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.

Learning dependent sources using mixtures of Dirichlet: applications on hyperspectral unmixing

This paper is an elaboration of the DECA algorithm [1] to blindly unmix hyperspectral data. The underlying mixing model is linear, meaning that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. The proposed method, as DECA, is tailored to highly mixed mixtures in which the geometric based approaches fail to identify the simplex of minimum volume enclosing the observed spectral vectors. We resort then to a statitistical framework, where the abundance fractions are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. With respect to DECA, we introduce two improvements: 1) the number of Dirichlet modes are inferred based on the minimum description length (MDL) principle; 2) The generalized expectation maximization (GEM) algorithm we adopt to infer the model parameters is improved by using alternating minimization and augmented Lagrangian methods to compute the mixing matrix. The effectiveness of the proposed algorithm is illustrated with simulated and read data.

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.

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.

Occupational exposure to Aflatoxin B1 in swine production and possible contamination sources

Although the adverse health consequences of ingestion of food contaminated with aflatoxin B1 (AFB1) are known, relatively few studies are available on the adverse effects of exposure in occupational settings. Taking this into consideration, our study was developed aiming to elucidate the possible effects of occupational exposure to AFB1 in Portuguese swine production facilities using a specific biomarker to assess exposure to AFB1. In total, 28 workers participated in this study, providing blood samples, and a control group (n  = 30) was composed of subjects without any type of agricultural activity. Fungal contamination was also studied by conventional methods through air, surfaces, and new and used floor coverage. Twenty-one workers (75%) showed detectable levels of AFB1 with values ranging from <1 ng/ml to 8.94 ng/ml and with a mean value of 1.91 ± 1.68 ng/ml. In the control group, the AFB1 values were all below 1 ng/ml. Twelve different Aspergillus species were identified. Aspergillus versicolor presented the highest airborne spore counts (3210 CFU/m3) and was also detected in higher values in surfaces (>300 CFU/cm2). Data indicate that exposure to AFB1 occurs in swine barns, and this site serves as a contamination source in an occupational setting.

Automatic vehicle detection and classification

This paper presents a proposal for an automatic vehicle detection and classification (AVDC) system. The proposed AVDC should classify vehicles accordingly to the Portuguese legislation (vehicle height over the first axel and number of axels), and should also support profile based classification. The AVDC should also fulfill the needs of the Portuguese motorway operator, Brisa. For the classification based on the profile we propose:he use of Eigenprofiles, a technique based on Principal Components Analysis. The system should also support multi-lane free flow for future integration in this kind of environments.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

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.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Molecular screening of 246 Portuguese Aspergillus isolates among different clinical and environmental sources

Clinical and environmental samples from Portugal were screened for the presence of Aspergillus and the distributions of the species complexes were determined in order to understand how their distributions differ based on their source. Fifty-seven Aspergillus isolates from clinical samples were collected from 10 health institutions. Six species complexes were detected by internal transcribed spacer sequencing; Fumigati, Flavi, and Nigri were found most frequently (50.9%, 21.0%, and 15.8%, respectively). β-tubulin and calmodulin sequencing resulted in seven cryptic species (A. awamorii, A. brasiliensis, A. fructus, A. lentulus, A. sydowii, A. tubingensis, Emericella echinulata) being identified among the 57 isolates. Thirty-nine isolates of Aspergillus were recovered from beach sand and poultry farms, 31 from swine farms, and 80 from hospital environments, for a total 189 isolates. Eleven species complexes were found in these 189 isolates, and those belonging to the Versicolores species complex were found most frequently (23.8%). There was a significant association between the different environmental sources and distribution of the species complexes; the hospital environment had greater variability of species complexes than other environmental locations. A high prevalence of cryptic species within the Circumdati complex was detected in several environments; from the isolates analyzed, at least four cryptic species were identified, most of them growing at 37ºC. Because Aspergillus species complexes have different susceptibilities to antifungals, knowing the species-complex epidemiology for each setting, as well as the identification of cryptic species among the collected clinical isolates, is important. This may allow preventive and corrective measures to be taken, which may result in decreased exposure to those organisms and a better prognosis.

Beyond methodological nationalism: The contribution of Robert E. Park to the history of intercultural communication

The legacy of nineteenth century social theory followed a “nationalist” model of society, assuming that analysis of social realities depends upon national boundaries, taking the nation-state as the primary unit of analysis, and developing the concept of methodological nationalism. This perspective regarded the nation-state as the natural - and even necessary - form of society in modernity. Thus, the constitution of large cities, at the end of the 19th century, through the intense flows of immigrants coming from diverse political and linguistic communities posed an enormous challenge to all social research. One of the most significant studies responding to this set of issues was The Immigrant Press and its Control, by Robert E. Park, one of the most prominent American sociologists of the first half of the 20th century. The Immigrant Press and its Control was part of a larger project entitled Americanization Studies: The Acculturation of Immigrant Group into American Society, funded by the Carnagie Corporation following World War I, taking as its goal to study the so-called “Americanization methods” during the 1920s. This paper revisits that particular work by Park to reveal how his detailed analysis of the role of the immigrant press overcame the limitations of methodological nationalism. By granting importance to language as a tool uniting each community and by showing how the strength of foreign languages expressed itself through the immigrant press, Park demonstrated that the latter produces a more ambivalent phenomenon than simply the assimilation of immigrants. On the one hand, the immigrant press served as a connecting force, driven by the desire to preserve the mother tongue and culture while at the same time awakening national sentiments that had, until then, remained diffuse. Yet, on the other hand, it facilitated the adjustment of immigrants to the American context. As a result, Park’s work contributes to our understanding of a particular liminal moment inherent within many intercultural contexts, the space between emigrant identity (emphasizing the country of origin) and immigrant identity (emphasizing the newly adopted country). His focus on the role played by media in the socialization of immigrant groups presaged later work on this subject by communication scholars. Focusing attention on Park’s research leads to other studies of the immigrant experience from the same period (e.g., Thomas & Znaniecki, The Polish Peasant in Europe and America), and also to insights on multi-presence and interculturality as significant but often overlooked phenomena in the study of immigrant socialization.

Eletrólise da água para produção de gás de síntese

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química e Biológica Ramo de Processos Químicos