Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETS1) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD. (C) 2009 Elsevier Ltd. All rights reserved.

Contrasting magmatic signatures in the Rairakhol and Koraput alkaline complexes, Eastern Ghats belt, India

The relation between alkaline magmatism and tectonism has been a contentious issue, particularly for the Precambrian continental regions. Alkaline complexes at the southwestern margin of Eastern Ghats belt, India, have been interpreted as rift-valley magmatism. However, those complexes occurring in granulite ensemble in the interior segments of the Eastern Ghats belt could not possibly be related to the rift-system, assumed for the western margin of the Eastern Ghats belt. Koraput complex was emplaced in a pull-apart structure, dominated by magmatic fabrics and geochemically similar to a fractionated alkaline complex, compatible with an alkalibasalt series. Rairakhol complex, on the other hand, shows dominantly solid-state deformation fabrics and geochemically similar to a fractionated calc-alkaline suite. Isotopic data for the Koraput complex indicate ca. 917 Ma alkaline magmatism from a depleted mantle source and postcrystalline thermal overprint at ca. 745 Ma, also recorded from sheared metapelitic country rocks. The calc-alkaline magmatism of the Rairakhol complex occurred around 938 Ma, from an enriched mantle source, closely following Grenvillian granulite facies imprint in the charnockitic country rocks.

The Guarguaraz Complex and the Neoproterozoic-Cambrian evolution of southwestern Gondwana: Geochemical signatures and geochronological constraints

The Guarguaraz Complex, in western Argentina, comprises a metasedimentary assemblage, associated with mafic sills and ultramafic bodies intruded by basaltic dikes, which are interpreted as Ordovician dismembered ophiolites. Two kinds of dikes are recognized, a group associated with the metasediments and the other ophiolite-related. Both have N-MORB signatures, with epsilon(Nd) between +3.5 and +8.2, indicating a depleted source, and Grenville model ages between 0.99 and 1.62 Ga. A whole-rock Sm-Nd isochron yielded an age of 655 +/- 76 Ma for these mafic rocks, which is compatible with cianobacteria and acritarchae recognized in the clastic metasedimentary platform sequences, that indicate a Neoproterozoic (Vendian)-Cambrian age of deposition. The Guarguaraz metasedimentary-ophiolitic complex represents, therefore, a remnant of an oceanic basin developed to the west of the Grenville-aged Cuyania terrane during the Neoproterozoic. The southernmost extension of these metasedimentary sequences in Cordon del Portillo might represent part of this platform and not fragments of the Chilenia terrane. An extensional event related to the fragmentation of Rodinia is represented by the mafic and ultramafic rocks. The Devonian docking of Chilenia emplaced remnants of ocean floor and slices of the Cuyania terrane (Las Yaretas Gneisses) in tectonic contact with the Neoproterozoic metasediments, marking the Devonian western border of Gondwana. (C) 2009 Elsevier Ltd. All rights reserved.

Nd and Pb isotope signatures on the Southeastern South American upper margin: Implications for sediment transport and source rocks

Neodymium and lead isotope values in sediment samples were used to interpret sediment transport and source rocks on the Southeastern South American upper margin. The sediments of the Argentinian margin exhibit an average epsilon(Nd) value of -1.9, indicating the influence of the Andean rocks as sediment sources. Sediments from the Rio de La Plata estuary show an average epsilon(Nd) value of -9.6 which is similar to that of the Southern Brazilian Upper Margin. Finally, sediments of Southeastern Brazil, which are associated with the transport of the Brazil Current exhibit an average epsilon(Nd) of -13.0. The Pb isotope signatures also confirm the differentiation of source rocks in the sedimentation of the study area. In addition, Pb isotopes helped to establish the extent of the influence of the Rio de La Plata on the sedimentation of the Southern Brazilian margin. In terms of Pb isotopes the sediments from the Rio de La Plata estuary and Southern Brazil are more radiogenic than those of Southeastem Brazil and the Argentinian margin. (c) 2007 Elsevier B.V. All rights reserved.

Measuring time series predictability using support vector regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.

UNIT VECTOR FIELDS ON ANTIPODALLY PUNCTURED SPHERES: BIG INDEX, BIG VOLUME

We establish in this paper a lower bound for the volume of a unit vector field (v) over right arrow defined ou S(n) \ {+/-x}, n = 2,3. This lower bound is related to the sum of the absolute values of the indices of (v) over right arrow at x and -x.

Periodic geodesics and geometry of compact Lorentzian manifolds with a Killing vector field

We study the geometry and the periodic geodesics of a compact Lorentzian manifold that has a Killing vector field which is timelike somewhere. Using a compactness argument for subgroups of the isometry group, we prove the existence of one timelike non self-intersecting periodic geodesic. If the Killing vector field is nowhere vanishing, then there are at least two distinct periodic geodesics; as a special case, compact stationary manifolds have at least two periodic timelike geodesics. We also discuss some properties of the topology of such manifolds. In particular, we show that a compact manifold M admits a Lorentzian metric with a nowhere vanishing Killing vector field which is timelike somewhere if and only if M admits a smooth circle action without fixed points.

Conserved tissue expression signatures of intronic noncoding RNAs transcribed from human and mouse loci

It has been postulated that noncoding RNAs (ncRNAs) are involved in the posttranscriptional control of gene expression, and may have contributed to the emergence of the complex attributes observed in mammalians. We show here that the complement of ncRNAs expressed from intronic regions of the human and mouse genomes comprises at least 78,147 and 39,660 transcriptional units, respectively. To identify conserved intronic sequences expressed in both humans and mice, we used custom-designed human cDNA microarrays to separately interrogate RNA from mouse and human liver, kidney, and prostate tissues. An overlapping tissue expression signature was detected for both species, comprising 198 transcripts; among these, 22 RNAs map to intronic regions with evidence of evolutionary conservation in humans and mice. Transcription of selected human-mouse intronic ncRNAs was confirmed using strand-specific RT-PCR. Altogether, these results support an evolutionarily conserved role of intronic ncRNAs in human and mouse, which are likely to be involved in the fine tuning of gene expression regulation in different mammalian tissues. (C) 2008 Elsevier Inc. All rights reserved.

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Color Segmentation using LVQ-Learning Vector Quantization

This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

Testing common nonlinear features in vector nonlinear autoregressive models

This paper studies a special class of vector smooth-transition autoregressive (VSTAR) models that contains common nonlinear features (CNFs), for which we proposed a triangular representation and developed a procedure of testing CNFs in a VSTAR model. We first test a unit root against a stable STAR process for each individual time series and then examine whether CNFs exist in the system by Lagrange Multiplier (LM) test if unit root is rejected in the first step. The LM test has standard Chi-squared asymptotic distribution. The critical values of our unit root tests and small-sample properties of the F form of our LM test are studied by Monte Carlo simulations. We illustrate how to test and model CNFs using the monthly growth of consumption and income data of United States (1985:1 to 2011:11).

Forecasting with Vector Nonlinear Time Series Models

This work concerns forecasting with vector nonlinear time series models when errorsare correlated. Point forecasts are numerically obtained using bootstrap methods andillustrated by two examples. Evaluation concentrates on studying forecast equality andencompassing. Nonlinear impulse responses are further considered and graphically sum-marized by highest density region. Finally, two macroeconomic data sets are used toillustrate our work. The forecasts from linear or nonlinear model could contribute usefulinformation absent in the forecasts form the other model.

Common Features in Vector Nonlinear Time Series Models

This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in these area. Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified. The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An Ftypetest for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail,and thereafter illustrated within two corresponding macroeconomic data sets.