An improved method for voice pathology detection by means of a HMM-based feature space transformation

This paper presents new a feature transformation technique applied to improve the screening accuracy for the automatic detection of pathological voices. The statistical transformation is based on Hidden Markov Models, obtaining a transformation and classification stage simultaneously and adjusting the parameters of the model with a criterion that minimizes the classification error. The original feature vectors are built up using classic short-term noise parameters and mel-frequency cepstral coefficients. With respect to conventional approaches found in the literature of automatic detection of pathological voices, the proposed feature space transformation technique demonstrates a significant improvement of the performance with no addition of new features to the original input space. In view of the results, it is expected that this technique could provide good results in other areas such as speaker verification and/or identification.     

Combining feature spaces for classification

In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous sources in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis-Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel matrix.     

Multi-category classifiers and sample width

In a recent paper, the authors introduced the notion of sample width for binary classifiers defined on the set of real numbers. It was shown that the performance of such classifiers could be quantified in terms of this sample width. This paper considers how to adapt the idea of sample width so that it can be applied in cases where the classifiers are multi-category and are defined on some arbitrary metric space.     

Using Semantic Classification Trees for WSD

This paper describes the evaluation of a WSD method withinSENSEVAL. This method is based on Semantic Classification Trees (SCTs)and short context dependencies between nouns and verbs. The trainingprocedure creates a binary tree for each word to be disambiguated. SCTsare easy to implement and yield some promising results. The integrationof linguistic knowledge could lead to substantial improvement.     

Synthesis and characterisation of alumina pillared Texas montmorillonite and determination of the effective Keggin ion charge

Controlled calcination of Texas montmorillonite has allowed production of a range of materials of cation exchange capacity from 70 mequiv./100 g dry clay to virtually zero. Pillaring of these with Chlorhydrol precursor solution has then provided a family of materials of pillar density ranging from about one per six unit cells to zero. The analytical technique based on isomorphous metal dilution (IMD) that follows pillaring is validated. The results provide a consistent value for the precursor (Keggin) ion charge, in the conditions normally used, of 3.15±0.10 rather than the formal value of 7. An approach to the attainment of pillar densities consistent with the introduction of shape/size selectivity on pillared layered materials is outlined.     

土石坝渗流及应对施工技术探析

对土石坝出现流土、管涌及冲刷破坏进行了探索分析．对当今水利技术条件下的土石坝应对技术做出了探讨性分析。     

基于多子空间线性判别分析的特征提取与分类

为了解决高维小样本数据的分类中Fisherface思想判别分析方法的不足,在最大散度差准则的基础上,提出了利用多线性子空间技术对每类样本进行单独描述的方法,该方法能更准确地反映样本在类内类间的分布关系.在分类中不是以距离作为判别依据,而是按照贝叶斯决策规则得到的隶属置信度作为衡量标准.实验结果表明了该方法的有效性,和同类方法相比,有更高的识别率.     

Polarization Phase-Based Method For Material Classification In Computer Vision

A robust and accurate polarization phase-based technique for material classification is presented. The novelty of this technique is three-fold in (i) its theoretical development, (ii) application, and, (iii) experimental implementation. The concept of phase of polarization of a light wave is introduced to computer vision for discrimination between materials according to their intrinsic electrical conductivity, such as distinguishing conducting metals, and poorly conducting dielectrics. Previous work has used intensity, color and polarization component ratios. This new method is based on the physical principle that metals retard orthogonal components of light upon reflection while dielectrics do not. This method has significant complementary advantages with respect to existing techniques, is computationally efficient, and can be easily implemented with existing imaging technology. Experiments for real circuit board inspection, nonconductive and conductive glass, and, outdoor object recognition have been performed to demonstrate its accuracy and potential capabilities.     

城镇土地定级信息系统的研制和开发

土地定级过程繁杂,周期长,数据处理量庞大,需要一套符合规程要求,科学、简便、实用的系统来辅助土地管理部门的工作,文中探讨了以计算机作为辅助工具,进行城镇土地定级系统的研制和开发。     

水压引信目标分类的研究

本文提出一种模糊统计分类 FSC 方法,用于研究水压引信目标分类问题。在建立水面目标场多级网络分类模型的基础上,分析讨论了此方法的特征提取、识别函数、模糊与冲突、搜索算法等技术问题,并通过仿真实验,对水面目标航速、吨位、横距进行分类处理,结果表明,FSC 方法行之有效。