Detection of the closure-burst transitions of stops and affricates in continuous speech using the plosion index

Automatic and accurate detection of the closure-burst transition events of stops and affricates serves many applications in speech processing. A temporal measure named the plosion index is proposed to detect such events, which are characterized by an abrupt increase in energy. Using the maxima of the pitch-synchronous normalized cross correlation as an additional temporal feature, a rule-based algorithm is designed that aims at selecting only those events associated with the closure-burst transitions of stops and affricates. The performance of the algorithm, characterized by receiver operating characteristic curves and temporal accuracy, is evaluated using the labeled closure-burst transitions of stops and affricates of the entire TIMIT test and training databases. The robustness of the algorithm is studied with respect to global white and babble noise as well as local noise using the TIMIT test set and on telephone quality speech using the NTIMIT test set. For these experiments, the proposed algorithm, which does not require explicit statistical training and is based on two one-dimensional temporal measures, gives a performance comparable to or better than the state-of-the-art methods. In addition, to test the scalability, the algorithm is applied on the Buckeye conversational speech corpus and databases of two Indian languages. (C) 2014 Acoustical Society of America.

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.

Performance enhancement of online handwritten Tamil symbol recognition with reevaluation techniques

In this article, we aim at reducing the error rate of the online Tamil symbol recognition system by employing multiple experts to reevaluate certain decisions of the primary support vector machine classifier. Motivated by the relatively high percentage of occurrence of base consonants in the script, a reevaluation technique has been proposed to correct any ambiguities arising in the base consonants. Secondly, a dynamic time-warping method is proposed to automatically extract the discriminative regions for each set of confused characters. Class-specific features derived from these regions aid in reducing the degree of confusion. Thirdly, statistics of specific features are proposed for resolving any confusions in vowel modifiers. The reevaluation approaches are tested on two databases (a) the isolated Tamil symbols in the IWFHR test set, and (b) the symbols segmented from a set of 10,000 Tamil words. The recognition rate of the isolated test symbols of the IWFHR database improves by 1.9 %. For the word database, the incorporation of the reevaluation step improves the symbol recognition rate by 3.5 % (from 88.4 to 91.9 %). This, in turn, boosts the word recognition rate by 11.9 % (from 65.0 to 76.9 %). The reduction in the word error rate has been achieved using a generic approach, without the incorporation of language models.

An Eighteen Serum Cytokine Signature for Discriminating Glioma from Normal Healthy Individuals

Glioblastomas (GBM) are largely incurable as they diffusely infiltrate adjacent brain tissues and are difficult to diagnose at early stages. Biomarkers derived from serum, which can be obtained by minimally invasive procedures, may help in early diagnosis, prognosis and treatment monitoring. To develop a serum cytokine signature, we profiled 48 cytokines in sera derived from normal healthy individuals (n = 26) and different grades of glioma patients (n = 194). We divided the normal and grade IV glioma/GBM serum samples randomly into equal sized training and test sets. In the training set, the Prediction Analysis for Microarrays (PAM) identified a panel of 18 cytokines that could discriminate GBM sera fromnormal sera with maximum accuracy (95.40%) and minimum error (4.60%). The 18-cytokine signature obtained in the training set discriminated GBM sera from normal sera in the test set as well (accuracy 96.55%; error 3.45%). Interestingly, the 18-cytokine signature also differentiated grade II/Diffuse Astrocytoma (DA) and grade III/Anaplastic Astrocytoma (AA) sera from normal sera very efficiently (DA vs. normal-accuracy 96.00%, error 4.00%; AA vs. normal-accuracy 95.83%, error 4.17%). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using 18 cytokines resulted in the enrichment of two pathways, cytokine-cytokine receptor interaction and JAK-STAT pathways with high significance. Thus our study identified an 18-cytokine signature for distinguishing glioma sera fromnormal healthy individual sera and also demonstrated the importance of their differential abundance in glioma biology.

Development of the 2003 CU-HTK conversational telephone speech transcription system

This paper describes the development of the 2003 CU-HTK large vocabulary speech recognition system for Conversational Telephone Speech (CTS). The system was designed based on a multi-pass, multi-branch structure where the output of all branches is combined using system combination. A number of advanced modelling techniques such as Speaker Adaptive Training, Heteroscedastic Linear Discriminant Analysis, Minimum Phone Error estimation and specially constructed Single Pronunciation dictionaries were employed. The effectiveness of each of these techniques and their potential contribution to the result of system combination was evaluated in the framework of a state-of-the-art LVCSR system with sophisticated adaptation. The final 2003 CU-HTK CTS system constructed from some of these models is described and its performance on the DARPA/NIST 2003 Rich Transcription (RT-03) evaluation test set is discussed.

Shallow Water Model On Cubed-Sphere By Multi-Moment Finite Volume Method

A global numerical model for shallow water flows on the cubed-sphere grid is proposed in this paper. The model is constructed by using the constrained interpolation profile/multi-moment finite volume method (CIP/MM FVM). Two kinds of moments, i.e. the point value (PV) and the volume-integrated average (VIA) are defined and independently updated in the present model by different numerical formulations. The Lax-Friedrichs upwind splitting is used to update the PV moment in terms of a derivative Riemann problem, and a finite volume formulation derived by integrating the governing equations over each mesh element is used to predict the VIA moment. The cubed-sphere grid is applied to get around the polar singularity and to obtain uniform grid spacing for a spherical geometry. Highly localized reconstruction in CIP/MM FVM is well suited for the cubed-sphere grid, especially in dealing with the discontinuity in the coordinates between different patches. The mass conservation is completely achieved over the whole globe. The numerical model has been verified by Williamson's standard test set for shallow water equation model on sphere. The results reveal that the present model is competitive to most existing ones. (C) 2008 Elsevier Inc. All rights reserved.

Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

Metodologia de projeto de turbinas eólicas de pequeno porte.

O potencial eólico do Brasil, de vento firme e com viabilidade econômica de aproveitamento, é de 143 GW. Isso equivale ao dobro de toda a capacidade da geração já instalada no país. No Brasil, a energia eólica tem uma sazonalidade complementar à energia hidrelétrica, porque os períodos de melhor condição de vento coincidem com os de menor capacidade dos reservatórios. O projeto desenvolvido neste trabalho nasceu de uma chamada pública do FINEP, e sob os auspícios do recém criado CEPER. Ao projeto foi incorporado um caráter investigativo, de contribuição científica original, resultando em um produto de tecnologia inovadora para aerogeradores de baixa potência. Dentre os objetivos do projeto, destacamos a avaliação experimental de turbinas eólicas de 5000 W de potência. Mais especificamente, dentro do objetivo geral deste projeto estão incluídas análise estrutural, análise aerodinâmica e análise de viabilidade de novos materiais a serem empregados. Para cada uma das diferentes áreas de conhecimento que compõem o projeto, será adotada a metodologia mais adequada. Para a Análise aerodinâmica foi realizada uma simulação numérica preliminar seguida de ensaios experimentais em túnel de vento. A descrição dos procedimentos adotados é apresentada no Capítulo 3. O Capítulo 4 é dedicado aos testes elétricos. Nesta etapa, foi desenvolvido um banco de testes para obtenção das características específicas das máquinas-base, como curvas de potência, rendimento elétrico, análise e perdas mecânicas e elétricas, e aquecimento. Este capítulo termina com a análise crítica dos valores obtidos. Foram realizados testes de campo de todo o conjunto montado. Atualmente, o aerogerador de 5kW encontra-se em operação, instrumentado e equipado com sistema de aquisição de dados para consolidação dos testes de confiabilidade. Os testes de campo estão ocorrendo na cidade de Campos, RJ, e abrangeram as seguintes dimensões de análise; testes de eficiência para determinação da curva de potência, níveis de ruído e atuação de dispositivos de segurança. Os resultados esperados pelo projeto foram atingidos, consolidando o projeto de um aerogerador de 5000W.

A Reliable Method for Rhythm Analysis during Cardiopulmonary Resuscitation

nterruptions in cardiopulmonary resuscitation (CPR) compromise defibrillation success. However, CPR must be interrupted to analyze the rhythm because although current methods for rhythm analysis during CPR have high sensitivity for shockable rhythms, the specificity for nonshockable rhythms is still too low. This paper introduces a new approach to rhythm analysis during CPR that combines two strategies: a state-of-the-art CPR artifact suppression filter and a shock advice algorithm (SAA) designed to optimally classify the filtered signal. Emphasis is on designing an algorithm with high specificity. The SAA includes a detector for low electrical activity rhythms to increase the specificity, and a shock/no-shock decision algorithm based on a support vector machine classifier using slope and frequency features. For this study, 1185 shockable and 6482 nonshockable 9-s segments corrupted by CPR artifacts were obtained from 247 patients suffering out-of-hospital cardiac arrest. The segments were split into a training and a test set. For the test set, the sensitivity and specificity for rhythm analysis during CPR were 91.0% and 96.6%, respectively. This new approach shows an important increase in specificity without compromising the sensitivity when compared to previous studies.

Cancer subtypes recognition and its gene expression profiles analysis based on geometrical learning

The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.

semantic oriented ontology cohesion metrics for ontology-based systems

Ontologies play a core role to provide shared knowledge models to semantic-driven applications targeted by Semantic Web. Ontology metrics become an important area because they can help ontology engineers to assess ontology and better control project management and development of ontology based systems, and therefore reduce the risk of project failures. In this paper, we propose a set of ontology cohesion metrics which focuses on measuring (possibly inconsistent) ontologies in the context of dynamic and changing Web. They are: Number of Ontology Partitions (NOP), Number of Minimally Inconsistent Subsets (NMIS) and Average Value of Axiom Inconsistencies (AVAI). These ontology metrics are used to measure ontological semantics rather than ontological structure. They are theoretically validated for ensuring their theoretical soundness, and further empirically validated by a standard test set of debugging ontologies. The related algorithms to compute these ontology metrics also are discussed. These metrics proposed in this paper can be used as a very useful complementarity of existing ontology cohesion metrics.

十八胺及其相关物质Langmuir-Blodgett膜的制备和性质研究

Langmuir-Blodgett（LB）膜技术由于在电子学、非线性光学以及化学传感器等领域具有潜在的应用前景而引起了人们的研究兴趣，其中它的热稳定性对LB膜的应用领域和范围具有一定的影响。本论文在此领域的主要研究内容如下： 利用LB膜技术分别制备了十八胺及硬脂酸、氘代硬脂酸的多层LB膜，采用变温傅立叶变换红外光谱研究了三种LB膜的相变行为。实验发现：十八胺LB膜在55-75 oC温度区间内发生相变，其CH2对称和反对称伸缩振动频率向高能量区发生明显移动；硬脂酸LB膜在70-80 oC的温度区间内发生了明显的相转变，CH2对称和反对称伸缩振动的强度比在升温过程中也有显著改变；氘代硬脂酸LB膜的相行为发生在65-70 oC的温度区间内。 利用LB膜技术制备了十八铵硬脂酸盐(C18H37NH3+C17H35COO-, ODASA)与十八铵氘代硬脂酸盐(C18H37NH3+C17D35COO-, ODASA-d35) Langmuir-Blodgett (LB)膜，使用变温傅立叶变换红外透射光谱研究了它们的热行为。发现LB膜中十八铵硬脂酸盐分子的两个碳氢链高度有序，然而在十八铵氘代硬脂酸盐LB分子中的来自于十八胺的碳氢链部分无序，即在常温下有一些扭曲构象存在于碳氢链中。而十八铵硬脂酸盐的热稳定性也与十八铵氘代硬脂酸盐的热稳定性有些不同。在十八铵硬脂酸盐LB膜中，碳氢链在85 oC到90 oC的温度区间内发生非常明显的有序-无序变化。而在十八铵氘代硬脂酸盐LB膜中，碳氢链和来自于硬脂酸的氘代的烃链各自呈现出不同的热行为，即：碳氢链在80-90 oC的温度区间发生有序-无序变化，尤其是在80-85 oC的温度范围内这个变化非常显著；而氘代的烃链则在70 oC到85 oC这个较长的温度区间发生缓慢的相变。 分别制备了十八铵十二酸盐 （C18H37NH3+C11H23COO-，ODALA）和十八铵二十四酸盐（C18H37NH3+C23H45COO-,ODATA）LB膜，并用变温傅立叶变换红外透射光谱法研究了十八铵十二酸盐和十八铵二十四酸盐LB膜的热行为，比较了十八铵十二酸盐、十八铵硬脂酸盐和十八铵二十四酸盐这三种双链化合物LB膜的热行为。温度相关的红外光谱显示，这三种物质LB膜的热稳定性取决于碳链的长度。其中，十八铵十二酸盐LB膜在50-65 oC的温度区间内发生相变。对应的，十八铵二十四酸盐LB膜在80-90 oC的温度范围内发生有序-无序变化。令人感兴趣的是，十八铵二十四酸盐LB膜的相变温度与十八铵硬脂酸盐LB膜的相变温度基本一样，都是80-90 oC，也即在十八铵二十四酸盐和十八铵硬脂酸盐两种LB膜中，即使二十四酸取代了硬脂酸对前者的热稳定性的影响非常小。以上结果说明，在双长链化合物中，有效链长度取决于双链中的较短的那个烃链，从而来决定膜的热稳定性。在十八铵二十四酸盐LB膜中，十八胺的全部碳链对膜的热稳定性有贡献，而二十四酸的碳链则只有部分（有效部分）烃链有贡献。 制备了十八胺单层和多层LB膜和粒径为几个纳米的金纳米粒子。由于十八胺在pH值小于10.3的溶液中氨基带正电荷，使其置于金纳米溶胶中，利用带正电荷的十八胺和附着负电荷的金纳米粒子之间的静电作用，使得金纳米颗粒成功地吸附组装到十八胺的有序分子膜中，形成有规律的纳米颗粒层。通过紫外-可见光谱、红外光谱以及扫描电镜观察到，金纳米颗粒通过这种方法能够很好的组装在有机分子膜上，而且由于十八胺LB膜的高度有序性使得金纳米颗粒的组装层有序。而且，不同层数的十八胺LB膜对金纳米粒子呈现出不同的吸附行为。 测量了含微量甲醇（体积分数为0.04%~0.24%）的系列乙醇水溶液的近红外光谱，利用近红外光谱分析建立了预测甲醇含量的定量分析模型。比较了用外部检验法（Test Set-Validation）和交叉检验法(Cross-Validaton)建立的数学模型以及研究了使用外部检验法时校正集和检验集样品数的改变对模型预测结果的影响。结果发现，当校正集样品数为15检验集样品数为6（总样品数为21）时，使用外部检验法建立的数学模型预测结果较好，外部检验与交叉检验的预测均方根误差（分别为RMSEE和RMSEP）都较小（分别为0.0105和0.0115）而且很接近。结果表明，近红外光谱方法简单，准确而且实用。

Separation of drug enantiomers by capillary electrophoresis in the presence of neutral cyclodextrins

This is a selected review, highlighting our results obtained in an extended screening program ("The German-Chinese Drug Screening Program"), with a focus on a set of original data obtained with heptakis(2,3,6-tri-O-methyl)-beta-cyclodextrin(TM-beta-CD) as the chiral solvating agent (CSA). The enantioseparation of 86 drugs by capillary zone electrophoresis in the presence of this CSA was successful for 47 drugs. The migration separation factors (alpha(m)) and the migration retardation factors (R-m) were compared with those found for native beta-cyclodextrin (beta-CD). The patterns thus obtained were also compared with those observed for hexakis(2,3,6-tri-O-methyl)-alpha-CD (TM-alpha-CD) and octakis(2,3,6-tri-O-methyl)-gamma-CD (TM-gamma-CD), respectively. From the statistical data, it can be concluded that there is a remarkable influence of the analyte structure on the electrophoretic data. A substructure 4H was found in the analyte structure that has a significant influence on the analytes' behaviour. Thus, analytes bearing the substructure 4H do not only have a strong affinity to the CDs but also a high rate of success of chiral separation in all systems reviewed. In light of this, the different ring sizes of native cyclodextrins (alpha-, beta- and gamma-CD) readily explain their behaviour towards a limited test set of chiral drugs. Sterical considerations point to the significance of side-on-binding versus inclusion in the cavity of the host. In addition to the findings from the screening program, numerous references to the Literature are given. (C) 2000 Elsevier Science B.V. All rights reserved.