A survey of fingerprint classification Part II: experimental analysis and ensemble proposal

In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.

Indexing Methods for Efficient Multiclass Recognition

Many real world image analysis problems, such as face recognition and hand pose estimation, involve recognizing a large number of classes of objects or shapes. Large margin methods, such as AdaBoost and Support Vector Machines (SVMs), often provide competitive accuracy rates, but at the cost of evaluating a large number of binary classifiers, thus making it difficult to apply such methods when thousands or millions of classes need to be recognized. This thesis proposes a filter-and-refine framework, whereby, given a test pattern, a small number of candidate classes can be identified efficiently at the filter step, and computationally expensive large margin classifiers are used to evaluate these candidates at the refine step. Two different filtering methods are proposed, ClassMap and OVA-VS (One-vs.-All classification using Vector Search). ClassMap is an embedding-based method, works for both boosted classifiers and SVMs, and tends to map the patterns and their associated classes close to each other in a vector space. OVA-VS maps OVA classifiers and test patterns to vectors based on the weights and outputs of weak classifiers of the boosting scheme. At runtime, finding the strongest-responding OVA classifier becomes a classical vector search problem, where well-known methods can be used to gain efficiency. In our experiments, the proposed methods achieve significant speed-ups, in some cases up to two orders of magnitude, compared to exhaustive evaluation of all OVA classifiers. This was achieved in hand pose recognition and face recognition systems where the number of classes ranges from 535 to 48,600.

Learning Embeddings for Indexing, Retrieval, and Classification, with Applications to Object and Shape Recognition in Image Databases

Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time, finding nearest neighbors accurately and efficiently can be challenging, especially when the database contains a large number of objects, and when the underlying distance measure is computationally expensive. This thesis proposes new methods for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed methods are domain-independent, and can be applied in arbitrary spaces, including non-Euclidean and non-metric spaces. In this thesis particular emphasis is given to computer vision applications related to object and shape recognition, where expensive non-Euclidean distance measures are often needed to achieve high accuracy. The first contribution of this thesis is the BoostMap algorithm for embedding arbitrary spaces into a vector space with a computationally efficient distance measure. Using this approach, an approximate set of nearest neighbors can be retrieved efficiently - often orders of magnitude faster than retrieval using the exact distance measure in the original space. The BoostMap algorithm has two key distinguishing features with respect to existing embedding methods. First, embedding construction explicitly maximizes the amount of nearest neighbor information preserved by the embedding. Second, embedding construction is treated as a machine learning problem, in contrast to existing methods that are based on geometric considerations. The second contribution is a method for constructing query-sensitive distance measures for the purposes of nearest neighbor retrieval and classification. In high-dimensional spaces, query-sensitive distance measures allow for automatic selection of the dimensions that are the most informative for each specific query object. It is shown theoretically and experimentally that query-sensitivity increases the modeling power of embeddings, allowing embeddings to capture a larger amount of the nearest neighbor structure of the original space. The third contribution is a method for speeding up nearest neighbor classification by combining multiple embedding-based nearest neighbor classifiers in a cascade. In a cascade, computationally efficient classifiers are used to quickly classify easy cases, and classifiers that are more computationally expensive and also more accurate are only applied to objects that are harder to classify. An interesting property of the proposed cascade method is that, under certain conditions, classification time actually decreases as the size of the database increases, a behavior that is in stark contrast to the behavior of typical nearest neighbor classification systems. The proposed methods are evaluated experimentally in several different applications: hand shape recognition, off-line character recognition, online character recognition, and efficient retrieval of time series. In all datasets, the proposed methods lead to significant improvements in accuracy and efficiency compared to existing state-of-the-art methods. In some datasets, the general-purpose methods introduced in this thesis even outperform domain-specific methods that have been custom-designed for such datasets.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

An FPGA Based Coprocessor for GLCM and Haralick Texture Features and Their Application in Prostate Cancer Classification

Grey Level Co-occurrence Matrix (GLCM), one of the best known tool for texture analysis, estimates image properties related to second-order statistics. These image properties commonly known as Haralick texture features can be used for image classification, image segmentation, and remote sensing applications. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of programmable hardware to accelerate the calculation of GLCM and Haralick texture features. Further, as an example of the speedup offered by programmable logic, a multispectral computer vision system for automatic diagnosis of prostatic cancer has been implemented. The performance is then compared against a microprocessor based solution.

Brief review of invariant texture analysis methods

This paper considers invariant texture analysis. Texture analysis approaches whose performances are not affected by translation, rotation, affine, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classified into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented first. Each approach is reviewed according to its classification, and its merits and drawbacks are outlined. The focus of possible future work is also suggested.

Classification of fundus autofluorescence patterns in early age-related macular disease

PURPOSE. To describe and classify patterns of abnormal fundus autofluorescence (FAF) in eyes with early nonexudative age-related macular disease (AMD). METHODS. FAF images were recorded in eyes with early AMD by confocal scanning laser ophthalmoscopy (cSLO) with excitation at 488 nm (argon or OPSL laser) and emission above 500 or 521 nm (barrier filter). A standardized protocol for image acquisition and generation of mean images after automated alignment was applied, and routine fundus photographs were obtained. FAF images were classified by two independent observers. The ? statistic was applied to assess intra- and interobserver variability. RESULTS. Alterations in FAF were classified into eight phenotypic patterns including normal, minimal change, focal increased, patchy, linear, lacelike, reticular, and speckled. Areas with abnormal increased or decreased FAF signals may or may not have corresponded to funduscopically visible alterations. For intraobserver variability, ? of observer I was 0.80 (95% confidence interval [CI]0.71-0.89) and of observer II, 0.74. (95% CI, 0.64-0.84). For interobserver variability, ? was 0.77 (95% CI, 0.67-0.87). CONCLUSIONS. Various phenotypic patterns of abnormal FAF can be identified with cSLO imaging. Distinct patterns may reflect heterogeneity at a cellular and molecular level in contrast to a nonspecific aging process. The results indicate that the classification system yields a relatively high degree of intra- and interobserver agreement. It may be applicable for determination of novel prognostic determinants in longitudinal natural history studies, for identification of genetic risk factors, and for monitoring of future therapeutic interventions to slow the progression of early AMD. Copyright © Association for Research in Vision and Ophthalmology.

Iris cysts in children:Classification, incidence, and management. The 1998 Torrence A Makley Jr Lecture

Background - Iris cysts in children are uncommon and there is relatively little information on their classification, incidence, and management. Methods - The records of all children under age 20 years who were diagnosed with iris cyst were reviewed and the types and incidence of iris cysts of childhood determined. Based on these observations recommendations were made regarding management of iris cysts in children. Results - Of 57 iris cysts in children, 53 were primary and four were secondary. There were 44 primary cysts of the iris pigment epithelium, 34 of which were of the peripheral or iridociliary type, accounting for 59% of all childhood iris cysts. It was most commonly diagnosed in the teenage years, more common in girls (68%), was not recognised in infancy, remained stationary or regressed, and required no treatment. The five mid-zonal pigment epithelial cysts were diagnosed at a mean age of 14 years, were more common in boys (83%), remained stationary, and required no treatment. The pupillary type of pigment epithelial cyst was generally recognised in infancy and, despite involvement of the pupillary aperture, also required no treatment. There were nine cases of primary iris stromal cysts, accounting for 16% of all childhood iris cysts. This cyst was usually diagnosed in infancy, was generally progressive, and required treatment in eight of the nine cases, usually by aspiration and cryotherapy or surgical resection. Among the secondary iris cysts, two were post-traumatic epithelial ingrowth cysts and two were tumour induced cysts, one arising from an intraocular lacrimal gland choristoma and one adjacent to a peripheral iris naevus. Conclusions - Most iris cysts of childhood are primary pigment epithelial cysts and require no treatment. However, the iris stromal cyst, usually recognised in infancy, is generally an aggressive lesion that requires treatment by aspiration or surgical excision.

Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification

Aims/hypothesis: Diabetic nephropathy is a major diabetic complication, and diabetes is the leading cause of end-stage renal disease (ESRD). Family studies suggest a hereditary component for diabetic nephropathy. However, only a few genes have been associated with diabetic nephropathy or ESRD in diabetic patients. Our aim was to detect novel genetic variants associated with diabetic nephropathy and ESRD. Methods: We exploited a novel algorithm, ‘Bag of Naive Bayes’, whose marker selection strategy is complementary to that of conventional genome-wide association models based on univariate association tests. The analysis was performed on a genome-wide association study of 3,464 patients with type 1 diabetes from the Finnish Diabetic Nephropathy (FinnDiane) Study and subsequently replicated with 4,263 type 1 diabetes patients from the Steno Diabetes Centre, the All Ireland-Warren 3-Genetics of Kidneys in Diabetes UK collection (UK–Republic of Ireland) and the Genetics of Kidneys in Diabetes US Study (GoKinD US). Results: Five genetic loci (WNT4/ZBTB40-rs12137135, RGMA/MCTP2-rs17709344, MAPRE1P2-rs1670754, SEMA6D/SLC24A5-rs12917114 and SIK1-rs2838302) were associated with ESRD in the FinnDiane study. An association between ESRD and rs17709344, tagging the previously identified rs12437854 and located between the RGMA and MCTP2 genes, was replicated in independent case–control cohorts. rs12917114 near SEMA6D was associated with ESRD in the replication cohorts under the genotypic model (p < 0.05), and rs12137135 upstream of WNT4 was associated with ESRD in Steno. Conclusions/interpretation: This study supports the previously identified findings on the RGMA/MCTP2 region and suggests novel susceptibility loci for ESRD. This highlights the importance of applying complementary statistical methods to detect novel genetic variants in diabetic nephropathy and, in general, in complex diseases.

Principles and methods for automated palynology

Pollen grains are microscopic so their identification and quantification has, for decades, depended upon human observers using light microscopes: a labour-intensive approach. Modern improvements in computing and imaging hardware and software now bring automation of pollen analyses within reach. In this paper, we provide the first review in over 15 yr of progress towards automation of the part of palynology concerned with counting and classifying pollen, bringing together literature published from a wide spectrum of sources. We
consider which attempts offer the most potential for an automated palynology system for universal application across all fields of research concerned with pollen classification and counting. We discuss what is required to make the datasets of these automated systems as acceptable as those produced by human palynologists, and present suggestions for how automation will generate novel approaches to counting and classifying pollen that have hitherto been unthinkable.

Kernelized structural classification for 3D dogs body parts detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

Patient classification for intelligent wheelchair adaptation

A importância e preocupação dedicadas à autonomia e independência das pessoas idosas e dos pacientes que sofrem de algum tipo de deficiência tem vindo a aumentar significativamente ao longo das últimas décadas. As cadeiras de rodas inteligentes (CRI) são tecnologias que podem ajudar este tipo de população a aumentar a sua autonomia, sendo atualmente uma área de investigação bastante ativa. Contudo, a adaptação das CRIs a pacientes específicos e a realização de experiências com utilizadores reais são assuntos de estudo ainda muito pouco aprofundados. A cadeira de rodas inteligente, desenvolvida no âmbito do Projeto IntellWheels, é controlada a alto nível utilizando uma interface multimodal flexível, recorrendo a comandos de voz, expressões faciais, movimentos de cabeça e através de joystick. Este trabalho teve como finalidade a adaptação automática da CRI atendendo às características dos potenciais utilizadores. Foi desenvolvida uma metodologia capaz de criar um modelo do utilizador. A investigação foi baseada num sistema de recolha de dados que permite obter e armazenar dados de voz, expressões faciais, movimentos de cabeça e do corpo dos pacientes. A utilização da CRI pode ser efetuada em diferentes situações em ambiente real e simulado e um jogo sério foi desenvolvido permitindo especificar um conjunto de tarefas a ser realizado pelos utilizadores. Os dados foram analisados recorrendo a métodos de extração de conhecimento, de modo a obter o modelo dos utilizadores. Usando os resultados obtidos pelo sistema de classificação, foi criada uma metodologia que permite selecionar a melhor interface e linguagem de comando da cadeira para cada utilizador. A avaliação para validação da abordagem foi realizada no âmbito do Projeto FCT/RIPD/ADA/109636/2009 - "IntellWheels - Intelligent Wheelchair with Flexible Multimodal Interface". As experiências envolveram um vasto conjunto de indivíduos que sofrem de diversos níveis de deficiência, em estreita colaboração com a Escola Superior de Tecnologia de Saúde do Porto e a Associação do Porto de Paralisia Cerebral. Os dados recolhidos através das experiências de navegação na CRI foram acompanhados por questionários preenchidos pelos utilizadores. Estes dados foram analisados estatisticamente, a fim de provar a eficácia e usabilidade na adequação da interface da CRI ao utilizador. Os resultados mostraram, em ambiente simulado, um valor de usabilidade do sistema de 67, baseado na opinião de uma amostra de pacientes que apresentam os graus IV e V (os mais severos) de Paralisia Cerebral. Foi também demonstrado estatisticamente que a interface atribuída automaticamente pela ferramenta tem uma avaliação superior à sugerida pelos técnicos de Terapia Ocupacional, mostrando a possibilidade de atribuir automaticamente uma linguagem de comando adaptada a cada utilizador. Experiências realizadas com distintos modos de controlo revelaram a preferência dos utilizadores por um controlo compartilhado com um nível de ajuda associado ao nível de constrangimento do paciente. Em conclusão, este trabalho demonstra que é possível adaptar automaticamente uma CRI ao utilizador com claros benefícios a nível de usabilidade e segurança.

Volatile terpenoids and C13 norisoprenoids in wines: development of rapid methods of analysis and evaluation of the sesquiterpenoids biological potential

Vitis vinifera L., the most widely cultivated fruit crop in the world, was the starting point for the development of this PhD thesis. This subject was exploited following on two actual trends: i) the development of rapid, simple, and high sensitive methodologies with minimal sample handling; and ii) the valuation of natural products as a source of compounds with potential health benefits. The target group of compounds under study were the volatile terpenoids (mono and sesquiterpenoids) and C13 norisoprenoids, since they may present biological impact, either from the sensorial point of view, as regards to the wine aroma, or by the beneficial properties for the human health. Two novel methodologies for quantification of C13 norisoprenoids in wines were developed. The first methodology, a rapid method, was based on the headspace solid-phase microextraction combined with gas chromatography-quadrupole mass spectrometry operating at selected ion monitoring mode (HS-SPME/GC-qMS-SIM), using GC conditions that allowed obtaining a C13 norisoprenoid volatile signature. It does not require any pre-treatment of the sample, and the C13 norisoprenoid composition of the wine was evaluated based on the chromatographic profile and specific m/z fragments, without complete chromatographic separation of its components. The second methodology, used as reference method, was based on the HS-SPME/GC-qMS-SIM, allowing the GC conditions for an adequate chromatographic resolution of wine components. For quantification purposes, external calibration curves were constructed with β-ionone, with regression coefficient (r2) of 0.9968 (RSD 12.51 %) and 0.9940 (RSD of 1.08 %) for the rapid method and for the reference method, respectively. Low detection limits (1.57 and 1.10 μg L-1) were observed. These methodologies were applied to seventeen white and red table wines. Two vitispirane isomers (158-1529 L-1) and 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) (6.42-39.45 μg L-1) were quantified. The data obtained for vitispirane isomers and TDN using the two methods were highly correlated (r2 of 0.9756 and 0.9630, respectively). A rapid methodology for the establishment of the varietal volatile profile of Vitis vinifera L. cv. 'Fernão-Pires' (FP) white wines by headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (HS-SPME/GCxGC-TOFMS) was developed. Monovarietal wines from different harvests, Appellations, and producers were analysed. The study was focused on the volatiles that seem to be significant to the varietal character, such as mono and sesquiterpenic compounds, and C13 norisoprenoids. Two-dimensional chromatographic spaces containing the varietal compounds using the m/z fragments 93, 121, 161, 175 and 204 were established as follows: 1tR = 255-575 s, 2tR = 0,424-1,840 s, for monoterpenoids, 1tR = 555-685 s, 2tR = 0,528-0,856 s, for C13 norisoprenoids, and 1tR = 695-950 s, 2tR = 0,520-0,960 s, for sesquiterpenic compounds. For the three chemical groups under study, from a total of 170 compounds, 45 were determined in all wines, allowing defining the "varietal volatile profile" of FP wine. Among these compounds, 15 were detected for the first time in FP wines. This study proposes a HS-SPME/GCxGC-TOFMS based methodology combined with classification-reference sample to be used for rapid assessment of varietal volatile profile of wines. This approach is very useful to eliminate the majority of the non-terpenic and non-C13 norisoprenic compounds, allowing the definition of a two-dimensional chromatographic space containing these compounds, simplifying the data compared to the original data, and reducing the time of analysis. The presence of sesquiterpenic compounds in Vitis vinifera L. related products, to which are assigned several biological properties, prompted us to investigate the antioxidant, antiproliferative and hepatoprotective activities of some sesquiterpenic compounds. Firstly, the antiradical capacity of trans,trans-farnesol, cis-nerolidol, α-humulene and guaiazulene was evaluated using chemical (DPPH• and hydroxyl radicals) and biological (Caco-2 cells) models. Guaiazulene (IC50= 0.73 mM) was the sesquiterpene with higher scavenger capacity against DPPH•, while trans,trans-farnesol (IC50= 1.81 mM) and cis-nerolidol (IC50= 1.48 mM) were more active towards hydroxyl radicals. All compounds, with the exception of α-humulene, at non-cytotoxic levels (≤ 1 mM), were able to protect Caco-2 cells from oxidative stress induced by tert-butyl hydroperoxide. The activity of the compounds under study was also evaluated as antiproliferative agents. Guaiazulene and cis-nerolidol were able to more effectively arrest the cell cycle in the S-phase than trans,trans-farnesol and α-humulene, being the last almost inactive. The relative hepatoprotection effect of fifteen sesquiterpenic compounds, presenting different chemical structures and commonly found in plants and plant-derived foods and beverages, was assessed. Endogenous lipid peroxidation and induced lipid peroxidation with tert-butyl hydroperoxide were evaluated in liver homogenates from Wistar rats. With the exception of α-humulene, all the sesquiterpenic compounds under study (1 mM) were effective in reducing the malonaldehyde levels in both endogenous and induced lipid peroxidation up to 35% and 70%, respectively. The developed 3D-QSAR models, relating the hepatoprotection activity with molecular properties, showed good fit (R2LOO > 0.819) with good prediction power (Q2 > 0.950 and SDEP < 2%) for both models. A network of effects associated with structural and chemical features of sesquiterpenic compounds such as shape, branching, symmetry, and presence of electronegative fragments, can modulate the hepatoprotective activity observed for these compounds. In conclusion, this study allowed the development of rapid and in-depth methods for the assessment of varietal volatile compounds that might have a positive impact on sensorial and health attributes related to Vitis vinifera L. These approaches can be extended to the analysis of other related food matrices, including grapes and musts, among others. In addition, the results of in vitro assays open a perspective for the promising use of the sesquiterpenic compounds, with similar chemical structures such as those studied in the present work, as antioxidants, hepatoprotective and antiproliferative agents, which meets the current challenges related to diseases of modern civilization.

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.