Performance-Effective Algorithm for Solving Large-Scale Forward Gravity Problem for Elliptical Objects Petr S. Martyshko, Igor V. Ladovskii, Denis D. Byzov, and Alexander I. Chernoskutov Page: 45-50 AbstractFull Text Download: 19 times https://doi.org/10.6025/stj/2024/13/2/45-50 Abstract: In this work, we aim to develop a computationally efficient algorithm for addressing the forward gravity issue related to elliptical objects. This algorithm is designed to support parallel processing, and it has been put into practice and evaluated using CUDA technology. Generally, the proposed approach can be utilized for density distribution models of any shape that can be trained.
Mathematical Modeling of the Autodyne Signal Characteristics at Strong Reflected Emission Vladislav Ya. Noskov, Kirill A. Ignatkov, and Andrey P. Chupakhin Page: 51-60 AbstractFull Text Download: 19 times https://doi.org/10.6025/stj/2024/13/2/51-60 Abstract: Based on a sophisticated mathematical model that describes how microwave oscillators interact with strong reflected emissions, and the use of numerical methods, the findings of the study are outlined, focusing on the characteristics of the autodyne signal. The research was conducted on a microwave oscillator model with a single-circuit oscillating system, considering its non-isochronicity and non-isodromicity. The study also explored how the inherent parameters and the delay in reflected emissions affect the formation of the autodyne response. The results include graphs that illustrate the normalized characteristics of the autodyne signal, along with its spectral analysis. The study also calculated the harmonic coefficients and amplitudes of the spectrum's harmonic components, along with the average levels of the autodyne response as a function of the reflection coefficient modulus at different distances from the radar object.
Signal Processing under Presence of Low Frequency Noise in the Low Speed Data Channel Alexander Yu. Parshin Page: 61-66 AbstractFull Text Download: 20 times https://doi.org/10.6025/stj/2024/13/2/61-66 Abstract: This paper explores the impact of low-frequency additive noise and interference on communication channels. It focuses on a specific channel that is used for both receiving and transmitting signals in both unmanned vehicle groups and the Internet of Things systems. To ensure the efficient operation of the receiving and transmitting devices, a low-power, narrowband signal is employed, along with a slow data transmission rate. The work discusses the use of fractal analysis to mitigate the effects of low-frequency flicker noise and introduces the fractal Brownian motion model for characterizing low-frequency flicker noise statistically. It also calculates the parameters of the communication channel under the influence of low-frequency interference. A maximum likelihood algorithm for signal detection in the presence of additive fractal jamming has been created. The findings suggest that employing fractal models can enhance signal processing efficiency in the presence of background noise, even when there are no other significant differences between the signal and noise. Recommendations are made for integrating signal processing techniques that can handle the low-frequency, fractal nature of the spectral power density.
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