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.     

Experimental evidence for mode shape influence on acceleration-induced frequency shifts in quartz resonators

Experimental results are presented to support the hypothesis that the shape and location of the active region of vibration in a thickness shear mode quartz resonator are the dominant factors in determining the acceleration sensitivity of the resonator. The shape and location of the mode in a real world resonator vary sufficiently from unit to unit (due to material and processing variations) that all other considerations are overwhelmed. It is shown experimentally that the mode shape and/or location can be trimmed with energy trapping by judicious addition or subtraction of mass to produce resonators with improved acceleration sensitivity.     

Tensile properties and plastic deformation modes ofβ Zr-Nb alloys

Tensile properties and plastic deformation modes of zirconium-niobium alloys were investigated at 290 and 77 K in the wide composition range from metastable to stable phase. Three types of plastic deformation modes, {332}113 twinning, {112}111 twinning and slip, were observed depending on alloy composition and temperature. {332}113 twinning, which occurs in metastable zirconium alloys, is related to the stability of phase to decomposition and leads to low yield stress and large elongation. On the other hand, {112}111 twinning, which appears in stable zirconium alloys, results from high critical stress for slip due to solution hardening and high Peierls stress and does not affect tensile properties significantly. The results obtained for zirconium-alloys are similar to those for titanium alloys, strongly suggesting that {332}113 twinning is an important plastic deformation mode which is common to phase alloys containing athermal phase.     

Effects of temperature and grain size on deformation and fracture in recrystallized Ni3Al doped with boron

The effects of temperature and grain size on the deformation and fracture behaviour of recrystallized Ni₃Al doped with boron were investigated by tensile tests at temperatures up to 973 K as a function of grain sizes from 1.6 to 105m. The yield stress showed a positive temperature dependence to a peak temperature in somewhat different manners depending on the grain size. For coarse-grained specimens, a rapid drop in elongation was observed with increasing temperature. The predominant fracture mode changed with temperature from the transgranular fracture of {1 1 1} cracking to brittle intergranular fracture. This embrittlement at elevated temperatures was considered to occur by a high stress concentration at grain boundaries arising from increased flow stress level and the occurrence of grain boundary sliding (GBS). In contrast, the elongation was not so markedly decreased with temperature for intermediate- and fine-grained specimens which exhibited ductile intergranular fracture and cavitation fracture, respectively, at elevated temperatures, and a slant-type fracture and cup-cone fracture, respectively, at low temperatures. The suppression of serious high-temperature embrittlement for intermediate-grained specimens was explained in terms of the slow propagation of a crack formed by GBS, owing to stress relaxation by dynamic recrystallization (DR) and plastic deformation. In the case of ultra-fine-grained specimens a large elongation was developed at elevated temperatures, which was interpreted as that the further occurrence of DR with increasing volume fraction of grain boundaries reduces the cavitation promoted by GBS, and that the limited sliding length due to extremely small grain diameter raises the stress for cavity formation.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters

Neural Computing and Applications - Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient...     

Automatic recognition fingerspelling gestures in multiple languages for a communication interface for the disabled

This paper presents the design and evaluation of a multi-lingual fingerspelling recognition module that is designed for an information terminal. Through the use of multimodal input and output methods, the information terminal acts as a communication medium between deaf and blind people. The system converts fingerspelled words to speech and vice versa using fingerspelling recognition, fingerspelling synthesis, speech recognition and speech synthesis in Czech, Russian, and Turkish languages. We describe an adaptive skin color based fingersign recognition system with a close to real-time performance and present recognition results on 88 different letters signed by five different signers, using above four hours of training and test videos.     

Mathematical models of the battle between parties for electorate or between companies for markets

A model of optimization of strategies for any number of political parties (companies) in election (advertising) campaigns is proposed. We formulated the problem of choosing an optimal strategy for allocating scarce resources among regions (markets) in which political parties (companies) have different influences. The optimization model is analyzed and the existence of a unique solution is proved for different functions of dependencies between investments and winning. An analytical solution to the model is found for the case of two parties (companies). A direction for further development is the study of the sensitivity of an optimal solution to model parameters and formation of a stochastic model.     

Resampling methods for meta-model validation with recommendations for evolutionary computation

Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features. A survey of meta-modeling techniques within evolutionary optimization is provided. In addition, practical examples illustrating some of the pitfalls associated with model selection and performance assessment are presented. Finally, recommendations are given for choosing a model validation technique for a particular setting.