CN107329401A - A kind of microscopy laboratory system - Google Patents

A kind of microscopy laboratory system Download PDF

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CN107329401A
CN107329401A CN201710495710.XA CN201710495710A CN107329401A CN 107329401 A CN107329401 A CN 107329401A CN 201710495710 A CN201710495710 A CN 201710495710A CN 107329401 A CN107329401 A CN 107329401A
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李昊天
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
    • G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P.I., P.I.D.
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0423—Input/output
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/21—Pc I-O input output
    • G05B2219/21137—Analog to digital conversion, ADC, DAC

Abstract

The invention belongs to detection device technical field, a kind of microscopy laboratory system is disclosed, including：The image sensing unit that light is reached via eyeglass；A/D converting units are used to the electric signal that image sensing unit is transmitted is carried out being converted to data signal, and DSP processing units, the data signal for A/D converting units to be transmitted is converted into digital image, and is transferred to memory cell；Memory cell is used to store the digital image after DSP processing unit processes；Controller, for performing when image sensing unit is measured, the regulation to eyeglass focal length；Sensor, for when image sensing unit is in measurement, obtaining the information of image.The image of automatic data collection of the present invention can be shown in dedicated display in real time, improve sex chromosome mosaicism directly perceived；The problem of image that the identification of memory cell image solves prior art presence is obscured, it is impossible to be that other miscellaneous signal is filtered out, it is ensured that the purity of image.

Description

A kind of microscopy laboratory system
Technical field
The invention belongs to detection device technical field, more particularly to a kind of microscopy laboratory system.
Background technology
As with comprising the relevant related system of microscopical measurement device, there is provided those discussed in PTL1 and PTL2 System.In PTL1, it is used for the grand inspection of the visually microtrauma (scratch) of observation wafer surface and deformation (strain) performing After looking into, perform micro-inspection critically to check the position for the confirmation for carrying out feature using microscope.For performing so Inspection structure in, afer rotates/inclined grand inspection unit can be made to be arranged between bearing part and micro-inspection unit. Although usually checking chip using the device (that is, grand inspection device and micro-inspection device) of separation, it is performed for so Such structure of inspection make it possible to simplify checking process.
In PTL2, a kind of structure is included in object lens and focus setting object lens in same optical axis, and object is set Between them.Here, after preliminary surveying is performed using focus setting object lens, actual measurement is performed using the object lens.Cause This, even if the thickness of the glassy layer of object changes, precision that still can be high sets the focus of object lens.Therefore, in such bag Containing in microscopical measurement device, usually using system configuration as follows, in the system configuration, by being used as survey in advance The result for measuring the various characteristics of object determines observation condition to perform actual measurement.Because when the result from preliminary surveying is pre- When first determining observation condition, it may be such that the operation in addition to the observation performed during actually measurement is minimum.
But, in recent years, when will use be measured microscopically substantial amounts of object when, exist for spend such as PTL1 and The need of the preliminary surveying for performing each object successively and the method for the actual method for measuring (imaging) shorter time in PTL2 Ask.
In current pathologic finding, Pathologic specimen is directly observed by human eye by using light microscope.In recent years, open Send out and obtained Pathologic specimen for for the microscope for the view data observed over the display.By such microscope, by In the view data for observing Pathologic specimen over the display, therefore, multiple observers can see described image data simultaneously.It is this The microscope of type enables the Telepathology doctor of shared view data to be diagnosed.But, the microscope of prior art Obtain the image of Pathologic specimen and be presented as view data and take a long time.
It is, it is necessary to using the object lens with narrow image pick-up range to obtain one of the reason for image takes a long time It is view data that Pathologic specimen with big image pickup region, which is obtained,.If the image pick-up range of object lens is narrow, then Need to perform multiple images pickup event, or, then captured image simultaneously connects each bar scan data to obtain while scanning Take single image data.In order to reduce the quantity of image pickup event and shorten the time for obtaining view data, it is necessary to have There are the object lens of big image pick-up range.
Japanese Patent Publication No.2009-063655 disclose by using the object lens with big image pick-up range and Wherein arrange the image pickup units of multiple images pickup device, pick up a plurality of of event acquisition by multiple images by connecting View data and obtain single image data.
Being used the microscope such as Japanese Patent Publication No.2009-063655 has big image pick-up range Object lens microscope in, compared with the microscope with narrow image pick-up range, it is expected to shorten image pickup time (i.e., From the start to finish of electrification storage), and the figure of the large area of such as whole image can be obtained in the short period of time As data.
But, captured image and the information for obtaining view data needs such as focal position and light exposure.Therefore, for obtaining The method for taking this information is also important for obtaining view data in a short time.If for example, picked up for multiple images Each image pickup event in event is taken to obtain this information, then the acquisition of information spends the long time, therefore, with this side Formula not necessarily obtains view data in a short time.
In summary, the problem of prior art is present be：The handling capacity of existing microscopic system measurement data is small, it is impossible to full The need for foot measurement；Accurate view data can not be obtained in a short time；The image purity degree of prior art is low.
The content of the invention
To solve the problem of prior art is present, it is an object of the invention to provide a kind of microscopy laboratory system.
The present invention is achieved in that a kind of microscopy laboratory system, and the microscopy laboratory system includes：
The image sensing unit that light is reached via eyeglass, image sensing unit is used to optical signal switching to electric signal, and A/D converting units are given by electric signal transmission；
A/D converting units are used to the electric signal that image sensing unit is transmitted is carried out being converted to data signal, and by numeral Signal is transferred to DSP processing units；
DSP processing units, the data signal for A/D converting units to be transmitted is converted into digital image, and is transferred to and deposits Storage unit；
Memory cell is used to store the digital image after DSP processing unit processes；
Controller, for performing when image sensing unit is measured, the regulation to eyeglass focal length；
Sensor, for when image sensing unit is in measurement, obtaining the information of image.2nd, it is as claimed in claim 1 Microscopy laboratory system, it is characterised in that the image sensing unit includes image collection device, display screen, ambiguity evaluation Module, fuzziness adjusting module；The fuzziness adjusting module is connected with display screen by printed line；The image collection device is used for Obtain ground image；
The ambiguity evaluation module is used for the image for obtaining the transmission of image collection device, and calculates image statisticses before and after filtering Information ratio；
The fuzziness adjusting module is connected with ambiguity evaluation module, and final figure is drawn for adjusting original image fuzziness Picture and image blur evaluation index.
Further, image collection device utilizes ambiguity evaluation module, fuzziness adjusting module to image blur evaluation side Method is：
Step one, image is obtained, and image to be evaluated is obtained by geological image collector；
Step 2, image gray processing, for convenience of the edge extracting of image, using the R of RGB image in Digital Image Processing, Coloured image is converted into gray level image by the pixel value of each passage of G, B and the transformational relation of gray level image pixel value, and formula is such as Under：
Gray=R*0.3+G*0.59+B*0.11；
Step 3, Edge extraction is made using the Roberts operator edge detections technology in digital image processing method The edge of image is obtained for gray level image, different detective operators have different edge detection templates, according to specific template The difference for intersecting pixel is calculated as current pixel value, it is as follows using template：
E (i, j)=| F (i, j)-F (i+1, j+1) |+| F (i+1, j)-F (i, j+1) |；
Step 4, image procossing is filtered processing to gray level image to be evaluated to construct using high pass/low pass filter The reference picture of image, using 3*3 mean filters, using each pixel of Filtering Template traversing graph picture, every time by template center Current pixel is placed in, the average value of all pixels is newly worth as current pixel using in template, and template is as follows：
Step 5, image border statistical information is calculated, and respective edge half-tone information, filtering before and after image filtering are calculated respectively The image F statistical informations to be evaluated of before processing are that the reference picture F2 statistical informations after sum_orig, filtering process are sum_ Filter, specific formula for calculation is as follows：
Wherein, w1 and w2 is according to from the weights set with a distance from center pixel, w1=1, w2=1/3；
Step 6, image blur index is calculated, the image filtering front and rear edges grey-level statistics that step 5 is drawn Ratio as fuzziness index, for convenience of evaluating, take larger for denominator, less is molecule, keeps the value between (0,1) Between；
Step 7, according to the DMOS scopes of the best visual effect draw a corresponding fuzziness indication range [min, Max], be specially：
Fuzziness adjusting range is drawn, 174 panel heights in LIVE2 are evaluated using the ambiguity evaluation method in above-mentioned steps This blurred picture, calculates their own ambiguity evaluation value, is then set up using fitting tool plot (value, DMOS) Mapping relations between evaluation of estimate value and DMOS, corresponding one is drawn according to the corresponding DMOS scopes of the best visual effect Fuzzy evaluation value scope [min, max]；
Step 8, image blur adjustment, if image blur index is less than min, according to step 6, judges image filtering Front and rear change is very big, and original image is excessively sharpened, then is filtered adjustment using low pass filter；If more than max, the filter of process decision chart picture Varied less after wavefront, original image is excessively obscured, then is filtered adjustment using high-pass filter, to reach more preferably vision effect Really；
Step 9, draws final image and the image blur evaluation index, and show on a display screen.
Further, DSP processing units are provided with synchronized orthogonal Frequency Hopping Signal blind source separating module, the step quadrature frequency hopping letter The signal processing method of number blind source separating includes：
Step one, passed using the array antenna received containing M array element from multiple synchronized orthogonal frequency hoppings The Frequency Hopping Signal of sensor, to being sampled per reception signal all the way, the M roads discrete time-domain mixed signal after being sampledThe interaction times of different time piece between array antenna node are gathered, according to obtaining Data setup time sequence, the interaction times of next timeslice between node are predicted by third index flatness, will be handed over The relative error of mutual number of times predicted value and actual value as node direct trust value；The specific calculation procedure of direct trust value For：Gather the interaction times of n timeslice between network observations node i and node j：Choose intervals t and be used as one Individual observation time piece, it is true to hand over using the interaction times of observer nodes i and tested node j in 1 timeslice as observation index Mutual number of times, is denoted as yt, the y of n timeslice is recorded successivelyn, and save it in the communications records table of node i；Prediction (n+1)th The interaction times of individual timeslice：According to the interaction times setup time sequence of the n timeslice collected, put down using three indexes Sliding method predicts the interaction times between next timeslice n+1 interior nodes i and j, predicts interaction times, is denoted asCalculation formula It is as follows：
Predictive coefficient an、bn、cnValue can by equation below calculate obtain：
Wherein：Be respectively once, secondary, Three-exponential Smoothing number, calculated by equation below Arrive：
It is the initial value of third index flatness, its value is
α is smoothing factor (0 ＜ α ＜ 1), embodies the time attenuation characteristic trusted, the i.e. timeslice nearer from predicted value ytWeight is bigger, the y of the timeslice more remote from predicted valuetWeight is smaller；If data fluctuations are larger, and long-term trend change width Degree is larger, and α when substantially rapidly rising or falling trend, which is presented, should take higher value (0.6~0.8), and increase Recent data is to prediction As a result influence；When data have a fluctuation, but long-term trend change it is little when, α can between 0.1~0.4 value；If data wave Dynamic steady, α should take smaller value (0.05~0.20)；
Calculate direct trust value：
Node j direct trust value TDijFor prediction interaction timesWith true interaction times yn+1Relative error,
Indirect trust values are calculated using calculating formula obtained from multipath trust recommendation mode；Trusted node is collected to node j Direct trust value：Node i meets TD to allik≤ φ credible associated nodes inquire its direct trust value to node j, its Middle φ is the believability threshold of recommended node, according to the precision prescribed of confidence level, and φ span is 0~0.4；Calculate letter indirectly Appoint value：Trust value collected by COMPREHENSIVE CALCULATING, obtains node j indirect trust values TRij,Its In, Set (i) is interacted and its direct trust value meets TD to have in observer nodes i associated nodes with j nodesik≤ φ section Point set；
Comprehensive trust value is drawn by direct trust value and indirect trust values conformity calculation；Comprehensive trust value (Tij) calculating it is public Formula is as follows：Tij=β TDij+(1-β)TRij, wherein β (0≤β≤1) represents the weight of direct trust value, as β=0, node i and Node j does not have direct interaction relation, and the calculating of comprehensive trust value arises directly from indirect trust values, and it is more objective to judge；When β=1 When, node i, all from direct trust value, in this case, judges more subjective to node j synthesis trust value, real Border calculates the value for determining β as needed；
Step 2, overlapping adding window Short Time Fourier Transform is carried out to M roads discrete time-domain mixed signal, obtains M mixing letter Number time-frequency domain matrixP=0,1 ..., P-1, q=0,1 ..., Nfft- 1, wherein P tables Show total window number, NfftRepresent FFT length；P, q) time-frequency index is represented, specific time-frequency value is (pCTs,), Here NfftThe length of FFT is represented, p represents adding window number of times, TsRepresent sampling interval, fsSample frequency is represented, C is integer, Represent the sampling number at Short Time Fourier Transform adding window interval, C ＜ Nfft, and Kc=Nfft/ C is integer, that is to say, that use It is the Short Time Fourier Transform of overlapping adding window；
Step 3, to the frequency-hopping mixing signal time-frequency domain square obtained in step 2 Pre-processed；
Step 4, estimates the jumping moment of each jump using clustering algorithm and respectively jumps corresponding normalized hybrid matrix Column vector, Hopping frequencies；It is right at p (p=0,1,2 ... the P-1) momentThe frequency values of expression are clustered, in obtained cluster Heart numberThe carrier frequency number that the expression p moment is present,Individual cluster centre then represents the size of carrier frequency, uses respectivelyRepresent；To each sampling instant p (p=0,1,2 ... P-1), clustering algorithm pair is utilizedEnter Row cluster, it is same availableIndividual cluster centre, is usedRepresent；To allAverage and round, obtain To the estimation of source signal numberI.e.：
Find outAt the time of, use phRepresent, to the p of each section of continuous valuehIntermediate value is sought, is usedTable Show the l sections of p that are connectedhIntermediate value, thenRepresent the estimation at l-th of frequency hopping moment；Obtained according to estimationAnd the 4th estimate that the obtained frequency hopping moment estimates in step and each jump correspondingIt is individual mixed Close matrix column vectorSpecifically formula is：
HereRepresent that l is jumped correspondingIndividual mixing Matrix column vector estimate；Estimation is each to jump corresponding carrier frequency, usesRepresent that l jumps correspondence 'sIndividual frequency estimation, calculation formula is as follows：
Step 5, estimates that obtained normalization hybrid matrix column vector estimates time-frequency domain frequency hopping source signal according to step 4；
Step 6, splices to the time-frequency domain frequency hopping source signal between different frequency hopping points；
Step 7, according to source signal time-frequency domain estimate, recovers time domain frequency hopping source signal；To each sampling instant p (p= 0,1,2 ...) frequency domain data Yn(p, q), q=0,1,2 ..., Nfft- 1 is NfftThe IFFT conversion of point, obtains p sampling instants pair The time domain frequency hopping source signal answered, uses yn(p,qt)(qt=0,1,2 ..., Nfft- 1) represent；The time domain that above-mentioned all moment are obtained Frequency hopping source signal yn(p,qt) processing is merged, final time domain frequency hopping source signal estimation is obtained, specific formula is as follows：
Here Kc=Nfft/ C, C are the sampling number at Short Time Fourier Transform adding window interval, NfftFor the length of FFT.
Further, the ambiguity evaluation module is combined using quantification with qualitative analysis, is evaluated with reference to actual set up Collection, sets up overall merit judgment matrix, the disturbance degree of the image transmitted according to each Failure Factors to image collection device and its right The total disturbance degree of weight calculation of the safety of image influence of image collection device transmission, abandon using the evaluation of single angle, it is undue according to The mode of bad or field data, considers all principal elements that influence image collection device transmits image, and clearly respectively influence Connect each other, make comprehensive evaluation.
Further, the overall merit judgment matrix of setting up includes：
Construct multilevel iudge matrix two-by-two：
Importance degree assignment, Judgement Matricies U are successively carried out according to 1~9 scaling law between any two to each key element =(uij)n×n, wherein uijExpression factor uiAnd ujRelative to the importance value of rule layer, matrix U has property：uii=1, uij=1/ uji, i, j=1,2 ..., n draw judgment matrix：By matrix X1~X5By row normalization, i.e.,：
Calculating matrix Y is：
(3) under single criterion element relative weighting calculating：
Y matrix by rowss are added, by formulaDraw：
W1=(2.652 0.686 0.253 0.409)T
W2=(1 1)T
W3=(1.273 0.371 0.221 2.135)T
W4=(1.9 0.319 0.781)T
W5=(2.121 0.604 0.275)T
Obtain and vector is normalized, by formulaWeight vector can be obtained：
Further, the overall merit judgment matrix of setting up also includes：
Construct fuzzy matrix for assessment：
By the weight vector of each indexFuzzy matrix for assessment B can be constructed with matrix R,
Calculate Comprehensive Evaluation result：
By fuzzy matrix for assessment B and the parameter column vector of evaluate collection, Comprehensive Evaluation result Z can be tried to achieve；
Z=BV
The result of fuzzy overall evaluation is arrived as available from the above equation, is provided further according to opinion rating, can be evaluated image collection device The image multifactor failure size of transmission.
Further, the controller is made up of fuzzy controller and Intelligent PID Control, if E0 is control threshold value, when | e |> During E0, using fuzzy controller, when deviation ratio is larger, be conducive to accelerating governing speed, system response using fuzzy controller It hurry up, when 0<|e|<During E0, using fuzzy intelligence self-regulated PID control, pid algorithm selection position model incomplete differential form：
In control process, the parameter of PID controller need to be adjusted according to current state：
α in formulaP, αIAnd αDThe correction factor respectively calculated by fuzzy reasoning, KP, KIAnd KDRespectively basic ratio Example, integration and differential coefficient.
Further, the digital modulation signals x (t) of sensor fractional lower-order ambiguity function is expressed as：
Wherein, τ is delay skew, and f is Doppler frequency shift, 0 ＜ a, b ＜ α/2, x*(t) x (t) conjugation is represented, as x (t) During for real signal, x (t)＜ p ＞=| x (t) |＜ p ＞sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]＜ p ＞=| x (t) |p-1x* (t)。
Further, need to be single beforehand through storage before the digital image after the memory cell storage DSP processing unit processes Picture recognition module built in member is identified, and the image information that can not be recognized is abandoned；When picture recognition module is recognized Transmission function be：
Wherein, ω0For the centre frequency of wave filter, for different ω0, k makes k/ ω0Keep constant.
In frequency domain construction wave filter, corresponding polar coordinates expression way is：
G (r, θ)=G (r, r) G (θ, θ)
In formula, Gr(r) it is the radial component of control filter bandwidht, Gθ(θ) is the angle component of control filter direction；
R represents radial coordinate, and θ represents angle coordinate, f0Centered on frequency, θ0For filter direction, σfEnglish determines band It is wide；
Bf=2 (2/ln2)1/2|lnσf|, σθDetermine angular bandwidth, Bθ=2 (2/ln2)1/2σθ。
The image of automatic data collection of the present invention can be shown in dedicated display in real time, improve sex chromosome mosaicism directly perceived；The present invention Picture appraisal be different from traditional evaluation, the present invention is set up on the basis of image own structural characteristics to be evaluated, is commented from relative The angle of valency is set out, and the reference picture of image to be evaluated is constructed using wave filter, calculates image border statistical information before and after change Ratio be used as evaluation index；The principle of the present invention is simple, realizes the content independence and real-time of image blur evaluation, Fuzziness that can quick and precisely between any image of evaluation comparison.
The present invention is not under conditions of any channel information is known, the mixing according only to the multiple Frequency Hopping Signals received is believed Number, frequency hopping source signal is estimated, multiple Frequency Hopping Signals can be carried out under conditions of reception antenna number is less than source signal number Blind estimate, with only Short Time Fourier Transform, and amount of calculation is small, easily realize, this method is carrying out blind point to Frequency Hopping Signal From while, moreover it is possible to partial parameters are estimated, it is practical, with stronger popularization and application value.
The present invention uses fuzzy overall evaluation, and quantification is combined with qualitative analysis, with reference to evaluate collection is actually set up, sets up Overall merit judgment matrix, the influence according to the distortion effect degree of each Failure Factors image and its to the weight calculation of influence always Rate, abandons by the way of the evaluation of single angle, undue dependence or field data, considers all masters of influence image fault Want factor, and it is clear and definite respectively influence connect each other, comprehensive evaluation is made on this basis；Whether can correctly draw can safe work The conclusion of work, moreover it is possible to the problem of solving image clearly；The reliability of the present invention is high, operability is good, enables assessment result more objective See the reality of truly image.
The present invention is using fuzzy intelligence PID regulation algorithms so that wide with adjustable range, improves degree of regulation.
The signal transacting model of inventive sensor overcomes the shortcoming that prior art receives signal difference.The storage of the present invention The problem of image that cell picture identification solves prior art presence is obscured, it is impossible to be that other miscellaneous signal is filtered out, is protected The purity of image is demonstrate,proved.
Brief description of the drawings
Fig. 1 is microscopy laboratory system schematic diagram provided in an embodiment of the present invention.
In figure：1st, image sensing unit；2nd, A/D converting units；3rd, DSP processing units；4th, memory cell；5th, controller； 6th, sensor.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to Limit the present invention.
The application principle of the present invention is described in detail below in conjunction with the accompanying drawings.
As shown in figure 1, microscopy laboratory system provided in an embodiment of the present invention, including：
The image sensing unit 1 that light is reached via eyeglass, image sensing unit is used to optical signal switching to electric signal, and A/D converting units are given by electric signal transmission；
A/D converting units 2 are used to the electric signal that image sensing unit is transmitted is carried out being converted to data signal, and by numeral Signal is transferred to DSP processing units；
DSP processing units 3, the data signal for A/D converting units to be transmitted is converted into digital image, and is transferred to and deposits Storage unit；
Memory cell 4 is used to store the digital image after DSP processing unit processes；
Controller 5, for performing when image sensing unit is measured, the regulation to eyeglass focal length；
Sensor 6, for when image sensing unit is in measurement, obtaining the information of image
The image sensing unit includes image collection device, display screen, ambiguity evaluation module, fuzziness adjusting module； The fuzziness adjusting module is connected with display screen by printed line；The image collection device is used to obtain ground image；
The ambiguity evaluation module is used for the image for obtaining the transmission of image collection device, and calculates image statisticses before and after filtering Information ratio；
The fuzziness adjusting module is connected with ambiguity evaluation module, and final figure is drawn for adjusting original image fuzziness Picture and image blur evaluation index.
Further, image collection device utilizes ambiguity evaluation module, fuzziness adjusting module to image blur evaluation side Method is：
Step one, image is obtained, and image to be evaluated is obtained by geological image collector；
Step 2, image gray processing, for convenience of the edge extracting of image, using the R of RGB image in Digital Image Processing, Coloured image is converted into gray level image by the pixel value of each passage of G, B and the transformational relation of gray level image pixel value, and formula is such as Under：
Gray=R*0.3+G*0.59+B*0.11；
Step 3, Edge extraction is made using the Roberts operator edge detections technology in digital image processing method The edge of image is obtained for gray level image, different detective operators have different edge detection templates, according to specific template The difference for intersecting pixel is calculated as current pixel value, it is as follows using template：
E (i, j)=| F (i, j)-F (i+1, j+1) |+| F (i+1, j)-F (i, j+1) |；
Step 4, image procossing is filtered processing to gray level image to be evaluated to construct using high pass/low pass filter The reference picture of image, using 3*3 mean filters, using each pixel of Filtering Template traversing graph picture, every time by template center Current pixel is placed in, the average value of all pixels is newly worth as current pixel using in template, and template is as follows：
Step 5, image border statistical information is calculated, and respective edge half-tone information, filtering before and after image filtering are calculated respectively The image F statistical informations to be evaluated of before processing are that the reference picture F2 statistical informations after sum_orig, filtering process are sum_ Filter, specific formula for calculation is as follows：
Wherein, w1 and w2 is according to from the weights set with a distance from center pixel, w1=1, w2=1/3；
Step 6, image blur index is calculated, the image filtering front and rear edges grey-level statistics that step 5 is drawn Ratio as fuzziness index, for convenience of evaluating, take larger for denominator, less is molecule, keeps the value between (0,1) Between；
Step 7, according to the DMOS scopes of the best visual effect draw a corresponding fuzziness indication range [min, Max], be specially：
Fuzziness adjusting range is drawn, 174 panel heights in LIVE2 are evaluated using the ambiguity evaluation method in above-mentioned steps This blurred picture, calculates their own ambiguity evaluation value, is then set up using fitting tool plot (value, DMOS) Mapping relations between evaluation of estimate value and DMOS, corresponding one is drawn according to the corresponding DMOS scopes of the best visual effect Fuzzy evaluation value scope [min, max]；
Step 8, image blur adjustment, if image blur index is less than min, according to step 6, judges image filtering Front and rear change is very big, and original image is excessively sharpened, then is filtered adjustment using low pass filter；If more than max, the filter of process decision chart picture Varied less after wavefront, original image is excessively obscured, then is filtered adjustment using high-pass filter, to reach more preferably vision effect Really；
Step 9, draws final image and the image blur evaluation index, and show on a display screen.
Further, DSP processing units are provided with synchronized orthogonal Frequency Hopping Signal blind source separating module, the step quadrature frequency hopping letter The signal processing method of number blind source separating includes：
Step one, sensed using the array antenna received containing M array element from multiple synchronized orthogonal frequency hoppings The Frequency Hopping Signal of device, to being sampled per reception signal all the way, the M roads discrete time-domain mixed signal after being sampledThe interaction times of different time piece between array antenna node are gathered, according to obtaining Data setup time sequence, the interaction times of next timeslice between node are predicted by third index flatness, will be handed over The relative error of mutual number of times predicted value and actual value as node direct trust value；The specific calculation procedure of direct trust value For：Gather the interaction times of n timeslice between network observations node i and node j：Choose intervals t and be used as one Individual observation time piece, it is true to hand over using the interaction times of observer nodes i and tested node j in 1 timeslice as observation index Mutual number of times, is denoted as yt, the y of n timeslice is recorded successivelyn, and save it in the communications records table of node i；Prediction (n+1)th The interaction times of individual timeslice：According to the interaction times setup time sequence of the n timeslice collected, put down using three indexes Sliding method predicts the interaction times between next timeslice n+1 interior nodes i and j, predicts interaction times, is denoted asCalculate public Formula is as follows：
Predictive coefficient an、bn、cnValue can by equation below calculate obtain：
Wherein：Be respectively once, secondary, Three-exponential Smoothing number, calculated by equation below Arrive：
It is the initial value of third index flatness, its value is
α is smoothing factor (0 ＜ α ＜ 1), embodies the time attenuation characteristic trusted, the i.e. timeslice nearer from predicted value ytWeight is bigger, the y of the timeslice more remote from predicted valuetWeight is smaller；If data fluctuations are larger, and long-term trend change width Degree is larger, and α when substantially rapidly rising or falling trend, which is presented, should take higher value (0.6~0.8), and increase Recent data is to prediction As a result influence；When data have a fluctuation, but long-term trend change it is little when, α can between 0.1~0.4 value；If data wave Dynamic steady, α should take smaller value (0.05~0.20)；
Calculate direct trust value：
Node j direct trust value TDijFor prediction interaction timesWith true interaction times yn+1Relative error,
Indirect trust values are calculated using calculating formula obtained from multipath trust recommendation mode；Collect trusted node pair Node j direct trust value：Node i meets TD to allik≤ φ credible associated nodes inquire its direct letter to node j Appoint value, wherein φ is the believability threshold of recommended node, according to the precision prescribed of confidence level, φ span for 0~ 0.4；Calculate indirect trust values：Trust value collected by COMPREHENSIVE CALCULATING, obtains node j indirect trust values TRij,Wherein, Set (i) is interacted and its is direct to have in observer nodes i associated nodes with j nodes Trust value meets TDik≤ φ node set；
Comprehensive trust value is drawn by direct trust value and indirect trust values conformity calculation；Comprehensive trust value (Tij) calculating it is public Formula is as follows：Tij=β TDij+(1-β)TRij, wherein β (0≤β≤1) represents the weight of direct trust value, as β=0, node i and Node j does not have direct interaction relation, and the calculating of comprehensive trust value arises directly from indirect trust values, and it is more objective to judge；When β=1 When, node i, all from direct trust value, in this case, judges more subjective to node j synthesis trust value, real Border calculates the value for determining β as needed；
Step 2, overlapping adding window Short Time Fourier Transform is carried out to M roads discrete time-domain mixed signal, obtains M mixing letter Number time-frequency domain matrixP=0,1 ..., P-1, q=0,1 ..., Nfft- 1, wherein P tables Show total window number, NfftRepresent FFT length；P, q) time-frequency index is represented, specific time-frequency value is (pCTs,), Here NfftThe length of FFT is represented, p represents adding window number of times, TsRepresent sampling interval, fsSample frequency is represented, C is integer, Represent the sampling number at Short Time Fourier Transform adding window interval, C ＜ Nfft, and Kc=Nfft/ C is integer, that is to say, that use It is the Short Time Fourier Transform of overlapping adding window；
Step 3, to the frequency-hopping mixing signal time-frequency domain square obtained in step 2 Pre-processed；
Step 4, estimates the jumping moment of each jump using clustering algorithm and respectively jumps corresponding normalized hybrid matrix Column vector, Hopping frequencies；It is right at p (p=0,1,2 ... the P-1) momentThe frequency values of expression are clustered, in obtained cluster Heart numberThe carrier frequency number that the expression p moment is present,Individual cluster centre then represents the size of carrier frequency, uses respectivelyRepresent；To each sampling instant p (p=0,1,2 ... P-1), clustering algorithm pair is utilizedEnter Row cluster, it is same availableIndividual cluster centre, is usedRepresent；To allAverage and round, obtain To the estimation of source signal numberI.e.：
Find outAt the time of, use phRepresent, to the p of each section of continuous valuehIntermediate value is sought, is usedTable Show the l sections of p that are connectedhIntermediate value, thenRepresent the estimation at l-th of frequency hopping moment；Obtained according to estimationAnd the 4th estimate that the obtained frequency hopping moment estimates in step and each jump correspondingIt is individual mixed Close matrix column vectorSpecifically formula is：
HereRepresent that l is jumped correspondingIndividual mixing Matrix column vector estimate；Estimation is each to jump corresponding carrier frequency, usesRepresent that l jumps correspondence 'sIndividual frequency estimation, calculation formula is as follows：
Step 5, estimates that obtained normalization hybrid matrix column vector estimates time-frequency domain frequency hopping source signal according to step 4；
Step 6, splices to the time-frequency domain frequency hopping source signal between different frequency hopping points；
Step 7, according to source signal time-frequency domain estimate, recovers time domain frequency hopping source signal；To each sampling instant p (p= 0,1,2 ...) frequency domain data Yn(p, q), q=0,1,2 ..., Nfft- 1 is NfftThe IFFT conversion of point, obtains p sampling instants pair The time domain frequency hopping source signal answered, uses yn(p,qt)(qt=0,1,2 ..., Nfft- 1) represent；The time domain that above-mentioned all moment are obtained Frequency hopping source signal yn(p,qt) processing is merged, final time domain frequency hopping source signal estimation is obtained, specific formula is as follows：
Here Kc=Nfft/ C, C are the sampling number at Short Time Fourier Transform adding window interval, NfftFor the length of FFT.
Further, the ambiguity evaluation module is combined using quantification with qualitative analysis, is evaluated with reference to actual set up Collection, sets up overall merit judgment matrix, the disturbance degree of the image transmitted according to each Failure Factors to image collection device and its right The total disturbance degree of weight calculation of the safety of image influence of image collection device transmission, abandon using the evaluation of single angle, it is undue according to The mode of bad or field data, considers all principal elements that influence image collection device transmits image, and clearly respectively influence Connect each other, make comprehensive evaluation.
Further, the overall merit judgment matrix of setting up includes：
Construct multilevel iudge matrix two-by-two：
Importance degree assignment, Judgement Matricies U are successively carried out according to 1~9 scaling law between any two to each key element =(uij)n×n, wherein uijExpression factor uiAnd ujRelative to the importance value of rule layer, matrix U has property：uii=1, uij=1/ uji, i, j=1,2 ..., n draw judgment matrix：By matrix X1~X5By row normalization, i.e.,：
Calculating matrix Y is：
(3) under single criterion element relative weighting calculating：
Y matrix by rowss are added, by formulaDraw：
W1=(2.652 0.686 0.253 0.409)T
W2=(1 1)T
W3=(1.273 0.371 0.221 2.135)T
W4=(1.9 0.319 0.781)T
W5=(2.121 0.604 0.275)T
Obtain and vector is normalized, by formulaWeight vector can be obtained：
Further, the overall merit judgment matrix of setting up also includes：
Construct fuzzy matrix for assessment：
By the weight vector of each indexFuzzy matrix for assessment B can be constructed with matrix R,
Calculate Comprehensive Evaluation result：
By fuzzy matrix for assessment B and the parameter column vector of evaluate collection, Comprehensive Evaluation result Z can be tried to achieve；
Z=BV
The result of fuzzy overall evaluation is arrived as available from the above equation, is provided further according to opinion rating, can be evaluated image collection device The image multifactor failure size of transmission.
Further, the controller is made up of fuzzy controller and Intelligent PID Control, if E0 is control threshold value, when | e |> During E0, using fuzzy controller, when deviation ratio is larger, be conducive to accelerating governing speed, system response using fuzzy controller It hurry up, when 0<|e|<During E0, using fuzzy intelligence self-regulated PID control, pid algorithm selection position model incomplete differential form：
In control process, the parameter of PID controller need to be adjusted according to current state：
α in formulaP, αIAnd αDThe correction factor respectively calculated by fuzzy reasoning, KP, KIAnd KDRespectively basic ratio Example, integration and differential coefficient.
Further, the digital modulation signals x (t) of sensor fractional lower-order ambiguity function is expressed as：
Wherein, τ is delay skew, and f is Doppler frequency shift, 0 ＜ a, b ＜ α/2, x*(t) x (t) conjugation is represented, as x (t) During for real signal, x (t)＜ p ＞=| x (t) |＜ p ＞sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]＜ p ＞=| x (t) |p-1x* (t)。
Further, need to be single beforehand through storage before the digital image after the memory cell storage DSP processing unit processes Picture recognition module built in member is identified, and the image information that can not be recognized is abandoned；When picture recognition module is recognized Transmission function be：
Wherein, ω0For the centre frequency of wave filter, for different ω0, k makes k/ ω0Keep constant.
In frequency domain construction wave filter, corresponding polar coordinates expression way is：
G (r, θ)=G (r, r) G (θ, θ)
In formula, Gr(r) it is the radial component of control filter bandwidht, Gθ(θ) is the angle component of control filter direction；
R represents radial coordinate, and θ represents angle coordinate, f0Centered on frequency, θ0For filter direction, σfEnglish determines band It is wide；
Bf=2 (2/ln2)1/2|lnσf|, σθDetermine angular bandwidth, Bθ=2 (2/ln2)1/2σθ。
The image of automatic data collection of the present invention can be shown in dedicated display in real time, improve sex chromosome mosaicism directly perceived；The present invention Picture appraisal be different from traditional evaluation, the present invention is set up on the basis of image own structural characteristics to be evaluated, is commented from relative The angle of valency is set out, and the reference picture of image to be evaluated is constructed using wave filter, calculates image border statistical information before and after change Ratio be used as evaluation index；The principle of the present invention is simple, realizes the content independence and real-time of image blur evaluation, Fuzziness that can quick and precisely between any image of evaluation comparison.
The present invention is not under conditions of any channel information is known, the mixing according only to the multiple Frequency Hopping Signals received is believed Number, frequency hopping source signal is estimated, multiple Frequency Hopping Signals can be carried out under conditions of reception antenna number is less than source signal number Blind estimate, with only Short Time Fourier Transform, and amount of calculation is small, easily realize, this method is carrying out blind point to Frequency Hopping Signal From while, moreover it is possible to partial parameters are estimated, it is practical, with stronger popularization and application value.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention Any modifications, equivalent substitutions and improvements made within refreshing and principle etc., should be included in the scope of the protection.

Claims (9)

1. a kind of microscopy laboratory system, it is characterised in that the microscopy laboratory system includes：
The image sensing unit that light is reached via eyeglass, image sensing unit is used to optical signal switching to electric signal, and by electricity Signal is transferred to A/D converting units；The image sensing unit includes image collection device；
The image collection device is to image blur evaluation method using ambiguity evaluation module, fuzziness adjusting module：
Step one, image is obtained, and image to be evaluated is obtained by geological image collector；
Step 2, image gray processing, for convenience of the edge extracting of image, R, G, B using RGB image in Digital Image Processing are each Coloured image is converted into gray level image by the pixel value of individual passage with the transformational relation of gray level image pixel value, and formula is as follows：
Gray=R*0.3+G*0.59+B*0.11；
Step 3, Edge extraction, using the Roberts operator edge detections technical role in digital image processing method in Gray level image obtains the edge of image, and different detective operators have different edge detection templates, according to specific formwork calculation Intersect the difference of pixel as current pixel value, it is as follows using template：
E (i, j)=| F (i, j)-F (i+1, j+1) |+| F (i+1, j)-F (i, j+1) |；
Step 4, image procossing is filtered processing to gray level image to construct image to be evaluated using high pass/low pass filter Reference picture, using 3*3 mean filters, using each pixel of Filtering Template traversing graph picture, template center is placed in every time Current pixel, the average value of all pixels is newly worth as current pixel using in template, and template is as follows：
Step 5, image border statistical information is calculated, and respective edge half-tone information, filtering process before and after image filtering are calculated respectively Preceding image F statistical informations to be evaluated are that the reference picture F2 statistical informations after sum_orig, filtering process are sum_filter, Specific formula for calculation is as follows：
Wherein, w1 and w2 is according to from the weights set with a distance from center pixel, w1=1, w2=1/3；
Step 6, image blur index is calculated, the ratio for the image filtering front and rear edges grey-level statistics that step 5 is drawn Value is as fuzziness index, for convenience of evaluating, and takes larger for denominator, and less is molecule, keep the value between (0,1) it Between；
Step 7, a corresponding fuzziness indication range [min, max] is drawn according to the DMOS scopes of the best visual effect, tool Body is：
Fuzziness adjusting range is drawn, 174 width Gaussian modes in LIVE2 are evaluated using the ambiguity evaluation method in above-mentioned steps Image is pasted, their own ambiguity evaluation value is calculated, then sets up evaluation of estimate value and DMOS using fitting tool plot Between mapping relations, corresponding fuzzy evaluation value scope is drawn according to the corresponding DMOS scopes of the best visual effect [min, max]；
Step 8, image blur adjustment, if image blur index is less than min, according to step 6, before and after judging image filtering Change is very big, and original image is excessively sharpened, then is filtered adjustment using low pass filter；If more than max, judging before image filtering After vary less, original image excessively obscure, then adjustment is filtered using high-pass filter, to reach more preferably visual effect；
Step 9, draws final image and the image blur evaluation index, and show on a display screen.
A/D converting units are used to the electric signal that image sensing unit is transmitted is carried out being converted to data signal, and by data signal It is transferred to DSP processing units；
DSP processing units, the data signal for A/D converting units to be transmitted is converted into digital image, and it is single to be transferred to storage Member；DSP processing units are provided with synchronized orthogonal Frequency Hopping Signal blind source separating module, the step quadrature frequency hopping signal blind source separating Signal processing method includes：
Step one, it is right using Frequency Hopping Signal of the array antenna received containing M array element from multiple synchronized orthogonal frequency hopping sensors Sampled per signal is received all the way, the M roads discrete time-domain mixed signal after being sampled The interaction times of different time piece between collection array antenna node, according to obtained data setup time sequence, pass through three fingers Number exponential smoothings predict the interaction times of next timeslice between node, by interaction times predicted value and the relative error of actual value It is used as the direct trust value of node；The specific calculation procedure of direct trust value is：Gather between network observations node i and node j N timeslice interaction times：Intervals t is chosen as an observation time piece, with observer nodes i and tested section Interaction times of the point j in 1 timeslice are as observation index, and true interaction times are denoted as yt, n timeslice is recorded successively Yn, and save it in the communications records table of node i；Predict the interaction times of (n+1)th timeslice：According to what is collected The interaction times setup time sequence of n timeslice, next timeslice n+1 interior nodes i is predicted using third index flatness Interaction times between j, predict interaction times, are denoted asCalculation formula is as follows：
Predictive coefficient an、bn、cnValue can by equation below calculate obtain：
Wherein：Be respectively once, secondary, Three-exponential Smoothing number, by equation below calculate obtain：
It is the initial value of third index flatness, its value is：
α is smoothing factor (0 ＜ α ＜ 1), embodies the y of the time attenuation characteristic, the i.e. timeslice nearer from predicted value trustedtWeight It is bigger, the y of the timeslice more remote from predicted valuetWeight is smaller；If data fluctuations are larger, and long-term trend amplitude of variation compared with Greatly, α when substantially rapidly rising or falling trend, which is presented, should take higher value (0.6~0.8), and increase Recent data is to predicting the outcome Influence；When data have a fluctuation, but long-term trend change it is little when, α values 0.1~0.4；If data fluctuations are steady, α takes 0.05~0.20；
Calculate direct trust value：
Node j direct trust value TDijFor prediction interaction timesWith true interaction times yn+1Relative error,
Indirect trust values are calculated using calculating formula obtained from multipath trust recommendation mode；Trusted node is collected to node j's Direct trust value：Node i meets TD to allik≤ φ credible associated nodes inquire its direct trust value to node j, wherein φ is the believability threshold of recommended node, according to the precision prescribed of confidence level, and φ span is 0~0.4；Calculate letter indirectly Appoint value：Trust value collected by COMPREHENSIVE CALCULATING, obtains node j indirect trust values TRij,Its In, Set (i) is interacted and its direct trust value meets TD to have in observer nodes i associated nodes with j nodesik≤ φ section Point set；
Comprehensive trust value is drawn by direct trust value and indirect trust values conformity calculation；Comprehensive trust value TijCalculation formula such as Under：Tij=β TDij+(1-β)TRij, wherein 0≤β≤1, β represents the weight of direct trust value, as β=0, node i and node j There is no direct interaction relation, the calculating of comprehensive trust value arises directly from indirect trust values, and it is more objective to judge；As β=1, section Point i, all from direct trust value, in this case, judges more subjective to node j synthesis trust value, actual to calculate β value is determined as needed；
Step 2, overlapping adding window Short Time Fourier Transform is carried out to M roads discrete time-domain mixed signal, obtains M mixed signal Time-frequency domain matrixP=0,1 ..., P-1, q=0,1 ..., Nfft- 1, wherein P is represented Total window number, NfftRepresent FFT length；P, q) time-frequency index is represented, specific time-frequency value isThis In NfftThe length of FFT is represented, p represents adding window number of times, TsRepresent sampling interval, fsSample frequency is represented, C is integer, table Show the sampling number at Short Time Fourier Transform adding window interval, C ＜ Nfft, and Kc=Nfft/ C is integer, that is to say, that used The Short Time Fourier Transform of overlapping adding window；
Step 3, to the frequency-hopping mixing signal time-frequency domain square obtained in step 2Carry out pre- Processing；
Step 4, estimate the jumping moment of each jump using clustering algorithm and respectively jump corresponding normalized mixed moment array to Amount, Hopping frequencies；It is right at p (p=0,1,2 ... the P-1) momentThe frequency values of expression are clustered, obtained cluster centre NumberThe carrier frequency number that the expression p moment is present,Individual cluster centre then represents the size of carrier frequency, uses respectivelyRepresent；To each sampling instant p (p=0,1,2 ... P-1), clustering algorithm pair is utilizedEnter Row cluster, it is same availableIndividual cluster centre, is usedRepresent；To allAverage and round, obtain To the estimation of source signal numberI.e.：
Find outAt the time of, use phRepresent, to the p of each section of continuous valuehIntermediate value is sought, is usedRepresent the The l sections of p that are connectedhIntermediate value, thenRepresent the estimation at l-th of frequency hopping moment；Obtained according to estimationAnd the 4th estimate that the obtained frequency hopping moment estimates in step and each jump correspondingIt is individual Hybrid matrix column vectorSpecifically formula is：
HereRepresent that l is jumped correspondingIndividual hybrid matrix Column vector estimate；Estimation is each to jump corresponding carrier frequency, usesRepresent that l is jumped corresponding Individual frequency estimation, calculation formula is as follows：
Step 5, estimates that obtained normalization hybrid matrix column vector estimates time-frequency domain frequency hopping source signal according to step 4；
Step 6, splices to the time-frequency domain frequency hopping source signal between different frequency hopping points；
Step 7, according to source signal time-frequency domain estimate, recovers time domain frequency hopping source signal；To each sampling instant p (p=0,1, 2 ...) frequency domain data Yn(p, q), q=0,1,2 ..., Nfft- 1 is NfftThe IFFT conversion of point, obtains p sampling instants corresponding Time domain frequency hopping source signal, uses yn(p,qt)(qt=0,1,2 ..., Nfft- 1) represent；The time domain frequency hopping that above-mentioned all moment are obtained Source signal yn(p,qt) processing is merged, final time domain frequency hopping source signal estimation is obtained, specific formula is as follows：
Here Kc=Nfft/ C, C are the sampling number at Short Time Fourier Transform adding window interval, NfftFor the length of FFT；
Memory cell is used to store the digital image after DSP processing unit processes；
Controller, for performing when image sensing unit is measured, the regulation to eyeglass focal length；
Sensor, for when image sensing unit is in measurement, obtaining the information of image.
2. microscopy laboratory system as claimed in claim 1, it is characterised in that the image sensing unit includes display Screen, ambiguity evaluation module, fuzziness adjusting module；The fuzziness adjusting module is connected with display screen by printed line；It is described Image collection device is used to obtain ground image；
The ambiguity evaluation module is used for the image for obtaining the transmission of image collection device, and calculates image statistics before and after filtering Ratio；
The fuzziness adjusting module is connected with ambiguity evaluation module, for adjust original image fuzziness draw final image and Image blur evaluation index.
3. microscopy laboratory system as claimed in claim 1, it is characterised in that the ambiguity evaluation module is using quantitative Change is combined with qualitative analysis, with reference to evaluate collection is actually set up, overall merit judgment matrix is set up, according to each Failure Factors pair The disturbance degree of the image of image collection device transmission and its weight calculation of the safety of image influence transmitted on image collection device are total Disturbance degree, is abandoned by the way of the evaluation of single angle, undue dependence or field data, is considered influence image collection device and is passed All principal elements of defeated image, and it is clear and definite respectively influence connect each other, make comprehensive evaluation.
4. microscopy laboratory system as claimed in claim 3, it is characterised in that described to set up overall merit judgment matrix bag Include：
Construct multilevel iudge matrix two-by-two：
Importance degree assignment, Judgement Matricies U=are successively carried out according to 1~9 scaling law between any two to each key element (uij)n×n, wherein uijExpression factor uiAnd ujRelative to the importance value of rule layer, matrix U has property：uii=1, uij=1/ uji, i, j=1,2 ..., n draw judgment matrix：By matrix X1~X5By row normalization, i.e.,：
Calculating matrix Y is：
(3) under single criterion element relative weighting calculating：
Y matrix by rowss are added, by formulaDraw：
W1=(2.652 0.686 0.253 0.409)T
W2=(1 1)T
W3=(1.273 0.371 0.221 2.135)T
W4=(1.9 0.319 0.781)T
W5=(2.121 0.604 0.275)T
Obtain and vector is normalized, by formulaWeight vector can be obtained：
5. microscopy laboratory system as claimed in claim 3, it is characterised in that described to set up overall merit judgment matrix also Including：
Construct fuzzy matrix for assessment：
By the weight vector of each indexFuzzy matrix for assessment B can be constructed with matrix R,
Calculate Comprehensive Evaluation result：
By fuzzy matrix for assessment B and the parameter column vector of evaluate collection, Comprehensive Evaluation result Z can be tried to achieve；
Z=BV
The result of fuzzy overall evaluation is arrived as available from the above equation, is provided further according to opinion rating, can evaluate the transmission of image collection device Image multifactor failure size.
6. microscopy laboratory system as claimed in claim 1, it is characterised in that the controller is by fuzzy controller and intelligence Energy PID control composition, if E0 is control threshold value, when | e |>During E0, using fuzzy controller, when deviation ratio is larger, using mould Fuzzy controllers are conducive to accelerating governing speed, faster system response, when 0<|e|<During E0, using fuzzy intelligence self-regulated PID control, Pid algorithm selects position model incomplete differential form：
In control process, the parameter of PID controller need to be adjusted according to current state：
α in formulaP, αIAnd αDThe correction factor respectively calculated by fuzzy reasoning, KP, KIAnd KDRespectively basic ratio, Integration and differential coefficient.
7. microscopy laboratory system as claimed in claim 1, it is characterised in that the digital modulation signals x of the sensor (t) fractional lower-order ambiguity function is expressed as：
Wherein, τ is delay skew, and f is Doppler frequency shift, 0 ＜ a, b ＜ α/2, x*(t) x (t) conjugation is represented, when x (t) is real During signal, x (t)＜ p ＞=| x (t) |＜ p ＞sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]＜ p ＞=| x (t) |p-1x*(t)。
8. microscopy laboratory system as claimed in claim 1, it is characterised in that the memory cell storage DSP processing is single Before digital image after member processing, it need to be identified beforehand through the picture recognition module built in memory cell, to that can not recognize Image information abandoned；Picture recognition module recognize when transmission function be：
Wherein, ω0For the centre frequency of wave filter, for different ω0, k makes k/ ω0Keep constant.
It is corresponding 9. microscopy laboratory system as claimed in claim 8, it is characterised in that in frequency domain construction wave filter Polar coordinates expression way is：
G (r, θ)=G (r, r) G (θ, θ)
In formula, Gr(r) it is the radial component of control filter bandwidht, Gθ(θ) is the angle component of control filter direction；
R represents radial coordinate, and θ represents angle coordinate, f0Centered on frequency, θ0For filter direction, σfEnglish determines bandwidth；
Bf=2 (2/ln2)1/2|lnσf|, σθDetermine angular bandwidth, Bθ=2 (2/ln2)1/2σθ。
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Publication number Priority date Publication date Assignee Title
CN111462005A (en) * 2020-03-30 2020-07-28 腾讯科技（深圳）有限公司 Method, apparatus, computer device and storage medium for processing microscopic image
CN116506719A (en) * 2023-06-21 2023-07-28 深圳华强电子网集团股份有限公司 Transmission management method based on photodiode CMOS image sensor
CN116506719B (en) * 2023-06-21 2023-09-15 深圳华强电子网集团股份有限公司 Transmission management method based on photodiode CMOS image sensor

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Application publication date: 20171107