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The nonlinear response and strong coupling effect of control channel in Deformable Mirrors make it difficult to obtain the desired mirror surface shapes. An efficient nonlinear model of deformation with respect to input voltages is presented using a back propagation neural network (BPNN). The residual relative error of the proposed model shows the improvement of accuracy of an order about 5 as compared...
In this paper, we propose a novel method to introduce spatial information in particle filters. This information may be expressed as spatial relations (orientation, distance, etc.), velocity, scaling, or shape information. Spatial information is modeled in a generic fuzzy-set framework. The fuzzy models are then introduced in the particle filter and automatically define transition and prior spatial...
We introduce in this work a new approach for learning spatial relationships between elements of hand-drawn patterns with the help of fuzzy mathematical morphology operators. Relying on mathematical morphology allows to take into account the actual shapes of hand-drawn patterns when modeling their spatial relationships, and thus to cope with the variability of handwriting signal. Extension of mathematical...
The successful design of non contact, magnetically levitated, massively actuated deformable mirrors and their control system requires the use of medium to high fidelity hybrid multidisciplinary simulation models, encompassing: deformable structures, fluid dynamics of the air film interposed between the mirror and its reference backplane, sensor and actuator dynamics, the control system. Such models...
A coverage problem is one of major issues of a wireless sensor network to prolong the lifetime while guaranteeing that the target region and objects are monitored by sufficient number of active nodes. There have been many proposals on the coverage problem, but most of them use geometric algorithms in order to determine whether to monitor around or sleep. As such, these algorithms require information...
In this paper GM(1, 1) cosine model was put forward for the periodic sequences existing widely. We solved the model and analyzed its properties. At the same time the paper studies GM(1, 1) cosine model's parameter space. GM(1, 1) cosine model not only has the properties of exponential curve, but can reflect the periodicity of the sequences. It is the generalization of GM(1, 1) model. An example showed...
This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. The contribution of the paper is that it deals with events with non-circular footprint. In the overwhelming majority of the papers that deal with event detection and localization, it is assumed that the source has a circular footprint in the sense...
Data provided by THEW was used to test QT gender differences. Three QT/RR models were used during analysis: a transfer function model (TRF), a model based on exponential weighting of RR intervals (EXP), and an EXP model with additive direct coupling with RR intervals (EXPDC). Data from 81 men and 73 women was analyzed. Women have a significantly higher QTc (p<;10-6), steeper GainL (QT/RR slope,...
This paper investigates hybrid computational models as an approach to efficient simulation of electrical activity in cardiac tissue. In the hybrid approach, computational requirements are reduced by modelling only a local region using a detailed model, and embedding this within a simpler model of a larger tissue. We argue that the validity of this approach is dependent upon the method used to couple...
The correct segmentation of textural pattern into different meaningful regions is one of the most important problems in automatic texture image recognition. In this paper, we presented a variational integration of shape prior statistics into a phase-field based segmentation process. By derivating the new phase field functionals with gradient shape policy, we obtain the interface evolution process...
Many object tracking methods based on Adaptive Appearance Models (online learning methods) have been developed in recent years. One problem that can be found with these methods is how to learn variations in object appearance without errors in the image sequence. This paper introduces a novel method, in which a solution to remove learning errors by using an offline learning is proposed; in addition,...
Generic non-rigid face fitting, namely the task of finding the configuration of a shape model describing a face in an image under variations in identity, illumination, pose and expression, is addressed in this work through an ensemble of local patch-based displacement experts. To account for appearance variations, these displacement experts are parameterized bilinearly, allowing the experts to adapt...
In this paper, we propose a two-level integrated model for accurate 3D tracking of rigid head motion and non-rigid facial animation. At the lowest level, the 2D shape of facial features is robustly extracted using a regularized shape model and a cascade multi-stage algorithm. At the highest level, we estimate both the facial animation and 3D pose parameters via minimizing an energy function comprising...
We present an approach to visual tracking based on dividing a target into multiple regions, or fragments. The target is represented by a Gaussian mixture model in a joint feature-spatial space, with each ellipsoid corresponding to a different fragment. The fragments are automatically adapted to the image data, being selected by an efficient region-growing procedure and updated according to a weighted...
We present a system for the estimation of unconstrained 3D human upper body movement from multiple cameras. Its main novelty lies in the integration of three components: single frame pose recovery, temporal integration and model adaptation. Single frame pose recovery consists of a hypothesis generation stage, where candidate 3D poses are generated based on hierarchical shape matching in the individual...
In this paper, we propose a novel predictive model for object boundary, which can integrate information from any sources. The model is a dynamic ldquoobjectrdquo model whose manifestation includes a deformable surface representing shape, a volumetric interior carrying appearance statistics, and an embedded classifier that separates object from background based on current feature information. Unlike...
Simulation of cardiac excitation is often a trade-off between accuracy and speed. A promising minimal, time-efficient cell model with four state variables has recently been presented together with parametrizations for ventricular cell behaviour. In this work, we adapt the model parameters to reproduce atrial excitation properties as given by the Courtemanche model. The action potential shape is considered...
Queueing behavior with long-range dependence input is important to determine the queue resource on the intermediate bottleneck nodes. The end-to-end performance especially depends on the provisioning of queue resources and link bandwidth. The purpose of this research is to explore queueing properties for self-similar traffic using the network simulator. In various ways to generate the self-similar...
Learning opponents’ preferences has a great impact on the success of negotiation, specially, when there is partial information about opponents. This incomplete information can be effectively utilized by intelligent agents equipped with adaptive capacities to learn opponents’ preferences during negotiation. This paper present a neural network based model, named ANUE, to estimate negotiators’ utility...
The importance of the deformation and physics based deformation methods are continuously increasing in computer graphics area. However, the user interaction and reality using them is still insufficient for the various applications such as modeling process and game industry. In this paper, we propose physics based deformation technology under AR (augmented reality) environments to improve the effectiveness...
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