Integrated vehicle classification system

This paper presents an integrated system for vehicle classification. This system aims to classify vehicles using different approaches: 1) based on the height of the first axle and_the number of axles; 2) based on volumetric measurements and; 3) based on features extracted from the captured image of the vehicle. The system uses a laser sensor for measurements and a set of image analysis algorithms to compute some visual features. By combining different classification methods, it is shown that the system improves its accuracy and robustness, enabling its usage in more difficult environments satisfying the proposed requirements established by the Portuguese motorway contractor BRISA.

A toolbox for hyperspectral image analysis

This paper introduces a new toolbox for hyperspectral imagery, developed under the MATLAB environment. This toolbox provides easy access to different supervised and unsupervised classification methods. This new application is also versatile and fully dynamic since the user can embody their own methods, that can be reused and shared. This toolbox, while extends the potentiality of MATLAB environment, it also provides a user-friendly platform to assess the results of different methodologies. In this paper it is also presented, under the new application, a study of several different supervised and unsupervised classification methods on real hyperspectral data.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Short-term Feature Space and Music Genre Classification

In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to class boundaries, and compare different processing techniques to partition this space. These partitions are evaluated in terms of accuracy on two genre classification tasks, with several types of classifiers. Experiments show that a randomized and unsupervised partition of the space, used in conjunction with a Markov Model classifier lead to accuracies comparable to the state of the art. We also show that unsupervised partitions of the space tend to create less hubs.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.

Propriedades psicométricas da versão portuguesa do Kansas City Cardiomyopathy Questionnaire na miocardiopatia dilatada com insuficiência cardíaca congestiva

Vários estudos demonstraram que os doentes com insuficiência cardíaca congestiva (ICC) têm um compromisso da qualidade de vida relacionada com a saúde (QVRS), tendo esta, nos últimos anos, vindo a tornar-se um endpoint primário quando se analisa o impacto do tratamento de situações crónicas como a ICC. Objectivos: Avaliar as propriedades psicométricas da versão portuguesa de um novo instrumento específico para medir a QVRS na ICC em doentes hospitalizados: o Kansas City Cardiomyopathy Questionnaire (KCCQ). População e Métodos: O KCCQ foi aplicado a uma amostra consecutiva de 193 doentes internados por ICC. Destes, 105 repetiram esta avaliação 3 meses após admissão hospitalar, não havendo eventos ocorridos durante este período de tempo. A idade era 64,4± 12,4 anos (entre 21 e 88), com 72,5% a pertencer ao sexo masculino, sendo a ICC de etiologia isquémica em 42%. Resultados: Esta versão do KCCQ foi sujeita a validação estatística semelhante à americana com a avaliação da fidelidade e validade. A fidelidade foi avaliada pela consistência interna dos domínios e dos somatórios, apresentando valores Alpha de Cronbach idênticos nos vários domínios e somatórios ( =0,50 a =0,94). A validade foi analisada pela convergência, pela sensibilidade às diferenças entre grupos e pela sensibilidade à alteração da condição clínica. Avaliou-se a validade convergente de todos os domínios relacionados com funcionalidade, pela relação verificada entre estes e uma medida de funcionalidade, a classificação da New York Heart Association (NYHA), tendo-se verificado correlações significativas (p<0,01), como medida para avaliar a funcionalidade em doentes com ICC. Efectuou-se uma análise de variância entre o domínio limitação física, os somatórios e as classes da NYHA, tendo-se encontrado diferenças estatisticamente significativas (F=23,4; F=36,4; F=37,4; p=0,0001), na capacidade de descriminação da gravidade da condição clínica. Foi realizada uma segunda avaliação em 105 doentes na consulta do 3º mês após a intervenção clínica, tendo-se observado alterações significativas nas médias dos domínios avaliados entre o internamento e a consulta (diferenças de 14,9 a 30,6 numa escala de 0-100), indicando que os domínios avaliados são sensíveis à mudança da condição clínica. A correlação interdimensões da qualidade de vida que compõe este instrumento é moderada, sugerindo dimensões independentes, apoiando a sua estrutura multifactorial e a adequabilidade desta medida para a sua avaliação. Conclusão: O KCCQ é um instrumento válido, sensível à mudança e específico para medir a QVRS numa população portuguesa com miocardiopatia dilatada e ICC. ABSTRACT - Several studies have shown that patients with congestive heart failure (CHF) have a compromised health-related quality of life (HRQL), and this, in recent years, has become a primary endpoint when considering the impact of treatment of chronic conditions such as CHF. Objectives: To evaluate the psychometric properties of the Portuguese version of a new specific instrument to measure HRQL in patients hospitalized for CHF: the Kansas City Cardiomyopathy Questionnaire (KCCQ). Methods: The KCCQ was applied to a sample of 193 consecutive patients hospitalized for CHF. Of these, 105 repeated the assessment 3 months after admission, with no events during this period. Mean age was 64.4±12.4 years (21-88), and 72.5% were 72.5% male. CHF was of ischemic etiology in 42% of cases. Results: This version of the KCCQ was subjected to statistical validation, with assessment of reliability and validity, similar to the American version. Reliability was assessed by the internal consistency of the domains and summary scores, which showed similar values of Cronbach alpha (0.50-0.94). Validity was assessed by convergence, sensitivity to differences between groups and sensitivity to changes in clinical condition. We evaluated the convergent validity of all domains related to functionality, through the relationship between them and a measure of functionality, the New York Heart Association (NYHA) classification. Significant correlations were found (p<0.01) for this measure of functionality in patients with CHF. Analysis of variance between the physical limitation domain, the summary scores and NYHA class was performed and statistically significant differences were found (F=23.4; F=36.4; F=37.4, p=0.0001) in the ability to discriminate severity of clinical condition. A second evaluation was performed on 105 patients at the 3-month follow-up outpatient appointment, and significant changes were observed in the mean scores of the domains assessed between hospital admission and the clinic appointment (differences from 14.9 to 30.6 on a scale of 0-100), indicating that the domains assessed are sensitive to changes in clinical condition. The correlation between dimensions of quality of life in the KCCQ is moderate, suggesting that the dimensions are independent, supporting the multifactorial nature of HRQL and the suitability of this measure for its evaluation. Conclusion: The KCCQ is a valid instrument, sensitive to change and a specific measure of HRQL in a population with dilated cardiomyopathy and CHF.

Seasonal variation of total particulate matter and children respiratory diseases at Lisbon primary schools using passive methods

In this work, 14 primary schools of Lisbon city, Portugal, followed a questionnaire of the ISAAC - International Study of Asthma and Allergies in Childhood Program, in 2009/2010. The questionnaire contained questions to identify children with respiratory diseases (wheeze, asthma and rhinitis). Total particulate matter (TPM) was passively collected inside two classrooms of each of 14 primary schools. Two types of filter matrices were used to collect TPM: Millipore (IsoporeTM) polycarbonate and quartz. Three campaigns were selected for the measurement of TPM: Spring, Autumn and Winter. The highest difference between the two types of filters is that the mass of collected particles was higher in quartz filters than in polycarbonate filters, even if their correlation is excellent. The highest TPM depositions occurred between October 2009 and March 2010, when related with rhinitis proportion. Rhinitis was found to be related to TPM when the data were grouped seasonally and averaged for all the schools. For the data of 2006/2007, the seasonal variation was found to be related to outdoor particle deposition (below 10 μm).

Learning techniques for automatic email message tagging

A organização automática de mensagens de correio electrónico é um desafio actual na área da aprendizagem automática. O número excessivo de mensagens afecta cada vez mais utilizadores, especialmente os que usam o correio electrónico como ferramenta de comunicação e trabalho. Esta tese aborda o problema da organização automática de mensagens de correio electrónico propondo uma solução que tem como objectivo a etiquetagem automática de mensagens. A etiquetagem automática é feita com recurso às pastas de correio electrónico anteriormente criadas pelos utilizadores, tratando-as como etiquetas, e à sugestão de múltiplas etiquetas para cada mensagem (top-N). São estudadas várias técnicas de aprendizagem e os vários campos que compõe uma mensagem de correio electrónico são analisados de forma a determinar a sua adequação como elementos de classificação. O foco deste trabalho recai sobre os campos textuais (o assunto e o corpo das mensagens), estudando-se diferentes formas de representação, selecção de características e algoritmos de classificação. É ainda efectuada a avaliação dos campos de participantes através de algoritmos de classificação que os representam usando o modelo vectorial ou como um grafo. Os vários campos são combinados para classificação utilizando a técnica de combinação de classificadores Votação por Maioria. Os testes são efectuados com um subconjunto de mensagens de correio electrónico da Enron e um conjunto de dados privados disponibilizados pelo Institute for Systems and Technologies of Information, Control and Communication (INSTICC). Estes conjuntos são analisados de forma a perceber as características dos dados. A avaliação do sistema é realizada através da percentagem de acerto dos classificadores. Os resultados obtidos apresentam melhorias significativas em comparação com os trabalhos relacionados.

Structural and Microanalytical Studies of CrO2 Thin Films on c-Sapphire by High Resolution Electron Microscopy Methods

Chromium dioxide (CrO2) has been extensively used in the magnetic recording industry. However, it is its ferromagnetic half-metallic nature that has more recently attracted much attention, primarily for the development of spintronic devices. CrO2 is the only stoichiometric binary oxide theoretically predicted to be fully spin polarized at the Fermi level. It presents a Curie temperature of ∼ 396 K, i.e. well above room temperature, and a magnetic moment of 2 mB per formula unit. However an antiferromagnetic native insulating layer of Cr2O3 is always present on the CrO2 surface which enhances the CrO2 magnetoresistance and might be used as a barrier in magnetic tunnel junctions.

PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods

Background: With the decrease of DNA sequencing costs, sequence-based typing methods are rapidly becoming the gold standard for epidemiological surveillance. These methods provide reproducible and comparable results needed for a global scale bacterial population analysis, while retaining their usefulness for local epidemiological surveys. Online databases that collect the generated allelic profiles and associated epidemiological data are available but this wealth of data remains underused and are frequently poorly annotated since no user-friendly tool exists to analyze and explore it. Results: PHYLOViZ is platform independent Java software that allows the integrated analysis of sequence-based typing methods, including SNP data generated from whole genome sequence approaches, and associated epidemiological data. goeBURST and its Minimum Spanning Tree expansion are used for visualizing the possible evolutionary relationships between isolates. The results can be displayed as an annotated graph overlaying the query results of any other epidemiological data available. Conclusions: PHYLOViZ is a user-friendly software that allows the combined analysis of multiple data sources for microbial epidemiological and population studies. It is freely available at http://www.phyloviz.net.

Methods for automatic and assisted image annotation

Personal memories composed of digital pictures are very popular at the moment. To retrieve these media items annotation is required. During the last years, several approaches have been proposed in order to overcome the image annotation problem. This paper presents our proposals to address this problem. Automatic and semi-automatic learning methods for semantic concepts are presented. The automatic method is based on semantic concepts estimated using visual content, context metadata and audio information. The semi-automatic method is based on results provided by a computer game. The paper describes our proposals and presents their evaluations.

A comparison of two attenuation correction methods in 111In-Pentetriotide abdominal SPECT

Tomographic image can be degraded, partially by patient based attenuation. The aim of this paper is to quantitatively verify the effects of attenuation correction methods Chang and CT in 111In studies through the analysis of profiles from abdominal SPECT, correspondent to a uniform radionuclide uptake organ, the left kidney.

Arrow Plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on samples subgroups

Background: A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software. Results: This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes. Conclusion: Our results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays.