Near-infrared spectroscopy (NIRS) in an imaging technique that has gained much attention in brain-computer interfaces (BCIs). Previous NIRS-BCI studies have primarily employed temporal features, derived from the time course of hemodynamic activity, despite potential value contained in the spatial attributes of a response. In an initial offline study, we investigated the value of using joint spatial-temporal pattern classification with dynamic NIR topograms to differentiate intentional cortical activation from rest. With the inclusion of spatiotemporal features, we demonstrated a significant increase in achievable classification accuracies from those obtained using temporal features alone (p < 10-4). In a second study, we evaluated the feasibility of implementing joint spatial-temporal pattern classification in an online system. We developed an online system-paced NIRS-BCI, and were able to differentiate two cortical states with high accuracy (77.4±10.5%). Collectively, these findings demonstrate the value of including spatiotemporal features in the classification of functional NIRS data for BCI applications.

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

Made available in DSpace on 2015-05-14T12:47:15Z (GMT). No. of bitstreams: 1 ArquivoTotalAnaPaula.pdf: 4539714 bytes, checksum: e81023113c80e20aab9cc31359a349d7 (MD5) Previous issue date: 2013-08-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Traffic accidents represent, in Brazil, a serious economic and especially social, relevant for magnitude of the mortality and number of people suffering from sequelae arising, thus becoming a serious public health problem. This research aimed to develop a model to support decision making based on fuzzy logic, supported by analyzes spatial and spatio-temporal (Scan method) to categorize neighborhoods according to priority intervention for prevention and control of traffic accidents that produce victims. Secondary data were georeferenced and recorded by Mobile Emergency Care Service in João Pessoa, Paraíba, in the years 2010 and 2011. Throughout study period, João Pessoa was 10,070 traffic accidents with victims. Of this total, 17.8% had breath ethanol and 0.8% died at the scene. The majority of victims were male (74.5%), belonging to the age group 20-29 years (37.7%). The accidents occurred mainly on Sundays (19.2%), Saturdays (18.7%) and on Fridays (14.4%) as well as in the months of December (10%), October (9.8% ) and May (8.9%). Most of the vehicles involved was composed by motorcycles (68.1%) and cars (36.5%). The nature of accident, collision was more frequent (46.2%), followed by fall motorcycle (30.7%) and pedestrian injuries (11.1%). In analysis of the relative risk and spatial distribution of these events, it was found that neighborhoods with high relative risk and formed significant spatial clusters concentrated in the north, northwest and northeast of the municipality. We identified 15 clusters space-time, which concentrated mainly in the northern, northeastern and coastal strip of the municipality. It was observed that neighborhoods reported by Mobile Emergency Care Service were categorized as priority by model, Valentina and Mandacaru were categorized as with tendency to priority, and Mangabeira was categorized as non-priority. The proposed decision model showed good agreement when compared with Mobile Emergency Care Service, thus satisfying the identification and classification of neighborhoods as a priority, with tendency to priority, with tendency to non-priority and non-priority. The results may be of relevance to both Mobile Emergency Care Service as to other public officials linked to road traffic, traffic education and care for victims produced by road traffic in João Pessoa. / Os acidentes de trânsito representam, no Brasil, um grave problema econômico e principalmente social, relevante pela magnitude da mortalidade e do número de pessoas portadoras de sequelas decorrentes, tornando-se assim um grave problema de saúde pública. Este trabalho objetivou elaborar um modelo de apoio à tomada de decisão baseado em lógica fuzzy, apoiado pelas análises espacial e espaço-temporal (método Scan), para categorizar os bairros de acordo com o grau de prioridade de intervenção para a prevenção e combate dos acidentes de trânsito que produzam vítimas. Foram utilizados dados secundários georreferenciados e registrados pelo Serviço de Atendimento Móvel de Urgência na cidade de João Pessoa, Paraíba, nos anos 2010 e 2011. Ao longo do período de estudo, João Pessoa apresentou 10.070 ocorrências de AT com vítimas. Deste total, 17,8% apresentaram hálito etílico e 0,8% morreram no local do acidente. A maioria das vítimas foi do sexo masculino (74,5%), pertencente à faixa etária de 20 a 29 anos (37,7%). Os acidentes ocorreram principalmente aos domingos (19,2%), aos sábados (18,7%) e às sextas-feiras (14,4%), bem como nos meses de dezembro (10%), outubro (9,8%) e maio (8,9%). A maioria dos veículos envolvidos foi composta por motocicletas (68,1%) e carros (36,5%). Quanto à natureza do acidente, a colisão foi mais frequente (46,2%), seguida por queda de motocicleta (30,7%) e atropelamento (11,1%). Na análise do risco relativo e da distribuição espacial destes eventos, verificou-se que os bairros com alto risco relativo e que formaram conglomerados espaciais significativos concentraram-se nas regiões norte, noroeste e nordeste do município. Foram identificados 15 conglomerados espaço-temporais, que se concentraram principalmente nas regiões norte, nordeste e faixa litorânea do município. Observou-se que os bairros relatados pelo SAMU/JP foram categorizados pelo modelo como prioritários, Mandacaru e Valentina, os quais foram categorizados como com tendência a prioritários, e Mangabeira, categorizado como não prioritário. O modelo de decisão proposto apresentou boa concordância quando comparado com o SAMU/JP, sendo assim satisfatório na identificação e classificação dos bairros como prioritários, com tendência a prioritários, com tendência a não prioritários e não prioritários. Os resultados desta pesquisa podem ser de relevância tanto para o SAMU/JP quanto para outros órgãos gestores públicos ligados ao trânsito, educação para o trânsito e atendimento às vítimas produzidas pelo trânsito no município de João Pessoa-PB.

[pt] Muitos fatores podem in uenciar a velocidade de um veículo numa rodovia ou estrada, mas dois deles são observados diariamente pelos motoristas: sua localização e o momento do dia. Obter modelos que retornem a velocidade média como uma função do espaço e do tempo é ainda uma tarefa desafiadora. São muitas as aplicações para esses tipos de modelos, como por exemplo: tempo estimado de chegada, caminho mais curto e previsão de tráfico, deteccção de acidente, entre outros. Este estudo propõe um modelo de previsão baseado em uma média espaço-temporal da velocidade média/instantânea coletada de dados históricos de GPS. A grande vantagem do modelo proposto é a sua simplicidade. Além disso, os resultados experimentais obtidos de caminhões de entrega de combustíveis, por todo o ano de 2013 no Brasil, indicaram que a maioria das observações podem ser preditas usando esse modelo dentro de uma tolerância de erro aceitável. / [en] Many factors may inuence a vehicle speed in a road, but two of them are usually observed by many drivers: its location and the time of the day. To obtain a model that returns the average speed as a function of position and time is still a challenging task. The application of such models can be in different scenarios, such as: estimated time of arrival, shortest route paths, traffic prediction, and accident detection, just to cite a few. This study proposes a prediction model based on a spatio-temporal partition and mean/instantaneous speeds collected from historic GPS data. The main advantage of the proposed model is that it is very simple to compute. Moreover, experimental results obtained from fuel delivery trucks, along the whole year of 2013 in Brazil, indicate that most of the observations can be predicted using this model within an acceptable error tolerance.

Made available in DSpace on 2014-06-11T19:30:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2012-02-24Bitstream added on 2014-06-13T18:59:56Z : No. of bitstreams: 1 bochini_gl_me_botib.pdf: 1046951 bytes, checksum: c853155f5ecbb629ac360d4f519b2981 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O presente estudo foi dividido em dois capítulos e tiveram como objetivos: cap.1- verificar a distribuição espaço-temporal de L. schmitti em três baías do litoral norte do estado de São Paulo, Ubatumirim (UBM), Ubatuba (UBA) e Mar Virado (MV); testar a relação dos fatores ambientais com a distribuição dos camarões durante um período de 2 anos (1998 e 1999) e cap. 2- averiguar a variação anual e sazonal da biomassa e do número de indivíduos do camarão, durante 4 anos de estudo (1998, 1999, 2002, 2006), com enfoque no período reprodutivo e de recrutamento juvenil informando qual a melhor época para a pesca. Os camarões foram capturados com um barco camaroneiro equipado com redes do tipo “double-rig” em profundidades até os 20 metros. Em 1998 e 1999, um total de 5658 indivíduos foi coletado, sendo 4437 no primeiro ano e 1221 no segundo ano. Em MV obteve-se a maior abundância (n= 2747), seguido de UBM (n= 1649) e UBA (n=1262). A salinidade da água variou de 28 a 37 com média de 34,6 ± 1,44, porém não houve correlação significativa desse fator com a abundância (p=0,90). A média da temperatura de fundo foi de 24,8 ± 2,84 °C com valor máximo de 31,4 °C e mínimo de 19 °C. Apesar dessa grande variação nos valores obtidos, não houve correlação significativa desse fator com a abundância (p= 0,11). A maior captura de camarões foi registrada em áreas onde silte + argila correspondem mais de 70 % do sedimento e em locais com maior porcentagem de matéria orgânica. Houve relação inversa da abundância com a pluviosidade, com as maiores abundâncias nos meses posteriores a temporada de chuvas. Porém, no ano em que houve maior pluviosidade, também houve uma maior captura de indivíduos. A quantidade de camarões seguiu uma tendência sazonal, sendo maior durante o outono e inverno... / The present study was divided in two chapters, which aimed: chapter 1 – to verify the spatiotemporal distribution of L. schmitti in three bays of north littoral of São Paulo State, Ubatumirim (UBM), Ubatuba (UBA) e Mar Virado (MV); to test the relation between environmental factors and shrimp distribution, during a 2-year period (1998 and 1999) and chapter 2 – to investigate the annual and seasonal variation of biomass and of the number of shrimp individuals, focusing reproductive period and juvenile recruitment; which is the best period for the open season; the rainfall influence on abundance during 4 years of study (1998, 1999, 2002 and 2006). Shrimps were captured with a shrimp fishing boat equipped with two double-rig nets at depths up to 20m. In 1998 and 1999, a total of 5658 individuals was collected, being 4437 on first year and 1221 on second year. The highest abundance was obtained at MV (n=2747), followed by UBM (n=1649) and then by UBA (n=1262). Water salinity has varied from 28 to 37, with average of 34.6 ± 1.44, although abundance has not had a significant correlation with this factor (p=0,90). The average bottom temperature was of 24,8 ± 2,84 °C, with a maximum of 31.4 °C and a minimum of 19°C. Besides the great variation on these values, abundance has not had a significant correlation with this factor (p=0.11). The higher shrimp capture was registered in areas where sediment was composed by more than 70% of silt + clay and at areas with highest percentage of organic-matter. There was an inverse correlation between abundance and rainfall, with the highest abundances on months after the rainy season. However, the highest individual catch occurred in the year with highest rainfall rates. Shrimp amount had a seasonal tendency, being higher during autumn and winter in both years. A total of 566 (13,171 g) individuals... (Complete abstract click electronic access below)

abstract: This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and related metadata of their daily activities from the website OpenStreetMap.org; and finally (3) preschool children's daily activities and interactions tagged with time and geographical location were collected with a novel TabletPC-based behavioral coding system. The proposed methodology is applied to these data to (1) automatically recommend optimal multi-day and multi-stay travel itineraries for travelers based on discovered attractions from geo-tagged photos, (2) automatically detect movement types of unknown moving objects from GPS trajectories, and (3) explore dynamic social and socio-spatial patterns of preschool children's behavior from both geographic and social perspectives. / Dissertation/Thesis / Ph.D. Geography 2012

Long and short-term exposure to particulate matter (PM) are linked to adverse heath endpoints. Evidence indicates that PM composition such as metals and organic carbon (OC) might drive the health effects. As airborne pollutants show significant intracity spatiotemporal variation, mobile sampling and distributed monitors are utilized to capture the variation pattern. The measurements are then fed to develop models to better characterize the relationship between exposure and health outcomes. Two sampling campaigns were conducted. One was sole mobile sampling in 2013 summer and winter in Pittsburgh, PA. Thirty-six sites were chosen based on three stratification variables: traffic density, proximity to point sources, and elevation. The other one was hybrid sampling network, incorporating a mobile sampling platform, 15 distributed monitors, and a supersite. We designed two case studies (transect and downtown), selected 14 neighborhoods (~1 km2), and conducted sampling in 2016 summer/fall and winter. Spatial variation of PM2.5 mass and composition was studied in the 2013 campaign. X-ray fluorescence (XRF) was used to analyze concentrations of 26 elements: Na, Mg, Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Zr, Cd, Sb, and Pb. Trace elements had a broad range of concentrations from 0 to 300 ng/m3. Comparison of data from mobile sampling with stationary monitors showed reasonable agreement. We developed Land use regression (LUR) models to describe spatial variation of PM2.5, Si, S, Cl, K, Ca, Ti, Cr, Fe, Cu, and Zn. Independent variables included traffic influence, land-use type, and facility emissions. Models had an average R2 of 0.57 (SD = 0.16). Traffic related variables explained the most variability with an average R2 contribution of 0.20 (SD = 0.20). Overall, these results demonstrated significant intra-urban spatial variability of fine particle composition. Spatial variation of OC was based on the 2013 campaign as well. We collected organic carbon (OC) on quartz filters, quantified different OC components with thermaloptical analysis, and grouped them based on volatility in decreasing order (OC1, OC2, OC3, OC4, and pyrolyzed carbon (PC)). We compared our ambient OC concentrations (both gas and particle phase) to similar measurements from vehicle dynamometer tests, cooking emissions, biomass burning emissions, and a highway traffic tunnel. OC2 and OC3 loading on ambient filters showed a strong correlation with primary emissions while OC4 and PC were more spatially homogenous. While we tested our hypothesis of OC2 and OC3 as markers of fresh source exposure for Pittsburgh, the relationship seemed to hold at a national level. Land use regression (LUR) models were developed for the OC fractions, and models had an average R2 of 0.64 (SD = 0.09). We demonstrate that OC2 and OC3 can be useful markers for fresh emissions, OC4 is a secondary OC indicator, and PC represents both biomass burning and secondary aerosol. People with higher OC exposure are likely inhaling more fresh OC2 and OC3, since secondary OC4 and PC varies much less drastically in space or with local primary sources. With the 2016 hybrid sampling campaign, we addressed the intracity exposure patterns, as they could be more complex than intercity ones because of local traffic, restaurants, land use, and point sources. This network studied a wide range of pollutants (CO2, CO, NO2, PM1 mass and composition, and particle number PN). Mobile measurements and distributed monitors show good agreement. PN hotspots are strongly associated with restaurants and highway traffic. PN at sites with large local source impacts tends to have larger diurnal variation than daily variation, while CO in downtown center shows the opposite trend. PN exhibits the largest spatial and temporal variations. Spatial variation is generally larger than temporal variation among all five pollutants (CO2, NO2, CO, PN, and PM1). These findings provide quantitative comparison between spatial and temporal variation in different scales, and support the theoretical validity of developing long-term exposure models from short-term mobile measurement. A combined sampling network with mobile and distributed monitor could prove more valuable in studying intracity air pollution. In the 2016 hybrid sampling campaign, we also studied spatial variability of air pollution in the vicinity of monitors. Monitoring network is essential for protecting public health, though evaluation is needed to assess spatial representativeness of monitors in different environments. Mobile sampling was conducted repeatedly around 15 distributed monitors. Substantial short-range spatial variability was observed. Spatial variation was consistently larger than temporal variation for NO2 and CO at different sites. Ultrafine particles were highly dynamic both in space and time. PM1 was less spatially and temporally variable. Urban locations had more frequent episodic source plume events compared with background sites. Using a single monitor measurement to represent surrounding ~1 km2 areas could introduce an average daily exposure misclassification of 46 ppb (SD = 26) for CO (30% of regional background), 3 ppb (SD = 2) for NO2 (43% of background), 4007 #/cm3 (SD = 1909) for ultrafine particle number (64% of background), and 1.2 μg/m3 (SD = 1.0) for PM1 (13% of background). Exposure differences showed fair correlation with traditional land use covariates such as traffic and restaurant density, and the magnitude of misclassification could be even bigger for urban neighborhoods.

De nos jours, le traitement et l’analyse d’images trouvent leur application dans de nombreux domaines. Dans le cas de la navigation d’un robot mobile (fauteuil roulant) en milieu intérieur, l’extraction de repères visuels et leur suivi constituent une étape importante pour la réalisation de tâches robotiques (localisation, planification, etc.). En particulier, afin de réaliser une tâche de franchissement de portes, il est indispensable de détecter et suivre automatiquement toutes les portes qui existent dans l’environnement. La détection des portes n’est pas une tâche facile : la variation de l’état des portes (ouvertes ou fermées), leur apparence (de même couleur ou de couleur différentes des murs) et leur position par rapport à la caméra influe sur la robustesse du système. D’autre part, des tâches comme la détection des zones navigables ou l’évitement d’obstacles peuvent faire appel à des représentations enrichies par une sémantique adaptée afin d’interpréter le contenu de la scène. Pour cela, les techniques de segmentation permettent d’extraire des régions pseudo-sémantiques de l’image en fonction de plusieurs critères (couleur, gradient, texture…). En ajoutant la dimension temporelle, les régions sont alors suivies à travers des algorithmes de segmentation spatio-temporelle. Dans cette thèse, des contributions répondant aux besoins cités sont présentées. Tout d’abord, une technique de détection et de suivi de portes dans un environnement de type couloir est proposée : basée sur des descripteurs géométriques dédiés, la solution offre de bons résultats. Ensuite, une technique originale de segmentation multirésolution et hiérarchique permet d’extraire une représentation en régions pseudosémantique. Enfin, cette technique est étendue pour les séquences vidéo afin de permettre le suivi des régions à travers le suivi de leurs contours. La qualité des résultats est démontrée et s’applique notamment au cas de vidéos de couloir. / Nowadays, image processing remains a very important step in different fields of applications. In an indoor environment, for a navigation system related to a mobile robot (electrical wheelchair), visual information detection and tracking is crucial to perform robotic tasks (localization, planning…). In particular, when considering passing door task, it is essential to be able to detect and track automatically all the doors that belong to the environment. Door detection is not an obvious task: the variations related to the door status (open or closed), their appearance (e.g. same color as the walls) and their relative position to the camera have influence on the results. On the other hand, tasks such as the detection of navigable areas or obstacle avoidance may involve a dedicated semantic representation to interpret the content of the scene. Segmentation techniques are then used to extract pseudosemantic regions based on several criteria (color, gradient, texture...). When adding the temporal dimension, the regions are tracked then using spatiotemporal segmentation algorithms. In this thesis, we first present joint door detection and tracking technique in a corridor environment: based on dedicated geometrical features, the proposed solution offers interesting results. Then, we present an original joint hierarchical and multiresolution segmentation framework able to extract a pseudo-semantic region representation. Finally, this technique is extended to video sequences to allow the tracking of regions along image sequences. Based on contour motion extraction, this solution has shown relevant results that can be successfully applied to corridor videos.

This study attempts to answer the research question ‘Can a novel model of health information system strengthen process for conducting research to understand the effects of air pollution on CVD in developing countries?’ There is limited research output from Asia and in particular, from India on studies of the deleterious effects of air pollution on CVD. This research aimed to investigate the barriers in developing countries and proposed the use of a spatiotemporal methodology to assess the effects of air pollution on CVD by developing an application based on a GIS platform. Choosing Bangalore as a case study area, secondary data from various governmental departments that included demographic data, air pollution data and mortality data were obtained. An Environmental Health Information system application based on GIS platform was developed specifically for Bangalore and with the characteristics of the datasets available. Data quality assessment was carried out on these datasets that resulted in the recommendation of a generalisable data quality framework to enable better data collection that will aid in strengthening health development policies. The data was analysed using spatial and non-spatial techniques. Results showed that levels of PM10 were of concern to the city with all areas having either high or critical levels of pollution. CVD deaths also were of concern contributing to almost 40% of total mortality. The potential years of life lost (PYLL), which is an estimate of the average years a person would have lived if he or she had not died prematurely was calculated for the years from 2010 to 2013; this revealed that 2.1 million person years were lost in Bangalore due to CVD alone. These potential years lost is an important factor to consider, as preventive measures taken by the Government will result in a significant economic impact on the city. The limitations of few monitoring stations were overcome by using spatial interpolation techniques such as Inverse Distance Weighted interpolation technique. The performance of the interpolation was tested using cross-validation techniques and the results revealed that Bangalore city would benefit from increased measuring stations for PM10. The logistic regression conducted showed that pollution especially PM10 was a likely predictor of CVD in the city. Spatial analysis was conducted and included buffering, overlay maps, queries and Hotspot analysis highlighting the zone hotspots. The results from the research guided the development of the novel 5-I model that would assist other similar developing cities to assess the effects of air pollution on CVD. The impetus is that based on evidence, intervention policies and programs may be implemented to inform research and practice which will ultimately have social, economic and health impact on the population. On implementation of the model, hotspots will be identified in order to roll out interventions to priority areas and populations most at risk that will ultimately prevent millions of deaths and enhance overall quality of life.

Real-time sensing brings the proliferation of big data that contains rich information of complex systems. It is well known that real-world systems show high levels of nonlinear and nonstationary behaviors in the presence of extraneous noise. This brings significant challenges for human experts to visually inspect the integrity and performance of complex systems from the collected data. My research goal is to develop innovative methodologies for modeling and optimizing complex systems, and create enabling technologies for real-world applications. Specifically, my research focuses on Mining Dynamic Recurrences in Nonlinear and Nonstationary Systems for Feature Extraction, Process Monitoring and Fault Diagnosis. This research will enable and assist in (i) sensor-driven modeling, monitoring and optimization of complex systems; (ii) integrating product design with system design of nonlinear dynamic processes; and (iii) creating better prediction/diagnostic tools for real-world complex processes. My research accomplishments include the following. (1) Feature Extraction and Analysis: I proposed a novel multiscale recurrence analysis to not only delineate recurrence dynamics in complex systems, but also resolve the computational issues for the large-scale datasets. It was utilized to identify heart failure subjects from the 24-hour heart rate variability (HRV) time series and control the quality of mobile-phone-based electrocardiogram (ECG) signals. (2) Modeling and Prediction: I proposed the design of stochastic sensor network to allow a subset of sensors at varying locations within the network to transmit dynamic information intermittently, and a new approach of sparse particle filtering to model spatiotemporal dynamics of big data in the stochastic sensor network. It may be noted that the proposed algorithm is very general and can be potentially applicable for stochastic sensor networks in a variety of disciplines, e.g., environmental sensor network and battlefield surveillance network. (3) Monitoring and Control: Process monitoring of dynamic transitions in complex systems is more concerned with aperiodic recurrences and heterogeneous types of recurrence variations. However, traditional recurrence analysis treats all recurrence states homogeneously, thereby failing to delineate heterogeneous recurrence patterns. I developed a new approach of heterogeneous recurrence analysis for complex systems informatics, process monitoring and anomaly detection. (4) Simulation and Optimization: Another research focuses on fractal-based simulation to study spatiotemporal dynamics on fractal surfaces of high-dimensional complex systems, and further optimize spatiotemporal patterns. This proposed algorithm is applied to study the reaction-diffusion modeling on fractal surfaces and real-world 3D heart surfaces.