Development of a rangefinding method for determining the coordinates of targets by a network of radar stations in counter-battery warfare

The increase in the accuracy of determining the coordinates of targets is explained by the use of a network of counter-battery radar stations and the rangefinding method for determining the coordinates of targets. The main advantage of using the rangefinding method for determining the coordinates of targets in a network of counter-battery radar stations is to ensure the required accuracy in determining the coordinates of targets without using accurate measurement of angular coordinates. The minimum geometry of the system, which ensures the use of the rangefinding method for determining coordinates, is given. The method of determining the coordinates of targets by a network of counter-battery radar stations has been improved. In contrast to the known ones, information about the range to the target is additionally used in a spatially distributed network of radar stations for counter-battery combat. The boundaries of the working zones of the network of two and three counter-battery radar stations are calculated. The features of creating a continuous strip using the rangefinding method for determining the coordinates of the target are considered. Statistical modeling of the rangefinding method for determining the plane coordinates of the target has been carried out. It has been established that the use of the rangefinding method ensures the determination of the planar coordinates of the target in a sector of at least 120°. The targets are at a distance of direct radio visibility in relation to the counter-battery radar. The root-mean-square error in determining the target range in this case is no more than 50 m. It has been established that the creation of continuous bands of a low-altitude radar field at a certain height is possible by arranging radar stations in a line. In this case, the distance between the counter-battery radar stations should be no more than half the target detection range at this heigh

An energy-efficient in-memory computing architecture for survival data analysis based on resistive switching memories

One of the objectives fostered in medical science is the so-called precision medicine, which requires the analysis of a large amount of survival data from patients to deeply understand treatment options. Tools like machine learning (ML) and deep neural networks are becoming a de-facto standard. Nowadays, computing facilities based on the Von Neumann architecture are devoted to these tasks, yet rapidly hitting a bottleneck in performance and energy efficiency. The in-memory computing (IMC) architecture emerged as a revolutionary approach to overcome that issue. In this work, we propose an IMC architecture based on resistive switching memory (RRAM) crossbar arrays to provide a convenient primitive for matrix-vector multiplication in a single computational step. This opens massive performance improvement in the acceleration of a neural network that is frequently used in survival analysis of biomedical records, namely the DeepSurv. We explored how the synaptic weights mapping strategy and the programming algorithms developed to counter RRAM non-idealities expose a performance/energy trade-off. Finally, we discussed how this application is tailored for the IMC architecture rather than being executed on commodity systems

About the Locomotive Simulator

Евдомаха Г. В. О тренажере машиниста локомотива / Г. В. Евдомаха, В. В. Глухов, К. И. Железнов и др. // Локомотив-информ. — 2011. — № 7. — С. 54—57.RU: В статье приводится описание программно-аппаратного комплекса «Тренажер машиниста», который предназначен для обучения машинистов безопасным и энергосберегающим технологиям вождения поездов, а также действиям в нестандартных и аварийных ситуациях.UK: У статті наводиться описпрограмно-апаратного комплексу «Тренажер машиніста», якийпризначений для навчаннямашиністівбезпечним і енергозберігаючимтехнологіямводінняпоїздів, а такождіям в нестандартних та аварійнихситуаціях.EN: The article describes the hardware and software "driver trainer", which is designed to teach drivers safe and energy-saving technologies driving the train, as well as actions in unusual and emergency situations