OFDM信号循环谱结构分析

利用数字调制信号的循环平稳特性可以完成许多盲信号处理任务,但前提是必须清楚信号的循环谱结构。该文把数字调制信号建模成线性周期时变变换,结合非周期的齐次马尔可夫链模型,推导和计算OFDM信号的循环谱公式,并通过计算机仿真给出了双频面上的OFDM循环谱幅度,结果与理论分析相吻合。     

Hidden Markov processes

An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie (1966) on finite-state finite-alphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximum-likelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed     

Optimal maintenance-policies for deteriorating systems undervarious maintenance strategies

This paper presents algorithms for deriving optimal maintenance policies to minimize the mean long-run cost-rate for continuous-time Markov deteriorating systems. The degree of deterioration (except failure) of the system is known only through inspection. The time durations of inspection and replacement are nonnegligible. The costs are for inspection, replacement, operation, and downtime (idle). In particular, the replacement time, replacement cost, and operating cost-rate increase as the system deteriorates. Five maintenance strategies are considered-failure replacement, age replacement, sequential inspection, periodic inspection, and continuous inspection. Iterative algorithms are developed to derive the optimal maintenance policy and the corresponding cost rate for each strategy. Under sufficient conditions, structural optimal policies are obtained     

Statistical analysis and spectral estimation techniques forone-dimensional chaotic signals

Signals arising out of nonlinear dynamics are compelling models for a wide range of both natural and man-made phenomena. In contrast to signals arising out of linear dynamics, extremely rich behavior is obtained even when we restrict our attention to one-dimensional (1-D) chaotic systems with certain smoothness constraints. An important class of such systems are the so-called Markov maps. We develop several properties of signals obtained from Markov maps and present analytical techniques for computing a broad class of their statistics in closed form. These statistics include, for example, correlations of arbitrary order and all moments of such signals. Among several results, we demonstrate that all Markov maps produce signals with rational spectra, and we can therefore view the associated signals as “chaotic ARMA processes,” with “chaotic white noise” as a special case. Finally, we also demonstrate how Markov maps can be used to approximate to arbitrary accuracy the statistics any of a broad class of non-Markov chaotic maps     

Particle filters for state estimation of jump Markov linear systems

Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem. Our algorithms combine sequential importance sampling, a selection scheme, and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure of JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problems of on-line deconvolution of impulsive processes and of tracking a maneuvering target are considered. It is shown that our algorithms outperform the current methods     

Multi-state homogeneous Markov models in reliability analysis

In the standard Markov technique applied to reliability analysis, components are characterized by two states: an up state and a down state. The present paper explores the possibility of studying system reliability, by modelling each component with a multi-state homogeneous Markov model (MHMM). It is shown that this approach is of value both in approximating non-exponential probability distributions and in helping to build up suitable models for physical processes. Examples are presented which illustrate how the multi-state technique fits many practical situations. Finally some open problems on this topic are suggested.     

Analysis of an Important Class of Non-Markov Systems

Probabilistic modeling of many types of systems generally assumes Markov behavior. However, some important practical systems exhibit memory. For example, in digital computer systems, the probability of occurrence of a transient failure is related to the time period the system has been operating correctly. Analytic methods do not yet exist that allow accurate modeling of such systems for the purpose of reliability analysis and fault-tolerant design. Methods are presented here to analyze an important class of non-Markov systems. In this class, the transition-probability-rate of an out-ward transition from a state is related to the duration the system has continuously been in that state. To analyze such systems, concept of memory profile has been introduced. Methods are first presented which enable computation of steady-state probabilities for both discrete-time and continuous-time processes with two states. These are then extended for general non-steady-state cases and also for systems with more than two states.     

The number of working periods of a repairable Markov system duringa finite time interval

In reliability analysis, continuous parameter homogeneous irreducible finite Markov processes are used to model repairable systems with time-independent transition rates between individual states. The state space is then partitioned into the set of up states and the set of down states. The number of completed repair events during a finite time interval is an important (undiscounted) cost measure for such a system; it can be expressed in terms of the number of working periods during the same time interval. This paper derives a closed-form expression for the PMF of this latter quantity. The tool used is a recent result on the joint distribution of sojourn times in finite Markov processes. The MatLab implementation of the Markov model of a 2-unit parallel power transmission system is used to demonstrate the utility of the formula. The quantity examined is the number of completed repairs during a finite time interval. The method is viable in this case whereas the more usual randomization technique is not     

Medical image reconstruction,processing, visualization,and analysis: the MIPG perspective. Medical Image Processing Group

In this paper, we identify and describe the key ideas, concepts and software related to Medical Imaging that emerged from our research group, which is commonly known by the acronym MIPG, denoting Medical Image Processing Group. For the purpose of our presentation, we consider the entire discipline of Medical Imaging to consist of the areas of acquisition, postprocessing, evaluation, and application. We are indeed fortunate to be able to discuss seminal research from our group in each of these areas. In Section II, we identify relevant moments of inception and describe our past contributions. Our achievements, current activities, and future directions are summarized in Section III, Section IV, and Section V, respectively; the keys to our success are listed in Section V     

Multiscale representations of Markov random fields

Recently, a framework for multiscale stochastic modeling was introduced based on coarse-to-fine scale-recursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. The authors show that this model class is also quite rich. In particular, they describe how 1-D Markov processes and 2-D Markov random fields (MRFs) can be represented within this framework. The recursive structure of 1-D Markov processes makes them simple to analyze, and generally leads to computationally efficient algorithms for statistical inference. On the other hand, 2-D MRFs are well known to be very difficult to analyze due to their noncausal structure, and thus their use typically leads to computationally intensive algorithms for smoothing and parameter identification. In contrast, their multiscale representations are based on scale-recursive models and thus lead naturally to scale-recursive algorithms, which can be substantially more efficient computationally than those associated with MRF models. In 1-D, the multiscale representation is a generalization of the midpoint deflection construction of Brownian motion. The representation of 2-D MRFs is based on a further generalization to a “midline” deflection construction. The exact representations of 2-D MRFs are used to motivate a class of multiscale approximate MRF models based on one-dimensional wavelet transforms. They demonstrate the use of these latter models in the context of texture representation and, in particular, they show how they can be used as approximations for or alternatives to well-known MRF texture models     

Finite-state Markov modeling of correlated Rician-fading channels

Stochastic properties of the binary channel that describe the successes and failures of the transmission of a modulated signal over a time-correlated flat-fading channel are considered for investigation. This analysis is employed to develop Kth-order Markov models for such a burst channel. The order of the Markov model that generates accurate analytical models is estimated for a broad range of fading environments. The parameterization and accuracy of an important class of hidden Markov models, known as the Gilbert-Elliott channel (GEC), are also investigated. Fading rates are identified in which the Kth-order Markov model and the GEC model approximate the fading channel with similar accuracy. The latter model is useful for approximating slowly fading processes, since it provides a more compact parameterization.     

Utilizing Multitemporal Data by a Stochastic Model

In remote sensing, a principle objective is to produce an accurate ground cover thematic classification map. In this paper a new classifier which makes use of multitemporal data is described. Ground cover types are considered as stochastic systems with nonstationary Gaussian processes as input and temporal variation of reflected and emitted electromagnetic energy as output. Then, by assumption that the behavior of these stochastic systems are governed by first-order Markov processes, multitemporal information is utilized. As a result of this approach for characterizing multitemporal data, a new processor, the Markov classifier, is developed. Experimental results from Landsat MSS data are included.     

Iddq testing for CMOS VLSI

It is little more than 15-years since the idea of Iddq testing was first proposed. Many semiconductor companies now consider Iddq testing as an integral part of the overall testing for all IC's. This paper describes the present status of Iddq testing along with the essential items and necessary data related to Iddq testing. As part of the introduction, a historical background and discussion is given on why this test method has drawn attention. A section on physical defects with in-depth discussion and examples is used to illustrate why a test method outside the voltage environment is required. Data with additional information from case studies is used to explain the effectiveness of Iddq testing. In Section IV, design issues, design styles, Iddq test vector generation and simulation methods are discussed. The concern of whether Iddq testing will remain useful in deep submicron technologies is addressed (Section V). The use of Iddq testing for reliability screening is described (Section VI). The current measurement methods for Iddq testing are given (Section VII) followed by comments on the economics of Iddq testing (Section VIII). In Section IX pointers to some recent research are given and finally, concluding remarks are given in Section X     

Information sources using chaotic dynamics

A sequence of binary random variables has found significant applications in modem digital communication systems. For such sequences, several kinds of linear feedback shift register sequences have been proposed. It is, however, well known in probability theory that the Bernoulli shift is a fundamental theoretic model of a sequence of independent identically distributed (i.i.d.) binary random variables. In this paper after reviewing fundamental subjects of chaotic dynamics, in particular a close relationship between information sources and Markov chains, we give the generation method of sequences of i.i.d. binary random variables using chaotic dynamics. Such a generation method is given as a sufficient condition composed of simple symmetric properties for some class of ergodic maps. Furthermore, we give the applications of such sequences: (1) to running-key sequences for stream cipher systems and (2) to a color image communication system through code-division multiple access channels and its extended version, a digital watermarking system. In addition, the performance of spread spectrum codes generated by a Markov chain is theoretically evaluated in asynchronous direct-sequence/code-division multiple access systems     

The Throughput of Packet Broadcasting Channels

Packet broadcasting is a form of data communications architecture which can combine the features of packet switching with those of broadcast channels for data communication networks. Much of the basic theory of packet broadcasting has been presented as a byproduct in a sequence of papers with a distinctly practical emphasis. In this paper we provide a unified presentation of packet broadcasting theory. In Section II we introduce the theory of packet broadcasting data networks. In Section III we provide some theoretical results dealing with the performance of a packet broadcasting network when the users of the network have a variety of data rates. In Section IV we deal with packet broadcasting networks distributed in space, and in Section V we derive some properties of power-limited packet broadcasting channels, showing that the throughput of such channels can approach that of equivalent point-to-point channels.     

Exponential filtering for uncertain Markovian jump time-delay systems with nonlinear disturbances

In this paper, we study the robust exponential filter design problem for a class of uncertain time-delay systems with both Markovian jumping parameters and nonlinear disturbances. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, and the parameter uncertainties appearing in the state and output equations are real, time dependent, and norm bounded. The time-delay and the nonlinear disturbances are assumed to be unknown. The purpose of the problem under investigation is to design a linear, delay-free, uncertainty-independent state estimator such that, for all admissible uncertainties as well as nonlinear disturbances, the dynamics of the estimation error is stochastically exponentially stable in the mean square, independent of the time delay. We address both the filtering analysis and synthesis issues, and show that the problem of exponential filtering for the class of uncertain time-delay jump systems with nonlinear disturbances can be solved in terms of the solutions to a set of linear (quadratic) matrix inequalities. A numerical example is exploited to demonstrate the usefulness of the developed theory.     

A dependent data extension of Wald's identity and its applicationto sequential test performance computation

The development of an exponential transformation of measure not restricted to independent identically distributed data is presented for a class of semi-Markov processes known as Markov-additive (MA) process. The MA measure transformation is applied to obtain a new form of the Wald identity. To demonstrate the utility of this extension to MA processes, the application of sequential tests with Markov data to error probability performance analysis is investigated. In particular, it is demonstrated how previous work dealing with robust error probability evaluation in the presence of uncertain serial dependency can be applied to sequential tests     

A lower bound to the distribution of computation for sequential decoding

In sequential decoding, the number of computations which the decoder must perform to decode the received digits is a random variable. In this paper, we derive a Paretian lower bound to the distribution of this random variable. We show thatP [C > L]L^{-rho}, whereCis the number of computations which the sequential decoder must perform to decode a block ofLambdatransmitted bits, and is a parameter which depends on the channel and the rate of the code. Our bound is valid for all sequential decoding schemes and all discrete memoryless channels. In Section II we give an example of a special channel for which a Paretian bound can be easily derived. In Sections III and IV we treat the general channel. In Section V we relate this bound to the memory buffer requirements of real-time sequential decoders. In Section VI, we show that this bound implies that certain moments of the distribution of the computation per digit are infinite, and we determine lower bounds to the rates above which these moments diverge. In most cases, our bounds coincide with previously known upper bounds to rates above which the moments converge. We conclude that the performance of systems using sequential decoding is limited by the computational and buffer capabilities of the decoder, not by the probability of making a decoding error. We further note that our bound applies only to sequential decoding, and that, in certain special cases (Section II), algebraic decoding methods prove superior.