CN108663576A - Weak electromagnetic red signal detection method under a kind of complex environment - Google Patents
Weak electromagnetic red signal detection method under a kind of complex environment Download PDFInfo
- Publication number
- CN108663576A CN108663576A CN201810431846.9A CN201810431846A CN108663576A CN 108663576 A CN108663576 A CN 108663576A CN 201810431846 A CN201810431846 A CN 201810431846A CN 108663576 A CN108663576 A CN 108663576A
- Authority
- CN
- China
- Prior art keywords
- signal
- data
- carried out
- detection
- sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/08—Measuring electromagnetic field characteristics
Landscapes
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- General Physics & Mathematics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The present invention provides a kind of quick detections of the weak electromagnetic leakage signal of information equipment in complex electromagnetic environment, and weak electromagnetic leakage signal is enhanced and detached by random resonance detection method, and judges risk of leakage.Accidental resonance detection directly is carried out to time-domain signal, noise energy is converted to signal energy in the time domain, enhances the electromagnetism red signal of time domain so that the detection weaker signal of energy is achieved.The optimization section of system structure parameter is widened so that system has the characteristics that adaptive.Parameter optimization to stochastic resonance method is reduced to one-dimensional parameter optimization from two-dimensional parameter optimizing, and time complexity reduces, and uses golden section search method, more efficient.Detection to electromagnetic signal carefully surveys two stages by small sample bigness scale and large sample, improves accuracy.The present invention is verified by system, and different electromagnetism red signals can be detected by the detecting system.
Description
Technical field
The present invention relates to electromagnetic leakage detection fields, particularly, are related to weak electromagnetic red signal under a kind of complex environment and examine
Survey method.
Background technology
Information equipment can all generate electromagnetic radiation at work, and this radiation can leak some useful informations (red information)
It goes out, serious information security threats can be caused.
Mainly there are conventional method and novel detection method for electromagnetic leakage detection method.
Conventional method is mainly detected (bibliography 1, bibliography 2) using test receiver, is observed from frequency domain
The larger doubtful leakage signal of amplitude, then magnified sweep, judgement leakage frequency point.
Novel detection method has had carries out the report of electromagnetic signal detection (with reference to text using accidental resonance nonlinear system
It offers 3), it is preliminary to attempt to be analyzed from frequency spectrum using accidental resonance, by the reinflated judgement of the big frequency point of spectrum energy, in flow
It is similar with conventional method.But the parameter optimization range setting of stochastic resonance system is empirically to judge.
It, usually will be random on other documents or patent merely for the optimizing of stochastic resonance system structural parameters
The Search Range of resonator system parameter is set as (0,1).Which has limited the Search Ranges in parameter, can not accomplish the overall situation most
It is excellent.
Accidental resonance is a kind of resonance effects generated between signal, noise and nonlinear system three, defeated by adjusting
The intensity or regulating system parameter for entering noise can reach accidental resonance, to realize signal detection.Bistable state is non-linear
System can be indicated with Langevin equations：
U (t)=s (t)+Γ (t)
V (x) is potential function：
Coefficient a>0,b>0
Arrangement can obtain
Wherein, Γ (t) is noise, and s (t) is signal.
Accidental resonance is generated by regulating system parameter a, b so that echo signal is enhanced.
It is detected for electromagnetic leakage signal, the shortcomings that traditional frequency spectrum detecting method is mainly reflected in two aspects：
First, detection speed is slow.Entire frequency domain is traversed using frequency spectrograph, speed depends on the frequency handover speed of frequency spectrograph
Degree；Suspicious frequency point is found under thicker spectral resolution and then is differentiated into line frequency with thinner frequency resolution.
Second is that the sensitivity of detection is low.Noise reduction process is carried out to signal using means of filtering, weakens the energy of signal, only
The larger leakage signal of energy can be detected, the lower leakage signal of energy can not be detected.
It is detected in existing method or frequency spectrum using the detection of accidental resonance electromagnetic leakage signal, with traditional frequency
Spectrum detection method has the shortcomings that identical；Meanwhile it also failing to enough accomplish on the Search Range of the adaptive and parameter of system optimal.
Invention content
Present invention aims at weak electromagnetic red signal detection methods under a kind of complex environment of offer, to solve in complicated electricity
In magnetic environment the technical issues of the quick detection of the weak electromagnetic leakage signal of information equipment, pass through random resonance detection method pair
Weak electromagnetic leakage signal is enhanced and is detached, and judges risk of leakage.
To achieve the above object, the present invention provides weak electromagnetic red signal detection methods under a kind of complex environment, including
Step：
(1) electromagnetic radiation information of information equipment is collected, discretization data are obtained after A/D conversions；
(2) the discretization data obtained to step (1) pre-process；
(3) energy conversion is carried out to the pretreated data of step (2) using the non-linear behavior of stochastic resonance system, by force
Change suspect signal；
(4) numeralization solves；
(5) parameter optimization is carried out；
(6) optimum structure parameter, output data are determined；
(7) feature extraction is carried out to the output data of step (6), and is carried out mutually with the sample data of electromagnetic signal sample database
Correlation computations carry out the preliminary matches comparison of small sample；
(8) the optimum structure parameter of step (6) is substituted into stochastic resonance system, acquires the data of longer time, carried out non-
Linear to calculate, the Data Matching that the output data of stochastic resonance system is carried out to large sample size with electromagnetic signal sample database compares,
Confirm electromagnetism red signal feature.
As one of preferred technical solution, the detection method further includes step (9), and the data of step (8) are carried out
The processing such as signal reconstruct, comentropy calculating, obtain the risk assessment grade of information equipment.
As one of preferred technical solution, it is as follows：
(1) electromagnetic radiation information that information equipment is collected by antenna, according to detection Frequency Band Selection respective antenna model, warp
It crosses high-speed data acquisition card and carries out A/D conversions, sample rate fs obtains discretization data, is expressed as un, n=1,2 ... N, N are
Sampling number；
(2) the discretization data of step (1) acquisition are pre-processed, obtain amplitude peak | U |=max (un),
It takes
The initial value a of computing system structural parameters a, b0,b0；
(3) energy conversion is carried out to the pretreated data of step (2) using the non-linear behavior of stochastic resonance system, it will
Part electromagnetic noise energy in free space is converted into the energy of detection echo signal, strengthens to suspect signal, improves
Detection sensitivity；
(4) numeralization solution is carried out using fourth-order Runge-Kutta method：
k1=h (axn-bxn 3+un)
k4=h [a (xn+k3)-b(xn+k3)3+un+1] formula 2
X in formula 2nFor the system output of n-th, k1For starting point slope, k2,k3For intermediate slope, k4For ending slope, it is
NumberA=a0, b=b0；
(5) parameter optimization is carried out using golden section search method, the search range of H is (0, | U |)
According to formula 1 and formula 3, new systematic parameter a, b are calculated；
(6) by adjusting structural parameters a, the b of stochastic resonance system, optimum structure parameter a*, b* are acquired, in sample set
In accomplish the global optimum of parameter, output data xn；
(7) to the system output data x of step (6)nCarry out feature extraction, the information such as extracting cycle, duty ratio, and with electricity
The sample data of magnetic signal sample database carries out cross-correlation calculation, carries out the preliminary matches comparison of small sample；
(8) by optimum structure parameter a*, b* the substitution stochastic resonance system of step (6), the data of longer time are acquired, into
Row NONLINEAR CALCULATION, by the output data x of stochastic resonance systemnThe Data Matching of large sample size is carried out with electromagnetic signal sample database
It compares, confirms electromagnetism red signal feature.
As further preferred one of technical solution, the detection method further includes step (9), by the number of step (8)
According to the processing such as signal reconstruct, comentropy calculating are carried out, the risk assessment grade of information equipment is obtained.
The invention has the advantages that：
The present invention realizes the quick detection of the weak electromagnetic leakage signal of the information equipment in complex electromagnetic environment, passes through
Random resonance detection method is enhanced and is detached to weak electromagnetic leakage signal, and judges risk of leakage.It is specific as follows：
(1) accidental resonance detection directly is carried out to time-domain signal, converts noise energy to signal energy in the time domain, increased
The strong electromagnetism red signal of time domain so that the detection weaker signal of energy is achieved.
(2) the optimization section for having widened system structure parameter (i.e. a, b), is not limited to (0,1), but by H and L determine i.e. by
The amplitude of mixed signal determines so that system has the characteristics that adaptive.
(3) it to the parameter optimization of stochastic resonance method, is reduced to one-dimensional parameter (i.e. H) from two-dimensional parameter (i.e. a, b) optimizing and seeks
Excellent, time complexity is reduced to O (n^1) from O (n^2), and uses golden section search method, more efficient.
(4) to the detection of electromagnetic signal, two stages is carefully surveyed by small sample bigness scale and large sample, improve accuracy.
The present invention is verified by system, and different electromagnetism red signals can be detected by the detecting system.
Other than objects, features and advantages described above, the present invention also has other objects, features and advantages.
Below with reference to figure, the present invention is described in further detail.
Description of the drawings
The attached drawing constituted part of this application is used to provide further understanding of the present invention, schematic reality of the invention
Example and its explanation are applied for explaining the present invention, is not constituted improper limitations of the present invention.In the accompanying drawings：
Fig. 1 is the detection of the synchronizing signal of interface；
Fig. 2 is the display font size detection of screen.
Specific implementation mode
The embodiment of the present invention is described in detail below in conjunction with attached drawing, but the present invention can be limited according to claim
Fixed and covering multitude of different ways is implemented.
Weak electromagnetic red signal detection method under a kind of complex environment, is as follows：
(1) electromagnetic radiation information that information equipment is collected by antenna, according to detection Frequency Band Selection respective antenna model, warp
It crosses high-speed data acquisition card and carries out A/D conversions, sample rate fs obtains discretization data, is expressed as un, n=1,2 ... N, N are
Sampling number；
(2) the discretization data of step (1) acquisition are pre-processed, obtain amplitude peak | U |=max (un),
It takes
The initial value a of computing system structural parameters a, b0,b0；
(3) energy conversion is carried out to the pretreated data of step (2) using the non-linear behavior of stochastic resonance system, it will
Part electromagnetic noise energy in free space is converted into the energy of detection echo signal, strengthens to suspect signal, improves
Detection sensitivity；
(4) numeralization solution is carried out using fourth-order Runge-Kutta method：
k1=h (axn-bxn 3+un)
k4=h [a (xn+k3)-b(xn+k3)3+un+1] formula 2
X in formula 2nFor the system output of n-th, k1For starting point slope, k2,k3For intermediate slope, k4For ending slope, it is
NumberA=a0, b=b0；
(5) parameter optimization is carried out using golden section search method, the search range of H is (0, | u |)
According to formula 1 and formula 3, new systematic parameter a, b are calculated；
(6) by adjusting structural parameters a, the b of stochastic resonance system, optimum structure parameter a*, b* are acquired, in sample set
In accomplish the global optimum of parameter, output data xn；
(7) to the system output data x of step (6)nCarry out feature extraction, the information such as extracting cycle, duty ratio, and with electricity
The sample data of magnetic signal sample database carries out cross-correlation calculation, carries out the preliminary matches comparison of small sample；
(8) by optimum structure parameter a*, b* the substitution stochastic resonance system of step (6), the data of longer time are acquired, into
Row NONLINEAR CALCULATION, by the output data x of stochastic resonance systemnThe Data Matching of large sample size is carried out with electromagnetic signal sample database
It compares, confirms electromagnetism red signal feature.
(9) data of step (8) processing, the risks for obtaining information equipment such as signal reconstruct, comentropy calculating is carried out to comment
Estimate grade.
The present invention is verified by system, and different electromagnetism red signals can be detected by the detecting system, and Fig. 1 is interface
The detection of synchronizing signal, Fig. 2 are the display font size detection of screen.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, any made by repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
【Bibliography】
1. Zhao Zhi is strong, Liu Taikang, detection and the analysis computer security of Jiang Yun computer display card electromagnetic information leakages
.2013.
2. the frequency spectrum detection of Zhao Lin, Liu Taikang, Jiang Yun computer input devices and analysis computer security .2013.
3. defending luxuriant, the stochastic resonance method and application study industry measurements .2017 of the detection of Shi Sen electromagnetic signals.
Claims (4)
1. weak electromagnetic red signal detection method under a kind of complex environment, which is characterized in that including step：
(1) electromagnetic radiation information of information equipment is collected, discretization data are obtained after A/D conversions；
(2) the discretization data obtained to step (1) pre-process；
(3) energy conversion is carried out to the pretreated data of step (2) using the non-linear behavior of stochastic resonance system, reinforcing waits for
Examine signal；
(4) numeralization solves；
(5) parameter optimization is carried out；
(6) optimum structure parameter, output data are determined；
(7) feature extraction is carried out to the output data of step (6), and cross-correlation is carried out with the sample data of electromagnetic signal sample database
It calculates, carries out the preliminary matches comparison of small sample；
(8) the optimum structure parameter of step (6) is substituted into stochastic resonance system, acquires the data of longer time, carried out non-linear
It calculates, the Data Matching that the output data of stochastic resonance system is carried out to large sample size with electromagnetic signal sample database compares, and confirms
Electromagnetism red signal feature.
2. detection method according to claim 1, which is characterized in that the detection method further includes step (9), by step
(8) processing such as data carry out signal reconstruct, comentropy calculates, obtain the risk assessment grade of information equipment.
3. detection method according to claim 1, which is characterized in that be as follows：
(1) electromagnetic radiation information that information equipment is collected by antenna, according to detection Frequency Band Selection respective antenna model, through excessively high
Fast data collecting card carries out A/D conversions, and sample rate fs obtains discretization data, is expressed as un, n=1,2 ... N, N are sampling
Points；
(2) the discretization data of step (1) acquisition are pre-processed, obtain amplitude peak | U |=max (un),
It takes
The initial value a of computing system structural parameters a, b0,b0；
(3) energy conversion is carried out to the pretreated data of step (2) using the non-linear behavior of stochastic resonance system, it will be free
Part electromagnetic noise energy in space is converted into the energy of detection echo signal, strengthens to suspect signal, improves detection
Sensitivity；
(4) numeralization solution is carried out using fourth-order Runge-Kutta method：
k1=h (axn-bxn 3+un)
k4=h [a (xn+k3)-b(xn+k3)3+un+1] formula 2
X in formula 2nFor the system output of n-th, k1For starting point slope, k2,k3For intermediate slope, k4For ending slope, coefficientA=a0, b=b0；
(5) parameter optimization is carried out using golden section search method, the search range of H is (0, | U |)
According to formula 1 and formula 3, new systematic parameter a, b are calculated；
(6) by adjusting structural parameters a, the b of stochastic resonance system, optimum structure parameter a*, b* is acquired, is done in sample set
To the global optimum of parameter, output data xn；
(7) to the system output data x of step (6)nFeature extraction, the information such as extracting cycle, duty ratio are carried out, and are believed with electromagnetism
The sample data of number sample database carries out cross-correlation calculation, carries out the preliminary matches comparison of small sample；
(8) optimum structure the parameter a*, b* of step (6) are substituted into stochastic resonance system, acquires the data of longer time, carries out non-
It is linear to calculate, by the output data x of stochastic resonance systemnThe Data Matching ratio of large sample size is carried out with electromagnetic signal sample database
It is right, confirm electromagnetism red signal feature.
4. detection method according to claim 3, which is characterized in that the detection method further includes step (9), by step
(8) processing such as data carry out signal reconstruct, comentropy calculates, obtain the risk assessment grade of information equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810431846.9A CN108663576A (en) | 2018-05-08 | 2018-05-08 | Weak electromagnetic red signal detection method under a kind of complex environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810431846.9A CN108663576A (en) | 2018-05-08 | 2018-05-08 | Weak electromagnetic red signal detection method under a kind of complex environment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108663576A true CN108663576A (en) | 2018-10-16 |
Family
ID=63778813
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810431846.9A Pending CN108663576A (en) | 2018-05-08 | 2018-05-08 | Weak electromagnetic red signal detection method under a kind of complex environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108663576A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109633289A (en) * | 2018-12-28 | 2019-04-16 | 集美大学 | A kind of red information detecting method of electromagnetism based on cepstrum and convolutional neural networks |
| CN110490154A (en) * | 2019-08-23 | 2019-11-22 | 集美大学 | A kind of multidimensional leakage information detection method, terminal device and storage medium |
| CN111814578A (en) * | 2020-06-15 | 2020-10-23 | 南京森林警察学院 | Method for extracting frequency of ultralow frequency Doppler signal |
| CN112307931A (en) * | 2020-10-26 | 2021-02-02 | 西安电子科技大学 | Electromagnetic information leakage rapid detection method based on template matching technology |
| CN117332410A (en) * | 2023-10-25 | 2024-01-02 | 北京航空航天大学 | Electromagnetic leakage red and black signal identification method based on information entropy characteristics |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101191804A (en) * | 2007-12-03 | 2008-06-04 | 中国人民解放军国防科学技术大学 | Self-adapting random resonant weak signal detection method |
| CN103699513A (en) * | 2013-12-20 | 2014-04-02 | 中国科学技术大学 | Stochastic resonance method based on multi-scale noise adjustment |
| CN104155518A (en) * | 2014-07-31 | 2014-11-19 | 桂林电子科技大学 | Signal frequency detection method based on stochastic resonance |
| CN104298878A (en) * | 2014-10-14 | 2015-01-21 | 天津工业大学 | Self-adaptive searching method for obtaining optimal parameters of stochastic resonance system |
| CN104678768A (en) * | 2015-01-30 | 2015-06-03 | 哈尔滨工程大学 | Autonomous underwater robot signal processing method based on particle swarm optimization monostable adaptive stochastic resonance |
| CN105823492A (en) * | 2016-03-18 | 2016-08-03 | 北京卫星环境工程研究所 | Method of extracting weak target signal in ocean current interference |
| CN106408087A (en) * | 2016-09-18 | 2017-02-15 | 厦门大学 | Reinforcement learning adaptive stochastic resonance method for underwater weak signal detection |
| CN107340056A (en) * | 2017-06-29 | 2017-11-10 | 西北工业大学 | One kind matching random resonance detection method |
| CN107809268A (en) * | 2017-11-09 | 2018-03-16 | 电子科技大学 | A kind of ultra-broadband signal Wave shape extracting method based on accidental resonance |
| CN107871109A (en) * | 2016-09-27 | 2018-04-03 | 重庆邮电大学 | The method for detecting weak signals of three-stable state accidental resonance under coloured noise |
| CN107870005A (en) * | 2016-09-27 | 2018-04-03 | 重庆邮电大学 | The normalization random resonant weak signal detection of empirical mode decomposition under over-sampling |
-
2018
- 2018-05-08 CN CN201810431846.9A patent/CN108663576A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101191804A (en) * | 2007-12-03 | 2008-06-04 | 中国人民解放军国防科学技术大学 | Self-adapting random resonant weak signal detection method |
| CN103699513A (en) * | 2013-12-20 | 2014-04-02 | 中国科学技术大学 | Stochastic resonance method based on multi-scale noise adjustment |
| CN104155518A (en) * | 2014-07-31 | 2014-11-19 | 桂林电子科技大学 | Signal frequency detection method based on stochastic resonance |
| CN104298878A (en) * | 2014-10-14 | 2015-01-21 | 天津工业大学 | Self-adaptive searching method for obtaining optimal parameters of stochastic resonance system |
| CN104678768A (en) * | 2015-01-30 | 2015-06-03 | 哈尔滨工程大学 | Autonomous underwater robot signal processing method based on particle swarm optimization monostable adaptive stochastic resonance |
| CN105823492A (en) * | 2016-03-18 | 2016-08-03 | 北京卫星环境工程研究所 | Method of extracting weak target signal in ocean current interference |
| CN106408087A (en) * | 2016-09-18 | 2017-02-15 | 厦门大学 | Reinforcement learning adaptive stochastic resonance method for underwater weak signal detection |
| CN107871109A (en) * | 2016-09-27 | 2018-04-03 | 重庆邮电大学 | The method for detecting weak signals of three-stable state accidental resonance under coloured noise |
| CN107870005A (en) * | 2016-09-27 | 2018-04-03 | 重庆邮电大学 | The normalization random resonant weak signal detection of empirical mode decomposition under over-sampling |
| CN107340056A (en) * | 2017-06-29 | 2017-11-10 | 西北工业大学 | One kind matching random resonance detection method |
| CN107809268A (en) * | 2017-11-09 | 2018-03-16 | 电子科技大学 | A kind of ultra-broadband signal Wave shape extracting method based on accidental resonance |
Non-Patent Citations (3)
| Title |
|---|
| 卫蔚等: "电磁信号检测的随机共振方法与应用研究", 《工业计量》 * |
| 宋巨龙等: "基于黄金分割的全局最优化方法", 《计算机工程与应用》 * |
| 明廷锋等: "随机共振模型结构参数自寻优方法与应用", 《天津大学学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109633289A (en) * | 2018-12-28 | 2019-04-16 | 集美大学 | A kind of red information detecting method of electromagnetism based on cepstrum and convolutional neural networks |
| CN110490154A (en) * | 2019-08-23 | 2019-11-22 | 集美大学 | A kind of multidimensional leakage information detection method, terminal device and storage medium |
| CN111814578A (en) * | 2020-06-15 | 2020-10-23 | 南京森林警察学院 | Method for extracting frequency of ultralow frequency Doppler signal |
| CN112307931A (en) * | 2020-10-26 | 2021-02-02 | 西安电子科技大学 | Electromagnetic information leakage rapid detection method based on template matching technology |
| CN117332410A (en) * | 2023-10-25 | 2024-01-02 | 北京航空航天大学 | Electromagnetic leakage red and black signal identification method based on information entropy characteristics |
| CN117332410B (en) * | 2023-10-25 | 2024-04-12 | 北京航空航天大学 | Electromagnetic leakage red and black signal identification method based on information entropy characteristics |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108663576A (en) | Weak electromagnetic red signal detection method under a kind of complex environment | |
| Liu et al. | Spectral decomposition for hydrocarbon detection based on VMD and Teager–Kaiser energy | |
| CN103488941B (en) | Hardware Trojan horse detection method and system | |
| Cheng | A new model for quantifying anisotropic scale invariance and for decomposition of mixing patterns | |
| Zhang et al. | 2-D entropy and short-time Fourier transform to leverage GPR data analysis efficiency | |
| CN105678273A (en) | Initial point detection algorithm of transient signal in radio frequency fingerprint identification technology | |
| CN109975273A (en) | A kind of petrographic classification method based on laser induced breakdown spectroscopy | |
| CN107390259A (en) | A kind of nuclide identification method based on SVD and SVM | |
| CN109885903A (en) | A kind of ground nuclear magnetic resonance signal peaks noise remove method based on model | |
| CN108304770A (en) | A method of the flow pattern of gas-liquid two-phase flow based on time frequency analysis algorithm combination deep learning theory | |
| CN104143115A (en) | Technological method for achieving soil water content classified identification through geological radar technology | |
| Li et al. | Knock detection in spark ignition engines using a nonlinear wavelet transform of the engine cylinder head vibration signal | |
| KR101362451B1 (en) | Method and device for determining a kind of land mind based on various characteristics | |
| CN113378680A (en) | Intelligent database building method for Raman spectrum data | |
| CN107764797A (en) | A kind of Raman spectral image data preprocessing method based on low-rank tensor algorithm | |
| Perrin et al. | Multisensor fusion in the frame of evidence theory for landmines detection | |
| Qu et al. | Study on the algorithm of vibration source identification based on the optical fiber vibration pre-warning system | |
| CN105807204A (en) | Spectrum refinement-based hardware Trojan detection method | |
| Qiuming | Multifractal modelling and spectrum analysis: Methods and applications to gamma ray spectrometer data from southwestern Nova Scotia, Canada | |
| Marmarelis et al. | High-order signal processing for ultra-wideband radar signals | |
| CN116610935A (en) | Mechanical fault detection method based on engine vibration signal multi-mode analysis | |
| CN108459209A (en) | A kind of computer video electromagnetic leakage synchronization parameter extracting method and device | |
| Liu et al. | CNN-based Super-resolution Full-waveform LiDAR | |
| Xianlei et al. | A Multi-frequency GPR data fusion method for underground disease detection | |
| Nie et al. | Fast extraction for Brillouin frequency shift in BOTDA system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181016 |