In this Thesis, the problem of the Free Vibrations Analysis of Doubly Stiffened Composite, Orthotropic and/or Isotropic, Base Plates or Panels (with Orthotropic Stiffening Plate Strips) is investigated. The composite plate or panel system is made of an Orthotropic and/or Isotropic Base Plate stiffened or reinforced by adhesively bonded Upper and Lower Orthotropic Stiffening Plate Strips. The plates are assumed to be the Mindlin Plates connected by relatively very thin adhesive layers. The general problem under study is considered in terms of three problems, namely Main PROBLEM I Main PROBLEM II and Main PROBLEM III. The theoretical formulation of the Main PROBLEMS is based on a First Order Shear Deformation Plate Theory (FSDPT) that is, in this case, the Mindlin Plate Theory. The entire composite system is assumed to have simple supports along the two opposite edges so that the Classical Levy&#039 / s Solutions can be applied in that direction. Thus, the transverse shear deformations and the rotary moments of inertia of plates are included in the formulation. The very thin, yet elastic deformable adhesive layers are considered as continua with transverse normal and shear stresses. The damping effects in the plates and the adhesive layers are neglected. The sets of the systems of equations of the Mindlin Plate Theory are reduced to a set of the Governing System of First Order Ordinary Differential Equations in the state vector form. The sets of the Governing System for each Main PROBLEM constitute a Two-Point Boundary Value Problem in the y-direction which is taken along the length of the plates. Then, the system is solved by the Modified Transfer Matrix Method (with Interpolation Polynomials and/or Chebyshev Polynomials)which is a relatively semi-analytical and numerical technique. The numerical results and important parametric studies of the natural modes and the corresponding frequencies of the composite system are presented.

This work consists in the development and design of an energy harvesting device to supply power to the new generation pacemakers, miniaturized leadless implants without battery placed directly in heart chambers. After analyzing different mechanical energy sources in the cardiac environment and associated energy harvesting mechanisms, a concept based on regular blood pressure variation stood out: an implant with a flexible packaging that transmits blood forces to an internal transducer. Advantages compared to traditional inertial scavengers are mainly: greater power density, adaptability to heartbeat frequency changes and miniaturization potential. Ultra-flexible 10-µm thin metal bellows have been designed, fabricated and tested. These prototypes acting as implant packaging that deforms under blood pressure actuation have validated the proposed harvesting concept. A new type of electrostatic transducer (3D multi-layer out-of-plane overlap structure with interdigitated combs) has been introduced and fully analyzed. Promising numerical results and associated fabrication processes are presented. Also, large stroke optimized piezoelectric spiral transducers including their complex electrodes patterns have been studied through a design analysis, numerical simulations, prototype fabrication and experimental testing. Apower density of 3 µJ/cm3/cycle has been experimentally achieved. With further addressed developments, the proposed device should provide enough energy to power autonomously and virtually perpetually the next generation of pacemakers.

Safety and reliability are prime concerns in aircraft performance due to the involved costs and risk to lives. Despite the best efforts in design methodology, quality evaluation in production and structural integrity assessment in-service, attainment of one hundred percent safety through development and use of a suitable in-flight health monitoring system is still a farfetched goal. And, evolution of such a system requires, first, identification of an appropriate Technique and next its adoption to meet the challenges posed by newer materials (advanced composites), complex structures and the flight environment. In fact, a quick survey of the available Non-Destructive Evaluation (NDE) techniques suggests Acoustic Emission (AE) as the only available method. High merit in itself could be a weakness - Noise is the worst enemy of AE. So, while difficulties are posed due to the insufficient understanding of the basic behavior of composites, growth and interaction of defects and damage under a specified load condition, high in-flight noise further complicates the issue making the developmental task apparently formidable and challenging. Development of an in-flight monitoring system based on AE to function as an early warning system needs addressing three aspects, viz., the first, discrimination of AE signals from noise data, the second, extraction of required information from AE signals for identification of sources (source characterization) and quantification of its growth, and the third, automation of the entire process. And, a quick assessment of the aspects involved suggests that Artificial Neural Networks (ANN) are ideally suited for solving such a complex problem. A review of the available open literature while indicates a number of investigations carried out using noise elimination and source characterization methods such as frequency filtering and statistical pattern recognition but shows only sporadic attempts using ANN. This may probably be due to the complex nature of the problem involving investigation of a large number of influencing parameters, amount of effort and time to be invested, and facilities required and multi-disciplinary nature of the problem. Hence as stated in the foregoing, the need for such a study cannot be over emphasized. Thus, this thesis is an attempt addressing the issue of analysis and automation of complex sets of AE data such as AE signals mixed with in-flight noise thus forming the first step towards in-flight monitoring using AE. An ANN can in fact replace the traditional algorithmic approaches used in the past. ANN in general are model free estimators and derive their computational efficiency due to large connectivity, massive parallelism, non-linear analog response and learning capabilities. They are better suited than the conventional methods (statistical pattern recognition methods) due to their characteristics such as classification, pattern matching, learning, generalization, fault tolerance and distributed memory and their ability to process unstructured data sets which may be carrying incomplete information at times and hence chosen as the tool. Further, in the current context, the set of investigations undertaken were in the absence of sufficient a priori information and hence clustering of signals generated by AE sources through self-organizing maps is more appropriate. Thus, in the investigations carried out under the scope of this thesis, at first a hybrid network named "NAEDA" (Neural network for Acoustic Emission Data Analysis) using Kohonen self-organizing feature map (KSOM) and multi-layer perceptron (MLP) that learns on back propagation learning rule was specifically developed with innovative data processing techniques built into the network. However, for accurate pattern recognition, multi-layer back propagation NN needed to be trained with source and noise clusters as input data. Thus, in addition to optimizing the network architecture and training parameters, preprocessing of input data to the network and multi-class clustering and classification proved to be the corner stones in obtaining excellent identification accuracy. Next, in-flight noise environment of an aircraft was generated off line through carefully designed simulation experiments carried out in the laboratory (Ex: EMI, friction, fretting and other mechanical and hydraulic phenomena) based on the in-flight noise survey carried out by earlier investigators. From these experiments data was acquired and classified into their respective classes through MLP. Further, these noises were mixed together and clustered through KSOM and then classified into their respective clusters through MLP resulting in an accuracy of 95%- 100% Subsequently, to evaluate the utility of NAEDA for source classification and characterization, carbon fiber reinforced plastic (CFRP) specimens were subjected to spectrum loading simulating typical in-flight load and AE signals were acquired continuously up to a maximum of three designed lives and in some cases up to failure. Further, AE signals with similar characteristics were grouped into individual clusters through self-organizing map and labeled as belonging to appropriate failure modes, there by generating the class configuration. Then MLP was trained with this class information, which resulted in automatic identification and classification of failure modes with an accuracy of 95% - 100%. In addition, extraneous noise generated during the experiments was acquired and classified so as to evaluate the presence or absence of such data in the AE data acquired from the CFRP specimens. In the next stage, noise and signals were mixed together at random and were reclassified into their respective classes through supervised training of multi-layer back propagation NN. Initially only noise was discriminated from the AE signals from CFRP failure modes and subsequently both noise discrimination and failure mode identification and classification was carried out resulting in an accuracy of 95% - 100% in most of the cases. Further, extraneous signals mentioned above were classified which indicated the presence of such signals in the AE signals obtained from the CFRP specimen. Thus, having established the basis for noise identification and AE source classification and characterization, two specific examples were considered to evaluate the utility and efficiency of NAEDA. In the first, with the postulation that different basic failure modes in composites have unique AE signatures, the difference in damage generation and progression can be clearly characterized under different loading conditions. To examine this, static compression tests were conducted on a different set of CFRP specimens till failure with continuous AE monitoring and the resulting AE signals were classified through already trained NAEDA. The results obtained shows that the total number of signals obtained were very less when compared to fatigue tests and the specimens failed with hardly any damage growth. Further, NAEDA was able to discriminate the"noise and failure modes in CFRP specimen with the same degree of accuracy with which it has classified such signals obtained from fatigue tests. In the second example, with the same postulate of unique AE signatures for different failure modes, the differences in the complexion of the damage growth and progression should become clearly evident when one considers specimens with different lay up sequences. To examine this, the data was reclassified on the basis of differences in lay up sequences from specimens subjected to fatigue. The results obtained clearly confirmed the postulation. As can be seen from the summary of the work presented in the foregoing paragraphs, the investigations undertaken within the scope of this thesis involve elaborate experimentation, development of tools, acquisition of extensive data and analysis. Never the less, the results obtained were commensurate with the efforts and have been fruitful. Of the useful results that have been obtained, to state in specific, the first is, discrimination of simulated noise sources achieved with significant success but for some overlapping which is not of major concern as far as noises are concerned. Therefore they are grouped into required number of clusters so as to achieve better classification through supervised NN. This proved to be an innovative measure in supervised classification through back propagation NN. The second is the damage characterization in CFRP specimens, which involved imaginative data processing techniques that proved their worth in terms of optimization of various training parameters and resulted in accurate identification through clustering. Labeling of clusters is made possible by marking each signal starting from clustering to final classification through supervised neural network and is achieved through phenomenological correlation combined with ultrasonic imaging. Most rewarding of all is the identification of failure modes (AE signals) mixed in noise into their respective classes. This is a direct consequence of innovative data processing, multi-class clustering and flexibility of grouping various noise signals into suitable number of clusters. Thus, the results obtained and presented in this thesis on NN approach to AE signal analysis clearly establishes the fact that methods and procedures developed can automate detection and identification of failure modes in CFRP composites under hostile environment, which could lead to the development of an in-flight monitoring system.

Large on-chip bandwidths required for high performance electronic chips will render optical components essential parts of future on-chip interconnects. Silicon photonics enables highly integrated photonic integrated circuit (PIC) using CMOS compatible process. In order to maximize the bandwidth density and design flexibility of PICs, vertical integration of electronic layers and photonics layers is strongly preferred. Comparing deposited silicon, single crystalline silicon offers low material absorption loss and high carrier mobility, which are ideal for multi-layer silicon PIC. Three different methods to build multi-layer silicon PICs based on single crystalline silicon are demonstrated in this dissertation, including double-bonded silicon-on-insulator (SOI) wafers, transfer printed silicon nanomembranes, and adhesively bonded silicon nanomembranes. 1-to-12 waveguide fanouts using multimode interference (MMI) couplers were designed, fabricated and characterized on both double-bonded SOI and transfer printed silicon nanomembrane, and the results show comparable performance to similar devices fabricated on SOI. However, both of these two methods have their limitations in optical interconnects applications. Large and defect-free silicon nanomembrane fabricated using adhesive bonding is identified as a promising solution to build multi-layer silicon PICs. A double-layer structure constituted of vertically integrated silicon nanomembranes was demonstrated. Subwavelength length based fiber-to-chip grating couplers were used to couple light into this new platform. Three basic building blocks of silicon photonics were designed, fabricated and characterized, including 1) inter-layer grating coupler based on subwavelength nanostructure, which has efficiency of 6.0 dB and 3 dB bandwidth of 41 nm, for light coupling between layers, 2) 1-to-32 H-tree optical distribution, which has excess loss of 2.2 dB, output uniformity of 0.72 dB and 3 dB bandwidth of 880 GHz, 3) waveguide crossing utilizing index-engineered MMI coupler, which has crossing loss of 0.019 dB, cross talk lower than -40 dB and wide transmission spectrum covering C-band and L-band. The demonstrated integration method and silicon photonic devices can be integrated into the CMOS back-end process for clock distribution and global signaling. / text

In this work, several beam finite element formulations are proposed for failure analysis of planar reinforced concrete beams and frames under monotonic static loading. The localized failure of material is modeled by the embedded strong discontinuity concept, which enhances standard interpolation of displacement (or rotation) with a discontinuous function, associated with an additional kinematic parameter representing jump in displacement (or rotation). The new parameters are local and are condensed on the element level. One stress resultant and two multi-layer beam finite elements are derived. The stress resultant Euler-Bernoulli beam element has embedded discontinuity in rotation. Bending response of the bulk of the element is described by elasto-plastic stress resultant material model. The cohesive relation between the moment and the rotational jump at the softening hinge is described by rigid-plastic model. Axial response is elastic. In the multi-layer beam finite elements, each layer is treated as a bar, made of either concrete or steel. Regular axial strain in a layer is computed according to Euler-Bernoulli or Timoshenko beam theory. Additional axial strain is produced by embedded discontinuity in axial displacement, introduced individually in each layer. Behavior of concrete bars is described by elastodamage model, while elasto-plasticity model is used for steel bars. The cohesive relation between the stress at the discontinuity and the axial displacement jump is described by rigid-damage softening model in concrete bars and by rigid-plastic softening model in steel bars. Shear response in the Timoshenko element is elastic. Finally, the multi-layer Timoshenko beam finite element is upgraded by including viscosity in the softening model. Computer code implementation is presented in detail for the derived elements. An operator split computational procedure is presented for each formulation. The expressions, required for the local computation of inelastic internal variables and for the global computation of the degrees of freedom, are provided. Performance of the derived elements is illustrated on a set of numerical examples, which show that the multi-layer Euler-Bernoulli beam finite element is not reliable, while the stress-resultant Euler-Bernoulli beam and the multi-layer Timoshenko beam finite elements deliver satisfying results.

The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties &ldquo / low&rdquo / , &ldquo / medium&rdquo / and &ldquo / high&rdquo / . In the rough fuzzy MLP, initial weights and near optimal number of hidden nodes are estimated using rough dependency rules. A rough fuzzy RBF structure similar to the rough fuzzy MLP is proposed. The rough fuzzy RBF was inspected whether dependencies like the ones in rough fuzzy MLP can be concluded.

In this thesis, a new technique for using quartz crystal microbalance (QCM) in magnetic field was explored. This technique would take advantage of the sensitive nature of QCM to vibration changes. The idea is to perturb the QCM vibrations with magnetic materials on it by applying magnetic field. A new instrument called magnetic QCM (MQCM) was constructed to explore this technique. The thesis contains three bodies of work. The first body describes the development of the MQCM instrument and the demonstration of the technique. The resonance frequency of a QCM with conducting polymer (polyaniline) suspension in poly(ethylene glycol) was observed to increase with increasing applied DC magnetic field. The change in population of free spins through doping with HCl vapor is reflected in increased frequency-field curve magnitude. The second body of work describes the study of QCM proximity phenomenon discovered during the MQCM instrument development process. When an object approaches a vibrating QCM, the resonant frequency changes. This proximity effect is seen at the distance of 10 mm in air and becomes more pronounced as the distance decreases. This effect depends on the value of quality factor, conductivity of the object, and electrical connection of the object to the QCM electrodes. A simple modified Butterworth van-Dyke model is used to describe this effect. It must be recognized that this effect may lead to experimental artifacts in a variety of analytical QCM applications. The third body of work describes an improved version of MQCM. The complex geometry such as particle suspension were simplified to alternating stack of ferromagnetic and diamagnetic layers. When magnetic field was applied, changes in the QCM admittance magnitude and phase curves were observed. A mass-equivalent stack of continuous consecutive layers of nickel and gold was also exposed to magnetic field but no changes were observed. Butterworth-van-Dyke model attributed the effect to internal shear friction loss among other losses is modulated by the magnetic field. Quantum effect was considered. However, after examining SEM surface images, the source of acoustic response to magnetic field is more likely from interfacial stresses.

The thesis reports a contribution to the development of neural-like network- based element-free methods for the numerical simulation of some non-Newtonian fluid flow problems. The numerical approximation of functions and solution of the governing partial differential equations are mainly based on radial basis function networks. The resultant micro-macroscopic approaches do not require any element-based discretisation and only rely on a set of unstructured collocation points and hence are truly meshless or element-free. The development of the present methods begins with the use of the multi-layer perceptron networks (MLPNs) and radial basis function networks (RBFNs) to effectively eliminate the volume integrals in the integral formulation of fluid flow problems. An adaptive velocity gradient domain decomposition (AVGDD) scheme is incorporated into the computational algorithm. As a result, an improved feed forward neural network boundary-element-only method (FFNN- BEM) is created and verified. The present FFNN-BEM successfully simulates the flow of several Generalised Newtonian Fluids (GNFs), including the Carreau, Power-law and Cross models. To the best of the author's knowledge, the present FFNN-BEM is the first to achieve convergence for difficult flow situations when the power-law indices are very small (as small as 0.2). Although some elements are still used to discretise the governing equations, but only on the boundary of the analysis domain, the experience gained in the development of element-free approximation in the domain provides valuable skills for the progress towards an element-free approach. A least squares collocation RBFN-based mesh-free method is then developed for solving the governing PDEs. This method is coupled with the stochastic simulation technique (SST), forming the mesoscopic approach for analyzing viscoelastic flid flows. The velocity field is computed from the RBFN-based mesh-free method (macroscopic component) and the stress is determined by the SST (microscopic component). Thus the SST removes a limitation in traditional macroscopic approaches since closed form constitutive equations are not necessary in the SST. In this mesh-free method, each of the unknowns in the conservation equations is represented by a linear combination of weighted radial basis functions and hence the unknowns are converted from physical variables (e.g. velocity, stresses, etc) into network weights through the application of the general linear least squares principle and point collocation procedure. Depending on the type of RBFs used, a number of parameters will influence the performance of the method. These parameters include the centres in the case of thin plate spline RBFNs (TPS-RBFNs), and the centres and the widths in the case of multi-quadric RBFNs (MQ-RBFNs). A further improvement of the approach is achieved when the Eulerian SST is formulated via Brownian configuration fields (BCF) in place of the Lagrangian SST. The SST is made more efficient with the inclusion of the control variate variance reduction scheme, which allows for a reduction of the number of dumbbells used to model the fluid. A highly parallelised algorithm, at both macro and micro levels, incorporating a domain decomposition technique, is implemented to handle larger problems. The approach is verified and used to simulate the flow of several model dilute polymeric fluids (the Hookean, FENE and FENE-P models) in simple as well as non-trivial geometries, including shear flows (transient Couette, Poiseuille flows)), elongational flows (4:1 and 10:1 abrupt contraction flows) and lid-driven cavity flows.

Les multicouches de graphène en empilement rhomboédrique sont considérés comme une phase prometteuse du carbone. Cela est due à la particularité de cette phase de pouvoir exhiber des états à forte corrélation électronique comme le magnétisme ou la supraconductivité à haute température critique. Ce qui est due, a son tour, à l’occurrence d’un état de surface avec une dispersion d’énergie électroniques quasi-nulle à proximité du niveau de Fermi. Malgré que le graphite Bernal soit la forme la plus stable du graphite, des échantillons a trois et quatre couches de graphène en empilement rhomboédrique ont pu être synthétisés. Plus récemment, des flocons d’épaisseur dépassant les 17 couches ont été isolés et provisoirement attribués à des séquences d’empilement rhomboédrique. Cette attribution à été faite via des expériences de spectroscopie Raman sous champ magnétique, bien que l’empreinte Raman des multicouche de graphène en empilement rhomboédrique est actuellement inconnue. Même le cas simple du spectre Raman résonnant à deux phonons (le pic 2D) du graphite Bernal n’est pas totalement compris. Dans ce travail de thèse, nous fournissons une description ab-initio complète du pic Raman 2D dans les systèmes de graphène à trois et quatre couches pour tous les empilements possibles, ainsi que pour le graphite Bernal, rhomboédrique et une alternance de graphite Bernal et rhomboédrique. / Multi-layer graphene with rhombohedral ABC stacking is considered as a promising carbon phase possibly displaying correlated states like magnetism or high-T c superconductivity due to the occurrence of an ultraflat electronic surface band at the Fermi level. Despite Bernal graphite being the most stable form of graphite, three and four layers graphene samples with rhombohedral stacking can be synthesized. Recently, flakes of thickness up to 17 layers were tentatively attributed ABC sequences although the Raman fingerprint of rhombohedral multilayer graphene is currently unknown and the 2D two-phonon resonant Raman spectrum of Bernal graphite not completely theoretically understood. Here we provide a complete first principles description of the 2D Raman peak in three and four layer graphene for all possible stackings, as well as for bulk Bernal, rhombohedral and an alternation of Bernal and rhombohedral graphite, that can be seen as a periodic sequence of ABA and ABC trilayers. Calculations for several laser energies are performed and we give practical prescriptions are proposed to identify long range sequences of ABC multi-layer graphene flakes.

A segmentaçãoo de imagens é fundamental para a visão computacional. Com essa finalidade, a textura tem sido uma propriedade bastante explorada por pesquisadores. Porém, a existência de diversos métodos de extração de textura, muitas vezes específicos para determinadas aplicações, dificulta a implementação de sistemas de escopo mais geral. Tendo esse contexto como motivação e inspirado no sucesso dos sistemas de visão naturais e em sua generalidade, este trabalho propõe a combinação de métodos por meio da seleção de características baseada na saliência das sinapses de um perceptron multicamadas (MLP). É proposto, também, um método alternativo baseado na capacidade do MLP de apreender textura que dispensa o uso de técnicas de extração de textura. Como principal contribuição, além da comparação da heurística de seleção proposta frente à busca exaustiva segundo o critério da distância de Jeffrey-Matusita, foi introduzida a técnica de Equalização da Entrada, que melhorou consideravelmente a qualidade da medida de saliência. É também apresentada a segmentação de imagens de cenas naturais, como exemplo de aplicação / Segmentation is a crucial step in Computer Vision. Texture has been a property largely employed by many researchers to achieve segmentation. The existence of a large amount of texture extraction methods is, sometimes, a hurdle to overcome when it comes to modeling systems for more general problems. Inside this context and following the excellence of natural vision systems and their generality, this work has adopted a feature selection method based on synaptic conexions salience of a Multilayer Perceptron and a method based on its texture inference capability. As well as comparing the proposed method with exhaustive search according to the Jeffrey-Matusita distance criterion, this work also introduces, as a major contribution, the Input Equalization technique, which contributed to significantly improve the segmentation results. The segmentation of images of natural scenes has also been provided as a likely application of the method