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Internet of Things (IoT) is becoming commonplace in people's daily life. Even, many governments' authorities have already deployed a very large number of IoT sensors toward their smart city initiative and development road-map. However, lack of semantics in the presentation of IoT-based sensory data represents the perception complexity by general people. Adding semantics to the IoT sensory data remains a challenge for smart cities and environments. In this thesis proposal, we present an implementation that provides a meaningful IoT sensory data notifications approach about indoor and outdoor environment status for people and authorities. The approach is based on analyzing spatio-temporal thresholds that compose of multiple IoT sensors readings. Our developed IoT sensory data analytics adds real-time semantics to the received sensory raw data stream by converting the IoT sensory data into meaningful and descriptive notifications about the environment status such as green locations, emergency zone, crowded places, green paths, polluted locations, etc. Our adopted IoT messaging protocol can handle a very large number of dynamically added static and dynamic IoT sensors publication and subscription processes. People can customize the notifications based on their preference or can subscribe to existing semantic notifications in order to be acknowledged of any concerned environmental condition. The thesis is supposed to come up with three contributions. The first, an IoT approach of a three-layer architecture that extracts raw sensory data measurements and converts it to a contextual-aware format that can be perceived by people. The second, an ontology that infers a semantic notification of multiple sensory data according to the appropriate spatio-temporal reasoning and description mechanism. We used a tool called Protégé to model our ontology as a common IDE to build semantic knowledge. We built our ontology through extending a well-known web ontology called Semantic Sensor Network (SSN). We built the extension from which six classes were adopted to derive our SENS-IT ontology and fulfill our objectives. The third, a fuzzy system approach is proposed to make our system much generic of providing broader semantic notifications, so it can be agile enough to accept more measurements of multiple sensory sources.

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Stormwater pipe infrastructure collects and conveys surface runoff resulting from rainfall or snowmelt to nearby streams. Traditionally, stormwater pipe systems were integrated with wastewater infrastructure through a combined sewer system. Many of these systems are being separated due to the impact of environmental laws and regulations; and the same factors have led to the creation of stormwater utilities. However, in the current ASCE Infrastructure Report Card, stormwater infrastructure is considered a sub-category of wastewater infrastructure. Stormwater infrastructure has always lacked attention compared to water and wastewater infrastructure. However, this notion has begun to shift, as aging stormwater pipes coupled with changes in climatic patterns and urban landscapes makes stormwater infrastructure more complex to manage. These changes and lack of needed maintenance has resulted in increased rates of deterioration and capacity. Stormwater utility managers have limited resources and funds to manage their pipe system. To effectively make decisions on allocating limited resources and funds, a utility should be able to understand and assess the performance of its pipe system. There is no standard rating system or comprehensive list of performance parameters for stormwater pipe infrastructure. Previous research has identified performance parameters affecting stormwater pipes and developed a performance index using a weighted factor method. However, the weighted performance index model does not capture interdependencies between performance parameters. This research developed a comprehensive list of parameters affecting stormwater pipe performance. This research also developed a performance index using fuzzy inference method to capture interdependencies among parameters. The performance index was evaluated and validated with the pipe ratings provided by one stormwater utility to document its effectiveness in real world conditions. / Master of Science / Stormwater pipe infrastructure collects and conveys the surface water resulting from rainfall or snowmelt to nearby streams. Traditionally, stormwater pipe system was integrated with wastewater infrastructure by combined sewer system. Environmental regulations forced creation of stormwater utilities and separate stormwater system, however, according to ASCE infrastructure report, stormwater infrastructure has been considered a sub-category of wastewater infrastructure. Stormwater infrastructure has always lacked attention compared to water and wastewater infrastructure. However, this notion has to shift, as aging stormwater pipes coupled with changes in climatic patterns and urban landscapes makes stormwater infrastructure complex to manage resulting in increased rate of deterioration and design capacity. Stormwater utility managers have limited resources and funds to manage their pipe system. To effectively make decisions on allocating limited resources and funds, a utility should be able to understand and assess the performance of its pipe system. There is no standard rating system for assessing the condition of stormwater pipe infrastructure. This research developed an index using fuzzy inference method to capture the interdependencies. Fuzzy inference method basically captures the interdependencies between parameters using if-then rule statements. Parameters are individual elements affecting the performance of stormwater pipes. The performance index was evaluated and validated with the pipe ratings provided by one stormwater utility to document its effectiveness in real world conditions.

Student Number : 9702018F - MSc(Eng) Dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / Due to the volatile nature of the world economies, investing is crucial in ensuring an individual is prepared for future financial necessities. This research proposes an application, which employs computational intelligent methods that could assist investors in making financial decisions. This system consists of 2 components. The Forecasting Component (FC) is employed to predict the closing index price performance. Based on these predictions, the Stock Quantity Selection Component (SQSC) recommends the investor to purchase stocks, hold the current investment position or sell stocks in possession. The development of the FC module involved the creation of Multi-Layer Perceptron (MLP) as well as Radial Basis Function (RBF) neural network classifiers. TCategorizes that these networks classify are based on a profitable trading strategy that outperforms the long-term “Buy and hold” trading strategy. The Dow Jones Industrial Average, Johannesburg Stock Exchange (JSE) All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. TIt has been determined that the MLP neural network architecture is particularly suited in the prediction of closing index price performance. Accuracies of 72%, 68%, 69% and 64% were obtained for the prediction of closing price performance of the Dow Jones Industrial Average, JSE All Share, Nasdaq 100 and Nikkei 225 Stock Average indices, respectively. TThree designs of the Stock Quantity Selection Component were implemented and compared in terms of their complexity as well as scalability. TComplexity is defined as the number of classifiers employed by the design. Scalability is defined as the ability of the design to accommodate the classification of additional investment recommendations. TDesigns that utilized 1, 4 and 16 classifiers, respectively, were developed. These designs were implemented using MLP neural networks, RBF neural networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4 classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of concern. It has also been determined that the neural network architecture as well as the Fuzzy Inference System implementation of this design performed equally well.

It has been shown by Roger Jang in his paper titled "Adaptive-network-based fuzzy inference systems" that the Adaptive Network based Fuzzy Inference System can model nonlinear functions, identify nonlinear components in a control system, and predict a chaotic time series. The system use hybrid-learning procedure which employs the back-propagation-type gradient descent algorithm and the least squares estimator to estimate parameters of the model. However the learning procedure has several shortcomings due to the fact that * There is a harmful and unforeseeable influence of the size of the partial derivative on the weight step in the back-propagation-type gradient descent algorithm. *In some cases the matrices in the least square estimator can be ill-conditioned. *Several estimators are known which dominate, or outperform, the least square estimator. Therefore this thesis develops a new system that overcomes the above problems, which is called the "Faster Adaptive Network Fuzzy Inference System" (FANFIS). The new system in this thesis is shown to significantly out perform the existing method in predicting a chaotic time series , modelling a three-input nonlinear function and identifying dynamical systems. We also use FANFIS to predict five major stock closing prices in New Zealand namely Air New Zealand "A" Ltd., Brierley Investments Ltd., Carter Holt Harvey Ltd., Lion Nathan Ltd. and Telecom Corporation of New Zealand Ltd. The result shows that the new system out performed other competing models and by using simple trading strategy, profitable forecasting is possible.

Made available in DSpace on 2014-06-11T19:22:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-07-29Bitstream added on 2014-06-13T19:27:43Z : No. of bitstreams: 1 anjos_ds_me_prud.pdf: 1639941 bytes, checksum: 0860c4a946325bd5e941068ec7106d5e (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A representação do relevo ou terreno é uma componente fundamental no processo cartográfico e dentre essas representações as que têm por objetivo analisar as diferentes curvaturas de uma vertente, ou seja, classificar as vertentes de um determinado terreno em retilíneas, côncavas ou convexas tem apresentado grande aplicabilidade em áreas como a agricultura, a construção civil, o estudo de microbacias entre outros. Assim, o desenvolvimento de algoritmos que classifiquem essas formas do relevo pode contribuir muito para a produção de informações relevantes à tomada de decisões em diversas áreas do conhecimento. Alguns algoritmos com esse intuito foram anteriormente desenvolvidos, porém apresentam claras necessidades de melhoria por classificarem apenas áreas pré-estabelecidas, não podendo ser utilizados para outras regiões. Visando sanar a necessidade de implementações mais completas este trabalho apresenta a metodologia utilizada na elaboração de um algoritmo para classificação de vertentes através de ferramentas matemáticas até então pouco utilizadas nas Ciências Cartográficas: a Thin Plate Spline (TPS) que será utilizada para adensar os dados de vertentes do município de Presidente Prudente, gerando Modelos Numérico de Terreno (MNTs) sob os quais a curvatura é calculada, e a Inferência Fuzzy que é uma ferramenta utilizada para discriminar classes que por diversas razões não possuem limites rígidos entre si, como é o caso das vertentes a serem analisadas, e, portanto, estará integrada a um produto final que será parte do estudo, isto é, um sistema que forneça modelos de classificação das vertentes em: retilíneas, côncavas e convexas e que possa ser comparada ao mapa geomorfólogico existente. / The relief or terrain representation is an essential component in the cartographic process. Representations which aim at classifying relief profiles of a certain terrain as rectilinear, concave and convex have reached great applicability in areas such as agriculture, civil construction, watershed studies, among others. Therefore, algorithms that classify these forms of relief can much contribute to the production of relevant information to the decision make in several areas of knowledge. The simplest algorithm, based on curvature value only is clearly not sufficient, but the literature brings fairly little in relation to a more adequate methodology. Attempting to contribute in the sense to aggregate more information and intelligence into this kind of classification so to achieve a more complete implementation, this work presents a methodology using two mathematical tools of little use so far in the Cartographic Science: 1) Thin Plate Spline (TPS) used to densify the existing data, for the Numerical Terrain Models on which the curvature shall be calculated and, 2) Fuzzy Inference used to discriminate classes that for several reasons do not possesses well defined boundaries, as is the curvature profile case. The validation used known and previously chosen data from Presidente Prudente so that a comparison with existing morphological map was possible.

Resumo: A representação do relevo ou terreno é uma componente fundamental no processo cartográfico e dentre essas representações as que têm por objetivo analisar as diferentes curvaturas de uma vertente, ou seja, classificar as vertentes de um determinado terreno em retilíneas, côncavas ou convexas tem apresentado grande aplicabilidade em áreas como a agricultura, a construção civil, o estudo de microbacias entre outros. Assim, o desenvolvimento de algoritmos que classifiquem essas formas do relevo pode contribuir muito para a produção de informações relevantes à tomada de decisões em diversas áreas do conhecimento. Alguns algoritmos com esse intuito foram anteriormente desenvolvidos, porém apresentam claras necessidades de melhoria por classificarem apenas áreas pré-estabelecidas, não podendo ser utilizados para outras regiões. Visando sanar a necessidade de implementações mais completas este trabalho apresenta a metodologia utilizada na elaboração de um algoritmo para classificação de vertentes através de ferramentas matemáticas até então pouco utilizadas nas Ciências Cartográficas: a Thin Plate Spline (TPS) que será utilizada para adensar os dados de vertentes do município de Presidente Prudente, gerando Modelos Numérico de Terreno (MNTs) sob os quais a curvatura é calculada, e a Inferência Fuzzy que é uma ferramenta utilizada para discriminar classes que por diversas razões não possuem limites rígidos entre si, como é o caso das vertentes a serem analisadas, e, portanto, estará integrada a um produto final que será parte do estudo, isto é, um sistema que forneça modelos de classificação das vertentes em: retilíneas, côncavas e convexas e que possa ser comparada ao mapa geomorfólogico existente. / Abstract: The relief or terrain representation is an essential component in the cartographic process. Representations which aim at classifying relief profiles of a certain terrain as rectilinear, concave and convex have reached great applicability in areas such as agriculture, civil construction, watershed studies, among others. Therefore, algorithms that classify these forms of relief can much contribute to the production of relevant information to the decision make in several areas of knowledge. The simplest algorithm, based on curvature value only is clearly not sufficient, but the literature brings fairly little in relation to a more adequate methodology. Attempting to contribute in the sense to aggregate more information and intelligence into this kind of classification so to achieve a more complete implementation, this work presents a methodology using two mathematical tools of little use so far in the Cartographic Science: 1) Thin Plate Spline (TPS) used to densify the existing data, for the Numerical Terrain Models on which the curvature shall be calculated and, 2) Fuzzy Inference used to discriminate classes that for several reasons do not possesses well defined boundaries, as is the curvature profile case. The validation used known and previously chosen data from Presidente Prudente so that a comparison with existing morphological map was possible. / Orientador: Messias Meneguette Júnior / Coorientador: João Osvaldo Rodrigues Nunes / Banca: Nilton Nobuhiro Imai / Banca: Ricardo Luis Barbosa / Mestre

Autism spectrum disorder (ASD) is a group of developmental disabilities characterized by impairments in social interaction and communication and by difficulties in emotion recognition and regulation. There is currently no cure for autism but psychosocial interventions and medical treatments exist. However, very few of them have been trialed on young children and others pose limitations. Strengthening young children's capacity to manage their emotions is important for academic success. Thus it becomes important to design and test the feasibility of an appropriate methodology that can teach emotion regulation to young children (age 3-6 years) with ASD. This thesis addresses the problem by proposing a novel framework that integrates physiology with Cognitive Behavior Theory to enable emotion regulation in the target population by exposing them to real-time stressful situations. The framework uses a feedback loop that measures the participant's physiology, estimates the level of stress being experienced and provides an audio feedback. The feasibility of the individual building blocks of the framework was tested by conducting pilot studies on nine typically developing children (age 3-6 years). The attention capturing capacity of different audio representations was tested, and a stress profile generating system was designed and developed to map the measured physiology of the participant on to a relative stress level. 33 out of 43 instances of audio representations proved to be successful in capturing the participants' attention and the stress profiles were found to be capable of distinguishing between stressed and relaxed state of the participants with an average accuracy of 83%. / Master of Science