Open-atmosphere structural depth profiling of multilayer samples of photovoltaic interest using laser-induced plasma spectrometry

The present work aims to assess Laser-Induced Plasma Spectrometry (LIPS) as a tool for the characterization of photovoltaic materials. Despite being a well-established technique with applications to many scientific and industrial fields, so far LIPS is little known to the photovoltaic scientific community. The technique allows the rapid characterization of layered samples without sample preparation, in open atmosphere and in real time. In this paper, we assess LIPS ability for the determination of elements that are difficult to analyze by other broadly used techniques, or for producing analytical information from very low-concentration elements. The results of the LIPS characterization of two different samples are presented: 1) a 90 nm, Al-doped ZnO layer deposited on a Si substrate by RF sputtering and 2) a Te-doped GaInP layer grown on GaAs by Metalorganic Vapor Phase Epitaxy. For both cases, the depth profile of the constituent and dopant elements is reported along with details of the experimental setup and the optimization of key parameters. It is remarkable that the longest time of analysis was ∼10 s, what, in conjunction with the other characteristics mentioned, makes of LIPS an appealing technique for rapid screening or quality control whether at the lab or at the production line.

Proceso metodológico para optimizar la toma de decisiones en el proyecto de envolventes arquitectónicas multicapa = Methodological process for the optimization of the decision making in multilayer building envelope projects

La presente tesis doctoral se enmarca dentro del concepto de la sistematización del conocimiento en arquitectura, más concretamente en el campo de las construcciones arquitectónicas y la toma de decisiones en la fase de proyecto de envolventes arquitectónicas multicapa. Por tanto, el objetivo principal es el establecimiento de las bases para una toma de decisiones informadas durante el proyecto de una envolvente multicapa con el fin de colaborar en su optimización. Del mismo modo que la historia de la arquitectura está relacionada con la historia de la innovación en construcción, la construcción está sujeta a cambios como respuesta a los fracasos anteriores. En base a esto, se identifica la toma de decisiones en la fase de proyecto como el estadio inicial para establecer un punto estratégico de reflexión y de control sobre los procesos constructivos. La presente investigación, conceptualmente, define los parámetros intervinientes en el proyecto de envolventes arquitectónicas multicapa a partir de una clasificación y sistematización de todos los componentes (elementos, unidades y sistemas constructivos) utilizados en las fachadas multicapa. Dicha sistematización se materializa en una hoja matriz de datos en la que, dentro de una organización a modo de árbol, se puede acceder a la consulta de cada componente y de su caracterización. Dicha matriz permite la incorporación futura de cualquier componente o sistema nuevo que aparezca en el mercado, relacionándolo con aquellos con los que comparta ubicación, tipo de material, etc. Con base en esa matriz de datos, se diseña la sistematización de la toma de decisiones en la fase de proyecto de una envolvente arquitectónica, en concreto, en el caso de una fachada. Operativamente, el resultado se presenta como una herramienta que permite al arquitecto o proyectista reflexionar y seleccionar el sistema constructivo más adecuado, al enfrentarse con las distintas decisiones o elecciones posibles. La herramienta se basa en las elecciones iniciales tomadas por el proyectista y se estructura, a continuación y sucesivamente, en distintas aproximaciones, criterios, subcriterios y posibilidades que responden a los distintos avances en la definición del sistema constructivo. Se proponen una serie de fichas operativas de comprobación que informan sobre el estadio de decisión y de definición de proyecto alcanzados en cada caso. Asimismo, el sistema permite la conexión con otros sistemas de revisión de proyectos para fomentar la reflexión sobre la normalización de los riesgos asociados tanto al proprio sistema como a su proceso constructivo y comportamiento futuros. La herramienta proporciona un sistema de ayuda para ser utilizado en el proceso de toma de decisiones en la fase de diseño de una fachada multicapa, minimizando la arbitrariedad y ofreciendo una cualificación previa a la cuantificación que supondrá la elaboración del detalle constructivo y de su medición en las sucesivas fases del proyecto. Al mismo tiempo, la sistematización de dicha toma de decisiones en la fase del proyecto puede constituirse como un sistema de comprobación en las diferentes fases del proceso de decisión proyectual y de definición de la envolvente de un edificio. ABSTRACT The central issue of this doctoral Thesis is founded on the framework of the concept of the systematization of knowledge in architecture, in particular, in respect of the field of building construction and the decision making in the design stage of multilayer building envelope projects. Therefore, the main objective is to establish the bases for knowledgeable decision making during a multilayer building envelope design process, in order to collaborate with its optimization. Just as the history of architecture is connected to the history of innovation in construction, construction itself is subject to changes as a response to previous failures. On this basis, the decisions made during the project design phase are identified as the initial state to establish an strategic point for reflection and control, referred to the constructive processes. Conceptually, this research defines the parameters involving the multilayer building envelope projects, on the basis of a classification and systematization for all the components (elements, constructive units and constructive systems) used in multilayer façades. The mentioned systematization is materialized into a data matrix sheet in which, following a tree‐like organization, the access to every single component and its characterization is possible. The above data matrix allows the future inclusion of any new component or system that may appear in the construction market. That new component or system can be put into a relationship with another, which it shares location, type of material,… with. Based on the data matrix, the systematization of the decision making process for a building envelope design stage is designed, more particularly in the case of a façade. Putting this into practice, it is represented as a tool which allows the architect or the designer, to reflect and to select the appropriate building system when facing the different elections or the different options. The tool is based on the initial elections taken by the designer. Then and successively, it is shaped on the form of different operative steps, criteria, sub‐criteria and possibilities which respond to a different progress in the definition of the building construction system. In order to inform about the stage of the decision and the definition reached by the project in every particular case, a range of operative sheets are proposed. Additionally, the system allows the connection with other reviewing methods for building projects. The aim of this last possibility is to encourage the reflection on standardization of the associated risks to the building system itself and its future performance. The tool provides a helping system to be used during the decision making process for a multilayer façade design. It minimizes the arbitrariness and offers a qualification previous to the quantification that will be done with the development of the construction details and their bill of quantities, that in subsequent project stages will be executed. At the same time, the systematization of the mentioned decision making during the design phase, can be found as a checking system in the different stages of the decision making design process and in the different stages of the building envelope definition.

Quantum spin Hall phase in multilayer graphene

The so-called quantum spin Hall phase is a topologically nontrivial insulating phase that is predicted to appear in graphene and graphenelike systems. In this paper we address the question of whether this topological property persists in multilayered systems. We consider two situations: purely multilayer graphene and heterostructures where graphene is encapsulated by trivial insulators with a strong spin-orbit coupling. We use a four-orbital tight-binding model that includes full atomic spin-orbit coupling and we calculate the Z2 topological invariant of the bulk states as well as the edge states of semi-infinite crystals with armchair termination. For homogeneous multilayers we find that even when the spin-orbit interaction opens a gap for all possible stackings, only those with an odd number of layers host gapless edge states while those with an even number of layers are trivial insulators. For heterostructures where graphene is encapsulated by trivial insulators, it turns out that interlayer coupling is able to induce a topological gap whose size is controlled by the spin-orbit coupling of the encapsulating materials, indicating that the quantum spin Hall phase can be induced by proximity to trivial insulators.

Design considerations for multilayer transmission line circuit boards.

Issued also as thesis (M.S.) University of Illinois.

Multigradient field-active contour model for multilayer boundary detection of ultrasound rectal wall image

Extraction and reconstruction of rectal wall structures from an ultrasound image is helpful for surgeons in rectal clinical diagnosis and 3-D reconstruction of rectal structures from ultrasound images. The primary task is to extract the boundary of the muscular layers on the rectal wall. However, due to the low SNR from ultrasound imaging and the thin muscular layer structure of the rectum, this boundary detection task remains a challenge. An active contour model is an effective high-level model, which has been used successfully to aid the tasks of object representation and recognition in many image-processing applications. We present a novel multigradient field active contour algorithm with an extended ability for multiple-object detection, which overcomes some limitations of ordinary active contour models—"snakes." The core part in the algorithm is the proposal of multigradient vector fields, which are used to replace image forces in kinetic function for alternative constraints on the deformation of active contour, thereby partially solving the initialization limitation of active contour for rectal wall boundary detection. An adaptive expanding force is also added to the model to help the active contour go through the homogenous region in the image. The efficacy of the model is explained and tested on the boundary detection of a ring-shaped image, a synthetic image, and an ultrasound image. The experimental results show that the proposed multigradient field-active contour is feasible for multilayer boundary detection of rectal wall

Dynamics of on-line gradient descent learning for multilayer neural networks

We consider the problem of on-line gradient descent learning for general two-layer neural networks. An analytic solution is presented and used to investigate the role of the learning rate in controlling the evolution and convergence of the learning process.

On-line learning in multilayer neural networks

We present an analytic solution to the problem of on-line gradient-descent learning for two-layer neural networks with an arbitrary number of hidden units in both teacher and student networks. The technique, demonstrated here for the case of adaptive input-to-hidden weights, becomes exact as the dimensionality of the input space increases.

Adaptive back-propagation in on-line learning of multilayer networks

An adaptive back-propagation algorithm is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, both numerical studies and a rigorous analysis show that the adaptive back-propagation method results in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.

Learning with noise and regularizers in multilayer neural networks

We study the effect of two types of noise, data noise and model noise, in an on-line gradient-descent learning scenario for general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labeled by a two-layer teacher network with an arbitrary number of hidden units. Data is then corrupted by Gaussian noise affecting either the output or the model itself. We examine the effect of both types of noise on the evolution of order parameters and the generalization error in various phases of the learning process.

Finite-size effects in on-line learning of multilayer neural networks

We complement recent advances in thermodynamic limit analyses of mean on-line gradient descent learning dynamics in multi-layer networks by calculating fluctuations possessed by finite dimensional systems. Fluctuations from the mean dynamics are largest at the onset of specialisation as student hidden unit weight vectors begin to imitate specific teacher vectors, increasing with the degree of symmetry of the initial conditions. In light of this, we include a term to stimulate asymmetry in the learning process, which typically also leads to a significant decrease in training time.

Learning with regularizers in multilayer neural networks

We study the effect of regularization in an on-line gradient-descent learning scenario for a general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labelled by a two-layer teacher network with an arbitrary number of hidden units which may be corrupted by Gaussian output noise. We examine the effect of weight decay regularization on the dynamical evolution of the order parameters and generalization error in various phases of the learning process, in both noiseless and noisy scenarios.

Globally optimal parameters for on-line learning in multilayer neural networks

We present a framework for calculating globally optimal parameters, within a given time frame, for on-line learning in multilayer neural networks. We demonstrate the capability of this method by computing optimal learning rates in typical learning scenarios. A similar treatment allows one to determine the relevance of related training algorithms based on modifications to the basic gradient descent rule as well as to compare different training methods.

Globally optimal learning rates in multilayer neural networks

A method for calculating the globally optimal learning rate in on-line gradient-descent training of multilayer neural networks is presented. The method is based on a variational approach which maximizes the decrease in generalization error over a given time frame. We demonstrate the method by computing optimal learning rates in typical learning scenarios. The method can also be employed when different learning rates are allowed for different parameter vectors as well as to determine the relevance of related training algorithms based on modifications to the basic gradient descent rule.