Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threat...Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.展开更多
The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiologi...The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.展开更多
The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the l...The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the low probability of intercept(LPI)radar.This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs(VG).More network and feature information can be extracted in the VG two-dimensional space,this algorithm can solve the problem of signal recognition using the autocorrelation function.Wavelet denoising processing is introduced into the signal to be tested,and the denoised signal is converted to the VG domain.Then,the signal detection is performed by using the constant false alarm of the VG average degree.Next,weight the converted graph.Finally,perform feature extraction on the weighted image,and use the feature to complete the recognition.It is testified that the proposed algorithm offers significant improvements,such as robustness to noise,and the detection and recognition accuracy,over the recent researches.展开更多
The specific emitter identification （SEI） technique some external feature measurements of the signal. determines the unique emitter of a given signal by using It has recently attracted a great deal of attention beca...The specific emitter identification （SEI） technique some external feature measurements of the signal. determines the unique emitter of a given signal by using It has recently attracted a great deal of attention because many applications can benefit from it. This work addresses the SEI problem using two methods, namely, the normalized visibility graph entropy （NVGE） and the normalized horizontal visibility graph entropy （NHVGE） based on treating emitters as nonlinear dynamical systems. Firstly, the visibility graph （VG） and the horizontal visibility graph （HVG） are used to convert the instantaneous amplitude, phase and frequency of received signals into graphs. Then, based on the information captured by the VG and the HVG, the normalized Shannon entropy （NSE） calculated from the corresponding degree distributions are utilized as the rf fingerprint. Finally, four emitters from the same manufacturer are utilized to evaluate the performance of the two methods. Experimental results demonstrate that both the NHVGE-based method and NVGE-based method are quite effective and they perform much better than the method based on the normalized permutation entropy （NPE） in the case of a small amount of data. The NVGE-based method performs better than the NHVGE-based method since the VG can extract more information than the HVG does. Moreover, our methods do not distinguish between the transient signal and the steady-state signal, making it practical.展开更多
To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited pene...To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited penetrable visibility graph(LPVG)is proposed.Firstly,seven types of radar antenna scans are analyzed,which include the circular scan,sector scan,helical scan,raster scan,conical scan,electromechanical hybrid scan and two-dimensional electronic scan.Then,the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network,and the feature parameters are extracted.Finally,the recognition result is obtained by using a support vector machine(SVM)classifier.The experimental results show that the recognition accuracy and noise resistance of this new method are improved,where the average recognition accuracy for radar antenna type is at least 90%when the signalto-noise ratio(SNR)is 5 dB and above.展开更多
Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important rese...Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.展开更多
The identification between chaotic systems and stochastic processes is not easy since they have numerous similarities. In this study, we propose a novel approach to distinguish between chaotic systems and stochastic p...The identification between chaotic systems and stochastic processes is not easy since they have numerous similarities. In this study, we propose a novel approach to distinguish between chaotic systems and stochastic processes based on the component reordering procedure and the visibility graph algorithm. It is found that time series and their reordered components will show diverse characteristics in the ＇visibility domain＇. For chaotic series, there are huge differences between the degree distribution obtained from the original series and that obtained from the corresponding reordered component. For correlated stochastic series, there are only small differences between the two degree distributions. For uncorrelated stochastic series, there are slight differences between them. Based on this discovery, the well-known Kullback Leible divergence is used to quantify the difference between the two degree distributions and to distinguish between chaotic systems, correlated and uncorrelated stochastic processes. Moreover, one chaotic map, three chaotic systems and three different stochastic processes are utilized to illustrate the feasibility and effectiveness of the proposed method. Numerical results show that the proposed method is not only effective to distinguish between chaotic systems, correlated and uncorrelated stochastic processes, but also easy to operate.展开更多
Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic r...Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.展开更多
A new concept, called the row-column visibility graph, is proposed to map two-dimensional landscapes to complex networks. A cluster coverage is introduced to describe the extensive property of node clusters on a Eucli...A new concept, called the row-column visibility graph, is proposed to map two-dimensional landscapes to complex networks. A cluster coverage is introduced to describe the extensive property of node clusters on a Euclidean lattice. Graphs mapped from fractals generated with the probability redistribution model behave scale-free. They have pattern-induced hierarchical organizations and comparatively much more extensive structures. The scale-free exponent has a negative correlation with the Hurst exponent, however, there is no deterministic relation between them. Graphs for fractals generated with the midpoint displacement model are exponential networks. When the Hurst exponent is large enough （e.g., H 〉 0.5）, the degree distribution decays much more slowly, the average coverage becomes significant large, and the initially hierarchical structure at H 〈 0.5 is destroyed completely. Hence, the row-column visibility graph can be used to detect the pattern-related new characteristics of two-dimensional landscapes.展开更多
An extreme event may lead to serious disaster to a complex system.In an extreme event series there exist generally non-trivial patterns covering different time scales.Investigations on extreme events are currently bas...An extreme event may lead to serious disaster to a complex system.In an extreme event series there exist generally non-trivial patterns covering different time scales.Investigations on extreme events are currently based upon statistics,where the patterns are merged into averages.In this paper from extreme event series we constructed extreme value series and extreme interval series.And the visibility graph is then adopted to display the patterns formed by the increases/decreases of extreme value or interval faster/slower than the linear ones.For the fractional Brownian motions,the properties for the constructed networks are the persistence,threshold,and event-type-independent,e.g.,the degree distributions decay exponentially with almost identical speeds,the nodes cluster into modular structures with large and similar modularity degrees,and each specific network has a perfect hierarchical structure.For the volatilities of four stock markets(NSDQ,SZI,FTSE100,and HSI),the properties for the former three's networks are threshold-and market-independent.Comparing with the factional Brownian motions,their degree distributions decay exponentially but with slower speeds,their modularity behaviors are significant but with smaller modularity degrees.The fourth market behaves similar qualitatively but different quantitatively with the three markets.Interestingly,all the transition frequency networks share an identical backbone composed of nine edges and the linked graphlets.The universal behaviors give us a framework to describe extreme events from the viewpoint of network.展开更多
Sleep is an essential integrant in everyone’s daily life;therefore,it is an important but challenging problem to characterize sleep stages from electroencephalogram(EEG)signals.The network motif has been developed as...Sleep is an essential integrant in everyone’s daily life;therefore,it is an important but challenging problem to characterize sleep stages from electroencephalogram(EEG)signals.The network motif has been developed as a useful tool to investigate complex networks.In this study,we developed a multiplex visibility graph motif-based convolutional neural network(CNN)for characterizing sleep stages using EEG signals and then introduced the multiplex motif entropy as the quantitative index to distinguish the six sleep stages.The independent samples t-test shows that the multiplex motif entropy values have significant differences among the six sleep stages.Furthermore,we developed a CNN model and employed the multiplex motif sequence as the input of the model to classify the six sleep stages.Notably,the classification accuracy of the six-state stage detection was 85.27%.Results demonstrated the effectiveness of the multiplex motif in characterizing the dynamic features underlying different sleep stages,whereby they further provide an essential strategy for future sleep-stage detection research.展开更多
In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distort...In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.展开更多
This paper provides a comprehensive examination of El Sallam Garden in Port Said City,concentrating on its landscape characteristics and potential for design enhancement.This study looks at how space syntax can be use...This paper provides a comprehensive examination of El Sallam Garden in Port Said City,concentrating on its landscape characteristics and potential for design enhancement.This study looks at how space syntax can be used to assess the impact of a tree planting design’s spatial configuration on an urban park’s visual fields.Trees play an important role in determining the spatial characteristics of an outdoor space.According to space syntax theory,an urban area is a collection of connected spaces that can be represented by a matrix of quantitative properties known as syntactic measures.Computer simulations can be used to measure the quantitative properties of these matrices.This study uses space syntax techniques to assess how tree configurations and garden area which can affect the social structures of small-scale gardens in Port Said.It also looks at how these techniques can be used to predict the social structures of four garden zones in El Sallam Garden.The study includes an observational and space syntax study through comparative analysis of four garden zones in El Sallam garden.The results of the study show that the area and planting configurations of the garden had a significant effect on the syntactic social and visual measures of the urban garden.The conclusions and recommendations can be a useful tool for landscape architects,urban planners,and legislators who want to enhance public areas and encourage social interaction in urban settings.展开更多
Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammog-raphy, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC)les...Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammog-raphy, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC)lesions. However, manual analysis is labor-intensive and prone to diagnostic errors. In this scenario, the large-scale deployment of computer-aided diagnosis using well-trained algorithms could significantly reduce themorbidity and mortality associated with this carcinoma. In this study, we used a similarity metric-based classi-fication of mammograms using graphical (with two different image sizes) and geometrical approaches (with asingle image size) for comparison to improve the specificity, sensitivity, and accuracy of BC prediction and triageof patients in the order of disease severity. Both classification techniques use two novel algorithms, hereafterreferred to as the normal and hybrid methods, to select representative images from the training sets of healthy andunhealthy groups of mammograms. The normal method identifies a representative image by comparing imageswithin a cohort, whereas the hybrid method adopts a comprehensive approach by comparing images from bothcohorts. This study explored the effects of image size and cardinality of the training set. Finally, we explored theuncharted territory of mapping accuracy versus computational expense for the different approaches adopted inthe current study.展开更多
Gas-liquid two-phase flow widely exits in production and transportation of petroleum industry.Characterizing gas-liquid flow and measuring flow parameters represent challenges of great importance,which contribute to t...Gas-liquid two-phase flow widely exits in production and transportation of petroleum industry.Characterizing gas-liquid flow and measuring flow parameters represent challenges of great importance,which contribute to the recognition of flow regime and the optimal design of industrial equipment.In this paper,we propose a novel complex network-based deep learning method for characterizing gas-liquid flow.Firstly,we map the multichannel measurements to multiple limited penetrable visibility graphs(LPVGs)and obtain their degree sequences as the graph representation.Based on the degree distribution,we analyze the complicated flow behavior under different flow structures.Then,we design a dual-input convolutional neural network to fuse the raw signals and the graph representation of LPVGs for the classification of flow structures and measurement of gas void fraction.We implement the model with two parallel branches with the same structure,each corresponding to one input.Each branch consists of a channel-projection convolutional part,a spatial-temporal convolutional part,a dense block and an attention module.The outputs of the two branches are concatenated and fed into several full connected layers for the classification and measurement.At last,our method achieves an accuracy of 95.3%for the classification of flow structures,and a mean squared error of 0.0038 and a mean absolute percent error of 6.3%for the measurement of gas void fraction.Our method provides a promising solution for characterizing gas-liquid flow and measuring flow parameters.展开更多
Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics...Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics,making the detection and estimation of LPI radar signals extremely difficult,and leading to highly required significant research on perception technology in the battlefield environment.This paper proposes a visibility graphs(VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation(TFR).On the one hand,the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms.On the other hand,the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency(IF).Simulation performance shows that,compared with the most advanced methods,the algorithm proposed in this paper has a valuable advantage.Meanwhile,the calculation cost of the algorithm is quite low,and it is achievable in the future battlefield.展开更多
Expo 2010 Shanghai China was a successful, splendid, and unforgettable event, leaving us with valuable experi- ences. The visitor flow pattern of the Expo is investigated in this paper. The Hurst exponent, the mean va...Expo 2010 Shanghai China was a successful, splendid, and unforgettable event, leaving us with valuable experi- ences. The visitor flow pattern of the Expo is investigated in this paper. The Hurst exponent, the mean value, and the standard deviation of visitor volume indicate that the visitor flow is fractal with long-term stability and correlation as well as obvious fluctuation in a short period. Then the time series of visitor volume is converted into a complex network by using the visibility algorithm. It can be inferred from the topological properties of the visibility graph that the network is scale-free, small-world, and hierarchically constructed, confirming that the time series are fractal and a close relationship exists among the visitor volumes on different days. Furthermore, it is inevitable that will be some extreme visitor volumes in the original visitor flow, and these extreme points may appear in a group to a great extent. All these properties are closely related to the feature of the complex network. Finally, the revised linear regression is performed to forecast the next-day visitor volume based on the previous 10-day data.展开更多
Automated valet parking(AVP)has attracted the attention of industry and academia in recent years.However,there are still many challenges to be solved,including shortest path search,optimal time efficiency,and applicab...Automated valet parking(AVP)has attracted the attention of industry and academia in recent years.However,there are still many challenges to be solved,including shortest path search,optimal time efficiency,and applicability of algorithm in complex scenarios.In this paper,a hierarchical AVP path planner is proposed,which divides a complete AVP path planning into the guided layer and the planning layer from the perspective of global decision-making.The guided layer is mainly used to divide a complex AVP path planning into several simple path plannings,which makes the hybrid A*algorithm more applicable in a complex parking environment.The planning layer mainly adopts different optimization methods for driving and parking path planning.The proposed method is verified by a large number of simulations which include the verification of the optimal parking position,the performance of the planner for perpendicular parking,and the scalability of the planner for parallel parking and inclined parking.The simulation results reveal that the efficiency of the algorithm is increased by more than 20 times,and the average path length is also shortened by more than 20%.Furthermore,the planner overcomes the problem that the hybrid A*algorithm is not applicable in complex parking scenarios.展开更多
基金supported by the Ministry of Industry and Information Technology(No.23100002022102001)。
文摘Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF1204803)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20190736)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.NJ2024029)the National Natural Science Foundation of China(Grant Nos.81701346 and 62201265).
文摘The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.
基金This work was supported by the National Defence Pre-research Foundation of China(30502010103).
文摘The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the low probability of intercept(LPI)radar.This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs(VG).More network and feature information can be extracted in the VG two-dimensional space,this algorithm can solve the problem of signal recognition using the autocorrelation function.Wavelet denoising processing is introduced into the signal to be tested,and the denoised signal is converted to the VG domain.Then,the signal detection is performed by using the constant false alarm of the VG average degree.Next,weight the converted graph.Finally,perform feature extraction on the weighted image,and use the feature to complete the recognition.It is testified that the proposed algorithm offers significant improvements,such as robustness to noise,and the detection and recognition accuracy,over the recent researches.
基金Supported by the National Natural Science Foundation of China under Grant No U1530126the Fundamental Research Funds for the Central Universities under Grant No ZYGX2015J022
文摘The specific emitter identification （SEI） technique some external feature measurements of the signal. determines the unique emitter of a given signal by using It has recently attracted a great deal of attention because many applications can benefit from it. This work addresses the SEI problem using two methods, namely, the normalized visibility graph entropy （NVGE） and the normalized horizontal visibility graph entropy （NHVGE） based on treating emitters as nonlinear dynamical systems. Firstly, the visibility graph （VG） and the horizontal visibility graph （HVG） are used to convert the instantaneous amplitude, phase and frequency of received signals into graphs. Then, based on the information captured by the VG and the HVG, the normalized Shannon entropy （NSE） calculated from the corresponding degree distributions are utilized as the rf fingerprint. Finally, four emitters from the same manufacturer are utilized to evaluate the performance of the two methods. Experimental results demonstrate that both the NHVGE-based method and NVGE-based method are quite effective and they perform much better than the method based on the normalized permutation entropy （NPE） in the case of a small amount of data. The NVGE-based method performs better than the NHVGE-based method since the VG can extract more information than the HVG does. Moreover, our methods do not distinguish between the transient signal and the steady-state signal, making it practical.
基金supported by the China Postdoctoral Science Foundation(2015M572694,2016T90979).
文摘To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited penetrable visibility graph(LPVG)is proposed.Firstly,seven types of radar antenna scans are analyzed,which include the circular scan,sector scan,helical scan,raster scan,conical scan,electromechanical hybrid scan and two-dimensional electronic scan.Then,the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network,and the feature parameters are extracted.Finally,the recognition result is obtained by using a support vector machine(SVM)classifier.The experimental results show that the recognition accuracy and noise resistance of this new method are improved,where the average recognition accuracy for radar antenna type is at least 90%when the signalto-noise ratio(SNR)is 5 dB and above.
基金Project supported by the Xuzhou Key Research and Development Program (Social Development) (Grant No. KC21304)the National Natural Science Foundation of China (Grant No. 61876186)。
文摘Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.
基金Supported by the National Natural Science Foundation of China under Grant No U1530126
文摘The identification between chaotic systems and stochastic processes is not easy since they have numerous similarities. In this study, we propose a novel approach to distinguish between chaotic systems and stochastic processes based on the component reordering procedure and the visibility graph algorithm. It is found that time series and their reordered components will show diverse characteristics in the ＇visibility domain＇. For chaotic series, there are huge differences between the degree distribution obtained from the original series and that obtained from the corresponding reordered component. For correlated stochastic series, there are only small differences between the two degree distributions. For uncorrelated stochastic series, there are slight differences between them. Based on this discovery, the well-known Kullback Leible divergence is used to quantify the difference between the two degree distributions and to distinguish between chaotic systems, correlated and uncorrelated stochastic processes. Moreover, one chaotic map, three chaotic systems and three different stochastic processes are utilized to illustrate the feasibility and effectiveness of the proposed method. Numerical results show that the proposed method is not only effective to distinguish between chaotic systems, correlated and uncorrelated stochastic processes, but also easy to operate.
基金Project supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20190736)the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.81701346 and 61603198)Qinglan Team of Universities in Jiangsu Province(Jiangsu Teacher Letter[2020]10 and Jiangsu Teacher Letter[2021]11).
文摘Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.
基金supported by the National Natural Science Foundation of China(Grant No.10975099)the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning,China+2 种基金the Innovation Program of Shanghai Municipal Education Commission,China(GrantNo.13YZ072)the Shanghai Leading Discipline Project,China(Grant No.XTKX2012)the Innovation Fund Project for Graduate Students of Shanghai,China(Grant No.JWCXSL1302)
文摘A new concept, called the row-column visibility graph, is proposed to map two-dimensional landscapes to complex networks. A cluster coverage is introduced to describe the extensive property of node clusters on a Euclidean lattice. Graphs mapped from fractals generated with the probability redistribution model behave scale-free. They have pattern-induced hierarchical organizations and comparatively much more extensive structures. The scale-free exponent has a negative correlation with the Hurst exponent, however, there is no deterministic relation between them. Graphs for fractals generated with the midpoint displacement model are exponential networks. When the Hurst exponent is large enough （e.g., H 〉 0.5）, the degree distribution decays much more slowly, the average coverage becomes significant large, and the initially hierarchical structure at H 〈 0.5 is destroyed completely. Hence, the row-column visibility graph can be used to detect the pattern-related new characteristics of two-dimensional landscapes.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11805128,11875042,and 11505114)the Shanghai Project for Construction of Top Disciplines,China(Grant No.USST-SYSBIO)。
文摘An extreme event may lead to serious disaster to a complex system.In an extreme event series there exist generally non-trivial patterns covering different time scales.Investigations on extreme events are currently based upon statistics,where the patterns are merged into averages.In this paper from extreme event series we constructed extreme value series and extreme interval series.And the visibility graph is then adopted to display the patterns formed by the increases/decreases of extreme value or interval faster/slower than the linear ones.For the fractional Brownian motions,the properties for the constructed networks are the persistence,threshold,and event-type-independent,e.g.,the degree distributions decay exponentially with almost identical speeds,the nodes cluster into modular structures with large and similar modularity degrees,and each specific network has a perfect hierarchical structure.For the volatilities of four stock markets(NSDQ,SZI,FTSE100,and HSI),the properties for the former three's networks are threshold-and market-independent.Comparing with the factional Brownian motions,their degree distributions decay exponentially but with slower speeds,their modularity behaviors are significant but with smaller modularity degrees.The fourth market behaves similar qualitatively but different quantitatively with the three markets.Interestingly,all the transition frequency networks share an identical backbone composed of nine edges and the linked graphlets.The universal behaviors give us a framework to describe extreme events from the viewpoint of network.
文摘Sleep is an essential integrant in everyone’s daily life;therefore,it is an important but challenging problem to characterize sleep stages from electroencephalogram(EEG)signals.The network motif has been developed as a useful tool to investigate complex networks.In this study,we developed a multiplex visibility graph motif-based convolutional neural network(CNN)for characterizing sleep stages using EEG signals and then introduced the multiplex motif entropy as the quantitative index to distinguish the six sleep stages.The independent samples t-test shows that the multiplex motif entropy values have significant differences among the six sleep stages.Furthermore,we developed a CNN model and employed the multiplex motif sequence as the input of the model to classify the six sleep stages.Notably,the classification accuracy of the six-state stage detection was 85.27%.Results demonstrated the effectiveness of the multiplex motif in characterizing the dynamic features underlying different sleep stages,whereby they further provide an essential strategy for future sleep-stage detection research.
基金supported by the National Natural Science Foundation of China under Grant No.61671185 and 62071153.
文摘In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.
文摘This paper provides a comprehensive examination of El Sallam Garden in Port Said City,concentrating on its landscape characteristics and potential for design enhancement.This study looks at how space syntax can be used to assess the impact of a tree planting design’s spatial configuration on an urban park’s visual fields.Trees play an important role in determining the spatial characteristics of an outdoor space.According to space syntax theory,an urban area is a collection of connected spaces that can be represented by a matrix of quantitative properties known as syntactic measures.Computer simulations can be used to measure the quantitative properties of these matrices.This study uses space syntax techniques to assess how tree configurations and garden area which can affect the social structures of small-scale gardens in Port Said.It also looks at how these techniques can be used to predict the social structures of four garden zones in El Sallam Garden.The study includes an observational and space syntax study through comparative analysis of four garden zones in El Sallam garden.The results of the study show that the area and planting configurations of the garden had a significant effect on the syntactic social and visual measures of the urban garden.The conclusions and recommendations can be a useful tool for landscape architects,urban planners,and legislators who want to enhance public areas and encourage social interaction in urban settings.
基金supported by the Commissioned Research through SRM University-AP,India,Research Grant–Central Facility under Grant No.SRMAP/URG/CF/2023-24/045.
文摘Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammog-raphy, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC)lesions. However, manual analysis is labor-intensive and prone to diagnostic errors. In this scenario, the large-scale deployment of computer-aided diagnosis using well-trained algorithms could significantly reduce themorbidity and mortality associated with this carcinoma. In this study, we used a similarity metric-based classi-fication of mammograms using graphical (with two different image sizes) and geometrical approaches (with asingle image size) for comparison to improve the specificity, sensitivity, and accuracy of BC prediction and triageof patients in the order of disease severity. Both classification techniques use two novel algorithms, hereafterreferred to as the normal and hybrid methods, to select representative images from the training sets of healthy andunhealthy groups of mammograms. The normal method identifies a representative image by comparing imageswithin a cohort, whereas the hybrid method adopts a comprehensive approach by comparing images from bothcohorts. This study explored the effects of image size and cardinality of the training set. Finally, we explored theuncharted territory of mapping accuracy versus computational expense for the different approaches adopted inthe current study.
基金supported by the National Natural Science Foundation of China under Grants 61922062 and 61873181。
文摘Gas-liquid two-phase flow widely exits in production and transportation of petroleum industry.Characterizing gas-liquid flow and measuring flow parameters represent challenges of great importance,which contribute to the recognition of flow regime and the optimal design of industrial equipment.In this paper,we propose a novel complex network-based deep learning method for characterizing gas-liquid flow.Firstly,we map the multichannel measurements to multiple limited penetrable visibility graphs(LPVGs)and obtain their degree sequences as the graph representation.Based on the degree distribution,we analyze the complicated flow behavior under different flow structures.Then,we design a dual-input convolutional neural network to fuse the raw signals and the graph representation of LPVGs for the classification of flow structures and measurement of gas void fraction.We implement the model with two parallel branches with the same structure,each corresponding to one input.Each branch consists of a channel-projection convolutional part,a spatial-temporal convolutional part,a dense block and an attention module.The outputs of the two branches are concatenated and fed into several full connected layers for the classification and measurement.At last,our method achieves an accuracy of 95.3%for the classification of flow structures,and a mean squared error of 0.0038 and a mean absolute percent error of 6.3%for the measurement of gas void fraction.Our method provides a promising solution for characterizing gas-liquid flow and measuring flow parameters.
基金supported by the National Defence Pre-research Foundation of China(30502010103).
文摘Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics,making the detection and estimation of LPI radar signals extremely difficult,and leading to highly required significant research on perception technology in the battlefield environment.This paper proposes a visibility graphs(VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation(TFR).On the one hand,the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms.On the other hand,the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency(IF).Simulation performance shows that,compared with the most advanced methods,the algorithm proposed in this paper has a valuable advantage.Meanwhile,the calculation cost of the algorithm is quite low,and it is achievable in the future battlefield.
基金Project supported by the National Natural Science Foundation of China (Grant No. 70871082)the Shanghai Leading Academic Discipline Project, China (Grant No. S30504)the Science and Technology Innovation Foundation of Shanxi Agricultural University, China (Grant No. 201208)
文摘Expo 2010 Shanghai China was a successful, splendid, and unforgettable event, leaving us with valuable experi- ences. The visitor flow pattern of the Expo is investigated in this paper. The Hurst exponent, the mean value, and the standard deviation of visitor volume indicate that the visitor flow is fractal with long-term stability and correlation as well as obvious fluctuation in a short period. Then the time series of visitor volume is converted into a complex network by using the visibility algorithm. It can be inferred from the topological properties of the visibility graph that the network is scale-free, small-world, and hierarchically constructed, confirming that the time series are fractal and a close relationship exists among the visitor volumes on different days. Furthermore, it is inevitable that will be some extreme visitor volumes in the original visitor flow, and these extreme points may appear in a group to a great extent. All these properties are closely related to the feature of the complex network. Finally, the revised linear regression is performed to forecast the next-day visitor volume based on the previous 10-day data.
基金appreciate the financial support of the Science and Technology Development Project of Jilin Province(Award Number 20200501009GX)and Exploration Foundation of State Key Laboratory of Automotive Simulation and Control.
文摘Automated valet parking(AVP)has attracted the attention of industry and academia in recent years.However,there are still many challenges to be solved,including shortest path search,optimal time efficiency,and applicability of algorithm in complex scenarios.In this paper,a hierarchical AVP path planner is proposed,which divides a complete AVP path planning into the guided layer and the planning layer from the perspective of global decision-making.The guided layer is mainly used to divide a complex AVP path planning into several simple path plannings,which makes the hybrid A*algorithm more applicable in a complex parking environment.The planning layer mainly adopts different optimization methods for driving and parking path planning.The proposed method is verified by a large number of simulations which include the verification of the optimal parking position,the performance of the planner for perpendicular parking,and the scalability of the planner for parallel parking and inclined parking.The simulation results reveal that the efficiency of the algorithm is increased by more than 20 times,and the average path length is also shortened by more than 20%.Furthermore,the planner overcomes the problem that the hybrid A*algorithm is not applicable in complex parking scenarios.
