Classificação de imagens de ambientes coralinos: uma abordagem empregando uma combinação de classificadores e máquina de vetor de suporte

The use of the maps obtained from remote sensing orbital images submitted to digital processing became fundamental to optimize conservation and monitoring actions of the coral reefs. However, the accuracy reached in the mapping of submerged areas is limited by variation of the water column that degrades the signal received by the orbital sensor and introduces errors in the final result of the classification. The limited capacity of the traditional methods based on conventional statistical techniques to solve the problems related to the inter-classes took the search of alternative strategies in the area of the Computational Intelligence. In this work an ensemble classifiers was built based on the combination of Support Vector Machines and Minimum Distance Classifier with the objective of classifying remotely sensed images of coral reefs ecosystem. The system is composed by three stages, through which the progressive refinement of the classification process happens. The patterns that received an ambiguous classification in a certain stage of the process were revalued in the subsequent stage. The prediction non ambiguous for all the data happened through the reduction or elimination of the false positive. The images were classified into five bottom-types: deep water; under-water corals; inter-tidal corals; algal and sandy bottom. The highest overall accuracy (89%) was obtained from SVM with polynomial kernel. The accuracy of the classified image was compared through the use of error matrix to the results obtained by the application of other classification methods based on a single classifier (neural network and the k-means algorithm). In the final, the comparison of results achieved demonstrated the potential of the ensemble classifiers as a tool of classification of images from submerged areas subject to the noise caused by atmospheric effects and the water column

Análise e classificação de imagens de lesões da pele por atributos de cor, forma e textura utilizando máquina de vetor de suporte

The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries

Classificação de distúrbios na rede elétrica usando redes neurais e wavelets

Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks

Detecção e classificação de modos de operação do bombeio mecânico via cartas dinamométricas

The precision and the fast identification of abnormalities of bottom hole are essential to prevent damage and increase production in the oil industry. This work presents a study about a new automatic approach to the detection and the classification of operation mode in the Sucker-rod Pumping through dynamometric cards of bottom hole. The main idea is the recognition of the well production status through the image processing of the bottom s hole dynamometric card (Boundary Descriptors) and statistics and similarity mathematics tools, like Fourier Descriptor, Principal Components Analysis (PCA) and Euclidean Distance. In order to validate the proposal, the Sucker-Rod Pumping system real data are used

Um sistema inteligente de classificação de sinais de EEG para Interface Cérebro-Computador

The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature

Classificação de padrões através de um comitê de máquinas aprimorado por aprendizagem por reforço

Reinforcement learning is a machine learning technique that, although finding a large number of applications, maybe is yet to reach its full potential. One of the inadequately tested possibilities is the use of reinforcement learning in combination with other methods for the solution of pattern classification problems. It is well documented in the literature the problems that support vector machine ensembles face in terms of generalization capacity. Algorithms such as Adaboost do not deal appropriately with the imbalances that arise in those situations. Several alternatives have been proposed, with varying degrees of success. This dissertation presents a new approach to building committees of support vector machines. The presented algorithm combines Adaboost algorithm with a layer of reinforcement learning to adjust committee parameters in order to avoid that imbalances on the committee components affect the generalization performance of the final hypothesis. Comparisons were made with ensembles using and not using the reinforcement learning layer, testing benchmark data sets widely known in area of pattern classification

Classificação Automática de Modulação Digital com uso de Correntropia para Ambientes de Rádio Cognitivo

Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licensed bands by sensing the spectrum and opportunistically access unused portions. Techniques like Automatic Modulation Classification (AMC) could help or be vital for such scenarios. Usually, AMC implementations rely on some form of signal pre-processing, which may introduce a high computational cost or make assumptions about the received signal which may not hold (e.g. Gaussianity of noise). This work proposes a new method to perform AMC which uses a similarity measure from the Information Theoretic Learning (ITL) framework, known as correntropy coefficient. It is capable of extracting similarity measurements over a pair of random processes using higher order statistics, yielding in better similarity estimations than by using e.g. correlation coefficient. Experiments carried out by means of computer simulation show that the technique proposed in this paper presents a high rate success in classification of digital modulation, even in the presence of additive white gaussian noise (AWGN)

Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

Medição da densidade óssea através de ondas eletromagnéticas e classificação por MVS

This work holds the purpose of presenting an auxiliary way of bone density measurement through the attenuation of electromagnetic waves. In order to do so, an arrangement of two microstrip antennas with rectangular configuration has been used, operating in a frequency of 2,49 GHz, and fed by a microstrip line on a substrate of fiberglass with permissiveness of 4.4 and height of 0,9 cm. Simulations were done with silica, bone meal, silica and gypsum blocks samples to prove the variation on the attenuation level of different combinations. Because of their good reproduction of the human beings anomaly aspects, samples of bovine bone were used. They were subjected to weighing, measurement and microwave radiation. The samples had their masses altered after mischaracterization and the process was repeated. The obtained data were inserted in a neural network and its training was proceeded with the best results gathered by correct classification on 100% of the samples. It comes to the conclusion that through only one non-ionizing wave in the 2,49 GHz zone it is possible to evaluate the attenuation level in the bone tissue, and that with the appliance of neural network fed with obtained characteristics in the experiment it is possible to classify a sample as having low or high bone density

Arquiteturas eficientes para sensoriamento espectral e classificação automática de modulações usando características cicloestacionárias

The increasing demand for high performance wireless communication systems has shown the inefficiency of the current model of fixed allocation of the radio spectrum. In this context, cognitive radio appears as a more efficient alternative, by providing opportunistic spectrum access, with the maximum bandwidth possible. To ensure these requirements, it is necessary that the transmitter identify opportunities for transmission and the receiver recognizes the parameters defined for the communication signal. The techniques that use cyclostationary analysis can be applied to problems in either spectrum sensing and modulation classification, even in low signal-to-noise ratio (SNR) environments. However, despite the robustness, one of the main disadvantages of cyclostationarity is the high computational cost for calculating its functions. This work proposes efficient architectures for obtaining cyclostationary features to be employed in either spectrum sensing and automatic modulation classification (AMC). In the context of spectrum sensing, a parallelized algorithm for extracting cyclostationary features of communication signals is presented. The performance of this features extractor parallelization is evaluated by speedup and parallel eficiency metrics. The architecture for spectrum sensing is analyzed for several configuration of false alarm probability, SNR levels and observation time for BPSK and QPSK modulations. In the context of AMC, the reduced alpha-profile is proposed as as a cyclostationary signature calculated for a reduced cyclic frequencies set. This signature is validated by a modulation classification architecture based on pattern matching. The architecture for AMC is investigated for correct classification rates of AM, BPSK, QPSK, MSK and FSK modulations, considering several scenarios of observation length and SNR levels. The numerical results of performance obtained in this work show the eficiency of the proposed architectures

Fibras de sisal (Agave sisalana) como isolante térmico de tubulações

In the last decades there was a significant increasing of the numbers of researchers that joint efforts to find alternatives to improve the development of low environmental impact technology. Materials based on renewable resources have enormous potentials of applications and are seen as alternatives for the sustainable development. Within other parameters, the sustainability depends on the energetic efficiency, which depends on the thermal insulation. Alternative materials, including vegetal fibers, can be applied to thermal insulation, where its first goal is to minimize the loss of energy. In the present research, it was experimentally analyzed the thermal behavior of fiber blankets of sisal (Agave sisalana) with and without surface treatment with oxide hidroxide (NaOH). Blankets with two densities (1100/1200 and 1300/1400 g/m2) were submitted to three rates of heat transfer (22.5 W, 40 W and 62.5 W). The analysis of the results allowed comparing the blankets treated and untreated in each situation. Others experiments were carried out to obtain the thermal conductivity (k), heat capacity (C) and the thermal diffusivity (α) of the blankets. Thermo gravimetric analyses were made to the verification of the thermal stability. Based on the results it was possible to relate qualitatively the effect of the heat transfer through the sisal blankets subjected to three heat transfer rates, corresponding to three temperature values (77 °C, 112 °C e 155 °C). To the first and second values of temperature it was verified a considerable reduction on the rate of heat transfer; nevertheless, to the third value of temperature, the surface of the blankets (treated and untreated) in contact with the heated surface of the tube were carbonized. It was also verified, through the analyses of the results of the measurements of k, C e α, that the blankets treated and untreated have values near to the conventional isolating materials, as glass wool and rock wool. It could be concluded that is technically possible the use of sisal blankets as constitutive material of thermal isolation systems in applications where the temperature do not reach values greater than 112 ºC

Fibras de licuri: um reforço vegetal alternativo de compósitos poliméricos

This research is based, at first, on the seeking of alternatives naturals reinforced in place of polymeric composites, also named reinforced plastics. Therein, this work starts with a whole licuri fiber micro structural characterization, as alternative proposal to polymeric composites. Licuri fiber is abundant on the Bahia state flora, native from a palm tree called Syagrus Coronata (Martius) Beccari. After, it was done only licuri fiber laminar composite developing studies, in order to know its behavior when impregnated with thermofix resin. The composite was developed in laminar structure shape (plate with a single layer of reinforcement) and produced industrially. The layer of reinforcement is a fabric-fiber unidirectional of licuri up in a manual loom. Their structure was made of polyester resin ortofitálica (unsaturated) only reinforced with licuri fibers. Fiber characterization studies were based on physical chemistry properties and their constitution. It was made by tension, scanning electron microscopy (SEM), x-ray diffraction (RDX) and thermal analyses (TG and DTA) tests, besides fiber chemistry analyses. Relating their mechanical properties of strength and hardness testing, they were determined through unit axial tension test and flexion in three points. A study in order to know fiber/matrix interface effects, in the final composites results, was required. To better understand the mechanical behavior of the composite, macroscopic and microscopic optical analysis of the fracture was performed

Desenvolvimento de um compósito de espuma rígida de poliuretana de mamona e fibras de sisal para isolação térmica

The search for sustainable technologies that can contribute to reduce energy consumption is a great challenge in the field of insulation materials. In this context, composites manufactured from vegetal sources are an alternative technology. The principal objectives of this work are the development and characterization of a composite composed by the rigid polyurethane foam derived from castor oil (commercially available as RESPAN D40) and sisal fibers. The manufacture of the composite was done with expansion controlled inside a closed mold. The sisal fibers where used in the form of needlepunched nonwoven with a mean density of 1150 g/m2 and 1350 g/m2. The composite characterization was performed through the following tests: thermal conductivity, thermal behavior, thermo gravimetric analysis (TG/DTG), mechanical strength in compression and flexural, apparent density, water absorption in percentile, and the samples morphology was analyzed in a MEV. The density and humidity percentage of the sisal fiber were also determined. The thermal conductivity of the composites was higher than the pure polyurethane foam, the addition of nonwoven sisal fibers will become in a higher level of compact foam, reducing empty spaces (cells) of polyurethane, inducing an increase in k value. The apparent density of the composites was higher than pure polyurethane foam. In the results of water absorption tests, was seen a higher absorption percent of the composites, what is related to the presence of sisal fibers which are hygroscopic. From TG/DTG results, with the addition of sisal fibers reduced the strength to thermal degradation of the composites, a higher loss of mass was observed in the temperature band between 200 and 340 °C, related to urethane bonds decomposition and cellulose degradation and its derivatives. About mechanical behavior in compression and flexural, composites presented a better mechanical behavior than the rigid polyurethane foam. An increase in the amount of sisal fibers induces a higher rigidity of the composites. At the thermal behavior tests, the composites were more mechanically and thermally resistant than some materials commonly used for thermal insulation, they present the same or better results. The density of nonwoven sisal fiber had influence over the insulation grade; this means that, an increaser in sisal fiber density helped to retain the heat

Sorção de petróleo por fibras vegetais

Nowadays, when accidents with oil tanker or shore tanks occur and there is oil spill, some arrangements are made in order to repress and to fix the situation. For the containment, barriers or detours are usually made of synthetic materials such as polyurethane foam. In order to clear water away, techniques like in loco burning, biodegradant agents, dispersant agents and sorbent materials application are used. The most of the sorbent materials are also synthetic and they are used because it is easy to store them and their availability in market. This dissertation introduces the study of vegetable fibers of pineapple leaf fibers (Ananas comosus (L.) Merr.), cotton fibers (Gossypium herbaceum L.), kapok fibers (Ceiba pentandra (L.) Gaertn.), curauá fibers (Ananas erectifolius L.B. Sm.) and sisal fibers (Agave sisalana Perrine) related to their capacity of sorption of oil in case of accidental spill in the ocean. This work evaluates the substitution possibility of synthetic materials by natural biodegradable materials with less cost

Desempenho mecânico de compósitos híbridos de fibras naturais e poliéster não saturado

With the objective to promote sustainable development, the fibres found in nature in abundance, which are biodegradable, of low cost in comparison to synthetic fibres are being used in the manufacture of composites. The mechanical behavior of the curauá and pineapple leaf fibre (PALF) composites in different proportions, 25% x 75% (P1), 50% x 50% (P2) e 75% x 25% (P3) were respectively studied, being initially treated with a 2% aqueous solution of sodium hydroxide. Mechanical analyses indicated that with respect to studies of traction, for the combination of P1 and P3, better results of 22.17 MPa and 16.98 MPa, were obtained respectively, which are higher than that of the combination P2. The results of the same pattern were obtained for analysis of bending resistance where P1 is 1.21% and P3 represents 0.96%. In the case of resistance to bending, best results were obtained for the combination P1 at 49.07 MPa. However, when Young's modulus values were calculated, the values were different to the pattern of the results of other tests, where the combination P2 with the value of 4.06 GPa is greater than the other combinations. This shows that the PALF had a greater influence in relation to curauá fibre. The analysis of the results generally shows that in combinations of two vegetable fibers of cellulosic origin, the fiber which shows higher percentage (75%) is the best option than to the composition of 50%/50%. In the meantime, according to the results obtained in this study, in the case where the application should withstand bending loads, the better composition would be 50%/50%