A Novel Noise Reduction Algorithm of MEMS Gyroscope Based on Compressive Sensing and Lifting Wavelet Transform

2014 ◽  
Vol 609-610 ◽  
pp. 1138-1143 ◽  
Author(s):  
Wen Jie Zhu ◽  
Guang Long Wang ◽  
Zhong Tao Qiao ◽  
Feng Qi Gao
Keyword(s):  
Wavelet Transform ◽  
Compressive Sensing ◽  
Noise Reduction ◽  
Reduction Algorithm ◽  
Lifting Wavelet Transform ◽  
Mems Gyroscope ◽  
Time Frequency ◽  
Time Consumption ◽  
Lifting Wavelet ◽  
Noise Reduction Algorithm

A novel noise reduction algorithm combined with compressive sensing (CS) and lifting wavelet transform (LWT) is proposed in this paper. This algorithm can overcome the limitations of traditional noise reduction methods based on Kalman filtering and wavelet threshold filtering. The characteristics of wavelet time-frequency distribution of the microelectromechanical system (MEMS) gyroscope are discussed to illustrate the demerit of the classical filtering methods. Noise reduction algorithm of MEMS gyroscope signal is studied in detail by combining CS theory with lifting wavelet transform. De-noising effect, time-consumption of computation as well as traditional CS reconstruction algorithms are analyzed. The results show that the signal reconstruction algorithm of conventional matching pursuit (MP) greedy algorithms contains more glitches and computation time-consumption, the basis pursuit de-noising (BPDN) algorithm is better and it has advantages of high computational efficiency and ease of implementation.

An improved noise reduction algorithm based on wavelet transformation for MEMS gyroscope

2015 ◽  
Vol 8 (4) ◽  
pp. 413-418 ◽  
Author(s):  
Jianguo Yuan ◽  
Yantao Yuan ◽  
Feilong Liu ◽  
Yu Pang ◽  
Jinzhao Lin
Keyword(s):  
Noise Reduction ◽  
Wavelet Transformation ◽  
Reduction Algorithm ◽  
Mems Gyroscope ◽  
Noise Reduction Algorithm

scholarly journals A Novel Approach based on Lifting Wavelet Transform for MIMO-OFDM Systems

2021 ◽  
Author(s):  
Elif Büşra Tuna ◽  
Yusuf İslam Tek ◽  
Ali Ozen
Keyword(s):  
Wavelet Transform ◽  
Average Power ◽  
Good Time ◽  
Ofdm Systems ◽  
Lifting Wavelet Transform ◽  
Time Frequency ◽  
High Data ◽  
Mimo Ofdm ◽  
Lifting Wavelet ◽  
Computer Simulation Studies

Abstract In this article, two methods are proposed to further increase the advantages of MIMO-OFDM systems such as high access quality, high data rates and spectral efficiency. The first of these is the combination of the MIMO-OFDM system with the fast Walsh Hadamard transform (FWHT) due to its high accomplishment with the ability to spread the data versus the disturbing influences of the channel. The second is the combination of Lifting wavelet transform (LWT), due to its superior advantages such as good time-frequency localization properties, ICI and ISI suppression capabilities due to its orthonormal structure, unlike fast Fourier transform (FFT), with MIMO-OFDM scheme. Computer simulation studies are carried out to verify the accomplishment of the suggested methods over the bit error rate (BER) and peak to average power ratio (PAPR) benchmark. From the acquired outcomes, it is noticed that approximately 6 dB of SNR gain and approximately 2 dB of PAPR gain are achieved with the proposed method.

Sparse-filtering in directional lifting wavelet transform domain based Bayesian compressive sensing

2014 ◽  
Vol 12 (06) ◽  
pp. 1450043 ◽  
Author(s):  
Xingsong Hou ◽  
Lan Zhang ◽  
Zan Chen ◽  
Chen Gong
Keyword(s):  
Wavelet Transform ◽  
Compressive Sensing ◽  
Signal To Noise Ratio ◽  
Natural Images ◽  
Transform Domain ◽  
Reconstruction Algorithms ◽  
Lifting Wavelet Transform ◽  
Multi Scale ◽  
Sparse Filtering ◽  
Lifting Wavelet

Compressive sensing (CS) has been proposed for images that are sparse under a certain transform domain. However, many natural images are not strictly sparse in the transform domain, causing a tail-folding effect that degrades the performance of the CS reconstruction. To decrease such effect, a sparse-filtering (SF) in Directional Lifting Wavelet Transform (DLWT) domain based Bayesian compressive sensing (BCS) algorithm (DLWT-SF-TSW-BCS) is proposed. At the encoder, DLWT, an efficient multi-scale geometrical analysis (MGA) tool, is used to produce the sparse representation for natural images. Then sparse-filtering is adopted to cut off the small DLWT coefficients before random measurement. At the decoder, the interscale tree-structure redundancy in DLWT domain is further exploited in Bayesian reconstruction. Experimental results show that the proposed DLWT-SF-TSW-BCS algorithm significantly outperforms other state-of-the-art CS reconstruction algorithms, for example, peak signal to noise ratio (PSNR) gain up to 10.00 dB over the tree structured wavelet compressive sensing (TSW-CS).

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