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.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Is tumour size an underestimated feature in the current TNM system for malignancies of the pancreatic head?

BACKGROUND: As the long-term survival of pancreatic head malignancies remains dismal, efforts have been made for a better patient selection and a tailored treatment. Tumour size could also be used for patient stratification. METHODS: One hundred and fourteen patients underwent a pancreaticoduodenectomy for pancreatic adenocarcinoma, peri-ampullary and biliary cancer stratified according to: ≤20 mm, 21-34 mm, 35-45 mm and >45 mm tumour size. RESULTS: Patients with tumour sizes of ≤20 mm had a N1 rate of 41% and a R1/2 rate of 7%. The median survival was 3.4 years. N1 and R1/2 rates increased to 84% and 31% for tumour sizes of 21-34 mm (P = 0.0002 for N, P = 0.02 for R). The median survival decreased to 1.6 years (P = 0.0003). A further increase in tumour size of 35-45 mm revealed a further increase of N1 and R1/2 rates of 93% (P < 0.0001) and 33%, respectively. The median survival was 1.2 years (P = 0.004). Tumour sizes >45 mm were related to a further decreased median survival of 1.1 years (P = 0.2), whereas N1 and R1/2 rates were 87% and 20%, respectively. DISCUSSION: Tumour size is an important feature of pancreatic head malignancies. A tumour diameter of 20 mm seems to be the cut-off above which an increased rate of incomplete resections and metastatic lymph nodes must be encountered and the median survival is reduced.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Local and Global Feature Extraction for Invariant Object Recognition

Perceiving the world visually is a basic act for humans, but for computers it is still an unsolved problem. The variability present innatural environments is an obstacle for effective computer vision. The goal of invariant object recognition is to recognise objects in a digital image despite variations in, for example, pose, lighting or occlusion. In this study, invariant object recognition is considered from the viewpoint of feature extraction. Thedifferences between local and global features are studied with emphasis on Hough transform and Gabor filtering based feature extraction. The methods are examined with respect to four capabilities: generality, invariance, stability, and efficiency. Invariant features are presented using both Hough transform and Gabor filtering. A modified Hough transform technique is also presented where the distortion tolerance is increased by incorporating local information. In addition, methods for decreasing the computational costs of the Hough transform employing parallel processing and local information are introduced.

Modelling additional costs caused by a new product feature

Työn tavoitteena oli mallintaa uuden tuoteominaisuuden aiheuttamat lisäkustannukset ja suunnitella päätöksenteon työkalu Timberjack Oy:n kuormatraktorivalmistuksen johtoryhmälle. Tarkoituksena oli luoda karkean tason malli, joka sopisi eri tyyppisten tuoteominaisuuksien kustannuksien selvittämiseen. Uuden tuoteominaisuuden vaikutusta yrityksen eri toimintoihin selvitettiin haastatteluin. Haastattelukierroksen tukena käytettiin kysymyslomaketta. Haastattelujen tavoitteena oli selvittää prosessit, toiminnot ja resurssit, jotka ovat välttämättömiä uuden tuoteominaisuuden tuotantoon saattamisessa ja tuotannossa. Malli suunniteltiin haastattelujen ja tietojärjestelmästä hankitun tiedon pohjalta. Mallin rungon muodostivat ne prosessit ja toiminnot, joihin uudella tuoteominaisuudella on vaikutusta. Huomioon otettiin sellaiset resurssit, joita uusi tuoteominaisuus kuluttaa joko välittömästi, tai välillisesti. Tarkasteluun sisällytettiin ainoastaan lisäkustannukset. Uuden tuoteominaisuuden toteuttamisesta riippumattomat, joka tapauksessa toteutuvat yleiskustannukset jätettiin huomioimatta. Malli on yleistys uuden tuoteominaisuuden aiheuttamista lisäkustannuksista, koska tarkoituksena on, että se sopii eri tyyppisten tuoteominaisuuksien aiheuttamien kustannusten selvittämiseen. Lisäksi malli soveltuu muiden pienehköjen tuotemuutosten kustannusten kartoittamiseen.

Integration of Economics into Life Cycle Assessment in the Finnish Pulp and Paper Industry

Taloudellisen laskennan yhdistäminen elinkaariarviointiin (LCA) on alkanut kiinnostaa eri teollisuuden aloja maailmanlaajuisesti viime aikoina. Useat LCA-tietokoneohjelmat sisältävät kustannuslaskentaominaisuuksia ja yksittäiset projektit ovat yhdistäneet ympäristö- ja talouslaskentamenetelmiä. Tässä projektissa tutkitaan näiden yhdistelmien soveltuvuutta suomalaiselle sellu- ja paperiteollisuudelle, sekä kustannuslaskentaominaisuuden lisäämistä KCL:n LCA-ohjelmaan, KCL-ECO 3.0:aan. Kaikki tutkimuksen aikana löytyneet menetelmät, jotka yhdistävät LCA:n ja taloudellista laskentaa, on esitelty tässä työssä. Monet näistä käyttävät elinkaarikustannusarviointia (LCCA). Periaatteessa elinkaari määritellään eri tavalla LCCA:ssa ja LCA:ssa, mikä luo haasteita näiden menetelmien yhdistämiselle. Sopiva elinkaari tulee määritellä laskennan tavoitteiden mukaisesti. Työssä esitellään suositusmenetelmä, joka lähtee suomalaisen sellu- ja paperiteollisuuden erikoispiirteistä. Perusvaatimuksena on yhteensopivuus tavanomaisesti paperin LCA:ssa käytetyn elinkaaren kanssa. Menetelmän yhdistäminen KCL-ECO 3.0:aan on käsitelty yksityiskohtaisesti.

STUDY ON THE SUGAR-ACID RATIO AND RELEVANT METABOLIZING ENZYME ACTIVITIES IN NAVEL ORANGE FRUITS FROM DIFFERENT ECO-REGIONS

ABSTRACT The flavor quality of citrus fruits is largely determined by the sugar-acid ratio, but it remains uncertain how sugar- and/or acid-metabolizing enzymes regulate the sugar-acid ratio of navel oranges and further affect the fruit quality. In the present study, Robertson navel oranges (Citrus sinesis Osb.) were collected from six representative habitats in three eco-regions of Sichuan, China. The changes in the sugar-acid ratio and the activities of sucrose phosphate synthase (SPS), sucrose synthase (SS), cytosolic cio-aconitase (ACO), and isocitrate dehydrogenase (IDH) were examined in navel oranges during fruit development. The results indicated that the sugar-acid ratio of fruits in different eco-regions changed significantly from 150 days after full bloom. The SPS and cytosolic ACO fruit activities had minor changes among different ecoregions throughout the experimental periods, whereas the activities of SS and IDH changed significantly in fruits among three eco-regions. Furthermore, the sugar-acid ratio and the activities of SS in the synthetic direction and IDH were the highest in south subtropics and the lowest in north mid-subtropics, probably due to the effects of climate conditions and/or other relevant eco-factors. It demonstrated that SS in the synthetic direction and IDH were of greater importance in regulating the sugar-acid ratio of navel oranges in different eco-regions, which provided new insights into the factors that determine the flavor quality of navel oranges and valuable data for guiding relevant agricultural practices.

Feature Selection, Ranking of Each Feature and Classification for the Diagnosis of Community Acquired Legionella Pneumonia

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.

Estudo comparativo entre marcadores ultra-sonográficos morfológicos preditores de parto pré-termo: sinal do afunilamento do colo e ausência do eco glandular endocervical

OBJETIVO: Avaliar o risco de parto pré-termo (PPT) espontâneo na população geral a partir do estudo comparativo dos marcadores ultra-sonográficos morfológicos do colo uterino, como o sinal do afunilamento e a ausência da área glandular endocervical. MATERIAIS E MÉTODOS: Foram arroladas 361 gestantes na população geral, submetidas a exame ultra-sonográfico transvaginal entre a 21ª e 24ª semana, e verificados os resultados perinatais. RESULTADOS: A incidência de PPT espontâneo foi de 5,0%. O sinal do afunilamento foi observado em 4,2% da população estudada e em 22,2% das pacientes que evoluíram para PPT espontâneo. Tal parâmetro mostrou associação significante com PPT (p < 0,001; risco relativo de 6,68). A ausência do eco glandular endocervical (EGE) foi detectada em 2,8% das pacientes estudadas e em 44,4% das pacientes que evoluíram para PPT espontâneo. Este parâmetro demonstrou forte associação com PPT espontâneo (p < 0,001; risco relativo de 28,57). A análise de regressão logística multivariada apontou a ausência do EGE como a única variável morfológica associada ao PPT espontâneo. CONCLUSÃO: A predição do PPT espontâneo a partir de sinais ultra-sonográficos deve ser realizada contemplando marcadores biométricos e morfológicos, entre estes, a ausência do EGE. Este estudo indica uma tendência clara da marcante importância da ausência do EGE como indicador do risco para PPT espontâneo, a ser confirmada futuramente em pesquisas multicêntricas.