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

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

Polymerization of ethylene using metallocene and aluminoxane systems

This paper describes ethylene polymerization using a number of metal-locene and aluminoxane catalyst systems, Cp2MR2 and methylaluminoxane [M = Zr, W, Nb; R = Cl, CH3]. Two types of methylaluminoxane, MAO (1) and MAO (2), were used as cocatalysts. The polymerization activities of the complexes Cp2WCl2 and Cp2NbCl2 were compared with that of Cp2ZrCl2. The Nb and W complexes were found to be less active than the Zr complex. Polyethylene characterization was also carried out by the following methods: gel permeation chromatography (GPC), differential scanning calorimetry (DSC) and nuclear magnetic resonance (NMR).info:eu-repo/semantics/publishedVersio

Inteligência Artificial em Radiologia: Do Processamento de Imagem ao Diagnóstico

The objective of this article is to present a view on the potential impact of Artificial Intelligence (AI) on processing medical images, in particular in relation to diagnostic. This topic is currently attracting major attention in both the medical and engineering communities, as demonstrated by the number of recent tutorials [1-3] and review articles [4-6] that address it, with large research hospitals, as well as engineering research centers contributing to the area. Furthermore, several large companies like General Electric (GE), IBM/Merge, Siemens, Philips or Agfa, as well as more specialized companies and startups are integrating AI into their medical imaging products. The evolution of GE in this respect is interesting. GE SmartSignal software was developed for industrial applications to identify impending equipment failures well before they happen. As written in the GE prospectus, with this added lead time, one can transform from reactive maintenance to a more proactive maintenance process, allowing the workforce to focus on fixing problems rather than looking for them. With this background experience from the industrial field, GE developed predictive analytics products for clinical imaging, that embodied the Predictive component of P4 medicine (predictive, personalized, preventive, participatory). Another interesting example is the Illumeo software from Philips that embeds adaptive intelligence, i. e. the capacity to improve its automatic reasoning process from its past experience, to automatically pop out related prior exams for radiology in face of a concrete situation. Actually, with its capacity to tackle massive amounts of data of different sorts (imaging data, patient exam reports, pathology reports, patient monitoring signals, data from implantable electrophysiology devices, and data from many other sources) AI is certainly able to yield a decisive contribution to all the components of P4 medicine. For instance, in the presence of a rare disease, AI methods have the capacity to review huge amounts of prior information when confronted to the patient clinical data

Sistema para pesquisa de imagens com retroacção de relevância

Recentemente, a retroacção de relevância tem sido utilizada para melhorar o desempenho dos sistemas de pesquisa em base de dados de imagens. Este artigo apresenta um método de Retroacção de Relevância baseado no classificador de Mínimos Quadrados Regularizado e numa técnica de selecção de imagens que permite aumentar a capacidade de aprendizagem do método. São apresentados resultados de testes experimentais.info:eu-repo/semantics/publishedVersio

Motor and cognitive deficits in the heterozygous leaner mouse, a Cav2.1 voltage-gated Ca2+ channel mutant

Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neurobiolaging.The leaner mutation in mice affects the Ca(v)2.1 voltage-gated calcium channel alpha(1A)-subunit gene (Cacna1a), causing a reduction in calcium currents predominantly in Purkinje cells. This reduction in calcium currents causes severe progressive cerebellar ataxia, beginning around postnatal day 10, in homozygous leaner mice (tg(la)/tg(la)), while their heterozygous littermates (tg(la)/+) present no obvious behavioral deficits. In humans, heterozygous mutations in the Cacna1a orthologous gene produce a broad range of neurological manifestations. To evaluate the phenotypic status of the tg(la)/+ animals, we assessed motor performance and cognition, at different ages, in these mutant mice. We were able to observe age-dependent impairment in motor and cognitive tasks; balance and motor learning deficits were found in demanding tasks on the rotarod and on the hanging wire test, while spatial learning and memory impairment was observed in the Morris water maze. Progressive dysfunction in escape reflexes, indicative of neurological impairment, was also present in tg(la)/+ animals. Although not presenting major motor alterations, tg(la)/+ mice show age-dependent motor and cognitive deficits.We would like to thank Carolina Lemos for her help with statistical analysis and Victor Mendes for image technical assistance. This work was supported by research grants POCTI/MGI/34517/00, POCTI/NSE/45352/2002 and POCI/SAU-MMO/56387/2004, FCT (Fundação para a Ciência e Tecnologia) and co-funded by FEDER. I.A. is recipient of a scholarship from FCT, Portugal