Análise de antenas de microfita em rampa com múltiplas camadas dielétricas

The present work deals with the ana1ysis of microstrip patch antennas printed on tapered dielectric substrates. We investigate the influence ofthe substrate height variations on the properties of configurations such as microstrip patch antennas, microstrip patch antennas with overlay and suspendeô microstrip patch antennas. The dielectric substrates can be isotropic or anisotropic ones. This accurate analysis is based on the full-wave formulation. It is carried out initially for the determination of the impedance matrix, through the use of the spectral¬domain immitance approach. We use a model based on a segmentation of the considered line into uniform microstrip line subsections. Normalized phase constants and characteristic impedances are obtained by means of the Galerkin numerical technique. Then, the cascaded combination of the uniform microstrip subsections are analyzed through an interactive procedure. Numerical results are presented for the input reflection coefficient, voltage standing wave ratio, resonant frequency, and radiation pattems ofthe E_plane and H-plane diagrams. It is found that the variations in the substrate height profile produce a great influence on the bandwidth of microstrip antennas. This procedure gives bandwidth improvements without altering considerably the resonant frequency. Furthermore, the tapered microstrip antenna can be used as a lightweight altemative for bandwidth control and to eXtend the use of microstiip antenna technology to a wider variety of applications. Finally, suggestions for the continuity of this work are presented

Exploiting the versatility of taste sensors based on impedance spectroscopy

The versatility of sensor arrays made from nanostructured Langmuir-Blodgett (LB) and layer-by-layer (LBL) films is demonstrated in two ways. First, different combinations of sensing units are employed to distinguish the basic tastes, viz. sweet, sour, bitter, and salty tastes, produced, respectively, by small concentrations (down to 0.01 g/mol) of sucrose, HCl, quinine, and NaCl solutions. The sensing units are comprised of LB and/or LBL films from semiconducting polymers, a ruthenium complex, and sulfonated lignin. Then, sensor arrays were used to identify wines from different sources, with the high distinguishing ability being demonstrated in principal component analysis (PCA) plots. Particularly important was the fact that the sensing ability does not depend on specific interactions between analytes and the film materials, but a judicious choice of materials is, nevertheless, required for the materials to respond differently to a given sample. It is also shown that the interaction with the analyte may affect the morphology of the nanostructured films, as indicated with scanning electron microscopy. For instance, in wine analysis these changes are not irreversible and the original film morphology is retrieved if the sensing unit is washed with copious amounts of water, thus allowing the sensor unit to be reused.

Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analysis of the Al-PANI interfaces by complex impedance spectroscopy

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Impedance of carrier injection at the metal-organic interface mediated by surface states in electron-only tris(8-hydroxyquinoline) aluminium (Alq(3)) thin layers

Capacitance spectra of thin (< 200 nm) Alq(3) electron-only devices have been measured as a function of bias voltage. Capacitance spectra exhibit a flat response at high frequencies (> 10(3) Hz) and no feature related to the carrier transit time is observed. Toward low frequencies the spectra reach a maximum and develop a negative excess capacitance. Capacitance response along with current-voltage (J-V) characteristics are interpreted in terms of the injection of electrons mediated by surface states at the metal organic interface. A detailed model for the impedance of the injection process is provided that highlights the role of the filling/releasing kinetics of energetically distributed interface states. This approach connects the whole capacitance spectra to the occupancy of interface states, with no additional information about bulk trap levels. Simulations based on the model allow to derive the density of interface states effectively intervening in the carrier injection (similar to 1.5 x 10(12) cm (2)). (C) 2008 Elsevier B.V. All rights reserved.

Time-domain analysis of piezoelectric impedance-based structural health monitoring using multilevel wavelet decomposition

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A New Impedance Measurement System for PZT-Based Structural Health Monitoring

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Influence of Excitation Signal on Impedance-based Structural Health Monitoring

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sizing PZT Transducers in Impedance-Based Structural Health Monitoring

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optimal Frequency Range Selection for PZT Transducers in Impedance-Based SHM Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Automatic dimming multi-lamp fluorescent management system with active input power factor corrector stage

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Real-time multi-sensors measurement system with temperature effects compensation for impedance-based structural health monitoring

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Structural integrity identification based on smart materials and neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically>30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, two sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with an experimental example, an investigation on a massive quarter scale model of a steel bridge section, in order to verify the performance of this proposed methodology.

Structural FRF acquisition via electric impedance measurement applied to damage location

Continuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward Variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.

Structural Health Monitoring in Smart Structures Through Time Series Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)