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Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment

Ge Wei, Qi Lin, Tong Lin, Zhu Jun, Zhang Jing, Zhao Dongyang, Li Ke

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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Vol. 71, nr 4

art. no. e145766

EN

The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification. In this study, approximate entropy is introduced into variational mode decomposition, whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is used to filter out the noise mode to improve SNR. We proposed a semi-supervised dimensionality reduction method named exponential semi-supervised discriminant analysis in order to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification. The experimental results show that the method performs better than the state-of-the-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch and model.

2

MHD free convective heat and mass transfer flow from a vertical porous surface with variable thermal conductivity, variable mass diffusivity and thermal diffusion including viscous dissipation and chemical reaction

Phakirappa J., Priyanka S., Veena P. H., Pravin V. K.

International Journal of Applied Mechanics and Engineering

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2022

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Vol. 27, no. 3

127--136

EN

analysis is carried out to study chemically reactive, viscous dissipative effects of an incompressible and electrically conducting fluid with MHD free convection adjacent to a vertical surface with variable thermal conductivity (VTD) and variable mass diffusivity (VMD). An approximate numerical solution for the steady laminar boundary layer flow over a wall of the surface in the presence of species concentration and thermal mass diffusion has been studied. Using numerical techniques the governing boundary layer equations are solved to get the exact solution. Numerical calculations are carried out for different values of dimensionless parameters. The results are exhibited through various graphs and it is observed from the analysis of the results that the velocity field is appreciably influenced by the magnetic effect, porous effect, chemical reaction and buoyancy ratio between the species and thermal diffusion at the wall of the surface.

3

An improved framework for Parkinson’s disease prediction using Variational Mode Decomposition-Hilbert spectrum of speech signal

Karan Biswajit, Sekhar Sahu Sitanshu

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

717--732

EN

Parkinson’s disease (PD) is a neuro-degenerative disease due to loss of brain cells, which produces dopamine. It is most common after Alzheimer’s disease specially seen in old age people. In the earlier stage of disease, it has been noticed that most of the people suffering from speech disorder. From last two decades many studies have been conducted for the analysis of vocal tremors in PD. This study explores the combined approach of Variational Mode Decomposition (VMD) and Hilbert spectrum analysis (HSA) to investigate the voice tremor of patients with PD. A new set of features Hilbert cepstral coefficients (HCCs) are proposed in this study. Proposed features are assessed using vowels and words of PC-GITA database. The effectiveness of HCC features is utilized to perform classification, and regression analysis for PD detection. The highest average classification accuracy up to 91% and 96% is obtained with vowel /a/ and word /apto/ respectively. Further the classification accuracy up to 82% is obtained with independent dataset, when tested with the optimized model developed using PC-GITA database. In dysarthria level prediction highest correlation up to 0.82 is obtained using vowel /a/ and 0.8 with word /petaka/. The outcomes of this study indicate that the proposed articulatory features are suitable and accurate for PD assessment.

4

A combined iCEEMDAN and VMD method for mitigating the impact of ionospheric scintillation on GNSS signals

Dey Abhijit, Chhibba Rohan, Ratnam Devananboyina Venkata, Sharma Nitin

Acta Geophysica

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2021

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Vol. 69, no. 5

1933--1948

EN

Severe amplitude and phase scintillation induced by the ionospheric plasma density irregularities degrades the performance of global navigation satellite system (GNSS) receivers. Scintillation typically has adverse effects at the tracking process and thus adversely affects the raw GNSS measurements used in a number of applications. Hence, it is important to develop robust methodologies for detecting and mitigating ionospheric effects on the GNSS signals. In this paper, we propose a novel method based on the combination of improved complete ensemble empirical mode decomposition with adaptive noise (iCEEMDAN) and variational mode decomposition (VMD) methods. The proposed method employs a detrended fuctuation analysis (DFA)-based metric for robust thresholding between the scintillation-free and amplitude scintillated GNSS signals. The major contribution of the proposed method is development of novel approaches for selection of intrinsic mode functions (IMFs) based on DFA and optimised selection of [K, 훼] parameters of the VMD. The performance of the proposed method was evaluated and was observed that it is better than existing ionospheric scintillation effects mitigation algorithms for both simulated and real-time GPS scintillation datasets. The proposed method can denoise approximately 9.23–15.30 dB scintillation noise from the synthetic and 0.2–0.48 from the real scintillation index (S4) values. Therefore, the proposed (iCEEMDAN-VMD) method is appropriate for mitigating the ionospheric scintillation effects on the GNSS signals.

5

Study on transformer fault diagnosis technology of VMD local signal de-noising based on kurtosis - approximate entropy

Chen Dingke, Mao Changbin, Qin Bin

Diagnostyka

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2020

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Vol. 21, No. 1

81--87

EN

With the needs of social development, the scale of power equipment continues to expand. Among them, the transformer, as the core equipment in the power system, plays a key role in the safe and stable operation of the power system. However, in the field where the field strength is too high, partial breakdown of insulating media, that is the partial discharge occurs, which brings certain threats and damage to the safe operation of the power system. Therefore, this article uses the kurtosis-approximate entropy variational mode decomposition (VMD) partial discharge signal denoising method is used to preprocess the UHF partial discharge signal, through the simulation analysis and the result comparison, the feasibility of the method for denoising the partial signal of the transformer is clarified, designed to improve the safety and reliability of transformer operation.