Information analysis of single photon emission computed tomography with count losses

An analysis is presented of the information transfer from emitter-space to detector-space in single photon emission computed tomography (SPECT) systems. The analysis takes into account the fact that count loss side information is generally not available at the detector. Side information corresponds to the number gamma-rays lost deleted due to lack of interaction with the detector data. It is shown that the information transfer depends on the structure of the likelihood function of the emitter locations associated with the detector data. This likelihood function is the average of a set of ideal-detection likelihood functions, each matched to a particular set of possible deleted gamma-ray paths. A lower bound is derived for the information gain due to incorporating the count loss side information at the detector. This is shown to be significant when the mean emission rate is small or when the gamma-ray deletion probability is strongly dependent on emitter location. Numerical evaluations of the mutual information, with and without side information, associated with information-optimal apertures and uniform parallel-hole collimators are presented.     

A methodology for modeling the distributions of medical images and their stochastic properties

The probabilistic distribution properties of a set of medical images are studied. It is shown that the generalized Gaussian function provides a good approximation to the distribution of AP chest radiographs. Based on this result and a goodness-of-fit test, a generalized Gaussian autoregressive model (GGAR) is proposed. Its properties and limitations are also discussed. It is expected that the GGAR model will be useful in describing the stochastic characteristics of some classes of medical images and in image data compression and other applications.     

Composition of acid tars from production of oils

Translated from Khimiya i Tekhnologiya Topliv i Masel, No. 2, pp. 32–35, February, 1990.     

Creation of progressive technology for production of lubricating oils

Translated from Khimiya i Tekhnologiya Topliv i Masel, No. 5, pp. 17–20, May, 1990.     

Training for an agricultural discrimination task

Skeleton weed, centaurea juncea, is a declared weed in Western Australia because it competes with grain crops for nutrients and moisture. When it is found during harvesting, mechanised teams search and eradicate it. In an earlier report of field trials (Hartley et al, 1989) it was reported that search teams' detection rate was poor and since search teams had usually never seen skeleton weeds, visual discrimination learning was to be expected and observed during searches. The present study investigated the nature of this discrimination learning in a laboratory by developing a training programme of colour photographic slides of weeds in stubble. Subjects receiving specific training with feedback on their performance compared with those receiving pseudo-training showed a significant improvement in detections. Subsequently the benefit of the programme was validated in a field trial.     

An accumulating interpreter for cognitive vision production systems

Knowledge-based recognition and analysis of high dimensional data such as aerial images often has high computational complexity. For most applications time and computational resources such as memory are limited. Therefore approximately correct interpreters with any-time capability are proposed. In this contribution a special software architecture is published, which can handle the administration of complex knowledge-based recognition and analysis in a tractable manner.     

Probability-confidence-kernel-based localized multiple kernel learning with lp norm

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features in terms of their discriminative power for each individual sample. However, models excessively fitting to a specific sample would obstacle the extension to unseen data, while a more general form is often insufficient for diverse locality characterization. Hence, both learning sample-specific local models for each training datum and extending the learned models to unseen test data should be equally addressed in designing LMKL algorithm. In this paper, for an integrative solution, we propose a probability confidence kernel (PCK), which measures per-sample similarity with respect to probabilistic-prediction-based class attribute: The class attribute similarity complements the spatial-similarity-based base kernels for more reasonable locality characterization, and the predefined form of involved class probability density function facilitates the extension to the whole input space and ensures its statistical meaning. Incorporating PCK into support-vectormachine-based LMKL framework, we propose a new PCK-LMKL with arbitrary l(p)-norm constraint implied in the definition of PCKs, where both the parameters in PCK and the final classifier can be efficiently optimized in a joint manner. Evaluations of PCK-LMKL on both benchmark machine learning data sets (ten University of California Irvine (UCI) data sets) and challenging computer vision data sets (15-scene data set and Caltech-101 data set) have shown to achieve state-of-the-art performances.     

Computer simulations of time-dependent suppression of EOF by polymer coatings

We use molecular dynamics simulations in order to investigate the time evolution of the effect of adsorbed polymer coatings on the electro-osmotic flow (EOF) in a capillary. Weakly adsorbed coatings show no time-dependent performance, but they do not strongly reduce the EOF. On the other hand, strongly adsorbed coatings made of longer polymer chains are often quenched in non-equilibrium conformations that can strongly reduce the EOF over extremely long periods of time. For intermediate adsorption strengths, we observe that the EOF increases as a function of time due to the relaxation of the coating layer. The concentration of polymers in solution and the length of the polymer chains also affect the time-dependence of the EOF. These results show that the quality of electrophoretic separations can depend on the waiting time between the formation of the coating and the beginning of the separation. We conclude by suggesting experimental tests of our predictions.