独立分量分析在混沌信号分析中的应用

本文从高阶统计信息角度来研究混沌信号,利用独立分量分析(Independent Component Analysis以下简称ICA)来探求混沌信号的特征结构.集中讨论了两个问题.一是基于ICA及相关的近似熵提出了一种新的方法来解决相空间重构中最佳延迟的选择问题;二是利用ICA来实现局部本征维数(LID)求取算法,从而对混沌吸引子的维数进行估计.实验结果表明该方法能正确分别混沌信号与非混沌信号,尤其是能正确判别1/f随机过程不是混沌过程.     

Information properties of order statistics and spacings

We explore properties of the entropy, Kullback-Leibler information, and mutual information for order statistics. The probability integral transformation plays a pivotal role in developing our results. We provide bounds for the entropy of order statistics and some results that relate entropy ordering of order statistics to other well-known orderings of random variables. We show that the discrimination information between order statistics and data distribution, the discrimination information among the order statistics, and the mutual information between order statistics are all distribution free and are computable using the distributions of the order statistics of the samples from the uniform distribution. We also discuss information properties of spacings for uniform and exponential samples and provide a large sample distribution-free result on the entropy of spacings. The results show interesting symmetries of information orderings among order statistics.     

Mitigating array sensor malfunctions with mixed second and fourthorder statistics

The authors propose a modified MUSIC algorithm which employs mixed second and fourth order statistics. When an array suffers from sensor malfunction, higher order statistics are used to extend the effective aperture of the faulty array. Simulation results support the potential of this approach which maximises the additional information available in fourth order statistics, while retaining the statistical reliability of second order sample estimates     

Inverting sampled traffic

Routers have the ability to output statistics about packets and flows of packets that traverse them. Since, however, the generation of detailed traffic statistics does not scale well with link speed, increasingly routers and measurement boxes implement sampling strategies at the packet level. In this paper, we study both theoretically and practically what information about the original traffic can be inferred when sampling, or "thinning", is performed at the packet level. While basic packet level characteristics such as first order statistics can be fairly directly recovered, other aspects require more attention. We focus mainly on the spectral density, a second-order statistic, and the distribution of the number of packets per flow, showing how both can be exactly recovered, in theory. We then show in detail why in practice this cannot be done using the traditional packet based sampling, even for high sampling rate. We introduce an alternative flow-based thinning, where practical inversion is possible even at arbitrarily low sampling rate. We also investigate the theory and practice of fitting the parameters of a Poisson cluster process, modeling the full packet traffic, from sampled data.     

基于测量数据的电磁环境分析软件开发

为了掌握电磁环境现状及发展趋势,提高电子信息系统的运用效率,采用数理统计和神经网络算法,对电磁环境测量数据进行归纳和总结。开发了电磁环境分析及预测软件,提供了关键电磁参数的建模方法,实现了对特定区域电磁环境发展变化情况的预测,为电子信息系统应对复杂电磁环境挑战提供了技术支撑。     

Adaptive scalar quantization without side information

In this paper, we introduce a novel technique for adaptive scalar quantization. Adaptivity is useful in applications, including image compression, where the statistics of the source are either not known a priori or will change over time. Our algorithm uses previously quantized samples to estimate the distribution of the source, and does not require that side information be sent in order to adapt to changing source statistics. Our quantization scheme is thus backward adaptive. We propose that an adaptive quantizer can be separated into two building blocks, namely, model estimation and quantizer design. The model estimation produces an estimate of the changing source probability density function, which is then used to redesign the quantizer using standard techniques. We introduce nonparametric estimation techniques that only assume smoothness of the input distribution. We discuss the various sources of error in our estimation and argue that, for a wide class of sources with a smooth probability density function (pdf), we provide a good approximation to a "universal" quantizer, with the approximation becoming better as the rate increases. We study the performance of our scheme and show how the loss due to adaptivity is minimal in typical scenarios. In particular, we provide examples and show how our technique can achieve signal-to-noise ratios within 0.05 dB of the optimal Lloyd-Max quantizer for a memoryless source, while achieving over 1.5 dB gain over a fixed quantizer for a bimodal source.     

Generalized flooding and Multicue PDE-based image segmentation.

Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. Moreover, the increasing demands of image analysis tasks in terms of segmentation results' quality introduce the necessity of employing multiple cues for improving image segmentation results. In this paper, we attempt to incorporate cues such as intensity contrast, region size, and texture in the segmentation procedure and derive improved results compared to using individual cues separately. We emphasize on the overall segmentation procedure, and we propose efficient simplification operators and feature extraction schemes, capable of quantifying important characteristics, like geometrical complexity, rate of change in local contrast variations, and orientation, that eventually favor the final segmentation result. Based on the well-known morphological paradigm of watershed transform segmentation, which exploits intensity contrast and region size criteria, we investigate its partial differential equation (PDE) formulation, and we extend it in order to satisfy various flooding criteria, thus making it applicable to a wider range of images. Going a step further, we introduce a segmentation scheme that couples contrast criteria in flooding with texture information. The modeling of the proposed scheme is done via PDEs and the efficient incorporation of the available contrast and texture information, is done by selecting an appropriate cartoon-texture image decomposition scheme. The proposed coupled segmentation scheme is driven by two separate image components: cartoon U (for contrast information) and texture component V. The performance of the proposed segmentation scheme is demonstrated through a complete set of experimental results and substantiated using quantitative and qualitative criteria.     

引入负相似的高光谱图像半监督分类

高光谱图像数据体现为波段多、地物标签获取困难大、谱信息抗干扰能力弱等特征,容易引起维数灾难、光谱空间变异性等问题,从而影响分类器的分类精度。针对这些问题,本文将负相似信息引入到拉普拉斯支持向量机（Laplacian Support Vector Machine, LapSVM）的流形正则化项中,提出了一种引入负相似的拉普拉斯支持向量机（Dissimilarity in Laplacian Support Vector Machine, Diss-LapSVM）分类算法,抑制光谱空间变异对分类结果的影响。同时,本文提出利用线性近邻传播（Linear Neighborhood Propagation, LNP）算法构造图的拉普拉斯矩阵,更有效地引入无标签样本的信息。实验结果表明,本文算法的分类精度得到了提高,特别是对光谱特征相似的地物。      

Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting.

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Unique identification information is embedded into each distributed copy of multimedia signal and serves as a digital fingerprint. Collusion attack is a cost-effective attack against digital fingerprinting, where colluders combine several copies with the same content but different fingerprints to remove or attenuate the original fingerprints. In this paper, we investigate the average collusion attack and several basic nonlinear collusions on independent Gaussian fingerprints, and study their effectiveness and the impact on the perceptual quality. With unbounded Gaussian fingerprints, perceivable distortion may exist in the fingerprinted copies as well as the copies after the collusion attacks. In order to remove this perceptual distortion, we introduce bounded Gaussian-like fingerprints and study their performance under collusion attacks. We also study several commonly used detection statistics and analyze their performance under collusion attacks. We further propose a preprocessing technique of the extracted fingerprints specifically for collusion scenarios to improve the detection performance.     

Statistical texture characterization from discrete waveletrepresentations

We conjecture that texture can be characterized by the statistics of the wavelet detail coefficients and therefore introduce two feature sets: (1) the wavelet histogram signatures which capture all first order statistics using a model based approach and (2) the wavelet co-occurrence signatures, which reflect the coefficients' second-order statistics. The introduced feature sets outperform the traditionally used energy. Best performance is achieved by combining histogram and co-occurrence signatures     

基于Profibus现场总线的车问生产过程自动化管理

为加强汽车总装车间生产组织过程的管理,使相关信息目视化,传递快捷化,工序过程透明化,提高生产组织效率,拟建立该系统。该系统作为生产全过程组织的一个辅助工具,能实现快速的申请呼叫、设备控制、信息传递、实时显示、统计分析、报表生成等,就工序作业、设备状态、质量问题、供应情况等过程进行实时信息传递和管理,对生产全过程构成支持。     

Application of higher order statistics to surface electromyogram signal classification

We propose a novel approach for surface electromyogram (sEMG) signal classification. This approach utilizes higher order statistics of sEMG signal to classify four primitive motions, i.e., elbow flexion, elbow extension, forearm supination, and forearm pronation. In documented research, the sEMG signal generated during isometric contraction is modeled by a stationary process whose probability density function (pdf) is assumed to be either Gaussian or Laplacian. In this paper, using Negentropy, we demonstrate that the level of non-Gaussianity of sEMG signal recorded in muscular forces below 25% of maximum voluntary contraction (MVC) is significant. Therefore, application of higher order statistics in sEMG signal processing is justified, due to the fact that more useful information can be extracted from the corresponding higher order statistics. An accurate classification is achieved by using the sequential forward selection (SFS) method for reducing of the dimensionality of feature space and the K-nearest neighbor (KNN) classifier. The results indicate that the proposed approach provides higher sEMG correct classification rates as compared to the existing methods.     

Three-dimensional nonlinear invisible boundary detection.

The human vision system can discriminate regions which differ up to the second-order statistics only. We present an algorithm designed to reveal "hidden" boundaries in gray level images, by computing gradients in higher order statistics of the data. We demonstrate it by applying it to the identification of possible "hidden" boundaries of glioblastomas as manifest themselves in three-dimensional (3-D) MRI scans, using a model driven approach. We also demonstrate the method using a nonmodel driven approach where we have no prior information about the location of possible boundaries. In this case, we use 3-D MRI data concerning schizophrenic patients and normal controls.     

Blind identification of multipath channels: a parametric subspaceapproach

In this paper, blind identification of single-input multiple-output (SIMO) systems using second-order statistics (SOS) only is considered. Using the assumption of a specular multipath channel, we investigate a parametric variant of the so-called subspace method. Nonparametric subspace-based methods require precise estimation of the model order; overestimation of the model order leads to inconsistent channel estimates. We show that the parametric subspace method gives consistent channel estimates when only an upper bound of the channel order is known. A new algorithm, which exploits parametric information on the channel structure, is presented. A statistical performance analysis of the proposed parametric subspace criterion is presented; limited Monte Carlo experiments show that the proposed algorithm is second-order optimal for a large class of channels     

Audit-based correction mechanism for malicious statistics information of data plane

In software-defined networking (SDN), the controller relies on the information collected from the data plane for route planning, load balancing, and other functions. Statistics information is the most important kind of information among them, so the correctness of statistics information is the key to the proper operation of the network. Most of the current research on data plane focuses on policy consistency, rule redundancy, forwarding anomalies, and so on, and little attention is paid to whether the statistics information uploaded by the switches to the controller is correct. However, incorrect statistics information inevitably leads the controller to make wrong decisions. Therefore, this paper proposes an audit-based malicious information correction mechanism to address the problem of wrong statistics information uploaded by the switches. This mechanism audits the statistics information and locates malicious switches before uploading the statistics information to the controller. It identifies and corrects the statistics information errors by combining flow path and statistics information. We have performed simulations on Nsfnet, Abilene, and Fat-Tree, and the results show that our method can correct about 70% of the statistical information errors with less computational cost. To the best of our knowledge, this paper is the first malicious statistics information correction scheme for wildcard rules.     

A multilayer differentiated protection services architecture

Metropolitan area and long-haul networks are migrating toward the deployment of optical mesh technologies. This requires, among other things, a new generation of highly intelligent protection and restoration mechanisms to perform functions of protection and bandwidth management. We introduce an architecture that provides differentiated protection services across multiple layers of network hierarchy. A connection at any client layer can request a protection against resource failures at any lower layer. A key aspect of the architecture is the hierarchical tree organization of shared risk link group (SRLG) resources. They represent routing-related failures across all layers of protocol stack. The architecture is very scalable in terms of communicating link-state and bandwidth information between adjacent layers. SRLG trees are used to aggregate this information and provide a summary to the client layer. We discuss the requirements and challenges for routing and signaling mechanisms in order to support the proposed architecture. The complexity of this architecture is evaluated and compared with the complexity of a nonhierarchical alternative.     

Reliability-Based Iterative Decoding of LDPC Codes Using Likelihood Accumulation

In this work, we present an improvement on low complexity serial concatenation of belief propagation (BP) - order statistic decoding (OSD) algorithm for low-density parity-check (LDPC) codes. We introduce a more reliable method to reconstruct ordered information sequence in terms of the accumulated log-likelihood ratio (LLR) transitions of variable nodes. We give a general expression for both the existing BP-OSD algorithm and our proposed new method according to a structure similar to an infinite impulse response (IIR) filter. This improved algorithm achieves noticeable performance gains with only modest increase in computation complexity.     

An optimum step-size assignment for incremental LMS adaptive networks based on average convergence rate constraint

This paper presents an optimum step-size assignment for incremental least-mean square adaptive networks in order to improve its robustness against the spatial variation of observation noise statistics over the network. We show that when the quality of measurement information (in terms of observation noise variances) is available, we can exploit it to adjust the step-size parameter in an adaptive network to obtain better performance. We formulate the optimum step-size assignment as a constrained optimization problem and then solve it via the Lagrange multipliers approach. The derived optimum step-size for each node requires information from other nodes, thus with some justifiable assumptions, near-optimum solutions are derived that depend only on local information. We show that the incremental LMS adaptive network with near-optimal step sizes has improved robustness and steady-state performance. Simulation results are also presented to support the theoretical results.     

A Fittingness Factor-Based Spectrum Management Framework for Cognitive Radio Networks

In order to increase cognitive radios (CRs) operation efficiency, there has been an increasing interest in strengthening awareness level about spectrum utilisation. In this respect, this paper proposes to exploit the fittingness factor concept to capture the suitability of spectral resources exhibiting time-varying characteristics to support a set of heterogeneous CR applications. First, a new knowledge management functional architecture for optimizing spectrum management has been constructed. It integrates a set of advanced statistics capturing the influence of the dynamic radio environment on the fittingness factor. Then, a knowledge manager (KM) exploiting these statistics to monitor time-varying suitability of spectrum resources has been proposed to support the spectrum selection (SS) decision-making process. In particular, a new Fittingness Factor-based strategy combining two SS and spectrum mobility (SM) functionalities has been proposed, following either a greedy or a proactive approach. Results have shown that, with a proper fittingness factor function, the greedy approach efficiently exploits the KM support at low loads and the SM functionality at high loads to introduce significant gains in terms of the user dissatisfaction probability. The proactive approach has been shown to maintain the introduced performance gain while minimizing the signalling requirements in terms of spectrum handover rate.     

Homomorphic wavelet-based statistical despeckling of SAR images

In this paper, we introduce the homomorphic /spl Gamma/-WMAP (wavelet maximum a posteriori) filter, a wavelet-based statistical speckle filter equivalent to the well known /spl Gamma/-MAP filter. We perform a logarithmic transformation in order to make the speckle contribution additive and statistically independent of the radar cross section. Further, we propose to use the normal inverse Gaussian (NIG) distribution as a statistical model for the wavelet coefficients of both the reflectance image and the noise image. We show that the NIG distribution is an excellent statistical model for the wavelet coefficients of synthetic aperture radar images, and we present a method for estimating the parameters. We compare the homomorphic /spl Gamma/-WMAP filter with the /spl Gamma/-MAP filter and and the recently introduced /spl Gamma/-WMAP filter, which are both based on the same statistical assumptions. The homomorphic /spl Gamma/-WMAP filter is shown to have better performance with regard to smoothing homogeneous regions. It may in some cases introduce a small bias, but in our studies it is always less than that introduced by the /spl Gamma/-MAP filter. Further, the speckle removed by the homomorphic /spl Gamma/-WMAP filter has statistics closer to the theoretical model than the speckle contribution removed with the other filters.