An Efficient Method for NMR Data Compression Based on Fast Singular Value Decomposition

Guo, Jiangfeng; Xie, Ranhong; Jin, Guowen

doi:10.1109/lgrs.2018.2872111

Cited by 20 publications

(17 citation statements)

References 26 publications

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“…Besides, Benes and Kruis [6] applied SVD to the compression of results from finite element solvers. Guo et al [7] proposed a fast SVD method to compress nuclear magnetic resonance echo data. SVD is good at dealing with global transformation, which at the same time leads to high complexity and is not suitable for streaming computing.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, researchers have shown an intensive research in approximate representation or compression of large-scale data. The most popular methods include Singular Value Decomposition (SVD) [3]- [7], Discrete Fourier Transform (DFT) [8]- [11], Discrete Wavelet Transform (DWT) [5], [12]- [15], Piecewise Aggregation Approximation (PAA) [16]- [18], Piecewise Linear Approximation (PLA) [1], [2], [19]- [23], Adaptive Piecewise Constant Approximation (APCA) [24]- [26], Piecewise Curve Fitting [27]- [38], and some other methods [39]. Piecewise curve fitting fits discrete data utilizing different fitting functions at different intervals.…”

Section: Introductionmentioning

confidence: 99%

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Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing

Gao

Zhang

et al. 2020

IEEE Access

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Streaming computing attracts intense attention because of the demand for massive data analyzing in real-time. Due to unbounded and continuous input, the volume of streaming data is so high that all the data cannot be permanently stored. Piecewise polynomial fitting is a popular data compression method that approximately represents the raw data stream with multiple polynomials. The polynomial coefficients corresponding to the best-fitting curve can be calculated by the method of least squares, which minimizes the sum of the squared residuals between observed and fitted values. However, built on several matrix calculations, the method of least squares always leads to high time complexity and is difficult to be applied to streaming computing. This paper puts forward a fast piecewise polynomial fitting for time-series data in streaming computing. The input data stream is dynamically segmented according to a given residual bound. Meanwhile, the data points in each segment are fitted using an improved polynomial fitting method, which has less time overhead than general polynomial fitting by reusing the intermediate calculation results. Experimental results on four time-series datasets show that our algorithm can achieve the highest speedup to the general piecewise polynomial fitting of 2.82x for periodically sampled time-series data and 1.85x for aperiodically sampled time-series data, without affecting the compression ratio and fitting accuracy. Moreover, the event-time latency comparison in a streaming environment indicates that the improved method can endure higher throughput than general piecewise polynomial fitting with the same latency. INDEX TERMS Least squares, piecewise polynomial fitting, streaming computing, time-series data.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing

Gao

Zhang

et al. 2020

IEEE Access

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“…In this paper, the orthogonal code mapping to multiple bit watermarks is a Hadamard sequence which taken from the Hadamard matrix. Denote the Hadamard matrix H r ∈ {−1, +1} r×r generated by [13,14] as…”

Section: An Overcomplete Dictionary With Ss-based Contentmentioning

confidence: 99%

Compressive Sampling with Multiple Bits Spread Spectrum-Based Data Hiding

Budiman¹,

Suksmono²,

Danudirdjo³

2020

Preprint

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We propose a novel data hiding method in an audio host with a compressive sampling technique. An over-complete dictionary represents a group of the watermark. Each row of the dictionary is a Hadamard sequence representing multiple bits of the watermark. Then, the singular values of segment-based host audio in a diagonal matrix multiply by the over-complete dictionary producing a lower size matrix. At the same time, we embed the watermark into the compressed audio. In the detector, we detect the watermark and reconstruct the audio. This proposed method offers not only hiding the information but also compressing the audio host. The application of the proposed method is a broadcast monitoring and biomedical signal recording. We can mark and secure the signal content by hiding the watermark inside the signal while we compress the signal for memory efficiency. We evaluate the performance in terms of payload, compression ratio, audio quality, and watermark quality. The proposed method can hide the data imperceptibly, in range 729-5292 bps with compression ratio 1.47-4.84 and perfect detected watermark.

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“…Moreover, the type of fluid within the rock can affect the imaging results [76,77]. The NMR inversion algorithm is another factor that influences the imaging quality; accordingly, research on NMR inversion algorithms is ongoing [78][79][80]. In summary, MRI is not currently employed to construct digital cores because of its limitations.…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Challenges and Prospects of Digital Core-Reconstruction Research

et al. 2019

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The simulation of various rock properties based on three-dimensional digital cores plays an increasingly important role in oil and gas exploration and development. The accuracy of 3D digital core reconstruction is important for determining rock properties. In this paper, existing 3D digital core-reconstruction methods are divided into two categories: 3D digital cores based on physical experiments and 3D digital core stochastic reconstructions based on two-dimensional (2D) slices. Additionally, 2D slice-based digital core stochastic reconstruction techniques are classified into four types: a stochastic reconstruction method based on 2D slice mathematical-feature statistical constraints, a stochastic reconstruction method based on statistical constraints that are related to 2D slice morphological characteristics, a physics process-based stochastic reconstruction method, and a hybrid stochastic reconstruction method. The progress related to these various stochastic reconstruction methods, the characteristics of constructed 3D digital cores, and the potential of these methods are analysed and discussed in detail. Finally, reasonable prospects are presented based on the current state of this research area. Currently, studies on digital core reconstruction, especially for the 3D digital core stochastic reconstruction method based on 2D slices, are still very rough, and much room for improvement remains. In particular, we emphasize the importance of evaluating functions, multiscale 3D digital cores, multicomponent 3D digital cores, and disciplinary intersection methods in the 3D construction of digital cores. These four directions should provide focus, alongside challenges, for this research area in the future. This review provides important insights into 3D digital core reconstruction.

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An Efficient Method for NMR Data Compression Based on Fast Singular Value Decomposition

Cited by 20 publications

References 26 publications

Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing

Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing

Compressive Sampling with Multiple Bits Spread Spectrum-Based Data Hiding

Challenges and Prospects of Digital Core-Reconstruction Research

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