Bond orbital description of the strain induced second order optical susceptibility in silicon

We develop a theoretical model, relying on the well established sp3 bond-orbital theory, to describe the strain-induced $\chi^{(2)}$ in tetrahedrally coordinated centrosymmetric covalent crystals, like silicon. With this approach we are able to describe every component of the $\chi^{(2)}$ tensor in terms of a linear combination of strain gradients and only two parameters $\alpha$ and $\beta$ which can be estimated theoretically. The resulting formula can be applied to the simulation of the strain distribution of a practical strained silicon device, providing an extraordinary tool for optimization of its optical nonlinear effects. By doing that, we were able not only to confirm the main valid claims known about $\chi^{(2)}$ in strained silicon, but also estimate the order of magnitude of the $\chi^{(2)}$ generated in that device

Electro-optic modulation in bulk silicon using surface plasmon resonance

The authors acknowledge funding from the EPSRC in the UK under the UK Silicon Photonics project.We propose and present simulated results for a new design of an optical modulator based on Surface Plasmon Polariton (SPP) resonance. The modulator is realized on a bulk silicon substrate, thus offering an opportunity for front-end integration with electronic circuits. The device consists of a dielectric waveguide evanescently coupled to a SPP mode at the interface between bulk silicon and metal. By using SPP resonance we achieved an ultra-high spectral sensitivity (∼5000 nm/refractive index unit) with large modulation bandwidth (90 nm). For a refractive index change of 0.02, we achieved 100 nm shift in resonance wavelength and a modulation depth of ∼10 dB.PostprintPeer reviewe

Understanding Water Equilibration Fundamentals as a Step for Rational Protein Crystallization

Background: Vapor diffusion is the most widely used technique for protein crystallization and the rate of water evaporation plays a key role on the quality of the crystals. Attempts have been made in the past to solve the mass transfer problem governing the evaporation process, either analytically or by employing numerical methods. Despite these efforts, the methods used for protein crystallization remain based on trial and error techniques rather than on fundamental principles. Methodology/Principal Findings: Here we present a new theoretical model which describes the hanging drop method as a function of the different variables that are known to influence the evaporation process. The model is extensively tested against experimental data published by other authors and considering different crystallizing conditions. Aspects responsible for the discrepancies between the existing theories and the measured evaporation kinetics are especially discussed; they include the characterization of vapor-liquid equilibrium, the role of mass transfer within the evaporating droplet, and the influence of the droplet-reservoir distance. Conclusions/Significance: The validation tests show that the proposed model can be used to predict the water evaporation rates under a wide range of experimental conditions used in the hanging drop vapor-diffusion method, with no parameter fitting or computational requirements. This model combined with protein solubility data is expected to become a usefu

Design e Tecnologia na Cidade: impactos da morfologia de um Dispositivo Individual de Notificação sobre a segurança de pessoas residentes em zonas

Este estudo objetiva discutir as relações entre propostas de design e a comunicação de sentidos específicos por meio de objetos projetados para a segurança. Este estudio tiene como objetivo discutir las relaciones entre las propuestas de diseño y la comunicación de significados específicos a través de objetos diseñados para la seguridad. This study aims to discuss the relationship between design proposals and the communication of specific meanings through objects designed for safety.&nbsp

Many-Objective Cooperative Co-evolutionary Feature Selection: A Lexicographic Approach

This paper presents a new wrapper method able to optimize simultaneously the parameters of the classifier while the size of the subset of features that better describe the input dataset is also being minimized. The search algorithm used for this purpose is based on a co-evolutionary algorithm optimizing several objectives related with different desirable properties for the final solutions, such as its accuracy, its final number of features, and the generalization ability of the classifier. Since these objectives can be sorted according to their priorities, a lexicographic approach has been applied to handle this many-objective problem, which allows the use of a simple evolutionary algorithm to evolve each one of the different sub-populations.Project TIN2015-67020-P (Spanish “Ministerio de Economía y Competitividad”)Project PGC2018-098813-B-C31 (Spanish “Ministerio de Ciencia, Innovación y Universidades”)European Regional Development Funds (ERDF

Effet Pockels dans les guides d'onde en silicium contraint : Vers la modulation optique à haute vitesse et faible consommation d'énergie dans le silicium

This work is devoted to the study of second order nonlinearities in silicon towards low power, high speed modulation. Being a centro-symmetric crystal, silicon does not possess a second order nonlinear susceptibility (X2), which inhibits Pockels effect, a linear electro-optic effect commonly used in the modulation of light in high speed communications. A possible solution to overcome this limitation is by straining/deforming the crystal lattice, which locally breaks the centro-symmetry of the crystal and generates X2.In this thesis, we approach the problem of generating X2 in silicon through the use of strain, covering all the research stages: we depart from newly developed theoretical grounds, simulate together the strain, optical and electrical effects together, describe the fabrication of the devices and present the experimental characterization.In our research work, we were able to detect very particular effects which are attributed to Pockels effect, such as a clear dependence of the crystal orientation on the modulation efficiency and high speed modulation, at frequencies higher than those expected from other contributions. This results are very promising and consist on a step further towards the possible implementation of high speed, low power modulation in silicon devices in the near future.Ce travail est centré sur l'étude des non-linéarités de deuxiéme ordre dans le silicium vers une modulation optique à faible puissance et haute vitesse. Étant un cristal centro-symétrique, le silicium ne possède pas une susceptibilité non linéaire de deuxiéme ordre (X2), ce qui inhibe l'effet Pockels, un effet électro-optique linéaire couramment utilisé dans la modulation de la lumière dans les communications optiques. Une solution possible pour vaincre cette limitation est par application de contraint et déformation de la maille cristalline, ce qui rompt localement la centro-symétrie du cristal et génère X2.Dans cette thèse, nous abordons le problème de la génération de X2 dans le silicium par l'utilisation de la contrainte, couvrant toutes les étapes de la recherche: nous partons de bases théoriques développées par nous, on simule l'ensemble des effets de contraints, optiques et électriques, on décrit la fabrication des dispositifs et finalement on présent la caractérisation expérimentale de ces dispositifs.Dans ce travail de recherche, nous avons pu détecter des effets très particuliers qui sont attribués au effet Pockels, comme par example une dépendance claire de l'orientation du cristal sur l'efficacité de la modulation et aussi la modulation à haute fréquences, plus élevées que celles attendues par autres contributions. Ces résultats sont très prometteurs et se composent d'une nouvelle étape vers la mise en œuvre, dans un avenir proche, de la modulation à grande vitesse et à faible puissance dans les dispositifs de silicium

“iCub, clean the table!” A robot learning from demonstration approach using Deep Neural Networks

Autonomous service robots have become a key research topic in robotics, particularly for household chores. A typical home scenario is highly unconstrained and a service robot needs to adapt constantly to new situations. In this paper, we address the problem of autonomous cleaning tasks in uncontrolled environments. In our approach, a human instructor uses kinestethic demonstrations to teach a robot how to perform different cleaning tasks on a table. Then, we use Task Parametrized Gaussian Mixture Models (TP-GMMs) to encode the demonstrations variability, while providing appropriate generalization abilities. TP-GMMs extend Gaussian Mixture Models with an auxiliary set of reference frames, in order to extrapolate the demonstrations to different task parameters such as movement locations, amplitude or orientations. However, the reference frames (that parametrize TP-GMMs) can be very difficult to extract in practice, as it may require segmenting the cluttered images of the working table-top. Instead, in this work the reference frames are automatically extracted from robot camera images, using a deep neural network that was trained during human demonstrations of a cleaning task. This approach has two main benefits: (i) it takes the human completely out of the loop while performing complex cleaning tasks; and (ii) the network is able to identify the specific task to be performed directly from image data, thus also enabling automatic task selection from a set of previously demonstrated tasks. The system was implemented on the iCub humanoid robot. During the tests, the robot was able to successfully clean a table with two different types of dirt (wiping a marker’s scribble or sweeping clusters of lentils).info:eu-repo/semantics/publishedVersio

Chemical Kinetic Strategies for High-Throughput Screening of Protein Aggregation Modulators

Insoluble aggregates staining positive to amyloid dyes are known histological hallmarks of different neurodegenerative disorders and of type II diabetes. Soluble oligomers are smaller assemblies whose formation prior to or concomitant with amyloid deposition has been associated to the processes of disease propagation and cell death. While the pathogenic mechanisms are complex and differ from disease to disease, both types of aggregates are important biological targets subject to intense investigation in academia and industry. Here we review recent advances in the fundamental understanding of protein aggregation that can be used on the development of anti-amyloid and anti-oligomerization drugs. Specifically, we pinpoint the chemical kinetic aspects that should be attended during the development of high-throughput screening assays and in the hit validation phase. The strategies here devised are expected to establish a connection between basic research and pharmaceutical innovation

A First Approach to a Fuzzy Classification System for Age Estimation based on the Pubic Bone

The study of human remains suffers from a lack of information for determining a reliable estimation of the age of an individual. One of the most extended methods for this task was proposed in the twenties of the past century and is based on the analysis of the pubic bone. The method describes some age changes occurring in the pubic bone and establishes ten different age ranges with a description of the morphological aspect of the bone in each one of them. These descriptions are sometimes vague and there is not a systematic way for using the method. In this contribution we propose two different preliminary fuzzy rule-based classification system designs for age estimation from the pubic bone that consider the main morphological characteristics of the bone as independent and linguistic variables. So, we have identified the problem variables and we have defined the corresponding linguistic labels making use of forensic expert knowledge, that is also considered to design a decision support fuzzy system. A brief collection of pubic bones labeled by forensic anthropologists has been used for learning the second fuzzy rule-based classification system by means of a fuzzy decision tree. The experiments developed report a best performance of the latter approach

A new multi-objective wrapper method for feature selection – Accuracy and stability analysis for BCI

Feature selection is an important step in building classifiers for high-dimensional data problems, such as EEG classification for BCI applications. This paper proposes a new wrapper method for feature selection, based on a multi-objective evolutionary algorithm, where the representation of the individuals or potential solutions, along with the breeding operators and objective functions, have been carefully designed to select a small subset of features that has good generalization capability, trying to avoid the over-fitting problems that wrapper methods usually suffer. A novel feature ranking procedure is also proposed in order to analyze the stability of the proposed wrapper method. Four different classification schemes have been applied within the proposed wrapper method in order to evaluate its accuracy and stability for feature selection on a real motor imagery dataset. Experimental results show that the wrapper method presented in this paper is able to obtain very small subsets of features, which are quite stable and also achieve high classification accuracy, regardless of the classifiers used.Project TIN2015-67020-P (Spanish “Ministerio de Economía y Competitividad”)European Regional Development Funds (ERDF