Data Fitting and Extraction of Time Constants
Extraction of Diffusion Constants from Data
... The author and colleagues are studying diffusion of materials in solids. They are interested in being able to extract information about the diffusive characteristics from the data. The two characteristics of most ...
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Real-Time Hyperbolae Recognition and Fitting in GPR Data
... Real Time Hyperbolae Recognition and Fitting in GPR Data Qingxu Dou, Lijun Wei, Derek ...and fitting hyperbolae from Ground Penetrating Radar (GPR) images is addressed, and a novel technique ...
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Two time constants for the binding of proteins to DNA from micromechanical data
... i⫽1 i max 共 n i 共 t 兲 ⫺ n 共 t 兲兲 2 . (14) These variances do not in any way represent statistical error bars for our routine, these are so small as to be practically invisible at this scale, but rather, the stochastic ...
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Theory and Methods for Modeling and Fitting Discrete Time Survival Data
... since BIC may over-penalize the dimension and sample size. Also, in Section 3.2, we carried out a detailed analysis of the subset of the SEER breast cancer data. Our study cohort consisted of the female subjects ...
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Fitting and Interpreting Continuous-Time Latent Markov Models for Panel Data
... panel data estimates of disease process functionals differed from fully observed counterparts, based on models assuming the same number of latent states per disease ...panel data estimates of hazard and ...
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Volume Deformation Based on Model-Fitting Surface Extraction
... octree data structure to separate the volume data set for implementing voxel-based masses and parameterized elastic characteristics in the designed mass- spring ...volume data set for GPU-based ...
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Automatic Multi-Model Fitting for Blood Vessel Extraction
... Figure 5.2: Random sampling result on a image data of size 102x96x34. There are many segments around thick vessels, but very few segments around thin vessels. We now discuss our random sampling strategy for the ...
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Clustering Time Series Gene Expression Data Based on Sum-of-Exponentials Fitting
... on fitting a sum-of-exponentials model to the nonuniformly sampled data, for clustering the time series of gene expression ...simulated data, and the superiority of the new selection criterion ...
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ELECTRIC SERVO MOTOR EQUATIONS AND TIME CONSTANTS
... In consulting published motor data, many motor manufacturers specify their motor’s parameter values, including resistance, using 25 0 C as the specified ambient temperature. NEMA, however, recommends 40 0 C as the ...
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Space and time efficient data structures in texture feature extraction
... Real texture is not a completely random process, i.e. the pixels are spatially correlated. In other words, the probability of co-occurrence of two grey levels at a certain interpixel distance and orientation in a given ...
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Time-varying model identification for time-frequency feature extraction from EEG data
... and data-based modelling framework from model identification that can produce an accurate but simple description of the dynamical relationships between different recording regions during brain ...the ...
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AMP: a new time frequency feature extraction method for intermittent time series data
... Because of its excellent performance in the previous section, we choose to use the EMD-AMP variant. 5.2.1 Results The aggregate and normalised IMFs of the MIT data are shown in Figure 6. Interestingly, many of the ...
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AMP: a new time-frequency feature extraction method for intermittent time-series data
... dynamic time warping (DTW) distance between individual time se- ...of time-series data is composed of 100 re- alisations of an almost periodically-driven stochastic process [3] (see Section ...
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Discount Functions for Fitting Individual Data
... Decreasing impatience holds if for all s < t, σ > 0, and gains x ≺ y, (s : x) ∼ (t : y) ⇒ (s + σ : x) 4 (t + σ : y). Increasing impatience holds if the implied preference always is the reverse. Under de- creasing ...
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Sharper Bounds for Regularized Data Fitting
... ) time, where s λ (Y ∗ ) ≤ k is the statistical dimension of Y ∗ , Y ∗ is an optimal Y , ε is an error parameter, and nnz(A) is the number of nonzero entries of ...
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Fitting Data with Different Error Models »
... The numerical computations show that the formulas developed by an ML estimator via symbolic computation to determine the parameters of a straight line to be fitted provide correct results and require considerably less ...
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Fitting high-dimensional Copulae to Data
... Markowitz. Recent developments strongly support the joint non-Gaussianity of asset returns and exploit numerous alternative approaches to model the underlying distribution. The key role of dependency can be best ...
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Data Engineering by the Best 1Convex Data Fitting Method
... Convex Data Fitting Method ...smoothed data, which gives a linear programming calculation that is subsequently solved by this ...The data set that is employed for illustration is the ...
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Behavior Formula Extraction for Object Trajectory using Curve Fitting Method
... the data and look for obvious trends such as linear, quadratic, or higher-order ...the data are ...the data on a semi log and/or log-log ...the data set into groups and considering the ...
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