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Measured Signal (measured + signal)
Selected AbstractsGlobal Techniques for Characterizing Phase Transformations , A Tutorial ReviewADVANCED ENGINEERING MATERIALS, Issue 6 2010Michel Perez To characterize phase transformations, it is necessary to get both local and global information. No experimental technique alone is capable of providing these two types of information. Local techniques are very useful to get information on morphology and chemistry but fail to deal with global information like phase fraction and size distribution since the analyzed volume is very limited. This is why, it is important to use, in parallel, global experimental techniques, that investigate the response of the whole sample to a stimulus (electrical, thermal, mechanical,). The aim of this paper is not to give an exhaustive list of all global experimental techniques, but to focus on a few examples of recent studies dealing with the characterization of phase transformations, namely (i) the measurement of the solubility limit of copper in iron, (ii) the tempering of martensite, (iii) the control of the crystallinity degree of a ultra high molecular weight polyethylene and (iii) a precipitation sequence in aluminum alloys. Along these examples, it will be emphasized that any global technique requires a calibration stage and some modeling to connect the measured signal with the investigated information. [source] Improved calculation of the net analyte signal in inverse multivariate calibrationJOURNAL OF CHEMOMETRICS, Issue 6 2001Joan Ferré Abstract The net analyte signal (NAS) is the part of the measured signal that a calibration model relates to the property of interest (e.g. analyte concentration). Accurate values of the NAS are required in multivariate calibration to calculate analytical figures of merit such as sensitivity, selectivity, signal-to-noise ratio and limit of detection. This paper presents an improved version of the calculation method for the NAS in inverse models proposed by Lorber et al. (Anal. Chem. 1997; 69: 1620). Model coefficients and predictions calculated with the improved NAS are the same as those from the common equations of principal component regression (PCR) and partial least squares (PLS) regression. The necessary alterations to the calculations of sensitivity, selectivity and the pseudounivariate presentation of the model are also provided. Copyright © 2001 John Wiley & Sons, Ltd. [source] Three-dimensional balanced steady state free precession imaging of the prostate: Flip angle dependency of the signal based on a two component T2-decay modelJOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 5 2010Tryggve H. Storås MS Abstract Purpose: To investigate the contrast of three-dimensional balanced steady state free precession (3D bSSFP) in the two component T2 model and to apply the results to optimize 3D bSSFP for prostate imaging at 1.5 Tesla. Materials and Methods: In each of seven healthy volunteers, six 3D bSSFP acquisitions were performed with flip angles (,) equally spaced between 10° and 110°. Predictions of signal and contrast were obtained from synthetic bSSFP images calculated from relaxation parameters obtained from a multi-spin-echo acquisition. One biexponential and two monoexponential models were applied. Measured and predicted signals were compared by simple linear regression. Results: The measured contrast to signal ratio increased continuously with ,. Mean R2 for the biexponential model was almost constant for , in the range 50,110°. The biexponential model was a better predictor of the measured signal than the monoexponential model. A monoexponential model restricted to the echoes TE = 50,125 ms performed similar to the biexponential model. The predicted contrast peaked at , between 50° and 90°. Conclusion: Prostate imaging with bSSFP benefited from high flip angles. The biexponential model provided good signal prediction while predictions from the monoexponential models are dependent on the range of TE used for T2 determination. J. Magn. Reson. Imaging 2010;31:1124,1131. © 2010 Wiley-Liss, Inc. [source] Diffusion measurements and diffusion tensor imaging with noisy magnitude dataJOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 1 2009Anders Kristoffersen Abstract Purpose To compare an unbiased method for estimation of the diffusion coefficient to the quick, but biased, log-linear (LL) method in the presence of noisy magnitude data. Materials and Methods The magnitude operation changes the signal distribution in magnetic resonance (MR) images from Gaussian to Rician. If not properly taken into account, this will introduce a bias in the estimated diffusion coefficient. We compare two methods by means of Monte Carlo simulations. The first one applies least-squares fitting of the measured signal to the median (MD) value of the probability density function. The second method is uncorrected LL estimation. We also perform a high-resolution diffusion tensor experiment. Results The uncorrected LL estimator is heavily biased at low signal-to-noise ratios. The bias has a significant effect on image quality. The MD estimator is accurate and produces images with excellent contrast. Conclusion In the presence of noisy magnitude data, unbiased estimation is essential in diffusion measurements and diffusion tensor imaging. J. Magn. Reson. Imaging 2009;29:237,241. © 2008 Wiley-Liss, Inc. [source] Biaxial testing and analysis of bicycle-welded components for the definition of a safety standardFATIGUE & FRACTURE OF ENGINEERING MATERIALS AND STRUCTURES, Issue 6 2003N. PETRONE ABSTRACT This paper presents the experimental evaluation of the fatigue behaviour of welded components under non-proportional variable amplitude biaxial loads. The study was undertaken on welded mountain bike handlebar stems, which were different in terms of geometry and technology and tested with load histories that were reconstructed and accelerated from recorded field data. Loads measured in the field were decomposed into bending and torsional components; a synchronous Peak-Valley counting, a spectrum inflation technique, a spline interpolation and a final amplification were applied to the measured signals in order to obtain test drive signals with the correct content of biaxial non-proportional loadings. After evaluation of the bending and torsion load-life curves of components under constant amplitude fatigue, the resulting data from biaxial variable amplitude fatigue tests were analysed in order to evaluate the damage contribution as a result of the two load components and an equivalent simplified two-stage constant amplitude fatigue test was proposed to the working group ISO/SC1/TC149/WG4. [source] Fetal heart rate monitoring from maternal body surface potentials using independent component analysisANIMAL SCIENCE JOURNAL, Issue 5 2004Wenxi CHEN ABSTRACT The fetal heart rate is indispensable for monitoring the health of unborn cattle fetuses. To monitor the fetal heart rate, a method employing independent component analysis (ICA) to extract the fetal electrocardiogram (fECG) from potentials measured on the maternal body surface and composed of a mixture of the maternal ECG (mECG), fECG, baseline drift and noise is described. A mixing of the raw data was simplified using a linear time-invariant model. To separate the fECG from the mECG, baseline drift, and noise, an ICA strategy was applied, using a hyperbolic tangent as the contrast function and treating mutual information with the minimization principle to find the optimum demixing matrix to derive the fECG from the measured signals. After the feasibility of this method was shown on simulated signals obtained by randomly mixing pure fECG, pure mECG, low frequency sinusoidal drift and noise, real signals from three cloned pregnant Holstein cows with 157, 177 and 224-day gestation periods were used to verify the separation method. The results show that the fECG, mECG, low-frequency sinusoidal drift and noise can be clearly segregated in simulations, and that the fECG, mECG, baseline drift and noise can be successfully derived from real signals. The ICA approach has great potential in effectively detecting the fECG from maternal body surface potentials. 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