Experimental and theoretical study on the structure, optical properties, and growth of metallic silver nanostructures in Ag3PO4

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optical parameters and crystallization of flash evaporated amorphous gallium antimonide films

Flash-evaporated GaSb films are analysed using a combination of optical, surface and x-ray diffraction techniques. The effects of thermal annealings on nearly stoichiometric GaSb films are studied.

Tailoring the structure, composition, optical properties and catalytic activity of Ag-Au nanoparticles by the galvanic replacement reaction

This Letter reports on the synthesis of Ag-Au nanoparticles (NPs) with controlled structures and compositions via a galvanic replacement reaction between Ag NPs and AuCl4(aq)- followed by the investigation of their optical and catalytic properties. Our results showed the formation of porous walls, hollow interiors and increased Au content in the Ag-Au NPs as the volume of AuCl4(aq)- employed in the reaction was increased. These variations led to a red shift and broadening of the SPR peaks and an increase of up to 10.9-folds in the catalytic activity towards the reduction of 4-nitrophenol relative to Ag NPs. (C) 2012 Elsevier B.V. All rights reserved.

Damage detection in beams by using artificial neural networks and dynamical parameters

In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network.

Optical Excitations and Field Enhancement in Short Graphene Nanoribbons

The optical excitations of elongated graphene nanoflakes of finite length are investigated theoretically through quantum chemistry semiempirical approaches. The spectra and the resulting dipole fields are analyzed, accounting in full atomistic details for quantum confinement effects, which are crucial in the nanoscale regime. We find that the optical spectra of these nanostructures are dominated at low energy by excitations with strong intensity, comprised of characteristic coherent combinations of a few single-particle transitions with comparable weight. They give rise to stationary collective oscillations of the photoexcited carrier density extending throughout the flake and to a strong dipole and field enhancement. This behavior is robust with respect to width and length variations, thus ensuring tunability in a large frequency range. The implications for nanoantennas and other nanoplasmonic applications are discussed for realistic geometries.

Extractive summarization using complex networks and syntactic dependency

The realization that statistical physics methods can be applied to analyze written texts represented as complex networks has led to several developments in natural language processing, including automatic summarization and evaluation of machine translation. Most importantly, so far only a few metrics of complex networks have been used and therefore there is ample opportunity to enhance the statistics-based methods as new measures of network topology and dynamics are created. In this paper, we employ for the first time the metrics betweenness, vulnerability and diversity to analyze written texts in Brazilian Portuguese. Using strategies based on diversity metrics, a better performance in automatic summarization is achieved in comparison to previous work employing complex networks. With an optimized method the Rouge score (an automatic evaluation method used in summarization) was 0.5089, which is the best value ever achieved for an extractive summarizer with statistical methods based on complex networks for Brazilian Portuguese. Furthermore, the diversity metric can detect keywords with high precision, which is why we believe it is suitable to produce good summaries. It is also shown that incorporating linguistic knowledge through a syntactic parser does enhance the performance of the automatic summarizers, as expected, but the increase in the Rouge score is only minor. These results reinforce the suitability of complex network methods for improving automatic summarizers in particular, and treating text in general. (C) 2011 Elsevier B.V. All rights reserved.

Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index

Various factors are believed to govern the selection of references in citation networks, but a precise, quantitative determination of their importance has remained elusive. In this paper, we show that three factors can account for the referencing pattern of citation networks for two topics, namely "graphenes" and "complex networks", thus allowing one to reproduce the topological features of the networks built with papers being the nodes and the edges established by citations. The most relevant factor was content similarity, while the other two - in-degree (i.e. citation counts) and age of publication - had varying importance depending on the topic studied. This dependence indicates that additional factors could play a role. Indeed, by intuition one should expect the reputation (or visibility) of authors and/or institutions to affect the referencing pattern, and this is only indirectly considered via the in-degree that should correlate with such reputation. Because information on reputation is not readily available, we simulated its effect on artificial citation networks considering two communities with distinct fitness (visibility) parameters. One community was assumed to have twice the fitness value of the other, which amounts to a double probability for a paper being cited. While the h-index for authors in the community with larger fitness evolved with time with slightly higher values than for the control network (no fitness considered), a drastic effect was noted for the community with smaller fitness. (C) 2012 Elsevier Ltd. All rights reserved.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Development of oil spill detection techniques for satellite optical sensors and their application to monitor oil spill discharge in Mediterranean sea

Satellite remote sensing has proved to be an effective support in timely detection and monitoring of marine oil pollution, mainly due to illegal ship discharges. In this context, we have developed a new methodology and technique for optical oil spill detection, which make use of MODIS L2 and MERIS L1B satellite top of atmosphere (TOA) reflectance imagery, for the first time in a highly automated way. The main idea was combining wide swaths and short revisit times of optical sensors with SAR observations, generally used in oil spill monitoring. This arises from the necessity to overcome the SAR reduced coverage and long revisit time of the monitoring area. This can be done now, given the MODIS and MERIS higher spatial resolution with respect to older sensors (250-300 m vs. 1 km), which consents the identification of smaller spills deriving from illicit discharge at sea. The procedure to obtain identifiable spills in optical reflectance images involves removal of oceanic and atmospheric natural variability, in order to enhance oil-water contrast; image clustering, which purpose is to segment the oil spill eventually presents in the image; finally, the application of a set of criteria for the elimination of those features which look like spills (look-alikes). The final result is a classification of oil spill candidate regions by means of a score based on the above criteria.

Influence of ice crystal habit and cirrus spatial inhomogeneities on the retrieval of cirrus optical thickness and effective radius

Diese Dissertation untersucht den Einfluss von Eiskristallform und räumlicher Inhomogenität von Zirren auf das Retrieval von optischer Wolkendicke und effektivem Eispartikelradius. Zu diesem Zweck werden flugzeuggetragene spektrale Messungen solarer Strahlung sowie solare und langwellige Strahlungstransfersimulationen durchgeführt. Flugzeuggetragene spektrale aufwärtsgerichtete Radianzen (Strahldichten) sind mit dem SMART-Albedometer (Spectral Modular Airborne Radiation measurement sysTem) während des CIRCLE-2 (CIRrus CLoud Experiment-2) Feldexperiments im Mai 2007 gemessen worden. Basierend auf diesen Radianzdaten werden mittels eines Wolkenretrievalalgorithmus optische Wolkendicken und effektive Eispartikelradien anhand von eindimensionalen Strahlungstransferrechnungen bestimmt. Die Auswirkung der Annahme unterschiedlicher Eiskristallformen auf die retrievten Parameter wird durch Variation der Einfachstreueigenschaften der Eispartikel untersucht. Darüber hinaus wird mittels Strahlungstransferrechnungen auch der Einfluss der Eiskristallform auf den Strahlungsantrieb von Eiswolken ermittelt. Die Frage nach dem relativen Einfluss von räumlicher Wolkeninhomogenität und Eiskristallform wird anhand von dreidimensionalen und independent pixel approximation (IPA) Strahlungssimulationen untersucht. Die Analyse basiert auf einer Modelleiswolke, die aus Daten des NASA (National Aeronautics and Space Administration) TC4 (Tropical Composition, Cloud, and Climate Coupling) Feldexperiments im Sommer 2007 in Costa Rica erzeugt wurde. Lokal gesehen können beide Effekte - Eiskristallform und räumliche Eiswolkeninhomogenität - die gleiche Grössenordnung haben und zu einer Unter- bzw. Überschätzung der retrievten Parameter um 40 – 60% führen. Gemittelt über die ganze Wolke ist jedoch der Einfluss der Eiskristallform viel bedeutender als der von räumlichen Inhomogenitäten.

Integrating wireless sensor networks and internet of things: A coap-based approach

L'obiettivo su cui è stata basata questa Tesi di Laurea è stato quello di integrare la tecnologia delle Wireless Sensor Networks (WSN) al contesto dell'Internet delle cose (IoT). Per poter raggiungere questo obiettivo, il primo passo è stato quello di approfondire il concetto dell'Internet delle cose, in modo tale da comprendere se effettivamente fosse stato possibile applicarlo anche alle WSNs. Quindi è stata analizzata l'architettura delle WSNs e successivamente è stata fatta una ricerca per capire quali fossero stati i vari tipi di sistemi operativi e protocolli di comunicazione supportati da queste reti. Infine sono state studiate alcune IoT software platforms. Il secondo passo è stato quindi di implementare uno stack software che abilitasse la comunicazione tra WSNs e una IoT platform. Come protocollo applicativo da utilizzare per la comunicazione con le WSNs è stato usato CoAP. Lo sviluppo di questo stack ha consentito di estendere la piattaforma SensibleThings e il linguaggio di programmazione utilizzato è stato Java. Come terzo passo è stata effettuata una ricerca per comprendere a quale scenario di applicazione reale, lo stack software progettato potesse essere applicato. Successivamente, al fine di testare il corretto funzionamento dello stack CoAP, è stata sviluppata una proof of concept application che simulasse un sistema per la rilevazione di incendi. Questo scenario era caratterizzato da due WSNs che inviavano la temperatura rilevata da sensori termici ad un terzo nodo che fungeva da control center, il cui compito era quello di capire se i valori ricevuti erano al di sopra di una certa soglia e quindi attivare un allarme. Infine, l'ultimo passo di questo lavoro di tesi è stato quello di valutare le performance del sistema sviluppato. I parametri usati per effettuare queste valutazioni sono stati: tempi di durata delle richieste CoAP, overhead introdotto dallo stack CoAP alla piattaforma Sensible Things e la scalabilità di un particolare componente dello stack. I risultati di questi test hanno mostrato che la soluzione sviluppata in questa tesi ha introdotto un overheadmolto limitato alla piattaforma preesistente e inoltre che non tutte le richieste hanno la stessa durata, in quanto essa dipende dal tipo della richiesta inviata verso una WSN. Tuttavia, le performance del sistema potrebbero essere ulteriormente migliorate, ad esempio sviluppando un algoritmo che consenta la gestione concorrente di richieste CoAP multiple inviate da uno stesso nodo. Inoltre, poichè in questo lavoro di tesi non è stato considerato il problema della sicurezza, una possibile estensione al lavoro svolto potrebbe essere quello di implementare delle politiche per una comunicazione sicura tra Sensible Things e le WSNs.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Fluctuation properties in random walks on networks and simple integrate and fire models

In questa tesi si è studiato l’insorgere di eventi critici in un semplice modello neurale del tipo Integrate and Fire, basato su processi dinamici stocastici markoviani definiti su una rete. Il segnale neurale elettrico è stato modellato da un flusso di particelle. Si è concentrata l’attenzione sulla fase transiente del sistema, cercando di identificare fenomeni simili alla sincronizzazione neurale, la quale può essere considerata un evento critico. Sono state studiate reti particolarmente semplici, trovando che il modello proposto ha la capacità di produrre effetti "a cascata" nell’attività neurale, dovuti a Self Organized Criticality (auto organizzazione del sistema in stati instabili); questi effetti non vengono invece osservati in Random Walks sulle stesse reti. Si è visto che un piccolo stimolo random è capace di generare nell’attività della rete delle fluttuazioni notevoli, in particolar modo se il sistema si trova in una fase al limite dell’equilibrio. I picchi di attività così rilevati sono stati interpretati come valanghe di segnale neurale, fenomeno riconducibile alla sincronizzazione.

Reticulate eruptions. Part 1: Vascular networks and physiology

Reticulate pattern is one of the most important dermatological signs of a pathological process involving the superficial vascular networks. Vascular malformations, such as cutis marmorata congenita telangiectasia and benign forms of livedo reticularis, and sinister conditions, such as meningococcal meningitis or Sneddon's syndrome, can all present with a reticulate pattern. The clinical presentation and morphology is determined by the nature and extent of the underlying pathology and the involvement of a particular vascular network. This review has been divided into four instalments. In the present paper, we discuss the anatomy and physiology of the complex network of vascular structures that support the function of the skin and subcutis.