Spectral representation of a periodic nonstationary random process

This paper deals with the periodic nonstationary process, the mean value and the correlation function of which are invariant under shift by a multiple of a certain period. The spectral representation is derived by making use of Loève's harmonizability theorem on a second-order nonstationary process. The process is represented as a sum of infinite stationary processes among which covariances exist. Each stationary process has a nonoverlapping frequency band of equal width, the center of which corresponds to a harmonic of the fundamental frequency determined by the period. The correlation function, dependent on two points, is represented in terms of a matrix-valued spectral density that is hermitian and nonnegative definite. The representations in other possible forms are also given. Finally some properties, special processes, and examples produced by a certain stationary random sequence are discussed.     

NFV中虚拟化网络功能生命周期安全管理措施

网络功能虚拟化的动态特性需要引入灵活可靠、能够实现自动化快速部署和资源弹性可扩展的嵌入式安全防护体系,要求安全技术和安全策略能够在VNF整个生命周期进行安全管理。描述了从VNF实例化、运行到退役的安全历程,并针对网络运营商需要在VNF生命周期关注的相应动态和静态安全策略进行分析探讨。     

Recognition of colored noise in pseudo-phase space by using BDS statistics

Algorithm for calculating BDS statistics based on determination of correlation integral is presented. The algorithm is based on statistical significance of correlation dimensionality of a process, represented in pseudo-phase space of the given dimensionality. For the first time a numerical method for classification of random processes by their “color” with using statistical properties of BDS statistics is suggested.     

On the accuracy of estimating the parameters of a regularstationary process

Any regular stationary random processes can be represented as the sum of a purely indeterministic process and a deterministic one. This paper considers the achievable accuracy in the joint estimation of the parameters of these two components, from a single observed realization of the process. An exact form of the Cramer-Rao bound (CRB) is derived, as well as a conditional CRB. The relationships between these bounds, and their relations to the previously derived asymptotic bound, are explored by analysis and numerical examples     

Nonparametric time series analysis for periodically correlatedprocesses

Correlation functions of continuous-time periodically correlated processes can be represented by a Fourier series with coefficient functions. It is shown that the usual estimator for stationary covariances, formed from a single sample path of the process, can be simply modified to provide a consistent (in quadratic mean) estimator for any of the coefficient functions resulting from the aforementioned representation. It is shown that, if the process is Gaussian and B _k(τ) is a Fourier integral with respect to a density function g_k(λ), a two-dimensional periodogram, formed from a single sample function, can be smoothed along a line of constant difference frequency to provide a consistent estimator for g_k(λ). This natural extension of the well-known procedure for stationary processes provides a method for nonparametric spectral analysis of periodically correlated processes     

Optimal nonlinear filtering for independent increment processes--Part II

The main result obtained in Part I [1]--which is contained in Theorem1of Part I--is generalized and extended in several ways. First, a new basic stochastic integro-partial differential equation for the conditional probability density function for the state of a nonlinear dynamic system with disturbance noise given noisy nonlinear measurements of the state is derived under the less restrictive assumption that the disturbance noise is an arbitrary independent increment process with an infinitely divisible distribution, and the measurement noise is a Gaussian independent increment process with an infinitely divisible distribution. It is then shown that, under proper restrictions, this basic equation reduces to either the Fokker-Planck equation for diffusion processes or the Kolmogorov-Feller equation for jump processes. Also, it is shown that this basic equation contains the earlier results of Kushner [2] and Wonhamt [3] as special cases. Next, it is shown how the result represented by this basic equation can be easily extended to include the case where the disturbance noise is a Markov process of the type initially assumed for the state of the dynamic system. Finally, it is shown that, in contrast to results obtained by Bryson and Johansen [4] and Cox [5] for the linear, Gaussian case, it is not generally possible to extend the result represented by this basic equation to include either the case where the measurement noise is a Markov process of the type initially assumed for the state of the dynamic system, or the case where the covariance matrix of the Gaussian measurement noise is singular. However, some incomplete results indicating when such extensions might be possible are given.     

A Nonstationary Model for the Electromyogram

A theoretical model of the electromyographic (EMG) signal has been developed. In the model, the neural pulse train inputs were considered to be point processes which passed through linear, time-invariant systems that represented the respective motor unit action potential. The outputs were then summed to produce the EMG. It was assumed, that in the production of muscle force, the controlled parameter was the number of active motor units, n(t). The model then showed that the EMG can be represented as an amplitude modulation process of the form EMG = [Kn(t)1/2 w(t) with the stochastic process, w(t), having the spectral and probability characteristics of the EMG during a constant contraction. Various assumptions made in the model development have been verified by experiments.     

Orthogonal functionals of independent-increment processes

In analogy with the Wiener-Itô theory of multiple integrals and orthogonal polynominals, a set of functionals of general square-integrable martingales is presented which, in the case of independent-increments processes, is orthogonal and complete in the sense that everyL^{2}-functional of the independent-increment process can be represented as an infinite sum of these elementary functionals. The functionals are iterated integrals of the basic martingales, similar to the multiple iterated integrals of Itô and can be also thought of as being the analogs of the powers1,x,x^{2}, cdotsof the usual calculus. The analogy is made even clearer by observing that expanding the Doleans-Dade formula for the exponential of the process in a Taylor-like series leads again to the above elementary functionals. A recursive formula for these functionals in terms of the basic martingale and of lower order functionals is given, and several connections with the theory of reproducing kernel Hilbert spaces associated with independent-increment processes are obtained.     

Segmentation of Gabor-filtered textures using deterministicrelaxation

A supervised texture segmentation scheme is proposed in this article. The texture features are extracted by filtering the given image using a filter bank consisting of a number of Gabor filters with different frequencies, resolutions, and orientations. The segmentation model consists of feature formation, partition, and competition processes. In the feature formation process, the texture features from the Gabor filter bank are modeled as a Gaussian distribution. The image partition is represented as a noncausal Markov random field (MRF) by means of the partition process. The competition process constrains the overall system to have a single label for each pixel. Using these three random processes, the a posteriori probability of each pixel label is expressed as a Gibbs distribution. The corresponding Gibbs energy function is implemented as a set of constraints on each pixel by using a neural network model based on Hopfield network. A deterministic relaxation strategy is used to evolve the minimum energy state of the network, corresponding to a maximum a posteriori (MAP) probability. This results in an optimal segmentation of the textured image. The performance of the scheme is demonstrated on a variety of images including images from remote sensing.     

The current-mode muddle

With the arrival of monolithic processes for the fabrication of bipolar transistors during the mid-1960s, it seemed like a good time to examine new paradigms for design. Current-mode (CM) was attractive because it suggested that small voltages swings could be used to process analog signals represented as currents; whatever voltages that did arise were treated as incidental. These were good ideas at the time. Are they still true today? It is important to know when CM offers a real advantage, to know when it is truly valuable, and to know when it is of only passing and academic interest.     

Managing technology CAD for competitive advantage: an efficientapproach for integrated circuit fabrication technology development

This paper describes a methodology to exploit the full capabilities of technology computer-aided design (TCAD) for the development of integrated circuit fabrication processes. The development process of integrated circuits is represented by a simple model that describes the technology specifications at the beginning and the product specifications at the end of the product development cycle. Considering this model, different intermediate tasks are defined to obtain initial guess process recipe from the target product specifications. The complete technology development is shown to be achieved using TCAD in three different phases such as the generation of initial guess process recipe, the optimization of process technology, and the evaluation of process manufacturability. A simple quantitative analysis to estimate the major advantages of TCAD in reducing the cycle time and cost of technology development is presented. The technical limitations of TCAD and the measures to address these limitations are discussed. The organizational and social issues of the implementation of TCAD and the managerial responsibilities in adopting TCAD for the development of integrated-circuit fabrication process are also discussed     

Performance of Voice/Data Integration on a TDM System

Data queueing is of primary concern in a voice/data integrated TDM system. The data queueing model is represented in the discrete-time domain with multiple servers and voice is given a higher priority than data. The data arrival process is assumed to be Poisson and the voice arrival process is characterized by a Markov chain. The correlation coefficient of the number of on voice calls between consecutive frames is used to measure the correlation behavior of the voice process. While the generating function approach may be used to analyze the queueing process, it involves the evaluation of a large number of boundary terms. On the assumption that the voice traffic consists ofNi.i.d. two-state Markov chains, we derive a simple expression for the mean queue size as a function of two variables in the form of the traffic departure processes. The results clearly reveal a significant influence of the correlation coefficient on the data queueing process. Then, an approximate analysis based on the departure processes is introduced. The numerical and simulation results indicate that this approximate approach yields reasonably accurate results.     

An application of time-dependent ARMA models to reliability-decay processes

This paper describes a somewhat different approach to the analysis of failure data of systems that operate under varying operational and/or environmental conditions. Surprisingly enough, it was found during the course of investigations that if the failure times of a system follow either Rayleigh, Weibull or exponential distributions and if the reliability-decay process of the system is represented by an AR(1) model with a specific time-dependent coefficient in each case, then the conditional mean of the AR(1) process, given that the initial value is equal to 1, is the same as the maximum likelihood estimate of the reliability function obtained by the classical method. Advantages of such a representation are discussed in a later section. Properties of time-dependent ARMA(1, 1) processes are discussed and asymptotic results are obtained in the last section.     

Wavelet-based representations for the 1/f family offractal processes

It is demonstrated that 1/f fractal processes are, in a broad sense, optimally represented in terms of orthonormal wavelet bases. Specifically, via a useful frequency-domain characterization for 1/f processes, the wavelet expansion's role as a Karhunen-Loeve-type expansion for 1/f processes is developed. As an illustration of potential, it is shown that wavelet-based representations naturally lead to highly efficient solutions to some fundamental detection and estimation problems involving 1/f processes     

Knowledge‐Based AOP Framework for Business Rule Aspects in Business Process

In recent years, numerous studies have identified and explored issues related to web‐service‐oriented business process specifications, such as business process execution language (BPEL). In particular, business rules are an important cross‐cutting concern that should be distinguished from business process instances. In this paper, we present a rule‐based aspect oriented programming (AOP) framework where business rule aspects contained in business processes can be effectively separated and executed. This is achieved by using a mechanism of the business rule itself at the business rule engine instead of using existing programming language‐based AOP technologies. Through some illustrative examples, this work also introduces a method by which business rule aspects, separated through an external rule engine, can be represented and evaluated. We also demonstrate how they can be dynamically woven and executed by providing an implementation example which uses two open‐source‐based products, the Mandarax rules engine and Bexee BPEL engine.     

Perturbation-Based Stochastic Modeling of Nonlinear Circuits

This paper presents a general model for a nonlinear circuit, in which, the circuit parameters (e.g. resistance and capacitance) are subject to random fluctuations due to noise, which vary with time. The fluctuating amplitudes of these parameters are assumed to be Ornstein–Uhlenbeck (O.U.) processes and not the white noise owing to temporal correlations. The nonlinear circuit is represented by a system of nonlinear differential equations depending upon a set of parameters that fluctuate slowly with time. To model these fluctuations, we use the theory of Ito’s stochastic differential equations (SDEs). Then the driving force of the circuit dynamics is in accordance with the general perturbation theory decomposed into the sum of a strong linear component and a weak nonlinear component by the introduction of a small perturbation parameter. The circuit states are expanded in the powers of this small perturbation parameter and recursive solutions to the various approximates obtained. Finally, the approximate expressions for the output states are obtained as stochastic integrals with respect to Brownian motion processes. The proposed method is applied to a half-wave rectifier circuit which is built out of a diode, a resistor and a capacitor. The diode is represented by nonlinear voltage–current equation, and resistance and capacitance are subject to random fluctuations due to noise, which vary slowly with time. The results, obtained using the proposed method, are compared with those obtained via the conventional perturbation-based deterministic differential equations model for a nonlinear circuit. Hence, the noise process component, present at the output, is obtained.     

Generalized feature extraction for time-varying autoregressivemodels

In this paper, a feature extraction scheme for a general type of nonstationary time series is described. A non-stationary time series is one in which the statistics of the process are a function of time; this time dependency makes it impossible to utilize standard globally derived statistical attributes such as autocorrelations, partial correlations, and higher order moments as features. In order to overcome this difficulty, the time series vectors are considered within a finite-time interval and are modeled as time-varying autoregressive (AR) processes. The AR coefficients that characterize the process are functions of time that may be represented by a family of basis vectors. A novel Bayesian formulation is developed that allows the model order of a time-varying AR process as well as the form of the family of basis vectors used in the representation of each of the AR coefficients to be determined. The corresponding basis coefficients are then invariant over the time window and, since they directly relate to the time-varying AR coefficients, are suitable features for discrimination. Results illustrate the effectiveness of the method     

Integrated GIS‐Based Logistics Process Monitoring Framework with Convenient Work Processing Environment for Smart Logistics

In today's highly competitive business environment, most companies try to manage their logistics function strategically to satisfy their orders as cost‐effectively as possible and maximize their present and future profits. In this environment, logistics process visibility is essential to companies wishing to competently track components, parts, or products in transit from the original suppliers to the final destinations. Thus, it is important to provide instantly and easily recognizable information about such visibility to all stakeholders, especially customers. To ensure a high‐level geographical visibility of the logistics processes, in this paper, we propose a way of implementing a GIS‐based logistics process monitoring environment and of integrating a performer's work processing environment on the monitoring system. Additionally, to provide more abundant monitoring information, we describe a procedure for creating and processing various monitoring information, which are represented as ECA rules in this work processing environment. Therefore, it is possible to provide intuitive and visible monitoring information regarding the logistics process and resource elements.     

Introduction to Cyclic Replacement Systems

A fundamental and unified treatment of problems akin to the classical Swedish Machine Problem is presented. Section I describes the nature of the systems known as cyclic replacement systems. In Section II pertinent facts about Markov processes are gathered. In Section III, it is shown that a certain class of cyclic systems behave as homogeneous Markov processes. The special class of homogeneous Markov processes known as homogeneous birth and death processes is considered in Section IV. Results of Section IV are applied to some cyclic replacement systems in Section V. In Section VI some systems are treated which cannot be represented as birth and death processes.     

Hardware/software codesign: a systematic approach targeting data-intensive applications

This article presents a systematic approach to hardware/software codesign targeting data-intensive applications. It focuses on the application processes that can be represented in directed acrylic graphs (DAGs) and use a synchronous dataflow (SDF) model, the popular form of dataflow employed in DSP systems when running the process. The codesign system is based on the ultrasonic reconfigurable platform, a system designed jointly at Imperial College and the SONY Broadcast Laboratory. This system is modeled as a loosely coupled structure consisting of a single instruction processor and multiple reconfigurable hardware elements. The paper also introduces and demonstrates a task-based hardware/software codesign environment specialized for real-time video applications. Both the automated partitioning and scheduling environment and the task manager program help to provide a fast robust for supporting demanding applications in the codesign system.