CN108245165A - A kind of backbone dynamic function detecting system - Google Patents

A kind of backbone dynamic function detecting system Download PDF

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Publication number
CN108245165A
CN108245165A CN201810090228.2A CN201810090228A CN108245165A CN 108245165 A CN108245165 A CN 108245165A CN 201810090228 A CN201810090228 A CN 201810090228A CN 108245165 A CN108245165 A CN 108245165A
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signal
module
image
sub
data
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Inventor
陈爽
马雪梅
常晓盼
代耀军
裴孝鹏
杨勇
孙宜保
卢中道
范富有
朱红鹤
祝孟坤
吕成国
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ZHENGZHOU CITY ORTHOPEDIC HOSPITAL
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ZHENGZHOU CITY ORTHOPEDIC HOSPITAL
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Priority to CN201810090228.2A priority Critical patent/CN108245165A/en
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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1077Measuring of profiles
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1075Measuring physical dimensions, e.g. size of the entire body or parts thereof for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00Modulated-carrier systems
    • H04L27/0012Modulated-carrier systems arrangements for identifying the type of modulation

Abstract

The invention belongs to backbone detection technique fields, disclose a kind of backbone dynamic function detecting system, and the backbone dynamic function detecting system includes:Indicate logging modle, 3-D scanning module, data acquisition module, main control module, monitoring module, rectification module, massage module, image processing module, display module.Mark logging modle, 3-D scanning module connect data acquisition module by circuit line respectively;Data acquisition module connects main control module by circuit line.The present invention can massage patient by massage module, fatigue of the environment patient in detection process;Complementary color processing is carried out to the sub-pix color data after sub-pix down-sampling using specific sub-pix color data complementary color Processing Algorithm by image processing module simultaneously, color mistake caused by backbone detection image resolution ratio can be effectively improved and eliminate sub-pix down-sampling technology in the case where not influencing picture clarity improves the accuracy of backbone detection data.

Description

A kind of backbone dynamic function detecting system
Technical field
The invention belongs to backbone detection technique field more particularly to a kind of backbone dynamic function detecting systems.
Background technology
Backbone inspection usually with depending on, touch, percussion be combined with each other, the curvature of main contents including backbone, whether there is deformity, The mobility of backbone and whether there is tenderness, percussion pain etc..There are four physiologicals to be bent for health adult's backbone:It dashes forward before cervical vertebra;Thoracic vertebrae Processus aboralis;It is prominent before lumbar vertebrae is apparent;Sacral processus aboralis.Those who are investigated should be allowed to make anterior flexion and rear stretching, left and right lateral bending and rotary motion, it is normal living Dynamic degree is:45 ° of cervical part of esophagus anterior flexion and rear stretching, left and right lateral bending is also up to 60 °, waist section 45 ° of anterior flexion and rear stretching under buttocks rigid condition, right left side Curved each 30 °, rotate to be pain without exception in 45 ° of inspections.However, existing backbone detecting system only has detection function, it is long Time detection patient is easily tired;The resolution ratio of the image of detection is not clear enough simultaneously, influences diagnostic result.
To sum up, problem of the existing technology is:Existing backbone detecting system only has detection function, examines for a long time It is easily tired to survey patient;The resolution ratio of the image of detection is not clear enough simultaneously, influences diagnostic result.
Invention content
In view of the problems of the existing technology, the present invention provides a kind of backbone dynamic function detecting systems.
The invention is realized in this way a kind of backbone dynamic function detecting system includes:
Indicate logging modle, 3-D scanning module, data acquisition module, main control module, monitoring module, correction mould Block, massage module, image processing module, display module;
Indicate logging modle, connect with data acquisition module, for the Mark Detection point being pasted onto on patient spine;
3-D scanning module, connect with data acquisition module, and three are carried out to patient spine for passing through 3-D scanning camera Tie up Scanning Detction;
Data acquisition module is connect with mark logging modle, 3-D scanning module, main control module, for that will indicate record The analog electric signal that module, 3-D scanning module obtain is converted to digital quantity signal, and be sent to main control module;
Main control module, with data acquisition module, monitoring module, rectification module, massage module, image processing module, Display module connects, and is analyzed for the data to data collecting module collected, while dispatches each electric elements and carry out just Often work;
The main control module processor digital demodulation signal identification method under non-gaussian noise, which is characterized in that the identification Method includes:
Step 1, docking collection of letters s (t) carry out nonlinear transformation;It docks collection of letters s (t) and carries out nonlinear transformation, by such as Lower formula carries out:
WhereinA represents the amplitude of signal, and a (m) represents letter Number symbol, p (t) represent shaping function, fcRepresent the carrier frequency of signal,The phase of signal is represented, by this It can obtain after nonlinear transformation:
Step 2 calculates the broad sense single order cyclic cumulants for receiving signal s (t)With broad sense second-order cyclic cumulantThe characteristic parameter of signal s (t) is received by calculatingClassify with using least mean-square error Device identifies 2FSK signals;Calculate the Generalized Cyclic cumulant for receiving signalIt carries out as follows:
WithIt is Generalized Cyclic square, is defined as:
Wherein s (t) is signal, and n is wide The exponent number of adopted Cyclic Moment, conjugation item are m;
Receive the characteristic parameter M of signal s (t)1Theoretical valueSpecific calculating process is such as Lower progress:
It is computed it is found that for 2FSK signals, the signalIt is 1, and for MSK, BPSK, QPSK, 8PSK, 16QAM and 64QAM signalsIt is 0, it is possible thereby to be gone out 2FSK signal identifications by least mean-square error grader Come, the expression-form of the grader is:
In formulaIt is characterized parameter M1Actual value;
Step 3 calculates the broad sense second-order cyclic cumulant for receiving signal s (t)Signal s (t) is received by calculating Characteristic parameterWith utilize least mean-square error grader, and pass through and detect Generalized Cyclic cumulant width Degree spectrumSpectral peak number identify bpsk signal and msk signal;Calculate the broad sense second-order cyclic for receiving signal s (t) CumulantIt carries out as follows:
Receive the characteristic parameter M of signal s (t)2Theoretical valueSpecific formula for calculation is:
By calculating it is found that bpsk signal and msk signalIt is 1, QPSK, 8PSK, 16QAM and 64QAM signal 'sBe 0, it is possible thereby to least mean-square error grader by BPSK, msk signal and QPSK, 8PSK, 16QAM, 64QAM signals separate;For bpsk signal, in Generalized Cyclic cumulant amplitude spectrumOn only in carrier frequency position There are an apparent spectral peak, and respectively there are one apparent spectral peaks at two frequencies for msk signal, thus can pass through characteristic parameter M2With Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number bpsk signal and msk signal are identified;
Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number specific method it is as follows:
Generalized Cyclic cumulant amplitude spectrum is searched for firstMaximum value Max and its position it is corresponding cycle frequency Rate α0, by its small neighbourhood [α0000] interior zero setting, wherein δ0For a positive number, if | α0-fc|/fc< σ0, wherein δ0It is one Close to 0 positive number, fcFor the carrier frequency of signal, then judge that this signal type for bpsk signal, otherwise continues search for second largest value The Max1 and its corresponding cycle frequency α in position1;If | Max-Max1 |/Max < σ0, and | (α01)/2-fc|/fc< σ0, then Judge this signal type for msk signal;
Step 4 calculates the broad sense quadravalence cyclic cumulants for receiving signal s (t)Signal s (t) is received by calculating Characteristic parameterWith using least mean-square error grader, QPSK signals, 8PSK letters are identified Number, 16QAM signals and 64QAM signals;Calculate the broad sense second-order cyclic cumulant for receiving signal s (t)As follows It carries out:
Receive the characteristic parameter M of signal s (t)3Theoretical valueSpecific calculating process is such as Under:
By calculating it is found that QPSK signalsFor 1,8PSK signalsFor 0,16QAM signalsFor 0.5747,64QAM signalBe 0.3580, from there through least mean-square error grader by QPSK, 8PSK, 16QAM and 64QAM signal identifications come out;
Monitoring module is connect with main control module, for detecting the heart rate situation of patient in real time, judging whether patient goes out Existing abnormal conditions simultaneously will determine that result is exported to display module;
Rectification module is connect with main control module, for carrying out corrective operations to patient's deformed spine position;
Massage module is connect with main control module, for massaging patient spine;
Image processing module is connect with main control module, is handled for the image to 3-D scanning module scans;
Described image processing module extracts partly overlapping multiple figures from the first non-negative image and/or the second non-negative image As block;Obtain the corresponding sparse coefficient of multiple images block;To first, non-negative image and/or the second non-negative image optimize and are asked Solution, obtains the optimization sparse solution for meeting object function, object function is:
Wherein, Ri∈ RM × N, Δ represent the first non-negative image or the second non-negative image, R
iThe image block that Δ expression is extracted from Δ, | | | | 2 represent 2- norms, | | | | 1 represents 1- norms, and γ is regularization Parameter, D represented complete dictionary, αiFor i-th of image block RiThe corresponding sparse coefficient of Δ, Γ are the sparse system of all image blocks Manifold is closed;
The Image Iterative model of described image processing module, the formula of iterative model are expressed as:
Wherein, X is the target image, and M is sytem matrix, and G is the data for projection, and i represents iterations, XiIt represents The iteration result obtained after ith iteration;λ represents convergence coefficient, and λ ∈ (0,1), M T represent the transposition to matrix M;Setting The initial value of the target image, and the iterative model is utilized in the target image according to pre-set iterations Each pixel be iterated update, obtain the target image, the current grayvalue of the pixel in the iterative model With the gray value Uniform approximat of previous iteration;It is described by gray value in target image be less than 0 pixel zero setting;
Display module is connect with main control module, for being shown to detection data and image.
Further, described image processing module processing method is as follows:
First, it obtains because carrying out corresponding to the more of each physical picture element obtained from sub-pix down-sampling to original image A sub-pix color data, original image includes multiple original pixels, and each original pixels includes corresponding multiple bases Data;
Then, multiple sub-pix color data of multiple physical picture elements are carried out with complementary color to handle to obtain corresponding to each object Sub-pix color data is to generate destination image data after multiple complementary colors processing of reason pixel;
Finally, complementary color processing is carried out to multiple sub-pix color data of multiple physical picture elements;
When carrying out complementary color processing to any one target sub-pix color data, mark off comprising target sub-pix number of colours According to the multiple sub-pix color data adjacent with target sub-pix color data block of pixels and based on pixel it is in the block multiple More bases of pending sub-pix color data and multiple original pixels corresponding with multiple pending sub-pix color data Non- Sample Color Data of the chromatic number in judges whether to need to carry out complementary color and determines complementary color position when needing to carry out complementary color With complementary color value size, plurality of pending sub-pix color data include target sub-pix color data and with target sub-pix Color data is the sub-pix color data of different colours type.
The present invention can massage patient by massage module, fatigue of the environment patient in detection process;Simultaneously By image processing module using specific sub-pix color data complementary color Processing Algorithm to the sub-pix after sub-pix down-sampling Color data carries out complementary color processing, can effectively improve backbone detection image resolution ratio and in the feelings for not influencing picture clarity Color mistake caused by eliminating sub-pix down-sampling technology under condition improves the accuracy of backbone detection data.
Description of the drawings
Fig. 1 is backbone dynamic function detecting system structure diagram provided in an embodiment of the present invention;
In figure:1st, indicate logging modle;2nd, 3-D scanning module;3rd, data acquisition module;4th, main control module;5th, state is supervised Control module;6th, rectification module;7th, massage module;8th, image processing module;9th, display module.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and coordinate attached drawing Detailed description are as follows.
The structure of the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in Figure 1, backbone dynamic function detecting system provided in an embodiment of the present invention includes:Indicate logging modle 1, three Tie up scan module 2, data acquisition module 3, main control module 4, monitoring module 5, rectification module 6, massage module 7, at image Manage module 8, display module 9.Indicate that logging modle 1,3-D scanning module 2 connect data acquisition module 3 by circuit line respectively; Data acquisition module 3 connects main control module 4 by circuit line.
Indicate logging modle 1, connect with data acquisition module 3, for the Mark Detection point being pasted onto on patient spine;
3-D scanning module 2 is connect with data acquisition module 3, and patient spine is carried out for passing through 3-D scanning camera 3-D scanning detects;
Data acquisition module 3 is connect with mark logging modle 1,3-D scanning module 2, main control module 4, for that will indicate The analog electric signal that logging modle 1,3-D scanning module 2 obtain is converted to digital quantity signal, and be sent to main control module 4;
Main control module 4, with data acquisition module 3, monitoring module 5, rectification module 6, massage module 7, image procossing Module 8, display module 9 connect, and the data for being acquired to data acquisition module 3 are analyzed, while dispatch each electric appliance member Part is worked normally;
The processor digital demodulation signal identification method under non-gaussian noise of the main control module 4, which is characterized in that the knowledge Other method includes:
Step 1, docking collection of letters s (t) carry out nonlinear transformation;It docks collection of letters s (t) and carries out nonlinear transformation, by such as Lower formula carries out:
WhereinA represents the amplitude of signal, and a (m) represents letter Number symbol, p (t) represent shaping function, fcRepresent the carrier frequency of signal,The phase of signal is represented, by this It can obtain after nonlinear transformation:
Step 2 calculates the broad sense single order cyclic cumulants for receiving signal s (t)With broad sense second-order cyclic cumulantThe characteristic parameter of signal s (t) is received by calculatingClassify with using least mean-square error Device identifies 2FSK signals;Calculate the Generalized Cyclic cumulant for receiving signalIt carries out as follows:
WithIt is Generalized Cyclic square, is defined as:
Receive the characteristic parameter M of signal s (t)1Theoretical valueSpecific calculating process is such as Lower progress:
It is computed it is found that for 2FSK signals, the signalIt is 1, and for MSK, BPSK, QPSK, 8PSK, 16QAM and 64QAM signalsIt is 0, it is possible thereby to be gone out 2FSK signal identifications by least mean-square error grader Come, the expression-form of the grader is:
In formulaIt is characterized parameter M1Actual value;
Step 3 calculates the broad sense second-order cyclic cumulant for receiving signal s (t)Signal s (t) is received by calculating Characteristic parameterWith utilize least mean-square error grader, and pass through and detect Generalized Cyclic cumulant width Degree spectrumSpectral peak number identify bpsk signal and msk signal;Calculate the broad sense second-order cyclic for receiving signal s (t) CumulantIt carries out as follows:
Receive the characteristic parameter M of signal s (t)2Theoretical valueSpecific formula for calculation is:
By calculating it is found that bpsk signal and msk signalIt is 1, QPSK, 8PSK, 16QAM and 64QAM signal 'sBe 0, it is possible thereby to least mean-square error grader by BPSK, msk signal and QPSK, 8PSK, 16QAM, 64QAM signals separate;For bpsk signal, in Generalized Cyclic cumulant amplitude spectrumOn only in carrier frequency position There are an apparent spectral peak, and respectively there are one apparent spectral peaks at two frequencies for msk signal, thus can pass through characteristic parameter M2With Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number bpsk signal and msk signal are identified;
Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number specific method it is as follows:
Generalized Cyclic cumulant amplitude spectrum is searched for firstMaximum value Max and its position it is corresponding cycle frequency Rate α0, by its small neighbourhood [α0000] interior zero setting, wherein δ0For a positive number, if | α0-fc|/fc< σ0, wherein δ0It is one Close to 0 positive number, fcFor the carrier frequency of signal, then judge that this signal type for bpsk signal, otherwise continues search for second largest value The Max1 and its corresponding cycle frequency α in position1;If | Max-Max1 |/Max < σ0, and | (α01)/2-fc|/fc< σ0, then Judge this signal type for msk signal;
Step 4 calculates the broad sense quadravalence cyclic cumulants for receiving signal s (t)Signal s (t) is received by calculating Characteristic parameterWith using least mean-square error grader, QPSK signals, 8PSK letters are identified Number, 16QAM signals and 64QAM signals;Calculate the broad sense second-order cyclic cumulant for receiving signal s (t)As follows It carries out:
Receive the characteristic parameter M of signal s (t)3Theoretical valueSpecific calculating process is such as Under:
By calculating it is found that QPSK signalsFor 1,8PSK signalsFor 0,16QAM signalsFor 0.5747,64QAM signalBe 0.3580, from there through least mean-square error grader by QPSK, 8PSK, 16QAM and 64QAM signal identifications come out;
Monitoring module 5 is connect with main control module 4, for detecting the heart rate situation of patient in real time, whether judging patient There are abnormal conditions and will determine that result is exported to display module 9;
Rectification module 6 is connect with main control module 4, for carrying out corrective operations to patient's deformed spine position;
Massage module 7 is connect with main control module 4, for massaging patient spine;
Image processing module 8 is connect with main control module 4, and the image for being scanned to 3-D scanning module 2 is handled;
Described image processing module extracts partly overlapping multiple figures from the first non-negative image and/or the second non-negative image As block;Obtain the corresponding sparse coefficient of multiple images block;To first, non-negative image and/or the second non-negative image optimize and are asked Solution, obtains the optimization sparse solution for meeting object function, object function is:
Wherein, Ri∈ RM × N, Δ represent the first non-negative image or the second non-negative image, R
iThe image block that Δ expression is extracted from Δ, | | | | 2 represent 2- norms, | | | | 1 represents 1- norms, and γ is regularization Parameter, D represented complete dictionary, αiFor i-th of image block RiThe corresponding sparse coefficient of Δ, Γ are the sparse system of all image blocks Manifold is closed;
The Image Iterative model of described image processing module, the formula of iterative model are expressed as:
Wherein, X is the target image, and M is sytem matrix, and G is the data for projection, and i represents iterations, XiIt represents The iteration result obtained after ith iteration;λ represents convergence coefficient, and λ ∈ (0,1), M T represent the transposition to matrix M;Setting The initial value of the target image, and the iterative model is utilized in the target image according to pre-set iterations Each pixel be iterated update, obtain the target image, the current grayvalue of the pixel in the iterative model With the gray value Uniform approximat of previous iteration;It is described by gray value in target image be less than 0 pixel zero setting;
Display module 9 is connect with main control module 4, for being shown to detection data and image.
8 processing method of image processing module provided by the invention is as follows:
First, it obtains because carrying out corresponding to the more of each physical picture element obtained from sub-pix down-sampling to original image A sub-pix color data, original image includes multiple original pixels, and each original pixels includes corresponding multiple bases Data;
Then, multiple sub-pix color data of multiple physical picture elements are carried out with complementary color to handle to obtain corresponding to each object Sub-pix color data is to generate destination image data after multiple complementary colors processing of reason pixel;
Finally, complementary color processing is carried out to multiple sub-pix color data of multiple physical picture elements;
When carrying out complementary color processing to any one target sub-pix color data, mark off comprising target sub-pix number of colours According to the multiple sub-pix color data adjacent with target sub-pix color data block of pixels and based on pixel it is in the block multiple More bases of pending sub-pix color data and multiple original pixels corresponding with multiple pending sub-pix color data Non- Sample Color Data of the chromatic number in judges whether to need to carry out complementary color and determines complementary color position when needing to carry out complementary color With complementary color value size, plurality of pending sub-pix color data include target sub-pix color data and with target sub-pix Color data is the sub-pix color data of different colours type.
Indicate that logging modle 1,3-D scanning module 2 acquire the information data of detection and image by data in the present invention Module 3 is converted to digital quantity signal, and is sent to main control module 4;Main control module 4 to the data that data acquisition module 3 acquires into Row analysis, while dispatch each electric elements and worked normally;Dispatch state monitoring module 5 detect in real time the heart of patient Rate situation judges whether patient abnormal conditions occurs and will determine that result is exported to display module 9;By rectification module 6 to suffering from Person's deformed spine position carries out corrective operations;If patient fatigue, patient spine is massaged by massage module 7, is alleviated Fatigue;The image scanned to 3-D scanning module 2 is handled by image processing module 8;It is shown by display module 9.
The above is only the preferred embodiments of the present invention, and not makees limitation in any form to the present invention, Every technical spirit according to the present invention any simple modification, equivalent change and modification made to the above embodiment, belongs to In the range of technical solution of the present invention.

Claims (2)

1. a kind of backbone dynamic function detecting system, which is characterized in that the backbone dynamic function detecting system includes:
Indicate logging modle, connect with data acquisition module, for the Mark Detection point being pasted onto on patient spine;
3-D scanning module, connect with data acquisition module, and patient spine progress three-dimensional is swept for passing through 3-D scanning camera Retouch detection;
Data acquisition module is connect with mark logging modle, 3-D scanning module, main control module, for will indicate logging modle, The analog electric signal that 3-D scanning module obtains is converted to digital quantity signal, and be sent to main control module;
Main control module, with data acquisition module, monitoring module, rectification module, massage module, image processing module, display Module connects, and is analyzed for the data to data collecting module collected, while dispatches each electric elements and carry out normal work Make;
The main control module processor digital demodulation signal identification method under non-gaussian noise, which is characterized in that the recognition methods Including:
Step 1, docking collection of letters s (t) carry out nonlinear transformation;It docks collection of letters s (t) and carries out nonlinear transformation, by following public affairs Formula carries out:
WhereinA represents the amplitude of signal, and a (m) represents signal Symbol, p (t) represent shaping function, fcRepresent the carrier frequency of signal,Represent the phase of signal, it is non-thread by this Property transformation after can obtain:
Step 2 calculates the broad sense single order cyclic cumulants for receiving signal s (t)With broad sense second-order cyclic cumulantThe characteristic parameter of signal s (t) is received by calculatingClassify with using least mean-square error Device identifies 2FSK signals;Calculate the Generalized Cyclic cumulant for receiving signalIt carries out as follows:
WithIt is Generalized Cyclic square, is defined as:
Wherein s (t) is signal, and n is followed for broad sense The exponent number of ring square, conjugation item are m;
Receive the characteristic parameter M of signal s (t)1Theoretical valueSpecific calculating process as follows into Row:
It is computed it is found that for 2FSK signals, the signalBe 1, and for MSK, BPSK, QPSK, 8PSK, 16QAM and 64QAM signalsIt is 0, it is possible thereby to be come out 2FSK signal identifications by least mean-square error grader, the classification The expression-form of device is:
In formulaIt is characterized parameter M1Actual value;
Step 3 calculates the broad sense second-order cyclic cumulant for receiving signal s (t)The spy of signal s (t) is received by calculating Levy parameterWith utilize least mean-square error grader, and pass through detect Generalized Cyclic accumulation discharge amplitude SpectrumSpectral peak number identify bpsk signal and msk signal;The broad sense second-order cyclic for calculating reception signal s (t) is tired out Accumulated amountIt carries out as follows:
Receive the characteristic parameter M of signal s (t)2Theoretical valueSpecific formula for calculation is:
By calculating it is found that bpsk signal and msk signalIt is 1, QPSK, 8PSK, 16QAM and 64QAM signalIt is 0, it is possible thereby to least mean-square error grader by BPSK, msk signal and QPSK, 8PSK, 16QAM, 64QAM Signal separates;For bpsk signal, in Generalized Cyclic cumulant amplitude spectrumOn only in carrier frequency position, there are one A apparent spectral peak, and respectively there are one apparent spectral peaks at two frequencies for msk signal, thus can pass through characteristic parameter M2It is wide with detection Adopted cyclic cumulants amplitude spectrumSpectral peak number bpsk signal and msk signal are identified;
Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number specific method it is as follows:
Generalized Cyclic cumulant amplitude spectrum is searched for firstMaximum value Max and its corresponding cycle frequency α in position0, By its small neighbourhood [α0000] interior zero setting, wherein δ0For a positive number, if | α0-fc|/fc< σ0, wherein δ0It is close for one 0 positive number, fcFor the carrier frequency of signal, then judge that this signal type for bpsk signal, otherwise continues search for second largest value Max1 And its corresponding cycle frequency α in position1;If | Max-Max1 |/Max < σ0, and | (α01)/2-fc|/fc< σ0, then judge This signal type is msk signal;
Step 4 calculates the broad sense quadravalence cyclic cumulants for receiving signal s (t)The spy of signal s (t) is received by calculating Levy parameterWith utilize least mean-square error grader, identify QPSK signals, 8PSK signals, 16QAM signals and 64QAM signals;Calculate the broad sense second-order cyclic cumulant for receiving signal s (t)As follows into Row:
Receive the characteristic parameter M of signal s (t)3Theoretical valueSpecific calculating process is as follows:
By calculating it is found that QPSK signalsFor 1,8PSK signalsFor 0,16QAM signalsFor 0.5747,64QAM signalBe 0.3580, from there through least mean-square error grader by QPSK, 8PSK, 16QAM and 64QAM signal identifications come out;
Monitoring module is connect with main control module, and for detecting in real time, the heart rate situation of patient, to judge whether patient occurs different Reason condition simultaneously will determine that result is exported to display module;
Rectification module is connect with main control module, for carrying out corrective operations to patient's deformed spine position;
Massage module is connect with main control module, for massaging patient spine;
Image processing module is connect with main control module, is handled for the image to 3-D scanning module scans;
Described image processing module extracts partly overlapping multiple images from the first non-negative image and/or the second non-negative image Block;Obtain the corresponding sparse coefficient of multiple images block;To first, non-negative image and/or the second non-negative image optimize and are asked Solution, obtains the optimization sparse solution for meeting object function, object function is:
Wherein, Ri∈ RM × N, Δ represent the first non-negative image or the second non-negative image, R
iThe image block that is extracted from Δ of Δ expression, | | | | 2 represent 2- norms, | | | | 1 represents 1- norms, and γ is regularization parameter, D Represented complete dictionary, αiFor i-th of image block RiThe corresponding sparse coefficient of Δ, Γ are the sparse coefficient set of all image blocks;
The Image Iterative model of described image processing module, the formula of iterative model are expressed as:
Wherein, X is the target image, and M is sytem matrix, and G is the data for projection, and i represents iterations, XiRepresent ith The iteration result obtained after iteration;λ represents convergence coefficient, and λ ∈ (0,1), M T represent the transposition to matrix M;The mesh is set The initial value of logo image, and the iterative model is utilized to each in the target image according to pre-set iterations Pixel is iterated update, obtains the target image, the current grayvalue of the pixel in the iterative model with it is previous The gray value Uniform approximat of iteration;It is described by gray value in target image be less than 0 pixel zero setting;
Display module is connect with main control module, for being shown to detection data and image.
2. backbone dynamic function detecting system as described in claim 1, which is characterized in that described image processing module processing side Method is as follows:
First, it obtains because of multiple Asias to corresponding to each physical picture element obtained from original image progress sub-pix down-sampling Pixel color data, original image includes multiple original pixels, and each original pixels includes corresponding multiple bases data;
Then, multiple sub-pix color data of multiple physical picture elements are carried out with complementary color to handle to obtain corresponding to each physics picture Sub-pix color data is to generate destination image data after multiple complementary colors processing of element;
Finally, complementary color processing is carried out to multiple sub-pix color data of multiple physical picture elements;
When carrying out complementary color processing to any one target sub-pix color data, mark off comprising target sub-pix color data and The block of pixels of the multiple sub-pix color data adjacent with target sub-pix color data simultaneously multiple is waited to locate based on pixel is in the block Manage the multiple bases number of sub-pix color data and multiple original pixels corresponding with multiple pending sub-pix color data Non- Sample Color Data in judges whether to need to carry out complementary color and determines complementary color position and benefit when needing to carry out complementary color Color value size, plurality of pending sub-pix color data include target sub-pix color data and with target sub-pix color Data are the sub-pix color data of different colours type.
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