Time series phase unwrapping algorithm using LP-norm optimization compressive sensing

Li, Shijin; Zhang, Shubi; Gao, Yandong; Li, Tao; Han, Jiazheng; Chen, Qiang; Zhang, Yansuo; Tian, Yu

doi:10.1016/j.jag.2023.103182

“…include Lempel-Ziv-Welsh (LZW), Lempel-Ziv-Markov chain algorithm, and adaptive Huffman coding [14]. The Compressed Sensing (CS) algorithm can accurately reconstruct all data with a small amount of sample, it can reconstruct a signal robustly and stably from under sampled noise observations by exploiting the sparse characteristics of the signal [15]. This advantage means CS has the potential to achieve a higher compression ratio.…”

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confidence: 99%

Multivariate time Series Collaborative Compression for Monitoring Systems in Securing Cloud-based Digital Twin

Miao,

Li,

Pan

2023

Preprint

0

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With the booming of cloud-based digital twin systems, monitoring key performance indicators has become crucial for ensuring system security and reliability. Due to the massive amount of monitoring data generated, data compression is necessary to save data transmission bandwidth and storage space. Although the existing research has proposed compression methods for multivariate time series (MTS), it is still a challenge to guarantee the correlation between data when compressing the MTS. This paper proposes an MTS Collaborative Compression (MTSCC) method based on the two-step compression scheme. First, shape-based clustering is implemented to group the MTS. Afterward, the compressed sensing is optimized to achieve collaborative compression of grouped data. Based on a real-world MTS dataset, the experimental results show that the proposed MTSCC can effectively preserve the complex temporal correlation between indicators while achieving efficient data compression, and the root mean squared error of correlation between the reconstructed and original data is only 0.0489 in the case of 30% compression ratio. Besides, it is verified that using the reconstructed data in the production environment has almost the same performance as using the original data.

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Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin

Miao,

Li,

Pan

2024

J Cloud Comp

0

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With the booming of cloud-based digital twin systems, monitoring key performance indicators has become crucial for ensuring system security and reliability. Due to the massive amount of monitoring data generated, data compression is necessary to save data transmission bandwidth and storage space. Although the existing research has proposed compression methods for multivariate time series (MTS), it is still a challenge to guarantee the correlation between data when compressing the MTS. This paper proposes an MTS Collaborative Compression (MTSCC) method based on the two-step compression scheme. First, shape-based clustering is implemented to group the MTS. Afterward, the compressed sensing is optimized to achieve collaborative compression of grouped data. Based on a real-world MTS dataset, the experimental results show that the proposed MTSCC can effectively preserve the complex temporal correlation between indicators while achieving efficient data compression, and the root mean squared error of correlation between the reconstructed and original data is only 0.0489 in the case of 30% compression ratio. Besides, it is verified that using the reconstructed data in the production environment has almost the same performance as using the original data.

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High-accuracy fringe projection profilometry without phase unwrapping based on multi-view geometry constraints

Fu,

Luo,

Zhong

et al. 2024

Opt. Express

0

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The number of fringes and phase unwrapping in fringe projection profilometry result in two key factors. The first is to avoid the problems of excessive fringe patterns, and the second is phase ambiguity. This paper presents a three-dimensional (3D) measurement method without phase unwrapping. This method benefits from the geometric constraints and does not require additional images. Meanwhile, epipolar rectification is performed to calibrate the rotation matrix relationship between the new plane of the dual camera and the plane of the projector. Subsequently, using depth constraints, the point pairs with incorrect 3D positions are effectively eliminated, and the initial parallax map is obtained by establishing epipolar lines of the left and right matching points in the projector domain, obtaining the intersection points, and setting up the threshold for filtering. Finally, a function combining the modulation intensity and phase is proposed to refine the parallax map such that the 3D result is insensitive to phase error. The standard step block and standard ball were used to verify the validity of the proposed method, and the experimental results showed that the root mean square error of the method was 0.052 mm.

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Time series phase unwrapping algorithm using LP-norm optimization compressive sensing

Cited by 3 publications

References 22 publications

Multivariate time Series Collaborative Compression for Monitoring Systems in Securing Cloud-based Digital Twin

Multivariate time Series Collaborative Compression for Monitoring Systems in Securing Cloud-based Digital Twin

Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin

High-accuracy fringe projection profilometry without phase unwrapping based on multi-view geometry constraints

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