fractional integration of aggregate world CO2 emissions and its five components – coal, oil, gas, cement, and gas flaring. We find that all variables are stationary and mean reverting, but exhibit long-term memory. Our results suggest that both coal and oil combustion emissions have the weakest degree of long-range dependence, while emissions from gas and gas flaring have the strongest. With evidence of long memory, we conclude that transitory policy shocks are likely to have long-lasting effects, but not permanent effects. Accordingly, permanent effects on CO2 emissions require a more permanent policy stance. In this context, if one were to rely only on testing for stationarity and non-stationarity, one would likely conclude in favour of non-stationarity, and therefore that even transitory policy shocks

Small Area Estimation of Economic Security

The objective of this thesis is the small area estimation of an economic security indicator. Economic security is a complex concept that carries a variety of meanings. In the literature there is no a formal unambiguous definition for economic security and in this work we refer to the definition recently provided for its opposite, economic insecurity, as the “anxiety produced by the possible exposure to adverse economic events and by the anticipation of the difficulty to recover from them” (Bossert and D’Ambrosio, 2013). In the last decade interest for economic insecurity/security has grown constantly, especially since the financial crisis of 2008, but even more in the last year after the economic consequences due to the Covid-19 pandemic. In this research, economic security is measures through a longitudinal indicator that takes into account the income levels of Italian households, from 2014 to 2016. The target areas are groups of Italian provinces, for which the indicator is estimated using longitudinal data taken from EU-SILC survey. We notice that the sample size is too low to obtain reliable estimates for our target areas. Therefore we resort to some Small Area Estimation strategies to improve the reliability of the results. In particular we consider small area models specified at area level. Besides the basic Fay-Herriot area-level model, we propose to consider some longitudinal extensions, including time-specific random effects following an autoregressive processes of order 1 (AR1) and a moving average of order 1 (MA1). We found that all the small area models used show a significant efficiency gain, especially MA1 model.