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<p> Hybrid task-lists are sets of periodic and asynchronous processes. To verifiably schedule hybrid tasks-lists with hard and soft real-time requirements, Xu and Lam proposed Integrated Pre-Run-Time scheduling (IPRTS) [13], a two phase method that first makes use of pre-run-time scheduling techniques, converting some asynchronous tasks with hard deadlines to periodic tasks and reserving processor capacity for the remaining hard deadline asynchronous tasks. These remaining asynchronous tasks are scheduled by a novel run-time scheduler that enforces arbitrary exclusion relations between any combination of periodic and asynchronous processes. The technique has two significant drawbacks: (i) a custom run-time scheduler is required that is not available on existing Real-Time Operating Systems (RTOS) and (ii) in many circumstances the reservation of processor capacity is overly pessimistic, causing the failure of the method for many simple task lists. To overcome these drawbacks, this thesis narrows the set of task-lists considered to those where the asynchronous tasks exclude periodic tasks and periodic processes do not exclude asynchronous tasks. A high priority polling server is then used to handle all hard asynchronous tasks. In cases where the method succeeds, it is easily implementable on any RTOS that has priority based scheduling with phased release times, and inherits the error handling and soft real-time process scheduling capabilities of the RTOS. A set of software tools which partially automates the technique, including an open source implementation of the Xu-Parnas pre-run-time scheduling algorithm [14], has been developed and applied to the examples in the thesis.</p> / Thesis / Master of Applied Science (MASc)

Wireless communication in industrial, scientific and medical applications have several benefits. The main benefits when using wireless technologies include ease-of-deployment, the simplicity to introduce new units into the network and mobility. However it also put higher demands on the communication, including reliability and predictability compared to wired communication. The reliability issues correlate to the radio communication and the possibility to ensure that the user data is received, and within the time frame of the system requirements. This doctoral thesis presents an empirical measurement approach to investigate and model the behaviour linked to reliability and predictability. The focus of the work presented is energy consumption, packet-error-rate and latency studies. This is performed for various radio technologies and standards in (radio?) harsh environments. The main contributions of this thesis are the measurements platforms and procedures that have been developed to meet the requirements to investigate modern radio technologies in terms of predictability and reliability. This thesis show that it is possible to predict wireless communication in radio harsh environments. However it is necessary to determine the characteristics of the environment to be able to choose a suitable radio technology. The measurement procedures presented in this thesis alongside the platform developed enable these types of investigations. In this thesis a model of the energy consumption for a Bluetooth radio in low-duty-cycle applications with point-to-multipoint communication is presented. The measurements show that distance and transmission power will not effect the energy consumption for a Bluetooth nor ZigBee module. However the packet-error-rate and number of retransmissions will affect the overall energy consumption, and these parameters can be correlated to distance and foremost the environmental characteristics. This thesis also presents two application-based solutions, a time synchronized ECG network with reliable data communication as well as a low-latency wireless I/O for a hydro plant. / Tesla / Gauss

Applying engineering principles to tessellation origami-based designs enables the control of certain design properties, such as flexibility, bending stiffness, mechanical advantage, shape conformance, and deployment motion. The ability to control these and other properties will enable augmented design capabilities in environments which currently limit the design to specific materials, including space, medicine, harsh environments, and scaled environments (such as MEMS applications). Other applications will be able to achieve more complex motions or better satisfy design and performance requirements.This research demonstrates augmented design capabilities of origami tessellations in engineering design in rigid-foldable and non-rigid-foldable applications. First, a method to determine Poisson's ratio and mechanical advantage for deployable, rigid-foldable tessellations is presented. The results enable the selection and tailoring of patterns based on deployment motion of specific patterns.Secondly, a model that predicts the deployment stability of the non-rigid-foldable triangulated cylinder is presented. This model defines the geometry needed to obtain a maximum deployed height, always return to a closed position, or remain in either the open or closed configurations. The Stability Transition Ratio is the ratio of the inner to outer diameter that marks the point between monostable and bistable behavior in a triangulated cylinder and is dependent only on the number of sides.Lastly, this work presents methods to reduce sag in adult diapers by increasing shape conformance, promoting wicking capabilities, and improving the structure through the implementation of origami tessellations. Several basic fold patterns were evaluated and the results reported. Reducing sag increases comfort and decreases leaking.

In modern processor architectures, caches are widely used to shorten the gap between the processor speed and memory access time. However, caches are time unpredictable, especially the shared L2 cache used by different cores on multicore processors. Thus, it can significantly increase the complexity of worst-case execution time (WCET) analysis, which is crucial for real-time systems. This dissertation designs several time-predictable scratchpad memory (SPM) based architectures for both VLIW (Very Long InstructionWord) based single-core and multicore processors. First, this dissertation proposes a time predictable two-level SPM based architecture for VLIW based single-core processors, and an ILP (Integer Linear Programming) based static memory objects allocation algorithm is extended to support the multi-level SPMs without harming the time predictability of SPMs. Second, several SPM based architectures for VLIW based multicore processors are designed. To support these architectures, the dynamic memory objects allocation based partition, the static memory objects allocation based partition and the static memory objects allocation based priority L2 SPM strategy are proposed, which retain the characteristic of time predictability. Also, both the WCET and worst-case energy consumption (WCEC) of our SPM based single-core and multicore architectures are completely evaluated in this dissertation. Last, to exploit the load/store latencies that are statically known in this architecture, we study a SPM-aware scheduling method to improve the performance. Our experimental results indicate the strengths and weaknesses of each proposed architecture and allocation method, which offers interesting memory design options to enable real-time computing. The strength of the two-level architecture is its superior performance compared to the one-level architecture, while the strength of the one-level architecture is its simple implementation. Also, the two-level architecture with separated L1 SPM for each core better fits for the data-intensive real-time applications, which not only retains good performance but also achieves a higher bandwidth by accessing both instruction and data SPM at the same time. Compared to the static based strategies, the dynamic allocation based partition L2 SPM strategy offers the better performance on each core because of the reuse of SPM space at the run-time, but has much higher complexity. In addition, the experimental results show that the timing and energy performance of our proposed SPM based architectures are superior to the similar cache based and hybrid architectures. Meanwhile, our architectures can ensure time predictability which is desirable for the real-time systems.

No description available.

Este trabalho apresenta um estudo inicial da aplicação de medidas de previsibilidade em sinais de voz. O objetivo é desenvolver métodos que sejam capazes de diferenciar sinais saudáveis e patológicos, inclusive separar patologias. Para isso, tenta-se medir a variação da incerteza e previsibilidade entre os sinais de voz dos grupos analisados. Algumas patologias de laringe, como nódulo e edema de Reinke, usadas neste estudo, causam modificações nos sinais de voz devido a mudanças na estrutura e funcionalidade do trato e pregas vocais. Nos casos patológicos, tem-se, principalmente, aumento de perturbações de freqüência e amplitude, adição de ruído e supressão de componentes harmônicos de alta freqüência da voz. Por causa disso, observa-se perda da estrutura quase-periódica dos sinais, aumentando-se a incerteza do sistema vocal e, portanto, diminuição de sua previsibilidade. Para avaliar essas mudanças, usam-se medidas de entropia de Shannon e entropia relativa entre os sinais saudáveis e patológicos. Além dessas, tem-se a potência de previsão (PP), a qual é uma medida baseada na entropia relativa entre o sinal de voz e seu erro de previsão obtido por um modelo. Inicialmente, optou-se pelo modelo autorregressivo (AR), consagrado em análise de voz, porém, devido a resultados não satisfatórios, apresentou-se um modelo baseado em decomposições por wavelets. Outra ferramenta utilizada foi a chamada análise de componentes previsíveis (PrCA), a qual realiza uma decomposição dos sinais em componentes ordenados por sua previsibilidade, sendo possível reconstruí-los usando somente os componentes mais previsíveis. Também, com essa técnica, analisaram-se representações tridimensionais dos sinais de voz em um espaço cujas coordenadas são dadas por versões atrasadas dos próprios sinais. Os algoritmos desenvolvidos foram testados com o auxílio de sinais de voz simulados, os quais possuíam variações de nível de ruído e perturbações de amplitude e freqüência. Com isso, foi possível detectar erros e solucionar problemas com os métodos. Após a avaliação dos algoritmos, estimou-se os valores de entropia dos sinais de voz, a entropia relativa entre os sinais saudáveis e os sinais dos grupos analisados, além de se calcular a PP usando o modelo AR e o modelo por wavelets. Por fim, utilizou-se a PrCA para obtenção de versões mais previsíveis dos sinais, então, calculando-se a PP para esses casos usando essa versão como previsão dos sinais. Aplicou-se, também, a PrCA para as representações tridimensionais dos sinais usando um modelamento AR multidimensional para obtenção de previsões. Com os ensaios de entropia dos sinais de voz, não foi possível diferenciar os grupos, mas com os resultados de entropia relativa, conseguiu-se distinguir eficientemente os sinais patológicos dos saudáveis. Porém, essa medida não possui muita aplicação prática, isso pois é necessário um banco de vozes diagnosticadas para servir de comparação. Nos ensaios de PP usando modelo AR, também não foi possível diferenciar os grupos, no entanto, com o modelo wavelet, os sinais saudáveis apresentaram significativamente maior previsibilidade do que os patológicos, mas, mesmo assim, não se conseguiu diferenciar as patologias. Com a PrCA, utilizando-se ambos os modelos, foi possível diferenciar os grupos patológicos do saudável, porém, frente ao modelo AR, os sinais saudáveis apresentaram menor previsibilidade. Isso demonstra que a previsibilidade depende do modelo usado para a análise, assim, as patologias da laringe podem diminuir ou aumentar a capacidade de previsão dos sinais de voz conforme o modelo usado. Com a avaliação dos resultados de PrCA das representações tridimensionais, tem-se comportamento semelhante ao obtido pela análise direta nos sinais de voz com o modelo AR, entretanto, essa forma e representação dos dados mostra se promissora em estudos futuros. Com esses ensaios, concluiu-se que este estudo foi muito útil para um maior conhecimento da dinâmica da produção vocal e que as medidas de previsibilidade são interessantes para avaliação de patologias da laringe, em especial, a presença de nódulo nas pregas vocais e edema de Reinke, pelo menos nestes estudos iniciais e usando os sinais de voz disponíveis. Mais estudos ainda são necessários, entretanto essa forma de análise já apresenta bons resultados, os quais podem ser aplicados para auxiliar o diagnóstico de disfonias por profissionais da saúde. / This thesis presents initial studies of the application of predictability measures to voice signal analysis. Its aim is to develop methods that are capable of differentiating healthy and pathological signals, also amongst pathologies. In order to do that, we perform an attempt to measure the uncertainty and predictability variations of the signals from the analyzed groups. Some larynx pathologies, such as nodule and Reinkes edema, that are used in this study, cause changes to the voice signals due to structure and functionality modifications of the vocal tract and folds. The main modifications are higher amplitude and frequency perturbations, noise addition, and supression of high frequency harmonic components. Because of that, the signals lose some of their almost periodic structure, the vocal system\'s uncertainty increases and, therefore, the predictability decreases. We use several measures to evaluate these changes, such as Shannons entropy and relative entropy between healthy and pathological signals. In addition, we use the predictive power (PP), that is based on the relative entropy between the voice signal and its prediction error given by a model. Firstly, we used the autoregressive model (AR), common for voice analysis, however, due to unsatisfactory results, we presented a model based on wavelet decomposition. We also took advantage of another tool, called predictable component analysis (PrCA), it performs a signal decomposition in components that are ordered by their predictability. Then it is possible to reconstruct the signals using only their most predictable components. Using this technique, we analyzed a kind of tridimensional representation of the voice signals in a space with coordinates given by delayed versions of the signals. We tested the developed algorithms with the aid of simulated voice signals, which had variations of noise level and amplitude and frequency perturbations. By means of that, it was possible to detect errors and solve method problems. After the algorithms evaluation, we estimated the entropy of the voice signals and the relative entropy between the healthy signals and all the signals. In addition, we estimated the PP using the AR and wavelet based models. After that, we used the PrCA in order to obtain more predictable versions of the signals and then, estimated the PP using this version as the signals prediction. Also, we applied the PrCA to the signals tridimensional representations using a multidimensional AR model as a predictor. Using the voice entropy results, we could not distinguish between the analyzed groups, but with the relative entropy values, the healthy and pathological signals were differentiated efficiently. In spite of that, this measure has no practical application, because a diagnosed voice database is necessary as a basis of comparison. For the PP with AR modeling, no distinction between the groups is observed, but with the wavelet modeling, the healthy signals showed significantly higher predictability than the pathological ones, however the pathologies were differentiated. Using the PrCA with both models, the pathological and healthy groups were distinguished, but for the AR model, the healthy signals presented smaller predictability. This shows that the predictability depends on the analysis model, thus the larynx pathologies can decrease or increase the prediction capacity of the voice signals according to the used model. The results of PrCA of the tridimensional representations show similar behavior of the ones from direct PrCA signal analisys with the AR model. Despite of these results, this form of data representation seems to be promising for future studies. Considering these results, we concluded that this study was very useful to acquire a better understanding of the dynamics of voice production and that the predictability measures are interesting for the evaluation of larynx pathologies, especially presence of nodule in the vocal folds and Reinke\'s edema, at least for this initial study using the available signals. More studies are still necessary, but this analysis method already presents good results, which can be applied to aid pathology diagnosis by health professionals.

Este trabalho apresenta um estudo inicial da aplicação de medidas de previsibilidade em sinais de voz. O objetivo é desenvolver métodos que sejam capazes de diferenciar sinais saudáveis e patológicos, inclusive separar patologias. Para isso, tenta-se medir a variação da incerteza e previsibilidade entre os sinais de voz dos grupos analisados. Algumas patologias de laringe, como nódulo e edema de Reinke, usadas neste estudo, causam modificações nos sinais de voz devido a mudanças na estrutura e funcionalidade do trato e pregas vocais. Nos casos patológicos, tem-se, principalmente, aumento de perturbações de freqüência e amplitude, adição de ruído e supressão de componentes harmônicos de alta freqüência da voz. Por causa disso, observa-se perda da estrutura quase-periódica dos sinais, aumentando-se a incerteza do sistema vocal e, portanto, diminuição de sua previsibilidade. Para avaliar essas mudanças, usam-se medidas de entropia de Shannon e entropia relativa entre os sinais saudáveis e patológicos. Além dessas, tem-se a potência de previsão (PP), a qual é uma medida baseada na entropia relativa entre o sinal de voz e seu erro de previsão obtido por um modelo. Inicialmente, optou-se pelo modelo autorregressivo (AR), consagrado em análise de voz, porém, devido a resultados não satisfatórios, apresentou-se um modelo baseado em decomposições por wavelets. Outra ferramenta utilizada foi a chamada análise de componentes previsíveis (PrCA), a qual realiza uma decomposição dos sinais em componentes ordenados por sua previsibilidade, sendo possível reconstruí-los usando somente os componentes mais previsíveis. Também, com essa técnica, analisaram-se representações tridimensionais dos sinais de voz em um espaço cujas coordenadas são dadas por versões atrasadas dos próprios sinais. Os algoritmos desenvolvidos foram testados com o auxílio de sinais de voz simulados, os quais possuíam variações de nível de ruído e perturbações de amplitude e freqüência. Com isso, foi possível detectar erros e solucionar problemas com os métodos. Após a avaliação dos algoritmos, estimou-se os valores de entropia dos sinais de voz, a entropia relativa entre os sinais saudáveis e os sinais dos grupos analisados, além de se calcular a PP usando o modelo AR e o modelo por wavelets. Por fim, utilizou-se a PrCA para obtenção de versões mais previsíveis dos sinais, então, calculando-se a PP para esses casos usando essa versão como previsão dos sinais. Aplicou-se, também, a PrCA para as representações tridimensionais dos sinais usando um modelamento AR multidimensional para obtenção de previsões. Com os ensaios de entropia dos sinais de voz, não foi possível diferenciar os grupos, mas com os resultados de entropia relativa, conseguiu-se distinguir eficientemente os sinais patológicos dos saudáveis. Porém, essa medida não possui muita aplicação prática, isso pois é necessário um banco de vozes diagnosticadas para servir de comparação. Nos ensaios de PP usando modelo AR, também não foi possível diferenciar os grupos, no entanto, com o modelo wavelet, os sinais saudáveis apresentaram significativamente maior previsibilidade do que os patológicos, mas, mesmo assim, não se conseguiu diferenciar as patologias. Com a PrCA, utilizando-se ambos os modelos, foi possível diferenciar os grupos patológicos do saudável, porém, frente ao modelo AR, os sinais saudáveis apresentaram menor previsibilidade. Isso demonstra que a previsibilidade depende do modelo usado para a análise, assim, as patologias da laringe podem diminuir ou aumentar a capacidade de previsão dos sinais de voz conforme o modelo usado. Com a avaliação dos resultados de PrCA das representações tridimensionais, tem-se comportamento semelhante ao obtido pela análise direta nos sinais de voz com o modelo AR, entretanto, essa forma e representação dos dados mostra se promissora em estudos futuros. Com esses ensaios, concluiu-se que este estudo foi muito útil para um maior conhecimento da dinâmica da produção vocal e que as medidas de previsibilidade são interessantes para avaliação de patologias da laringe, em especial, a presença de nódulo nas pregas vocais e edema de Reinke, pelo menos nestes estudos iniciais e usando os sinais de voz disponíveis. Mais estudos ainda são necessários, entretanto essa forma de análise já apresenta bons resultados, os quais podem ser aplicados para auxiliar o diagnóstico de disfonias por profissionais da saúde. / This thesis presents initial studies of the application of predictability measures to voice signal analysis. Its aim is to develop methods that are capable of differentiating healthy and pathological signals, also amongst pathologies. In order to do that, we perform an attempt to measure the uncertainty and predictability variations of the signals from the analyzed groups. Some larynx pathologies, such as nodule and Reinkes edema, that are used in this study, cause changes to the voice signals due to structure and functionality modifications of the vocal tract and folds. The main modifications are higher amplitude and frequency perturbations, noise addition, and supression of high frequency harmonic components. Because of that, the signals lose some of their almost periodic structure, the vocal system\'s uncertainty increases and, therefore, the predictability decreases. We use several measures to evaluate these changes, such as Shannons entropy and relative entropy between healthy and pathological signals. In addition, we use the predictive power (PP), that is based on the relative entropy between the voice signal and its prediction error given by a model. Firstly, we used the autoregressive model (AR), common for voice analysis, however, due to unsatisfactory results, we presented a model based on wavelet decomposition. We also took advantage of another tool, called predictable component analysis (PrCA), it performs a signal decomposition in components that are ordered by their predictability. Then it is possible to reconstruct the signals using only their most predictable components. Using this technique, we analyzed a kind of tridimensional representation of the voice signals in a space with coordinates given by delayed versions of the signals. We tested the developed algorithms with the aid of simulated voice signals, which had variations of noise level and amplitude and frequency perturbations. By means of that, it was possible to detect errors and solve method problems. After the algorithms evaluation, we estimated the entropy of the voice signals and the relative entropy between the healthy signals and all the signals. In addition, we estimated the PP using the AR and wavelet based models. After that, we used the PrCA in order to obtain more predictable versions of the signals and then, estimated the PP using this version as the signals prediction. Also, we applied the PrCA to the signals tridimensional representations using a multidimensional AR model as a predictor. Using the voice entropy results, we could not distinguish between the analyzed groups, but with the relative entropy values, the healthy and pathological signals were differentiated efficiently. In spite of that, this measure has no practical application, because a diagnosed voice database is necessary as a basis of comparison. For the PP with AR modeling, no distinction between the groups is observed, but with the wavelet modeling, the healthy signals showed significantly higher predictability than the pathological ones, however the pathologies were differentiated. Using the PrCA with both models, the pathological and healthy groups were distinguished, but for the AR model, the healthy signals presented smaller predictability. This shows that the predictability depends on the analysis model, thus the larynx pathologies can decrease or increase the prediction capacity of the voice signals according to the used model. The results of PrCA of the tridimensional representations show similar behavior of the ones from direct PrCA signal analisys with the AR model. Despite of these results, this form of data representation seems to be promising for future studies. Considering these results, we concluded that this study was very useful to acquire a better understanding of the dynamics of voice production and that the predictability measures are interesting for the evaluation of larynx pathologies, especially presence of nodule in the vocal folds and Reinke\'s edema, at least for this initial study using the available signals. More studies are still necessary, but this analysis method already presents good results, which can be applied to aid pathology diagnosis by health professionals.

In this research study three subject didactical measuring instruments were compared with regard to certain psychometric characteristics of a group of grade nine learners in the Tzaneen and Phalaborwa region, in the Northern Province. The primary goal of this study was to optimize learning competencies with regard to subject context. The SOM-, LEMOSS(II)- and LCH-questionnaires can be applied at the start or at the end of an academic year to an individual or to a group in order to identify learners who need specific help, counseling, remediation and support in the subjects mathematics, natural and domestic sciences in order to facilitate learning. The following aspects have been handled in this study, such as: In chapter 1 an attempt is made to clarify and define the title and certain relevant concepts. The research statement and questions are formulated. The research design is briefly discussed and the primary goal and objectives are set. Other aspects that enjoy attention include the types of research that were used, the method of sampling, data gathering and analysis, as well as the progress of the study. Literature on subject didactics is discussed in chapter 2 for the conceptualising of basic related aspects by means of a macro-model for the optimising of learning competencies in the context of a specific subject. This model emphasises subject content, form and competencies that should be taken into consideration when planning a learning opportunity. Other aspects concerning learning, namely relevant theories, approaches, models, styles, strategies and motives were discussed in order to facilitate learning in mathematics, natural and domestic sciences. In chapter 3 the SOM-, LEMOSS(II)- and LCH-questionnaires as diagnostic instruments are described. The diagnostic value, background, categories and psychometric aspects of the measuring instruments are briefly discussed. In chapter 4 the method of investigation used for the comparison of the SOM- LEMOSS(II)- and LCH-instruments regarding their reliability and validity is described and it includes the goal, hypothesis and method of sampling as an extension of the research design. The way in which descriptive and inferential statistics are conveyed is theoretically described In chapter 5 the results and qualitative conclusions of the empiric-analytical investigation are included. The SOM-, LEMOSS(II)- and LCH-questionnaires are compared with regard to reliability and validity of a group grade nine learners in the Tzaneen and Phalaborwa region. Reliability is discussed by means of test-retest reliability and pared observations. Pared observations include staggering, locality and pared t- and sign-rank testing. Validity refers to criteria referenced validity with regards to simultaneous and predictable validity. Simultaneous validity is described by means of the relation between the subject mark and the field, and by stepwise and multiple regression analysis. In chapter 6 the study is concluded with the brief answering of the research questions and by describing the research findings by means of the research statement. Possible limitations are shown and recommendations for future research are made. / Dissertation (PhD)--University of Pretoria, 2001. / Humanities Education / Unrestricted

No description available.