Ventricle Boundary in CT: Partial Volume Effect and Local Thresholds

We present a mathematical frame to carry out segmentation of cerebrospinal fluid (CSF) of ventricular region in computed tomography (CT) images in the presence of partial volume effect (PVE). First, the image histogram is fitted using the Gaussian mixture model (GMM). Analyzing the GMM, we find global threshold based on parameters of distributions for CSF, and for the combined white and grey matter (WGM). The parameters of distribution of PVE pixels on the boundary of ventricles are estimated by using a convolution operator. These parameters are used to calculate local thresholds for boundary pixels by the analysis of contribution of the neighbor pixels intensities into a PVE pixel. The method works even in the case of an almost unimodal histogram; it can be useful to analyze the parameters of PVE in the ground truth provided by the expert

Development of Neural Convolutional Networks in the World and Child Features

This article discusses the main directions of development of machine learning and neural networks of intelligence, the rules of teaching children of preschool and school age when working with computers and the features of the graphical application interface for children

Методика исследования пространственного распределения параметров среды и продуктов горения в жилом помещении и смежных с ним пространствах

This article is dedicated to the issue of efficiency increase of fire detection equipment in living accommodation and adjacent spaces. This research is intended to develop the testing methodology for spatial pattern of combustion products, including toxic gases with asphyxiant and irritant effects, in both height and area of rooms in a standard apartment building, as well as correlation identification between controlled by fire alarm parameters of environment in living rooms and dangerous fire factors.Proposed methodology includes the concentration measurement of the basic gaseous combustion products (asphyxiant and irritant), generated in the process of materials burning in living accommodation. Justification of measurement facilities location in the fire scene and adjacent spaces is provided, considering the most likely position of a person during the evacuation and leisure time. The impact of fire stage on the flow rate of optical radiation diffused by smoke is shown.The findings will enable to develop the testing methodology for fire detectors designed to protect living accommodations and people located in them, formulate the functioning effectiveness criteria (operation algorithms) for detectors, used in living accommodations.Статья посвящена проблеме повышения эффективности технических средств обнаружения пожара в жилых помещениях и смежных с ними пространствах. Целью настоящей работы являлась разработка методики исследования пространственного распределения продуктов горения, включая токсичные продукты удушающего и раздражающего действия как по высоте помещений стандартной квартиры в жилом доме, так и по ее площади, а также определение зависимости между контролируемыми пожарной сигнализацией параметрами окружающей среды в жилых помещениях и опасными факторами пожара.Предложенная методика предусматривает измерение концентрации основных газообразных продуктов сгорания (удушающих и раздражающих), образующихся при горении материалов характерных для жилых помещений. Дано обоснование мест расположения средств измерения в помещении, где возник пожар, и в смежных с ним пространствах, учитывающие наиболее вероятное положение человека как во время эвакуации, так и во время отдыха. Показано влияние стадии пожара на величину потока оптического излучения, рассеянного дымом.Полученные результаты позволят разработать методику испытаний пожарных извещателей, предназначенных для защиты жилых помещений и находящихся в них людей, сформулировать критерии эффективности функционирования (алгоритмы работы) извещателей, используемых для защиты жилых помещений

The Impact of a Number of Samples on Unsupervised Feature Extraction, Based on Deep Learning for Detection Defects in Printed Circuit Boards

Deep learning provides new ways for defect detection in automatic optical inspections (AOI). However, the existing deep learning methods require thousands of images of defects to be used for training the algorithms. It limits the usability of these approaches in manufacturing, due to lack of images of defects before the actual manufacturing starts. In contrast, we propose to train a defect detection unsupervised deep learning model, using a much smaller number of images without defects. We propose an unsupervised deep learning model, based on transfer learning, that extracts typical semantic patterns from defect-free samples (one-class training). The model is built upon a pre-trained VGG16 model. It is further trained on custom datasets with different sizes of possible defects (printed circuit boards and soldered joints) using only small number of normal samples. We have found that the defect detection can be performed very well on a smooth background; however, in cases where the defect manifests as a change of texture, the detection can be less accurate. The proposed study uses deep learning self-supervised approach to identify if the sample under analysis contains any deviations (with types not defined in advance) from normal design. The method would improve the robustness of the AOI process to detect defects

Castles and transportation networks in Podvine, Belarus, during the Livonian War (1558-1583)

The study of warfare in Belarusian Podvine (Polack and Viciebsk Voivodeships along with the Braslaŭ district) throughout the Livonian Wars has garnered consider able scholarly attention. However, several pivotal aspects have not been explicated, including the objectives behind various war campaigns and the efficacy of strate gies employed by the adversaries – the Tsardom of Muscovy and the Grand Duchy of Lithuania (Polish-Lithuanian Commonwealth from 1569). A thorough analysis of military logistics, with its focal points being transportation routes and fortifications, is essential for a comprehensive insight into these issues. The assessment of military potential associated with Lithuanian and Muscovite castles is most effectively executed through an in-depth analysis of their arsenals and garrisons. Evidently, the supply of weaponry indicated that Vilnius anticipated an intense struggle for control over the Daugava River, which was significant for dominance in Livonia. The Polack campaign led by Ivan the Terrible during the winter of 1562/1563 should be evaluated as a rational, yet daring manoeuvre. This endeavour instigated the construction of a robust network of fortresses in the occupied territories, the upkeep of which proved very expensive, and the lack of waterways further exacerbated the problem. Conversely, the Lithuanians adeptly devised a defensive framework on the left bank of the Daugava River, and successfully thwarted efforts to block Viciebsk – a city that maintained control over river transportation. The skilful utilisation of roads and watercourses within Podvine emerged as a pivotal factor contributing to the triumphs of Stephen Batory’s military campaigns from 1579 to 1582. Keywords: Livonian wars, military logistics, defensive infrastructure, fortifications, Grand Duchy of Lithuania, Polish-Lithuanian Commonwealth, Tsardom of Muscovy

Experimental Test Procedure of the Spatial Distribution of Environmental Parameters and Products of Combustion in a Residential Area and Adjacent Spaces

This article is dedicated to the issue of efficiency increase of fire detection equipment in living accommodation and adjacent spaces. This research is intended to develop the testing methodology for spatial pattern of combustion products, including toxic gases with asphyxiant and irritant effects, in both height and area of rooms in a standard apartment building, as well as correlation identification between controlled by fire alarm parameters of environment in living rooms and dangerous fire factors.Proposed methodology includes the concentration measurement of the basic gaseous combustion products (asphyxiant and irritant), generated in the process of materials burning in living accommodation. Justification of measurement facilities location in the fire scene and adjacent spaces is provided, considering the most likely position of a person during the evacuation and leisure time. The impact of fire stage on the flow rate of optical radiation diffused by smoke is shown.The findings will enable to develop the testing methodology for fire detectors designed to protect living accommodations and people located in them, formulate the functioning effectiveness criteria (operation algorithms) for detectors, used in living accommodations

The Impact of a Number of Samples on Unsupervised Feature Extraction, Based on Deep Learning for Detection Defects in Printed Circuit Boards

Deep learning provides new ways for defect detection in automatic optical inspections (AOI). However, the existing deep learning methods require thousands of images of defects to be used for training the algorithms. It limits the usability of these approaches in manufacturing, due to lack of images of defects before the actual manufacturing starts. In contrast, we propose to train a defect detection unsupervised deep learning model, using a much smaller number of images without defects. We propose an unsupervised deep learning model, based on transfer learning, that extracts typical semantic patterns from defect-free samples (one-class training). The model is built upon a pre-trained VGG16 model. It is further trained on custom datasets with different sizes of possible defects (printed circuit boards and soldered joints) using only small number of normal samples. We have found that the defect detection can be performed very well on a smooth background; however, in cases where the defect manifests as a change of texture, the detection can be less accurate. The proposed study uses deep learning self-supervised approach to identify if the sample under analysis contains any deviations (with types not defined in advance) from normal design. The method would improve the robustness of the AOI process to detect defects