CN115379021A - Coal mine microseismic data compression and acquisition method based on elliptic curve pseudorandom sequence - Google Patents
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Abstract
The invention provides a coal mine microseismic data compression acquisition method based on an elliptic curve pseudorandom sequence, which comprises the following steps: step 1, representing the correlation between coal mine microseismic data and internal data based on a Fourier basis and a mixed support set model; step 2, constructing a deterministic sparse binary measurement matrix based on an elliptic curve pseudorandom sequence, and performing nonlinear projection on coal mine microseismic data by using the matrix in a coal mine microseismic sensor to realize compression and acquisition of the coal mine microseismic data; and 3, in a ground server or an edge computing node, recovering the original data by utilizing the correlation between the coal mine microseismic data and the data. The invention has low calculation complexity, only needs a small amount of storage space, and is more suitable for coal mine microseismic sensors with limited calculation resources and storage resources than other measurement matrixes.
Description
Technical Field
The invention belongs to the technical field of signal processing, and particularly relates to a coal mine microseismic data compression and acquisition method based on an elliptic curve pseudorandom sequence.
Background
The coal mine micro-seismic monitoring system is widely applied to the health monitoring of the geological structure of a coal mine, and can effectively prevent natural disasters such as roof collapse, rock burst and the like. The wired communication technology is high in cost and inconvenient to deploy, the wireless communication technology has wide application prospects in the field of coal mine micro-seismic monitoring, and a large number of micro-seismic wireless sensors are self-organized into a wireless sensor network to acquire and transmit data in a coal mine micro-seismic monitoring system. The energy, the computing power, the storage space and other resources of the wireless micro-seismic sensor are limited, the traditional Nyquist sampling theorem requires that the sampling frequency is more than twice of the signal bandwidth, the micro-seismic sensor needs to transmit a large amount of data, the energy of the micro-seismic wireless sensor is quickly exhausted, the performance of the wireless sensor network is seriously hindered, and a high-efficiency information acquisition method needs to be researched urgently.
The compressed sensing is a novel information acquisition theory, breaks through the limitation of the Nyquist sampling theorem, can acquire the information of data with a very small sampling number for the data with sparsity, can recover the original data by solving an optimization problem at a decoding end, and can realize the high-efficiency compressed acquisition of information. However, compressed sensing only exploits intra-data correlations.
The distributed compressed sensing is an expansion of a compressed sensing theory in the field of multiple sensors, can utilize the intra-data correlation and the inter-data correlation, and has higher-efficiency information acquisition efficiency than the compressed sensing. The existing distributed compressed sensing model for describing the correlation between data and data mainly comprises a JSM-1 model, a JSM-2 model, a JSM-3 model and a mixed support set model, wherein the JSM-1 model needs to divide the data into a public information part and an independent information part, but the non-zero element positions and the numerical values of the public information parts of all the data are completely the same, and the data are not in line with practical application; the JSM-2 model only has a public information part and does not accord with the characteristics of coal mine microseismic data; the JSM-3 model divides data into a public information part and an independent information part, but the data of the public information part cannot be sparsely represented, and is not in line with practical application;
the measurement matrix is a key technology of distributed compressed sensing, the measurement matrix acquires information through nonlinear projection of data, and the performance of the measurement matrix determines the compression rate of the data. Measurement matrices are mainly classified into random measurement matrices and deterministic measurement matrices. The Gaussian random measurement matrix and the Bernoulli random measurement matrix can meet constraint equidistant conditions with high probability and have good performance, but the performance of the Gaussian random measurement matrix and the Bernoulli random measurement matrix has uncertainty, and the Gaussian random measurement matrix and the Bernoulli random measurement matrix are dense measurement matrices, need to occupy a large amount of storage space and calculation resources and are not suitable for wireless microseismic sensors with limited resources. The chaotic measurement matrix utilizes the characteristic that the chaotic sequence has good cross correlation and has good performance, but the chaotic measurement matrix is a dense measurement matrix, has high calculation complexity and is not suitable for a wireless microseismic sensor with limited calculation resources. Dimakis et al have demonstrated that the check matrix of low-density parity-check codes can be used as a measurement matrix, and have proposed a deterministic measurement matrix based on a progressive step-size algorithm, which has a low computational complexity, but the performance needs to be improved.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems that resources such as energy, computing power, storage space and the like of a coal mine micro-seismic wireless sensor are limited and the defects of high computing complexity, large required storage space and poor performance of the conventional measurement matrix, the coal mine micro-seismic data compression acquisition method based on the elliptic curve pseudorandom sequence is provided, and comprises the following steps:
step 1, describing the correlation between the coal mine microseismic data and the internal data by using a Fourier basis and mixed support set model;
step 2, in the coal mine microseismic sensor, nonlinear projection is carried out on coal mine microseismic data by using a deterministic measurement matrix based on an elliptic curve pseudorandom sequence, so that coal mine microseismic data compression and collection are realized;
and 3, transmitting the data compressed and collected in the step 2 to a computing center (the computing center is a ground server or an edge computing node), and recovering the data in the computing center.
Further, step 1 comprises:
step 1-1, for J N-dimensional coal mine microseismic data x j J =1,2, \ 8230, J, sparse representation using fourier basis respectively, J-th N-dimensional coal mine microseismic data x j The sparse representation on the fourier basis is as follows:
θ j ＝Ψ T x j
wherein Ψ represents a Fourier sparse basis;
step 1-2, based on a distributed compressed sensing mixed support set model, sparsely representing each data theta j Reserving a part of large-amplitude coefficients (for example, 80% of coal mine microseismic data is generally reserved), and then dividing the large-amplitude coefficients into a public information part and an independent information part;
in step 1-1, the Fourier sparse basis Ψ is:
in the formula
In step 1-2, each sparsely represented data θ j Expressed as:
θ j ＝c j +z ji ，j∈{1,2,…,J}，
wherein c is j For the common information part of the jth coal mine microseismic data, the common information parts of different coal mine microseismic data have the same non-zero element position, but different values, z ji The position and the value of the nonzero element of the independent information part of the j-th coal mine microseismic data are different.
Further, in step 2, in the coal mine microseismic sensor, a measuring matrix is constructed and usedNonlinear projection of coal mine microseismic data by measurement matrix to realize compressed acquisition y j ＝Φx j Where Φ is the measurement matrix, x j Coal mine microseismic data, y, collected for the jth sensor j Compressing the coal mine microseismic data collected by the jth sensor;
in step 2, the constructing of the measurement matrix specifically includes:
step 2-1, for an elliptic curve xi, the order (the number of rational points on the elliptic curve) is t, G is the first rational point of the elliptic curve xi, and the l-th rational point on the elliptic curve xi is represented as lG = (u l ,v l ) Wherein l is more than or equal to 1 and less than or equal to t-1 l Abscissa, v, representing the ith rational point l Expressing the ordinate of the l-th rational point, an elliptic curve pseudorandom sequence S = { S } of length t is constructed by the following formula 0 ,s 2 ,…,s t-1 }
Where mod is the remainder function, s l Representing the l-th element in an elliptic curve pseudorandom sequence S;
step 2-2, using elliptic curve pseudorandom sequence S = { S = } S 1 ,s 2 ,…,s t-1 Constructing a temporary matrix Q, wherein the first column of the matrix is the elliptic curve pseudorandom sequence S, and the ith column is a sequence obtained by circularly shifting the element of the elliptic curve pseudorandom sequence S for i-1 times to the left:
wherein 1< -i is less than or equal to t;
step 2-2, randomly extracting M rows from the matrix Q to construct a deterministic measurement matrix with the size of M multiplied by N, wherein 1-M-N, M is the row number of the measurement matrix, and N is the column number of the measurement matrix; the first column of the matrix Q is the pseudo-random sequence S, and the ith column is the sequence of the first column cyclically shifted i-1 times to the left.
In a further step 3, the data after compression and collection are transmitted to a computing center, and in the computing center, the data theta after sparse representation is recovered based on a joint orthogonal matching pursuit algorithm by utilizing the correlation between the coal mine microseismic data and the data in the coal mine microseismic data described in the step 1 j Of the set of non-zero element positions I j Then recovering theta by least squares j Then on the sparsely represented data theta j And restoring the coal mine microseismic data by Fourier inverse transformation.
The Fourier basis and the mixed support set model used accord with the characteristics of coal mine micro-seismic data, and the mixed support set model is an extended model of JSM-1 and JSM-2, so that the method has stronger practicability and can better describe the correlation between coal mine micro-seismic data and the internal data.
The measurement matrix can be constructed by only a few parameters, almost does not occupy storage space, and is particularly suitable for the characteristic that the storage space of the coal mine micro-seismic wireless sensor is limited.
The measurement matrix is a deterministic sparse binary measurement matrix, only comprises two elements of '0' and '1', only needs a small amount of addition operation when the coal mine microseismic data is subjected to nonlinear projection, is low in calculation complexity, and is particularly suitable for the characteristic that the coal mine microseismic wireless sensor is limited in calculation resources.
The measurement matrix is smaller than the cross correlation coefficient of a traditional Gaussian random measurement matrix, a Bernoulli measurement matrix, a chaotic measurement matrix and a progressive step size measurement matrix with a Fourier basis.
Under the condition of using the same compression rate and recovery algorithm, the relative error of the method is smaller than that of the traditional Gaussian random measurement matrix, the random sparse measurement matrix and the chaotic measurement matrix when the method is applied to coal mine microseismic data compression and acquisition.
The invention has the following beneficial effects:
the Fourier basis and the mixed support set model used accord with the characteristics of coal mine micro-seismic data, and the mixed support set model is an extended model of JSM-1 and JSM-2, so that the method has stronger practicability and can better describe the correlation in and among the coal mine micro-seismic data.
The deterministic measurement matrix based on the elliptic curve pseudorandom sequence only comprises two elements of '0' and '1', only needs to be subjected to addition operation on the sensor, has low calculation complexity, can be constructed by only a few parameters, occupies less storage space of the sensor, and is more suitable for the characteristics of limited calculation resources and limited storage space of the coal mine microseismic wireless sensor than other measurement matrices.
The deterministic measurement matrix based on the elliptic curve pseudorandom sequence and the Fourier basis have lower cross correlation coefficient than other measurement matrixes and have lower recovery error when the same recovery algorithm and the same compression rate are used.
Drawings
The foregoing and/or other advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Fig. 1 is a distributed compressed sensing mixed support set model taking three data as an example.
FIG. 2 is a schematic step diagram of a coal mine microseismic data compression acquisition method based on an elliptic curve pseudorandom sequence.
FIG. 3 is a cross-correlation coefficient comparison graph of a pseudo-random sequence measurement matrix based on an elliptic curve, other measurement matrices and Fourier bases.
FIG. 4 is a graph showing the experimental results of the present invention.
Detailed Description
With reference to fig. 2, the invention provides a coal mine microseismic data compression acquisition method based on an elliptic curve pseudorandom sequence, which comprises the following steps:
step 1, describing the correlation between coal mine microseismic data and the interior of the data by using a Fourier basis and a mixed support set model;
step 2, in the coal mine microseismic sensor, nonlinear projection is carried out on coal mine microseismic data by using a deterministic measurement matrix based on an elliptic curve pseudorandom sequence, so that coal mine microseismic data compression and collection are realized;
and 3, transmitting the data acquired by compression in the step 2 to a ground server or an edge computing node, and recovering the data by utilizing the correlation between the coal mine microseismic data and the data in the ground server and the edge computing node based on a distributed compression sensing model.
Further, in the step 1, the correlation between the coal mine microseismic data and the internal data is described by using a Fourier basis and a mixed support model, and the steps are as follows:
step 1-1, J N-dimensional coal mine microseismic data x j J =1,2, \8230J, J, respectively expressed sparsely by using Fourier basis, J is N dimension coal mine microseismic data x j The sparse representation on the fourier basis is as follows:
θ j ＝Ψ T x j
wherein the Fourier sparse basis Ψ is:
in the formula
Step 1-2, data theta after each sparse representation is obtained j Into a common information part and an independent information part, theta j ＝c j +z ji J ∈ {1,2, \8230;, J }, where c j For the common information part of the jth coal mine microseismic data, the common information parts of different coal mine microseismic data have the same non-zero element position, but different values, z ji The position and the value of non-zero elements of the independent information part of the j-th coal mine micro-seismic data are different. Taking three data as an example, the mixed support set model is shown in fig. 1, where colors represent non-zero elements, colors represent different values, and white represents a zero element.
Further, in the step 2, in the coal mine microseismic sensor, the measurement matrix is used for carrying out nonlinear projection on coal mine microseismic data to realize compressed acquisition y j ＝Φx j Where Φ is the measurement matrix, x j Coal mine microseismic collected for jth sensorData, y j For compressed coal mine microseismic data acquired by the jth sensor, the step of constructing a measurement matrix is as follows:
step 2-1, for an elliptic curve xi, its order (the number of rational points on the elliptic curve) is t, G is the first rational point of the elliptic curve xi, and the l-th rational point on the elliptic curve xi can be represented as lG = (u l ,v l ) Wherein l is more than or equal to 1 and less than or equal to t-1 l Abscissa, v, representing the ith rational point l Expressing the ordinate of the l-th rational point, an elliptic curve pseudorandom sequence S = { S } of length t is first constructed by the following formula 0 ,s 2 ,…,s t-1 }
Step 2-2, using elliptic curve pseudorandom sequence S = { S = } S 1 ,s 2 ,…,s t-1 Constructing a temporary matrix Q, the first column of which is the pseudo-random sequence S of the elliptic curve, the ith (1) }<i is less than or equal to t) is a sequence obtained by circularly shifting elements of the elliptic curve pseudorandom sequence S for i-1 times to the left:
and 2-3, randomly extracting M rows from the matrix Q to construct a deterministic measurement matrix with the size of M multiplied by N, and 1-type-M-type-N, wherein M is the row number of the matrix, and N is the column number of the matrix.
Further, the data after compression and collection are transmitted to a computing center in the step 3, and in the computing center, the data theta after sparse representation is recovered on the basis of a combined orthogonal matching pursuit algorithm by utilizing the correlation between the coal mine microseismic data and the data in the step 1 j Of the set of non-zero element positions I j Then recovering theta by least squares j Then on the sparsely represented data theta j And restoring the coal mine microseismic data by performing Fourier inversion.
Researches show that the smaller the cross-correlation coefficient of the measurement matrix and the sparse basis is, the more excellent the performance is, and in order to verify the performance of the measurement matrix based on the elliptic curve pseudorandom sequence, the cross-correlation coefficients of the measurement matrix based on the elliptic curve pseudorandom sequence, the chaotic measurement matrix, the random sparse measurement matrix, the Gaussian random measurement matrix and the Fourier basis, which are provided by the invention, are compared, as shown in FIG. 3. The cross-correlation coefficient μ (a) is calculated as:
wherein A = phi psi, a i Is the ith column of matrix a. Obviously, the cross-correlation coefficient of the measurement matrix based on the elliptic curve pseudorandom sequence and the Fourier basis is smaller than that of other measurement matrixes, and the measurement matrix has better performance.
Furthermore, in order to verify the performance of the method, the performance of the method applied to coal mine microseismic data compression and acquisition is compared, and compared objects comprise a Gaussian random measurement matrix, a random sparse measurement matrix and a chaotic measurement matrix. The experimental data source is from a coal mine microseismic monitoring system, the sensor is an ADXL362 3 axis MEMS acceleration sensor, and the deployment position is a coal mine working face. The length of coal mine microseismic data used in an experiment is 540, the sparsity is 100, the number of sensors is 3, the coal mine microseismic data is subjected to nonlinear projection at the sensors through measurement matrixes, data compression and acquisition are realized, a combined orthogonal matching pursuit algorithm is used for recovering the data at a ground server or an edge computing node, and the relative error of data recovery corresponding to each measurement matrix is shown in figure 4. Obviously, the relative error corresponding to the measurement matrix based on the elliptic curve pseudorandom sequence is smaller.
Resources such as energy, storage space and computing power of the coal mine micro-seismic wireless sensor are limited, and in order to verify that the elliptic curve-based pseudo-random sequence measurement matrix provided by the invention is more suitable for the coal mine micro-seismic wireless sensor, the occupation conditions of various measurement matrices with the size of MxN on the coal mine micro-seismic sensor resources are compared, and the occupation conditions are shown in table 1. The elliptic curve-based pseudo-random sequence measurement matrix provided by the invention can be constructed by only a few parameters, hardly occupies the storage space of the sensor, only comprises two elements of '0' and '1', only needs to carry out a few addition operations, occupies few computing resources of the coal mine micro-seismic wireless sensor, and is suitable for the coal mine micro-seismic wireless sensor with limited resources.
TABLE 1
In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, where the computer storage medium can store a computer program, and the computer program can run the inventive content of the coal mine microseismic data compression acquisition method based on the elliptic curve pseudorandom sequence and part or all of the steps in each embodiment provided by the present invention when executed by the data processing unit. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like.
It is clear to those skilled in the art that the technical solutions in the embodiments of the present invention can be implemented by means of a computer program and its corresponding general-purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be essentially or partially implemented in the form of a computer program or a software product, where the computer program or the software product may be stored in a storage medium and include instructions for enabling a device (which may be a personal computer, a server, a single chip microcomputer, an MUU, or a network device) including a data processing unit to execute the method according to the embodiments or some parts of the embodiments of the present invention.
The invention provides a coal mine microseismic data compression acquisition method based on an elliptic curve pseudorandom sequence, and a plurality of methods and ways for realizing the technical scheme are provided, the above description is only a preferred embodiment of the invention, and it should be noted that, for a person skilled in the art, a plurality of improvements and decorations can be made without departing from the principle of the invention, and the improvements and decorations should also be regarded as the protection scope of the invention. All the components not specified in the present embodiment can be realized by the prior art.
Claims (7)
1. The coal mine microseismic data compression acquisition method based on the elliptic curve pseudorandom sequence is characterized by comprising the following steps of:
step 1, describing the correlation between coal mine microseismic data and the interior of the data by using a Fourier basis and a mixed support set model;
step 2, in the coal mine microseismic sensor, nonlinear projection is carried out on coal mine microseismic data by using a deterministic measurement matrix based on an elliptic curve pseudorandom sequence, so that coal mine microseismic data compression and collection are realized;
and 3, transmitting the data compressed and collected in the step 2 to a computing center, and recovering the data in the computing center.
2. The method of claim 1, wherein step 1 comprises:
step 1-1, for J N-dimensional coal mine microseismic data x j J =1,2, \ 8230, J, sparse representation using fourier basis respectively, J-th N-dimensional coal mine microseismic data x j The sparse representation on the fourier basis is as follows:
θ j ＝Ψ T x j
wherein Ψ represents a Fourier sparse basis;
step 1-2, based on a distributed compressed sensing mixed support set model, sparsely representing each data theta j The reserved portion is a large amplitude coefficient and then divided into a common information portion and an independent information portion.
3. The method according to claim 2, wherein in step 1-1, the fourier sparsity Ψ is:
in the formula
0≤a≤N-1，0≤d≤N-1。
4. The method according to claim 3, wherein in step 1-2, each sparsely represented data θ j Expressed as:
θ j ＝c j +z ji ，j∈{1,2,…,J}，
wherein c is j Is the common information part of the jth coal mine microseismic data, z ji Is an independent information part of the j coal mine microseismic data.
5. The method as claimed in claim 4, wherein in step 2, in the coal mine microseismic sensor, a measurement matrix is constructed, and the compressed acquisition y is realized by carrying out nonlinear projection on coal mine microseismic data by using the measurement matrix j ＝Φx j Where Φ is the measurement matrix, x j Coal mine microseismic data, y, collected for the jth sensor j And compressing the coal mine microseismic data collected by the jth sensor.
6. The method according to claim 5, characterized in that in step 2, said constructing a measurement matrix comprises in particular:
step 2-1, for an elliptic curve xi, the order is t, G is a rational point of the elliptic curve xi, and the l-th rational point on the elliptic curve xi is represented as lG = (u l ,v l ) Wherein l is more than or equal to 1 and less than or equal to t-1 l Abscissa, v, representing the ith rational point l Expressing the ordinate of the l-th rational point, an elliptic curve pseudorandom sequence S = { S } with the length of t is constructed by the following formula 0 ,s 2 ,…,s t-1 }：
Where mod is the remainder function, s l Representing the ith element in an elliptic curve pseudorandom sequence S;
step 2-2, using elliptic curve pseudorandom sequence S = { S = } S 1 ,s 2 ,…,s t-1 Constructing a temporary matrix Q, wherein the first column of the matrix is an elliptic curve pseudorandom sequence S, and the ith column is a sequence obtained by circularly shifting the element of the elliptic curve pseudorandom sequence S for i-1 times to the left:
wherein 1 is formed by (i) and less than or equal to t;
step 2-2, randomly extracting M rows from the matrix Q to construct a deterministic measurement matrix with the size of M multiplied by N, wherein 1-M-N, M is the row number of the measurement matrix, and N is the column number of the measurement matrix; the first column of the matrix Q is the pseudo-random sequence S, and the ith column is the sequence of the first column cyclically shifted i-1 times to the left.
7. The method as claimed in claim 6, wherein in step 3, the compressed and collected data is transmitted to a computing center, and in the computing center, the correlation between the coal mine microseismic data and the data described in step 1 is utilized to recover the sparsely represented data theta based on a joint orthogonal matching pursuit algorithm j Of the set of non-zero element positions I j Then recovering theta by least squares j Then on the sparsely represented data theta j And restoring the coal mine microseismic data by Fourier inverse transformation.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007050680A2 (en) * | 2005-10-25 | 2007-05-03 | William Marsh Rice University | Method and apparatus for on-line compressed sensing |
| CN103020018A (en) * | 2012-12-27 | 2013-04-03 | 南京师范大学 | Construction method of compressive sensing matrix based on multi-dimensional pseudo-random sequence |
| CN107045142A (en) * | 2017-07-04 | 2017-08-15 | 吉林大学 | Wavelet field geological data Real Time Compression and High precision reconstruction method based on compressed sensing |
| CN107070459A (en) * | 2017-03-13 | 2017-08-18 | 中国矿业大学 | A kind of distributed process monitors the highly reliable encoding and decoding algorithm of the low computation complexity of information source |
| CN107666322A (en) * | 2017-09-08 | 2018-02-06 | 山东科技大学 | A kind of adaptive microseism data compression sensing method based on dictionary learning |
| CN107942377A (en) * | 2018-01-05 | 2018-04-20 | 吉林大学 | A kind of seismic data compression and reconstructing method |
| CN112543092A (en) * | 2020-10-26 | 2021-03-23 | 北京计算机技术及应用研究所 | Chaotic binary sequence family matrix construction method based on image compressed sensing encryption |
| CN114396311A (en) * | 2021-12-20 | 2022-04-26 | 煤炭科学技术研究院有限公司 | Coal mine microseismic signal processing method and device |
-
2022
- 2022-07-08 CN CN202210805307.3A patent/CN115379021A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007050680A2 (en) * | 2005-10-25 | 2007-05-03 | William Marsh Rice University | Method and apparatus for on-line compressed sensing |
| CN103020018A (en) * | 2012-12-27 | 2013-04-03 | 南京师范大学 | Construction method of compressive sensing matrix based on multi-dimensional pseudo-random sequence |
| CN107070459A (en) * | 2017-03-13 | 2017-08-18 | 中国矿业大学 | A kind of distributed process monitors the highly reliable encoding and decoding algorithm of the low computation complexity of information source |
| CN107045142A (en) * | 2017-07-04 | 2017-08-15 | 吉林大学 | Wavelet field geological data Real Time Compression and High precision reconstruction method based on compressed sensing |
| CN107666322A (en) * | 2017-09-08 | 2018-02-06 | 山东科技大学 | A kind of adaptive microseism data compression sensing method based on dictionary learning |
| CN107942377A (en) * | 2018-01-05 | 2018-04-20 | 吉林大学 | A kind of seismic data compression and reconstructing method |
| CN112543092A (en) * | 2020-10-26 | 2021-03-23 | 北京计算机技术及应用研究所 | Chaotic binary sequence family matrix construction method based on image compressed sensing encryption |
| CN114396311A (en) * | 2021-12-20 | 2022-04-26 | 煤炭科学技术研究院有限公司 | Coal mine microseismic signal processing method and device |
Non-Patent Citations (1)
| Title |
|---|
| 徐永华，刘海强等: ""基于分布式压缩感知的煤矿井下图像采集方法研究"", 《金陵科技学院学报》 * |
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