In this paper, we precisely discuss two necessary methods to manipulate artificially a rheology object by another rigid object via force feedback (haptics) devise. the rheology object is quite popular material in our ...
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In this paper, we precisely discuss two necessary methods to manipulate artificially a rheology object by another rigid object via force feedback (haptics) devise. the rheology object is quite popular material in our living life, e.g., food materials such as several kinds of noodles, human bodies such as organs and muscles, and an article of clothing. For this reason, it is very important for us to manipulate a rheology object by another rigid object. In our previous works, we finish modeling and calibrating MSD rheology model. therefore, in this paper, we define some collision model to make and calibrate a set of external forces based on the swept volume of rheology object by rigid body. Also, we revisit some force transformation method between encountered surfaces of rheology object and rigid body. In this research, we successively regard bothmethods as penalty method and bilinear approximation method
the problem of terrain modeling is basically a type of function approximation problem. this type of problem has been widely studied in the soft computing community. In recent years, neural networks have been successfu...
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the problem of terrain modeling is basically a type of function approximation problem. this type of problem has been widely studied in the soft computing community. In recent years, neural networks have been successfully applied to surface reconstruction and classification problems involving scattered data. However, due to the iterative nature of training a neural network, the resulting high cost in computational time limits the implementation of machine learning based methods in many real world applications (for example, navigation applications in unmanned aerial vehicles) that require fast generation of terrain models. A recently proposed machine learning method, the extreme learning machine (ELM), is able to train single-layer feed forward neural networks with excellent speed and good generalization. In this paper, we present terrain modeling using various machine learning methods, and we compare the performances of these methods with ELM. We also present a comparison of terrain modeling performances between ELM and the popular choice of terrain and surface modeling technique, the Delaunay triangulation with linear interpolation. Our results show that machine learning using ELM offers a potential solution to terrain modeling problems with good performances
We present a method based on the morphological 3D h-maxima transform to segment the kidneys in image volumes obtained by magnetic resonance imaging after injection of a contrast agent. Manual cropping techniques had b...
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We present a method based on the morphological 3D h-maxima transform to segment the kidneys in image volumes obtained by magnetic resonance imaging after injection of a contrast agent. Manual cropping techniques had been heavily relied upon to sever connections to other organs (arteries, liver, spleen, intestines, etc.) when routine methods produced oversegmented results. We show that simply generated rectangular masks and edge information may be incorporated to guide the segmentation. this approach is particularly useful when severe pathology or congenital abnormalities are present as no prior information about location or appearance is assumed. Results and validation from experiments on a number of selected MR datasets are demonstrated
An algorithm to estimate pan, tilt and zoom (PTZ) parameters of a PTZ camera from meta data and frame-to-frame (F2F) correspondences at different sampling rates is proposed in a real-time video surveillance and automa...
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An algorithm to estimate pan, tilt and zoom (PTZ) parameters of a PTZ camera from meta data and frame-to-frame (F2F) correspondences at different sampling rates is proposed in a real-time video surveillance and automatic object tracking system. Two extended Kalman filters are designed to simultaneously estimate zoom and pan-tilt parameters. Uncorrelated constant velocity models are used to model the kinematics of focal length, pan and tilt motions, while the F2F homography is employed to model the relative motion of the camera. Experiment results from both synthetic and realtime system data demonstrate that the F2F correspondence information can enhance the PTZ estimation accuracy as long as its error is smaller than a particular threshold
the autonomous underwater vehicles (AUVs) have been developed over the last three decades for potential uses in scientific, commercial, environmental, and military purposes. the improvement of the computer technology ...
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the autonomous underwater vehicles (AUVs) have been developed over the last three decades for potential uses in scientific, commercial, environmental, and military purposes. the improvement of the computer technology has allowed the expansion of control algorithms into untethered AUVs motions. the paper applies the continuous time model predictive control algorithm designed using Laguerre functions to the dynamic motions of the vehicle. the nonlinear model of the AUVs is established in six degree of freedom and converts into a state space model. the continuous time model predictive control (MPC) algorithm, using Laguerre orthogonal functions, is extended to include intermittent features for manipulation of rudder and stern angle signals. the proposed approach allows the continuous time MPC to tolerate slower computational time and longer sampling interval
Automatic segmentation of the liver has the potential to assist in the diagnosis of disease, preparation for organ transplantation, and possibly assist in treatment planning. this paper presents initial results from w...
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Automatic segmentation of the liver has the potential to assist in the diagnosis of disease, preparation for organ transplantation, and possibly assist in treatment planning. this paper presents initial results from work that extends on previous two-dimensional (2D) segmentation methods by implementing full three-dimensional (3D) liver segmentation, using a self-reparameterising discrete deformable model. this method overcomes many of the weaknesses inherent in 2D segmentation techniques, such as the inability to automatically segment separate lobes of the liver in each image slice, and sensitivity to individual-slice noise. Results are presented showing volumetric and overlap comparison of twelve automatically segmented livers withtheir corresponding manually segmented livers, which were treated as the gold standard for this study
the subjects described in this paper are a realtime adaptive modeling method for deformable objects and an adaptive cutting method for 3D surface meshes. In the literature so far, adaptive models with high resolution ...
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the subjects described in this paper are a realtime adaptive modeling method for deformable objects and an adaptive cutting method for 3D surface meshes. In the literature so far, adaptive models with high resolution representations at regions of high deformation have not been investigated. In this paper we propose a new method for real-time modeling of soft objects, of which high resolutions dynamically adapt to the regions of high deformation. In order to reduce the computational cost in comparison withthe previous methods we use the bisection refinement algorithm. the experimental results show the effectiveness of the proposed method. the cutting operation of 3D surface meshes plays an important role in surgery simulators. One of the important requirements for surgical simulators is the visual reality. We propose a new strategy for cutting on surface mesh: refinement and separate strategy consisting of the refinement followed by the separation of the refined mesh element. Since the advantage of the low computational cost, the bisection refinement method is utilized for the refinement process. the proposed strategy gives the faithful representation of the interaction path
In this paper, application of a modified nonlinear model predictive control (NMPC) method with simplified optimization problem has been studied. the NMPC scheme used here is based on Wiener model which requires only l...
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In this paper, application of a modified nonlinear model predictive control (NMPC) method with simplified optimization problem has been studied. the NMPC scheme used here is based on Wiener model which requires only little more effort in development than a standard linear step-response model, yet offers superior characterization of systems with highly nonlinear gains. In this work, Wiener model has been incorporated into model predictive control scheme in a way that effectively removes the nonlinearity from the control problem. this scheme is based on identification of inverse nonlinear part of the Wiener model. the presented method results in an optimization problem withthe implementation and computation levels of difficulty, that are nearly equivalent to the case where the plant and constraints are linear. A standard quadratic programming (QP) algorithm is enough to solve the optimization problem. PH neutralization, as a highly nonlinear plant, is used in simulations to demonstrate the efficiency of the presented method
the proposed study introduces a total least squares with structure selection (TLSS) algorithm to identify continuous time differential equation models from generalized frequency response function matrix (GFRFM) of mul...
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the proposed study introduces a total least squares with structure selection (TLSS) algorithm to identify continuous time differential equation models from generalized frequency response function matrix (GFRFM) of multiple-input multiple-output (MIMO) nonlinear system. the estimation procedure is progressive where the parameters of each degree of nonlinearity of each subsystem is estimated beginning withthe estimation of linear terms and then adding higher order nonlinear terms. the algorithm combines the advantages of boththe total least squares and orthogonal least squares with structure selection (OLSSS). the error reduction ratio (ERR) feature of OLSSS are exploited to provide an effective way of detecting the correct model structure or which terms to include into the model and the total least squares algorithm provides accurate estimates of the parameters when the data is corrupted with noise. the performance of the algorithm has been compared withthe weighted complex orthogonal estimator and has been shown to be superior
A straight line needle trajectory is typically used in medical needle insertion for percutaneous intervention. Flexible needle steering may be able to avoid obstacles, and reach regions that are currently inaccessible...
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A straight line needle trajectory is typically used in medical needle insertion for percutaneous intervention. Flexible needle steering may be able to avoid obstacles, and reach regions that are currently inaccessible using straight line trajectory. the success of flexible needle insertion in clinical application, for example, biopsy to obtain a tissue sample from human liver organ is dependent on how accurate the motion path can be planned and simulated. We developed a motion planning algorithm for flexible needle insertion with an integrative model of human liver organ, and finite element models of needle and needle-tissue interaction. the aim of image based integrative modeling is to have a unified organ model of patient comprising finite element and implicit surface models of blood vessels, normal and pathological tissues. the trajectory path can be determined in an interactive manner. the generated trajectory for flexible needles avoids obstacles (vessels) to reach target (tumor) inaccessible to rigid needles. the algorithm can also be used to simulate needle bending due to dynamic contact with tumor
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