任意概率分布下Golomb码和扩展Gamma码的性能分析

以信源的平均值给出了任意概率分布下Golomb码的平均码长的上下界和最优的参数选择准则。在Golomb码的基础上,进一步推广了Elias的码,提出了扩展的码,同时给出了其性能界和最优的参数选择准则。扩展码是一类通用码,而且在一定的条件下可以达到渐近最优的性能。最后,提出了一个低复杂性的基于Golomb码和扩展码的通用数据压缩框架,并通过构建一个样例系统说明了该数据压缩框架的实际应用价值。     

Averaging entropy

It is pointed out that a uniform distribution on probability n-tuples is not necessarily the best distribution to use in calculating an average entropy. The noninformative prior of Bayesian statistics and certain-distributions which arise in differential-geometry approaches to statistics are other candidates. The mean and variance of the entropy are calculated when probability n-tuples are distributed according to these distributions     

The nearest-neighbor rule for small samples drawn from uniform distributions (Corresp.)

It is shown in the classification problem, when independent samples are taken from uniform distributions, that for small sample sizes the probability of misclassification when using the nearest-neighbor rule is "close" to its asymptotic value. It is also shown that when using this rule the probability of classification in many cases is close to its Bayes optimum even for small sample sizes. Moreover, if one is restricted to a small sample size from one population, it is shown that it is not necessary to "make up" this deficiency by taking a large sample from the other population; best results may be obtained when both sample sizes are small.     

A comparison of two radio propagation channel impulse responsedetermination techniques

A comparison of two radio propagation channel impulse response determination techniques is described. Presented are typical impulse response and transfer functions obtained from each measurement system. Also included for comparison are average impulse response envelopes and cumulative probability distributions for the RMS delay spread of static indoor radio channels calculated from 120 measurements using each system. The comparisons show good agreement between results     

合肥地区大气风速风向联合概率密度函数研究

在光电扫描系统中,热晕驻区会对激光大气传输能量产生严重的衰减,而热晕驻区出现的概率是由大气风速风向联合概率决定的.由合肥地区2007年5月至2011年6月的对流层风廓线雷达1h平均风场数据,根据最小二乘法原理,分别拟合Frechet、Gumbel和Weibull概率分布,得到拟合参数及相关系数和剩余标准差,从而比较得到最优分布模型;在上述方法拟合不出较好结果的情况下,由样本数据,根据圆形正态概率分布的特点,计算出纬向风和经向风平均值和均方差,这样也可以得到风场概率随风速和风向的关系,从而计算某一风场对应的概率.     

Distinguishing between lognormal and Weibull distributions[time-to-failure data]

A previous method for deciding if a set of time-to-fail data follows a lognormal distribution or a Weibull distribution is expanded upon. Pearson's s-correlation coefficient is calculated for lognormal and Weibull probability plots of the time-to-fail data. The test statistic is the ratio of the two s-correlation coefficients. When "standardized", the lognormal and Weibull variables map into 1 of 2 gamma distributions with no dependence on the shape or scaling factors, confirming earlier observations. Using a set of Monte Carlo simulations, the test statistic was found to be s-normally distributed to good approximation. Formulas for estimating the mean and standard deviation of the test statistic were derived, allowing for an estimate of the probability of hypothesis test errors. As anticipated, the test capability increases with increasing sample size, but only if a substantial fraction of the parts actually fail. If less than 10% of the parts are stressed to failure, then it is almost impossible to distinguish between lognormal and Weibull distributions. If all parts are stressed to failure, the probability of making a correct choice is fair for sample sizes as small as 10, and becomes quite good if the sample size is at least 50. The statistical technique for distinguishing lognormal from Weibull distributions is presented. Its theoretical foundation is given at a qualitative level, and the range of useful application is explored. An approximate form for the distribution of the test statistic is inferred from Monte Carlo simulation     

基于MODIS资料的陕西水云特征研究

陕西位于西北内陆地区,是中国气候的敏感区,为准确认识其上空的水云特征,利用MODIS的MYD06二级云产品数据,对陕西水云的概率分布、云顶高度、粒子有效半径、光学厚度进行了统计分析。结果表明:(1) 水云概率分布显示出单峰结构,峰值出现在11月, 5月出现概率最低。水云在秋季出现概率最高,春季出现概率最低。 (2) 7月和9月水云云顶高度的概率分布会产生显著的变化,7月和8月的分布形态与其余各月有显著差别。水云云顶高度平均最大值出现在春季的4~5月,最小值出现在冬季的12~2月。(3)水云的粒子有效半径在10月~次年5月, 分布形态相似,6~9月分布形态与之明显不同。水云粒子尺度平均最大值出现在夏季的6~8月, 最小值出现在秋季的11月。(4) 光学厚度在0~5之间的水云在10月~次年5月,出现概率最高, 峰值出现在12~2月; 6~9月光学厚度在5~10之间的水云出现概率最高, 峰值出现在7~8月。水云光学厚度最大值出现在秋季的9~11月,最小值出现在夏季的7~8月。     

The performance of continuous-phase-coded DS/SSMA communications

A class of spread-spectrum systems with continuous-phase spreading signals is proposed. We present a characterization of the spectral-spreading signals that allows computation of various measures of the system performance, such as the signal-to-noise ratio (SNR), spectral density, and average bit-error probability. The error probability can be evaluated if the distributions of the multiple-access interferences (MAIs) are available. We obtain the conditional distributions of random variables that characterize the MAI given the time delays. These conditional distributions are used to compute vectors, allowing the average bit-error probability to be computed with arbitrary accuracy. Since most of the computations only involve array additions and multiplications, these operations can be performed efficiently on array processors     

Fading distributions and co-channel interference in wirelesssystems

This paper describes the physical models behind the various fading distributions applicable in wireless-communications systems, and presents the probability density functions, both for the signal envelope and for the power of the mobile radio signal. It addresses the problem of co-channel interference in terms of its mean value, for macrocellular and microcellular structures, and illustrates how the outage probability is influenced by a series of factors such as reuse pattern, modulation scheme, traffic load, and others     

Extreme Value Characteristics of Distributions of Cumulative Processes

The paper introduces the concept of a cumulative stochastic process and derives the general mathematical expression of the distribution corresponding to such processes when they can be assumed to be Markovian. The behaviour of such a distribution in correspondence to accumulation functions of the type u(t) = atb and u(t) = l ln(l + t) is explored. It is shown how the exponential, Weibull, gamma, normal and lognormal distributions are particular cases of the general distribution. Next, the characteristics of the extreme values of n independent observations coming from such a general distribution are investigated. The central characteristics of the extreme values distributions are related to the hazard rate of the initial distribution. In particular, a simple method for relating the modal smallest value and the modal largest value to the sample size using the asymptotic expression of the hazard rate is given. The tail characteristics of the extreme values distributions are investigated numerically or analytically. The mathematical findings are applied to the volume effect on the failure probability of materials.     

Stochastic Analysis of CAN-Based Real-Time Automotive Systems

Many automotive applications, including most of those developed for active safety and chassis systems, must comply with hard real-time deadlines, and are also sensitive to the average latency of the end-to-end computations from sensors to actuators. A characterization of the timing behavior of functions is used to estimate the quality of an architecture configuration in the early stages of architecture selection. In this paper, we extend previous work on stochastic analysis of response times for software tasks to controller area network messages, then compose them with sampling delays to compute probability distributions of end-to-end latencies. We present the results of the analysis on a realistic complex distributed automotive system. The distributions predicted by our method are very close to the probability of latency values measured on a simulated system. However, the faster computation time of the stochastic analysis is much better suited to the architecture exploration process, allowing a much larger number of configurations to be analyzed and evaluated.      

Discriminating Among the Log-Normal, Weibull, and Generalized Exponential Distributions

We consider model selection and discrimination among three important lifetime distributions. These three distributions have been used quite effectively to analyze lifetime data. We study the probability of correct selection using the maximized likelihood method, as it has been used in the literature. We further compute the asymptotic probability of correct selection, and compare the theoretical, and simulation results for different sample sizes, and for different model parameters. The results have been extended for Type-I censored data also. The theoretical, and simulation results match quite well. Two real data sets have been analyzed for illustrative purposes. We also suggest a method to determine the minimum sample size required to discriminate among the three distributions for a given probability of correct selection, and a user specified protection level.     

Mean field and mixed mean field iterative decoding of low-density parity-check codes

In this paper, the mean field (MF) and mixed mean field (MMF) algorithms for decoding low-density parity-check (LDPC) codes are considered. The MF principle is well established in statistical physics and artificial intelligence. Instead of using a single completely factorized approximated distribution as in the MF approach, the mixed MF algorithm forms a weighted average of several MF distributions as an approximation of the true posterior probability distribution. The MF decoding algorithm for linear block codes is derived and shown to be an approximation of the a posteriori probability (APP) decoding algorithm. The MF approach is then developed in the context of iterative decoding and presented as an approximation of the popular belief propagation decoding method. These results are extended to iterative decoding with the MMF algorithm. Simulation results show that the MF and MMF decoding algorithms yield a good performance-complexity tradeoff, especially when employed for decoding LDPC codes based on finite geometries.     

基于贝叶斯分区数据挖掘的光纤网络异常分析算法

光纤网络通信中异常信息的快速、准确识别是保证通信稳定的关键,随着光纤网络通信数据的激增,也成为了近年来的一个研究热点。文中结合异常信息识别算法的精度与收敛速度之间的制约机理,提出了基于贝叶斯分区数据挖掘的异常信息识别算法。首先,采用贝叶斯定量完成数据样本的特征分类,通过极大化分析修正先验概率;然后,依据异常信息的不同类型设置挖掘特征参数及概率化系数;最后,依据贝叶斯分区分别对样本数据进行具有针对性的数据挖掘。实验以光纤局域网的通信状态数据为样本,将该算法与人工神经网络算法和遗传算法的识别结果进行对比,计算了三种算法的识别正确率、收敛速度以及算法稳定性。该算法的识别正确率均值为93.83%,在数据量增大时未发生明显的降低。收敛速度与遗传算法相近,均值为3.25 s。漏检率和误检率均值分别为0.10%和0.54%。结果表明:该算法识别正确率与收敛速度均得到了提高,稳定性好,并能够在漏检率与误检率之间通过参数控制进行微调,具有较好的应用价值。     

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     

Compound exponential distributions for undermoded reverberation chambers

Novel theoretical first-order probability density functions are derived for the energy density and magnitude of electromagnetic fields inside mode-tuned or mode-stirred reverberation chambers operated at relatively low frequencies. Deviations of physical characteristics for fields in undermoded chambers from those for ideal reverberation are quantified. These deviations are then used as parameters of the distributions. The distribution parameters can be easily and independently calculated from the measured tuner sweep data functions. The derivation is based on an eigenvalue decomposition of the 3/spl times/3 polarization matrix for the stir-averaged local field, followed by a polarization decomposition of the principal components. The theoretical distributions are compared with measured data, showing improved agreement and a significantly lower mismatch at lower frequencies compared to ideal /spl chi//sub 6//sup (2)/ distributions. The previously observed "flattening" of the cumulative distribution function is confirmed, resulting in a now calculable decrease of the mean value and an increase of the uncertainty for field statistics as frequency is lowered.     

Mean time to finish a randomly disturbed 2-phase mission

This paper shows the average time to finish a mission consisting of a sequence of two noninterruptible phases of deterministic, given lengths, when after a short disturbance the interrupted phase has to be restarted. Disturbances, e.g., intermittent faults, are modeled according to a renewal point-process. In order to overcome limitations of Markov modeling, this point process need not be Poisson. Fairly simple closed-form solutions are derived. The Poisson case treated, is a practical example. Upper bounds are derived for easy conservative approximations. The approach can be easily extended to cover the determination of probability distributions instead of only mean values     

HRF estimation in fMRI data with an unknown drift matrix by iterative minimization of the Kullback-Leibler divergence

Hemodynamic response function (HRF) estimation in noisy functional magnetic resonance imaging (fMRI) plays an important role when investigating the temporal dynamic of a brain region response during activations. Nonparametric methods which allow more flexibility in the estimation by inferring the HRF at each time sample have provided improved performance in comparison to the parametric methods. In this paper, the mixed-effects model is used to derive a new algorithm for nonparametric maximum likelihood HRF estimation. In this model, the random effect is used to better account for the variability of the drift. Contrary to the usual approaches, the proposed algorithm has the benefit of considering an unknown and therefore flexible drift matrix. This allows the effective representation of a broader class of drift signals and therefore the reduction of the error in approximating the drift component. Estimates of the HRF and the hyperparameters are derived by iterative minimization of the Kullback-Leibler divergence between a model family of probability distributions defined using the mixed-effects model and a desired family of probability distributions constrained to be concentrated on the observed data. The performance of proposed method is demonstrated on simulated and real fMRI data, the latter originating from both event-related and block design fMRI experiments.     

A probability inequality for band-limited pulse trains

Band-limited pulse trains derived from a discrete stochastic process are considered. An upper bound is obtained for the average probability that the magnitude of the departure of the instantaneous signal value from the mean signal value exceedslambdatimes the standard deviation, as a function oflambda. This upper bound is useful in the analysis and prediction of intersymbol interference, quantizing errors, and digit errors in digital data communication systems.     

Design of Channel-Estimate-Dependent Space–Time Block Codes

Adaptive Channel Estimation and Equalization for Rapidly Mobile Communication Channels The paper presents a reduced-complexity maximum-likelihood sequence estimation receiver, based on the Viterbi algorithm (VA), suitable for rapidly fading mobile communication channels. The channel impulse response is expanded onto a set of basis sequences and time-invariant (TI) expansion parameters. The proposed receiver continuously estimates the time instant of the VA, the accumulated metrics of the survivor paths are compared, and the survivors whose metrics are lower than the average value of the accumulated survivor metrics are retained. The other survivors are used to update the estimate of the TI expansion parameters. The convergence properties of the estimation algorithm are investigated, and the steady-state value of the mean square error or estimation is derived. The performance of the proposed receiver, in terms of the symbol-error probability, is compared with other receivers. The effects of time offset and frequency offset on the performance of receiver are studied.