Matlab在确定可靠性分布模型中的应用

利用概率知识，基于Matlab进行编程，由一组关于刹车距离的数据推断出可靠性分布模型，并对其进行了仿真。通过亲身实践得到的体会，具有一定的借鉴性。     

Detection of radar signals in conditions of full prior information when using stochastic signals for probing

Probabilistic characteristics of coherent detection of reflected signals with the fully known parameters when using stochastic soundings radio signals are determined. Analytical expression for probability density of decision statistics for the cases of presence at detector??s input of reflected signal only, interference only, and both signal and interference is obtained. Dependency of false alarm probability on threshold ratio and dependency of hit probability on signal-to-interference ratio for different values of bandwidth-duration product of stochastic signal are computed. A family of radar detection performances characteristics for fixed bandwidth-duration product and different values of false alarm probability is calculated.     

Synthesis of discrete-time signals from distributions

With minor modification, Sussman's algorithm is demonstrated to be directly applicable to the problem of least-squares synthesis of the discrete-time Wigner distribution provided that it is appropriately defined.<>     

Estimation for a probabilistic stress-strength model

Estimation for an unknown strength distribution is considered in two situations: (1) independent identically distributed stresses are applied to the component until it fails (no cumulative damage); and (2) each applied stress causes damage to the component and damage cumulates until the component fails. Both situations lead to mixtures of probability distributions, with the strength distribution playing the role of the mixing distribution. Based on the observation of cycles to failure of several independent components and on the theory of mixtures of distributions, estimators of the mixing distributions are obtained using linear programming. In particular, the solution to the linear-programming problem yields a probability mass function which approximates the unknown strength distribution. From this estimate of the strength distribution, an estimate of the mean strength of the item can be obtained by the usual computation of the mean of a probability distribution. Hence, the results provide a method of estimating the mean strength, or other parameters of the strength distribution, without requiring observations directly on the strengths of the test components     

Bayesian analysis of the mixture of exponential failure distributions

In this paper, a Bayesian reliability analysis is carried out for the mixture of two exponential failure distributions on the basis of failure time data x₁ < x₂ <…< x_r, for a preassigned r, and (n − r) survivors from this mixed failure model. Under the assumptions of the squared error loss function (SELF) and suitable prior densities on the underlying parameter space, exact results for the Bayes estimators of the mean life and the reliability function are obtained.     

Shift covariant time-frequency distributions of discrete signals

Many commonly used time-frequency distributions are members of the Cohen (1989) class. This class is defined for continuous signals, and since time-frequency distributions in the Cohen class are quadratic, the formulation for discrete signals is not straightforward. The Cohen class can be derived as the class of all quadratic time-frequency distributions that are covariant to time shifts and frequency shifts. We extend this method to three types of discrete signals to derive what we call the discrete Cohen classes. The properties of the discrete Cohen classes differ from those of the original Cohen class. To illustrate these properties, we also provide explicit relationships between the classical Wigner distribution and the discrete Cohen classes     

Effective appearance model for probabilistic object tracking

This paper presents a robust object tracking approach via a spatially constrained colour model. Local image patches of the object and spatial relation between these patches are informative and stable during object tracking. So, we propose to partition an object into patches and develop a Spatially Constrained Colour Model (SCCM) by combining the colour distributions and spatial configuration of these patches. The likelihood of the candidate object is given by estimating the confidences of the pixels in the ...     

Stationary distributions for the random waypoint mobility model

In simulations of mobile ad hoc networks, the probability distribution governing the movement of the nodes typically varies over time and converges to a "steady-state" distribution, known in the probability literature as the stationary distribution. Some published simulation results ignore this initialization discrepancy. For those results that attempt to account for this discrepancy, the practice is to discard an initial sequence of observations from a simulation in the hope that the remaining values will closely represent the stationary distribution. This approach is inefficient and not always reliable. However, if the initial locations and speeds of the nodes are chosen from the stationary distribution, convergence is immediate and no data need be discarded. We derive the stationary distributions for location, speed, and pause time for the random waypoint mobility model. We then show how to implement the random waypoint mobility model in order to construct more efficient and reliable simulations for mobile ad hoc networks. Simulation results, which verify the correctness of our method, are included. In addition, implementation of our method for the NS-2 simulator is available.     

A minimal data probabilistic model for spectrum management

One of the most common problems facing present-day communicators in developed and developing nations is the maximal utilization of the radio frequency spectrum. In this area, one of the most pressing problems is the requirement for a model that will provide a suitable indicator of performance for receivers that are mobile, in uncertain locations, or dispersed over a known area. Examples of these are mobile receivers and broadcast receivers. The problem is further complicated by the fact that present available data for management of the radio spectrum is very limited, and, for the case of prime interest in our study, does not include any appreciable amount of receiver data. One of the requirements for the model was that it utilize a minimal amount of data commensurate with what is readily available. The model described herein provides a probability of interference based on the geographical aspects of the problem. It allows for the addition of temporal data (duty cycles, etc.) if such data is available. The model allows for management of the spectrum based on technical considerations of compatibility and, as such, could be a key aspect of the development of spectrum management at a national level.     

Robust parameter estimation for mixture model

In pattern recognition, when the ratio of the number of training samples to the dimensionality is small, parameter estimates become highly variable, causing the deterioration of classification performance. This problem has become more prevalent in remote sensing with the emergence of a new generation of sensors with as many as several hundred spectral bands. While the new sensor technology provides higher spectral and spatial resolution, enabling a greater number of spectrally separable classes to be identified, the needed labeled samples for designing the classifier remain difficult and expensive to acquire. Better parameter estimates can be obtained by exploiting a large number of unlabeled samples in addition to training samples, using the expectation maximization algorithm under the mixture model. However, the estimation method is sensitive to the presence of statistical outliers. In remote sensing data, miscellaneous classes with few samples are often difficult to identify and may constitute statistical outliers. Therefore, the authors propose to use a robust parameter-estimation method for the mixture model. The proposed method assigns full weight to training samples, but automatically gives reduced weight to unlabeled samples. Experimental results show that the robust method prevents performance deterioration due to statistical outliers in the data as compared to the estimates obtained from the direct EM approach     

A new fundamental mode field-radius definition usable for non-Gaussian and noncircular field distributions

A new definition for the mode field radius of fundamental mode fibers is proposed which can be applied to circular as well as to noncircular and non-Gaussian field distributions. The mode field radius according to this definition can be strictly determined by the well-established transverse offset technique. Moreover, an alternative test method is presented offering the advantage of using one standard launching fiber for each measurement.     

Carrier initial phases estimation for single-channel adjacent-frequency mixture of digitally modulated signals

For single-channel mixture of adjacent-frequency digitally modulated signals which have the fixed frame length and sync codes in non-cooperative satellite communications,a carrier initial phases estimation algorithm which can be applied to both low-order and high-order digitally modulated mixtures was provided according to the differences of parameters in the source signals.The sync data eliminated the frequency offset was added in the interval of frame length firstly,then timing synchronization was accomplished for the added result in terms of power maximization rule,thus the initial phases information was derived.Moreover,the modified Cramer-Rao bound (MCRB) of carrier initial phases for single-channel mixture of adjacent-frequency digitally modulated signals was derived for comparison.Simulation results show that,difference between estimation performance of the proposed algorithm and MCRB becomes smaller with the increase in the number of added frames.     

Analysis and synthesis of multicomponent signals using positivetime-frequency distributions

A new approach to the analysis and reconstruction of multicomponent nonstationary signals from their time-frequency distribution (TFD) is presented. Specifically, we consider a TFD based on the recently introduced minimum cross entropy principle (MCE). This positive TFD is cross-terms free and, hence, has an advantage over the family of bilinear distributions. Based on the MCE-TFD, a new algorithm for reconstructing the phase and amplitude parameters of each component of the signal is developed. To evaluate the accuracy of the algorithm. Monte Carlo simulations are presented and compared with the corresponding Cramer-Rao bound. It is shown that the new algorithm is superior to presently available methods in both efficiency and performance. It is concluded that together with the MCE-TFD representation, the proposed approach provides a powerful tool for analysis of nonstationary multicomponent signals embedded in additive Gaussian noise     

Modified Cohen-Lee time-frequency distributions and instantaneousbandwidth of multicomponent signals

Cohen (1989, 1995) has introduced and extensively studied and developed the concept of the instantaneous bandwidth of a signal. Specifically, instantaneous bandwidth is interpreted as the spread in frequency about the instantaneous frequency, which is itself interpreted as the average frequency at each time. This view stems from a joint time-frequency distribution (TFD) analysis of the signal, where instantaneous frequency and instantaneous bandwidth are taken to be the first two conditional spectral moments, respectively, of the distribution. However, the traditional definition of instantaneous frequency, namely, as the derivative of the phase of the signal, is not consistent with this interpretation, and new definitions have therefore been proposed previously. We show that similar problems arise with the Cohen-Lee (1988, 1989) instantaneous bandwidth of a signal and propose a new formulation for the instantaneous bandwidth that is consistent with its interpretation as the conditional standard deviation in frequency of a TFD. We give the kernel constraints for a distribution to yield this new result, which is a modification of the kernel proposed by Cohen and Lee. These new kernel constraints yield a modified Cohen-Lee TFD whose first two conditional moments are interpretable as the average frequency and bandwidth at each time, respectively     

A probabilistic model for sub-carrier allocation in OFDMA systems

Orthogonal frequency division multiple-access technique showed a successful utilization of channel features. It implements an orthogonal sub-carrier space to be shared among different users. The management of these sub-carriers in both power and frequency allocation is reflected on the systems performance as better utilization of bandwidth, and hence, better capacity is obtained. Sub-carrier allocation is used to avoid deep fading that might occur at some user’s locations but not at other user’s locations. In this paper, we devise an algorithm based on probabilistic model for sub-carrier allocation to avoid deep fading in some user’s signals. By controlling the sub-carrier allocation for each user, we can create a full rank channel for each user and hence, provide maximum capacity for the system. Simulation results showed that using the devised algorithm will avoid deep fading and utilize the bandwidth up to 40% more than localized allocation strategies.     

Radial dependence of the level of amplitude fluctuations of spacecraft radio signals probing circumsolar plasma

The statistical characteristics of amplitude fluctuations of radio signals propagating through circumsolar plasma at various distances from the Sun are investigated during various phases of solar activity. Primary data obtained in large-scale series of radio-probing sessions are systemized. During these sessions, circumsolar plasma was probed with signals from spacecraft launched by Russia and other countries. It is found that the dependences of the relative level of amplitude fluctuations (scintillation index) on the solar-offset distance of a radio beam can be approximated with power functions with exponents depending on the heliocentric distance. When the distance between a probed region and the photosphere is large, this exponent is close to the value 1.5. As the Sun is approached, the exponent grows to a value of 1.8–2.05 reached at a heliocentric distance of about four solar radii. The scintillation index is calculated theoretically with allowance for the radial dependence of turbulence characteristics, such as the spectral index and outer scale.     

一种基于频移滤波器的混合信号盲恢复算法

针对单天线接收的频谱混叠的混合信号盲恢复问题,在频移滤波器结构上,提出了一种基于相关函数误差准则的自适应频移滤波信号盲恢复算法.该算法利用滤波器输出信号和参考信号以 及混合信号与参考信号之间的相关函数误差来调整自适应滤波器输出权值.分析了该算法的稳态性能.仿真结果表明:在信噪比大于-5 dB的条件下,该算法对混合...     

Simple mixture model for sparse overcomplete ICA

The use of mixture of Gaussians (MoGs) for noisy and overcomplete independent component analysis (ICA) when the source distributions are very sparse is explored. The sparsity model can often be justified if an appropriate transform, such as the modified discrete cosine transform, is used. Given the sparsity assumption, a number of simplifying approximations are introduced to the observation density that avoid the exponential growth of mixture components. An efficient clustering algorithm is derived whose complexity grows linearly with the number of sources and it is shown that it is capable of performing reasonable separation.     

A probabilistic model for anonymity analysis of anonymous communication networks

An anonymous communication network (ACN) is supposed to provide its users with anonymity attributes. As a practical matter, we need to have a means to predict the level of the anonymity provided by ACN. In this paper, we propose a probabilistic model for the security analysis of ACNs, thereby quantifying their loss of anonymity. To be precise, we have tried to obtain the probability distribution of potential senders of a message sent from an unknown source to a specific destination. With the probability distribution in hand, it is possible to define and derive some anonymity metrics. The evaluated metrics help us to gain an understanding of how much such a network may be vulnerable to attacks aiming at revealing the identity of senders of messages. Consequently, new rerouting policies and strategies can be utilized to increase the anonymity level of the network. The quantitative analysis is performed from a general perspective and the applicability of the model is not limited to a specific network. The evaluation process of the metrics using the proposed model is clarified by an illustrative example.