Quality and error assessment is an essential part of spatial analysis which with the increasingamount of applications resulting from today’s extensive access to spatial data, such as satelliteimagery and computer power is extra important to address. This study evaluates the impact ofinput errors associated with satellite sensor noise for a spatial method aimed at characterisingaspects of landscapes associated with the historical village structure, called the HybridCharacterisation Model (HCM), that was developed as a tool to monitor sub goals of theSwedish Environmental Goal “A varied agricultural landscape”. The method and errorsimulation method employed for generating random errors in the input data, is implemented andautomated as a Python script enabling easy iteration of the procedure. The HCM is evaluatedqualitatively (by visual analysis) and quantitatively comparing kappa index values between theoutputs affected by error. Comparing the result of the qualitative and quantitative evaluationshows that the kappa index is an applicable measurement of quality for the HCM. Thequalitative analysis compares impact of error for two different scales, the village scale and thelandscape scale, and shows that the HCM is performing well on the landscape scale for up to30% error and on the village scale for up to 10% and shows that the impact of error differsdepending on the shape of the analysed feature. The Python script produced in this study couldbe further developed and modified to evaluate the HCM for other aspects of input error, such asclassification errors, although for such studies to be motivated the potential errors associatedwith the model and its parameters must first be further evaluated.

In practice, most approaches for text error detection and correction are based on a conventional domain-dependent background dictionary that represents a fixed and static collection of correct words of a given language and, as a result, satisfactory correction can only be achieved if the dictionary covers most tokens of the underlying correct text. Again, most approaches for text correction are for only one or at best a very few types of errors. The purpose of this thesis is to propose an unsupervised approach to detecting and correcting text errors, that can compete with supervised approaches and answer the following questions: Can an unsupervised approach efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature? What is the magnitude of error coverage, in terms of the number of errors that can be corrected? We conclude that (1) it is possible that an unsupervised approach can efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature. Error types include: real-word spelling errors, typographical errors, lexical choice errors, unwanted words, missing words, prepositional errors, article errors, punctuation errors, and many of the grammatical errors (e.g., errors in agreement and verb formation). (2) The magnitude of error coverage, in terms of the number of errors that can be corrected, is almost double of the number of correct words of the text. Although this is not the upper limit, this is what is practically feasible. We use engineering approaches to answer the first question and theoretical approaches to answer and support the second question. We show that finding inherent properties of a correct text using a corpus in the form of an n-gram data set is more appropriate and practical than using other approaches to detecting and correcting errors. Instead of using rule-based approaches and dictionaries, we argue that a corpus can effectively be used to infer the properties of these types of errors, and to detect and correct these errors. We test the robustness of the proposed approach separately for some individual error types, and then for all types of errors. The approach is language-independent, it can be applied to other languages, as long as n-grams are available. The results of this thesis thus suggest that unsupervised approaches, which are often dismissed in favor of supervised ones in the context of many Natural Language Processing (NLP) related tasks, may present an interesting array of NLP-related problem solving strengths.

Many high-accuracy regional GPS continuously operating reference (CORS) networks have been established globally. These networks are used to facilitate better positioning services, such as high accuracy real-time positioning. GPSnet is the first state-wide CORS network in Australia. In order to maximize the benefits of the expensive CORS geospatial infrastructure, the state of Victoria in collaboration with three universities (RMIT University, the University of NSW and the University of Melbourne) embarked on research into regional atmospheric error modelling for Network-based RTK (NRTK) via an Australian Research Council project in early 2005. The core of the NRTK technique is the modelling of the spatially-correlated errors. The accuracy of the regional error model is a determining factor for the performance of NRTK positioning. In this research, a number of error models are examined and comprehensively analysed. Among them, the following three models are tested: 1) the Linear Interpolation Method (LIM); 2) the Distance-Based interpolation method (DIM); and 3) the Low-order surface model (LSM). The accuracy of the three models is evaluated using three different observation sessions and a variety of network configurations of GPSnet. Results show that the LIM and DIM can be used to significantly reduce the double-differenced (DD) residuals (up to 60% improvement), and the LIM is slightly better than the DIM (most at mm level). However the DD residuals with the LSM corrections are, in some cases, not only much worse than that of the LIM and DIM but also even must greater/worse than the DD residuals without any corrections applied at all. This indicates that there are no advantages by using the LSM for the error modelling for NRTK in GPSnet, even though it is the most commonly used method by researchers. The performance difference of the LIM for different GPSnet configurations is also tested. Results show that in most cases, the performance difference mainly caused by the number of reference stations used is not significant. This implies that more redundant reference stations may not contribute much to the accuracy improvement of the LIM. However, it may mitigate the station specific errors (if any). The magnitude of the temporal variations of both the tropospheric and ionospheric effects in GPSnet observations is also investigated. Test results suggest that the frequency of generating and transmitting the tropospheric corrections should not be significantly different from that for the ionospehric corrections. Thus 1Hz frequency (i.e. once every second) is recommended for the generation and transmission for both types of the atmospheric corrections for NRTK in GPSnet. The algorithms of the NRTK software package used are examined and extensive analyses are conducted. The performance and limitation of the NRTK system in terms of network ambiguity resolution are assessed. The methodology for generating virtual reference station (VRS) observations in the system is presented. The validation of the algorithms for the generated VRS observations is undertaken. It is expected that this research is significant for both the selection of regional error models and the implementation of the NRTK technique in GPSnet or in the Victorian region.

A necessidade de suportar serviços multimídia impulsiona o desenvolvimento das redes sem fio. Com isso, torna-se importante fornecer confiabilidade na transmissão de dados em um ambiente sujeito a variações espaciais, temporais e de freqüência, causadas por fenômenos físicos que, geralmente, causam erros nos dados transmitidos. Esses erros são basicamente de dois tipos: erros em rajada e erros aleatórios (Additive White Gaussian Noise - AWGN). Simular o comportamento dos canais sem fio afetados por erros é objeto de pesquisa há diversos anos. Entretanto, grande parte das pesquisas não considera a aplicação dos dois tipos de erros simultaneamente, o que pode gerar imprecisões nos resultados das simulações. Sendo assim, este trabalho propõe um injetor capaz de gerar tanto seqüências de erros em rajada quanto AWGN, além de propor um modelo de erros híbrido que considera a injeção de ambos os tipos de erros para simular o comportamento de um canal sem fio. O injetor de erros proposto é empregado em um estudo de caso referente à análise de desempenho do mecanismo de codificação de canal em redes que seguem o padrão IEEE 802.16, tanto nomádicas (fixas) quanto móveis. É avaliada a capacidade de correção dos codificadores Forward Error Correction (FEC), de emprego obrigatório de acordo com o referido padrão. Além disso, estuda-se o impacto causado pela aplicação de técnicas que consistem na adição de diversidade temporal à transmissão, em cenários cuja ocorrência dos erros é em rajada, e em cenários cujos erros são modelados de acordo com seqüências AWGN. Finalmente, realiza-se um estudo teórico sobre a vazão que pode ser atingida nos cenários nomádicos e móveis, além de uma discussão sobre os avanços tecnológicos trazidos pela multiplexação de canal baseada em Orthogonal Frequency Division Multiple Access (OFDMA), empregado em redes IEEE 802.16 móveis. / The demand for providing multimedia services is increasing the development of wireless networks. Therefore, an important issue is to guarantee correct transmissions over channels that are affected by time and frequency variant conditions caused by physical impairments that lead to the occurrence of errors during the transmission. These errors are basically of two types: burst errors and random errors, typically modeled as Additive White Gaussian Noise (AWGN). Simulating the behavior of wireless channels affected by physical impairments has been subject of several investigations in the past years. Nevertheless, part of the current researches does not consider the occurrence of both errors at the same time. This approach may lead to imprecisions on the results obtained through simulations. This work proposea an error sequence generator which is able of generating both burst and AWGN error models. Moreover, the proposed model can generate hybrid errors sequences composed of both error types simultaneously. The proposed error sequence generator is applied to a case study that aims to evaluate the performance of the channel encoder of nomadic (fixed) and mobile IEEE 802.16 networks. In this context, we evaluate the error correction capability of FEC encoders which are mandatory according to IEEE 802.16 standard. Furthermore, we study the impact caused by the application of time diversity techniques on the transmission, considering scenarios affected by burst errors and AWGN. We also present a study about the theoretical throughput that can be reached by nomadic and mobile technologies. Finally, we discuss the technological advances brought by Orthogonal Frequency Division Multiple Access (OFDMA) channel multiplexing technique, which is employed in IEEE 802.16 mobile networks.

A necessidade de suportar serviços multimídia impulsiona o desenvolvimento das redes sem fio. Com isso, torna-se importante fornecer confiabilidade na transmissão de dados em um ambiente sujeito a variações espaciais, temporais e de freqüência, causadas por fenômenos físicos que, geralmente, causam erros nos dados transmitidos. Esses erros são basicamente de dois tipos: erros em rajada e erros aleatórios (Additive White Gaussian Noise - AWGN). Simular o comportamento dos canais sem fio afetados por erros é objeto de pesquisa há diversos anos. Entretanto, grande parte das pesquisas não considera a aplicação dos dois tipos de erros simultaneamente, o que pode gerar imprecisões nos resultados das simulações. Sendo assim, este trabalho propõe um injetor capaz de gerar tanto seqüências de erros em rajada quanto AWGN, além de propor um modelo de erros híbrido que considera a injeção de ambos os tipos de erros para simular o comportamento de um canal sem fio. O injetor de erros proposto é empregado em um estudo de caso referente à análise de desempenho do mecanismo de codificação de canal em redes que seguem o padrão IEEE 802.16, tanto nomádicas (fixas) quanto móveis. É avaliada a capacidade de correção dos codificadores Forward Error Correction (FEC), de emprego obrigatório de acordo com o referido padrão. Além disso, estuda-se o impacto causado pela aplicação de técnicas que consistem na adição de diversidade temporal à transmissão, em cenários cuja ocorrência dos erros é em rajada, e em cenários cujos erros são modelados de acordo com seqüências AWGN. Finalmente, realiza-se um estudo teórico sobre a vazão que pode ser atingida nos cenários nomádicos e móveis, além de uma discussão sobre os avanços tecnológicos trazidos pela multiplexação de canal baseada em Orthogonal Frequency Division Multiple Access (OFDMA), empregado em redes IEEE 802.16 móveis. / The demand for providing multimedia services is increasing the development of wireless networks. Therefore, an important issue is to guarantee correct transmissions over channels that are affected by time and frequency variant conditions caused by physical impairments that lead to the occurrence of errors during the transmission. These errors are basically of two types: burst errors and random errors, typically modeled as Additive White Gaussian Noise (AWGN). Simulating the behavior of wireless channels affected by physical impairments has been subject of several investigations in the past years. Nevertheless, part of the current researches does not consider the occurrence of both errors at the same time. This approach may lead to imprecisions on the results obtained through simulations. This work proposea an error sequence generator which is able of generating both burst and AWGN error models. Moreover, the proposed model can generate hybrid errors sequences composed of both error types simultaneously. The proposed error sequence generator is applied to a case study that aims to evaluate the performance of the channel encoder of nomadic (fixed) and mobile IEEE 802.16 networks. In this context, we evaluate the error correction capability of FEC encoders which are mandatory according to IEEE 802.16 standard. Furthermore, we study the impact caused by the application of time diversity techniques on the transmission, considering scenarios affected by burst errors and AWGN. We also present a study about the theoretical throughput that can be reached by nomadic and mobile technologies. Finally, we discuss the technological advances brought by Orthogonal Frequency Division Multiple Access (OFDMA) channel multiplexing technique, which is employed in IEEE 802.16 mobile networks.

A necessidade de suportar serviços multimídia impulsiona o desenvolvimento das redes sem fio. Com isso, torna-se importante fornecer confiabilidade na transmissão de dados em um ambiente sujeito a variações espaciais, temporais e de freqüência, causadas por fenômenos físicos que, geralmente, causam erros nos dados transmitidos. Esses erros são basicamente de dois tipos: erros em rajada e erros aleatórios (Additive White Gaussian Noise - AWGN). Simular o comportamento dos canais sem fio afetados por erros é objeto de pesquisa há diversos anos. Entretanto, grande parte das pesquisas não considera a aplicação dos dois tipos de erros simultaneamente, o que pode gerar imprecisões nos resultados das simulações. Sendo assim, este trabalho propõe um injetor capaz de gerar tanto seqüências de erros em rajada quanto AWGN, além de propor um modelo de erros híbrido que considera a injeção de ambos os tipos de erros para simular o comportamento de um canal sem fio. O injetor de erros proposto é empregado em um estudo de caso referente à análise de desempenho do mecanismo de codificação de canal em redes que seguem o padrão IEEE 802.16, tanto nomádicas (fixas) quanto móveis. É avaliada a capacidade de correção dos codificadores Forward Error Correction (FEC), de emprego obrigatório de acordo com o referido padrão. Além disso, estuda-se o impacto causado pela aplicação de técnicas que consistem na adição de diversidade temporal à transmissão, em cenários cuja ocorrência dos erros é em rajada, e em cenários cujos erros são modelados de acordo com seqüências AWGN. Finalmente, realiza-se um estudo teórico sobre a vazão que pode ser atingida nos cenários nomádicos e móveis, além de uma discussão sobre os avanços tecnológicos trazidos pela multiplexação de canal baseada em Orthogonal Frequency Division Multiple Access (OFDMA), empregado em redes IEEE 802.16 móveis. / The demand for providing multimedia services is increasing the development of wireless networks. Therefore, an important issue is to guarantee correct transmissions over channels that are affected by time and frequency variant conditions caused by physical impairments that lead to the occurrence of errors during the transmission. These errors are basically of two types: burst errors and random errors, typically modeled as Additive White Gaussian Noise (AWGN). Simulating the behavior of wireless channels affected by physical impairments has been subject of several investigations in the past years. Nevertheless, part of the current researches does not consider the occurrence of both errors at the same time. This approach may lead to imprecisions on the results obtained through simulations. This work proposea an error sequence generator which is able of generating both burst and AWGN error models. Moreover, the proposed model can generate hybrid errors sequences composed of both error types simultaneously. The proposed error sequence generator is applied to a case study that aims to evaluate the performance of the channel encoder of nomadic (fixed) and mobile IEEE 802.16 networks. In this context, we evaluate the error correction capability of FEC encoders which are mandatory according to IEEE 802.16 standard. Furthermore, we study the impact caused by the application of time diversity techniques on the transmission, considering scenarios affected by burst errors and AWGN. We also present a study about the theoretical throughput that can be reached by nomadic and mobile technologies. Finally, we discuss the technological advances brought by Orthogonal Frequency Division Multiple Access (OFDMA) channel multiplexing technique, which is employed in IEEE 802.16 mobile networks.

In practice, most approaches for text error detection and correction are based on a conventional domain-dependent background dictionary that represents a fixed and static collection of correct words of a given language and, as a result, satisfactory correction can only be achieved if the dictionary covers most tokens of the underlying correct text. Again, most approaches for text correction are for only one or at best a very few types of errors. The purpose of this thesis is to propose an unsupervised approach to detecting and correcting text errors, that can compete with supervised approaches and answer the following questions: Can an unsupervised approach efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature? What is the magnitude of error coverage, in terms of the number of errors that can be corrected? We conclude that (1) it is possible that an unsupervised approach can efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature. Error types include: real-word spelling errors, typographical errors, lexical choice errors, unwanted words, missing words, prepositional errors, article errors, punctuation errors, and many of the grammatical errors (e.g., errors in agreement and verb formation). (2) The magnitude of error coverage, in terms of the number of errors that can be corrected, is almost double of the number of correct words of the text. Although this is not the upper limit, this is what is practically feasible. We use engineering approaches to answer the first question and theoretical approaches to answer and support the second question. We show that finding inherent properties of a correct text using a corpus in the form of an n-gram data set is more appropriate and practical than using other approaches to detecting and correcting errors. Instead of using rule-based approaches and dictionaries, we argue that a corpus can effectively be used to infer the properties of these types of errors, and to detect and correct these errors. We test the robustness of the proposed approach separately for some individual error types, and then for all types of errors. The approach is language-independent, it can be applied to other languages, as long as n-grams are available. The results of this thesis thus suggest that unsupervised approaches, which are often dismissed in favor of supervised ones in the context of many Natural Language Processing (NLP) related tasks, may present an interesting array of NLP-related problem solving strengths.

The largest and most difficult numerical approximation error to estimate is discretization error. Residual-based discretization error estimation methods are a category of error estimators that use an estimate of the source of discretization error and information about the specific application to estimate the discretization error using only one grid level. The higher-order terms are truncated from the discretized equations and are the local source of discretization error. The accuracy of the resulting discretization error estimate depends solely on the accuracy of the estimated truncation error. Residual-based methods require only one grid level compared to the more commonly used Richardson extrapolation which requires at least two. Reducing the required number of grid levels reduces computational expense and, since only one grid level is required, can be applied to unstructured grids where multiple quality grid levels are difficult to produce. The two residual-based discretization error estimators of interest are defect correction and error transport equations. The focus of this work is the development, improvement, and evaluation of various truncation error estimation methods considering the accuracy of the truncation error estimate and the resulting discretization error estimates. The minimum requirements for accurate truncation error estimation is specified along with proper treatment for several boundary conditions. The methods are evaluated using various Euler and Navier-Stokes applications. The discretization error estimates are compared to Richardson extrapolation. The most accurate truncation error estimation method was found to be the k-exact method where the fine grid with a correction factor was considerably reliable. The single grid methods including the k-exact require that the continuous operator be modified at the boundary to be consistent with the implemented boundary conditions. Defect correction showed to be more accurate for areas of larger discretization error; however, the cost was substantial (although cheaper than the primal problem) compared to the cost of solving the ETEs which was essential free due to the linearization. Both methods showed significantly more accurate estimates compared to Richardson extrapolation especially for smooth problems. Reduced accuracy was apparent with the presence of stronger shocks and some possible modifications to adapt to singularies are proposed for future work. / Ph. D.

Computational fluid dynamics (CFD) simulations can provide tremendous insight into complex physical processes and are often faster and more cost-effective to execute than experiments. However, each CFD result inherently contains numerical errors that can significantly degrade the accuracy of a simulation. Discretization error is typically the largest contributor to the overall numerical error in a given simulation. Discretization error can be very difficult to estimate since the generation, transport, and diffusion of these errors is a highly nonlinear function of the computational grid and discretization scheme. As CFD is increasingly used in engineering design and analysis, it is imperative that CFD practitioners be able to accurately quantify discretization errors to minimize risk and improve the performance of engineering systems. In this work, improvements are made to the accuracy and efficiency of existing error estimation techniques. Discretization error is estimated by deriving and solving an error transport equation (ETE) for the local discretization error everywhere in the computational domain. Truncation error is shown to act as the local source for discretization error in numerical solutions. An equivalence between adjoint methods and ETE methods for functional error estimation is presented. This adjoint/ETE equivalence is exploited to efficiently obtain error estimates for multiple output functionals and to extend the higher-order properties of adjoint methods to ETE methods. Higher-order discretization error estimates are obtained when truncation error estimates are sufficiently accurate. Truncation error estimates are demonstrated to deteriorate on grids with a non-smooth variation in grid metrics (e.g., unstructured grids) regardless of how smooth the underlying exact solution may be. The loss of accuracy is shown to stem from noise in the discrete solution on the order of discretization error. When using conventional least-squares reconstruction techniques, this noise is exactly captured and introduces a lower-order error into the truncation error estimate. A novel reconstruction method based on polyharmonic smoothing splines is developed to smoothly reconstruct the discrete solution and improve the accuracy of error estimates. Furthermore, a method for iteratively improving discretization error estimates is devised. Efficiency and robustness considerations are discussed. Results are presented for several inviscid and viscous flow problems. To facilitate the study of discretization error estimation, a new, higher-order finite-volume solver is developed. A detailed description of the code base is provided along with a discussion of best practices for CFD code design. / Ph. D. / Computational fluid dynamics (CFD) is a branch of computational physics at the intersection of fluid mechanics and scientific computing in which the governing equations of fluid motion, such as the Euler and Navier-Stokes equations, are solved numerically on a computer. CFD is utilized in numerous fields including biomedical engineering, meteorology, oceanography, and aerospace engineering. CFD simulations can provide tremendous insight into physical processes and are often preferred over experiments because they can be performed more quickly, are typically more cost-effective, and can provide data in regions where it may be difficult to measure. While CFD can be an extremely powerful tool, CFD simulations are inherently subject to numerical errors. These errors, which are generated when the governing equations of fluid motion are solved on a computer, can have a significant impact on the accuracy of a CFD solution. If numerical errors are not accurately quantified, ill-informed decision-making can lead to poor system performance, increased risk of injury, or even system failure. In this work, research efforts are focused on numerical error estimation for the finite -volume method, arguably the most widely used numerical algorithm for solving CFD problems. The error estimation techniques provided herein target discretization error, the largest contributor to the overall numerical error in a given simulation. Discretization error can be very difficult to estimate since these errors are generated, convected, and diffused by the same physical processes embedded in the governing equations. In this work, improvements are made to the accuracy and efficiency of existing discretization error estimation techniques. Results are presented for several inviscid and viscous flow problems. To facilitate the study of these error estimators, a new, higher-order finite -volume solver is developed. A detailed description of the code base is provided along with a discussion of best practices for CFD code design.

This paper analyses the sources of income measurement error in surveys with a unique dataset.We use the Austrian 2008-2011 waves of EU-SILC which provide individual information on wages,pensions and unemployment benefits from survey interviews and officially linked administrativerecords. Thus, we do not have to fall back on complex two-sample matching procedures likerelated studies. We empirically investigate four sources of measurement error, namely (i) socialdesirability, (ii) socio-demographic characteristics of the respondent, (iii) the survey design, and(iv) the presence of learning effects. We find strong evidence for a social desirability bias inincome reporting, while the presence of learning effects is mixed and depends on the income typeunder consideration. An Owen value decomposition reveals that social desirability is a majorexplanation of misreporting in wages and pensions, whereas socio-demographic characteristicsare most relevant for mismatches in unemployment benefits. / Series: INEQ Working Paper Series