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At least 361 records · Page 20Linked to original sources

PubMed1997

Scale selection for second-order (non-linear) stereopsis.

In addition to the conventional luminance spatial frequency-dependent, disparity processing mode, there is a second-order luminance spatial frequency-independent type of processing available to the stereoscopic system. Here we use gaussian-enveloped, amplitude-modulated grating patches to determine how the stereoscopic system responds to the presence of two sources of second-order disparity information at different scales when there is no disparity information available via the conventional luminance-based system. In the first experiment we show that the stereoscopic system uses the disparity signal provided by the stimulus envelope, even though it is at a coarser scale than that provided by the amplitude modulation (AM). We then demonstrate that if the stimulus envelope is degraded via blurring, or if it is fixed at zero disparity, then performance depends on the finer-scale AM disparity signal. To show that the stereoscopic system uses the disparity signal provided by the AM we extend the carrier grating outside the borders of the AM stimulus, thereby making the boundary of the patch less discernible. Results obtained using this stimulus suggest that when two sources of second-order disparity information are present within the same stimulus (i.e., with no reliable luminance-based disparity signal available), the disparity signal provided by the coarser-scale contrast envelope vetos the finer-scale disparity signal. The coarse-scale disparity information dominates as long at it provides an adequate disparity signal. When it is degraded, however, the finer-scale signal takes precedence.

Depth Perception↗

PubMedDate not supplied

Quantification of sensory information transmission using timeseries decorrelation techniques.

To estimate the information transmitted across a neuronal sensory system one has to deal with serial dependence among consecutive samples of the stimulus and the response signal. Common methods usually require a huge amount of data, or are restricted to Gaussian stimuli. Here, we describe stimulus and response as stochastic processes, i.e. as sequences of random variables, in the same coordinate system. Stimulus-response pairs of these random variables must not be considered independently because otherwise the transinformation is overestimated. To account for the linear fraction of the serial dependence, we present two decorrelation techniques based on coordinate transformation. They provide a representation of the processes with uncorrelated random variables and yield a more precise estimate of the transinformation.

Animals↗

PubMed2001

Noise-assisted traffic of spikes through neuronal junctions.

The presence of noise, i.e., random fluctuations, in the nervous system raises at least two different questions. First, is there a constructive role noise can play for signal transmission in a neuron channel? Second, what is the advantage of the power spectra observed for the neuron activity to be shaped like 1/f(k)? To address these questions a simple stochastic model for a junction in neural spike traffic channels is presented. Side channel traffic enters main channel traffic depending on the spike rate of the latter one. The main channel traffic itself is triggered by various noise processes such as Poissonian noise or the zero crossings of Gaussian 1/f(k) noise whereas the variation of the exponent k gives rise to a maximum of the overall traffic efficiency. It is shown that the colored noise is superior to the Poissonian and, in certain cases, to deterministic, periodically ordered traffic. Further, if this periodicity itself is modulated by Gaussian noise with different spectral exponents k, then such modulation can lead to noise-assisted traffic as well. The model presented can also be used to consider car traffic at a junction between a main and a side road and to show how randomness can enhance the traffic efficiency in a network. (c) 2001 American Institute of Physics.

Journal Article↗

PubMed2005

Style consistent classification of isogenous patterns.

In many applications of pattern recognition, patterns appear together in groups (fields) that have a common origin. For example, a printed word is usually a field of character patterns printed in the same font. A common origin induces consistency of style in features measured on patterns. The features of patterns co-occurring in a field are statistically dependent because they share the same, albeit unknown, style. Style constrained classifiers achieve higher classification accuracy by modeling such dependence among patterns in a field. Effects of style consistency on the distributions of field-features (concatenation of pattern features) can be modeled by hierarchical mixtures. Each field derives from a mixture of styles, while, within a field, a pattern derives from a class-style conditional mixture of Gaussians. Based on this model, an optimal style constrained classifier processes entire fields of patterns rendered in a consistent but unknown style. In a laboratory experiment, style constrained classification reduced errors on fields of printed digits by nearly 25 percent over singlet classifiers. Longer fields favor our classification method because they furnish more information about the underlying style.

Algorithms↗

PubMed1998

[Native tissue harmonic imaging].

Native Tissue Harmonic Imaging is a new way of ultrasound image processing that addresses the difficult-to-image patient. By using a Gaussian shaped transmit pulse it is possible to separate from the returning echo the harmonic component without overlapping with fundamental reflections. The improved image quality leads to higher diagnostic confidence so that the use of further imaging modalitites is unnecessary.

Artifacts↗

PubMed2006

[Analyzing the effect of field frequencies on reconstructive accuracy of rebuilt field with spectroscopy].

The field to be reconstructed was simulated with numerical simulation technique. The effect of a three-dimensional field's characteristics, especially, its frequency components, on the reconstructive accuracy was discussed with spectroscopy. A double-peaked field model was built with Gaussian function, and its frequency components were analyzed by Fourier transform. With algebraic reconstruction technique, the model field was reconstructed. The reconstructed field was analyzed with many error indexes. As a result, the reconstructed field was similar to the model one in respect of the position, pointing and shape of the peaks, but the heights of the peaks were shorter than those of corresponding ones in the model. The border of the reconstructive field showed obvious fluctuation. It was considered that the main causation of the reconstructive result was the filtering inserted in iterating process. To confirm this idea, the convergence factor, represented with A, of Gaussian function was changed from six to thirty in order to change the frequency components of the model field, and the same numerical simulation as the one for the first model field with A=18 was carried out for these new model ones consisting of different frequency. As a result, the idea proved to be right. At the same time, it was found that the characteristics of the field at the edge of the reconstructed zone had a deeper effect on the reconstructive accuracy. When the precondition that the field edge approximated to zero could no longer existed, the reconstructive accuracy declined, and furthermore, it would make iterating process divergent. From all the above, the characteristics of the field at the edge should draw much attention when we reconstruct a field with Algebraic reconstruction technique.

English Abstract↗

PubMed2004

Fast converging minimum probability of error neural network receivers for DS-CDMA communications.

We consider a multilayer perceptron neural network (NN) receiver architecture for the recovery of the information bits of a direct-sequence code-division-multiple-access (DS-CDMA) user. We develop a fast converging adaptive training algorithm that minimizes the bit-error rate (BER) at the output of the receiver. The adaptive algorithm has three key features: i) it incorporates the BER, i.e., the ultimate performance evaluation measure, directly into the learning process, ii) it utilizes constraints that are derived from the properties of the optimum single-user decision boundary for additive white Gaussian noise (AWGN) multiple-access channels, and iii) it embeds importance sampling (IS) principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.

Communication↗

PubMed2003

Determination of firing times for the stochastic Fitzhugh-Nagumo neuronal model.

We present for the first time an analytical approach for determining the time of firing of multicomponent nonlinear stochastic neuronal models. We apply the theory of first exit times for Markov processes to the Fitzhugh-Nagumo system with a constant mean gaussian white noise input, representing stochastic excitation and inhibition. Partial differential equations are obtained for the moments of the time to first spike. The observation that the recovery variable barely changes in the prespike trajectory leads to an accurate one-dimensional approximation. For the moments of the time to reach threshold, this leads to ordinary differential equations that may be easily solved. Several analytical approaches are explored that involve perturbation expansions for large and small values of the noise parameter. For ranges of the parameters appropriate for these asymptotic methods, the perturbation solutions are used to establish the validity of the one-dimensional approximation for both small and large values of the noise parameter. Additional verification is obtained with the excellent agreement between the mean and variance of the firing time found by numerical solution of the differential equations for the one-dimensional approximation and those obtained by simulation of the solutions of the model stochastic differential equations. Such agreement extends to intermediate values of the noise parameter. For the mean time to threshold, we find maxima at small noise values that constitute a form of stochastic resonance. We also investigate the dependence of the mean firing time on the initial values of the voltage and recovery variables when the input current has zero mean.

Models, Neurological↗

PubMed2005

A comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response.

Blood oxygenation level-dependent (BOLD) contrast-based functional magnetic resonance imaging (fMRI) has been widely utilized to detect brain neural activities and great efforts are now stressed on the hemodynamic processes of different brain regions activated by a stimulus. The focus of this paper is the comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response. The convolutions are between the perfusion function of the neural response to a stimulus and a Gaussian or Gamma function. The parameters of the two models are estimated by a nonlinear least-squares optimal algorithm for the fMRI data of eight subjects collected in a visual stimulus experiment. The results show that the Gaussian model is better than the Gamma model in fitting the data. The model parameters are different in the left and right occipital regions, which indicate that the dynamic processes seem different in various cerebral functional regions.

Algorithms↗

PubMed2000

Relationship between the crystallization rates of amorphous nifedipine, phenobarbital, and flopropione, and their molecular mobility as measured by their enthalpy relaxation and (1)H NMR relaxation times.

Isothermal crystallization of amorphous nifedipine, phenobarbital, and flopropione was studied at temperatures above and below their glass transition temperatures (T(g)). A sharp decrease in the crystallization rate with decreasing temperature was observed for phenobarbital and flopropione, such that no crystallization was observed at temperatures 20-30 degrees C lower than their T(g) within ordinary experimental time periods. In contrast, the crystallization rate of nifedipine decreased moderately with decreasing temperature, and considerable crystallization was observed at 40 degrees C below its T(g) within 4 months. The molecular mobility of these amorphous drugs was assessed by enthalpy relaxation and (1)H-NMR relaxation measurements. The enthalpy relaxation time of nifedipine was smaller than that of phenobarbital or flopropinone at the same T - T(g) values, suggesting higher molecular mobility of nifedipine. The spin-lattice relaxation time in the rotating frame (T(1rho)) decreased markedly at temperature above T(g). The slope of the Arrhenius type plot of the T(1rho) for nifedipine protons changed at about 10 degrees C below the T(g), whereas the slope for phenobarbital protons became discontinuous at about 10 degrees C above the T(g). Even at temperatures below its T(g), the spin-spin relaxation process of nifedipine could be described by the sum of its Gaussian relaxation, which is characteristic of solid protons, and its Lorentzian relaxation, which is characteristic of protons with higher mobility. In contrast, no Lorentzian relaxation was observed for phenobarbital or flopropione at temperatures below their T(g). These results also suggest that nifedipine has higher molecular mobility than phenobarbital and flopropione at temperatures below T(g). The faster crystallization of nifedipine than that of phenobarbital or flopropione observed at temperatures below its T(g) may be partly ascribed to its higher molecular mobility at these temperatures.

Crystallization↗

PubMed1996

Classification of errors in locating a rigid body.

This paper discusses the manner in which random Gaussian errors affect the determination of body segment kinematics. For the process of modelling rigid body (RB) motion, three types of kinematic errors, input, measured and theoretical, are identified. These correspond to errors in: the determination of three-dimensional observed points, the RB fit of those points, and the estimation of true RB positions, respectively. Of these, the theoretical error is most critical and most pivotal. Accuracy is provided when the theoretical error is minimised, yet only the measured error can be minimised by RB modelling algorithms. In computer simulations one may determine the effect that such manipulations have on theoretical error, yet in most experimental conditions this value may not even be calculated. Fortunately, computer simulations can be performed to determine the inter-relationships between types of RB modelling errors. Such simulations can also be used to investigate the effects of RB shape. In this paper, Monte Carlo simulations were performed on three unit radius RBs; a triangle, a square and a tetrahedron. Although the use of the triangle provided the lowest measured error, this also coincided with the greatest theoretical error. The use of redundant points was found to yield superior theoretical accuracies. A slight advantage was gained with use of the non-planar point arrangement on the tetrahedron, both the measured and theoretical errors were reduced. Finally, the superiority of RB modelling over individual point tracking was reflected in all of the results; between 33 and 50% of the input error was eliminated with the use of RB modelling.

Evaluation Studies as Topic↗

PubMed2004

Visualization of the Barkhausen effect by magnetic force microscopy.

By visualization of the Barkhausen effect using magnetic force microscopy we are able to provide detailed information about the physical principles that govern the magnetization reversal of a granular ferromagnetic thin film with perpendicular anisotropy. Individual Barkhausen volumes are localized and distinguished as either newly nucleated or grown by domain wall propagation. The Gaussian size distribution of nucleated Barkhausen volumes indicates an uncorrelated random process, while grown Barkhausen volumes exhibit an inverse power law distribution, which points towards a critical behavior during domain wall motion.

Journal Article↗

PubMed2002

Parameter estimation in longitudinal studies with outcome-dependent follow-up.

In many observational studies, individuals are measured repeatedly over time, although not necessarily at a set of prespecified occasions. Instead, individuals may be measured at irregular intervals, with those having a history of poorer health outcomes being measured with somewhat greater frequency and regularity; i.e., those individuals with poorer health outcomes may have more frequent follow-up measurements and the intervals between their repeated measurements may be shorter. In this article, we consider estimation of regression parameters in models for longitudinal data where the follow-up times are not fixed by design but can depend on previous outcomes. In particular, we focus on general linear models for longitudinal data where the repeated measures are assumed to have a multivariate Gaussian distribution. We consider assumptions regarding the follow-up time process that result in the likelihood function separating into two components: one for the follow-up time process, the other for the outcome process. The practical implication of this separation is that the former process can be ignored when making likelihood-based inferences about the latter; i.e., maximum likelihood (ML) estimation of the regression parameters relating the mean of the longitudinal outcomes to covariates does not require that a model for the distribution of follow-up times be specified. As a result, standard statistical software, e.g., SAS PROC MIXED (Littell et al., 1996, SAS System for Mixed Models), can be used to analyze the data. However, we also demonstrate that misspecification of the model for the covariance among the repeated measures will, in general, result in regression parameter estimates that are biased. Furthermore, results of a simulation study indicate that the potential bias due to misspecification of the covariance can be quite considerable in this setting. Finally, we illustrate these results using data from a longitudinal observational study (Lipshultz et al., 1995, New England Journal of Medicine 332, 1738-1743) that explored the cardiotoxic effects of doxorubicin chemotherapy for the treatment of acute lymphoblastic leukemia in children.

Adolescent↗

PubMed1999

Measurement of thickness and density of thin structures by computed tomography: a simulation study.

The limited spatial resolution of clinical CT systems causes difficulties in the measurement of the density and thickness of thin structures such as the vertebral cortical shell. We simulated the imaging process by convolving experimentally determined point spread functions with rectangular and Gaussian profiles, for various fields of view or pixel sizes and reconstruction kernels. The simulations successfully explained the reported overestimation of thickness and underestimation of density when imaging thin structures. Both effects are larger for Gaussian profiles. For the rectangular profiles, experimental estimates of thickness and density will only be accurate when the true thickness is greater than about 1.5 times (for the bone reconstruction kernel) or 2.0 times (for the standard kernel) the full width at half maximum of the point spread function (PSF) of the imaging system. For Gaussian profiles imaged by a system with a Gaussian PSF, there are straightforward analytical expressions for the overestimation of thickness and underestimation of density: and these are useful approximations to the simulations of Gaussian profiles with experimental (pseudo-Gaussian) PSFs. We have demonstrated that thresholding of the vertebral image cannot provide accurate estimates of cortical thickness and density because the appropriate threshold level requires foreknowledge of the cortical thickness. To circumvent such difficulties we suggest that the average value of the peak CT numbers measured along the medial axis of the cortical shell be adopted as an index of cortical shell strength, since its value depends on both the density and the thickness of the shell.

Computer Simulation↗

PubMed2006

Self-interferometric technique for visualization of phase patterns encoded onto a liquid-crystal display.

We report a new self-interferometric technique for visualizing phase patterns that are encoded onto a phase-only liquid-crystal display (LCD). In our approach, the LCD generates both the desired object beam as well as the reference beam. Normally the phase patterns are encoded with a phase depth of 2pi radians, and all of the incident energy is diffracted into the first-order beam. However, by reducing this phase depth, we can generate an additional zero-order diffracted beam, which acts as the reference beam. We work at distances such that these two patterns spatially interfere, producing an interference pattern that displays the encoded phase pattern. This approach was used recently to display the phase vortices of helical Ince-Gaussian beams. Here we show additional experimental results and analyze the process.

Journal Article↗

PubMedDate not supplied

Gel filtration chromatographic studies of the isolated membrane domain of band 3.

We have investigated the oligomeric state of the membrane domain of band 3 (MDB3) in non-ionic detergent solution using Sepharose CL-4B gel filtration chromatography to study the hydrodynamic properties of the protein as a function of its concentration. The studies were performed in a C12E9 (polyoxyethylene-9-lauryl ether) buffer containing phosphatidylcholine and sodium chloride, which significantly slow a dilution-induced band 3 conformational change, and an associated aggregation process. Under these conditions native MDB3 eluted predominantly as a single Gaussian peak with a Stokes radius of 76 +/- 14 A, at all protein concentrations studies between 0.2 and 12 microM. This value agrees with the calculated Stokes radius (74 A) determined from the crystal structure of the MDB3 dimer. The Stokes radius of the MDB3 monomer was obtained experimentally by treating native MDB3 with 0.5% SDS, and exchanging the SDS for C12E9 on the Sepharose column. SDS-treated MDB3 showed two peaks whose ratio was strongly dependent on applied protein concentration. The peak representing the largest material had a Stokes radius of 69.7 +/- 14 A, which is essentially the same as the native MDB3 dimer. The peak representing the smaller material had a Stokes radius of 36 +/- 9 A, and was assigned as the MDB3 monomer in C12E9. Evidence is discussed which indicates that the C12E9 monomer specifically self-associates to form a functional MDB3 dimer. We conclude that native MDB3 exists as a stable dimer in mixed micellar solutions composed of C12E9 and phosphatidylcholine, and that the dimer can be dissociated to monomers only by denaturation.

4,4'-Diisothiocyanostilbene-2,2'-Disulfonic Acid↗

PubMed1998

Statistical Inference (part II): The Normal and Related Distributions.

The normal (or gaussian) is the most important probability distribution in the statistical inference process. By transforming the normal distribution to a standard distribution (z-distribution) it is possible to determine the probabilities where observations or values fall in certain intervals for variables with different means and standard deviations. Areas under the normal distribution may be represented in a table where the z is the number of standard deviations away from the mean. For example, the area under the normal distribution delimited by a z value of 1.96 to the left and 1.96 to the right of the mean corresponds to 95% of the total area of the normal distribution. Sampling distribution of estimates of population parameters may also be described by the normal distribution. It is important to note, however, that the estimation of a population mean based on the normal distribution is conditional to the assumption that the population standard deviation is a known parameter. The t distribution is used to infer about a population mean when the population standard deviation is estimated by the sample data. In statistical inference about proportions, the normal approximation of the binomial distribution may be used provided the data fit certain assumptions. Statistical methods that do not depend on the form of the distribution (distribution-free or nonparametric methods) and those based on the actual probability distribution, called exact methods, are often used in situations where the data do not comply with the assumption that the distribution of the estimate is approximately normal.

Journal Article↗

PubMed2000

Variability in centred house-of-cards mutation models.

Convergence of variability in phenotypic models with balance between selection and mutation is analysed. The mutation assumed occurs with weak probability and brings down the evolutionary process built up by selection around the mean in the population. Gaussian approximations are used.

Biological Evolution↗