The role of “leads” in the dynamic OLS estimation of cointegrating regression models

In this paper, we consider the role of “leads” of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. Specifically, we investigate Stock and Watson’s [J.H. Stock, M.W. Watson’s, A simple estimator of cointegrating vectors in higher order integrated systems, Econometrica 61 (1993) 783–820] claim that the role of leads is related to the concept of Granger causality by a Monte Carlo simulation. From the simulation results, we find that the dynamic OLS estimator without leads substantially outperforms that with leads and lags; we therefore recommend testing for Granger non-causality before estimating models.     

Uncertainty of transfer function modelling using prior estimated noise models

Assuming “small” model errors (unmodelled dynamics and/or nonlinear distortions) and “large” signal-to-noise ratios we derive in this paper explicit expressions for the covariance matrix of a frequency domain estimator using prior estimated noise models. These analytic expressions (i) give a clear insight in the behaviour of the covariance matrix as a function of the signal-to-noise ratio, the unmodelled dynamics and the nonlinear distortions, and (ii) allow to predict accurately the order of magnitude of the actual uncertainty of the estimates. The link with the classical prediction error approach is also established.     

Modeling of Asymmetry between Gasoline and Crude Oil Prices: A Monte Carlo Comparison

An Engle–Granger two-step procedure is commonly used to estimate cointegrating vectors and consequently asymmetric error-correction models. This study uses Monte Carlo methods and demonstrates that the Engle–Granger two-step method leads to biased estimates of asymmetric parameters and in some cases suggests symmetry in the asymmetric data generating process (DGP). The single equation error correction models (SEECM) based on ordinary least squares (OLS) and nonlinear least squares (NLS) are employed for simultaneous estimation of the cointegrating vector and the ECM. The SEECMs perform better than Engle–Granger two-step procedures in estimating the asymmetry and making inferences on its existence in various DGPs. We show that SEECM estimations are less biased and inferences are less likely to be misleading compared to the Engle–Granger two-step procedure. Unlike the asymmetric specifications based on Engle–Granger two-step approach, the asymmetric SEECMs do not refute the possibility of long-run asymmetry by allowing different cointegrating vectors for positive and negative regimes. Examining the model with real data also supports the Monte Carlo results. While the conventional approaches imply symmetry, the proposed asymmetric SEECM, which has been embedded in a Threshold Autoregressive model, uncovers asymmetry at the presence of different cointegrating vectors for positive and negative regimes.     

Fast macroblock mode decision for H.264/AVC baseline profile video transcoder based on support vector machines

In video transcoding, accuracy and efficiency of macroblock mode decision are critical issues at the re-encoder side due to the changes in frame size, frame rate, and bit rate. In this paper, a fast macroblock mode decision scheme based on support vector machines is proposed for H.264/AVC baseline profile video transcoder. Features including motion vectors, residual data, pre-encoded macroblock modes, and quantization parameters are extracted from incoming bitstream in both of training stage and classification stage. Feature extraction methods are investigated for spatial resolution transcoder, temporal resolution transcoder, and bit-rate transcoder. After off-line training and simplification of support vectors, the obtained support vector machine classifier can determine macroblock mode in the re-encoder accurately. Extensive experiments are carried out on different types of transcoders and results show that the proposed method can save about 80% in computational complexity compared to full mode search algorithm implemented in the latest H.264/AVC reference software (JM17.1), while maximum peak signal-to-noise ratio is degraded by 0.2–1.1?dB depending on different sequences and bit rate.     

水平门限同积模型参数的拟极大似然估计

提出了用于非平稳非线性时间序列建模的水平门限同积模型,给出了模型参数的拟极大似然估计,由于对门限参数和同积向量似然函数既不可微也不光滑,不能直接运用传统的极大似然估计．因此首先利用遗传模拟退火算法估计门限参数和同积向量,然后用极大似然估计计算其余的参数,仿真结果表明,拟极大似然估计不受模型维数限制具有有效性和可行性,此外,数值计算结果的比较分析表明遗传模拟退火优于传统的遗传算法、模拟退火和随机搜索等优化算法．     

Disturbance rejection with information constraints: Performance limitations of a scalar system for bounded and Gaussian disturbances

This paper derives performance limitations for disturbance rejection of scalar systems under information constraints subject to either bounded or Gaussian disturbances. Two kinds of disturbance are treated in a unified manner, using appropriate entropies and distortions. It is shown that the achievable performance cannot be improved even if the maximum information constraint is relaxed to an average information constraint. Another observation is that, while the information constraints are weaker than bit-rate or signal-to-noise ratio constraints on the communication channel, the same performance levels are achieved by the best encoder and decoder for disturbance rejection with the information constraints.     

Improved accuracy of time-resolved micro-Particle Image Velocimetry using phase-correlation and confocal microscopy

Micro-Particle Image Velocimetry (μPIV) measurements often suffer from poor image quality because of volume illumination effects, out of focus particles, and low seeding densities. As a result, measurements are typically ensemble averaged in time to improve the signal-to-noise ratio (SNR) of the resulting cross correlations. To achieve reliable, time-accurate μPIV measurements we need to improve the SNR of the recorded images and/or the SNR treatment of the resulting cross correlations. In this paper, we improve image quality and cross correlation SNR by comparing the use of confocal microscopy with spectral filtering. Steady-state spatiotemporally resolved data from widefield and confocal μPIV experiments were used and cross correlations were performed using standard techniques and the Robust Phase Correlation (RPC) method that employs a PIV-optimized spectral filter on the cross-correlation planes. The accuracy improvements were assessed by comparison against the time-averaged ensemble cross correlation, which currently represents the most accurate and accepted approach for steady-state μPIV measurements. Results show 24.77 % erroneous vectors for two-pass standard cross correlation with widefield imaging, which was reduced to 9.08 % erroneous vectors when using the RPC and confocal imaging. Furthermore, a 59.2 % reduction of error referenced to the ensemble correlation was observed when using RPC with confocal imaging over baseline cases. Improvements seen for RPC and confocal cases result from synergistically improving the correlation signal-to-noise ratio, resulting in correlation planes with sharper primary peaks and lower background levels.     

Exploiting multi-scale support vector regression for image compression

Unlike traditional neural networks that require predefined topology of the network, support vector regression (SVR) approach can model the data within the given level of accuracy with only a small subset of the training data, which are called support vectors (SVs). This property of sparsity has been exploited as the basis for image compression. In this paper, for still image compression, we propose a multi-scale support vector regression (MS-SVR) approach, which can model the images with steep variations and smooth variations very well resulting in good performance. We test our proposed MS-SVR based algorithm on some standard images. The experimental results verify that the proposed MS-SVR achieves better performance than standard SVR. And in a wide range of compression ratio, MS-SVR is very close to JPEG in terms of peak signal-to-noise ratio (PSNR) but exhibits better subjective quality. Furthermore, MS-SVR even outperforms JPEG on both PSNR and subjective quality when the compression ratio is higher enough, for example 25:1 for Lena image. Even when compared with JPEG-2000, the results show greatly similar trend as those in JPEG experiments, except that the compression ratio is a bit higher where our proposed MS-SVR will outperform JPEG-2000.     

基于互信息与二次方变换的超声宽景成像

超声宽景成像技术能获取超过正常超声图像显示范围的图像信息。由于超声图像的噪声和图像采集时组织的复杂运动,使用互信息和二次方变换实现宽景成像。对高斯预处理后的图像使用基于互信息的图像配准技术得到运动向量。利用基于角度的校正提高向量精度,通过二次方变换估计图像间的非刚体运动。最后,使用基于非线性方程组的多边形填充融合方法获取宽景图像。实验表明,获得的高质量宽景图信噪比和对比度有明显提高。     

Semi-blind quality estimation of compressed videos using digital watermarking

A semi-blind method that estimates the quality of a video distorted by H.264/AVC compression/decompression is introduced. The method embeds pseudo-random binary watermarks in the I-frames of the original undistorted video. To assess the quality of a segment of a distorted watermarked video, the watermark bits are extracted and the quality is estimated based on the similarity between the embedded and the extracted watermarks. To enable quality assessment for a large range of distortions, the derivative vectors of different scaled versions of each I-frame of the original video are obtained, using wavelet transform. The watermark bits that are embedded in the small wavelet scales are used to estimate the small distortions, while the bits embedded in the large wavelet scales are used to estimate large distortions. This is because the latter bits are more robust (to distortions) than those bits embedded in the small wavelet scales. The proposed method was tested on different video sequences which were distorted by compression/decompression using H.264/AVC with different quality factors. The simulation results show that the proposed method can accurately estimate the quality of a video and its frames in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) quality measures.     

基于信噪比估计和矢量平均的干涉仪抗噪声测向方法

针对低信噪比条件下干涉仪测向准确度低的问题,提出了一种基于信噪比估计和相位差矢量平均的自适应测向方法。本文方法通过对多次测量的相位差复数矢量求平均来提高相位差的测量精度和稳定性,从而提高测向性能。提出的自适应准则通过估计来波信噪比,可快速确定不同信噪比下矢量平均所需样本量,使处理后信号达到设定信噪比阈值,获得稳定的测向准确度。分析了信噪比阈值对本方法测向性能的影响。本文方法计算复杂性小,对测向实时性影 响小。理论分析和仿真结果表明：本方法在低信噪比条件下可以达到很高的测向准确度,对低信噪比条件下的测向性能改善明显。     

Face recognition using Gabor-based direct linear discriminant analysis and support vector machine

This paper presents a novel and uniform framework for face recognition. This framework is based on a combination of Gabor wavelets, direct linear discriminant analysis (DLDA) and support vector machine (SVM). First, feature vectors are extracted from raw face images using Gabor wavelets. These Gabor-based features are robust against local distortions caused by the variance of illumination, expression and pose. Next, the extracted feature vectors are projected to a low-dimensional subspace using DLDA technique. The Gabor-based DLDA feature vectors are then applied to SVM classifier. A new kernel function for SVM called hyperhemispherically normalized polynomial (HNP) is also proposed in this paper and its validity on the improvement of classification accuracy is theoretically proved and experimentally tested for face recognition. The proposed algorithm was evaluated using the FERET database. Experimental results show that the proposed face recognition system outperforms other related approaches in terms of recognition rate.     

Artificial neural network visual model for image quality enhancement

An artificial neural network visual model is developed, which extracts multi-scale edge features from the decompressed image and uses these visual features as input to estimate and compensate for the coding distortions. This provides a generic postprocessing technique that can be applied to all the main coding methods. Experimental results involving postprocessing of the JPEG and quadtree coding systems show that the proposed artificial neural network visual model significantly enhances the quality of reconstructed images, both in terms of the objective peak signal-to-noise ratio and subjective visual assessment.     

Testing linearity in a cointegrating STR model for the money demand function: International evidence from G-7 countries

The motivation behind this paper is to re-investigate the stability of the long-run money demand function (MDF) in a non-linear cointegrating framework for G-7 countries. Previous studies on non-linearity in the MDF are only related to the short-run dynamics and assume that long-run cointegrating relations are linear, which according to economic theory need not be the case. Thus, we really need to focus on the variables in the long-run MDF and their determinants through the adoption of a cointegrating smooth transition regression (CSTR) test developed by [I. Choi, P. Saikkonen, Testing linearity in cointegrating smooth transition regressions, Economet. J. 7 (2004) 341–365]. The reason is due to this model being more general than the traditional STR model in that it may contain several transition functions and has more than a single transition variable. Our evidence demonstrates the existence of a non-linear cointegrating relationship, and as such several transition variables should be of more concern under the non-linear hypothesis. Overall, we propose more possibilities that will bring about the unstable phenomenon of the long-run MDF.     

音素关联的多文种语音融合编码方法

依据异类文种之间、同类文种不同语音之间存在音素数据关联的特性,提出多文种语音数据融合编码方法。将不同文种存在的相同音素数据段块按段块模板截取语音样本序列,小波变换,提取特征矢量,生成共享模板集;任意字音或语句音串均按共享模板集提供的元素进行编码与解码;以模板音素串构成的语音记录库按（音节、音素）索引。实验结果表明,单字语音数据压缩比、语音数据存储量、语音还原分段信噪比、主观评价得分等参数均明显优于已有方法,语音还原质量良好。     

马尔可夫协整回归模型的动态滤波估计

本文提出动态滤波估计方法估计马尔可夫协整回归模型的参数.利用领先和滞后方法构造辅助的动态回归模型,以消除解释变量和误差序列间的相关性以及误差自相关性对估计结果的影响.在Hamilton滤波基础上,应用极大似然方法估计辅助模型的参数.模拟计算结果表明动态滤波估计方法能降低误差序列相关性造成的估计偏差.对1990年1月至2011年10月的中国进出口贸易数据,利用所提方法建立了马尔可夫协整回归模型.     

A robust alignment-free fingerprint hashing algorithm based on minimum distance graphs

Abstraction of a fingerprint in the form of a hash can be used for secure authentication. The main challenge is in finding the right choice of features which remain relatively invariant to distortions such as rotation, translation and minutiae insertions and deletions, while at the same time capturing the diversity across users. In this paper, an alignment-free novel fingerprint hashing algorithm is proposed which uses a graph comprising of the inter-minutia minimum distance vectors originating from the core point as a feature set called the minimum distance graph. Matching of hashes has been implemented using a corresponding search algorithm. Based on the experiments conducted on the FVC2002-DB1a and FVC2002-DB2a databases, we obtained an equal error rate of 2.27%. The computational cost associated with our fingerprint hash generation and matching processes is relatively low, despite its success in capturing the minutia positional variations across users.     

Resampled confidence bounds on effects diagrams for signal-to-noise

A computer simulation investigation on the use of resampling for the construction of confidence bounds on signal-to-noise ratios is presented. The resampling technique employed is bootstrapping. Results indicate accurate confidence bounds may be constructed for the most common signal-to-noise ratio provided an adequate sample size is employed.     

Evaluation of Objective Quality Measures for Speech Enhancement

In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based, and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the quality of enhanced speech along three dimensions: signal distortion, noise distortion, and overall quality. This paper reports on the evaluation of correlations of several objective measures with these three subjective rating scales. Several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.     

小样本情况下Fisher线性鉴别分析的理论及其验证

线性鉴别分析是特征抽取中最为经典和广泛使用的方法之一。近几年,在小样本情况下如何抽取F isher最优鉴别特征一直是许多研究者关心的问题。本文应用投影变换和同构变换的原理,从理论上解决了小样本情况下最优鉴别矢量的求解问题,即最优鉴别矢量可在一个低维空间里求得;给出了特征抽取模型,并给出求解模型的PPCA+LDA算法;在ORL人脸库3种分辨率灰度图像上进行实验。实验结果表明,PPCA+LDA算法抽取的鉴别向量有较强的特征抽取能力,在普通的最小距离分类器下能达到较高的正确识别率,而且识别结果十分稳定。