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We describe an engineering-psychological experiment that continues the study of ways to adapt a person to the increasing complexity of logical problems by presenting a series of problems of increasing complexity, which is determined by the volume of initial data. Tasks require calculations in an associative or non-associative system of operations. By the nature of the change in the time of solving the problem, depending on the number of necessary operations, we can conclude that a purely sequential method of solving problems or connecting additional brain resources to the solution in parallel mode. In a previously published experimental work, a person in the process of solving an associative problem recognized color images with meaningful images. In the new study, a similar problem is solved for abstract monochrome geometric shapes. Analysis of the result showed that for the second case, the probability of the subject switching to a parallel method of processing visual information is significantly reduced. The research method is based on presenting a person with two types of tasks. One type of problem contains associative calculations and allows a parallel solution algorithm. Another type of problem is the control one, which contains problems in which calculations are not associative and parallel algorithms are ineffective. The task of recognizing and searching for a given object is associative. A parallel strategy significantly speeds up the solution with relatively small additional resources. As a control series of problems (to separate parallel work from the acceleration of a sequential algorithm), we use, as in the previous experiment, a non-associative comparison problem in cyclic arithmetic, presented in the visual form of the game “rock, paper, scissors”. In this problem, the parallel algorithm requires a large number of processors with a small efficiency coefficient. Therefore, the transition of a person to a parallel algorithm for solving this problem is almost impossible, and the acceleration of processing input information is possible only by increasing the speed. Comparing the dependence of the solution time on the volume of source data for two types of problems allows us to identify four types of strategies for adapting to the increasing complexity of the problem: uniform sequential, accelerated sequential, parallel computing (where possible), or undefined (for this method) strategy. The Reducing of the number of subjects, who switch to a parallel strategy when encoding input information with formal images, shows the effectiveness of codes that cause subject associations. They increase the speed of human perception and processing of information. The article contains a preliminary mathematical model that explains this phenomenon. It is based on the appearance of a second set of initial data, which occurs in a person as a result of recognizing the depicted objects.

Deep learning’s power stems from complex architectures; however, these can lead to overfitting, where models memorize training data and fail to generalize to unseen examples. This paper proposes a novel probabilistic approach to mitigate this issue. We introduce two key elements: Truncated Log-Uniform Prior and Truncated Log-Normal Variational Approximation, and Automatic Relevance Determination (ARD) with Bayesian Deep Neural Networks (BDNNs). Within the probabilistic framework, we employ a specially designed truncated log-uniform prior for noise. This prior acts as a regularizer, guiding the learning process towards simpler solutions and reducing overfitting. Additionally, a truncated log-normal variational approximation is used for efficient handling of the complex probability distributions inherent in deep learning models. ARD automatically identifies and removes irrelevant features or weights within a model. By integrating ARD with BDNNs, where weights have a probability distribution, we achieve a variational bound similar to the popular variational dropout technique. Dropout randomly drops neurons during training, encouraging the model not to rely heavily on any single feature. Our approach with ARD achieves similar benefits without the randomness of dropout, potentially leading to more stable training.

To evaluate our approach, we have tested the model on two datasets: the Canadian Institute For Advanced Research (CIFAR-10) for image classification and a dataset of Macroscopic Images of Wood, which is compiled from multiple macroscopic images of wood datasets. Our method is applied to established architectures like Visual Geometry Group (VGG) and Residual Network (ResNet). The results demonstrate significant improvements. The model reduced overfitting while maintaining, or even improving, the accuracy of the network’s predictions on classification tasks. This validates the effectiveness of our approach in enhancing the performance and generalization capabilities of deep learning models.

This paper considers the inverse problem of seismic exploration — determining the structure of the media based on the recorded wave response from it. Large cracks are considered as target objects, whose size and position are to be determined.

he direct problem is solved using the grid-characteristic method. The method allows using physically based algorithms for calculating outer boundaries of the region and contact boundaries inside the region. The crack is assumed to be thin, a special condition on the crack borders is used to describe the crack.

The inverse problem is solved using convolutional neural networks. The input data of the neural network are seismograms interpreted as images. The output data are masks describing the medium on a structured grid. Each element of such a grid belongs to one of two classes — either an element of a continuous geological massif, or an element through which a crack passes. This approach allows us to consider a medium with an unknown number of cracks.

The neural network is trained using only samples with one crack. The final testing of the trained network is performed using additional samples with several cracks. These samples are not involved in the training process. The purpose of testing under such conditions is to verify that the trained network has sufficient generality, recognizes signs of a crack in the signal, and does not suffer from overtraining on samples with a single crack in the media.

The paper shows that a convolutional network trained on samples with a single crack can be used to process data with multiple cracks. The networks detects fairly small cracks at great depths if they are sufficiently spatially separated from each other. In this case their wave responses are clearly distinguishable on the seismogram and can be interpreted by the neural network. If the cracks are close to each other, artifacts and interpretation errors may occur. This is due to the fact that on the seismogram the wave responses of close cracks merge. This cause the network to interpret several cracks located nearby as one. It should be noted that a similar error would most likely be made by a human during manual interpretation of the data. The paper provides examples of some such artifacts, distortions and recognition errors.

The practical application of nuclear medicine demonstrates the continued information deficiency of the algorithms and programs that provide visualization and analysis of medical images. The aim of the study was to determine the principles of optimizing the processing of planar osteostsintigraphy on the basis of сomputer aided diagnosis (CAD) for analysis of texture descriptions of images of metastatic zones on planar scintigrams of skeleton. A computer-aided diagnosis system for analysis of skeletal metastases based on planar scintigraphy data has been developed. This system includes skeleton image segmentation, calculation of textural, histogram and morphometrical parameters and the creation of a training set. For study of metastatic images’ textural characteristics on planar scintigrams of skeleton was developed the computer program of automatic analysis of skeletal metastases is used from data of planar scintigraphy. Also expert evaluation was used to distinguishing ‘pathological’ (metastatic) from ‘physiological’ (non-metastatic) radiopharmaceutical hyperfixation zones in which Haralick’s textural features were determined: autocorrelation, contrast, ‘forth moment’ and heterogeneity. This program was established on the principles of сomputer aided diagnosis researches planar scintigrams of skeletal patients with metastatic breast cancer hearths hyperfixation of radiopharmaceuticals were identified. Calculated parameters were made such as brightness, smoothness, the third moment of brightness, brightness uniformity, entropy brightness. It has been established that in most areas of the skeleton of histogram values of parameters in pathologic hyperfixation of radiopharmaceuticals predominate over the same values in the physiological. Most often pathological hyperfixation of radiopharmaceuticals as the front and rear fixed scintigramms prevalence of brightness and smoothness of the image brightness in comparison with those of the physiological hyperfixation of radiopharmaceuticals. Separate figures histogram analysis can be used in specifying the diagnosis of metastases in the mathematical modeling and interpretation bone scintigraphy. Separate figures histogram analysis can be used in specifying the diagnosis of metastases in the mathematical modeling and interpretation bone scintigraphy.

The work continues research on the ability of a person to improve the productivity of information processing, using parallel work or improving the performance of analyzers. A person receives a series of tasks, the solution of which requires the processing of a certain amount of information. The time and the validity of the decision are recorded. The dependence of the average solution time on the amount of information in the problem is determined by correctly solved problems. In accordance with the proposed method, the problems contain calculations of expressions in two algebras, one of which is associative and the other is nonassociative. To facilitate the work of the subjects in the experiment were used figurative graphic images of elements of algebra. Non-associative calculations were implemented in the form of the game “rock-paper-scissors”. It was necessary to determine the winning symbol in the long line of these figures, considering that they appear sequentially from left to right and play with the previous winner symbol. Associative calculations were based on the recognition of drawings from a finite set of simple images. It was necessary to determine which figure from this set in the line is not enough, or to state that all the pictures are present. In each problem there was no more than one picture. Computation in associative algebra allows the parallel counting, and in the absence of associativity only sequential computations are possible. Therefore, the analysis of the time for solving a series of problems reveals a consistent uniform, sequential accelerated and parallel computing strategy. In the experiments it was found that all subjects used a uniform sequential strategy to solve non-associative problems. For the associative task, all subjects used parallel computing, and some have used parallel computing acceleration of the growth of complexity of the task. A small part of the subjects with a high complexity, judging by the evolution of the solution time, supplemented the parallel account with a sequential stage of calculations (possibly to control the solution). We develop a special method for assessing the rate of processing of input information by a person. It allowed us to estimate the level of parallelism of the calculation in the associative task. Parallelism of level from two to three was registered. The characteristic speed of information processing in the sequential case (about one and a half characters per second) is twice less than the typical speed of human image recognition. Apparently the difference in processing time actually spent on the calculation process. For an associative problem in the case of a minimum amount of information, the solution time is near to the non-associativity case or less than twice. This is probably due to the fact that for a small number of characters recognition almost exhausts the calculations for the used non-associative problem.

We suggested some approaches for solving image processing tasks in the decision support system (DSS) of the Center for Automated Recording of Administrative Offenses of the Road Traffic (CARAO). The main task of this system is to assist the operator in obtaining accurate information about the vehicle registration plate and the vehicle brand/model based on images obtained from the photo and video recording systems. We suggested the approach for vehicle registration plate recognition and brand/model classification on the images based on modern neural network models. LPRNet neural network model supplemented by Spatial Transformer Layer was used to recognize the vehicle registration plate. The ResNeXt-101-32x8d neural network model was used to classify for vehicle brand/model. We suggested the approach to construct the training set for the neural network of vehicle registration plate recognition. The approach is based on computer vision methods and machine learning algorithms. The SIFT algorithm was used to detect and describe local features on images with the vehicle registration plate. DBSCAN clustering was used to detect and delete outliers in such local features. The accuracy of vehicle registration plate recognition was 96% on the testing set. We suggested the approach to improve the efficiency of using the ResNeXt-101-32x8d model at additional training and classification stages. The approach is based on the new architecture of convolutional neural networks with “freezing” weight coefficients of convolutional layers, an additional convolutional layer for parallelizing the classification process, and a set of binary classifiers at the output. This approach significantly reduced the time of additional training of neural network when new vehicle brand/model classification was needed. The final accuracy of vehicle brand/model classification was 99% on the testing set. The proposed approaches were tested and implemented in the DSS of the CARAO of the Republic of Tatarstan.

The advent of laser scanning technologies has revolutionized forestry. Their use made it possible to switch from studying woodlands using manual measurements to computer analysis of stereo point images called point clouds.

Automatic calculation of some tree parameters (such as trunk diameter) using a point cloud requires the removal of foliage points. To perform this operation, a preliminary segmentation of the stereo image into the “foliage” and “trunk” classes is required. The solution to this problem often involves the use of machine learning methods.

One of the most popular classifiers used for segmentation of stereo images of trees is a random forest. This classifier is quite demanding on the amount of memory. At the same time, the size of the machine learning model can be critical if it needs to be sent by wire, which is required, for example, when performing distributed learning. In this paper, the goal is to find a classifier that would be less demanding in terms of memory, but at the same time would have comparable segmentation accuracy. The search is performed among classifiers such as logistic regression, naive Bayes classifier, and decision tree. In addition, a method for segmentation refinement performed by a decision tree using logistic regression is being investigated.

The experiments were conducted on data from the collection of the University of Heidelberg. The collection contains hand-marked stereo images of trees of various species, both coniferous and deciduous, typical of the forests of Central Europe.

It has been shown that classification using a decision tree, adjusted using logistic regression, is able to produce a result that is only slightly inferior to the result of a random forest in accuracy, while spending less time and RAM. The difference in balanced accuracy is no more than one percent on all the clouds considered, while the total size and inference time of the decision tree and logistic regression classifiers is an order of magnitude smaller than of the random forest classifier.

The article shows the relevance of research work in the field of creating control systems for unmanned vehicles based on computer vision technologies. Computer vision tools are used to solve a large number of different tasks, including to determine the location of the car, detect obstacles, determine a suitable parking space. These tasks are resource intensive and have to be performed in real time. Therefore, it is important to develop effective models, methods and tools that ensure the achievement of the required time and accuracy for use in unmanned vehicle control systems. In this case, the choice of the image representation model is important. In this paper, we consider a model based on the wavelet transform, which makes it possible to form features characterizing the energy estimates of the image points and reflecting their significance from the point of view of the contribution to the overall image energy. To form a model of energy characteristics, a procedure is performed based on taking into account the dependencies between the wavelet coefficients of various levels and the application of heuristic adjustment factors for strengthening or weakening the influence of boundary and interior points. On the basis of the proposed model, it is possible to construct descriptions of images their characteristic features for isolating and analyzing, including for isolating contours, regions, and singular points. The effectiveness of the proposed approach to image analysis is due to the fact that the objects in question, such as road signs, road markings or car numbers that need to be detected and identified, are characterized by the relevant features. In addition, the use of wavelet transforms allows to perform the same basic operations to solve a set of tasks in onboard unmanned vehicle systems, including for tasks of primary processing, segmentation, description, recognition and compression of images. The such unified approach application will allow to reduce the time for performing all procedures and to reduce the requirements for computing resources of the on-board system of an unmanned vehicle.

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 work analyzes the problem of traffic signs recognition in intelligent transport systems. The basic concepts of computer vision and image recognition tasks are considered. The most effective approach for solving the problem of analyzing and recognizing images now is the neural network method. Among all kinds of neural networks, the convolutional neural network has proven itself best. Activation functions such as Relu and SoftMax are used to solve the classification problem when recognizing traffic signs. This article proposes a technology for recognizing traffic signs. The choice of an approach for solving the problem based on a convolutional neural network due to the ability to effectively solve the problem of identifying essential features and classification. The initial data for the neural network model were prepared and a training sample was formed. The Google Colaboratory cloud service with the external libraries for deep learning TensorFlow and Keras was used as a platform for the intelligent system development. The convolutional part of the network is designed to highlight characteristic features in the image. The first layer includes 512 neurons with the Relu activation function. Then there is the Dropout layer, which is used to reduce the effect of overfitting the network. The output fully connected layer includes four neurons, which corresponds to the problem of recognizing four types of traffic signs. An intelligent traffic sign recognition system has been developed and tested. The used convolutional neural network included four stages of convolution and subsampling. Evaluation of the efficiency of the traffic sign recognition system using the three-block cross-validation method showed that the error of the neural network model is minimal, therefore, in most cases, new images will be recognized correctly. In addition, the model has no errors of the first kind, and the error of the second kind has a low value and only when the input image is very noisy.