A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

Global periodicity conditions for maps and recurrences via Normal Forms

We face the problem of characterizing the periodic cases in parametric families of (real or complex) rational diffeomorphisms having a fixed point. Our approach relies on the Normal Form Theory, to obtain necessary conditions for the existence of a formal linearization of the map, and on the introduction of a suitable rational parametrization of the parameters of the family. Using these tools we can find a finite set of values p for which the map can be p-periodic, reducing the problem of finding the parameters for which the periodic cases appear to simple computations. We apply our results to several two and three dimensional classes of polynomial or rational maps. In particular we find the global periodic cases for several Lyness type recurrences

The prognostic value of minimally involved melanoma sentinel lymph nodes

Introduction: la biopsie du ganglion sentinelle (GS) est une procédure reconnue et fiable pour établir le stade ganglionnaire du mélanome cutané. Le GS est le facteur pronostique le plus puissant pour la survie des patients atteints d'un mélanome à risque intermédiaire, cliniquement localisé. Celui-ci est métastatique dans environ 15-30% des cas. Lorsque le GS est positif, un curage de l'aire ganglionnaire concernée est généralement entrepris. Néanmoins, seuls 20-25% de ces patients présentent des ganglions non-sentinelles (GNS) métastatiques. Ces données suggèrent que le curage, et les risques opératoires qui y sont associés, n'est peut-être pas nécessaire chez le trois-quarts de ces patients. Un autre aspect est que l'impact sur la survie des curages basé sur le résultat du GS n'est pas clairement démontré. La nécessité de ce curage d'emblé est actuellement en cours d'évaluation par un protocole international (Multicenter Selective Lymphadenectomy Trial II : MSLT II). Plusieurs auteurs ont essayé de classifier la charge tumorale du GS afin d'évaluer s'il était possible d'épargner le curage à certains patients et de mieux affiner ce facteur pronostic sans succès. En 2009, le Groupe Mélanome de l'EORTC (European Organisation for Research and Treatment of Cancer) a recommandé un protocole d'évaluation anatomopathologique du GS-positif en trois items: (1) la localisation micro-anatomique des métastases à l'intérieur du ganglion selon Dewar (A = sous-capsulaire, B = combinée sous-capsulaire and parenchymateuse, C = parenchymateuse, D = multifocale, and'E = extensive) ; (2) la mesure de la taille tumorale dans le ganglion selon les critères de Rotterdam pour le diamètre maximal. Le diamètre de la plus grande métastase est exprimé en nombre absolu et (3) la taille tumorale stratifiée par catégories : <0.1mm, 0.1-1.0mm et >1.0 mm. Le but de cette étude rétrospective d'une cohorte de patients, était d'investiguer les résultats des GS-positifs et d'analyser les facteurs pronostiques de la survie à la lumière des recommandations de l'EORTC. Ainsi que de comparer les sous-groupes du GS-positif avec une invasion minimale (taille tumorale <0.1mm et/ou atteinte sous-capsulaire) avec le GS-négatif. Les facteurs pouvant prédire la présence de GNS- positif ont également été analysés. Matériel et méthode : une étude des dossiers a été réalisée pour les 499 patients consécutifs entre 1997 et 2008 qui ont eu une biopsie du GS dans notre institution. Le dégrée d'envahissement du GS-positif a été entièrement revue par l'équipe référente de l'Institut de Pathologie (Dresse E. Saiji et Dresse H. Bouzourène) selon les recommandations de l'EORTC. Des analyses univariées et multivariées des potentiels facteuis pronostics ont été réalisées. Des analyses de survie ont également été effectuées avec des courbes d'estimation de Kaplan-Meier combinées à une régression de Cox. Le protocole a été accepté par la Commission d'Ethique. Résultats: un GS-positif a été trouvé chez 123 (25%) patients panni les 499 qui ont bénéficié d'une biopsie. Avec un suivi médian de 52 mois, la survie à 5 ans sans récidive (SSR), spécifique à la maladie (SS) et globale (SG) étaient de 88%, 94%, et 90% respectivement pour les patients avec GS-négatif. Concernant les GS avec invasion minimale, 21 patients étaient dans le sous-groupe <0.1 mm selon les critères de Rotterdam et 52 patients dans le sous-groupe sous-capsulaire selon Dewar. La survie dans ces deux sous-groupes était de 80% et 57% pour la SSR, 87% et 70% pour la SS, 87% et 68% pour la SG, respectivement. L'analyse multivariée des GS-positifs a montré que les facteuis suivants influençaient significativement la survie (SSR, SS et SG): l'épaisseur selon Breslow de la tumeur primaire (p=0.002, 0.006, 0.004), la taille tumorale du GS-positif >0.1 mm (p= 0.01, 0.04, 0.03), le genre masculin (p=0.06, 0.005, 0.002) et l'ulcération de la tumeur primaire (p=0.05, 0.03, 0.007). L'analyse des sous-groupes avec invasion minimale n'a pas permis d'établir de facteur pour prédire la négativité des GNSs. Conclusion: La classification du GS-positif par la taille tumorale selon les critères de Rotterdam est un facteur pronostique simple et utile pour évaluer la survie des patients atteints de mélanome. Nous avons observé une tendance (non statistiquement significative) d'une survie diminuée pour le sous-groupe des patients avec GS-positif et une taille de la métastase <0.1 mm comparée à celle des patients avec GS-négatif. Ceci nous incite à conclure que ce sous-groupe de patients ne devrait pas être assimilé et traité comme ceux qui ont un GS-négatif. D'autre part nos résultats montrent que la localisation micro-anatomique selon Dewar n'est pas un outil pronostique utile pour évaluer la survie, ni pour prédire le status des GNSs.

An epidemiological evaluation of pediatric long bone fractures - a retrospective cohort study of 2716 patients from two Swiss tertiary pediatric hospitals.

BACKGROUND: Children and adolescents are at high risk of sustaining fractures during growth. Therefore, epidemiological assessment is crucial for fracture prevention. The AO Comprehensive Injury Automatic Classifier (AO COIAC) was used to evaluate epidemiological data of pediatric long bone fractures in a large cohort. METHODS: Data from children and adolescents with long bone fractures sustained between 2009 and 2011, treated at either of two tertiary pediatric surgery hospitals in Switzerland, were retrospectively collected. Fractures were classified according to the AO Pediatric Comprehensive Classification of Long Bone Fractures (PCCF). RESULTS: For a total of 2716 patients (60% boys), 2807 accidents with 2840 long bone fractures (59% radius/ulna; 21% humerus; 15% tibia/fibula; 5% femur) were documented. Children's mean age (SD) was 8.2 (4.0) years (6% infants; 26% preschool children; 40% school children; 28% adolescents). Adolescent boys sustained more fractures than girls (p < 0.001). The leading cause of fractures was falls (27%), followed by accidents occurring during leisure activities (25%), at home (14%), on playgrounds (11%), and traffic (11%) and school accidents (8%). There was boy predominance for all accident types except for playground and at home accidents. The distribution of accident types differed according to age classes (p < 0.001). Twenty-six percent of patients were classed as overweight or obese - higher than data published by the WHO for the corresponding ages - with a higher proportion of overweight and obese boys than in the Swiss population (p < 0.0001). CONCLUSION: Overall, differences in the fracture distribution were sex and age related. Overweight and obese patients seemed to be at increased risk of sustaining fractures. Our data give valuable input into future development of prevention strategies. The AO PCCF proved to be useful in epidemiological reporting and analysis of pediatric long bone fractures.

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

IFN Stimulated Gene Expression in the Liver is a Better Predictor of Treatment Response in Chronic Hepatitis C than the IL28B (IFN lambda 3) Genotype

BACKGROUND: Therapy of chronic hepatitis C (CHC) with pegIFNα/ribavirin achieves a sustained virologic response (SVR) in ∼55%. Pre-activation of the endogenous interferon system in the liver is associated with non-response (NR). Recently, genome-wide association studies described associations of allelic variants near the IL28B (IFNλ3) gene with treatment response and with spontaneous clearance of the virus. We investigated if the IL28B genotype determines the constitutive expression of IFN stimulated genes (ISGs) in the liver of patients with CHC. METHODS: We genotyped 93 patients with CHC for 3 IL28B single nucleotide polymorphisms (SNPs, rs12979860, rs8099917, rs12980275), extracted RNA from their liver biopsies and quantified the expression of IL28B and of 8 previously identified classifier genes which discriminate between SVR and NR (IFI44L, RSAD2, ISG15, IFI22, LAMP3, OAS3, LGALS3BP and HTATIP2). Decision tree ensembles in the form of a random forest classifier were used to calculate the relative predictive power of these different variables in a multivariate analysis. RESULTS: The minor IL28B allele (bad risk for treatment response) was significantly associated with increased expression of ISGs, and, unexpectedly, with decreased expression of IL28B. Stratification of the patients into SVR and NR revealed that ISG expression was conditionally independent from the IL28B genotype, i.e. there was an increased expression of ISGs in NR compared to SVR irrespective of the IL28B genotype. The random forest feature score (RFFS) identified IFI27 (RFFS = 2.93), RSAD2 (1.88) and HTATIP2 (1.50) expression and the HCV genotype (1.62) as the strongest predictors of treatment response. ROC curves of the IL28B SNPs showed an AUC of 0.66 with an error rate (ERR) of 0.38. A classifier with the 3 best classifying genes showed an excellent test performance with an AUC of 0.94 and ERR of 0.15. The addition of IL28B genotype information did not improve the predictive power of the 3-gene classifier. CONCLUSIONS: IL28B genotype and hepatic ISG expression are conditionally independent predictors of treatment response in CHC. There is no direct link between altered IFNλ3 expression and pre-activation of the endogenous system in the liver. Hepatic ISG expression is by far the better predictor for treatment response than IL28B genotype.

J-shaped association between serum glucose and functional outcome in acute ischemic stroke.

BACKGROUND AND PURPOSE: Hyperglycemia after stroke is associated with larger infarct volume and poorer functional outcome. In an animal stroke model, the association between serum glucose and infarct volume is described by a U-shaped curve with a nadir ≈7 mmol/L. However, a similar curve in human studies was never reported. The objective of the present study is to investigate the association between serum glucose levels and functional outcome in patients with acute ischemic stroke. METHODS: We analyzed 1446 consecutive patients with acute ischemic stroke. Serum glucose was measured on admission at the emergency department together with multiple other metabolic, clinical, and radiological parameters. National Institutes of Health Stroke Scale (NIHSS) score was recorded at 24 hours, and Rankin score was recorded at 3 and 12 months. The association between serum glucose and favorable outcome (Rankin score ≤2) was explored in univariate and multivariate analysis. The model was further analyzed in a robust regression model based on fractional polynomial (-2-2) functions. RESULTS: Serum glucose is independently correlated with functional outcome at 12 months (OR, 1.15; P=0.01). Other predictors of outcome include admission NIHSS score (OR, 1.18; P<0001), age (OR, 1.06; P<0.001), prestroke Rankin score (OR, 20.8; P=0.004), and leukoaraiosis (OR, 2.21; P=0.016). Using these factors in multiple logistic regression analysis, the area under the receiver-operator characteristic curve is 0.869. The association between serum glucose and Rankin score at 12 months is described by a J-shaped curve with a nadir of 5 mmol/L. Glucose values between 3.7 and 7.3 mmol/L are associated with favorable outcome. A similar curve was generated for the association of glucose and 24-hour NIHSS score, for which glucose values between 4.0 and 7.2 mmol/L are associated with a NIHSS score <7. Discussion-Both hypoglycemia and hyperglycemia are dangerous in acute ischemic stroke as shown by a J-shaped association between serum glucose and 24-hour and 12-month outcome. Initial serum glucose values between 3.7 and 7.3 mmol/L are associated with favorable outcome.

Equidistribution of Fekete Points on the Sphere

Fekete points are the points that maximize a Vandermonde-type determinant that appears in the polynomial Lagrange interpolation formula. They are well suited points for interpolation formulas and numerical integration. We prove the asymptotic equidistribution of Fekete points in the sphere. The way we proceed is by showing their connection to other arrays of points, the so-called Marcinkiewicz-Zygmund arrays and interpolating arrays, that have been studied recently.

Performance of juveniles of Pseudoplatystoma fasciatum fed graded levels of corn gluten meal

The objective of this work was to evaluate corn gluten meal (CGM) as a substitute for fish meal in diets for striped catfish (Pseudoplatystoma fasciatum) juveniles. Eight isonitrogenous (46% crude protein) and isoenergetic (3,450 kcal kg-1 digestible energy) diets, with increasing levels of CGM - 0, 6, 12, 18, 24, 30, 36, and 42% -, were fed to juvenile striped catfish (113.56±5.10 g) for seven weeks. Maximum values for weight gain, specific growth rate, protein efficiency ratio and feed conversion ratio, evaluated by polynomial quadratic regression, were observed with 10.4, 11.4, 15.4 and 15% of CGM inclusion, respectively. Feed intake decreased significantly from 0.8% CGM. Mesenteric fat index and body gross energy decreased linearly with increasing levels of CGM; minimum body protein contents were observed with 34.1% CGM. Yellow pigmentation of fillets significantly increased until 26.5% CGM, and decreased from this point forth. Both plasma glucose and protein concentrations decreased with increased CGM levels. The inclusion of 10-15% CGM promotes optimum of striped catfish juveniles depending on the parameter evaluated. Yellow coloration in fillets produced by CGM diets can have marketing implications.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.

The ability to obtain gene expression profiles from human disease specimens provides an opportunity to identify relevant gene pathways, but is limited by the absence of data sets spanning a broad range of conditions. Here, we analyzed publicly available microarray data from 16 diverse skin conditions in order to gain insight into disease pathogenesis. Unsupervised hierarchical clustering separated samples by disease as well as common cellular and molecular pathways. Disease-specific signatures were leveraged to build a multi-disease classifier, which predicted the diagnosis of publicly and prospectively collected expression profiles with 93% accuracy. In one sample, the molecular classifier differed from the initial clinical diagnosis and correctly predicted the eventual diagnosis as the clinical presentation evolved. Finally, integration of IFN-regulated gene programs with the skin database revealed a significant inverse correlation between IFN-β and IFN-γ programs across all conditions. Our study provides an integrative approach to the study of gene signatures from multiple skin conditions, elucidating mechanisms of disease pathogenesis. In addition, these studies provide a framework for developing tools for personalized medicine toward the precise prediction, prevention, and treatment of disease on an individual level.

Multispectral remote sensing for site-specific nitrogen fertilizer management

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

On the Condition Monitoring of Induction machines

The research of condition monitoring of electric motors has been wide for several decades. The research and development at universities and in industry has provided means for the predictive condition monitoring. Many different devices and systems are developed and are widely used in industry, transportation and in civil engineering. In addition, many methods are developed and reported in scientific arenas in order to improve existing methods for the automatic analysis of faults. The methods, however, are not widely used as a part of condition monitoring systems. The main reasons are, firstly, that many methods are presented in scientific papers but their performance in different conditions is not evaluated, secondly, the methods include parameters that are so case specific that the implementation of a systemusing such methods would be far from straightforward. In this thesis, some of these methods are evaluated theoretically and tested with simulations and with a drive in a laboratory. A new automatic analysis method for the bearing fault detection is introduced. In the first part of this work the generation of the bearing fault originating signal is explained and its influence into the stator current is concerned with qualitative and quantitative estimation. The verification of the feasibility of the stator current measurement as a bearing fault indicatoris experimentally tested with the running 15 kW induction motor. The second part of this work concentrates on the bearing fault analysis using the vibration measurement signal. The performance of the micromachined silicon accelerometer chip in conjunction with the envelope spectrum analysis of the cyclic bearing faultis experimentally tested. Furthermore, different methods for the creation of feature extractors for the bearing fault classification are researched and an automatic fault classifier using multivariate statistical discrimination and fuzzy logic is introduced. It is often important that the on-line condition monitoring system is integrated with the industrial communications infrastructure. Two types of a sensor solutions are tested in the thesis: the first one is a sensor withcalculation capacity for example for the production of the envelope spectra; the other one can collect the measurement data in memory and another device can read the data via field bus. The data communications requirements highly depend onthe type of the sensor solution selected. If the data is already analysed in the sensor the data communications are needed only for the results but in the other case, all measurement data need to be transferred. The complexity of the classification method can be great if the data is analysed at the management level computer, but if the analysis is made in sensor itself, the analyses must be simple due to the restricted calculation and memory capacity.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.