English syllable confusion and imitation in Korean bilingual and monolingual children and adults

In the present study, Korean-English bilingual (KEB) and Korean monolingual (KM) children, between the ages of 8 and 13 years, and KEB adults, ages 18 and older, were examined with one speech perception task, called the Nonsense Syllable Confusion Matrix (NSCM) task (Allen, 2005), and two production tasks, called the Nonsense Syllable Imitation Task (NSIT) and the Nonword Repetition Task (NRT; Dollaghan & Campbell, 1998). The present study examined (a) which English sounds on the NSCM task were identified less well, presumably due to interference from Korean phonology, in bilinguals learning English as a second language (L2) and in monolinguals learning English as a foreign language (FL); (b) which English phonemes on the NSIT were more challenging for bilinguals and monolinguals to produce; (c) whether perception on the NSCM task is related to production on the NSIT, or phonological awareness, as measured by the NRT; and (d) whether perception and production differ in three age-language status groups (i.e., KEB children, KEB adults, and KM children) and in three proficiency subgroups of KEB children (i.e., English-dominant, ED; balanced, BAL; and Korean-dominant, KD). In order to determine English proficiency in each group, language samples were extensively and rigorously analyzed, using software, called Systematic Analysis of Language Transcripts (SALT). Length of samples in complete and intelligible utterances, number of different and total words (NDW and NTW, respectively), speech rate in words per minute (WPM), and number of grammatical errors, mazes, and abandoned utterances were measured and compared among the three initial groups and the three proficiency subgroups. Results of the language sample analysis (LSA) showed significant group differences only between the KEBs and the KM children, but not between the KEB children and adults. Nonetheless, compared to normative means (from a sample length- and age-matched database provided by SALT), the KEB adult group and the KD subgroup produced English at significantly slower speech rates than expected for monolingual, English-speaking counterparts. Two existing models of bilingual speech perception and production—the Speech Learning Model or SLM (Flege, 1987, 1992) and the Perceptual Assimilation Model or PAM (Best, McRoberts, & Sithole, 1988; Best, McRoberts, & Goodell, 2001)—were considered to see if they could account for the perceptual and production patterns evident in the present study. The selected English sounds for stimuli in the NSCM task and the NSIT were 10 consonants, /p, b, k, g, f, θ, s, z, ʧ, ʤ/, and 3 vowels /I, ɛ, æ/, which were used to create 30 nonsense syllables in a consonant-vowel structure. Based on phonetic or phonemic differences between the two languages, English sounds were categorized either as familiar sounds—namely, English sounds that are similar, but not identical, to L1 Korean, including /p, k, s, ʧ, ɛ/—or unfamiliar sounds—namely, English sounds that are new to L1, including /b, g, f, θ, z, ʤ, I, æ/. The results of the NSCM task showed that (a) consonants were perceived correctly more often than vowels, (b) familiar sounds were perceived correctly more often than unfamiliar ones, and (c) familiar consonants were perceived correctly more often than unfamiliar ones across the three age-language status groups and across the three proficiency subgroups; and (d) the KEB children perceived correctly more often than the KEB adults, the KEB children and adults perceived correctly more often than the KM children, and the ED and BAL subgroups perceived correctly more often than the KD subgroup. The results of the NSIT showed (a) consonants were produced more accurately than vowels, and (b) familiar sounds were produced more accurately than unfamiliar ones, across the three age-language status groups. Also, (c) familiar consonants were produced more accurately than unfamiliar ones in the KEB and KM child groups, and (d) unfamiliar vowels were produced more accurately than a familiar one in the KEB child group, but the reverse was true in the KEB adult and KM child groups. The KEB children produced sounds correctly significantly more often than the KM children and the KEB adults, though the percent correct differences were smaller than for perception. Production differences were not found among the three proficiency subgroups. Perception on the NSCM task was compared to production on the NSIT and NRT. Weak positive correlations were found between perception and production (NSIT) for unfamiliar consonants and sounds, whereas a weak negative correlation was found for unfamiliar vowels. Several correlations were significant for perceptual performance on the NSCM task and overall production performance on the NRT: for unfamiliar consonants, unfamiliar vowels, unfamiliar sounds, consonants, vowels, and overall performance on the NSCM task. Nonetheless, no significant correlation was found between production on the NSIT and NRT. Evidently these are two very different production tasks, where immediate imitation of single syllables on the NSIT results in high performance for all groups. Findings of the present study suggest that (a) perception and production of L2 consonants differ from those of vowels; (b) perception and production of L2 sounds involve an interaction of sound type and familiarity; (c) a weak relation exists between perception and production performance for unfamiliar sounds; and (d) L2 experience generally predicts perceptual and production performance. The present study yields several conclusions. The first is that familiarity of sounds is an important influence on L2 learning, as claimed by both SLM and PAM. In the present study, familiar sounds were perceived and produced correctly more often than unfamiliar ones in most cases, in keeping with PAM, though experienced L2 learners (i.e., the KEB children) produced unfamiliar vowels better than familiar ones, in keeping with SLM. Nonetheless, the second conclusion is that neither SLM nor PAM consistently and thoroughly explains the results of the present study. This is because both theories assume that the influence of L1 on the perception of L2 consonants and vowels works in the same way as for production of them. The third and fourth conclusions are two proposed arguments: that perception and production of consonants are different than for vowels, and that sound type interacts with familiarity and L2 experience. These two arguments can best explain the current findings. These findings may help us to develop educational curricula for bilingual individuals listening to and articulating English. Further, the extensive analysis of spontaneous speech in the present study should contribute to the specification of parameters for normal language development and function in Korean-English bilingual children and adults.

Landscape unit discrimination for pedological surveys by orbital spectral response

Landscape unit discrimination for pedological surveys by orbital spectral response. The objective of tins study was compare two soil survey methods. The first was performed by methods traditionally used to distinguish landscape units and soil class discrimination. The second was based on soil class distinction through orbital spectral response. In order to establish soil characteristics and their classification, soil samples were collected at two depths in a grid system, with a distance of 500 meters between points. With these samples, physical and chemical analyses were carried out. In the sampling points, the apparent reflectance of the soil, front the orbital image, was determined and, through cluster analysis landscape units were established. In order to evaluate the resemblance reliability between the landscape units established in each method, the Kappa index was used, the value set for the confusion matrix was 0.43, indicating high quality in the comparison, showing that the non-conventional method was as close as the one carried out by photointerpretation.

Analysis of urban land use and land cover changes: a case of study in Bahir Dar, Ethiopia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies

Identification and characterization of deforestation hot spots in Venezuela using MODIS satellite images

Land cover changes over time as a result of human activity. Nowadays deforestation may be considered one of the main environmental problems. The objective of this study was to identify and characterize changes to forest cover in Venezuela between 2005-2010. Two maps of deforestation hot spots were generated on the basis of MODIS data, one using digital techniques and the other by means of direct visual interpretation by experts. These maps were validated against Landsat ETM+ images. The accuracy of the map obtained digitally was estimated by means of a confusion matrix. The overall accuracy of the maps obtained digitally was 92.5%. Expert opinions regarding the hot spots permitted the causes of deforestation to be identified. The main processes of deforestation were concentrated to the north of the Orinoco River, where 8.63% of the country's forests are located. In this region, some places registered an average annual forest change rate of between 0.72% and 2.95%, above the forest change rate for the country as a whole (0.61%). The main causes of deforestation for the period evaluated were agricultural and livestock activities (47.9%), particularly family subsistence farming and extensive farming which were carried out in 94% of the identified areas.

Aplicación de imágenes radar de satélite a la cartografía de la vegetación en zonas boreales

El presente estudio se enmarca en el proyecto europeo SIBERIA. Trata de explorar el uso de imágenes radar de satélite (ERS y JERS) para la actualización de la cartografía de vegetación de zonas boreales. Se dispone de 8 imágenes de amplitud y coherencia tomadas en 1998, así como de un inventario de vegetación georreferenciado de dos pequeñas zonas. Se proponen tres tipos de clasificaciones supervisadas por el método de máxima verosimilitud. La primera con las imágenes de satélite, la segunda añadiendo algunas imágenes texturales, y la tercera utilizando sólo las imágenes de los componentes principales más significativos. Se siguen los criterios establecidos en el proyecto SIBERIA para la obtención de áreas de entrenamiento. Se propone una doble validación, por una parte vía matrices de confusión a partir de áreas de verdad-terreno obtenidas por el mismo método que las áreas de entrenamiento, y por otra parte contrastando y correlacionando las clasificaciones con los parámetros de inventario disponibles para dos pequeñas áreas de verdad-terreno. Los resultados indican una sensible mejora en la clasificación con la incorporación de imágenes texturales (la precisión aumenta de un 66% a un 75%), y señalan el parámetro biomasa como el mejor correlacionado con las clasificaciones derivadas (coeficiente de correlación r de hasta 0,49). Diferentes fuentes de error permiten augurar un margen de mejora para posteriores estudios.

Comparison measures of maps generated by geostatistical methods

This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps.

Comparison of maps of spatial variability of soil resistance to penetration constructed with and without covariables using a spatial linear model

A study about the spatial variability of data of soil resistance to penetration (RSP) was conducted at layers 0.0-0.1 m, 0.1-0.2 m and 0.2-0.3 m depth, using the statistical methods in univariate forms, i.e., using traditional geostatistics, forming thematic maps by ordinary kriging for each layer of the study. It was analyzed the RSP in layer 0.2-0.3 m depth through a spatial linear model (SLM), which considered the layers 0.0-0.1 m and 0.1-0.2 m in depth as covariable, obtaining an estimation model and a thematic map by universal kriging. The thematic maps of the RSP at layer 0.2-0.3 m depth, constructed by both methods, were compared using measures of accuracy obtained from the construction of the matrix of errors and confusion matrix. There are similarities between the thematic maps. All maps showed that the RSP is higher in the north region.

Quantifying uncertainty in remotely sensed land cover maps

Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.

Bayesian analysis of uncertainty in the GlobCover 2009 land cover product at climate model grid scale

Land cover data derived from satellites are commonly used to prescribe inputs to models of the land surface. Since such data inevitably contains errors, quantifying how uncertainties in the data affect a model’s output is important. To do so, a spatial distribution of possible land cover values is required to propagate through the model’s simulation. However, at large scales, such as those required for climate models, such spatial modelling can be difficult. Also, computer models often require land cover proportions at sites larger than the original map scale as inputs, and it is the uncertainty in these proportions that this article discusses. This paper describes a Monte Carlo sampling scheme that generates realisations of land cover proportions from the posterior distribution as implied by a Bayesian analysis that combines spatial information in the land cover map and its associated confusion matrix. The technique is computationally simple and has been applied previously to the Land Cover Map 2000 for the region of England and Wales. This article demonstrates the ability of the technique to scale up to large (global) satellite derived land cover maps and reports its application to the GlobCover 2009 data product. The results show that, in general, the GlobCover data possesses only small biases, with the largest belonging to non–vegetated surfaces. In vegetated surfaces, the most prominent area of uncertainty is Southern Africa, which represents a complex heterogeneous landscape. It is also clear from this study that greater resources need to be devoted to the construction of comprehensive confusion matrices.

A Nonstaining and Tasteless Hydrophobic Salt of Chlorhexidine

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicações de redes neurais e neuro fuzzy em engenharia biomédica e agronomia

Pós-graduação em Engenharia Elétrica - FEIS

Desempenho perceptivo-auditivo de crianças na identificação de contrastes fonológicos entre as oclusivas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

COMPARISON MEASURES OF MAPS GENERATED BY GEOSTATISTICAL METHODS

This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps.

Comparison measures of maps generated by geostatistical methods

This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.