WI-BIO: redes de monitoramento de pacientes em ambientes de automação hospitalar utilizando o padrão IEEE 802.11

The monitoring of patients performed in hospitals is usually done either in a manual or semiautomated way, where the members of the healthcare team must constantly visit the patients to ascertain the health condition in which they are. The adoption of this procedure, however, compromises the quality of the monitoring conducted since the shortage of physical and human resources in hospitals tends to overwhelm members of the healthcare team, preventing them from moving to patients with adequate frequency. Given this, many existing works in the literature specify alternatives aimed at improving this monitoring through the use of wireless networks. In these works, the network is only intended for data traffic generated by medical sensors and there is no possibility of it being allocated for the transmission of data from applications present in existing user stations in the hospital. However, in the case of hospital automation environments, this aspect is a negative point, considering that the data generated in such applications can be directly related to the patient monitoring conducted. Thus, this thesis defines Wi-Bio as a communication protocol aimed at the establishment of IEEE 802.11 networks for patient monitoring, capable of enabling the harmonious coexistence among the traffic generated by medical sensors and user stations. The formal specification and verification of Wi-Bio were made through the design and analysis of Petri net models. Its validation was performed through simulations with the Network Simulator 2 (NS2) tool. The simulations of NS2 were designed to portray a real patient monitoring environment corresponding to a floor of the nursing wards sector of the University Hospital Onofre Lopes (HUOL), located at Natal, Rio Grande do Norte. Moreover, in order to verify the feasibility of Wi-Bio in terms of wireless networks standards prevailing in the market, the testing scenario was also simulated under a perspective in which the network elements used the HCCA access mechanism described in the IEEE 802.11e amendment. The results confirmed the validity of the designed Petri nets and showed that Wi-Bio, in addition to presenting a superior performance compared to HCCA on most items analyzed, was also able to promote efficient integration between the data generated by medical sensors and user applications on the same wireless network

Modelo 2,5D de predição de propagação para ambientes interiores utilizando o método do traçado de raios

A 2.5D ray-tracing propagation model is proposed to predict radio loss in indoor environment. Specifically, we opted for the Shooting and Bouncing Rays (SBR) method, together with the Geometrieal Theory of Diffrartion (GTD). Besides the line-of-sight propagation (LOS), we consider that the radio waves may experience reflection, refraction, and diffraction (NLOS). In the Shooting and Bouncing Rays (SBR) method, the transmitter antenna launches a bundle of rays that may or may not reach the receiver. Considering the transmitting antenna as a point, the rays will start to launch from this position and can reach the receiver either directly or after reflections, refractions, diffractions, or even after any combination of the previous effects. To model the environment, a database is built to record geometrical characteristics and information on the constituent materials of the scenario. The database works independently of the simulation program, allowing robustness and flexibility to model other seenarios. Each propagation mechanism is treated separately. In line-of-sight propagation, the main contribution to the received signal comes from the direct ray, while reflected, refracted, and diffracted signal dominate when the line-of-sight is blocked. For this case, the transmitted signal reaches the receiver through more than one path, resulting in a multipath fading. The transmitting channel of a mobile system is simulated by moving either the transmitter or the receiver around the environment. The validity of the method is verified through simulations and measurements. The computed path losses are compared with the measured values at 1.8 GHz ftequency. The results were obtained for the main corridor and room classes adjacent to it. A reasonable agreement is observed. The numerical predictions are also compared with published data at 900 MHz and 2.44 GHz frequencies showing good convergence

Simulador computacional do comportamento em regime de poços de petróleo equipados com gas-lift contínuo

Amongst the results of the AutPoc Project - Automation of Wells, established between UFRN and Petrobras with the support of the CNPq, FINEP, CTPETRO, FUNPEC, was developed a simulator for equipped wells of oil with the method of rise for continuous gas-lift. The gas-lift is a method of rise sufficiently used in production offshore (sea production), and its basic concept is to inject gas in the deep one of the producing well of oil transform it less dense in order to facilitate its displacement since the reservoir until the surface. Based in the use of tables and equations that condense the biggest number of information on characteristics of the reservoir, the well and the valves of gas injection, it is allowed, through successive interpolations, to simulate representative curves of the physical behavior of the existing characteristic variable. With a simulator that approaches a computer of real the physical conditions of an oil well is possible to analyze peculiar behaviors with very bigger speeds, since the constants of time of the system in question well are raised e, moreover, to optimize costs with assays in field. The simulator presents great versatility, with prominance the analysis of the influence of parameters, as the static pressure, relation gas-liquid, pressure in the head of the well, BSW (Relation Basic Sediments and Water) in curves of request in deep of the well and the attainment of the curve of performance of the well where it can be simulated rules of control and otimization. In moving the rules of control, the simulator allows the use in two ways of simulation: the application of the control saw software simulated enclosed in the proper simulator, as well as the use of external controllers. This implies that the simulator can be used as tool of validation of control algorithms. Through the potentialities above cited, of course one another powerful application for the simulator appears: the didactic use of the tool. It will be possible to use it in formation courses and recycling of engineers

Análise de escalabilidade de uma implementação paralela do simulated annealing acoplado

This paper analyzes the performance of a parallel implementation of Coupled Simulated Annealing (CSA) for the unconstrained optimization of continuous variables problems. Parallel processing is an efficient form of information processing with emphasis on exploration of simultaneous events in the execution of software. It arises primarily due to high computational performance demands, and the difficulty in increasing the speed of a single processing core. Despite multicore processors being easily found nowadays, several algorithms are not yet suitable for running on parallel architectures. The algorithm is characterized by a group of Simulated Annealing (SA) optimizers working together on refining the solution. Each SA optimizer runs on a single thread executed by different processors. In the analysis of parallel performance and scalability, these metrics were investigated: the execution time; the speedup of the algorithm with respect to increasing the number of processors; and the efficient use of processing elements with respect to the increasing size of the treated problem. Furthermore, the quality of the final solution was verified. For the study, this paper proposes a parallel version of CSA and its equivalent serial version. Both algorithms were analysed on 14 benchmark functions. For each of these functions, the CSA is evaluated using 2-24 optimizers. The results obtained are shown and discussed observing the analysis of the metrics. The conclusions of the paper characterize the CSA as a good parallel algorithm, both in the quality of the solutions and the parallel scalability and parallel efficiency

Recuperação e purificação de quitosanases usando adsorção em leito expandido com streamline DEAE com modelagem e simulação usando redes neurais

Expanded Bed Adsorption (EBA) is an integrative process that combines concepts of chromatography and fluidization of solids. The many parameters involved and their synergistic effects complicate the optimization of the process. Fortunately, some mathematical tools have been developed in order to guide the investigation of the EBA system. In this work the application of experimental design, phenomenological modeling and artificial neural networks (ANN) in understanding chitosanases adsorption on ion exchange resin Streamline® DEAE have been investigated. The strain Paenibacillus ehimensis NRRL B-23118 was used for chitosanase production. EBA experiments were carried out using a column of 2.6 cm inner diameter with 30.0 cm in height that was coupled to a peristaltic pump. At the bottom of the column there was a distributor of glass beads having a height of 3.0 cm. Assays for residence time distribution (RTD) revelead a high degree of mixing, however, the Richardson-Zaki coefficients showed that the column was on the threshold of stability. Isotherm models fitted the adsorption equilibrium data in the presence of lyotropic salts. The results of experiment design indicated that the ionic strength and superficial velocity are important to the recovery and purity of chitosanases. The molecular mass of the two chitosanases were approximately 23 kDa and 52 kDa as estimated by SDS-PAGE. The phenomenological modeling was aimed to describe the operations in batch and column chromatography. The simulations were performed in Microsoft Visual Studio. The kinetic rate constant model set to kinetic curves efficiently under conditions of initial enzyme activity 0.232, 0.142 e 0.079 UA/mL. The simulated breakthrough curves showed some differences with experimental data, especially regarding the slope. Sensitivity tests of the model on the surface velocity, axial dispersion and initial concentration showed agreement with the literature. The neural network was constructed in MATLAB and Neural Network Toolbox. The cross-validation was used to improve the ability of generalization. The parameters of ANN were improved to obtain the settings 6-6 (enzyme activity) and 9-6 (total protein), as well as tansig transfer function and Levenberg-Marquardt training algorithm. The neural Carlos Eduardo de Araújo Padilha dezembro/2013 9 networks simulations, including all the steps of cycle, showed good agreement with experimental data, with a correlation coefficient of approximately 0.974. The effects of input variables on profiles of the stages of loading, washing and elution were consistent with the literature

Modelagem dos algorítmos simples e Simulated Annealing por cadeias de Markov

Os Algoritmos Genético (AG) e o Simulated Annealing (SA) são algoritmos construídos para encontrar máximo ou mínimo de uma função que representa alguma característica do processo que está sendo modelado. Esses algoritmos possuem mecanismos que os fazem escapar de ótimos locais, entretanto, a evolução desses algoritmos no tempo se dá de forma completamente diferente. O SA no seu processo de busca trabalha com apenas um ponto, gerando a partir deste sempre um nova solução que é testada e que pode ser aceita ou não, já o AG trabalha com um conjunto de pontos, chamado população, da qual gera outra população que sempre é aceita. Em comum com esses dois algoritmos temos que a forma como o próximo ponto ou a próxima população é gerada obedece propriedades estocásticas. Nesse trabalho mostramos que a teoria matemática que descreve a evolução destes algoritmos é a teoria das cadeias de Markov. O AG é descrito por uma cadeia de Markov homogênea enquanto que o SA é descrito por uma cadeia de Markov não-homogênea, por fim serão feitos alguns exemplos computacionais comparando o desempenho desses dois algoritmos