Azimuth Resolution Enhancement in Bistatic Ground Stepped-Frequency Noise SAR using its Intrerferometry Operational Mode

Author(s):  
Konstantin Lukin ◽  
Volodimyr Palamarchuk ◽  
Sergii Lukin
2008 ◽  
Author(s):  
Guoqing Liu ◽  
Ken Yang ◽  
Brian Sykora ◽  
Imad Salha

Author(s):  
Konstantin Lukin ◽  
Pavlo Vyplavin ◽  
Oleg Zemlyaniy ◽  
Vladimir Palamarchuk ◽  
Jong Phill Kim ◽  
...  

Author(s):  
K. Lukin ◽  
V. Palamarchuk ◽  
O. Zemlyaniy ◽  
D. Tatyanko ◽  
S. Lukin

2021 ◽  
Vol 64 (1) ◽  
pp. 97-102
Author(s):  
Gao Yunkai ◽  
◽  
Yang Zhaotong ◽  
Wang Shihui ◽  
◽  
...  

Aiming at the reduction of low-frequency noise problem for large equipment, a damping optimization method based on the OMA (Operational Mode Analysis) is proposed. Due to the stability of the mode frequencies and shapes, damping application could make efficient noise reduction without bringing new problems compared with structural optimizations, which makes it one of the most important means for finalized products. Taking the engine compartment of an excavator as the study object, a damping optimization method based on the OMA test is proposed in this article, which makes a more efficient optimization for large equipment by its feasible modal test. Through simulation and experimental verification, the method is effective. The test results show that based on the OMA damping application method, the low-frequency sound power level has been significantly reduced, and after the damping application, the sound radiation power level defined by the national standard has also been reduced.


1976 ◽  
Vol 30 (1) ◽  
pp. 23-27 ◽  
Author(s):  
Keith R. Betty ◽  
Gary Horlick

A number of signal processing operations can be carried out on spectra using a digital filter based on a simple trapezoid function. The filter is applied to the Fourier transform of the spectral signal. Several signal processing examples are presented to illustrate the capabilities of this filter. These examples include filtering and diagnosis of high frequency noise on a signal, removal of fixed frequency noise, minimization of quantization noise, and differentiation and approximate deconvolution for the purpose of resolution enhancement.


Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3305
Author(s):  
Li Fan ◽  
Zelin Wang ◽  
Yuxiang Lu ◽  
Jianguang Zhou

Scanning electron microscopy (SEM) plays a crucial role in the characterization of nanoparticles. Unfortunately, due to the limited resolution, existing imaging techniques are insufficient to display all detailed characteristics at the nanoscale. Hardware-oriented techniques are troubled with costs and material properties. Computational approaches often prefer blurry results or produce a less meaningful high-frequency noise. Therefore, we present a staged loss-driven neural networks model architecture to transform low-resolution SEM images into super-resolved ones. Our approach consists of two stages: first, residual channel attention network (RCAN) with mean absolute error (MAE) loss was used to get a better peak signal-to-noise ratio (PSNR). Then, discriminators with adversarial losses were activated to reconstruct high-frequency texture features. The quantitative and qualitative evaluation results indicate that compared with other advanced approaches, our model achieves satisfactory results. The experiment in AgCl@Ag for photocatalytic degradation confirms that our proposed method can bring realistic high-frequency structural detailed information rather than meaningless noise. With this approach, high-resolution SEM images can be acquired immediately without sample damage. Moreover, it provides an enhanced characterization method for further directing the preparation of nanoparticles.


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