Recent developments in the use of time-frequency analysis for radar-based applications

Ahmad, Ashraf Adamu; Ahmad, Sadiq A.; Muhammad, Abdulkadir L.

doi:10.1109/afrcon.2015.7332030

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…11 The Wigner Ville distribution has been applied in radar and acoustic signal analysis. 12 In terms of EMD, it is an adaptive signal decomposition method that can decompose complex nonstationary signals into a series of intrinsic mode functions (IMFs), each of which has a clear physical meaning. Through EMD, nonlinear and nonstationary signals are linearized and stabilized step by step, while preserving data characteristics and separating fluctuations and variations at different scales.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the short-time Fourier transform is widely used in speech and music signal processing . The Wigner Ville distribution has been applied in radar and acoustic signal analysis . In terms of EMD, it is an adaptive signal decomposition method that can decompose complex nonstationary signals into a series of intrinsic mode functions (IMFs), each of which has a clear physical meaning.…”

Section: Introductionmentioning

confidence: 99%

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Characteristic Investigation on Fluid Signals Based on a Combination of Empirical Mode Decomposition and Hilbert Transform in a Jet Impact-Negative Pressure Deamination Reactor

Zhang,

Chai,

Qiu

et al. 2024

Ind. Eng. Chem. Res.

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The jet impact-negative pressure reactor (JI-NPR) is a novel wastewater treatment technology developed for the efficient removal of high-concentration ammonia nitrogen. However, the complex and transient nature of the flow behavior within the JI-NPR poses significant challenges for understanding the underlying fluid dynamics. In this work, a comprehensive signal-processing framework was developed to elucidate the flow characteristics inside the JI-NPR. First, a flow signal acquisition platform was established to capture the negative pressure signals during the treatment process. The empirical mode decomposition (EMD) technique was then employed to decompose the turbulent flow signals into a series of intrinsic mode functions (IMFs), representing multiscale turbulent eddy characteristics. To mitigate the effects of local noise and abrupt changes, various curve fitting methods, including cubic spline interpolation, piecewise cubic Hermite interpolating polynomial, and Makima interpolation, were utilized to smooth the IMF signals. The Hilbert transform was subsequently applied to extract the instantaneous frequency features of the smoothed IMFs, enabling more accurate quantification of the nonstationary and nonlinear flow behavior. The results revealed that the low-frequency IMFs were associated with the interactions between the wastewater jet and negative pressure, while the highfrequency IMFs reflected the internal dynamic evolution of the fluid. Furthermore, the multiple regression analysis approach was adopted to quantify the relationship between the IMF feature parameters and the critical performance metric of the denitrification efficiency. The decision tree regression model was identified as a particularly suitable technique, as it can flexibly capture both linear and nonlinear dependencies and effectively identify the most influential variables. This integrated approach of EMD, curve fitting, Hilbert transform, and regression analysis methods provides valuable insights into the quantitative impact of multiscale turbulent eddies on the overall performance of the JI-NPR system. These findings are expected to guide targeted optimization of the reactor design to enhance the denitrification efficiency, a crucial goal for the practical application of this wastewater treatment technology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%