Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior perfo...Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.展开更多
The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-n...The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.展开更多
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve it...Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.展开更多
An automatic method for classifying frequency shift keying(FSK),minimum shift keying(MSK),phase shift keying(PSK),quadrature amplitude modulation(QAM),and orthogonal frequency division multiplexing(OFDM)is proposed by...An automatic method for classifying frequency shift keying(FSK),minimum shift keying(MSK),phase shift keying(PSK),quadrature amplitude modulation(QAM),and orthogonal frequency division multiplexing(OFDM)is proposed by simultaneously using normality test,spectral analysis,and geometrical characteristics of in-phase-quadrature(I-Q)constellation diagram.Since the extracted features are unique for each modulation,they can be considered as a fingerprint of each modulation.We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio(SNR)and success rate.For example,the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%.Another advantage of the proposed method is its wide SNR range;such that the probability of classification for 16-QAM at SNR=3 dB is almost 1.The proposed method also provides a database for geometrical features of I-Q constellation diagram.By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal,the processing gain of 4 dB is obtained.Whatever can be mentioned about the preference of the proposed algorithm are low complexity,low SNR,wide range of modulation set,and enhanced recognition at higher-order modulations.展开更多
Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel dat...Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.展开更多
Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin c...Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.展开更多
To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the cl...To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.展开更多
To meet the actual requirement of automatic monitoring of the shortwave signals under wide band ranges, a technique for automatic recognition is studied in this paper. And basing upon the spectrum and modulation chara...To meet the actual requirement of automatic monitoring of the shortwave signals under wide band ranges, a technique for automatic recognition is studied in this paper. And basing upon the spectrum and modulation characters of amplitude modulation （AM） signals, an automatic recognition scheme for AM signals is proposed. The proposed scheme is achieved by a joint judgment with four different characteristic parameters. Experiment results indicate that the proposed scheme can effectively recognize AM signals in practice.展开更多
Objective:to study the effect of automatic tube current modulation on abdominal CT image quality and radiation dose. Methods: a total of 70 patients who underwent abdominal CT examination in our hospital from August 2...Objective:to study the effect of automatic tube current modulation on abdominal CT image quality and radiation dose. Methods: a total of 70 patients who underwent abdominal CT examination in our hospital from August 2019 to August 2020 were collected and divided into control group (35 cases) and study group (35 cases) according to the random number table. CT scan was performed in the control group according to the conventional parameters, while CT scan was performed in the research group using automatic tube current modulation technology. The CT image quality and radiation dose of patients in the two groups were compared. Results: the image quality of the study group was better than that of the control group (P < 0.05). When NI ≥8, the radiation dose in the study group was lower than that in the control group (P < 0.05). Conclusion Automatic tube current modulation technique can obtain better image quality in CT detection, and has significant significance in reducing radiation dose. It is worthy of affirmation, praise and application.展开更多
In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intr...In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intra-pulse modulation signal type based on deep residual network.The basic principle of the recognition method is to obtain the transformation relationship between the time and frequency of complex radar intra-pulse modulation signal through short-time Fourier transform(STFT),and then design an appropriate deep residual network to extract the features of the time-frequency map and complete a variety of complex intra-pulse modulation signal type recognition.In addition,in order to improve the generalization ability of the proposed method,label smoothing and L2 regularization are introduced.The simulation results show that the proposed method has a recognition accuracy of more than 95%for complex radar intra-pulse modulation sig-nal types under low SNR(2 dB).展开更多
In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to m...In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.展开更多
This paper presents work on modulated signal recognition using an artificial neural network （ANN） developed using the Python programme language. The study is basically on the analysis of analog modulated signals. Fo...This paper presents work on modulated signal recognition using an artificial neural network （ANN） developed using the Python programme language. The study is basically on the analysis of analog modulated signals. Four of the best-known analog modulation types are considered namely： amplitude modulation （AM）, double sideband （DSB） modulation, single sideband （SSB） modulation and frequency modulation （FM）. Computer simulations of the four modulated signals are carried out using MATLAB. MATLAB code is used in simulating the analog signals as well as the power spectral density of each of the analog modulated signals. In achieving an accurate classification of each of the modulated signals, extensive simulations are performed for the training of the artificial neural network. The results of the study show accurate and correct performance of the developed automatic modulation recognition with average success rate above 99.5%.展开更多
With the deepening development of communication technology,the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields.This paper ...With the deepening development of communication technology,the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields.This paper mainly studies the implementation of automatic modulation recognition using Deep Learning as a computing tool,focusing on CNN neural network and LSTM neural network,and conducting simulation experiments on public data sets.Based on the original CNN neural network,this paper introduces the structure of LSTM neural network and combines the advantages of the two types of neural networks to explore a combined neural network that is superior to the originally used CNN network.The experimental results of this thesis show that introducing the features of dynamic time series modeling of LSTM networks into Deep Learning networks can capture the global and local information of signals more effectively and improve the accuracy of neural networks in automatic modulation recognition.展开更多
E-commerce refers to a kind of business and trade activities, which are conducted under the situation that buyers and sellers do not meet each other by using a fast, easy and low-cost way. Along with the promotion and...E-commerce refers to a kind of business and trade activities, which are conducted under the situation that buyers and sellers do not meet each other by using a fast, easy and low-cost way. Along with the promotion and application of computer office automation, it is necessary for e-commerce module to develop towards automation, in which the core lies in making a good design for operation module. In this paper, the promotion of e-commerce in the information age is reviewed by the authors, the unique characteristics of e-commerce functional modules are analyzed, and also the most important point of the design of e-commerce automatic operation module is discussed. Hopefully, helps can be provided for relevant follow-up studies in the future.展开更多
E-commerce refers to a kind of business and trade activities, which are conducted under the situation that buyers and sellers do not meet each other by using a fast, easy and low-cost way. Along with the promotion and...E-commerce refers to a kind of business and trade activities, which are conducted under the situation that buyers and sellers do not meet each other by using a fast, easy and low-cost way. Along with the promotion and application of computer office automation, it is necessary for e-commerce module to develop towards automation, in which the core lies in making a good design for operation module. In this paper, the promotion of e-commerce in the information age is reviewed by the authors, the unique characteristics of e-commerce functional modules are analyzed, and also the most important point of the design of e-commerce automatic operation module is discussed. Hopefully, helps can be provided for relevant follow-up studies in the future.展开更多
Aiming at the problem that the recognition rate of existing Automatic Modulation Recognition(AMR)models needs to be improved under high signal-to-noise ratio conditions,a model consisting of phase transformation,resid...Aiming at the problem that the recognition rate of existing Automatic Modulation Recognition(AMR)models needs to be improved under high signal-to-noise ratio conditions,a model consisting of phase transformation,residual Convolutional Neural Network(CNN)network and bidirectional Long Short-Term Memory(LSTM)network is proposed.First,the DeepSig RadioML 2018.01A dataset is normalized as the model input;the phase parameter is extracted through the phase recognition module,and then the phase is corrected according to the phase parameter;then the spatial features are extracted through the residual CNN network to avoid gradient vanishing and explosion;then the data is passed to the bidirectional LSTM network to extract the bidirectional time series features of the data;finally,the deep neural network is used for classification and recognition.Experimental results show that under high signal-to-noise ratio conditions,the model improves the recognition rate of modulation modes such as 16PSK,and the highest recognition rate and average recognition rate reach 96.79%and 62.13%respectively.Compared with other existing models,the overall optimization of recognition rate and model efficiency is achieved.展开更多
The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks....The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.展开更多
The key difficulty of restoring a fuzzy image is to estimate its point spread function( PSF). In the paper,PSF is modelled based on modulation transfer function( MTF). The first step is calculating the image MTF. In t...The key difficulty of restoring a fuzzy image is to estimate its point spread function( PSF). In the paper,PSF is modelled based on modulation transfer function( MTF). The first step is calculating the image MTF. In the traditional slanted-edge method,a sub-block is always manually extracted from original image and its MTF will be viewed as the result of the whole image. However,handcraft extraction is inefficient and will lead to inaccurate results. Given this,an automatic MTF computation algorithm is proposed,which extracts and screens out all the effective sub-blocks and calculates their average MTF as the final result. Then,a two-dimensional MTF restoration model is constructed by multiplying the horizontal and vertical MTF,and it is combined with conventional image restoration methods to restore fuzzy image. Experimental results indicate the proposed method implementes a fast and accurate MTF computation and the MTF model improves the performance of conventional restoration methods significantly.展开更多
基金supported by the Beijing Natural Science Foundation (L202003)National Natural Science Foundation of China (No. 31700479)。
文摘Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation 2022M720419 to provide fund for conducting experiments。
文摘The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
基金supported in part by National Key Research and Development Program of China under Grant 2021YFB2900404.
文摘Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.
文摘An automatic method for classifying frequency shift keying(FSK),minimum shift keying(MSK),phase shift keying(PSK),quadrature amplitude modulation(QAM),and orthogonal frequency division multiplexing(OFDM)is proposed by simultaneously using normality test,spectral analysis,and geometrical characteristics of in-phase-quadrature(I-Q)constellation diagram.Since the extracted features are unique for each modulation,they can be considered as a fingerprint of each modulation.We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio(SNR)and success rate.For example,the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%.Another advantage of the proposed method is its wide SNR range;such that the probability of classification for 16-QAM at SNR=3 dB is almost 1.The proposed method also provides a database for geometrical features of I-Q constellation diagram.By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal,the processing gain of 4 dB is obtained.Whatever can be mentioned about the preference of the proposed algorithm are low complexity,low SNR,wide range of modulation set,and enhanced recognition at higher-order modulations.
基金supported in part by the National Natural Science Foundation of China under Grant(62171045,62201090)in part by the National Key Research and Development Program of China under Grants(2020YFB1807602,2019YFB1804404).
文摘Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.
文摘Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.
文摘To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.
文摘To meet the actual requirement of automatic monitoring of the shortwave signals under wide band ranges, a technique for automatic recognition is studied in this paper. And basing upon the spectrum and modulation characters of amplitude modulation （AM） signals, an automatic recognition scheme for AM signals is proposed. The proposed scheme is achieved by a joint judgment with four different characteristic parameters. Experiment results indicate that the proposed scheme can effectively recognize AM signals in practice.
文摘Objective:to study the effect of automatic tube current modulation on abdominal CT image quality and radiation dose. Methods: a total of 70 patients who underwent abdominal CT examination in our hospital from August 2019 to August 2020 were collected and divided into control group (35 cases) and study group (35 cases) according to the random number table. CT scan was performed in the control group according to the conventional parameters, while CT scan was performed in the research group using automatic tube current modulation technology. The CT image quality and radiation dose of patients in the two groups were compared. Results: the image quality of the study group was better than that of the control group (P < 0.05). When NI ≥8, the radiation dose in the study group was lower than that in the control group (P < 0.05). Conclusion Automatic tube current modulation technique can obtain better image quality in CT detection, and has significant significance in reducing radiation dose. It is worthy of affirmation, praise and application.
文摘In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intra-pulse modulation signal type based on deep residual network.The basic principle of the recognition method is to obtain the transformation relationship between the time and frequency of complex radar intra-pulse modulation signal through short-time Fourier transform(STFT),and then design an appropriate deep residual network to extract the features of the time-frequency map and complete a variety of complex intra-pulse modulation signal type recognition.In addition,in order to improve the generalization ability of the proposed method,label smoothing and L2 regularization are introduced.The simulation results show that the proposed method has a recognition accuracy of more than 95%for complex radar intra-pulse modulation sig-nal types under low SNR(2 dB).
文摘In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.
文摘This paper presents work on modulated signal recognition using an artificial neural network （ANN） developed using the Python programme language. The study is basically on the analysis of analog modulated signals. Four of the best-known analog modulation types are considered namely： amplitude modulation （AM）, double sideband （DSB） modulation, single sideband （SSB） modulation and frequency modulation （FM）. Computer simulations of the four modulated signals are carried out using MATLAB. MATLAB code is used in simulating the analog signals as well as the power spectral density of each of the analog modulated signals. In achieving an accurate classification of each of the modulated signals, extensive simulations are performed for the training of the artificial neural network. The results of the study show accurate and correct performance of the developed automatic modulation recognition with average success rate above 99.5%.
文摘With the deepening development of communication technology,the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields.This paper mainly studies the implementation of automatic modulation recognition using Deep Learning as a computing tool,focusing on CNN neural network and LSTM neural network,and conducting simulation experiments on public data sets.Based on the original CNN neural network,this paper introduces the structure of LSTM neural network and combines the advantages of the two types of neural networks to explore a combined neural network that is superior to the originally used CNN network.The experimental results of this thesis show that introducing the features of dynamic time series modeling of LSTM networks into Deep Learning networks can capture the global and local information of signals more effectively and improve the accuracy of neural networks in automatic modulation recognition.
文摘E-commerce refers to a kind of business and trade activities, which are conducted under the situation that buyers and sellers do not meet each other by using a fast, easy and low-cost way. Along with the promotion and application of computer office automation, it is necessary for e-commerce module to develop towards automation, in which the core lies in making a good design for operation module. In this paper, the promotion of e-commerce in the information age is reviewed by the authors, the unique characteristics of e-commerce functional modules are analyzed, and also the most important point of the design of e-commerce automatic operation module is discussed. Hopefully, helps can be provided for relevant follow-up studies in the future.
文摘E-commerce refers to a kind of business and trade activities, which are conducted under the situation that buyers and sellers do not meet each other by using a fast, easy and low-cost way. Along with the promotion and application of computer office automation, it is necessary for e-commerce module to develop towards automation, in which the core lies in making a good design for operation module. In this paper, the promotion of e-commerce in the information age is reviewed by the authors, the unique characteristics of e-commerce functional modules are analyzed, and also the most important point of the design of e-commerce automatic operation module is discussed. Hopefully, helps can be provided for relevant follow-up studies in the future.
文摘Aiming at the problem that the recognition rate of existing Automatic Modulation Recognition(AMR)models needs to be improved under high signal-to-noise ratio conditions,a model consisting of phase transformation,residual Convolutional Neural Network(CNN)network and bidirectional Long Short-Term Memory(LSTM)network is proposed.First,the DeepSig RadioML 2018.01A dataset is normalized as the model input;the phase parameter is extracted through the phase recognition module,and then the phase is corrected according to the phase parameter;then the spatial features are extracted through the residual CNN network to avoid gradient vanishing and explosion;then the data is passed to the bidirectional LSTM network to extract the bidirectional time series features of the data;finally,the deep neural network is used for classification and recognition.Experimental results show that under high signal-to-noise ratio conditions,the model improves the recognition rate of modulation modes such as 16PSK,and the highest recognition rate and average recognition rate reach 96.79%and 62.13%respectively.Compared with other existing models,the overall optimization of recognition rate and model efficiency is achieved.
基金funded by the Enterprise Ireland Innovation Partnership Programme with Ericsson under grant agreement IP/2011/0135[6]supported by the National Natural Science Foundation of China(No.61373131,61303039,61232016,61501247)+1 种基金the PAPDCICAEET funds
文摘The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.
基金Supported by the National High Technology Research and Development Programme of China(No.2012AA12A305)the National Key Technology R&D Program of the Ministry of Science and Technology(No.2013BAH03B01)+1 种基金Fundamental Research Funds for the Central Universities of China(No.2042015kf0059)China Postdoctoral Science Foundation(No.2015M582277)
文摘The key difficulty of restoring a fuzzy image is to estimate its point spread function( PSF). In the paper,PSF is modelled based on modulation transfer function( MTF). The first step is calculating the image MTF. In the traditional slanted-edge method,a sub-block is always manually extracted from original image and its MTF will be viewed as the result of the whole image. However,handcraft extraction is inefficient and will lead to inaccurate results. Given this,an automatic MTF computation algorithm is proposed,which extracts and screens out all the effective sub-blocks and calculates their average MTF as the final result. Then,a two-dimensional MTF restoration model is constructed by multiplying the horizontal and vertical MTF,and it is combined with conventional image restoration methods to restore fuzzy image. Experimental results indicate the proposed method implementes a fast and accurate MTF computation and the MTF model improves the performance of conventional restoration methods significantly.
