Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

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.

Big Data Analytics Using Graph Signal Processing

The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore,this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,etc.Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

Gearbox Fault Diagnosis using Adaptive Zero Phase Time-varying Filter Based on Multi-scale Chirplet Sparse Signal Decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Totally Coded Method for Signal Flow Graph Algorithm

After a code-table has been established by means of node association information from signal flow graph, the totally coded method (TCM) is applied merely in the domain of code operation beyond any figure-earching algorithm. The code-series (CS) have the holo-information nature, so that both the content and the sign of each gain-term can be determined via the coded method. The principle of this method is simple and it is suited for computer programming. The capability of the computer-aided analysis for switched current network (SIN) can be enhanced.

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

RECONSTRUCTION OF ONE DIMENSIONAL MULTI-LAYERED MEDIA BY USING A TIME DOMAIN SIGNAL FLOW GRAPH TECHNIQUE

A novel inverse scattering method to reconstruct the permittivity profile of one-dimensional multi-layered media is proposed in this paper.Based on the equivalent network ofthe medium,a concept of time domain signal flow graph and its basic principles are introduced,from which the reflection coefficient of the medium in time domain can be shown to be a series ofDirac δ-functions(pulse responses).In terms of the pulse responses,we will reconstruct both thepermittivity and the thickness of each layer will accurately be reconstructed.Numerical examplesverify the applicability of this

Graph Laplacian Matrix Learning from Smooth Time-Vertex Signal

In this paper,we focus on inferring graph Laplacian matrix from the spatiotemporal signal which is defined as“time-vertex signal”.To realize this,we first represent the signals on a joint graph which is the Cartesian product graph of the time-and vertex-graphs.By assuming the signals follow a Gaussian prior distribution on the joint graph,a meaningful representation that promotes the smoothness property of the joint graph signal is derived.Furthermore,by decoupling the joint graph,the graph learning framework is formulated as a joint optimization problem which includes signal denoising,timeand vertex-graphs learning together.Specifically,two algorithms are proposed to solve the optimization problem,where the discrete second-order difference operator with reversed sign(DSODO)in the time domain is used as the time-graph Laplacian operator to recover the signal and infer a vertex-graph in the first algorithm,and the time-graph,as well as the vertex-graph,is estimated by the other algorithm.Experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively infer meaningful time-and vertex-graphs from noisy and incomplete data.

Time-Varying Bandpass Filter Based on Assisted Signals for AM-FM Signal Separation: A Revisit

In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.

Fragmental Weight-Conservation Combining Scheme for Statistical Signal Transmissions under Fast Time-Varying Channels

Statistical Signal Transmission(SST)is a technique based on orthogonal frequency-division multiplexing(OFDM)and adopts cyclostationary features,which can transmit extra information without additional bandwidth.However,the more complicated environment in 5G communication systems,especially the fast time-varying scenarios,will dramatically degrade the performance of the SST.In this paper,we propose a fragmental weight-conservation combining(FWCC)scheme for SST,to overcome its performance degradation under fast time-varying channels.The proposed FWCC scheme consists of three phases:1、incise the received OFDM stream into pieces;2、endue different weights for fine and contaminated pieces,respectively;3、combine cyclic autocorrelation function energies of all the pieces;and 4、compute the final feature and demodulate data of SST.Through these procedures above,the detection accuracy of SST will be theoretically refined under fast time-varying channels.Such an inference is confirmed through numerical results in this paper.It is demonstrated that the BER performance of proposed scheme outperforms that of the original scheme both in ideal channel estimation conditions and in imperfect channel estimation conditions.In addition,we also find the experiential optimal weight distribution strategy for the proposed FWCC scheme,which facilitates practical applications.

Analysis of Electronic Circuits with the Signal Flow Graph Method

In this work a method called “signal flow graph (SFG)” is presented. A signal-flow graph describes a system by its signal flow by directed and weighted graph;the signals are applied to nodes and functions on edges. The edges of the signal flow graph are small processing units, through which the incoming signals are processed in a certain form. In this case, the result is sent to the outgoing node. The SFG allows a good visual inspection into complex feedback problems. Furthermore such a presentation allows for a clear and unambiguous description of a generating system, for example, a netview. A Signal Flow Graph (SFG) allows a fast and practical network analysis based on a clear data presentation in graphic format of the mathematical linear equations of the circuit. During creation of a SFG the Direct Current-Case (DC-Case) was observed since the correct current and voltage directions was drawn from zero frequency. In addition, the mathematical axioms, which are based on field algebra, are declared. In this work we show you in addition: How we check our SFG whether it is a consistent system or not. A signal flow graph can be verified by generating the identity of the signal flow graph itself, illustrated by the inverse signal flow graph (SFG−1). Two signal flow graphs are always generated from one circuit, so that the signal flow diagram already presented in previous sections corresponds to only half of the solution. The other half of the solution is the so-called identity, which represents the (SFG−1). If these two graphs are superposed with one another, so called 1-edges are created at the node points. In Boolean algebra, these 1-edges are given the value 1, whereas this value can be identified with a zero in the field algebra.

A Distributed Newton Method for Processing Signals Defined on the Large-Scale Networks

In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously perform the local computation,which calls for heavy computational and communication costs.Moreover,in many real-world networks,such as those with straggling nodes,the homogeneous manner may result in serious delay or even failure.To this end,we propose active network decomposition algorithms to select non-straggling nodes(normal nodes)that perform the main computation and communication across the network.To accommodate the decomposition in different kinds of networks,two different approaches are developed,one is centralized decomposition that leverages the adjacency of the network and the other is distributed decomposition that employs the indicator message transmission between neighboring nodes,which constitutes the main contribution of this paper.By incorporating the active decomposition scheme,a distributed Newton method is employed to solve the least squares problem in GSP,where the Hessian inverse is approximately evaluated by patching a series of inverses of local Hessian matrices each of which is governed by one normal node.The proposed algorithm inherits the fast convergence of the second-order algorithms while maintains low computational and communication cost.Numerical examples demonstrate the effectiveness of the proposed algorithm.

结合项目属性协作信号减少无关邻域的推荐

在推荐系统中,知识图谱(knowledge graph,KG)作为辅助信息,提高了算法的性能以及可解释性。但在聚合多跳邻居时,它通常把所有的实体信息加以聚合并传播。KG中不是所有的信息都有助于改善推荐结果,当聚合邻域信息不加以区分时,实体的嵌入就会受到不相关实体的干扰。针对上述问题,提出一个项目属性协作信号和筛选高相关的邻域策略的模型(RUNCS),用以提高推荐的效果。具体来说,把点击过相同项目的用户称为相似邻居,通过相似邻居点击的项目和KG中的项目属性相结合,从而得到项目属性协作集;通过计算项目属性的相似性,得到相关性分数,用以筛选高相关的邻居;利用注意力机制对其分配权重进行信息聚合。在音乐和电影两个基准数据集中的实验结果表明,与现有最优主流方法相比,该模型在CTR预测上AUC提升0.6~2.7个百分点。

基于建筑信息模型数据驱动的铁路设备运维多模态知识图谱构建

铁路信号设备是保障行车安全、提高运输效率的核心装备,加强信号设备智能运维是降低铁路运行风险的必要基础保障。目前,针对我国基于建筑信息模型(BIM)的智能运维平台存在不能精准映射各设备的行为规律和相互之间互馈作用的机理,须同时依靠经验知识进行推断等问题。首先构建了铁路设备运维文本知识图谱;其次构建卷积神经网络(CNN)-团组图卷积神经网络(cgGCN)模型对BIM图像模态数据进行处理,完成对20种铁路信号设备零件图信息的标注,实验结果表明模型准确率达到95.38%,精确率和召回率的调和平均值F1达到95.58%;最后将BIM图像信息以视觉模态嵌入运维文本知识图谱,利用Neo4j图数据库实现多模态知识图谱可视化展示,从而精准映射各信号设备相互之间互馈作用的机理,为后续现场铁路运维人员实施安全管理和运维决策提供在线服务和指导。

稀疏分解和图拉普拉斯正则化的图像前景背景分割方法

针对现有图像前景背景分割方法的分割结果存在孤立像素点的问题,利用图信号处理理论和稀疏分解模型,提出新的图像前景背景分割方法.将图像的内在结构建模为图,通过图模型有效地刻画像素之间的内在关联性.将图像的像素强度建模为图信号,其中图像背景作为平滑分量,由一组图傅里叶变换基函数线性表示,叠加在背景上的前景为稀疏分量,前景像素间的连通性可由图拉普拉斯正则化项进行刻画.将图像前景背景分割问题归结为包含稀疏分解模型和图拉普拉斯正则化项的约束优化问题,采用交替方向乘子法对该优化问题进行求解.实验结果表明,与现有的其他方法相比,所提方法具有更好的分割效果.

异构多平台信号处理任务调度研究

简单的并行计算或单一异构平台已经无法满足计算量大、复杂度高的信号处理和任务调度需求,异构多平台系统已经成为信号处理和任务调度的发展趋势。针对提高平台的吞吐量、处理器的利用率以及任务的感知等问题,文中对异构多平台信号处理模型进行了研究,并利用有向无环图对调度任务和软硬件资源建模。基于已提出的调度算法,对任务调度进行了归纳总结、对比分析,发现基于任务感知的混合调度算法能够较好地满足平台调度需求。利用基于任务感知的混合调度算法解决信号处理中的任务调度将是未来研究发展的趋势。

行为增强的多层次协同Top-N推荐

传统推荐系统只利用单一用户行为,然而用户行为间是具有关联性,忽视用户行为会丢失辅助行为对目标行为的影响。本文提出了一种行为增强的多层次协同Top-N推荐,在推荐二分图和元路径图上利用注意力机制传播信息,学习多层次高阶和异质协同信号(包括用户-项目间的和项目间的)以提高推荐性能,这样可以更好地利用推荐图结构,并充分考虑到推荐图结构上各种行为间的相互影响。在经典数据集上做了全方位实验验证模型有效性,在电商推荐数据上取得了很好效果。

BDS信号失锁下基于自适应因子图的列车组合定位模型

针对列车运行环境复杂,容易产生BDS信号失锁,影响BDS/IMU列车定位系统的精确性问题,提出了基于自适应因子图的BDS/IMU/OD列车组合定位模型。在BDS/IMU列车定位系统的基础上,引入里程计(Odometer,OD)定位技术,利用北斗卫星导航系统(Beidou Navigation Satellite System,BDS)与惯性测量元件(Inertial Measurement Unit,IMU)、OD 3类传感器获取列车量测信息,根据因子图理论,将多源量测信息描述为状态空间方程,抽象BDS、IMU、OD因子节点和先验因子,确定因子节点与变量节点之间的无向连接关系,建立多变量列车组合定位因子图模型,解算列车的位置信息。当BDS信号产生变化时,借助因子图的即插即用特性,提出了自适应因子算法,动态调整列车组合定位因子图模型结构。在BDS信息部分失锁时,利用BDS的部分信息,建立BDS/IMU/OD列车组合定位因子图模型,在BDS信息完全失锁时,转换为IMU/OD列车组合定位因子图模型,抑制BDS完全失锁造成的发散误差。利用卡尔曼算法、因子图算法、自适应因子图算法进行了列车定位的仿真分析,在BDS信息部分失锁时,自适应因子图模型的定位位置均方根误差比卡尔曼算法分别降低了52.3%、48.2%和42.7%,比因子图算法分别降低了34.8%、27.0%和25.2%。在BDS信息完全失锁时,自适应因子图模型的定位位置误差比卡尔曼算法分别降低了46.7%、46.7%和50%。自适应因子图算法提高了BDS信息失锁的情况下的列车定位精度,实现了不同传感器之间的即插即用,为构建高精确性、强鲁棒性、高可扩展性的列车组合定位系统提供模型支持。

基于多信号流图的大气数据系统故障诊断

针对传统无人机大气数据系统故障诊断依赖专家经验、故障定位不准确的问题,提出了基于多信号流图的大气数据系统故障诊断方法。通过分析大气数据系统的组成结构和故障模式影响与危害度分析,采用多信号流图方法对某型无人机大气数据系统关键部件进行测试性分析,得到大气数据系统的故障-测试相关性矩阵。针对传统实时测试性工程与维护系统(Real-Time Testability Engineering and Ma-intenance System,TEAMS-RT)算法诊断速度较慢的问题,对TEAMS-RT算法进行优化研究,提出了一种基于TEAMS-RT的矩阵分解优化(简称Tree-RT)算法,在此基础上对D矩阵分别采用Tree-RT算法和测试一致性算法对大气数据系统进行故障注入试验。试验结果表明,Tree-RT算法对故障注入试验的诊断率为94.62%,高于基于测试一致性算法85.73%的诊断率,证明了大气数据系统多信号流图和Tree-RT算法的有效性,为无人机大气数据系统的故障诊断相关研究提供了技术支撑。

Spatial distribution characteristics and mechanism of nonhydrological time-variable gravity in China's Mainland

The purpose of this study is to explore nonhydrological mass transfer in China's Mainland.For this purpose,gravity recovery and climate experiment(GRACE)data were obtained to study the spatial distribution of time variant gravity signals in China's Mainland.Then,from auxiliary hydrological data processed according to the current hydrological model,a new more comprehensive hydrological model of China's Mainland was constructed.Finally,the time variant signals of this new hydrological model were removed from the time variant gravity field computed from GRACE data,thus obtaining a description of the nonhydrological mass transfer of China's Mainland.The physical sources and mechanisms of the resulting mass transfer are then discussed.The improved,more realistic,hydrological model used here was created by selecting the hydrological components with the best correlations in existing hydrological models,by use of correlation calculation,analysis,and comparison.This improved model includes water in soils and deeper strata,in the vegetation canopy,in lakes,snow,and glaciers,and in other water components(mainly reservoir storage,swamps,and rivers).The spatial distribution of the transfer signals due to nonhydrological mass in China's Mainland was obtained by subtracting the combined hydrological model from the GRACE time-variable gravity field.The results show that the nonhydrological signals in China's Mainland collected in GRACE data were mainly positive signals,and were distributed in the Bohai Rim and the northern and eastern parts of the Tibetan Plateau.The above nonhydrological mass transfer signals have been studied further and are discussed.The results show that the nonhydrological mass migration signals in the Bohai Rim region originate primarily from sea level change and marine sediment accumulation.The mass accumulation from Indian plate collision in the Tibetan Plateau appears to be the main reason for the increase in the residual gravity field in that region.

Research on LPI radar signal detection and parameter estimation technology

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.