In this paper we consider the application of a naïve Bayes model for the evaluation of fraud risk connected with government agencies. This model applies probabilistic classifiers to support a generic risk assessment model, allowing for more efficient and effective use of resources for fraud detection in government transactions, and assisting audit agencies in transitioning from reactive to proactive fraud detection model.

This paper introduces a new method for the computation of contact forces during the haptic interaction between a rigid probe and a soft virtual object. Tradicional methods used to estimate forces inside a haptic loop are based on a penetration distance of the haptic probe inside the virtual objects. This unnatural approach creates some visual incoherences when simulating the contact with rigid objects, but works fine on the force estimation side. For soft objects however, the use of a penetration distance makes less sense and creates many problems both visually and haptically. We propose a method that considers the penetration of the probe inside the virtual object as being an approximation error, and performs an iterative model adjustment estimating a local elasticity for the deformable object. Forces are computed incrementally. The proposed approach is independent from any particular implementation used for simulating the deformable object. The force estimation is based on the actual shape of the object, considering its deformations, allowing multiple users to interact with a same object while feeling the influence of each other. Experimental results are presented.

This paper presents LEM (long elements method), a new method for physically based simulation of deformable objects, suitable for real time animation and virtual environment interaction. The approach implements a static solution for elastic global deformations of objects filled with fluid based on the Pascal's principle and volume conservation. The volumes are discretised in long elements, defining meshes one order of magnitude smaller than tetrahedral or cubic meshes. The physics of the objects are modeled using bulk variables: pressure, density, volume and stress. No pre-calculations or condensations are needed. The approach is particularly interesting for soft tissue real time simulation and for graphic and haptic rendering.

This paper proposes a method for interfacing a force-feedback device of type PHANToM to a spring-damper model of the human thigh. The model was defined from experimental data and it is simulated using implicit integration. The main difficulty encountered is that while the PHANToM needs to receive the force values at a rate of 1 KHz, the physical model runs at a maximum speed of 100 Hz. Supplying forces at this frequency leads to unrealistic vibration in the force feedback. The novelty of our approach is the use of a local model supplying reliable force values at a high frequency. The purpose of this work is to contribute for the implementation of an echographic simulator with force-feedback.

Presents an approach to implementing haptic interfaces in generic virtual reality systems. Typical implementations of virtual environment simulations with haptic rendering conceive the haptic interface intrinsically linked to the virtual scene update. In this framework, the haptic feedback estimation depends on the methods used to simulate the virtual scene and the haptic feedback rate depends on the scene update rate. This dependence ties the development of the simulation system and strongly limits the complexity of simulated scenes due to the high feedback rate (/spl sim/1 KHz) demanded by a realistic haptic rendering. We propose a new structure to systems having a haptic interface, decoupling the haptic rendering from the virtual environment simulation and introducing a "buffer model" between them. We successfully applied this approach to interface a deformable objects simulator with a Phantom haptic device.

In this paper we propose an original modeling method. Our work can be considered as somewhere between classical modeling methods and learning based methods. We show that in the scope of a particular but quite general functional class it is possible to automatically choose the best equation form to represent a physical process, avoiding the hard work of analytical model definition that the designer should do. Our method uses an experimental protocol where the parameters identification is limited to one entry dimension problems, reducing the amount of data required by the model acquisition. The models generated by our method can be easily differentiated, corrected and reused. The method can be particularly useful in robotics where the functional form the method hands can be easily found in many kinds of problems.

A computational approach to direct and generalized inverse model acquisition is presented. The approach is based on a proposed method to direct model acquisition from partial information. The method decomposes a hyper-space function in one variable functions, simplifying the learning problem. The acquired direct model is then implemented in a tree-like structure that can be used in the inverse sense without additional learning effort. The authors' approach is able to acquire complete models in hyper-spaces requiring only selected data focused in one-dimension sub-spaces, strongly reducing the data acquisition effort. The authors' approach is particularly interesting for applications in robotics. The acquisition of direct models in robotics frequently takes place in high dimension phase spaces. When traditional approximation methods are used, enormous data bases, containing the examples to be interpolated, are required.