Análise e síntese de antenas e superfícies seletivas de frequência utilizando computação evolucionária e inteligência de enxames

The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

Efeitos do tratamento com Hidrogel na cicatrização de úlceras venosas de membros inferiores: revisão sistemática

Chronic venous disease (CVD) is evident among the chronic diseases and affects the elderly population and primarily is responsible for leg ulcers in this population. The use of dressings in the care of a venous ulcer is a fundamental part of the treatment for healing, however, evidence to assist in choosing the best dressing is scarce. The main objective of this study was to evaluate the effectiveness of treatment with hydrogel in the healing of venous ulcers using search methods, synthesis of information and statistical research through a systematic review and meta-analysis. Randomized controlled trials were selected in the following databases: CENTRAL; DARE; NHS EED; MEDLINE; EMBASE; CINAHL. Beyond these databases three websites were consulted to identify ongoing studies: ClinicalTrials.gov, OMS ICTRP e ISRCTN. The primary outcomes were analyzed: complete wound healing, incidence of wound infection and the secondary were: changes in ulcer size, time to ulcer healing, recurrence of ulcer, quality of life of participants, pain and costs of treatment. Four studies are currently included in the review with a total of 250 participants. The use of hydrogel appears to be superior to conventional dressing, gauze soaked in saline, for the healing of venous leg ulcers; 16/30 patients showed complete healing of ulcers (RR 5,33, 95%CI [1,73,16,42]). The alginate gel was shown to be more effective when compared to the hydrogel dressing in reduction of the wound area; 61,2% (± 26,2%) with alginate e 19,4% (± 24,3%) with hydrogel at the end of four weeks of treatment. Manuka honey has shown to be similar to the hydrogel dressings in percentage of area reduction. This review demonstrated that there is no evidence available about the effectiveness of the hydrogel compared to other types of dressings on the healing of venous leg ulcers of the lower limbs, thus demonstrating the need of future studies to assist health professionals in choosing the correct dressing.

Classificador neural híbrido para imagens obtidas por sensoriamento remoto

Remote sensing is one technology of extreme importance, allowing capture of data from the Earth's surface that are used with various purposes, including, environmental monitoring, tracking usage of natural resources, geological prospecting and monitoring of disasters. One of the main applications of remote sensing is the generation of thematic maps and subsequent survey of areas from images generated by orbital or sub-orbital sensors. Pattern classification methods are used in the implementation of computational routines to automate this activity. Artificial neural networks present themselves as viable alternatives to traditional statistical classifiers, mainly for applications whose data show high dimensionality as those from hyperspectral sensors. This work main goal is to develop a classiffier based on neural networks radial basis function and Growing Neural Gas, which presents some advantages over using individual neural networks. The main idea is to use Growing Neural Gas's incremental characteristics to determine the radial basis function network's quantity and choice of centers in order to obtain a highly effective classiffier. To demonstrate the performance of the classiffier three studies case are presented along with the results.

Desenvolvimento de estruturas integradas de filtros e antenas para aplicações em sistemas de comunicações sem fio

This paper presents a study of the integration of filters and microstrip antennas, yielding devices named as filtennas for applications in wireless communications systems. The design of these structures is given from the observation of filtennas based integration between horn antennas and frequency selective surfaces (FSS), used in the band X. The choice of microstrip line structures for the development of a new configuration filtennas justifies the wide application of these transmission lines, in recent decades, always resulting in the production of circuit structures with planar light-weight, compact size, low cost, easy to construct and particularly easy to integrate with other microwave circuits. In addition, the antenna structure considered for the composition of filtennas consists of a planar monopole microstrip to microstrip filters integrated in the feed line of the antenna. In particular, are considered elliptical monopole microstrip (operating in UWB UWB) microstrip filters and (in structures with associated sections in series and / or coupled). In addition, the monopole microstrip has a proper bandwidth and omnidirectional radiation pattern, such that its integration with microstrip filters results in decreased bandwidth, but with slight changes in the radiation pattern. The methods used in the analysis of monopoles, and filters were filtennas finite elements and moments by using commercial software Ansoft Designer and HFSS Ansoft, respectively. Specifically, we analyze the main characteristics of filtennas, such as radiation pattern, gain and bandwidth. Were designed, constructed and measures, several structures filtennas, for validation of the simulated results. Were also used computational tools (CAD) in the process of building prototypes of planar monopoles, filters and filtennas. The prototypes were constructed on substrates of glass-fiber (FR4). Measurements were performed at the Laboratory for Telecommunications UFRN. Comparisons were made between simulated and measured, and found good agreement in the cases considered

Observação da ação na reabilitação do membro superior de pacientes com acidente vascular cerebral: uma revisão sistemática com metanálise

Background: stroke causes long-term impairments, limitation of activities and restriction in participation in daily life situations, especially for upper limb impairment (UL). Action Observation (AO) has been used for the rehabilitation of UL in these patients. It's a multisensory therapy which consists in observing a healthy subject performing a motor task, followed by physical practice. Objectives: assess whether the AO improves motor function of UL and dependence for activities of daily living (ADLs) of stroke patients or cause any adverse effects. Search methods: a search strategy was words and terms used for the identification of articles, in the following scientific basis Cochrane Central Register of Controlled Trials; MEDLINE; PsycINFO; CINAHL and LILACS. In addition to manual search of the references of articles and search for theses and dissertations in Portal Capes and LILACS. The identification of the studies was conducted from October to December 2015, being the last search on December 3. Selection criteria: randomised controlled trials (RCT) involving adults with stroke who had deficits in upper limb function and used AO as an intervention. Data collection and analysis: the data extracted from the studies were used to analyze the risk of bias, the effect of the treatment and the quality of the body of evidence. Main results: 6 studies were included, totaling 270 patients. The primary outcome analyzed was the motor function of MS. Were combined in meta-analyzes studies comparing AO versus placebo or an active control, considering the immediate and long-term effect (n=241). Regarding the motor function of the arm (5 trials), the estimated effect for the therapy was not significant. However, when considering the hand function estimating the effect was favorable to the group that conducted the AO, in short (mean difference = 6.93, 95% CI 1.48 to 12.39; P = 0.01) and long-term (mean difference = 7.57; 95% CI 1.34 the 13.80; p = 0.02). Unable to perform the analysis for functional dependency. The studies showed a low or uncertain risk of bias, but the quality of evidence the body was considered low and very low quality. Authors’ conclusions: AO was effective in improving hand function of stroke patients. Despite the low quality evidence that the use of OA in clinical practice should not be discouraged. RCT new studies should be conducted with greater methodological rigor and larger samples, covering important outcomes such as functional dependence for ADLs.

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

Contribuições a técnicas de agrupamento e visualização de dados multivariados utilizando mapas auto-organizáveis

Self-organizing maps (SOM) are artificial neural networks widely used in the data mining field, mainly because they constitute a dimensionality reduction technique given the fixed grid of neurons associated with the network. In order to properly the partition and visualize the SOM network, the various methods available in the literature must be applied in a post-processing stage, that consists of inferring, through its neurons, relevant characteristics of the data set. In general, such processing applied to the network neurons, instead of the entire database, reduces the computational costs due to vector quantization. This work proposes a post-processing of the SOM neurons in the input and output spaces, combining visualization techniques with algorithms based on gravitational forces and the search for the shortest path with the greatest reward. Such methods take into account the connection strength between neighbouring neurons and characteristics of pattern density and distances among neurons, both associated with the position that the neurons occupy in the data space after training the network. Thus, the goal consists of defining more clearly the arrangement of the clusters present in the data. Experiments were carried out so as to evaluate the proposed methods using various artificially generated data sets, as well as real world data sets. The results obtained were compared with those from a number of well-known methods existent in the literature

Utilizando Pesos estáticos e dinâmicos em sistemas multi-classificadores com diferentes níveis de diversidade

Although some individual techniques of supervised Machine Learning (ML), also known as classifiers, or algorithms of classification, to supply solutions that, most of the time, are considered efficient, have experimental results gotten with the use of large sets of pattern and/or that they have a expressive amount of irrelevant data or incomplete characteristic, that show a decrease in the efficiency of the precision of these techniques. In other words, such techniques can t do an recognition of patterns of an efficient form in complex problems. With the intention to get better performance and efficiency of these ML techniques, were thought about the idea to using some types of LM algorithms work jointly, thus origin to the term Multi-Classifier System (MCS). The MCS s presents, as component, different of LM algorithms, called of base classifiers, and realized a combination of results gotten for these algorithms to reach the final result. So that the MCS has a better performance that the base classifiers, the results gotten for each base classifier must present an certain diversity, in other words, a difference between the results gotten for each classifier that compose the system. It can be said that it does not make signification to have MCS s whose base classifiers have identical answers to the sames patterns. Although the MCS s present better results that the individually systems, has always the search to improve the results gotten for this type of system. Aim at this improvement and a better consistency in the results, as well as a larger diversity of the classifiers of a MCS, comes being recently searched methodologies that present as characteristic the use of weights, or confidence values. These weights can describe the importance that certain classifier supplied when associating with each pattern to a determined class. These weights still are used, in associate with the exits of the classifiers, during the process of recognition (use) of the MCS s. Exist different ways of calculating these weights and can be divided in two categories: the static weights and the dynamic weights. The first category of weights is characterizes for not having the modification of its values during the classification process, different it occurs with the second category, where the values suffers modifications during the classification process. In this work an analysis will be made to verify if the use of the weights, statics as much as dynamics, they can increase the perfomance of the MCS s in comparison with the individually systems. Moreover, will be made an analysis in the diversity gotten for the MCS s, for this mode verify if it has some relation between the use of the weights in the MCS s with different levels of diversity