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In our work we present generalization of well-known approach for construction of invariant feature vectors of images in computer vision applications. Basic feature of the suggested algorithm is replacement of commonly used Gaussian filter by convolution of image function with Green’s function of evolution operator, which inherits symmetries of this operator. The use of general filtration allows to obtain additional characteristics of invariant feature vectors.

GIS INTEGRO is the geo-information software system forming the basis for the integrated interpretation of geophysical data in researching a deep structure of Earth. GIS INTEGRO combines a variety of computational and analytical applications for the solution of geological and geophysical problems. It includes various interfaces that allow you to change the form of representation of data (raster, vector, regular and irregular network of observations), the conversion unit of map projections, application blocks, including block integrated data analysis and decision prognostic and diagnostic tasks.

The methodological approach is based on integration and integrated analysis of geophysical data on regional profiles, geophysical potential fields and additional geological information on the study area. Analytical support includes packages transformations, filtering, statistical processing, calculation, finding of lineaments, solving direct and inverse tasks, integration of geographic information.

Technology and software and analytical support was tested in solving problems tectonic zoning in scale 1:200000, 1:1000000 in Yakutia, Kazakhstan, Rostov region, studying the deep structure of regional profiles 1:S, 1-SC, 2-SAT, 3-SAT and 2-DV, oil and gas forecast in the regions of Eastern Siberia, Brazil.

The article describes two possible approaches of parallel calculations for data processing 2D or 3D nets in the field of geophysical research. As an example presented realization in the environment of GRID of the application software ZondGeoStat (statistical sensing), which create 3D net model on the basis of data 2d net. The experience has demonstrated the high efficiency of the use of environment of GRID during realization of calculations in field of geophysical researches.

The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high energy and nuclear physics.

The amount of data in modern experiments is so large that classical tracking methods such as Kalman filter can not process them fast enough. To solve this problem, we have developed two neural network algorithms of track recognition, based on deep learning architectures, for local (track by track) and global (all tracks in an event) tracking in the GEM tracker of the BM@N experiment at JINR (Dubna). The advantage of deep neural networks is the ability to detect hidden nonlinear dependencies in data and the capability of parallel execution of underlying linear algebra operations.

In this work we generalize these algorithms to the cylindrical GEM inner tracker of BESIII experiment. The neural network model RDGraphNet for global track finding, based on the reverse directed graph, has been successfully adapted. After training on Monte Carlo data, testing showed encouraging results: recall of 98% and precision of 86% for track finding.

The local neural network model TrackNETv2 was also adapted to BESIII CGEM successfully. Since the tracker has only three detecting layers, an additional neuro-classifier to filter out false tracks have been introduced. Preliminary tests demonstrated the recall value at the first stage of 99%. After applying the neuro-classifier, the precision was 77% with a slight decrease of the recall to 94%. This result can be improved after the further model optimization.

In single-photon emission computed tomography (SPECT), patients with bone disorders receive a radiopharmaceutical (RP) that accumulates selectively in pathological lesions. Accurate quantification of RP uptake plays a critical role in disease staging, prognosis, and the development of personalized treatment strategies. Traditionally, the accuracy of quantitative assessment is evaluated through in vitro clinical trials using the standardized physical NEMA IEC phantom, which contains six spheres simulating lesions of various sizes. However, such experiments are limited by high costs and radiation exposure to researchers. This study proposes an alternative in silico approach based on numerical simulation using a digital twin of the NEMA IEC phantom. The computational framework allows for extensive testing under varying conditions without physical constraints. Analogous to clinical protocols, we calculated the recovery coefficient (RC_max), defined as the ratio of the maximum activity in a lesion to its known true value. The simulation settings were tailored to clinical SPECT/CT protocols involving ^99mTc for patients with bone-related diseases. For the first time, we systematically analyzed the impact of lesion-to-background ratios and post-reconstruction filtering on RC_max values. Numerical experiments revealed the presence of edge artifacts in reconstructed lesion images, consistent with those observed in both real NEMA IEC phantom studies and patient scans. These artifacts introduce instability into the iterative reconstruction process and lead to errors in activity quantification. Our results demonstrate that post-filtering helps suppress edge artifacts and stabilizes the solution. However, it also significantly underestimates activity in small lesions. To address this issue, we introduce post-reconstruction correction factors derived from our simulations to improve the accuracy of quantification in lesions smaller than 20 mm in diameter.

This paper proposes the implementation of a coherent transceiver with a constant delay and the ability to select any clock frequency grid used for clocking peripheral DACs and ADCs, tasks of device synchronization and data transmission. The choice of the required clock frequency grid directly affects the data transfer rate in the network, however, it allows one to flexibly configure the network for the tasks of transmitting clock signals and subnanosecond generation of sync signals on all devices in the network. A method for increasing the synchronization accuracy to tenths of nanoseconds by using digital phase detectors and a Phase Locked Loop (PLL) system on the slave device is proposed. The use of high-speed fiber-optic communication lines (FOCL) for synchronization tasks allows simultaneously exchanging control commands and signaling data. To simplify and reduce the cost of devices of a synchronous network of transceivers, it is proposed to use a clock signal restored from a data transmission line to filter phase noise and form a frequency grid in the PLL system for heterodyne signals and clock peripheral devices, including DAC and ADC. The results of multiple synchronization tests in the proposed synchronous network are presented.

Industrial imaging or “machine vision” is currently a key technology in many industries as it can be used to optimize various processes. The purpose of this work is to create a software and hardware complex for measuring the overall and weight characteristics of cargo based on an intelligent system using neural network identification methods that allow one to overcome the technological limitations of similar complexes implemented on ultrasonic and infrared measuring sensors. The complex to be developed will measure cargo without restrictions on the volume and weight characteristics of cargo to be tariffed and sorted within the framework of the warehouse complexes. The system will include an intelligent computer program that determines the volume and weight characteristics of cargo using the machine vision technology and an experimental sample of the stand for measuring the volume and weight of cargo.

We analyzed the solutions to similar problems. We noted that the disadvantages of the studied methods are very high requirements for the location of the camera, as well as the need for manual operations when calculating the dimensions, which cannot be automated without significant modifications. In the course of the work, we investigated various methods of object recognition in images to carry out subject filtering by the presence of cargo and measure its overall dimensions. We obtained satisfactory results when using cameras that combine both an optical method of image capture and infrared sensors. As a result of the work, we developed a computer program allowing one to capture a continuous stream from Intel RealSense video cameras with subsequent extraction of a three-dimensional object from the designated area and to calculate the overall dimensions of the object. At this stage, we analyzed computer vision techniques; developed an algorithm to implement the task of automatic measurement of goods using special cameras and the software allowing one to obtain the overall dimensions of objects in automatic mode.

Upon completion of the work, this development can be used as a ready-made solution for transport companies, logistics centers, warehouses of large industrial and commercial enterprises.

Big science projects are producing data at ever increases rates. Typical techniques involve storing the data to disk, after minor filtering, and then processing it in large computer farms. Data production has reached a point where on-line processing is required in order to filter the data down to manageable sizes. A potential solution involves using low-cost, low-power ARM processors in large arrays to provide massive parallelisation for data stream computing (DSC). The main advantage in using System on Chips (SoCs) is inherent in its design philosophy. SoCs are primarily used in mobile devices and hence consume less power while maintaining relatively good performance. A benchmarking characterisation of three different models of ARM processors will be presented.