利用相锁值算法的脑电相同步测谎研究

相锁值（Phase Locking Value,PLV）是由相同步的概念下提出一种描述不同信号相关性（同步性）的算法,在脑电信号领域,其有效性已经得到了验证.本文针对当前测谎方法中脑电信号特征提取困难的问题,首次将相锁值的算法应用到脑电测谎领域中,研究谎言脑活动下不同脑区之间的相关性,通过相关性发现谎言的认知机制,并利用该相关性作为特征,使用支持向量机对说谎者和诚实者的两类信号进行模式识别,得到了88.50%的准确率,提出的方法验证了PLV在测谎应用中的有效性,为基于脑电信号的测谎提供了一种新的途径.     

Self-correcting time synchronization using reference broadcast in wireless sensor network

Time synchronization is one of the most fundamental services for numerous wireless sensor network applications. In this article the definition and basic concepts of time synchronization are introduced, and the related work is summarized in brief. Through analyzing the characteristics of the existing typical synchronization protocols and making a comprehensive comparison of the performance of various algorithms, we present a common guideline for designing the time synchronization protocol in WSN. Following this guideline, we develop a new time synchronization protocol called Self-Correcting Time Synchronization (SCTS), which converts the time synchronization problem into an online dynamic self-adjusting optimizing process to make the offset compensation and drift compensation simultaneously. The time and space complexities of the algorithm implementation are very low. In addition, the SCTS protocol fully exploits the inherent broadcast property of wireless channel, so the communication overhead is rather low. Because the algorithm implementation is based on the phase locked loop principle, an equivalent digital PLL without an actual voltage controlled oscillator is also proposed to avoid introducing the extra hardware required by a traditional PLL circuit. Finally, we validate SCTS on the Berkeley Mica2 experimental platform, and the performance is evaluated and compared to the existing typical time synchronization protocol.     

混沌同步新方法研究

从数字通信技术角度提出了混沌同步定义，指出混沌数字保密通信的应用研究重点是混沌严格同步技术。从网络通信协议的思路出发，在混沌脉冲同步的启发下，提出了一种新的混沌同步方法——混沌协议同步技术．并加以改进，从而解决了混沌数字保密通信的关键技术难题。最后构建了一个基于协议同步的网络混沌语音保密通信系统．验证了此方法的可行性。     

Towards optimal synchronization in NFV-based environments

Network Function Virtualization (NFV) is known for its ability to reduce deployment costs and improve the flexibility and scalability of network functions. Due to processing capacity limitations, the infrastructure provider may need to instantiate multiple instances of the same network function. However, most of network functions are stateful, meaning that the instances of the same function need to keep a common state and hence the need for synchronization among them. In this paper, we address this problem with the goal of identifying the optimal synchronization pattern between the instances in order to minimize the synchronization costs and delay. We propose a novel network function named Synchronization Function able to carry out data collection and further minimize these costs. We first mathematically model this problem as an integer linear program that finds the optimal synchronization pattern and the optimal placement and number of synchronization functions that minimize synchronization costs and ensure a bounded synchronization delay. We also put forward three greedy algorithms to cope with large-scale scenarios of the problem, and we explore the possibility to migrate network function instances to further reduce costs. Extensive simulations show that the proposed algorithms efficiently find near-optimal solutions with minimal computation time and provide better results compared to existing solutions.     

Joint synchronization in Eureka 147 DAB system based on abruptphase change detection

We present a joint symbol, frame, and carrier synchronization method for the Eureka 147 DAB signal. Symbol timing is determined first by detecting an abrupt change in the phase angle of the complex product between the last quarter of a useful symbol and its cyclic extension in the guard interval. The detection of this abrupt change is based an the maximal likelihood (hit) principle. Frequency offset of fractional carrier spacing is estimated from the phase angle of the autocorrelation after symbol timing is estimated. Coarse frame synchronization and null symbol detection can also be achieved through this correlation information. Frequency offset of integral carrier spacing is determined from the convolution outputs between a received phase reference symbol and several locally generated but frequency-shifted phase reference symbols. We found the length of a guard interval is the most important parameter for the synchronization algorithm to work. Simulation results show that the performance of this synchronization method approaches to the ideal synchronization case in both an additive white Gaussian noise (AWGN) channel and a two-path Rayleigh fading channel     

Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings

The mechanisms underlying the transition of brain activity toward epileptic seizures remain unclear. Based on nonlinear analysis of both intracranial and scalp electroencephalographic (EEG) recordings, different research groups have recently reported dynamical smooth changes in epileptic brain activity several minutes before seizure onset. Such preictal states have been detected in populations of patients with mesial temporal lobe epilepsy (MTLE) and, more recently, with different neocortical partial epilepsies (NPEs). In this paper, we are particularly interested in the spatio-temporal organization of epileptogenic networks prior to seizures in neocortical epilepsies. For this, we characterize the network of two patients with NPE by means of two nonlinear measures of interdependencies. Since the synchronization of neuronal activity is an essential feature of the generation and propagation of epileptic activity, we have analyzed changes in phase synchrony between EEG time series. In order to compare the phase and amplitude dynamics, we have also studied the degree of association between pairs of signals by means of a nonlinear correlation coefficient. Recent findings have suggested changes prior to seizures in a wideband frequency range. Instead, for the examples of this study, we report a significant decrease of synchrony in the focal area several minutes before seizures (>30 min in both patients) in the frequency band of 10-25 Hz mainly. Furthermore, the spatio-temporal organization of this preictal activity seems to be specifically related to this frequency band. Measures of both amplitude and phase coupling yielded similar results in narrow-band analysis. These results may open new perspectives on the mechanisms of seizure emergence as well as the organization of neocortical epileptogenic networks. The possibility of forecasting the onset of seizures has important implications for a better understanding, diagnosis and a potential treatment of the epilepsy.     

Detection and visualization method of dynamic state transition for biological spatio-temporal imaging data

In the statistical analysis of functional brain imaging data, regression analysis and cross correlation analysis between time series data on each grid point have been widely used. The results can be graphically represented as an activation map on an anatomical image, but only activation signal, whose temporal pattern resembles the predefined reference function, can be detected. In the present study, we propose a fusion method comprising innovation approach in time series analysis and statistical test. Autoregressive (AR) models were fitted to time series data of each pixel for the range sufficiently before or after the state transition. Then, the remaining time series data were filtered using these AR parameters to obtain its innovation (filter output). The proposed method could extract brain neural activation as a phase transition of dynamics in the system without employing external information such as the reference function. The activation could be detected as temporal transitions of statistical test values. We evaluated this method by applying to optical imaging data obtained from the mammalian brain and the cardiac sino-atrial node (SAN), and demonstrated that our method can precisely detect spatio-temporal activation profiles in the brain or SAN.     

A distributed transmit beamforming synchronization strategy for multi-element radar systems

The distributed transmit beamforming has recently been discussed as an energy-effective technique in wireless communication systems. A common ground of various techniques is that the destination node transmits a beacon signal or feedback to assist source nodes to synchronize signals. However, this approach is not appropriate for a radar system since the destination is a non-cooperative target of an unknown location. In our paper, we propose a novel synchronization strategy for a distributed multiple-element beamfoming radar system. Source nodes estimate parameters of beacon signals transmitted from others to get their local synchronization information. The channel information of the phase propagation delay is transmitted to nodes via the reflected beacon signals as well. Next, each node generates appropriate parameters to form a beamforming signal at the target. Transmit beamforming signals of all nodes will combine coherently at the target compensating for different propagation delay. We analyse the influence of the local oscillation accuracy and the parameter estimation errors on the performance of the proposed synchronization scheme. The results of numerical simulations illustrate that this synchronization scheme is effective to enable the transmit beamforming in a distributed multi-element radar system.     

Envelope and phase delays correction in an EER radio architecture

This article deals with synchronization in the Envelope Elimination and Restoration (EER) type of transmitter architecture. To illustrate the performances of such solution, we choose to apply this architecture to a 64 carriers 16QAM modulated OFDM. We first introduce the problematic of the realisation of a highly linear transmitter. We then present the Envelope Elimination and Restoration solution and draw attention to its major weakness: a high sensitivity to desynchronization between the phase and envelope signal paths. To address this issue, we propose an adaptive synchronization algorithm relying on a feedback loop, a Least Mean Square formulation and involving an interpolation step. It enables the correction of delay mismatches and tracking of possible variations. We demonstrate that the quality of the interpolator has a direct impact on Error Vector Magnitude (EVM) value and output spectrum. Implementation details are provided along with an analysis of the behaviour and performances of the method. We present Agilent-ADS and Matlab simulation results and then focus on the enhancement of the transmitter performances using the proposed algorithm.     

Modulation classification for asynchronous high‐order QAM signals

Automatic modulation classification (MC) is beneficial to digital radio receivers. Conventional MC schemes usually assume that perfect synchronization has been accomplished and then rely on certain statistical characteristics to distinguish different modulation formats. In practice, however, this perfect synchronization assumption is not reasonable since modulation classifiers operate in a non‐cooperative manner and therefore, the receiver has little prior knowledge about a transmitted signal and no training is available. In this paper, we first address asynchronous MC for high‐order QAMs through blind time synchronization. A characteristic function (CF) based approach is proposed to improve the performance of the conventional cumulant method. We then move on to consider asynchronous MC in the presence of frequency offset. We propose a hybrid MC scheme based on blind time synchronization, differential processing, and cumulants to solve this difficult problem. Copyright © 2010 John Wiley & Sons, Ltd.     

Using replicator dynamics for analyzing fMRI data of the human brain

The understanding of brain networks becomes increasingly the focus of current research. In the context of functional magnetic resonance imagery (fMRI) data of the human brain, networks have been mostly detected using standard clustering approaches. In this work, we present a new method of detecting functional networks using fMRI data. The novelty of this method is that these networks have the property that every network member is closely connected with every other member. This definition might to be better suited to model important aspects of brain activity than standard cluster definitions. The algorithm that we present here is based on a concept from theoretical biology called "replicator dynamics."     

Group replicator dynamics: a novel group-wise evolutionary approach for sparse brain network detection

Functional magnetic resonance imaging (fMRI) is increasingly used for studying functional integration of the brain. However, large inter-subject variability in functional connectivity, particularly in disease populations, renders detection of representative group networks challenging. In this paper, we propose a novel technique, "group replicator dynamics" (GRD), for detecting sparse functional brain networks that are common across a group of subjects. We extend the replicator dynamics (RD) approach, which we show to be a solution of the nonnegative sparse principal component analysis problem, by integrating group information into each subject's RD process. Our proposed strategy effectively coaxes all subjects' networks to evolve towards the common network of the group. This results in sparse networks comprising the same brain regions across subjects yet with subject-specific weightings of the identified brain regions. Thus, in contrast to traditional averaging approaches, GRD enables inter-subject variability to be modeled, which facilitates statistical group inference. Quantitative validation of GRD on synthetic data demonstrated superior network detection performance over standard methods. When applied to real fMRI data, GRD detected task-specific networks that conform well to prior neuroscience knowledge.     

Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings

In magnetoencephalography (MEG) and electroencephalography (EEG), independent component analysis is widely applied to separate brain signals from artifact components. A number of different methods have been proposed for the automatic or semiautomatic identification of artifact components. Most of the proposed methods are based on amplitude statistics of the decomposed MEG/EEG signal. We present a fully automated approach based on amplitude and phase statistics of decomposed MEG signals for the isolation of biological artifacts such as ocular, muscle, and cardiac artifacts (CAs). The performance of different artifact identification measures was investigated. In particular, we show that phase statistics is a robust and highly sensitive measure to identify strong and weak components that can be attributed to cardiac activity, whereas a combination of different measures is needed for the identification of artifacts caused by ocular and muscle activity. With the introduction of a rejection performance parameter, we are able to quantify the rejection quality for eye blinks and CAs. We demonstrate in a set of MEG data the good performance of the fully automated procedure for the removal of cardiac, ocular, and muscle artifacts. The new approach allows routine application to clinical measurements with small effect on the brain signal.      

基于前导脉冲辅助的相位调制1090ES信号载频同步算法

基于相位调制扩容1090ES信号为ADS-B带来新的功能和应用,载波频率同步是扩容信号需解决的一项关键技术。针对扩容1090ES信号的突发载频同步问题,提出了一种基于频偏捕获和相位误差跟踪环的快速同步方法,频偏捕获采用前导辅助脉冲相关性判决进行二叉树频率搜索,捕获载频偏差并校正得到频偏较小的基带信号,相位误差跟踪环实现对小频偏信号进行频率估计和补偿。仿真结果表明:该方法适用于基于相位调制的扩容1090ES信号接收系统。     

Performance Analysis of SyncML Server System Using Stochastic Petri Nets

Synchronization Markup Language (SyncML) is a specification of a common data synchronization framework for synchronizing data on networked devices. SyncML is designed for use between mobile devices that are intermittently connected to a network and network services that are continuously available on the network. We have designed and developed a data synchronization system based on the SyncML protocol and evaluated the throughput of the system using the stochastic Petri nets package (SPNP) and analyzed the relationship between the arrival rate and the system resources. Using this model, we evaluate various performance measures in different situations, and we estimate the relationship between the arrival rate and the system resources. From the results, we can estimate the optimal amount of resources due to the arrival rate before deploying the developed system.     

Efficiently self-synchronized audio watermarking for assured audio data transmission

In this paper, we propose a self-synchronization algorithm for audio watermarking to facilitate assured audio data transmission. The synchronization codes are embedded into audio with the informative data, thus the embedded data have the self-synchronization ability. To achieve robustness, we embed the synchronization codes and the hidden informative data into the low frequency coefficients in DWT (discrete wavelet transform) domain. By exploiting the time-frequency localization characteristics of DWT, the computational load in searching synchronization codes has been dramatically reduced, thus resolving the contending requirements between robustness of hidden data and efficiency of synchronization codes searching. The performance of the proposed scheme in terms of SNR (signal to noise ratio) and BER (bit error rate) is analyzed. An estimation formula that connects SNR with embedding strength has been provided to ensure the transparency of embedded data. BER under Gaussian noise corruption has been estimated to evaluate the performance of the proposed scheme. The experimental results are presented to demonstrate that the embedded data are robust against most common signal processing and attacks, such as Gaussian noise corruption, resampling, requantization, cropping, and MP3 compression.     

基于同步性脑网络的支持张量机情绪分类研究

一直以来,情绪是心理学、教育学、信息科学等多个学科的研究热点,脑电信号(EEG)因其客观、不易伪装的特点,在情绪识别领域受到广泛关注。由于人类情绪是大脑多个脑区相互作用产生的,该文提出一种基于同步性脑网络的支持张量机情绪分类算法(SBN-STM),该算法采用相位锁定值(PLV)构建了同步性脑网络,分析多导联脑电信号之间的同步性和相关性,并生成2阶张量序列作为训练集,运用支持张量机(STM)模型实现正负情绪的二分类。该文基于DEAP脑电情绪数据库,详细分析了同步性脑网络张量序列的选取方法,最佳张量序列窗口的大小和位置,解决了传统情绪分类算法特征冗余的问题,提高了模型训练速度。仿真实验表明,基于支持张量机的同步性脑网络分类方法的情绪准确率优于支持向量机、C4.5决策树、人工神经网络、K近邻等以向量为特征的情绪分类模型。     

Retrospective evaluation of intersubject brain registration

Although numerous methods to register brains of different individuals have been proposed, no work has been done, as far as we know, to evaluate and objectively compare the performances of different nonrigid (or elastic) registration methods on the same database of subjects. In this paper, we propose an evaluation framework, based on global and local measures of the relevance of the registration. We have chosen to focus more particularly on the matching of cortical areas, since intersubject registration methods are dedicated to anatomical and functional normalization, and also because other groups have shown the relevance of such registration methods for deep brain structures. Experiments were conducted using 6 methods on a database of 18 subjects. The global measures used show that the quality of the registration is directly related to the transformation's degrees of freedom. More surprisingly, local measures based on the matching of cortical sulci did not show significant differences between rigid and non rigid methods.