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Analysis of the feeding behavior of pigs using different models.

Short-term feeding behavior is conventionally analysed using random process models. The assumption underlying these models have recently been questioned and this article describes the application of both random, and more biologically based, models to the feeding behavior of pigs. Feeder visits of 16 growing pigs, housed individually from 17 to 52 kg live weight, were recorded electronically over a continuous period of 35 days. Daily food intake increased linearly with time, but there was considerable individuality in the degree of order. Pigs made between 18.8 and 80.3 (mean 47.9) daily visits to the feeder. Intervals between visits could be described by two log-normal distributions. Two Gaussian density functions were fitted to the distribution of the log-transformed intervals. For the combined data from all animals the within- and between-meal intervals were 11.2 s and 100.1 min, respectively. A model with three Gaussian functions gave an improved fit to the interval distribution. The within and between meal intervals were then estimated to be 4.2 s and 93.9 min, respectively. The middle distribution of intervals ranged from 0.5 to 38.1 min. The intervals were also described by random process models; again, a three-process model gave an improved fit compared to a two-process model. The mean estimated number of meals per day from the three Gaussian model was 14.3, and from the three process random model, 16.3. A biological interpretation of the three types of interval suggests that: (1) pigs eat in meals separated by long intervals; (2) meals consist of clusters of eating bouts separated by shorter intervals, sometimes associated with drinking; (3) within each eating bout short intervals occur as pigs constantly move in and out of the feeder. It remains unclear what underlies the observed patterns of eating.

Animals↗

Adaptive processing techniques based on hidden Markov models for characterizing very small channel currents buried in noise and deterministic interferences.

Techniques for characterizing very small single-channel currents buried in background noise are described and tested on simulated data to give confidence when applied to real data. Single channel currents are represented as a discrete-time, finite-state, homogeneous, Markov process, and the noise that obscures the signal is assumed to be white and Gaussian. The various signal model parameters, such as the Markov state levels and transition probabilities, are unknown. In addition to white Gaussian noise, the signal can be corrupted by deterministic interferences of known form but unknown parameters, such as the sinusoidal disturbance stemming from AC interference and a drift of the base line owing to a slow development of liquid-junction potentials. To characterize the signal buried in such stochastic and deterministic interferences, the problem is first formulated in the framework of a Hidden Markov Model and then the Expectation Maximization algorithm is applied to obtain the maximum likelihood estimates of the model parameters (state levels, transition probabilities), signals, and the parameters of the deterministic disturbances. Using fictitious channel currents embedded in the idealized noise, we first show that the signal processing technique is capable of characterizing the signal characteristics quite accurately even when the amplitude of currents is as small as 5-10 fA. The statistics of the signal estimated from the processing technique include the amplitude, mean open and closed duration, open-time and closed-time histograms, probability of dwell-time and the transition probability matrix. With a periodic interference composed, for example, of 50 Hz and 100 Hz components, or a linear drift of the baseline added to the segment containing channel currents and white noise, the parameters of the deterministic interference, such as the amplitude and phase of the sinusoidal wave, or the rate of linear drift, as well as all the relevant statistics of the signal, are accurately estimated with the algorithm we propose. Also, if the frequencies of the periodic interference are unknown, they can be accurately estimated. Finally, we provide a technique by which channel currents originating from the sum of two or more independent single channels are decomposed so that each process can be separately characterized. This process is also formulated as a Hidden Markov Model problem and solved by applying the Expectation Maximization algorithm. The scheme relies on the fact that the transition matrix of the summed Markov process can be construed as a tensor product of the transition matrices of individual processes.

Algorithms↗

Two-dimensional spectral processing of sequential evoked potentials.

The processing of sequential evoked potentials (EPs) is investigated using two-dimensional processing techniques. Two-dimensional EP arrays or images are formed by stacking sequential recordings. Processing is accomplished in the frequency domain by 2-D low-pass filtering using Gaussian filters. Auditory brainstem responses (ABRs), which are the early auditory EPs, are used to investigate the effects of the 2-D filtering on real data. Gaussian filtering improves signal-to-noise ratios by reducing high frequency noise effectively in both intra-EP and inter-EP dimensions. Applications to intra-operative monitoring are simulated with real ABR data.

Evoked Potentials, Auditory, Brain Stem↗

An evaluation of measurement uncertainties in the on-line measurement of coal ash content by gamma-ray transmission.

In this paper, a significant effect producing systematic errors in the on-line measurement using gamma-ray transmission is revealed. Ash content fluctuations or thickness changes lead to a permanent negative systematic error in the results of the measurements. To study uncertainties in the measurements applicable to time-independent ash content indicators and to investigate the characteristics of the radiation attenuation process, the behavior of the quantity in question in modeled with a stationary Gaussian distribution. A systematic error-producing effect has been found, and a quantitative correction is given to compensate for it. For some other quantities in question that vary in time, a linear model is used to discuss the systematic errors in the case of automated coal gangue separator. Results of experiments that demonstrate different systematic errors for different sampling intervals are presented. The reason for these errors is the nonlinearity of the relationship between the radiation intensity, on the one hand, and the sample thickness and mass attention, on the other.

Journal Article↗

Effects of geometric distortions on face-recognition performance.

The importance of 'configural' processing for face recognition is now well established, but it remains unclear precisely what it entails. Through four experiments we attempted to clarify the nature of configural processing by investigating the effects of various affine transformations on the recognition of familiar faces. Experiment 1 showed that recognition was markedly impaired by inversion of faces, somewhat impaired by shearing or horizontally stretching them, but unaffected by vertical stretching of faces to twice their normal height. In experiment 2 we investigated vertical and horizontal stretching in more detail, and found no effects of either transformation. Two further experiments were performed to determine whether participants were recognising stretched faces by using configural information. Experiment 3 showed that nonglobal vertical stretching of faces (stretching either the top or the bottom half while leaving the remainder undistorted) impaired recognition, implying that configural information from the stretched part of the face was influencing the process of recognition--ie that configural processing involves global facial properties. In experiment 4 we examined the effects of Gaussian blurring on recognition of undistorted and vertically stretched faces. Faces remained recognisable even when they were both stretched and blurred, implying that participants were basing their judgments on configural information from these stimuli, rather than resorting to some strategy based on local featural details. The tolerance of spatial distortions in human face recognition suggests that the configural information used as a basis for face recognition is unlikely to involve information about the absolute position of facial features relative to each other, at least not in any simple way.

Adult↗

Proton loss model for therapeutic beam dose calculations.

A transport algorithm called the proton loss (PL) model is developed for proton pencil beams of therapeutic energies. The PL model takes into account inelastic nuclear reactions, pathlength straggling, and energy-loss straggling and predicts the 3D dose distribution from a proton pencil beam. In proton beams, the multiple scattering and ionizational energy loss processes approach their diffusional limit where scattering and energy loss probability densities become Gaussian. Therefore we chose to derive the PL model from the Fermi-Eyges diffusional multiple scattering theory and the Gaussian theory of energy straggling. We first introduce a generalization of the Fermi-Eyges equation for proton pencil beams, labeled the proton loss (PL) transport equation. This new equation includes terms that model inelastic nuclear reactions as a depth-dependent absorption and pathlength straggling as a quasi-absorption. Then energy straggling is taken into account by using a weighted superposition of a discrete number of elementary pencil beams. These elementary pencil beams have different initial energies and lose energy according to the CSDA, thus they have different ranges of penetration. A final solution for the proton beam transport is obtained as a linear combination of elementary pencil beam solutions with weights defined by the Gaussian evolution of the proton energy spectrum with depth. A numerical comparison of the dose distribution predictions of the PL model with measurements and PTRAN Monte Carlo simulations indicates the model is both computational fast and accurate.

Algorithms↗

Edge extraction by active defocusing.

A novel edge extraction method that employs an active defocusing technique is presented. The method is based on the principle that a Laplacian-of-Gaussian (LOG) operation can be approximated by a Difference-of-Gaussian (DOG) operation. While such an operation is usually done in digital image processing, it can also be very effective conducted in a combination of optical techniques and digital processing. In this edge extraction method, a focused image of an object in a scene is first acquired. The image of the scene is then slightly defocused by changing the focal length of the camera. A real time subtraction operation is applied to subtract the defocused image from the previously acquired image. It produces a residual image that emphasizes abrupt intensity variations. An objective evaluation, called an edge index, is performed on the resulting image. The amount of defocusing is carefully adjusted according to this measurement so that a desired edge image is generated. Boundaries of objects can then be obtained by further enhancement of the edge image. Since this edge detection method is an optical-based process aided by digital processing, it is fast and relatively inexpensive.

Artificial Intelligence↗

Fabrication and properties of spray-dried nanofeatured spherical Ni(OH)2 materials.

Spherical agglomerates of nanostructured beta-phase Ni(OH)2 with the general formula Ni1-xCox(OH)2 (x = 0, 0.1, 0.3) for use as cathode materials were produced by a modified method including coprecipitation of Ni or Ni composite hydroxide and further spray drying of the precipitated and washed slurry. This process leads to the formation of spherical agglomerate particles with a narrow Gaussian-type distribution range. The method permits faster and cheaper production of cathode materials with a higher specific surface area and similar or better capacity and cycle life compared with the materials prepared via conventional technology.

Aerosols↗

Optimum receivers for pattern recognition in the presence of Gaussian noise with unknown statistics.

We develop algorithms to detect a known pattern or a reference signal in the presence of additive, disjoint background, and multiplicative white Gaussian noise with unknown statistics. The presence of three different types of noise processes with unknown statistics presents difficulties in estimating the unknown parameters. The standard methods such as expected-maximization-type algorithms are iterative, and in the framework of hypothesis testing they are time-consuming, because corresponding to each hypothesis one must estimate a set of parameters. Other standard methods such as setting the gradient of the likelihood function with respect to the unknown parameters will lead to a nonlinear system of equations that do not have a closed-form solution and require iterative methods. We develop an approach to overcome these handicaps and derive algorithms to detect a known object. We present new methods to estimate unknown parameters within the framework of hypothesis testing. The methods that we present are direct and provide closed-form estimates of the unknown parameters. Computer simulations are used to show that for the images tested, the receivers that we have designed perform better than existing receivers.

Journal Article↗

[Non-parameter estimation algorithm to determine stellar effective temperature].

The effective temperature of a star is one of the most important parameters, which determine the continuum and spectral lines in the stellar spectrum. A non-parameter estimation algorithm is proposed to estimate the stellar effective temperature in the present paper. Firstly, the spectrum data is processed by principal component analysis(PCA), then, an estimating model based on a Gaussian kernel function is set up using the PCA data and their temperatures. Experiments were carried out to verify the efficiency, and numerical robustness of the algorithm is also tested.

English Abstract↗

Dissociation of normal feature analysis and deficient processing of letter-strings in dyslexic adults.

Neuroimaging studies have revealed that the functional organization of reading differs between developmentally dyslexic and non-impaired individuals. However, it is not clear how early in the reading process the differences between fluent and dyslexic readers start to emerge. We studied cortical activity of ten dyslexic adults using magnetoencephalography (MEG), as they silently read words or viewed symbol-strings which were clearly visible or degraded with Gaussian noise. This method has previously been used to dissociate between analysis of local features and pre-lexical word processing in fluent adult readers. Signals peaking around 100 ms after stimulus onset and originating in the postero-medial extrastriate cortex were associated with increasing local luminance contrast in the noise patches. These early visual responses were similar in dyslexic and non-impaired readers. In contrast, the letter-string-specific responses peaking around 150 ms predominantly in the left inferior occipito-temporal cortex in fluent readers were undetectable in dyslexic readers. Thus, while the early visual processing seems intact in dyslexic adults, the pattern of cortical activation starts to differ from that of fluent readers at the point where letter-string-specific signals first emerge during reading.

Adult↗

Binaural processing of noisy stimuli: internal/external noise ratios for diotic and dichotic stimuli.

A set of ten digitized statistically similar Gaussian maskers was used in one-internal tone-in-noise detection experiments under diotic (NoSo) and dichotic (NoS pi) interaural conditions. Stimulus/response matrices were generated for each masker in the presence or absence of a target 500-Hz tone. For both NoSo and NoS pi, nonparametric analyses show that response probabilities and sensitivities vary significantly across noise waveforms, indicating a considerable external noise component in subject response variability. A parametric model is developed that maps individual stimulus waveforms onto a decision axis, facilitating evaluation of internal/external noise variance ratios. For both NoSo and NoS pi, internal and external noise variance are of similar magnitude.

Acoustic Stimulation↗

Quantification of epithelial area by image processing applied to endometrial carcinomas: a comparison with ovarian tumors.

In endometrial carcinomas, the epithelial area measured by interactive morphometry is an important feature in the classification of tumors of varying histologic grades. This report describes an image analysis technique for the fully automated estimation of the area percentages of epithelium and stroma in tissue sections of the endometrium obtained from hysterectomy specimens. The method is evaluated using endometrial carcinomas with varying degrees of malignancy. From standard paraffin sections stained with pararosanilin Feulgen and naphthol yellow, a blue-yellow image pair was recorded. The blue image was used to determine the total tissue area and the yellow image was used to determine the epithelial area. Image processing of the blue image was comprised of correction for shading, segmentation of the tissue area, and restoration of the segmented image by removing small artefacts and closing small tears in the tissue. Image processing of the yellow image was based on the fact that epithelial nuclei are generally more tightly packed than stromal nuclei and consists of the following steps: correction for shading, gaussian blurring, segmentation of nuclei, and editing the segmented image by removing small objects and closing small spaces between the epithelial nuclei. These image processing steps are compared with those used for quantification of the epithelial area in ovarian tumors. The performance of the method was evaluated using 120 image pairs from 30 endometrial carcinomas of varying histologic grades. The epithelial area percentages, as assessed by digital image processing, strongly correlate to control percentages that were established by interactive morphometry (r = .987).

Cell Nucleus↗

Enhanced specular peaks in diffuse light scattering from weakly rough metal surfaces.

We employ Monte Carlo techniques based on the reduced Rayleigh equations to study an enhanced specular peak that appears in the light scattered from weakly rough metal surfaces. This peak is not associated with the specular reflection but instead appears, with finite angular width, at the specular angle of the mean diffusely scattered intensity. As is the case with backscattering enhancement, the specular peak arises from the interference of contributions of multiple-scattering processes related to surface plasmon polariton excitation. We demonstrate that the specular peak is seen clearly for surface roughness that has a conventional Gaussian power spectrum. Further, we show that the peak appears more distinctly for roughness whose power spectrum has a new rectangular form, which is proposed here with the intent of better isolation of the scattering processes essential to the specular peak. Finally, for a pair of rough surfaces that have appropriately correlated surface roughness, it is found that the cross correlation of scattered amplitudes presents a well-isolated specular peak, which directly demonstrates the constructive interference that produces the effect.

Journal Article↗

Bayesian restoration using a new nonstationary edge-preserving image prior.

In this paper, we propose a class of image restoration algorithms based on the Bayesian approach and a new hierarchical spatially adaptive image prior. The proposed prior has the following two desirable features. First, it models the local image discontinuities in different directions with a model which is continuous valued. Thus, it preserves edges and generalizes the on/off (binary) line process idea used in previous image priors within the context of Markov random fields (MRFs). Second, it is Gaussian in nature and provides estimates that are easy to compute. Using this new hierarchical prior, two restoration algorithms are derived. The first is based on the maximum a posteriori principle and the second on the Bayesian methodology. Numerical experiments are presented that compare the proposed algorithms among themselves and with previous stationary and non stationary MRF-based with line process algorithms. These experiments demonstrate the advantages of the proposed prior.

Algorithms↗

Gaussian factorization of hydrodynamic correlation functions and mode-coupling memory kernels.

A simple method to determine mode-coupling memory functions in generalized Langevin equations is obtained by explicitly expressing the random force of the slow hydrodynamic modes in terms of pair interactions in liquids and by Gaussian factoring the resulting multiple-point time correlation functions into products of linear correlation functions. The approach is used to derive the mode-coupling memory kernels for the velocity autocorrelation function, four-point bilinear density correlation function, and density correlation function of linear molecular liquids. These generalized Langevin equations and their associated memory kernels are useful for calculating relaxation processes and spectroscopic measurements in liquids and solvents. As a central result of our analysis, the non-Gaussian behavior of the bilinear density correlation function is quantitatively related to the nonexponential nature of linear hydrodynamic modes. This relation aids in the understanding of recent simulation results of non-Gaussian indicators in supercooled liquids.

Journal Article↗

Bilateral spatial filtering: refining methods for localizing brain activation in the presence of parenchymal abnormalities.

Functional MRI (fMRI) is an important tool for pre-surgical localization of eloquent cortex prior to resection of brain lesions. To increase the inherently low activation signal to noise ratio, fMRI pre-processing steps often include spatial smoothing. However, the effects of smoothing in the presence of brain lesions have not been studied. We have adapted the widely used method of Gaussian spatial filtering to include an "edge stopping" function. This method, termed bilateral filtering, minimizes blurring of apparent brain activity across anatomic boundaries and into regions of non-activation. fMRI data were acquired in a patient with a known low grade glioma during a blocked finger-tapping paradigm. Simulated activity was superimposed on baseline images of non-activated brain using the same paradigm, with additive signal equal to 1, 3, and 5% of the mean physiologic background. Comparison of Gaussian and bilateral filtering suggests that the modified technique more accurately locates brain activation and increases the significance of activation bordering sharp transitions. Thus, spatial pre-processing with a bilateral filter may be particularly useful in the pre-operative assessment of brain lesions.

Brain↗

A simulation of transit time effects in Doppler ultrasound signals.

A signal model is proposed which can be used to study frequency extraction techniques for Doppler ultrasound. The signal is based on the physics of the Doppler process and depends on a sliding window used to average a set of independent Gaussian random numbers. This window is related to the shape of the sample volume for the Doppler pulse and depends on the Doppler angle. Simulation results compare favorably with results from flow experiments in terms of the variance of the estimated Doppler shift, the shape of the power spectra and the behavior of the signals with respect to Burg autoregressive power spectra. A potential use of the signal in the study of spectral analysis techniques is presented.

Models, Theoretical↗