Search

In information security systems, the algorithm of the Blum-Blum-Shub (BBS) generator, which is based on the use of a one-way function and is a cryptographically secure pseudorandom number generator, became widespread. In this paper, the problem of the analysis of modified algorithms of the BBS generator operation is considered to improve their statistical characteristics, namely, the sequence repetition period. It has been established that in order to improve the characteristics of the classic BBS algorithm, it is necessary to systematize approaches to change the recurrent equation itself, the relationship between the current and the previous members of the sequence. For this purpose, a generalized unified model of the modification of the classical BBS algorithm is derived. The repetition period with computational complexity were analyzed for classical algorithm and 80 proposed modifications. A gain in statistical characteristics is improved with slight increase in the required computing power of the system. The proposed modified BBS pseudorandom sequence generator can be used in training of students when teaching cryptographic stability of information security systems. The study of this generator combines the knowledge of students acquired in both digital electronics and mathematics.

The paper describes the design and technical parameters of a medical thermoelectric device developed for diagnosing and monitoring the ophthalmic diseases. The main elements of the device are a specially designed thermoelectric heat flux sensor and a thermocouple temperature sensor connected to a data acquisition unit. The sensor is a thermoelectric micro-module that converts the heat flux into an electric voltage, which is recorded by the measuring channel of the data acquisition unit. The device allows high-precision measurements of both heat flux and temperature from the ocular surface. The paper contains examples of clinical piloting of the device.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Phase methods of measuring physical quantities and phase measuring equipment are widely used in various fields of science and technology. The article proposes a signal processing methodology based on a combination of the discrete Hilbert transform (DHT) and deterministic, as well as statistical methods of phase measurement. This methodology makes it possible to more fully use the information resource of the measuring signal phase in a wide range of the signal-to-noise ratio. It can be used both in computerized measurement and testing systems, as well as in the processing of measurement data. The benefits of the DHT are considered. The possibility of using statistics of directional data for phase measurements is shown. Circular statistics, such as the mean ring value, circular variance and the resulting vector length, were proposed for use in the phase measurements. Some examples of the use of this methodology in measurements and non-destructive testing are given.

The article presents the modified structure of the two-level digital frequency synthesizer (TLDFS), which combines the properties of classical digital frequency synthesizers (DFS) and Poisson pulse sequence generators (PPSG). The analysis of the statistical characteristics of synthesizer output signal, obtained in computer modelling with the use of appropriate software, has been carried out, which allowed determining the effective range of values of its control codes. The proposed generators can be effectively used to simulate various natural and technical processes, in particular, to simulate the output signals of dosimetric detectors during the design, adjustment and testing of dosimetric devices.

Error due to inhomogeneity is the main problem of thermocouples (TCs), e.g., during the operation of a type K TC, this error can reach 11-30 °C. Thus, metrological reliability of TCs is threatened by this error because there is a high risk of exceeding the permissible error when the temperature distribution along the TC legs changes. Such a large error, in turn, can threaten a proper operation or even safety of a measured object. A TC with controlled temperature field was proposed to cope with this error. An information-measuring system to perform proper measurements, measurement data acquisition and collection to construct mathematical models is proposed. Its property is high diurnal stability of ±(0.0025+0,002(X/XMAX–1) %. The requirements for the information-measuring system and its structure are considered in this paper. In particular, one of the key problems of such a sensor is how stable is its own temperature field under the influence of the temperature field of a measured object. The experimental studies were carried out using the developed system. They showed that the coefficient of penetration of the temperature field of the measured object is about 0.04. This allows decreasing error due to inhomogeneity by about 10-20 times.

The simplified metrological software test (MST) for modeling the method of determining the thermocouple (TC) error in situ during operation is considered in the paper. The interaction between the proposed MST and a temperature measuring system is also reflected in order to study the error of determining the TC error in situ during operation. The modelling studies of the random error influence of the temperature measuring system, as well as interference magnitude (both the common and normal mode noises) on the error of determining the TC error in situ during operation using the proposed MST, have been carried out. The noise and interference of the order of 5-6 μV cause the error of about 0.2-0.3°C. It is shown that high noise immunity is essential for accurate temperature measurements using TCs.

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including carfollowing, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management. [ABSTRACT FROM AUTHOR]

Segmenting medical images is a principal component of computer vision. The UNet model framework has taken over as the standard framework for this activity across a wide range of medical picture segmentation applications. Due to convolutional neural networks (CNNs) convolution operation limitations, the model's global modeling ability is not absolutely perfect. Moreover, a single convolution operation cannot gather feature information at various scales, which will have an impact on the quality of the global feature extraction as well as the localization of local details. The DCELANM‐Net structure, which this article offers, is a model that ingeniously combines a Dual Channel Efficient Layer Aggregation Network (DCELAN) and a Micro Masked Autoencoder (Micro‐MAE). On the one hand, for the DCELAN, the features are more effectively fitted by deepening the network structure, which in turn can successfully learn and fuse the features, helping to locate the local feature information more accurately; and the utilization of each layer of channels is more effectively improved by widening the network structure and residual connections. We adopt Micro‐MAE as the learner of the model. In addition to being straightforward in its methodology, it also includes a self‐supervised learning method, which has the benefit of being incredibly scalable for the model. This scalable method enables the generalization of high‐volume models, and the models can show good scaling behavior. It is also shown that Micro‐MAE is a powerful and adaptable learner that we can incorporate it into our network design to improve the model's accuracy and stability for tasks involving medical picture segmentation. Superior metrics and good generalization are demonstrated by DCELANM‐Net on the datasets Kvasir‐SEG and CVC‐ClinicDB. In the experiments, we set DCELANM‐S and DCELANM‐L to represent different sizes of the model, and since DCELANM‐L has the best performance, DCELANM‐L is determined as the base model for all experiments, called DCELANM. [ABSTRACT FROM AUTHOR]

Methodological aspects of assessing harmful impacts on the natural environment are presented, aimed at determining the indicator of ecosystem stability. The use of such an indicator makes it possible to determine environmental changes as a result of anthropogenic activity, as well as to determine the significance of these changes. A system is presented that systematizes the variety of consequences of anthropogenic impact on CLS. A qualitative scale of reducing harmful anthropogenic impact is proposed. It is proposed to conduct assessment of the categories of significance of harmful effects and ecological risk on the basis of a comprehensive evaluation of impacts on individual storeys and subsystems in the compartment from different sources of influence, taking into account their magnitude and intensity. The corresponding scales, a way of complex formation, categories of impact significance have been developed; also, an example of constructing an environmental risk matrix has been presented [ABSTRACT FROM AUTHOR]