30 results about "Amplitude histogram" patented technology

An optical signal quality monitoring system is provided, by which the quality of optical signals can be examined using a single monitoring system, not depending on the bit rate of each signal. In the system, an optical signal having a bit rate N.f0, that is, N times as much as basic clock frequency f0, is sampled by using a pulse repetition frequency f0 / n1-DELTAf or f0 / n1+DELTAf where n1 is a predetermined natural number and the pulse repetition frequency slightly differs from f0 / n1 by DELTAf, and an amplitude histogram of the optical signal is determined based on results of the sampling. Regarding the sampling points which constitute the histogram, a set of higher-level points and a set of lower-level points are extracted and a ratio of a difference between an average level of the set of higher-level points within a predetermined period and an average level of the set of lower-level points within a predetermined period, to the sum of standard deviations of both sets within each predetermined period is calculated as a coefficient of the S / N, and the quality of the optical signal is examined based on the coefficient. By performing optical sampling, quality of optical signals having bit rates of a few dozen Gbit / s or more can be monitored.

The invention discloses a rolling mill transmission system key component fatigue life early warning method based on load spectrum analysis. The method is characterized by comprising the following steps: 1, obtaining a rolling mill transmission system key component load spectrum through real-time monitoring in a rolling mill working process; 2, based on a computer simulation technology, obtaining a stress distribution and conversion coefficient of a rolling mill transmission system key component; 3, obtaining a corresponding stress spectrum by performing conversion processing on the load spectrum measured in the first step; 4, compiling an equivalent stress amplitude histogram by performing statistics on the stress spectrum after processing in the third step by use of a cyclic rain flow counting method; and 5, calculating fatigue life circulation frequency of a transmission part by use of a correction Miner method, and converting the fatigue life circulation frequency into a fatigue life taking a year as a unit. The method solves the problems of large calculation amount and high calculation complexity in a conventional fatigue life calculation method, has quite high applicability and operability and is high in engineering practicality.

The waveform deterioration detection range is broadened and multi bit rates can be handled. A chromatic dispersion compensator (or polarization mode dispersion compensator) (102) receives a waveform-deteriorated NRZ optical signal entered through an input fiber (101) and compensates it. On the other hand, an optical detector (106) receives part of output light and a sampling circuit (A / D converter) (107) performs asynchronous sampling of received waveform intensity. A control circuit (110) calculates the nth even moment (n is 4 or more) from an obtained waveform amplitude histogram and performs control to minimize its value.

An optical signal quality monitoring apparatus includes an optical detector for directly receiving an optical signal modulated in an optical path and converting the optical signal to an electric signal, an asynchronous sampling unit for asynchronously sampling the electric signal of the optical detector at a reduced speed, and a digital signal processor for monitoring an optical signal quality by finding a synchronized amplitude histogram of data sampled in the asynchronous sampling unit. An optical signal quality monitoring method includes (a) a step of allowing an optical detector to directly receive a modulated optical signal and to convert the optical signal to an electric signal; (b) a step of allowing an asynchronous sampling unit to asynchronously sample the electric signal; and (c) a step of allowing a digital signal processor to monitor an optical signal quality by generating a synchronized amplitude histogram of sampled data.

The invention provides a non-reference definition evaluating method and a non-reference definition evaluating system for a full-slice image. The non-reference definition evaluating method comprises the steps of: adopting a pyramid multi-layer data structure for storing digital images, and segmenting the images in each layer of the pyramid multi-layer data structure into a plurality of image blocks evenly; dividing a strong edge in a gradient amplitude histogram of the image blocks by adopting an Otsu threshold segmentation method, calculating to obtain strong edge intensity according to a gradient maximum value and width of the strong edge, correcting the strong edge intensity by utilizing background complexity, and determining definition of each single image block; setting a standard value of the definition, and determining whether a current image block is a clear image or a blurred image; and finally determining whether the images in each layer are clear images or not according to proportions of clear image blocks in each layer. The non-reference definition evaluating method and the non-reference definition evaluating system avoid the image definition score differences caused by image content differences, correct the influence of the image background complexity on the image definition, and realizes effective identification of the image which is partially clear and partially blurred.

A method for determining signal quality in optical transmission systems, wherein the effective signal-to-noise ratio is determined by measuring amplitude histograms of a signal and by calculating characteristic histogram moments and additional interference is ascertained by comparing the characteristic histogram moments with the optical signal-to-noise ratio.

A monitoring system includes first and second evaluation sections for obtaining an averaged Q-factor parameter and a waveform distortion parameter from an optical signal amplitude histogram collected from optical signals under measurement. The monitoring system further includes a third evaluation section for determining both averaged Q-factor parameter and waveform distortion parameter, and for making a decision as to whether the main factor of the optical signal quality degradation is waveform distortion or not by comparing the averaged Q-factor parameter and waveform distortion parameter with their initial values or initial characteristics which are obtained when no optical signal quality degradation is present.

The invention discloses a flexible optical network time domain equalization method and system based on a composite neural network, and belongs to the field of optical fiber communication systems, andthe method comprises the steps: (1) preprocessing a received signal transmitted by a flexible optical network; (2) calculating an amplitude distribution histogram of the preprocessed received signal;(3) inputting the amplitude distribution histogram into a first-stage multi-task neural network classifier, and outputting transmission parameters of the flexible optical network; (4) setting a weightand an offset parameter of a second-stage neural network regression device according to the transmission parameter of the flexible optical network; (5) carrying out time domain equalization on the preprocessed received signal by adopting a second-stage neural network regression device, wherein the number of input neurons of the first-stage multi-task neural network classifier is the same as the number of groups of amplitude histograms, and the number of output neurons of the first-stage multi-task neural network classifier is the same as transmission parameters of the flexible optical network. The time domain equalization method and system disclosed by the invention are wider in application range.

A method for monitoring the transmission quality of an optical transmission system, such as, for example, an optical wavelength division-multiplex network. An amplitude histogram of an optical signal (transmission signal) transmitted over the transmission system may be plotted and classified, with the assistance of a neural network, according to bit error rates and / or causes of faults. The need for setting requirements for transmission mode, transmission format and / or transmission timing cycle of the transmission system may be eliminated. The amplitude histogram may be implemented for any signal, and causes of faults, which are not able to be determined by a conventional bit rate classification, may be allocated.

A technique for determining averaged Q-factor, Qavg, for an optical binary signal transmitted at a bit rate X, based on amplitude histogram evaluation and on asynchronous sampling. The technique is inexpensive since it uses sampling pulses, which have bit rate Y lower than X and are not synchronized with the optical signal. The technique proposes conducting N sampling sessions and constructing N respective amplitude histograms for the sessions, wherein an initial session is conducted at a randomly selected initial phase of the sampling pulses, and N−1 sessions are held at different phases shifted in respect of the initial phase. The technique then compares the obtained N amplitude histograms to select there-from the amplitude histogram having the minimal cumulative value of cross-point data. The averaged Q-factor is determined from the selected histogram, with accuracy comparable with that of synchronous methods.

An embodiment of the invention discloses a texture identification method based on complete local characteristics, which belongs to the technical field of mode identification. The method comprises the following steps of calculating an amplitude histogram hm, a symbol histogram hs and a center coded histogram hc of a trained grayscale texture image; based on texture identification characteristic vectors of the histograms, using a support vector machine for carrying out training to obtain a texture identification classification model; and obtaining a texture identification characteristic vector for testing the grayscale texture image, and inputting the texture identification characteristic vector into the texture identification classification model to obtain a texture identification result. A transformation matrix is used for processing the grayscale texture image to achieve the object of adapting to the environment, so that the robustness of texture identification is achieved.

A method for controlling the gain and zero of a multiple pixel photon counter device, and light-measuring system implementing said method.Signals provided by the device are acquired for predefined periods, until a predefined total measurement time is reached, an amplitude histogram is formed from the acquired signals, the positions of two consecutive peaks, measurable on the histogram, are determined, an error signal is produced, which is equal to the deviation between both peaks and, by means of the same, the voltage supplying the device is regulated, so as to keep the deviation equal to a predefined set point.

In a method of analyzing an optical communications signal, a threshold is established under control of a processor, and a decision circuit compares the amplitude of the signal to the threshold. A counter samples the output of the decision circuit and counts, over many periods of the signal, those samples indicating that the amplitude of the signal is above the threshold. The threshold generating, comparing, and counting are repeated for several thresholds within a range corresponding to an expected amplitude range of the signal. The stored counts and thresholds represent an amplitude histogram N(VT) for the signal. The derivative dN / dVT of the function N(VT) represents the probability density function (PDF) for the signal amplitude and can be used to derive performance information such as bit error rate and optical signal-to-noise ratio (OSNR).

The invention discloses an abnormal behavior detection method based on an optical flow algorithm. The method is specifically implemented according to the following steps: step 1, extracting optical flow information by the optical flow algorithm: extracting the optical flow information generated when a human body moves by adopting a Farneback dense optical flow algorithm; step 2, extracting behavior characteristics: counting the optical flow information extracted in the step 1 into a direction amplitude histogram; and step 3, result analysis and abnormality judgment: judging whether an abnormalbehavior occurs or not by calculating the direction and amplitude entropy of the histogram, the larger the direction and amplitude entropy of the histogram is, the more disordered the current motionis, the higher the possibility of the abnormal behavior is, and the problem of misjudgment of the abnormal behavior in the prior art is solved.

The waveform deterioration detection range is broadened and multi bit rates can be handled. A chromatic dispersion compensator (or polarization mode dispersion compensator) (102) receives a waveform-deteriorated NRZ optical signal entered through an input fiber (101) and compensates it. On the other hand, an optical detector (106) receives part of output light and a sampling circuit (A / D converter) (107) performs asynchronous sampling of received waveform intensity. A control circuit (110) calculates the nth even moment (n is 4 or more) from an obtained waveform amplitude histogram and performs control to minimize its value.

The invention discloses a modulation and recognition device and a method for union high-order statistic and spectral peak features. The modulation and recognition device comprises a signal preprocessing module, a high-order statistic feature extraction module, a spectral peak feature extraction module and a union recognition module. Signals to be recognized and modulated are input into the signal preprocessing module so that filtering, carrier frequency estimating and normalization processing can be carried out to obtain the preprocessing signals, and then the preprocessing signals are input into the high-order statistic feature extraction module and the spectral peak feature extraction module so that the features can be extracted, wherein the features include high-order moment features, high-order accumulation features, constellation cluster point values, feature power spectrum variance features, first-order differential amplitude histogram spectral peak number features and the like. The extracted feature information is input into the union recognition module. A classifier based on the union features carries out mode feature matching on the input signals and outputs recognition results. According to the device and the method, recognition of the SC-FDMA modulation mode is achieved for the first time, the recognition rate of the high-order QAM modulation mode is improved, the judgment threshold of recognition in MFSK classes is expanded, and the device and the method can be applied to the fields of frequency spectrum management, electronic countermeasures and the like.

The invention provides a blind signal modulation type recognition method based on a convolutional neural network, and the method comprises the steps: employing three phase difference histograms calculated at different sampling rates and an amplitude histogram as signal feature parameters, carrying out the classification of the signal feature parameters through a convolutional neural network classifier, and obtaining a signal modulation type. Because the texture of the phase difference histogram is insensitive to frequency offset and inaccurate bandwidth, the method has strong immunity to frequency offset and inaccurate bandwidth, and is suitable for blind signal modulation type identification in a complex electromagnetic environment.

The invention discloses a method for collecting data in a digital pre-distortion system. The method comprises the following steps that (1) a parallel processing device collects emitting signals and samples the collected emitting signals, and a serial processing device determines the start position of frames; (2) the serial processing device searches for the largest value to determine the start position of the frames and transmits the synchronous start position of the frames to the parallel processing device; (3) the parallel processing device starts a long-time histogram statistic module to count amplitude distributions of all frames of the emitting signals; (4) the parallel processing device collects the emitting signals, stores the emitting signals in short time duration, transmits the collected emitting signals and amplitude histograms counted in a long time period to the serial processing device; (5) the serial processing device receives the emitting signals in short time duration, calculates the distributions of the amplitude histograms, and compares the similarity of the distributions of the amplitude histograms and the amplitude histograms calculated in a long time period, if the similarity is higher than the threshold, a pre-distortion factor is calculated, and if the similarity is not higher than the threshold, the similarity is abandoned, and data are obtained from the parallel processing device again. The method has the advantages of being high in adaptability, and the like.

The invention provides a monitoring method and device for an optical signal-to-noise ratio in a nonlinear region. The method comprises the following steps: carrying out processing on an output signal of an optical fiber coherent transmission system through a digital signal processor, and acquiring an amplitude histogram and a self-adaptive filter tap coefficient for assisting the calibration of nonlinear noise, wherein the above processing comprises IQ imbalance compensation processing, electric dispersion compensation processing, depolarization multiplexing processing, frequency offset compensation processing, phase damage recovery processing and intersymbol interference equalization processing; extracting a first number of features based on the amplitude histogram from the amplitude histogram, wherein the features based on the amplitude histogram comprise a peak position feature, a standard deviation feature and a peak maximum value feature; and selecting sample data from a data set, weighting and inputting the sample data to a pre-established neural network model for optical signal-to-noise ratio monitoring so as to obtain an optical signal-to-noise ratio monitoring result based on the neural network model, wherein the sample data in the data set comprises the extracted features based on the amplitude histogram and the self-adaptive filter tap coefficient.

A method for controlling the gain and zero of a multiple pixel photon counter device, and light-measuring system implementing said method.Signals provided by the device are acquired for predefined periods, until a predefined total measurement time is reached, an amplitude histogram is formed from the acquired signals, the positions of two consecutive peaks, measurable on the histogram, are determined, an error signal is produced, which is equal to the deviation between both peaks and, by means of the same, the voltage supplying the device is regulated, so as to keep the deviation equal to a predefined set point.

The present invention provides a reference-free evaluation method and system for a full-slice image, which uses a pyramid multi-layer data structure to store digital images, and divides each layer image of the pyramid multi-layer data structure into several image blocks on average; through Otsu threshold segmentation The method divides the strong edge in the gradient magnitude histogram of the image block, calculates the strong edge strength from the gradient maximum value and width of the strong edge, and uses the background complexity to correct the strong edge strength to determine the definition of a single image block; Set the standard value of sharpness to determine whether the current image block is a clear image or a blurred image; according to the proportion of clear image blocks in each layer, finally determine whether the image of each layer is a clear image. The invention avoids the difference of image clarity score caused by the difference of image content, corrects the influence of image background complexity on image clarity, and realizes the effective identification of partly clear and partly blurred images.

The invention discloses a data acquisition method in a digital predistortion system, which comprises the following steps: 1. A parallel processing device collects and samples a transmission signal, and a serial processing device determines the starting position of a frame; 2. The serial processing device searches for the maximum The value determines the frame start position and transmits the frame synchronization start position to the parallel processing device; 3. The parallel processing device starts the long-time histogram statistical module to count the amplitude distribution of each frame of the transmitted signal; 4. The parallel processing device collects the transmitted signal And save the transmission signal of short time length and transmit it to the serial processing device with the amplitude histogram of long-term statistics; The similarity of the amplitude histogram that has been counted for a long time, if the similarity is higher than the threshold, the pre-distortion coefficient is calculated, otherwise, the similarity is discarded and the data is reacquired from the parallel processing device. It has the advantages of strong adaptability and so on.

An optical signal quality monitoring apparatus includes an optical detector for directly receiving an optical signal modulated in an optical path and converting the optical signal to an electric signal, an asynchronous sampling unit for asynchronously sampling the electric signal of the optical detector at a reduced speed, and a digital signal processor for monitoring an optical signal quality by finding a synchronized amplitude histogram of data sampled in the asynchronous sampling unit. An optical signal quality monitoring method includes (a) a step of allowing an optical detector to directly receive a modulated optical signal and to convert the optical signal to an electric signal; (b) a step of allowing an asynchronous sampling unit to asynchronously sample the electric signal; and (c) a step of allowing a digital signal processor to monitor an optical signal quality by generating a synchronized amplitude histogram of sampled data.

The invention belongs to the technical field of signal processing, and particularly relates to a signal clipping detection method and device, a terminal and a computer readable storage medium, and the signal clipping detection method comprises the steps: obtaining a to-be-detected signal; determining an amplitude histogram of the to-be-detected signal, wherein the amplitude histogram comprises a plurality of amplitude intervals; if a first amplitude interval of the amplitude histogram jumps, determining an amplitude corresponding to the first amplitude interval as a clipping threshold of the to-be-detected signal; carrying out clipping detection on the to-be-detected signal by utilizing the clipping threshold value; and improving the precision of signal clipping detection.

The invention relates to a method for estimating the number of people in group abnormal emergencies. The method comprises the following steps: selecting multiple frames of video images as training samples; extracting optical flow amplitude histogram features of each frame of image of each training sample; obtaining a support vector machine model; combining the area feature and the key point feature of the foreground image into a combined feature of the training sample; obtaining a Gaussian regression model; selecting multiple frames of video images as test samples; extracting optical flow amplitude histogram features of each frame of image of each test sample; carrying out group behavior detection by utilizing a support vector machine model, and judging whether a group abnormal emergency occurs or not; and when group abnormal emergencies occur, estimating the number of people by adopting a Gaussian regression model. The invention further relates to a system for estimating the number of people in the group abnormal emergencies. The invention can accurately detect the number of crowds in allusion to sudden abnormal dangerous events of crowds in public places, and is high in environment adaptability and high in accuracy.

The invention discloses a multi-core optical fiber crosstalk monitoring method based on a neural network. The method comprises the following steps: collecting an original time sequence signal at a receiving end, and measuring by using a power meter to obtain a crosstalk value; carrying out asynchronous sampling on the original time sequence signal to obtain an asynchronous amplitude histogram; inputting the asynchronous amplitude histogram and the corresponding crosstalk label value into a neural network for training to obtain a trained neural network; and a signal in a current link is obtained, and after an asynchronous amplitude histogram is obtained through asynchronous sampling, the asynchronous amplitude histogram is input into the trained neural network, so that real-time online monitoring of the crosstalk of the multi-core optical fiber in the space division multiplexing system can be realized. According to the multi-core optical fiber crosstalk monitoring method based on the neural network, the crosstalk which fluctuates severely in a space division multiplexing link can be monitored dynamically in real time, and a solid operation and maintenance technical support is provided for stable transmission of a space division multiplexing system. The invention further provides a corresponding multi-core optical fiber crosstalk monitoring system based on the neural network.

The embodiment of the invention discloses a texture recognition method based on complete local features, which belongs to the technical field of pattern recognition. The method comprises the following steps: calculating the amplitude histogram hm, the sign histogram hs and the center coding histogram hc of the grayscale texture image for training; forming a texture recognition feature vector based on the histogram, using a support vector machine for training, and obtaining Texture recognition classification model; obtain the texture recognition feature vector of the test grayscale texture image, input it into the texture recognition classification model, and obtain the texture recognition result. The invention achieves the purpose of adapting to the environment by using the transformation matrix to process the grayscale texture image, thereby improving the robustness of texture recognition.

The invention relates to a radar narrow-band interference identification method based on envelope histogram distribution. Aiming at the defects of fuzziness and subjectivity caused by more dependence on manual judgment and setting on traditional radar interference type confirmation and anti-interference measure selection, the invention provides a method for accurately classifying four typical narrow-band interferences, namely narrow pulse interference, suppressing interference, deception interference and convolution interference. The method comprises the specific steps of echo amplitude envelope construction, constant amplitude histogram distribution statistics, near window fine compensation, adaptive classification and recognition and the like. According to the invention, the automatic selection capability of anti-interference measures is effectively enhanced, and the operational effectiveness of the radar in a complex electromagnetic environment is improved.