50 results about "Rényi entropy" patented technology

The invention provides a method of automatically removing ocular artifacts from an electroencephalogram signal without setting a threshold value, belongs to the field of biological information technology, and is mainly applied to the preprocessing process of the electroencephalogram signal. The method particularly comprises the following steps: performing an independent component decomposition to a captured electroencephalogram signal containing the ocular artifacts; gaining the kurtosis, the sequence renyi entropy and the sample entropy of each independent component as feature vectors, so as to automatically recognize an independent component containing the ocular artifacts by k-means cluster analysis, and setting the independent component to be zero and other components to be constant, reconstructing the signal, and obtaining a pure electroencephalogram signal. The method provided by the invention solves the problems that the artifacts are identified by means of manual work during the traditional process for removing the ocular artifacts, so that time and labors are wasted and the workload is heavy. In addition, the method provided by the invention can realize the purposes of automatically identifying and removing the ocular artifacts without setting the threshold value by manual work, so that the shortcoming in the existing method that a researcher is required to have definite future knowledges and strong subjectivity during the setting of the the threshold value is overcame.

The invention relates to a radar radiation source identification method based on feature fusion. The method comprises the steps of generating a radar radiation source unintentional modulation signal set; extracting AR model coefficients, Renyi entropy features and spectral kurtosis features; computing smoothed pseudo Wigner-Ville distribution, generating time-frequency images, and carrying out graying and adaptive binarization to obtain adaptive binarized images; extracting pseudo Zernike matrix and Hu matrix features of the images; extracting signal time-frequency image unintentional modulation features through application of an AlexNet convolutional neural network, carrying out normalization, and carrying out feature fusion to obtain fused feature vectors; and inputting the fused featurevectors into a support vector machine, training the support vector machine optimized through particle swarm optimization, and inputting the radar radiation source signal set into a system to finish identifying radar radiation sources. According to the method, the signals are analyzed from a time domain, a frequency domain and a time-frequency domain, various unintentional modulation features areextracted comprehensively, and the problems that the extracted unintentional modulation features are low applicability and reliability and the radiation sources are difficult to identify is solved.

The invention belongs to the technical field of radar emitter signal modulation recognition, and particularly relates to a modulation recognition method for extracting time-frequency image features byjoint entropy and pre-training CNN. The method includes the following steps: firstly, performing time-frequency transformation on 9 types of radar signal sets to be identified to obtain a time-frequency image; and then based on a pre-training convolutional neural network model imagenet-vgg-verydeep-19 provided by the MatConvNet official website, constituting an FT-VGGNet-fc6 feature migration extraction module from an input layer to an fc6 full connection layer; and then, sending an adjusted image to the feature migration extraction module, and outputting time-frequency image features of radar signals; performing graying on the adjusted image, and manually extracting the Renyi entropy of the processed image; and then, dividing a training set and a test set according to a certain proportion, and selecting the training set to train an SVM classifier; and finally, adopting the trained SVM classifier to recognize the training set of time-frequency images, and adopting a data set composedof 9 types of radar signals with multiple signal-to-noise ratios to verify a recognition rate of an FT-VGGNET-fc6-SVM classifier.

The invention provides a heart rate variability feature classification method based on multi-scale Renyi entropy and belongs to the field of electrocardiosignal processing. R wave positioning is performed after pretreatment such as interference and baseline drift removal is performed on to-be-processed original electrocardiosignals, and the interval of adjacent R waves is calculated to obtain an HRV sequence; discrete wavelet coefficients are obtained through discrete wavelet conversion of the HRV sequence; a proper q value is selected according to requirements to calculate the Renyi entropy of the wavelet coefficient of each layer; feature vectors are constructed by using the calculated Renyi entropy values of all scales for classification and identification of the electrocardiosignals.

The invention discloses a semi-supervised hyperspectral remote sensing image classification method based on information entropies and relates to the field of remote sensing. The method specifically comprises the steps of (1) inputting a hyperspectral remote sensing image, (2) inputting a training sample set, (3) inputting a category set corresponding to training samples, (4) calculating the probability of the category which each image element in the hyperspectral remote sensing image represents through a multi-classification linear regression method, (5) outputting the categories corresponding to all the image elements according to the calculated probabilities of all the image elements, (6) outputting the classification result and judging the accuracy of the output result, (7) converting the probabilities of all the image elements in the remote sensing image into indeterminacy through the Renyi entropy algorithm, (8) converting unmarked label image elements in the hyperspectral remote sensing image into marked label image elements according to the indeterminacy, (9) adding new marked labels into the training set, and (10) conducting iteration operation. The hyperspectral remote sensing image classification method has the advantages of being easy to realize, low in calculation complexity and the like.

The invention discloses a non-local means filtering method for a speckle noise pollution image. The method comprises that iterative computation of a neurons firing state image series of the speckle noise pollution image is conducted through a pulse transmission cortex model, a Renyi entropy vector is extracted by the neurons firing state image series, and non-local means filtering is conducted on the speckle noise pollution image based on the Renyi entropy vector, so that a denoised gray value is obtained. By the aid of the method, rotation invariance, translation invariance and scaling invariance can be extracted from the image containing speckle noise, the method can use more image information for denoising than traditional methods, besides, the similarity between two image pixel blocks can be calculated reasonably, image noise can be suppressed significantly, and the peak signal-to-noise ratio of the image can be improved, so that detailed information of the image can be effectively protected.

The invention provides a combined classifier multi-signal modulation identification method based on fractional Fourier transform. The method includes steps: firstly, solving the problem of multi-signal separation by employing an FRFT technology, then realizing accurate classification and identification of a strong signal with the combination of a SVM by employing a pre-trained CNN in time and frequency domains, extracting characteristics including the spectrum kurtosis, a root-mean-square value, an AR characteristic coefficient and a Renyi entropy etc. of a weak signal to realize characteristic fusion, performing dimension reduction on fusion characteristics by employing a principal component analysis (PCA) to improve the system validity, and inputting the characteristics after dimension reduction to the SVM to realize second signal classification and identification and finally realize accurate and rapid identification of the multi-signal modulation mode with low signal to noise ratio.

The invention relates to a video significance processing method based on spectral analysis. The method comprises the following steps of: extracting a key frame from an input video by utilizing a method for extracting a video key frame according to motion information to acquire a video key frame sequence; extracting a video key frame gray level image sequence and a video key frame motion information characteristic map sequence; calculating Renyi entropy of the video key frame gray level image sequence and the video key frame motion information characteristic map sequence; measuring the distribution of significance points in the air domain significant maps by the Renyi entropy and using a result as a significant value of an air domain significant map; and finally linearly fusing the obvious vale of the video key frame air domain significant value and an obvious value of the time domain so as to acquire an overall obvious value of the video key frame. By the method, the significance information carried by magnitude spectra and phase spectrum in the video key frame field is fully utilized, the calculated amount is small and the effect is prominent; and the distribution of the significance points in the significant maps is measured by the Renyi entropy, and the result is used as the significant value of the significant pattern, so that the problem of overall significance measurement of the significant maps are well solved; and therefore, the method can be applied to video analysis processing systems of various of military use or civil use.

The invention provides an image registration method based on mixed mutual information and improved particle swarm optimization. The image registration method comprises the following steps that mutual information of a floating image and a reference template image is calculated by Renyi entropy; the population size, the maximum number of steps of algorithm iteration, a nonlinear adjustment parameter n and the minimum threshold are set; the new speed and position of each particle are calculated according to the speed updating and position updating formula of a PSO algorithm so that the local optimal solution Pid and the global optimal solution Pgd are updated; local optimization searching is performed on the global optimal solution Pgd with shannon entropy acting as similarity measure so that global optimal solution Rid is calculated; the global optimal solution Rid and the preset minimum threshold Fmin are compared until iteration ends or algorithm registration fails and then the algorithm ends; and mapping of the gray value of each pixel of the transformed images is performed. Compared with the methods in the prior art, the image registration method is high in calculation speed, short in registration time and more accurate in registration parameter.

The present invention belongs to the image segmentation field and relates to an image threshold segmentation method and device based on a fuzzy set and the Otsu. According to the method and device ofthe invention, a new fuzzy enhanced membership function is provided based on the fuzzy set; the inter-class variance of the Otsu is constructed through using a discretization method of mean square error; and with the Renyi entropy theory used in combination and weight calculation introduced, the Renyi entropy of an image is provided, and the threshold of a maximum Renyi entropy is adopted to accomplish image segmentation. Compared with a traditional threshold segmentation algorithm, the method of the invention has advantages of high edge segmentation accuracy, high robustness to noises, high stability and favorable segmentation effect, and can effectively improve the accuracy of image segmentation.

The invention provides an underwater acoustic communication signal modulation mode identification method based on a support vector machine. The method comprises the following steps: preprocessing an underwater acoustic signal containing complex ocean background noise through a nonlinear piecewise exponential function; extracting the time domain L-Z complexity feature, the frequency domain fractalbox dimension feature and the time-frequency domain Renyi entropy feature of the underwater acoustic signal as the input of an SVM classifier, and then selecting a proper kernel function and parameters to complete the modulation mode recognition of the underwater acoustic communication signal. According to the method, the influence of complex ocean background noise is eliminated through a nonlinear transformation preprocessing method, three features in a time domain, a frequency domain and a time-frequency domain are extracted to form feature vectors; compared with a single-feature method, themethod improves the performance obviously. The method can be used for monitoring and identifying shallow sea underwater acoustic communication signals, perceives underwater acoustic communication behaviors of enemies in advance, and improves the coastal defense strength of China.

The invention discloses a rolling bearing fault detection method based on an actual measurement signal. The rolling bearing fault detection method comprises the steps of: firstly, converting a rollingbearing fault time domain vibration signal to an angle domain through employing an order tracking technology; secondly, carrying out parameter optimization on variational mode decomposition through adpopting a longicorn beard search algorithm, and decomposing all state vibration signals of a rolling bearing to obtain a series of intrinsic mode functions, wherein frequency band energy in differentintrinsic mode functions can change when different faults happen to the bearing; thirdly, extracting Renyi entropy features from modal components containing main fault information, and constructing afeature subset; and finally, using normal state vibration signals easy to obtain for training, extracting fault characteristic quantities, establishing fault data samples and incremental learning data samples, acquiring a fault recognition model through training by adopting a single-class support vector machine incremental learning algorithm, judging whether the rolling bearing breaks down or notaccurately, and achieving fault early warning.

The invention relates to an underwater information anti-interference method based on compressed sensing and Renyi entropy, and belongs to the technical field of underwater analog information conversion and compressed sensing. The method comprises the following steps: 1) reading underwater acquired information, calculating the Renyi entropy of each row, solving the row corresponding to the maximumvalue, and calculating the pixel maximum value of the row as the Renyi entropy threshold T1; 2) filtering by taking W * W as a unit based on T1; 3) carrying out discrete two-dimensional wavelet transform; 4) calculating Renyi entropy denoising thresholds of the high-frequency coefficients according to rows, and then denoising the high-frequency coefficients; 5) constructing a Gaussian random observation matrix to perform compressed observation on the denoised high-frequency coefficient; 6) performing AMP reconstruction on all the filtered high-frequency coefficients; and 7), performing waveletinverse transform on the low-frequency sub-coefficient and the reconstructed high-frequency sub-coefficient for obtaining recovered underwawter acquired information; according to the method, part ofnoise can be filtered out, the peak signal-to-noise ratio of the denoised underwater acquired image is effectively improved, and the average structural similarity of the denoised underwater image is optimized.

The invention belongs to the technical field of radar signal processing in a non-Gaussian clutter environment, and discloses a radar signal identification method based on generalized variational modedecomposition combined with a support vector machine, which comprises the following steps: carrying out generalized variational mode decomposition on a received radar radiation source signal to obtainan intrinsic mode component IMF; computing smooth pseudo Wigner-Ville time-frequency distribution matrix for each intrinsic mode component, and extracting Renyi entropy features of time-frequency distribution to construct a feature vector T; and finally, carrying out classification and identification by utilizing a support vector machine. When the generalized signal-to-noise ratio is larger than5dB, the identification rate of various signals reaches 65% or above, especially when the generalized signal-to-noise ratio is larger than 10dB, the identification rate of various signals reaches 90%or above, and therefore the identification effect of the method is good.

The present invention belongs to the technical field of digital information transmission, and discloses a recognition method of different types of barrage jamming and deception jamming. The method comprises the steps of performing bispectrum calculation on a received radar active jamming signal to obtain a bispectrum of the jamming signal; respectively calculating Renyi entropies of the bispectrumunder different parameters, and combining the Renyi entropies into new feature vectors; and classifying the constructed feature vectors by using an RBF neural network classifier. When a jamming-to-noise ratio is greater than 0dB, recognition rates of amplitude modulation noise jamming and radio frequency noise jamming are 100%, when the jamming-to-noise ratio is greater than 3dB, a recognition rate of frequency modulation noise jamming is 95% or above and gradually approaches 100%; and recognition rates of three types of deception jamming rapidly grow up along with increase of the jamming-to-noise ratio, when the jamming-to-noise ratio is greater than 5dB, the recognition rates of the three types of deception jamming are all 94% or above and gradually approach 100%, and the method still has a good recognition effect for the deception jamming.

The invention provides a control method and a device for a flywheel energy storage system. The method comprises the steps of a, with the angular velocity, the d-axis current and the q-axis current of A, a flywheel as state variables, and the energy storage capacity of the flywheel as a control variable, constructing a model for the energy storage system of the flywheel, and discretizing the model according to the sampling time; B, constructing a performance index based on a second-order Renyi entropy, and determining an optimal input control variable increment with the partial derivative of the input control variable increment to be zero based on the performance index; C, determining a current optimal input control variable based on the optimal input control variable increment. According to the control method and device, based on the simplified energy storage model of the flywheel, a controller for the energy storage system of the flywheel can be designed. Meanwhile, the excess energy of the flywheel is stored, or the deficient energy of the flywheel is supplemented. In this way, a wind turbine generator set is ensured to transmit a smoothing power to the power grid.

The invention belongs to the signal processing field, and specifically relates to clutter filtering on opto-acoustic signals through Renyi entropy so as to improve signal to noise ratio and opto-acoustic imaging quality of the opto-acoustic signals. The opto-acoustic image reconstruction prefilter based on Renyi entropy includes the following steps: 1) acquiring opto-acoustic signals; 2) performing time frequency distribution solution on the opto-acoustic signals; 3) performing Renyi entropy solution on each opto-acoustic signal point; 4) determining the threshold; 5) performing filtering processing; and 6) performing image reconstruction on the opto-acoustic signals which are processed through filtering. Compared with the prior art, the opto-acoustic image reconstruction prefilter based on Renyi entropy has the following advantages: 1) the signal to noise ratio of the reconstructed image is obviously improved when opto-acoustic image reconstruction is performed through the opto-acoustic signals which are processed by the prefilter; and 2) the mean square error of the reconstructed image is obviously reduced when opto-acoustic image reconstruction is performed through the opto-acoustic signals which are processed by the prefilter.

The invention discloses an object performance detecting method combined with obviousness information under a complicated background. The method comprises the steps that firstly, obviousness images of all channels under eight set Gaussian kernels are respectively obtained based on the SSS obviousness detecting algorithm; secondly, the Renyi entropy values of the eight obviousness images are respectively calculated, and the largest Renyi entropy value changing position is marked; thirdly, the eight obviousness images are divided into two parts, and the obviousness images with the minimum Renyi entropy values are selected from the two parts respectively; fourthly, the two obviousness images are normalized and overlapped to serve as the final obviousness image of the channel; fifthly, the above steps are repeatedly executed, the respective final obviousness images of the three channels are worked out; sixthly, the obtained final obviousness images of the three channels are multiplied with the respective channel images and combined again; seventhly, the object performance detection is carried out on the combined image through the Bing algorithm. The method has the advantages of being simple in principle, good in operability, high in detecting efficiency and the like.

The invention relates to an information enhancement and transmission method based on wavelet, threshold filtering and compressed sensing, and belongs to the technical field of image enhancement. The method comprises the following steps: carrying out discrete two-dimensional wavelet sparse basis on information to obtain high-frequency and low-frequency coefficients; observing the high-frequency coefficient, and outputting an observation result matrix; performing quantization, channel coding, modulation, transmission, demodulation, channel decoding and inverse quantization on the observation result matrix and the low-frequency coefficient, and outputting recovered high-frequency and low-frequency coefficients; denoising the high-frequency coefficient based on Renyi entropy, and then performing compressed sensing reconstruction; and performing Butterworth high-pass filtering on the low-frequency coefficient, and performing wavelet inverse transformation on the low-frequency coefficient and the recovered high-frequency coefficient to obtain recovered information. According to the method, the contour of the image with the blurred contour can be enhanced through high-pass filtering so that the processed image is clearer; part of noise can be filtered out in the compressed sensing process, and wavelet coefficient reconstruction with unknown sparsity is achieved; and channel coding iscombined so that the cost is reduced and the bit error rate is also reduced.

The invention discloses a user load classification method based on an optimal clustering number and an apparatus thereof. The method comprises the following steps of acquiring time sequence curve data of a user load and carrying out normalization processing; extracting a user electricity usage behavior characteristic; using an Renyi entropy gradient descent method to carry out optimal clustering number training; and using the optimal clustering number to carry out hierarchical clustering. In the invention, a similarity and relevance among different users are comprehensively considered; a problem that precision of a traditional Euclidean distance similarity during high dimension data processing is not high is overcome; the different clustering numbers of Renyi entropy values are calculated so that the optimal clustering number can be found; a disadvantage that the clustering number needs to be set in advance in a traditional algorithm is overcome; operation efficiency is increased and operation cost is reduced.

The present invention discloses a medical image registration method based on the alpha-Renyi mutual information (RMIR). The method comprises the following steps: the step of acquiring image pixel information: acquiring pixel information of a reference image and a floating image; the step of acquiring image grayscale information: acquiring grayscale information of the reference image and the floating image; the step of acquiring image mutual information: acquiring the Renyi mutual information of the reference image and the floating image; and the step of image registration: performing rigid registration on the Renyi mutual information obtained in the step of acquiring image mutual information, and obtaining the optimal transformation through the registration function, so that the Renyi mutual information reaches a maximum value. The Renyi entropy is used to introduce the mutual information metric, and the registration function is used to register, so that the registration accuracy is effectively improved; and the appropriate alpha initial parameters are found out through a large number of registration experiments, so that the operation load in the registration process is effectivelyreduced.

The invention discloses a time series analysis method based on Renyi entropy and MMA. The invention makes use of the advantage of the MMA analysis method in multi-scale analysis of time series and theadvantage of Renyi entropy in dealing with heavy tail problem of time series analysis, has the ability to analyze time series with multi-attributes, multi-scales and multi-fractals, can reveal the short-term and long-term characteristics of financial time series, and especially can quantify the volatility, importance and relationship between the attributes of time series.

The invention relates to a wavelet soft threshold image denoising method based on Renyi entropy, and belongs to the technical field of image denoising and wavelet transform. The method comprises the following steps: 1, splitting an RGB color image into three sub-images with a single color; 2, performing discrete wavelet transform on the three sub-images output in the step 1, and outputting high-frequency coefficients of the Jth layer, the J-1 layer and the J-2 layer; wherein the wavelet generating function is one of bior2.2 and sym4, and the wavelet generating function is one of bior2.2 and sym4; 3, taking interval boundaries corresponding to the two-dimensional Renyi entropy values of the high-frequency coefficients of the Jth layer, the J-1 layer and the J-2 layer output in the step 2 asoptimal segmentation thresholds; 4, adjusting thresholds of the Jth layer, constructing the J-1 layer and the J-2 layer, and performing wavelet denoising on the high-frequency coefficient of the Jthlayer with the adjusting thresholds as soft thresholds; and reconstructing the image to complete two-dimensional discrete wavelet inverse transformation to obtain a denoised image. According to the method, the point noise in the image signal can be effectively separated from the image signal.

The invention discloses a censored data parameter self-adaption estimation method based on information theory learning. The invention comprises following steps of 1: designing estimation error functions of censored signals; 2: calculating secondary Renyi entropy of the estimation errors; 3: obtaining local estimation of all node parameters by means of gradient descent; 4: designing weighing coefficient according to local estimation of each node and neighbor node thereof; and 5: exchanging local estimation of the node and the neighbor node and calculating weighing estimation of the node and the neighbor node thereof by use of the weighing coefficient obtained in the step 4. According to the invention, 1) self-adaption estimation of parameters included in the censored data is achieved; 2) collaborative estimation of all nodes of a sensor network is achieved; 3) impacts on estimation performance by signal abnormal values caused by attacks suffered by the network nodes can be effectively reduced; and 4) higher estimation precision can be provided under a gaussian or non-gausssian noise environment.

The invention relates to the field of bearing fault diagnosis, and discloses a weighted sliding window second-order synchronous compression S transformation bearing fault diagnosis method, which is used for improving the precision of bearing fault feature extraction and fault type identification. According to the invention, an optimal weighted sliding window module is constructed according to bearing fault vibration signal characteristics; second-order synchronous compression S transformation is fused into a plurality of step modules, a Gaussian window best matched with a fault feature signalis obtained in a self-adaptive mode in the signal time-frequency analysis process through an optimization algorithm, the weight is calculated through the obtained time-frequency analysis Renyi entropy, and then overall signal reconstruction output is achieved. The bearing fault characteristics are accurately extracted and the fault type is accurately identified through the time-frequency spectrogram, and the method is suitable for rolling bearing fault diagnosis.

The invention provides an image data dimension reduction method based on two-dimensional kernel entropy component analysis. The method comprises the following steps that (1) image data are read in; (2) a kernel function is estimated through a Parzen window; (3) a kernel matrix for calculating all the image data in row is set; (4) the eigenvalue and eigenvector of a correlation matrix of the image data are calculated; (5) the Renyi entropy of the image data is calculated; (6) the eigenvector of the correlation matrix of the image data is mapped through a two-dimensional kernel entropy component analysis method, and data dimension reduction is achieved. According to the method, through the two-dimensional analysis method, kernel conversion is directly carried out on the rows or lines of an image, the entropy estimated by the kernel matrix of the image data is sorted, the intrinsic dimension of the image data obtained after dimension reduction is obtained, and the space structure information of the image data can be kept. According to the method, due to the fact that the kernel matrix directly calculates the image data in row or in line, two-dimensional image data do not need to be converted into one-dimensional vectors, and calculation complexity is reduced when the correlation matrix is obtained through kernel conversion.

The invention discloses an integrated circuit fault diagnosis method based on information entropy. The fault diagnosis method comprises steps as follows: the characteristic that entropy information is sensitive to parameters of a to-be-tested circuit is used, a probability density function of output responses of the to-be-tested circuit is derived with the Lagrange multiplier method, then a free parameter alpha in a Renyi entropy definition formula of an output of the to-be-tested circuit is acquired through estimation with a maximum likelihood method, finally, Renyi entropy of the output of the to-be-tested circuit is acquired through computation by the aid of the probability density function and the free parameter alpha, and fault diagnosis is finished by the aid of the difference between Renyi entropy corresponding to an output of the unknown fault circuit and Renyi entropy corresponding to an output of a fault-free circuit. Compared with the prior art, the method has the advantages that the diagnosis effect on parametric faults in noises is good, the robustness is high, only a single test point is required, the method is applicable to current signals and voltage signals, and the computation complexity is low.

The invention discloses a wind turbine generator main bearing fault diagnosis method containing unknown faults. The method comprises the following steps: carrying out efficient time-frequency decomposition on a vibration signal of a main bearing of a wind turbine generator by adopting K-S conversion; extracting features from a complex number time-frequency matrix obtained through K-S decomposition, extracting ten features including a peak value, a mean value, a standard deviation, a variance, skewness, kurtosis, a root mean square value, a peak-to-peak value, Shannon entropy and Renyi entropy from a high frequency domain, a middle frequency domain and a low frequency domain respectively, and constructing a 30-dimensional original feature set; performing descending sorting on the 30-dimensional features according to the feature Gini importance, and selecting the first 15-dimensional features having the maximum influence on classification to construct an optimal feature subset; and finally, identifying the mechanical state of the main bearing of the wind turbine generator containing the unknown fault by adopting an OCSVM and RF combined hierarchical hybrid classifier. According to the invention, new faults of the main bearing of the wind turbine generator can be well identified, potential safety hazards of the main bearing of the wind turbine generator can be found as early as possible, and the operation reliability of equipment is improved.

The invention discloses a no-reference multi-focus image fusion evaluation measure based on alpha mutual information, comprising construction of conditional Renyi entropy, construction of alpha mutualinformation, no-reference image fusion evaluation measure based on alpha mutual information and other steps. First, character of the Renyi entropy is studied to construct the conditional Renyi entropy; second, alpha mutual information is constructed according to Arimoto's conditional Renyi entropy definition, character of the alpha mutual information is studied, and the alpha mutual information is used to measure dependence degrees of two random variables; third, alpha mutual information of an input image and fused image is estimated to provide a no-reference image fusion evaluation measure that evaluates the effects of different multi-focus image fusion algorithms. The no-reference multi-focus image fusion evaluation measure based on alpha mutual information has the advantages that statistical information of the input image and fused image is comprehensively considered, informatics measure is used to measure the fused image to obtain information quantity, no reference to images is required during fusion effect evaluation, and the no-reference multi-focus image fusion evaluation measure based on alpha mutual information is widely applicable and has good evaluation effect.

The invention discloses an integrated circuit fault diagnosis method based on information entropy. The fault diagnosis method uses the characteristic that entropy information is sensitive to the parameters of the tested circuit, uses the Lagrangian multiplier method to derive the probability density function of the output response of the tested circuit, and then uses the maximum likelihood method to estimate the output of the tested circuit. Rényi entropy defines the free parameter α in the formula, and finally uses the probability density function and the free parameter α to calculate the Rényi entropy of the output of the circuit under test, and uses the difference between the Rényi entropy corresponding to the output of the unknown fault circuit and the output of the non-fault circuit, Complete troubleshooting. Compared with the prior art, the invention has good effect in diagnosing parametric faults in noise, has strong robustness, only needs a single measuring point, is applicable to both current signals and voltage signals, and has low computational complexity.