Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of five and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.     

Probabilistic framework for brain connectivity from functional MR images

This paper unifies our earlier work on detection of brain activation (Rajapakse and Piyaratna, 2001) and connectivity (Rajapakse and Zhou, 2007) in a probabilistic framework for analyzing effective connectivity among activated brain regions from functional magnetic resonance imaging (fMRI) data. Interactions among brain regions are expressed by a dynamic Bayesian network (DBN) while contextual dependencies within functional images are formulated by a Markov random field. The approach simultaneously considers both the detection of brain activation and the estimation of effective connectivity and does not require a priori model of connectivity. Experimental results show that the present approach outperforms earlier fMRI analysis techniques on synthetic functional images and robustly derives brain connectivity from real fMRI data.     

Adaptive feedforward/feedback architectures for multiuser detectionin high data rate wireless CDMA networks

We consider the design and performance of nonlinear minimum mean-square-error multiuser detectors for direct sequence code-division multiple-access (CDMA) networks. With multiple users transmitting asynchronously at high data rates over multipath fading channels, the detectors contend with both multiple-access interference (MAI) and intersymbol interference (ISI). The cyclostationarity of the MAI and ISI is exploited through a feedforward filter (FFF), which processes samples at the output of parallel chip-matched filters, and a feedback filter (FBF), which processes detected symbols. By altering the connectivity of the FFF and FBF, we define four architectures based on fully connected (FC) and nonconnected (NC) filters. Increased connectivity of the FFF gives each user access to more samples of the received signal, while increased connectivity of the FBF provides each user access to previous decisions of other users. We consider three methods for specifying the FFF sampling and propose a nonuniform FFF sampling scheme based on multipath ray tracking that can offer improved performance relative to uniform FFF sampling. For the FC architecture, we capitalize on the sharing of filter contents among users by deriving a multiuser recursive least squares (RLS) algorithm and direct matrix inversion approach, which determine the coefficients more efficiently than single-user algorithms. We estimate the uncoded bit-error rate (BER) of the feedforward/feedback detectors for CDMA systems with varying levels of power control and timing control for multipath channels with quasi-static Rayleigh fading. Simulations of packet-based QPSK transmission validate the theoretical BER analysis and demonstrate that the multiuser RLS adapted detectors train in several hundred symbols and avoid severe error propagation during data transmission mode     

A new method of determination of the I-Vcharacteristics of negative differential conductance devices

A method for mapping the complete I-V characteristic of a negative differential conductance (NDC) device has been investigated. This method employs the measurable positive differential conductance (PDC) portions of the DC I-V curve together with the measured conductances at a fixed DC bias voltage in the PDC region with different RF signal levels using a standard semiconductor analyzer. The NDC regime of the I-V curve is numerically constructed from the measured conductances at a fixed DC bias voltage in the PDC region with different signal levels using a large-signal nonlinear-circuit analysis     

基于LTE-A异构网中双连接技术的切换流程设计与实现

3GPP在LTE-A异构网络中提出双连接技术是为了增强型小小区,终端同时连接到MeNB(Master eNB)和SeNB(Secondary eNB)可以提高单个用户的吞吐量和移动的强健性,用户吞吐量的提升是通过聚合至少两个基站的无线资源实现的.其中MeNB主要负责信令的传输和移动性管理,SeNB主要实现的是对数据的分流.提供了双连接技术的应用场景,在架构上实现了用户平面和控制平面的分离,设计了双连接场景下的切换流程以及搭建了基于TTCN-3(Testing and Test Control Notation version 3)测试系统的终端无线资源管理(Radio Resource Management,RRM)一致性测试平台,并对流程设计进行了验证.     

Application of the Link Management Protocol to Discovery and Forwarding Adjacencies

The link management protocol (LMP) is used to maintain control channel connectivity, to verify the physical connectivity of data channels, to correlate link property information at both endpoints of a data link and to aggregate multiple data links into a single traffic engineering (TE) link. LMP also provides assistance in fault localization, both in opaque and transparent optical networks. In this paper, we show how LMP relates to the generalized multi-protocol label switching (GMPLS) suite of protocols and integrates into a distributed GMPLS control plane. We describe the role of LMP in two applications: data link (auto-)discovery and the establishment of forwarding adjacency label-switched paths (FA-LSPs) including their initiation, verification and bundling, and conclude that they can be built upon basic functions of LMP, namely control channel management, link verification and link property correlation.     

基于PMU的电力系统监控体系结构优化设计

文中提出了一种利用有限数量的相量测量单元(PMU)和相量数据集中器(PDC)设计最优监控结构的方法.通过在大量的设定值场景下,使电力系统可观测性曲线的期望值最大化,同时使通信基础设施成本最小化,最终确定PMU和PDC的最佳位置.提出了一种非线性动态扩展卡尔曼滤波(EFK)状态观测器.这种状态观测器可以将暂态行为转换为由...     

SLM‐based PAPR reduction method using partial data circulation and side information insertion in OFDM systems

In this paper, two selected mapping (SLM)‐based schemes are proposed to reduce the peak‐to‐average power ratio (PAPR) in the orthogonal frequency division multiplexing (OFDM) systems. One is SLM‐based partial data circulation (PDC) scheme. The other is SLM‐based PDC with hamming coded side information (SI) scheme. In the PDC scheme, the partial samples within the data sequence are scrambled by circularly shifting to obtain a set of candidate sequences and, then, each sequence is combined with the corresponding SI to form the candidate OFDM symbol. With the selection of the candidate OFDM symbols with the lowest PAPR, the PAPR of the transmitted signal could be greatly reduced. In the other scheme, hamming code is used to protect the SI to improve the system performance. Simulation results show that the proposed method could reduce PAPR and it has a better bandwidth efficiency and less modules of inverse fast Fourier transform than that with the SLM‐based dummy sequence insertion scheme. Copyright © 2008 John Wiley & Sons, Ltd.     

A Connectivity-Based Method for Defining Regions-of-Interest in fMRI Data

In this paper, we describe a new methodology for defining brain regions-of-interset (ROIs) in functional magnetic resonance imaging (fMRI) data. The ROIs are defined based on their functional connectivity to other ROIs, i.e., ROIs are defined as sets of voxels with similar connectivity patterns to other ROIs. The method relies on 1) a spatially regularized canonical correlation analysis for identifying maximally correlated signals, which are not due to correlated noise; 2) a test for merging ROIs which have similar connectivity patterns to the other ROIs; and 3) a graph-cuts optimization for assigning voxels to ROIs. Since our method is fully connectivity-based, the extracted ROIs and their corresponding time signals are ideally suited for a subsequent brain connectivity analysis.      

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain

Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period.     

Ultralong lightwave systems with incomplete dispersioncompensations

Ultralong nonreturn-to-zero optical transmission systems with incomplete dispersion compensations are studied. The dispersion of transmission fiber is periodically under- or overcompensated. Postdispersion compensation (PDC) at the receiver is used to compensate for the residual dispersion caused by incomplete compensation and to tailor the signal pulse shape. Formulas estimating the change of pulse width in the absence of amplifier noise during signal transmission and after PDC are given. During signal transmission, pulse width may be compressed or broadened by the combined effect of the dispersion and self-phase modulation (SPM). The change of pulse width nearly increases with the square of the distance during signal transmission. With amplifier noise, system performance evaluated by Q factor is studied. Several types of transmission fibers are considered. The Q factor can be significantly improved by proper PDC. Signal pulse is compressed when PDC is optimized. The characteristics of the maximum Q factor and the residual dispersion are studied, in which PDC is optimized. The results show that to achieve the best system performance, fiber dispersion should be undercompensated for positive dispersion parameter and overcompensated for negative dispersion parameter. The optimal fiber dispersion lies in the range from 4 to 10 ps/km/nm for the considered systems, and the optimal ratio of residual dispersion and fiber dispersion is about 1%     

A DTI-Derived Measure of Cortico-Cortical Connectivity

We arm researchers with a simple method to chart a macroscopic cortico-cortical connectivity network in living human subjects. The researcher provides a diffusion-magnetic resonance imaging (MRI) data set and $N$ cortical regions of interest. In return, we provide an $N times N$ structural adjacency matrix (SAM) quantifying the relative connectivity between all cortical region pairs. We also return a connectivity map for each pair to enable visualization of interconnecting fiber bundles. The measure of connectivity we devise is: 1) free of length bias, 2) proportional to fiber bundle cross-sectional area, and 3) invariant to an exchange of seed and target. We construct a 3-D lattice scaffolding (graph) for white-matter by drawing a link between each pair of voxels in a 26-voxel neighborhood for which their two respective principal eigenvectors form a sufficiently small angle. The connectivity between a cortical region pair is then measured as the maximum number of link-disjoint paths that can be established between them in the white-matter graph. We devise an efficient Edmonds–Karp-like algorithm to compute a conservative bound on the maximum number of link-disjoint paths. Using both simulated and authentic diffusion-tensor imaging data, we demonstrate that the number of link-disjoint paths as a measure of connectivity satisfies properties 1)–3), unlike the fraction of intersecting streamlines—the measure intrinsic to most existing probabilistic tracking algorithms. Finally, we present connectivity maps of some notoriously difficult to track longitudinal and contralateral fasciculi.      

Performance Analysis with Traffic Accident for Cooperative Active Safety Driving in VANET/ITS

Recently, by using vehicle-to-vehicle and vehicle-to-infrastructure communications for VANET/ITS, the cooperative active safety driving (ASD) providing vehicular traffic information sharing among vehicles significantly prevents accidents. Clearly, the performance analysis of ASD becomes difficult because of high vehicle mobility, diverse road topologies, and high wireless interference. An inaccurate analysis of packet connectivity probability significantly affects and degrades the VANET/ITS performance. Especially, most of related studies seldom concern the impact factors of vehicular accidents for the performance analyses of VANET/ITS. Thus, this paper proposes a two-phase approach to model a distributed VANET/ITS network with considering accidents happening on roads and to analyze the connectivity probability. Phase 1 proposes a reliable packet routing and then analyzes an analytical model of packet connectivity. Moreover, the analysis is extended to the cases with and without exhibiting transportation accidents. In phase 2, by applying the analysis results of phase 1 to phase 2, an adaptive vehicle routing, namely adaptive vehicle routing (AVR), is proposed for accomplishing dynamic vehicular navigation, in which the cost of a road link is defined in terms of several critical factors: traffic density, vehicle velocity, road class, etc. Finally, the path with the least path cost is selected as the optimal vehicle routing path. Numerical results demonstrate that the analytical packet connectivity probability and packet delay are close to that of simulations. The yielded supreme features justify the analytical model. In evaluations, the proposed approach outperforms the compared approaches in packet connectivity probability, average travel time, average exhausted gasoline. However, the proposed approach may lead to a longer travel distance because it enables the navigated vehicle to avoid traversing via the roads with a higher traffic density.     

On diversity reception over fading channels with impulsive noise

In this paper, we analyze the performance of different diversity combining techniques over fading channels with impulsive noise. We use Middleton's Class A model for the noise distribution and adopt two noise models, which assume dependent and independent noise components on each branch. We systematically analyze the performance of maximum ratio combing (MRC), equal gain combining (EGC), selection combining (SC), and post-detection combining (PDC) under these impulsive noise models, and derive insightful lower and upper bounds. We show that even under impulsive noise, the diversity order is retained for each combining scheme. However, we also show that under both models, there is a fundamental tradeoff between diversity gain and coding gain. Under the independent noise model, PDC is shown to combat impulsive noise more effectively than MRC, EGC, and SC. Our simulation results also corroborate our analysis.     

Network virtualization for cloud computing

Cloud computing enables a transparent access to information technology (IT) services such that the users do not need to know the location and characteristics of the relevant resources. While IT resource virtualization and service abstraction have been widely investigated, data transport within the cloud and its efficient control have not received much attention in the technical literature. In fact, connectivity is, itself, a service that contributes to the overall performance of the cloud. This paper introduces a novel classification of the Network as a Service (NaaS) such that it can be orchestrated with other cloud services. Then, it proposes a network virtualization platform (NVP) as the mediation layer able to provide NaaS to cloud computing by exploiting the functionality provided by control plane (CP)-enabled networks. In particular, the proposed NVP maps the end-point addresses and perceived Quality of Service parameters of a NaaS requests in the parameters characterizing the connectivity as viewed by transport networks using the information obtained from the CP at the boundary of the network. The NVP uses these parameters to fulfill connectivity requests to the CP. Finally, this paper presents a complete design from both the software implementation and network signaling perspective of two use cases in which NaaS is involved as stand-alone facility for the connectivity service provisioning or is combined with other cloud services for a storage service provisioning.     

A design technique for reliable networks under a nonuniform trafficdistribution

Many network-reliability analysis techniques define and compute a variety of reliability measures. Most techniques assume that network connectivity is the only determining factor in network reliability; and merely analyze an existing network structure but do not provide any methodology for reliable design. This paper presents a heuristic design algorithm to enhance the reliability of existing communication networks by modifying their topology. This algorithm improves the reliability of the least reliable node (reliability is the probability that messages transmitted from a given node reach their destination). To use this algorithm on large networks, a reliability analysis method is developed which determines approximate network reliability values in linear-time when an upper bound is placed upon the in-degree of all network nodes. The heuristic network design algorithm uses this approximate reliability analysis technique to place additional links. The goal of this link placement is to improve the reliability of the least-reliable node. The placement of additional links is a function of both the traffic distribution and the connectivity of the network. This process continues until either a desired level of network reliability is achieved or a maximum number of additional links has been added. A unique feature of this design strategy is that it has quadratic time-complexity when the maximum in-degree of all network nodes is limited     

一种改进的PDC块匹配运动估值算法

像素差分类（PDC）块匹配运动估值算法是一种考虑独立像素特征的运动估值算法，针对PDC算法的最优匹配不唯一的问题，本文提出了一种基于块运动最小相对位移约束的方法予以改进。本文还引入了双门限法以降低PDC算法的门限敏感性。实验表明本算法不仅克服了原算法的上述缺点，用于视频压缩编码时，还可使解码信噪比和主观质量有所改善。硬件实现的复杂性仅有很小的增加。     

Industrial electronics [technology 1999 analysis and forecast]

In old markets and new, power electronics, robots, intelligent control, and connectivity are becoming the pillars of industrial electronics and factory automation. Consider the integrated gate-commutated thyristor (IGCT). In the 18 months since its introduction by a Wisconsin company, motor drives based on the thyristor are already making a dent in the market for medium voltage and power levels-up to 6.6 kV and 10 MW-not least because other devices operate at similar power levels but higher losses. In the control and connectivity fields, open systems are vigorously pursued. Software and hardware advances in Java language technology are allowing manufacturers to break through the restrictions of proprietary systems and test real-time factory automation over the Internet, involving text, numerical data, and graphics. Open networking for the factory is strongly advocated by such industry leaders as Hirschmann Network Systems, in Riverdale, NJ and on printed-circuit boards, the use of vias under 150 μm in diameter (microvias) is taking hold, as packaging concepts like ball grid arrays cause component leads to shrink     

AR order selection in the case when the model parameters are estimated by forgetting factor least-squares algorithms

During the last decades, the use of information theoretic criteria (ITC) for selecting the order of autoregressive (AR) models has increased constantly. Because the ITC are derived under the strong assumption that the measured signals are stationary, it is not straightforward to employ them in combination with the forgetting factor least-squares algorithms. In the previous literature, the attempts for solving the problem were focused on the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the predictive least squares (PLS). In connection with PLS, an ad hoc criterion called SRM was also introduced. In this paper, we modify the predictive densities criterion (PDC) and the sequentially normalized maximum likelihood (SNML) criterion such that to be compatible with the forgetting factor least-squares algorithms. Additionally, we provide rigorous proofs concerning the asymptotic approximations of four modified ITC, namely PLS, SRM, PDC and SNML. Then, the four criteria are compared by simulations with the modified variants of BIC and AIC.