Results 21 to 30 of about 36,871 (305)
Bayesian multifractal signal denoising [PDF]
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004 This work presents an approach for signal/image denoising in a semi-parametric frame. Our model is a wavelet-based one, which essentially assumes a minimal local regularity. This assumption translates into constraints on the multifractal spectrum of the signals.
Lévy Véhel, Jacques, Legrand, Pierrick
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On denoising and best signal representation [PDF]
IEEE Transactions on Information Theory, 1999 Summary: We propose a best basis algorithm for signal enhancement in white Gaussian noise. The best basis search is performed in families of orthonormal bases constructed with wavelet packets or local cosine bases. We base our search for the ``best'' basis on a criterion of minimal reconstruction error of the underlying signal.
Hamid Krim +3 more
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Advances in Mechanical Engineering, 2022 Aero-engine electrostatic monitoring technology (EMT) is a novel and effective condition monitoring technology. With the help of EMT, effective monitoring of early failures can be achieved. Since the electrostatic monitoring of the running engine will be
Zhirong Zhong, Hongfu Zuo, Heng Jiang
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A Hybrid Algorithm for Noise Suppression of MEMS Accelerometer Based on the Improved VMD and TFPF
Micromachines, 2022 High-G MEMS accelerometer (HGMA) is a new type of sensor; it has been widely used in high precision measurement and control fields. Inevitably, the accelerometer output signal contains random noise caused by the accelerometer itself, the hardware circuit
Yongjun Zhou, Huiliang Cao, Tao Guo
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Block-Adaptive Rényi Entropy-Based Denoising for Non-Stationary Signals
Sensors, 2022 This paper approaches the problem of signal denoising in time-variable noise conditions. Non-stationary noise results in variable degradation of the signal’s useful information content over time.
Nicoletta Saulig +3 more
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Radar Complex Intermediate Frequency Signal Denoising Based on Convolutional Auto-Encoder Network
IEEE Access, 2023 In radar systems, target state features are commonly extracted from intermediate frequency signals. However, these signals often have a low signal-to-noise ratio due to noisy environments and limitations of the radar hardware.
Haihua Xie, Yi Yuan, Sanyou Zeng
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A Gyroscope Signal Denoising Method Based on Empirical Mode Decomposition and Signal Reconstruction [PDF]
Sensors, 2019 Shi Zhen
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Applied Sciences, 2020 Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST ...
Xiang Li +4 more
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Using AutoEncoders for Radio Signal Denoising [PDF]
Proceedings of the 15th ACM International Symposium on QoS and Security for Wireless and Mobile Networks, 2019 We investigated the use of a Deep Learning approach to radio signal de-noising. This data-driven approach has does not require explicit use of expert knowledge to set up the parameters of the denoising procedure and grants great flexibility across many channel conditions.
Almazrouei E. +4 more
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Sensors, 2019 In this paper, a novel hybrid method combining adaptive chirp mode pursuit (ACMP) with an adaptive multiscale Savitzky−Golay filter (AMSGF) based on adaptive moving average (AMA) is proposed for offline denoising micro-electromechanical system ...
Jingjing He, Changku Sun, Peng Wang
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