压缩感知(compressed sensing,CS)技术在采样中完成对数据的压缩,相比传统Nyquist采样方法有效降低采样信号数据量,克服采样端压缩复杂度高,对硬件需求大的缺点。该文通过理论证明指出电网信号基波–谐波稀疏度特性,并基于此特性提出一...压缩感知(compressed sensing,CS)技术在采样中完成对数据的压缩,相比传统Nyquist采样方法有效降低采样信号数据量,克服采样端压缩复杂度高,对硬件需求大的缺点。该文通过理论证明指出电网信号基波–谐波稀疏度特性,并基于此特性提出一种新型基波滤除谱投影梯度算法(SPGFF)。通过西门子Benchmark 0.4 k V电网通用模型实验,结果表明SPG-FF算法比现有方法有效提升了谐波检测精度和信号重构精度,对谐波和间谐波的检测误差分别小于6.8×10-5和6.2×10-3,重构信号的信噪比高于89 d B。展开更多
The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (VSS-NLMS) algorithm in the context of Echo Cancellation ...The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (VSS-NLMS) algorithm in the context of Echo Cancellation (EC). The core parameter in this algorithm is the Background Noise Power (BNP); in the estimation of BNP, the power difference between the desired signal and the filter output, statistically equaling to the error signal power, has been widely used in a rough manner. In this study, a precise BNP estimate is implemented by multiplying the rough estimate with a corrective factor, taking into consideration the fact that the error signal consists of background noise and misalignment noise. This corrective factor is obtained by subtracting half of the latest VSS value from 1 after analyzing the ratio of BNP to the misalignment noise. Based on the precise BNP estimate, the PVSS-NLMS algorithm suitable for the EC system is eventually proposed. In practice, the proposed algorithm exhibits a significant advantage of easier controllability application, as prior knowledge of the EC environment can be neglected. The simulation results support the preciseness of the BNP estimation and the effectiveness of the proposed algorithm.展开更多
In this paper, an adaptive noise detection and removal algorithm using local statistics for salt-and-pepper noise are proposed. In order to determine constraints for noise detection, the local mean, varianoe, and maxi...In this paper, an adaptive noise detection and removal algorithm using local statistics for salt-and-pepper noise are proposed. In order to determine constraints for noise detection, the local mean, varianoe, and maximum value are used. In addition, a weighted median filter is employed to remove the detected noise. The simulation results show the capability of the proposed algorithm removes the noise effectively.展开更多
文摘压缩感知(compressed sensing,CS)技术在采样中完成对数据的压缩,相比传统Nyquist采样方法有效降低采样信号数据量,克服采样端压缩复杂度高,对硬件需求大的缺点。该文通过理论证明指出电网信号基波–谐波稀疏度特性,并基于此特性提出一种新型基波滤除谱投影梯度算法(SPGFF)。通过西门子Benchmark 0.4 k V电网通用模型实验,结果表明SPG-FF算法比现有方法有效提升了谐波检测精度和信号重构精度,对谐波和间谐波的检测误差分别小于6.8×10-5和6.2×10-3,重构信号的信噪比高于89 d B。
文摘The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (VSS-NLMS) algorithm in the context of Echo Cancellation (EC). The core parameter in this algorithm is the Background Noise Power (BNP); in the estimation of BNP, the power difference between the desired signal and the filter output, statistically equaling to the error signal power, has been widely used in a rough manner. In this study, a precise BNP estimate is implemented by multiplying the rough estimate with a corrective factor, taking into consideration the fact that the error signal consists of background noise and misalignment noise. This corrective factor is obtained by subtracting half of the latest VSS value from 1 after analyzing the ratio of BNP to the misalignment noise. Based on the precise BNP estimate, the PVSS-NLMS algorithm suitable for the EC system is eventually proposed. In practice, the proposed algorithm exhibits a significant advantage of easier controllability application, as prior knowledge of the EC environment can be neglected. The simulation results support the preciseness of the BNP estimation and the effectiveness of the proposed algorithm.
基金supported by the Korea Science and Engineering Foundation(KOSEF)granted bythe Korea government(MEST)(No.2009-0079776)
文摘In this paper, an adaptive noise detection and removal algorithm using local statistics for salt-and-pepper noise are proposed. In order to determine constraints for noise detection, the local mean, varianoe, and maximum value are used. In addition, a weighted median filter is employed to remove the detected noise. The simulation results show the capability of the proposed algorithm removes the noise effectively.
