Real-time method for fitting time-resolved reflectance and transmittance measurements with a monte carlo model

An efficient method is proposed for the evaluation of theabsorption and the transport scattering coefficients from atime-resolved reflectance or transmittance distribution. Theprocedure is based on a library of Monte Carlo simulations and is fastenough to be used in a nonlinear fitting algorithm. Tests performedagainst both Monte Carlo simulations and experimental measurements ontissue phantoms show that the results are significantly better thanthose obtained by fitting the data with the diffusion approximation, especially for low values of the scattering coefficient. The methodrequires an a priori assumption on the value of theanisotropy factor g. Nonetheless, the transportscattering coefficient is rather independent of the exact knowledge ofthe g value within the range 0.7 < g < 0.9.     

Risk reduction in composites processing using prototype data,process simulation,and Bayesian statistics

There is often significant risk and uncertainty associated with the development of manufacturing processes for large integrated composites structures. A good understanding of the outcome of the process is required so that process tooling and other process parameters can be designed appropriately and costly redesign and rework avoided. This paper presents an approach to risk reduction in composites processing using prior knowledge, prototype data, and model results. A Bayesian methodology for combining this information into a probability density function of the outcome of the process that explicitly accounts for reliability of the data sources is developed. The use and effectiveness of the approach is demonstrated with a case study.     

Bayesian analysis of data: reconstruction of track structure at 100% detection efficiency for a track-nanodosimetric counter

An algorithmic reconstruction of the probability distribution of ionisation cluster-size formation from measurements performed with detectors having a non-uniform sensitive volume is presented. From such a data analysis, ionisation spectra, which correspond to a detection efficiency of 100% are extracted for the track-nanodosimetric counter at L.N.L.-I.N.F.N.     

Multiwavelength three-dimensional near-infrared tomography of the breast: initial simulation,phantom, and clinical results

Three-dimensional (3D), multiwavelength near-infrared tomography has the potential to provide new physiological information about biological tissue function and pathological transformation. Fast and reliable measurements of multiwavelength data from multiple planes over a region of interest, together with adequate model-based nonlinear image reconstruction, form the major components of successful estimation of internal optical properties of the region. These images can then be used to examine the concentration of chromophores such as hemoglobin, deoxyhemoglobin, water, and lipids that in turn can serve to identify and characterize abnormalities located deep within the domain. We introduce and discuss a 3D modeling method and image reconstruction algorithm that is currently in place. Reconstructed images of optical properties are presented from simulated data, measured phantoms, and clinical data acquired from a breast cancer patient. It is shown that, with a relatively fast 3D inversion algorithm, useful images of optical absorption and scatter can be calculated with good separation and localization in all cases. It is also shown that, by use of the calculated optical absorption over a range of wavelengths, the oxygen saturation distribution of a tissue under investigation can be deduced from oxygenated and deoxygenated hemoglobin maps. With this method the reconstructed tumor from the breast cancer patient was found to have a higher oxy-deoxy hemoglobin concentration and also a higher oxygen saturation level than the background, indicating a ductal carcinoma that corresponds well to histology findings.     

Optimal sparse solution for fluorescent diffuse optical tomography: theory and phantom experimental results

We present a method to accurately localize small fluorescent objects within the tissue using fluorescent diffuse optical tomography (FDOT). The proposed method exploits the localized or sparse nature of the fluorophores in the tissue as a priori information to considerably improve the accuracy of the reconstruction of fluorophore distribution. This is accomplished by minimizing a cost function that includes the L1 norm of the fluorophore distribution vector. Experimental results for a milk-based phantom using a fiber-based cw FDOT system demonstrate the capability of this method in accurately localizing small fluorescent objects deep in the phantom.     

Phaseless imaging with experimental data: facts and challenges

Two-dimensional target characterization using inverse profiling approaches with total-field phaseless data is discussed. Two different inversion schemes are compared. In the first one, the intensity-only data are exploited in a minimization scheme, thanks to a proper definition of the cost functional. Specific normalization and starting guess are introduced to avoid the need for global optimization methods. In the second scheme [J. Opt. Soc. Am. A21, 622 (2004)], one exploits the field properties and the theoretical results on the inversion of quadratic operators to derive a two-step solution strategy, wherein the (complex) scattered fields embedded in the available data are retrieved first and then a traditional inverse scattering problem is solved. In both cases, the analytical properties of the fields allow one to properly fix the measurement setup and identify the more convenient strategy to adopt. Also, indications on the number and types of sources and receivers to be used are given. Results from experimental data show the efficiency of these approaches and the tools introduced.     

Application of orthogonal sampling to incomplete data interferometric tomography: numerical simulation and experimental analysis

Orthogonal projection sampling mode was proposed to reconstruct the incomplete-data flow field in optical computerized tomography (OCT). With numerical simulation technique, a two-peak plane symmetric flow field was reconstructed in different sampling modes and discussed in simulated results is the reconstructive accuracy with error indexes, such as mean square error (MSE) and peak error (PE). The corresponding experiments were researched with a Fabry-Perot rotary interferometer. The results indicated that the errors were drastically reduced and the precision was improved when orthogonal projection sampling mode was adopted in the reconstruction of the incomplete data field. The MSE obtained with orthogonal sampling mode was decreased 72.81% from that of the sequential projection sampling mode (the difference between the MSE obtained with the orthogonal sampling mode and that with the sequential sampling mode divided by the MSE of the sequential sampling mode) and the PE was decreased by 73.97%. The precision obtained from the experimental results reached 10%, which showed the orthogonal projection sampling could be a practicable sampling mode for the incomplete data field reconstruction in OCT and could provide some guidance for the flow-field measurement and apparatus design in the practical situation.     

Comparison of radiation dose to patient and staff for two interventional cardiology units: a phantom study

The purpose of this investigation was to measure the patient and staff dose during routine interventional cardiology procedures for an image intensifier-based and a flat detector system using a water phantom. The Integris BH3000 image intensifier-based (Philips) and the Axiom Artis flat detector-based (Siemens) angiography units were used in this study. The accuracy of tubes potential and irradiation timers and also internal dosimeters were verified and confirmed. A water phantom with a thickness of 18 cm was used for patient and staff dose measurements. For the Philips system, phantom entrance dose rates were 2.77 and 38.97 microGym(2) s(-1) during fluoroscopy and cineangiography. The respective dose rates for the Siemens were 1.98 and 13.46 microGym(2) s(-1). Phantom entrance dose rate was 28.5 and 65% higher for the Philips system during fluoroscopy and cineangiography, respectively. Comparing the scattered dose rates at the operator location showed that the flat detector-based Siemens system delivers five times lower dose to the operator in comparison with the image intensifier-based Philips unit. The results suggest that the decrease in received dose of the patient and staff is achievable using the flat detector system. In addition, application of lead curtain and glass is recommended to lower the cardiologist dose especially for the image intensifier-based Philips system.     

3-Dimensional rotational X-ray imaging, 3D-RX: image quality and patient dose simulation for optimisation studies

We have developed a full-scale simulation model as a tool for X-ray system design, image quality optimisation and patient dose reduction. The model supports the (de-)composition of system level requirements and takes inherent care of the prerequisite mutual coherence between component requirements. Furthermore, the model is equipped with several types of automatic X-ray control loop simulations. All relevant patient dose and image quality items are calculated, such as contrast, sharpness, lag and noise and finally combined in 'IQ figures of merit'. The short calculation times realised enable comprehensive multi-parameter optimisation studies. Initially, the IQ model dealt with the performance of 'flat' projection X-ray systems referred to as two-dimensional (2D). Nowadays, 2D X-ray systems are increasingly used for three-dimensional rotational X-ray imaging (3D-RX). An additional 3D-RX module, dealing with patient dose as well as image quality, will simplify and accelerate specific 3D-RX optimisations concerning, e.g. exposure factors, dose control concepts and image processing algorithms.     

Image reconstruction: a unifying model for resolution enhancement and data extrapolation. Tutorial

In reconstructing an object function F(r) from finitely many noisy linear-functional values integral of F(r)Gn(r)dr we face the problem that finite data, noisy or not, are insufficient to specify F(r) uniquely. Estimates based on the finite data may succeed in recovering broad features of F(r), but may fail to resolve important detail. Linear and nonlinear, model-based data extrapolation procedures can be used to improve resolution, but at the cost of sensitivity to noise. To estimate linear-functional values of F(r) that have not been measured from those that have been, we need to employ prior information about the object F(r), such as support information or, more generally, estimates of the overall profile of F(r). One way to do this is through minimum-weighted-norm (MWN) estimation, with the prior information used to determine the weights. The MWN approach extends the Gerchberg-Papoulis band-limited extrapolation method and is closely related to matched-filter linear detection, the approximation of the Wiener filter, and to iterative Shannon-entropy-maximization algorithms. Non-linear versions of the MWN method extend the noniterative, Burg, maximum-entropy spectral-estimation procedure.     

Multilevel double loop Monte Carlo and stochastic collocation methods with importance sampling for Bayesian optimal experimental design

An optimal experimental set-up maximizes the value of data for statistical inferences. The efficiency of strategies for finding optimal experimental set-ups is particularly important for experiments that are time-consuming or expensive to perform. In the situation when the experiments are modeled by partial differential equations (PDEs), multilevel methods have been proven to reduce the computational complexity of their single-level counterparts when estimating expected values. For a setting where PDEs can model experiments, we propose two multilevel methods for estimating a popular criterion known as the expected information gain (EIG) in Bayesian optimal experimental design. We propose a multilevel double loop Monte Carlo, which is a multilevel strategy with double loop Monte Carlo, and a multilevel double loop stochastic collocation, which performs a high-dimensional integration on sparse grids. For both methods, the Laplace approximation is used for importance sampling that significantly reduces the computational work of estimating inner expectations. The values of the method parameters are determined by minimizing the computational work, subject to satisfying the desired error tolerance. The efficiencies of the methods are demonstrated by estimating EIG for inference of the fiber orientation in composite laminate materials from an electrical impedance tomography experiment.     

Review and experimental evaluation of models for drivability simulation with focus on tire modeling

Forschung im Ingenieurwesen - Recent research showed a&nbsp;significant role of the interaction between traction and torsional vibrations on control design in passenger cars. However, there is...     

A correlation between experimental and simulation data for the self-diffusion and shear viscosity coefficient of nonpolar liquids along the saturation line

Simple and accurate evaluations for the self-diffusion and shear viscosity coefficients of nonpolar liquids along the saturation line have been obtained from simulation data for the hard-sphere fluid. The obtained deviations are lower than those reported for other correlations.     

Two-dimensional measurement technique for birefringence vector distributions: data processing and experimental verification

A two-dimensional measurement method for a birefringence vector distribution differs from a point-measurement method not only in the two-dimensional system nonuniformity but also in the system reliability. The previously proposed intrinsic vector allowed for the elimination of the influence of the system nonuniformity, whereas the two-dimensional system reliability is ensured by both an online diagnosis technique and an image lock-in processing. It is revealed that the measured intrinsic vector is relevant not only with the birefringence vector distribution in a sample but also with the natural birefringence vector distributions that exist in the optic components. The complete solution from the measured intrinsic vector results in a bidirectional vector for the desired birefringence vector distribution. The correctness of the two-dimensional measurement principle is examined by means of a comparison of the measured data with that calculated from a finite-element analysis based on the photoelastic effect.     

Comparison of finite element solutions with analytical and experimental data for elastic-plastic cracked problems

Non-linear finite element results from a round robin are compared with empirical and experimental data obtained for three types of geometries: compact specimen, three-point bend specimen and a center-cracked panel subject to uniaxial loading. The solution parameters to be compared in various forms are: applied load, clip gauge displacement, RiceJ-integral (with no limit placed on method of calculation),K ₁ crack opening profiles and plastic zone development.

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Résumé Les résultats d'éléments finis non linéaires résultant d'une consultation circulaire sont comparés avec des données empiriques et expérimentales obtenues pour trois types de géométrie: éprouvette compacte, éprouvette de flexion en trois points et panneau à fissure centrale soumis à une charge uniaxiale. Les paramètres de la solution qui doivent être comparés dans les différentes formes sont: la charge appliquée, le déplacement d'un extensiomètre, l'intégraleJ (sans réserve sur sa méthode de calcul), KI, les profils d'ouverture de fissure ainsi que le développement de la zone plastique.

     

Techniques for optimization via simulation: an experimental study on an (s,S) inventory system

Various methods have been proposed to conduct optimization via discrete-event simulation. Prevalent among these are gradient-based algorithms, and more recently, so-called retrospective approaches that deterministically optimize over simulated outcomes. In this paper we compare and contrast the performance of these two approaches for the optimization of a periodic review (s S) inventory system. In practice, gradient-based methods often exhibit undesirably slow convergence, whereas the retrospective approach can be computationally burdensome. We propose a hybrid algorithm that combines aspects of both approaches to mitigate these shortcomings.     

A hybrid transient model for simulation of air-cooled refrigeration systems: Description and experimental validation

In this paper are described a hybrid dynamic model for transient simulation of refrigeration systems as well as dynamic experiments that have been performed on an air/water heap pump. The machine under consideration is made of an evaporator, a condenser, an expansion valve, a variable speed scroll compressor and a receiver. The refrigerant and second fluid flows in heat exchangers are approximated by a cascade of Continuous Stirred Tank Reactors (CSTRs). This model is quite flexible since a unique structure is used for the evaporator and the condenser models according to different boundary conditions. This is due to the use of a switching procedure between different configurations based on a phase stability test that is designed to ensure the continuity of the system simulation. An analytical thermodynamic model of the refrigerant based on an equation of state is used. Good agreement between simulation results and experimental data is achieved.     

Microphysical particle parameters from extinction and backscatter lidar data by inversion with regularization: simulation

A sensitivity study with an inversion scheme that permits one to retrieve physical parameters of tropospheric particle size distributions, e.g., effective radius, volume, surface-area, and number concentrations, as well as the mean complex refractive index from backscatter and extinction coefficients at multiple wavelengths is presented. The optical data for the analysis are derived from Mie-scattering calculations for monomodal and bimodal logarithmic-normal distributions in the particle size range between 0.01 and 10 microm. The complex refractive index is taken between 1.33 and 1.8 in the real part and between 0 and 0.1 in the imaginary part. The choice of these parameters takes account of properties of optically active atmospheric particles. The wavelengths were chosen at 355, 400, 532, 710, 800, and 1064 nm for the backscatter and at 355 and 532 nm for the extinction data, which are the available wavelengths of the two lidar systems at the Institute for Tropospheric Research. Cases of erroneous optical data of the order of as much as 20%, an unknown refractive index, which may also be wavelength and size dependent, as well as the a priori unknown modality of the particle size distribution were considered. It is shown that both extinction channels are necessary for determining the above-mentioned parameters within reasonable limits, i.e., effective radius, surface-area, and volume concentrations to an accuracy of +/-50%, the real part of the complex refractive index to +/-0.1, and the imaginary part to +/-50%. The number concentration may have errors larger than 50%. The overall performance of the inversion scheme permits the evaluation of experimental data on a routine basis.