Fitting noncausal autoregressive signal plus noise models to noisy non-Gaussian linear processes

The problem of estimating the parameters of a noncausal autoregressive signal model from noisy observations is considered. The signal is assumed to be non-Gaussian. The measurement noise is allowed to be non-Gaussian. Two techniques that use both autocorrelations and third-order autocumulants of the data are presented for parameter estimation. Knowledge of the probability distribution of the driving noise is not required. Several simulation examples are presented to illustrate the two methods. The problem of model order selection is also addressed.     

Testing serial correlation in fixed effects regression models based on asymptotically unbiased autocorrelation estimators

Testing the presence of serial correlation in the error terms in fixed effects regression models is important for many reasons. This paper proposes portmanteau tests based on the sum of the squares of autocorrelation estimators. This approach is a direct extension of the Box–Pierce or Ljung–Box test from single time series to panel data settings. In fixed effects regression analysis, we may estimate the autocorrelations using the within-group autocorrelations of the residuals. However, the within-group autocorrelations may be severely biased when the length of the time series is not very large compared with the cross-sectional sample size, as a result of the incidental parameters problem. We overcome this problem by using asymptotically unbiased autocorrelation estimators for long panel data recently proposed by the author. Monte Carlo simulations reveal that the proposed tests have good size properties and are powerful against a wide range of alternatives.     

On confidence intervals and tests for autocorrelations

We propose a statistical procedure for estimating the asymptotic variances and covariances of sample autocorrelations from a stationary time series so that confidence regions and tests on a finite subset of autocorrelations can be implemented. The corresponding algorithm is described. The accuracy of the asymptotic confidence intervals for finite samples is studied by Monte Carlo simulations. Further, our method is illustrated with examples from the literature.     

Mutual information expansion for studying the role of correlations in population codes: how important are autocorrelations?

The role of correlations in the activity of neural populations responding to a set of stimuli can be studied within an information theory framework. Regardless of whether one approaches the problem from an encoding or decoding perspective, the main measures used to study the role of correlations can be derived from a common source: the expansion of the mutual information. Two main formalisms of mutual information expansion have been proposed: the series expansion and the exact breakdown. Here we clarify that these two formalisms have a different representation of autocorrelations, so that even when the total information estimated differs by less than 1%, individual terms can diverge. More precisely, the series expansion explicitly evaluates the informational contribution of autocorrelations in the count of spikes, that is, count autocorrelations, whereas the exact breakdown does not. We propose a new formalism of mutual information expansion, the Poisson exact breakdown, which introduces Poisson equivalents in order to explicitly evaluate the informational contribution of count autocorrelations with no approximation involved. Because several widely employed manipulations of spike trains, most notably binning and pooling, alter the structure of count autocorrelations, the new formalism can provide a useful general framework for studying the role of correlations in population codes.     

Split-operator technique for propagating phase space functions: Exploring chaotic,dissipative and relativistic dynamics

We conduct a comprehensive analysis of the split-operator method for propagating phase space distribution functions in different scenarios of classical mechanics. A numerical method based on Fast Fourier Transform allows to propagate almost any sampled or exact localized initial state, as well as the direct calculation of current densities in phase space. In order to demonstrate the potential of the proposed numerical scheme some simulations involving chaotic, dissipative and relativistic dynamics are performed. In the conducted simulations, dynamical functions like autocorrelations as well as the detailed structures in phase space are discussed. We find that the split-operator technique demonstrates the effectiveness for studying time evolution of interacting one-dimensional classical systems.     

语音识别中谱包自相关技术

提出了一种语音识别线性预测分析方法：基于谱自相关和频率抽样获得谱包，即由归一化频率估计谱包，此谱包规定在Mel频率级；再由语音信号谱包估计抽样自相关，用IDFT提取抽样自相关估计。从抽样自相关的结果，最终获得谱包倒谱系数。HMM识别试验显示：谱包倒谱系数与其他算法相比较，在低信噪比时，识别率可提高10％以上，识别性能明显提高，在噪声环境下也能达到好的识别效果。     

Modelling long-memory volatilities with leverage effect: A-LMSV versus FIEGARCH

A new stochastic volatility model, called A-LMSV, is proposed to cope simultaneously with leverage effect and long-memory in volatility. Its statistical properties are derived and compared with the properties of the FIEGARCH model. It is shown that the dependence of the autocorrelations of squares on the parameters measuring the asymmetry and the persistence is different in both models. The kurtosis and autocorrelations of squares do not depend on the asymmetry in the A-LMSV model while they increase with the asymmetry in the FIEGARCH model. Furthermore, the autocorrelations of squares increase with the persistence in the A-LMSV model and decrease in the FIEGARCH model. On the other hand, if the correlation between returns and future volatilities is negative, the autocorrelations of absolute returns increase with the magnitude of the asymmetry in the FIEGARCH model while they decrease in the A-LMSV model. Finally, the cross-correlations between squares and original observations are, in general, larger in absolute value in the FIEGARCH model than in the A-LMSV model. The results are illustrated by fitting both models to represent the dynamic evolution of volatilities of daily returns of the S&P500 and DAX indexes.     

A real-time responsiveness measurement method of linux-based mobile systems for P2P cloud systems

Linux-based mobile computing systems such as robots, electronic control devices, and smart-phone are the most important types of P2P cloud systems in recent days. To improve the overall performance of networked systems, each mobile computing system requires real-time characteristics. For this reason, mobile computing system developers want to know how well real-time responsiveness is supported; several real-time measurement tools have been proposed. However, those previous tools have their own measurement schemes and we think that the results from those models do not show how responsive those systems are. In this paper, we propose ELRM, a new real-time measurement method that has clear measurement interval definitions and an accurate measurement method for real-time responsiveness. We evaluate ELRM on various mobile computing systems and compare it with other existing models. As a result, our method can obtain more accurate and intuitive real-time responsiveness measurement results.     

Blind source extraction using generalized autocorrelations.

This letter addresses blind (semiblind) source extraction (BSE) problem when a desired source signal has temporal structures, such as linear or nonlinear autocorrelations. Using the temporal characteristics of sources, we develop objective functions based on the generalized autocorrelations of primary sources. Maximizing the objective functions, we propose simple fixed-point source extraction algorithms. We give the stability analysis and prove convergence properties of the algorithms as the generalized autocorrelation function is linear or nonlinear. Especially, as the generalized autocorrelation function is linear, the algorithm has interesting character of "one-iteration" convergence under some conditions. Computer simulations and real-data application experiments show that the algorithms are appealing BSE methods for temporal signals of interest by capturing the linear or nonlinear autocorrelations of the desired sources.     

Fuzzy-wavelet based prediction of Earth rotation parameters

Prediction of Earth rotation parameters (ERPs) is of importance especially for near real-time applications including navigation, remote sensing, and hazard monitoring. Therefore, prediction of ERPs at least over a few days in the future is necessary.Fuzzy-inference systems (FIS) are increasingly popular and have advantage over classical FFT that lacks stochastic stability due to non-stationarity, multiscaling, and persistent autocorrelations. Wavelet filtering can be used to handle such phenomenon. A FIS rule-base created from ERP time series, where the volatilities (returns) of the preprocessed series are used, and high frequency signals removed, is summarized. The performance of this system, trained using the fuzzy-wavelet method, is compared with that of a conventional FIS, trained on raw time series. The results show that the predictions by the fuzzy-wavelet method are superior to the FIS-only model for short-term predictions (up to 10 days in future). The improvement of prediction accuracy is found to be about 30% in terms of RMS error.     

语音线性预测技术新探

本文提出了一种线性预测分析方法。通过估计频率抽样获得谱包，由归一化频率估计谱包；谱包规定在mel频率级，由IDFT提取抽样自相关估计，我们从抽样自相关的结果最终获得谱包cepstral系数(SEC)。HMM(Hidden Markov Model)识别实验表明，SEC与其它算法相比较，在低信噪比时，识别性能明显提高。     

Segmentation of unsorted cloud of points data from full field optical measurement for metrological validation

Modern industry requires the increase of quality of manufactured products with the simultaneous minimization of production time and cost. Therefore the development of faster, more precise measurement techniques is needed. There are many full field optical systems in use that offer multi directional measurement that meet these requirements. The raw output measurement data from these systems is in the form of unsorted clouds of points which may include millions of measurement points. This data has a different structure than data acquired by traditional contact methods. In addition phenomena connected with optical measurement such as reflection and occlusion result in various errors in the obtained cloud. Therefore a new method of analysis has to be developed to process the data and prepare it for metrological verification. This article presents an algorithm to manage measured data from full field optical systems. This includes segmentation of clouds of points so that each point is associated with its corresponding surface of the CAD model and then exported to certified metrological software for analysis.     

Blind Source Extraction for Noisy Mixtures by Combining Gaussian Moments and Generalized Autocorrelations

In the blind source extraction problem, the concept of generalized autocorrelations has been successfully used when the desired signal has special temporal structures. However, their applications are only limited to noise-free mixtures, which is not realistic. Therefore, this paper addresses the extraction of the noisy model based on these temporal characteristics of sources. An objective function, which combines Gaussian moments and generalized autocorrelations, is proposed. Maximizing this objective function, we present a blind source extraction algorithm for noisy mixtures. Simulations on synthesized signals, images, artificial electrocardiogram (ECG) data and the real-world ECG data show the better performance of the proposed algorithm. Moreover, comparisons with the existing algorithms further indicate its validity and also show its robustness to the estimated error of time delay.     

Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images

This paper presents a novel image representation method for generic object recognition by using higher-order local autocorrelations on posterior probability images. The proposed method is an extension of the bag-of-features approach to posterior probability images. The standard bag-of-features approach is approximately thought of as a method that classifies an image to a category whose sum of posterior probabilities on a posterior probability image is maximum. However, by using local autocorrelations of posterior probability images, the proposed method extracts richer information than the standard bag-of-features. Experimental results reveal that the proposed method exhibits higher classification performances than the standard bag-of-features method.     

A fuzzy-based approach to remove clock skew and reset from one-way delay measurement

One-way delay (OWD) traces are important measurements for analyzing end-to-end performance on the Internet. It is still a great challenge to provide a scalable solution for large-scale OWD measurement. Because the clocks at end systems are usually not synchronized, the OWD measurements are often inaccurate. For the more challenging case with clock resets to some reference times during the measurement, the OWD measurements are more inaccurate. Furthermore, the measurement data often exhibit considerable network-induced noise when the network is heavily loaded. All the existing OWD measurement techniques, such as linear programming and convex-hull approach (CHA), try to solve this problem by deterministic mathematics model. However, they often fail to distinguish clock resets from temporary Internet congestion. Based on the fuzzy-clustering analysis, this paper proposes a new algorithm to estimate and remove the clock skews and resets from measurement results. This algorithm has been implemented as a tool called fuzzy-based OWD corrector (FOC). The paper then presents OWD measurements of several Internet paths using FOC. Numerical experiments demonstrate that FOC is more accurate and robust than the existing techniques. FOCs computation complexity O(N) is similar to that of CHA and its computing time is much less than that of convex-hull technique.     

测量系统分析在四轮定位仪中的应用

测量系统在企业发展中具有非常重要的作用。为了了解四轮定位仪测量系统状况,对车轮前束、车轮外倾角、主销后倾角3个测量系统进行了分析。结果显示:3个测量系统中,研究变异以及部件间变异占总过程变异均未超过标准,说明该测量系统能够满足过程控制要求。但是,在测量系统分辨力方面,其中有2个测量系统经统计软件Minitab计算后,结果小于5,判定该测量系统不能接受。针对这一互为矛盾的结果,给出了行之有效的解决方案。系统测试表明:当一个测量系统中出现ndc与其他准则评断结论相悖时,应该用更精确的ndc=4.497作为评判准则。     

Thickness measurement in ultrathick multilayer microstructures

Microparts are more and more mass products and quality assurance becomes an important factor in manufacturing. Based on a new optical principle, a tool was developed and tested which is well suited for easy non-destructive measurements at microstructures as well as for measurement at transparent layers. Thickness measurement of ca. 1 mm ultrathick photoresist layers on non-patterned high-reflective gold surface was demonstrated. As well, measurement of multi layer systems consisting of each several hundred micrometers thick transparent layers was verified at capped microfluidic channel structures.     

功能点的计算方法

目前,软件复杂度越来越高,有的甚至会成为管理系统的系统,虽然这些软件有着不同的目标,但最终它们都必须相互协调的在一起共同发展。然而,随着软件的复杂度的上升,软件的可控性却在下降。由此,软件度量在软件开发过程中的地位日益增强。因为只有做好软件度量工作,才能在很短的时间内,做出正确的决策,保证产品的质量,才能在日益激烈的竞争中取得胜利。而软件度量的方法有很多,例如FPA、LOC等等,本文主要关注的功能点分析方法,主要是探讨一下FPA中的功能点的计算方法。     

Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler?

A Gibbs sampler for a Poisson regression model including spatial effects is presented and evaluated. The approach is based on that a Poisson regression model can be transformed into an approximate normal linear model by data augmentation using the introduction of two sequences of latent variables. It is shown how this methodology can be extended to spatial Poisson regression models and details of the resulting Gibbs sampler are given. In particular, the influence of model parameterisation and different update strategies on the mixing of the MCMC chains is discussed. The developed Gibbs samplers are analysed in two simulation studies and applied to model the expected number of claims for policyholders of a German car insurance company. The mixing of the Gibbs samplers depends crucially on the model parameterisation and the update schemes. The best mixing is achieved when collapsed algorithms are used, reasonable low autocorrelations for the spatial effects are obtained in this case. For the regression effects however, autocorrelations are rather high, especially for data with very low heterogeneity. For comparison a single component Metropolis-Hastings algorithms is applied which displays very good mixing for all components. Although the Metropolis-Hastings sampler requires a higher computational effort, it outperforms the Gibbs samplers which would have to be run considerably longer in order to obtain the same precision of the parameters.     

Experimental evaluation of an object-oriented function point measurement procedure

This paper presents an empirical study that evaluates OO-Method Function Points (OOmFP), a functional size measurement procedure for object-oriented systems that are specified using the OO-Method approach. A laboratory experiment with students was conducted to compare OOmFP with the IFPUG – Function Point Analysis (FPA) procedure on a range of variables, including efficiency, reproducibility, accuracy, perceived ease of use, perceived usefulness and intention to use. The results show that OOmFP is more time-consuming than FPA but the measurement results are more reproducible and accurate. The results also indicate that OOmFP is perceived to be more useful and more likely to be adopted in practice than FPA in the context of OO-Method systems development. We also report lessons learned and suggest improvements to the experimental procedure employed and replications of this study using samples of industry practitioners.