36 results about "Nonlinear equalization" patented technology

A data channel on an integrated circuit device includes a non-linear equalizer having as inputs digitized samples of signals on the data channel, decoding circuitry configured to determine from outputs of the non-linear equalizer a respective value of each of the signals, and adaptation circuitry configured to adapt parameters of the non-linear equalizer based on respective ones of the value. The non-linear equalizer includes a non-linear filter portion, and a front-end filter portion configured to reduce numbers of the inputs from the digitized samples. The non-linear equalizer may be a neural network equalizer, such as a multi-layer perceptron neural network equalizer, a reduced complexity multi-layer perceptron neural network equalizer, or a radial-basis function neural network equalizer. Alternatively, the non-linear equalizer may include a linear filter and a non-linear activation function, which may be a hyperbolic tangent function.

This invention discloses a fiber optic nonlinear equalization method based on perturbation theory using a complex-valued convolutional neural network, comprising the following steps: S1, constructing a complex-valued channel feature map from symbol samples using perturbation theory to form an original training set, and inputting it into the neural network; S2, training the complex-valued convolutional neural network using the original training dataset, fitting the training data, and finally obtaining the most suitable nonlinear equalizer model; S3, constructing a complex-valued channel input feature map for the test signal, and inputting it as the test set into the fitted nonlinear equalizer model for equalization, obtaining the output result. The fiber optic nonlinear equalization method based on perturbation theory proposed in this invention utilizes perturbation theory combined with a complex-valued convolutional neural network to extract more useful feature information, solving the problems of high bit error rate and poor compensation effect during nonlinear equalization, improving the nonlinear compensation effect, and greatly enhancing system performance.

The invention discloses a nonlinear equalization method and device for digital subcarrier multiplexing signals and an optical fiber communication system. The method comprises the following steps: caching an original symbol of a transmitting end, and preprocessing a received signal to obtain a signal to be equalized; constructing a power model according to the link parameters and deducing a back propagation power model; inputting the data into an improved digital back transmission algorithm, and calculating a nonlinear accumulation amount in each equalization step length; determining an initial splitting ratio parameter by using a nonlinear symmetry principle, and embedding the initial splitting ratio parameter into the neural network as a trainable variable; inputting a received signal and an original symbol into a network, and optimizing a splitting ratio through gradient descent to obtain an optimal value; therefore, the position of a nonlinear operator in the improved digital back transmission algorithm is adjusted, linear and nonlinear combined compensation of the signal is realized, and a high-precision equalization result is output. According to the method, extra communication overhead is not needed, and the link power non-uniformity characteristic can be effectively adapted.

The application discloses an optical fiber nonlinear equalization method, system, electronic equipment and medium, and relates to the technical field of optical fiber communication, and the method comprises the following steps: acquiring a target symbol sequence; the target symbol sequence is a symbol sequence of a received optical signal to be tested received by a receiving end; a perturbation triple product eigenvalue of each symbol in the target symbol sequence is constructed by using a perturbation theory to obtain a target eigenvalue set; the target eigenvalue set is input into an equalizer model to obtain a data category of the target symbol sequence after equalization; wherein the equalizer model is obtained by training a complex full-connection neural network by using training data. The application can obtain an equalizer model closer to a real value, and improve the performance of optical fiber signal nonlinear equalization.

The invention discloses a nonlinear equalizer for terahertz communication and a nonlinear equalization method of CNN-RES-LSTM combination, and belongs to the technical field of terahertz communication. The invention designs a nonlinear equalizer applied between a signal generator and a signal modulator of a terahertz communication system, which is used for carrying out nonlinear equalization processing on the terahertz communication system. The nonlinear equalizer refers to a neural network model based on CNN-RES-LSTM combination, and the processing steps of the CNN-RES-LSTM combination method are as follows: (1) improved CNN output retention: after spatial features are extracted from an improved CNN layer, the output is retained; (2) feature transformation and alignment: adjusting the feature dimension output by the CNN, and enabling the feature dimension to be matched with the dimension input by the LSTM; and (3) jump connection: directly splicing the adjusted CNN output with the LSTM. According to the invention, the problem of loss in a terahertz communication system is overcome, the performance and efficiency of balanced electric signals are calculated on demand, and the stability of signal transmission is improved.

The present invention discloses a fiber optic nonlinear equalization method, system, device and storage medium, belonging to the field of optical transmission technology. The method includes: obtaining the M-QAM signal sequence received at the receiving end; inputting the M-QAM signal sequence into a trained bidirectional long short-term memory neural network based on a memristor array to output a score matrix; using the Viterbi algorithm to obtain the optimal label sequence corresponding to each input feature sequence according to the score matrix and the training score matrix obtained during the training process. The intermediate label values of all the optimal label sequences form the label values of the M-QAM signal sequence, and the data categories corresponding to the label values are subjected to constellation mapping to obtain the original signal sequence transmitted by the transmitting end, thus completing the nonlinear equalization. The present invention uses a memristor array to implement synaptic weights, greatly reducing the complexity of the network, and uses the bidirectional feature to process the current input information by combining the information of the previous and subsequent sequences, improving the signal quality and transmission performance.

The invention discloses a high-precision communication sensing integration method, belongs to the technical field of communication sensing integration, and aims to solve the problems of complicated steps and low accuracy of traditional microalgae density detection. The method comprises the following steps: preparing a microalgae sample and obtaining a density reference value, deducing a communication bit error rate mathematical expression related to multiple parameters, designing an OOK / PAM4 modulation and low-complexity nonlinear equalization algorithm, obtaining a bit error rate data set by taking a microalgae solution as a transmission medium, and constructing and verifying a microalgae density-bit error rate correlation model. Reversely deducing a density value, optimizing a test interval, repeatedly verifying the accuracy rate, outputting multiple parameters, and analyzing an association relationship; communication and sensing collaboration is achieved, the average accuracy rate of microalgae density monitoring exceeds 95%, the adaptation rate range is wide, chip integration of an equalizer is easy, no contact is achieved, environmental protection is achieved, and the system is suitable for microalgae cultivation, marine environment monitoring and other scenes.

The invention provides a non-linear optical channel equalization method and system and a medium, and relates to the technical field of optical fiber communication, and the method comprises the steps: carrying out the signal element definition of an optical signal, receiving the optical signal transmitted by an optical channel, executing the signal element verification, and if there is non-linear disturbance, carrying out the signal element verification; the method comprises the following steps of: performing probability density distribution reconstruction based on amplitude on an optical signal, performing optical signal equalization processing under entropy maximization by measuring distribution entropy and triggering a probability distribution equalizer aiming at the probability density distribution, outputting a target optical signal after nonlinear equalization of the optical signal, and importing the target optical signal into an optical channel, and the probability distribution equalizer is arranged at a peripheral interface of the optical channel in an embedded manner. The technical problems that in the prior art, non-linear optical channel equalization calculation is poor in real-time performance, compensation precision is insufficient, and consequently optical communication performance and efficiency are low are solved. The technical effects that the optical signal transmission quality is efficiently and accurately improved, and thus the performance and efficiency of the whole optical communication are improved are achieved.

The invention relates to a digital signal processing system for a signal receiving end, which comprises a first 4 * 4 MIMO (Multiple Input Multiple Output) equalizer, a carrier phase recovery module, a Wollaston nonlinear equalizer, a second 4 * 4 MIMO equalizer, a third 4 * 4 MIMO equalizer and an error rate calculation module which are connected in sequence, and the first, second and third 4 * 4 MIMO equalizers all meet the following conditions: in polarization demultiplexing, each of the first, second and third 4 * 4 MIMO equalizers meets the following conditions: 1, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 9, 10, 11, 12, 13 X polarization of an input signal is split into an I component and a Q component, Y polarization of the input signal is split into the I component and the Q component, four independent real-value filters are arranged for the I component and the Q component corresponding to the X polarization and the I component and the Q component corresponding to the Y polarization respectively, 16 real-value filters are deployed in total, and the MIMO equalizer is obtained. The digital signal processing system for the signal receiving end constructed by the invention can effectively and reliably restore the received signal.

This invention discloses an optical transmission system based on FEC (Fiber Optic Expansion Coefficient) extended perturbation fiber nonlinearity compensation technology, belonging to the field of optical fiber communication technology. Its receiver includes, in sequence, an adaptive perturbation equalization module, a soft-decision demodulation module, a deinterleaving module, an FEC decoding module, an interleaving module, and a soft-information-based symbol reconstruction module. The adaptive perturbation nonlinear equalization module has a dual-input structure, connected to the output of the soft-information-based symbol reconstruction module in addition to the received signal. The output data of the interleaving module is added to the output data of the soft-decision demodulation module and then input to the second input of the soft-decision demodulation module. The output of the FEC decoding module uses the same decoding method as the transmitter to output the received bit result. This invention can improve the receiver sensitivity and fiber nonlinearity tolerance in optical systems.

The present invention discloses an MZM non - linear equalization compensation method based on the LM - BP neural network algorithm, belonging to the field of communication. First, at the transmitting end of the system, the generation of random signals and the modulation of PAM4 signals are implemented in Matlab. A PAM4 signal IM / DD back - to - back transmission system is built using the VPI simulation platform to simulate the non - linear damage caused by MZM and transmit the signals. The signal data after transmission is collected, and digital signal processing is performed through Matlab after going offline. The compensation algorithm of the present invention is to put the offline data into the neural network model, generate a large amount of empirical data through learning and exploration of the data to train the model, and finally extract the trained neural network model to verify the data signal. This method has an extremely fast operation speed, high accuracy after training, low scheme cost, good effect, can ensure the stability of the IM / DD system transmission, achieve precise compensation for MZM; and while not increasing the algorithm complexity, reduce the transmission error rate and improve the transmission efficiency of the system.

This invention proposes a field recovery-assisted iterative nonlinear channel equalization method for use in intensity modulation direct detection (IM / DD) fiber optic transmission systems. The method cascades the Gerchberg-Saxton (GS) iterative algorithm with a Volterra decision feedback equalizer (VDFE) in a digital receiver, forming a GS-VDFE equalizer. The GS iterative module proceeds first, iteratively updating the optical field by inferring the phase from the optical intensity signal, initially compensating for fiber dispersion and increasing the power at fading frequencies. The VDFE module follows, employing a simplified structure of a second-order feedforward Volterra and a first-order feedback DFE, to perform nonlinear equalization on the GS output signal to eliminate residual distortion. GS preprocessing eliminates the need for high-order nonlinear compensation in the VDFE, reducing equalizer complexity and noise sensitivity. The VDFE handles residual compensation, reducing the number of GS iterations to below 20, thus lowering processing latency. This invention effectively recovers high-speed digital signals in high-dispersion channels and mitigates spectral fading and self-reflection interference without altering the existing system structure.

The invention belongs to the technical field of communication, and particularly relates to a simplified second-order Volterra nonlinear equalization algorithm and device for digital signal processing of a receiving end in a communication system. The algorithm comprises the following steps: training: based on a second-order Volterra algorithm, balancing received data according to two groups of first-order weight coefficients and second-order weight coefficients, and calculating errors by taking sent data as reference; updating and optimizing each weight coefficient; simplification is carried out, the finally optimized second-order nonlinear weight coefficients are arranged from large to small according to absolute values of amplitudes, 70% of smaller parts are deleted, and values of the remaining parts are used as simplified second-order nonlinear weight coefficients; and balancing: successively balancing the effective data except the training sequence by using the obtained first-order weight coefficient and the simplified second-order weight coefficient. The method solves the problem that a traditional nonlinear equalizer is high in calculation complexity, has good universality, and is suitable for digital signal processing of receiving ends of various communication systems.

The application relates to the technical field of optical fiber communication, and particularly discloses an optical fiber nonlinear equalization method and system based on a bit-by-bit estimation neural network. The method comprises the following steps: acquiring M-QAM data of a coherent optical communication system sender, converting the M-QAM data into bits corresponding to a modulation format of the M-QAM data, and taking the bits as neural network labels; performing linear equalization on a signal transmitted through the coherent optical communication system to obtain data only retaining a nonlinear effect, and taking the data as neural network data; processing the data, constructing a feature sequence corresponding to the data, and constructing a training set and a test set based on the feature sequence and the labels; constructing a CNN-BiGRU neural network based on bit-by-bit estimation, training and optimizing the neural network by using the training set; and predicting corresponding sender data based on the test set by using the trained neural network. The method improves the optical fiber nonlinear equalization performance and reduces the complexity, and can be applied to a long-distance, high-order modulation format and a multi-channel system.

The invention discloses a simplified super-Nyquist transmission method for optical interconnection in a high-speed data center, and relates to the technical field of optical communication. Comprising the following steps that: a transmitting end pre-codes a transmitting signal by using a simplified pre-coding technology to obtain a pre-coded signal; the pre-coded signal is transmitted to a receiving end through an optical fiber, and the receiving end receives the pre-coded signal sent by the transmitting end; and finally, decoding the pre-coded signal to obtain a transmission signal. According to the method, the processing of the signal is split to the transmitting end and the receiving end, and the transmitting signal is pre-coded before the signal is transmitted, namely the transmitting end, so that the inter-symbol interference possibly generated in the subsequent process of the transmitting signal is eliminated in advance, and error propagation is suppressed; and residual damage is eliminated by using simplified nonlinear equalization at a receiving end. According to the method, the system complexity is greatly reduced while the performance is maintained.

Disclosed are a nonlinear equalization method and apparatus for an optical transmission system, relating to the technical field of optical communications. The method comprises: on the basis of a specifically structured frame received, training a noise-filtering network and a main network, wherein the noise-filtering network is configured for tracking and filtering out time-varying noise, and the main network is configured for static nonlinear equalization; the specifically structured frame comprises a reference frame, an interference frame, and a test frame, wherein the reference frame is configured for training the main network so as to achieve convergence of a weight coefficient of the main network, the interference frame and the reference frame are configured for training the noise-filtering network so as to achieve real-time convergence of the noise-filtering network, and the test frame is configured for carrying a random service signal and testing system performance; performing nonlinear equalization on the test frame using the trained noise-filtering network and the trained main network, and outputting an equalized test frame. The present invention solves the problems of large nonlinear equalization convergence errors and poor generalization performance caused by the introduction of time-varying noise, has good anti-noise performance and low algorithmic complexity, and meets practical application requirements.

The invention relates to the technical field of underwater communication, in particular to an underwater AUV (Autonomous Underwater Vehicle) blue laser-SiPM detection equalization communication system and method, and particularly relates to a signal processing method applying a linear and nonlinear equalization technology. The underwater AUV blue light laser communication system integrates four modules of optical communication transmitting and receiving, signal processing and balancing, and underwater positioning and tracking. The transmitting module generates a blue laser signal, the receiving module converts the blue laser signal into an electric signal, the signal processing module suppresses distortion, and the positioning and tracking module adjusts the transmitting direction in real time to ensure signal alignment. According to the system, the underwater optical communication transmission distance is remarkably increased, and the underwater optical communication transmission distance can reach 250 meters under the standard seawater condition and is far beyond that of a traditional system. And meanwhile, the data transmission rate is greatly improved, and the high-bandwidth application requirement is met. The underwater communication performance is optimized through the overall design, and an efficient and stable communication solution is provided for deep sea detection, underwater robots and other high-requirement applications.

The invention provides a construction method of a communication perception integrated model and the communication perception integrated model, and relates to the field of communication, and the method comprises the steps: employing a set modulation mode to transmit a plurality of OFDM frames between a transmitter and a receiver, and obtaining a frequency domain OFDM signal segment corresponding to each OFDM frame through the receiver, and each OFDM frame comprises a data subcarrier and a pilot frequency subcarrier; each sample instance comprises a frequency domain OFDM signal segment, and a real symbol and a real distance corresponding to the frequency domain OFDM signal segment; the communication and perception integrated model comprises a communication network architecture and a perception network architecture which share the same complex value residual time sequence convolutional network; the communication network architecture carries out equalization processing based on the frequency domain OFDM signal segment of the input communication and perception integrated model so as to output a prediction symbol, and the perception network architecture carries out distance estimation based on the frequency domain OFDM signal segment of the input communication and perception integrated model so as to output a prediction distance between the transmitter and the receiver. Therefore, joint nonlinear equalization and transmitting-receiving end distance estimation are realized.

The invention discloses an optical fiber communication nonlinear equalization method based on early-stage monitoring and machine learning, which belongs to the field of optical fiber communication, and comprises the following steps of: before nonlinear equalization is carried out on a signal, accurately identifying the nonlinear damage degree of the signal through a monitoring module established based on machine learning; therefore, additional guidance information is provided for the nonlinear compensation module, a proper compensation model is selected from the compensation module to carry out nonlinear compensation on the signal so as to obtain an optimal compensation effect, and the nonlinear equalization performance is effectively improved. Moreover, the method only needs to output the monitoring result to the compensation module once in a short time, does not need continuous real-time monitoring, and is lower in algorithm complexity. Therefore, according to the method provided by the invention, the nonlinear equalization performance of the algorithm can be remarkably improved with relatively low algorithm complexity cost.

The application discloses a large dynamic anti-interference reconfigurable receiver, and belongs to the technical field of communication, radar, electronic countermeasure and integrated radio frequency. The receiver is composed of a coupling amplification branch network, an array radio frequency amplification filter, an array scrambling mixing, an array intermediate frequency amplification filter, an array scrambling sampling, an array descrambling synthesis, a nonlinear equalization, a calibration and an interference generation module. Through the combination of interference cancellation, array synthesis, power backoff, forced scrambling and nonlinear equalization, the performance of specific devices is freed from the limitation of the performance of the receiver, the dynamic range and the anti-interference ability of the receiver are improved by multiple orders of magnitude, and the receiver is especially suitable for use in a receiving system in the technical field of communication, radar, electronic countermeasure and integrated radio frequency.

The invention discloses an optical transmission system and an optical receiving device, and relates to the technical field of optical communication. The optical transmission system comprises an optical transmitting device and an optical receiving device. The light sending device comprises a light source assembly and a filtering assembly. The light source assembly is used for outputting a light double-sideband modulation signal. The filtering assembly is used for receiving the optical double-sideband modulation signal, filtering the optical double-sideband modulation signal and outputting an optical residual sideband signal; the light receiving device comprises a photoelectric conversion assembly and a signal processing assembly, and the photoelectric conversion assembly is used for receiving the light residual sideband signal, converting the light residual sideband signal into an analog electric signal and converting the analog electric signal into a digital electric signal; and the signal processing assembly is used for receiving the digital electric signal and carrying out linear equalization processing and nonlinear equalization processing on the digital electric signal so as to compensate linear and nonlinear damage caused by dispersion in the light residual sideband signal. By adopting the optical transmission system, the transmission distance of the optical signal in the optical fiber can be increased.

To provide a receiver with nonlinear neural network equalizers for a high speed data channel and provide a method of use.SOLUTION: The present invention provides a method performed by a receiver for use in a data channel on an integrated circuit device. The method comprises steps of: performing nonlinear equalization of digitized samples of an input signal on a data channel; determining respective values of each of the output signals from the nonlinear equalized output signals; and adapting parameters of the nonlinear equalization based on the respective values.SELECTED DRAWING: Figure 11

The present invention provides a short-distance high-speed optical fiber communication equalization method and system. A Gaussian mixture model is constructed for an optical signal modulated by a set-order quadrature amplitude modulation. First, the particle swarm optimization algorithm with an initially increasing and then decreasing inertia weight is used to complete the parameter initialization of the Gaussian mixture model. The data features of two dimensions, namely the in-phase component and the quadrature component of the data, are input into the Gaussian mixture model. Then, the expectation-maximization algorithm is used to solve the parameters of the Gaussian mixture model. The membership degree of each data point corresponding to the clustering label of each univariate Gaussian distribution is determined through the parameters, and it is assigned to the corresponding clustering label through the maximum a posteriori probability to complete the nonlinear equalization. The present invention can accurately capture the memory property of signal nonlinear damage, improve the accuracy of nonlinear clustering, and reduce the computational complexity of the nonlinear clustering model under the same clustering effect.

The invention discloses an optical signal processing method and device, electronic equipment and a nonvolatile storage medium. The method comprises the following steps: acquiring a first optical signal; a complex numerical convolutional neural network is adopted to perform feature extraction on the first optical signal to obtain a first feature sequence, and the complex numerical convolutional neural network is used for removing noise in the first optical signal through feature extraction; and determining a target dependency relationship according to the first feature sequence by adopting a bidirectional long-short-term memory network, and eliminating intersymbol interference in the first optical signal according to the target dependency relationship to obtain a second optical signal, the target dependency relationship is used for representing the dependency relationship between each complex value symbol in the first optical signal and other adjacent complex value symbols, and the second optical signal is an optical signal after nonlinear equalization. The technical problems of high complexity and poor effect of a scheme for carrying out nonlinear equalization on a high-order optical signal in related technologies are solved.

A method, system and apparatus for mapping a symbol from a N-QAM constellation into a binary bit set, where N is the number of symbols in the constellation, and reducing the length of the binary bit set according to at least one property of nonlinear distortion by generating a lookup table (LUT) address from a LUT address generator, and adding the LUT address to a LUT of size smaller than N{circumflex over ( )}(2M+1), where 2M is the number of neighboring symbols to be stored.

The present application relates to the technical field of optical fiber communication, and particularly relates to a GRU neural network nonlinear equalization method based on a regression coupling value, which first reshapes the data structure of a received signal sequence and matrixes it; then in the training stage, a nonlinear damage error factor based on a nonlinear Schrodinger equation is used to guide network updating, and in the application stage, a signal is input into a GRU neural network, and a signal nonlinear damage compensation result is obtained through the propagation algorithm of the network. The method of the present application uses a GRU neural network algorithm based on a regression coupling value to realize the capture and compensation of signal nonlinear damage, solves the problems of high algorithm complexity, multiple iteration numbers and limited nonlinear compensation capacity in digital back propagation and learning type equalization algorithms, further improves the effectiveness and practicability of the nonlinear equalization algorithm, and has an important application prospect in the field of digital signal processing related to optical communication.

The application provides a nonlinear equalization circuit, which can be applied to the field of high-speed optical communication technology, and the circuit comprises N basic units, wherein N is a positive integer, the basic unit comprises an adjustable delay circuit, a pulse current generation circuit and an equalization strength adjustable circuit, the adjustable delay circuit comprises a first adjustable delay circuit and a second adjustable delay circuit, the first adjustable delay circuit and the second adjustable delay circuit are used for delaying input differential data to generate a first delay signal and a second delay signal, the equalization strength adjustable circuit is used for adjusting the amplitude of the pulse current generated by the pulse current generation circuit to adjust the equalization strength, and the pulse current generation circuit is used for logically combining the first delay signal and the second delay signal to generate a pulse current for compensating the dynamic nonlinearity of a laser. Various requirements for nonlinear compensation are supported, and meanwhile, the main signal is not weakened.

The present application relates to the field of optical fiber communications, and in particular to a method, apparatus, device, medium, and product for recovering four-dimensional modulated signals. The method comprises: dynamically obtaining, for each target point information, its corresponding filter tap coefficient and phase information required for phase compensation, wherein the filter tap coefficient is updated based on the filter tap coefficient of the previous target point information and the original transmitted signal sequence, and the phase information corresponding to the target point information is updated based on the phase information of the previous target point information and the original transmitted signal sequence, thereby achieving low-complexity and robust phase estimation for irregular constellation points of four-dimensional modulation; utilizing a MIMO nonlinear equalizer to perform nonlinear signal equalization in combination with the target point information and the filter tap coefficient; performing phase compensation based on the equalized output signal and the phase information, and performing phase recovery simultaneously with multi-dimensional equalization, thereby reducing the impact of equalization-enhanced phase noise and improving the transmission reliability of the four-dimensional modulated signal.

The application relates to a probability shaping signal equalization method based on an improved LSTM nonlinear equalizer, and the method is characterized in that the method comprises the following steps: obtaining a transmission signal by sampling in an optical fiber transmission system; pre-processing the transmission signal; inputting the processed transmission signal into an improved LSTM nonlinear equalizer trained with MSE as a loss function to perform nonlinear compensation, and restoring original sending symbols. Compared with the prior art, the application has the advantages of avoiding overfitting, improving the generalization ability of the model, and effectively compensating for the nonlinear damage in optical fiber communication.

The application relates to a Volterra nonlinear equalization method and system, which comprises the following steps: obtaining an input signal; inputting the input signal into an improved Volterra equalizer after a pre-DSP step, and outputting an equalization result; when the improved Volterra equalizer is designed, the traditional algorithm in the prior art, in which all training sequences are directly used for training in sequence, is improved into a new algorithm in which the training sequences are grouped, intercepted and shuffled, the training set and the test set are divided, and training is carried out in two stages. Compared with the prior art, the application effectively solves the invisible and uncontrollable problem of overfitting during equalizer training while improving the training efficiency of the equalizer, thereby improving the compensation effect, and the application has good universality.