Recursive bayesian identification of nonlinear autonomous systems

This paper concerns the recursive identification of nonlinear discrete-time systems for which the original equations of motion are not known. Since the true model structure is not available, we replace it with a generic nonlinear model. This generic model discretizes the state space into a finite grid and associates a set of velocity vectors to the nodes of the grid. The velocity vectors are then interpolated to define a vector field on the complete state space. The proposed method follows a Bayesian framework where the identified velocity vectors are selected by the maximum a posteriori (MAP) criterion. The resulting algorithms allow a recursive update of the velocity vectors as new data is obtained. Simulation examples using the recursive algorithm are presented

Alignment of velocity fields for video surveillance

Velocity fields play an important role in surveillance since they describe typical motion behaviors of video objects (e.g., pedestrians) in the scene. This paper presents an algorithm for the alignment of velocity fields acquired by different cameras, at different time intervals, from different viewpoints. Velocity fields are aligned using a warping function which maps corresponding points and vectors in both fields. The warping parameters are estimated by minimizing a non-linear least squares energy. Experimental tests show that the proposed model is able to compensate significant misalignments, including translation, rotation and scaling

Offline Bayesian Identification of Jump Markov Nonlinear Systems

This paper presents a framework for the offline identification of nonlinear switched systems with unknown model structure. Given a set of sampled trajectories, and under the assumption that they were generated by switching among a number of models, we estimate a set of vector fields and a stochastic switching mechanism that best describes the observed data. The switching mechanism is described by a position dependent hidden Markov model that provides the probabilities of the next active model given the current active model and the state vector. The vector fields and the stochastic matrix is obtained by interpolating a set of nodes distributed over a relevant region in the state space. The work follows a Bayesian formulation where the EM-algorithm is used for optimization

An Overview of Sustainability Assessment Frameworks in Agriculture

Recent research established a link between environmental alterations due to agriculture intensification, social damage and the loss of economic growth. Thus, the integration of environmental and social dimensions is key for economic development. In recent years, several frameworks have been proposed to assess the overall sustainability of farms. Nevertheless, the myriad of existing frameworks and the variety of indicators result in difficulties in selecting the most appropriate framework for study site application. This manuscript aims to: (i) understand the criteria to select appropriate frameworks and summarize the range of those being used to assess sustainability; (ii) identify the available frameworks to assess agricultural sustainability; and (iii) analyze the strengths, weaknesses and applicability of each framework. Six frameworks, namely SAFA, RISE, MASC, LADA, SMART and public goods (PG), were identified. Results show that SMART is the framework that considers, in a balanced way, the environmental, sociocultural and economic dimensions of sustainability, whereas others focused on the environmental (RISE), environmental and economic (PG) and sociocultural (SAFA) dimension. However, depending on the scale assessment, sector of application and the sustainability completeness intended, all frameworks are suitable for the assessment. We present a decision tree to help future users understand the best option for their objective

Efficient Optimization Algorithm for Space-Variant Mixture of Vector Fields

This paper presents a new algorithm for trajectory classifi- cation of human activities. The presented framework uses a mixture of parametric space-variant vector fields to describe pedestrian’s trajecto- ries. An advantage of the proposed method is that the vector fields are not constant and depend on the pedestrian’s localization. This means that the switching motion among vector fields may occur at any image location and should be accurately estimated. In this paper, the model is equipped with a novel methodology to estimate the switching probabilities among motion regimes. More specifically, we propose an iterative optimization of switching probabilities based on the natural gradient vector, with respect to the Fisher information metric. This approach follows an information geometric framework and contrasts with more traditional approaches of constrained optimization in which euclidean gradient based methods are used combined with probability simplex constraints. We testify the per- formance superiority of the proposed approach in the classification of pedestrian’s trajectories in synthetic and real data sets concerning farfield surveillance scenarios

Neutron detection in the SNO+ water phase

SNO+ is a multipurpose neutrino experiment located approximately 2 km underground in SNOLAB, Sudbury, Canada. The detector started taking physics data in May 2017 and is currently completing its first phase, as a pure water Cherenkov detector. The low trigger threshold of the SNO+ detector allows for a substantial neutron detection efficiency, as observed with a deployed ^{241}Am^{9}Be source. Using a statistical analysis of one hour AmBe calibration data, we report a neutron capture constant of 208.2 + 2.1(stat.) us and a lower bound of the neutron detection efficiency of 46% at the center of the detector.Peer Reviewe

Secundary Diabetes Mellitus, the tip of the iceberg: a report of two clinical cases

Descrevemos dois casos clínicos raros de Diabetes Mellitus (DM) secundária, cuja avaliação etiológica revelou um macroadenoma hipofisário secretor de GHgh e um tumor secretor de cortisol, localizado unilateralmente na cortical da suprarrenal. Ambos os casos têm a particularidade de terem sido internados pelo Serviço de Urgência com quadros agudos associados à diabetes, antes de serem conhecidas as patologias endócrinas subjacentes. O primeiro (Acromegalia) foi internado por hiperglicemia grave e hiperosmolalidade. O segundo foi internado por hipoglicemia iatrogénica e tratava-se de um carcinoma, forma rara de manifestação da Síndroma de Cushing. Tecemos algumas considerações teóricas sobre a fisiopatologia da hiperglicémia secundária a estas doenças. Referimos alguns conhecimentos actuais sobre a epidemiologia, o diagnóstico, o tratamento e o prognóstico de ambas as endocrinopatias