A SIMPLE GIBBS SAMPLER FOR THE STATE ESTIMATION IN WIRELESS COMMUNICATIONS

In the recent years, wireless communications are extremely useful in many disciplines including health monitoring, environment monitoring, signal processing etc. State estimation and prediction are quite challenging tasks in wireless communications. Traditionally, in the literature, dynamic state-space models have been used for the state estimation and predic- tion purpose. The estimation method is based on Kalman-Filter which is computationally demanding. In this work, we consider computationally simpler Gibbs sampler algorithm for the state estimation. We consider three different cases, (i) continuous state values, (ii) binary (0/1) state values, and (iii) categorical state values with more than two categories. We consider a simple linear model for the prediction purpose, and the underlying regression coefficients are estimated by Gibbs sampler. We compute the misclassification proportions for assessing the practical usefulness of our estimation approach. Areal dataset where 200 wireless sensor nodes are used for measuring the temperature of a chamber is analysed in this work

Modeling habitat suitability of the invasive clam Corbicula fluminea in a Neotropical shallow lagoon, southern Brazil / Modelagem da adequabilidade de habitat do bivalve invasor Corbicula fluminea em uma lagoa rasa Neotropical

Abstract This study aimed to model the habitat suitability for an invasive clam Corbicula fluminea in a coastal shallow lagoon in the southern Neotropical region (–30.22, –50.55). The lagoon (19km2, maximum deep 2.5m) was sampled with an Ekman dredge in an orthogonal matrix comprising 84 points. At each sampling point, were obtained environmental descriptors as depth, organic matter content (OMC), average granulometry (Avgran), and the percentage of sand (Pcsand). Prediction performance of Generalized Linear Models (GLM), Generalized Additive Models (GAM) and Boosted Regression Tree (BRT) were compared. Also, niche overlapping with other native clam species (Castalia martensi, Neocorbicula limosa and Anodontites trapesialis) was examined. A BRT model with 1400 trees was selected as the best model, with cross-validated correlation of 0.82. The relative contributions of predictors were Pcsand-42.6%, OMC-35.8%, Avgran-10.9% and Depth-10.8%. Were identified that C. fluminea occur mainly in sandy sediments with few organic matter, in shallow areas nor by the shore. The PCA showed a wide niche overlap with the native clam species C. martensi, N. limosa and A. trapesialis.

Resumo O objetivo de deste estudo foi modelar a adequabilidade de habitat do bivalve invasor Corbicula fluminea em uma lagoa costeira na região Neotropical (–30.22, –50.55). A lagoa (19km2, 2,5 m de profundidade máxima) foi amostrada com uma draga Ekman em uma matriz ortogonal compreendendo 84 pontos. Em cada ponto de amostragem foram obtidos descritores ambientais como a profundidade, teor de matéria orgânica (OMC), granulometria média (Avgran), e a percentagem de areia (Pcsand). O poder preditivo dos métodos Modelos Lineares Generalizados (GLM), Modelos Aditivos Generalizados (GAM) e Boosted Regression Trees (BRT) foram comparados. Além disso, a sobreposição de nicho com espécies de moluscos nativos (Castalia martensi, Neocorbicula limosa e Anodontites trapesialis) foi examinada. Um modelo BRT com 1.400 árvores foi selecionado como o melhor modelo, com correlação da validação cruzada de 0,82. As contribuições relativas dos preditores foram Pcsand-42,6%, OMC-35,8%, Avgran-10,9% e profundidade-10,8%. Foi demonstrado que C. fluminea está associada a sedimentos arenosos com pouca matéria orgânica, em áreas rasas próximo às margens. A PCA mostrou uma ampla sobreposição de nicho com as espécies de moluscos nativos C. martensi, N. limosa e A. trapesialis.

Modelling the growth of tambaqui, Colossoma macropomum (Cuvier, 1816) in floodplain lakes: model selection and multimodel inference / Modelagem do crescimento do tambaqui (Colossoma macropomum Cuvier, 1816): seleção de modelos e inferência multimodelos

The tambaqui, Colossoma macropomum, is one of the most commercially valuable Amazonian fish species, and in the floodplains of the region, they are caught in both rivers and lakes. Most growth studies on this species to date have adjusted only one growth model, the von Bertalanffy, without considering its possible uncertainties. In this study, four different models (von Bertalanffy, Logistic, Gompertz and the general model of Schnüte-Richards) were adjusted to a data set of fish caught within lakes from the middle Solimões River. These models were adjusted by non-linear equations, using the sample size of each age class as its weight. The adjustment evaluation of each model was based on the Akaike Information Criterion (AIC), the variation of AIC between the models (Δi) and the evidence weights (wi). Both the Logistic (Δi = 0.0) and Gompertz (Δi = 1.12) models were supported by the data, but neither of them was clearly superior (wi, respectively 52.44 and 29.95%). Thus, we propose the use of an averaged-model to estimate the asymptotic length (L∞). The averaged-model, based on Logistic and Gompertz models, resulted in an estimate of L∞=90.36, indicating that the tambaqui would take approximately 25 years to reach average size.

O tambaqui, Colossoma macropomum, é uma das espécies de peixes amazônicos de maior valor comercial, sendo capturado em rios e lagos da planície alagável da região. Até o presente, a maioria dos estudos sobre essa espécie tem ajustado um único modelo de crescimento, o de von Bertalanffy, sem considerer as possíveis incertezas associadas ao uso do modelo. Neste estudo, quatro modelos diferentes (von Bertalanffy, Logístico, Gompertz e o modelo geral de Schnüte-Richards) foram ajustados a um conjunto de dados de peixes capturados no interior de lagos situados no médio Solimões. Esses modelos foram ajustados por equações não lineares e o número de tambaquis em cada classe de tamanho foi usado como peso no ajuste. A avaliação do ajuste de cada modelo foi baseada no Critério de Informação de Akaike (AIC), na diferença do AIC entre os modelos (Δi) e nos pesos de evidência (wi). Tanto o modelo Logístico (Δi = 0,0) como o de Gompertz (Δi = 1,12) foram suportados pelos dados, mas nenhum deles foi claramente superior (wi, respectivamente, de 52,44 e 29,95%). Assim, é proposto o uso de um modelo médio para estimar o comprimento assintótico (L∞). O modelo médio, baseado nos modelos Logístico e de Gompertz, resultou em uma estimativa de L∞ = 90,36 e indicou que o tambaqui levaria aproximadamente 25 anos para atingir esse tamanho.

Effective connectivity between superior temporal gyrus and Heschl’s gyrus during white noise listening: linear versus non-linear models

Purpose: This fMRI study is about modelling the effective connectivity between Heschl’s gyrus (HG) and the superior temporal gyrus (STG) in human primary auditory cortices. Materials & methods: Ten healthy male participants were required to listen to white noise stimuli during functional magnetic resonance imaging (fMRI) scans. Statistical parametric mapping (SPM) was used to generate individual and group brain activation maps. For input region determination, two intrinsic connectivity models comprising bilateral HG and STG were constructed using dynamic causal modelling (DCM). The models were estimated and inferred using DCM while Bayesian Model Selection (BMS) for group studies was used for model comparison and selection. Based on the winning model, six linear and six non-linear causal models were derived and were again estimated, inferred, and compared to obtain a model that best represents the effective connectivity between HG and the STG, balancing accuracy and complexity. Results: Group results indicated significant asymmetrical activation (puncorr < 0.001) in bilateral HG and STG. Model comparison results showed strong evidence of STG as the input centre. The winning model is preferred by 6 out of 10 participants. The results were supported by BMS results for group studies with the expected posterior probability,r = 0.7830 and exceedance probability, φ = 0.9823. One-sample t-tests performed on connection values obtained from the winning model indicated that the valid connections for the winning model are the unidirectional parallel connections from STG to bilateral HG (p < 0.05). Subsequent model comparison between linear and non-linear models using BMS prefers non-linear connection (r = 0.9160, φ = 1.000) from which the connectivity between STG and the ipsi- and contralateral HG is gated by the activity in STG itself. Conclusion: We are able to demonstrate that the effective connectivity between HG and STG while listening to white noise for the respective participants can be explained by a non-linear dynamic causal model with the activity in STG influencing the STG-HG connectivity non-linearly.

Adequação de modelo compartimental para exames de tomografia por emissão de pósitrons / Adequacy of compartmental model for positron emission tomography examinations

O presente trabalho objetiva a seleção do modelo compartimental mais adequado ao estudo da dinâmica fisiológica em exames de tomografia por emissão de pósitrons (PET). Para tanto, propõe-se utilizar o critério de informação de Akaike para a seleção do modelo, e a metodologia de equações de sensibilidade e o algoritmo de Levenberg-Marquardt para a tarefa de estimação de parâmetros característicos e, consequentemente, do erro residual da estimativa. São consideradas três estruturas compartimentais compostas, respectivamente, por dois compartimentos e duas constantes características, três compartimentos e quatro constantes características e quatro compartimentos e seis constantes características. Os dados considerados neste trabalho foram sintetizados preocupando-se em reunir as principais características de um exame de tomografia real, tais como tipo e nível de ruído e morfologia de função de excitação do sistema. Aplicando-se a metodologia proposta em três níveis de ruído (baixo, médio e alto), obteve-se concordância do melhor modelo em graus forte e considerável (com índices de Kappa iguais a 0,95, 0,93 e 0,63, respectivamente). Observou-se que, com elevado nível de ruído e modelos mais complexos (quatro compartimentos), a classificação se deteriora devido ao pequeno número de dados para a decisão. Foram desenvolvidos programas e uma interface gráfica que podem ser utilizados na investigação, elaboração, simulação, estimativa de parâmetros e identificação do modelo compartimental para apoio e análise de diagnósticos clínicos e práticas científicas.

The objective of this work is the determination of the most adequate compartmental model for the study of physiological dynamics based on positron emission tomography (PET) exams. We propose the use of Akaike’s information criterion for the optimal model selection, and Levenberg-Marquardt algorithm with sensitivity equations for the task of estimating the characteristic parameters of the differential equations describing the models. We have considered three compartmental structures represented, respectively, by two compartments and two characteristic constants, three compartments and four characteristic constants and four compartments and six characteristics constants. The data considered in this work were synthesized taking into account key features of a real tomography exam, such as type and level of noise and morphology of the input function of the system. Applying the proposed methodology with three noise levels (low, medium and high), we obtained agreement of the best model with strong and considerable degrees (with Kappa indexes equal to  0.95, 0.93 and  0.63, respectively). It was observed that, with high noise level and more complex models (four compartments), the classification is deteriorated due to lack of data for the decision. Programs have been developed and a graphical interface that can be used in research, development, simulation and parameter identification of compartmental models, supporting analysis of clinical diagnostics and scientific practices.