A comparison of photoluminescence properties of InGaAs GaAs quantum dots with a single quantum well

Variable temperature photoluminescence (PL) measurements for In0.3Ga0.7As(6 nm)/GaAs(34 nm) quantum dot superlattices with a period of 20 and an In0.3Ga0.7As(6 nm)/GaAs(34 nm) reference single quantum well have been conducted. It is found that the temperature dependence is different between the quantum dots and the reference single quantum well. The PL peak energy of the single quantum well decreases faster than that of the quantum dots with increasing temperature. The PL peak energy for the InGaAs/GaAs quantum dots closely follows the InAs band gap in the temperature range from 11 to 170 K, while the PL peak energy for the InGaAs/GaAs quantum well closely follows the GaAs band gap. In comparison with InAs/GaAs quantum dots, the InGaAs/GaAs quantum dots are more typical as a zero-dimensional system since the unusual PL results, which appear in the former, are not obvious for the latter. (C) 1999 American Institute of Physics. [S0021-8979(99)08615-6].

Electronic structure and optical gain saturation of InAs1-xNx/GaAs quantum dots

The electronic band structures and optical gains of InAs1-xNx/GaAs pyramid quantum dots (QDs) are calculated using the ten-band k . p model and the valence force field method. The optical gains are calculated using the zero-dimensional optical gain formula with taking into consideration of both homogeneous and inhomogeneous broadenings due to the size fluctuation of quantum dots which follows a normal distribution. With the variation of QD sizes and nitrogen composition, it can be shown that the nitrogen composition and the strains can significantly affect the energy levels especially the conduction band which has repulsion interaction with nitrogen resonant state due to the band anticrossing interaction. It facilitates to achieve emission of longer wavelength (1.33 or 1.55 mu m) lasers for optical fiber communication system. For QD with higher nitrogen composition, it has longer emission wavelength and less detrimental effect of higher excited state transition, but nitrogen composition can affect the maximum gain depending on the factors of transition matrix element and the Fermi-Dirac distributions for electrons in the conduction bands and holes in the valence bands respectively. For larger QD, its maximum optical gain is greater at lower carrier density, but it is slowly surpassed by smaller QD as carrier concentration increases. Larger QD can reach its saturation gain faster, but this saturation gain is smaller than that of smaller QD. So the trade-off between longer wavelength, maximum optical, saturation gain, and differential gain must be considered to select the appropriate QD size according to the specific application requirement. (C) 2009 American Institute of Physics. [DOI: 10.1063/1.3143025]

Computational information geometry and its applications

Digitization is the main feature of modern Information Science. Conjoining the digits and the coordinates, the relation between Information Science and high-dimensional space is consanguineous, and the information issues are transformed to the geometry problems in some high-dimensional spaces. From this basic idea, we propose Computational Information Geometry (CIG) to make information analysis and processing. Two kinds of applications of CIG are given, which are blurred image restoration and pattern recognition. Experimental results are satisfying. And in this paper, how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is also introduced. Lots of the algorithms have been realized using software.

Coordinate-Independent Computations on Differential Equations

This project investigates the computational representation of differentiable manifolds, with the primary goal of solving partial differential equations using multiple coordinate systems on general n- dimensional spaces. In the process, this abstraction is used to perform accurate integrations of ordinary differential equations using multiple coordinate systems. In the case of linear partial differential equations, however, unexpected difficulties arise even with the simplest equations.

Learning Embeddings for Indexing, Retrieval, and Classification, with Applications to Object and Shape Recognition in Image Databases

Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time, finding nearest neighbors accurately and efficiently can be challenging, especially when the database contains a large number of objects, and when the underlying distance measure is computationally expensive. This thesis proposes new methods for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed methods are domain-independent, and can be applied in arbitrary spaces, including non-Euclidean and non-metric spaces. In this thesis particular emphasis is given to computer vision applications related to object and shape recognition, where expensive non-Euclidean distance measures are often needed to achieve high accuracy. The first contribution of this thesis is the BoostMap algorithm for embedding arbitrary spaces into a vector space with a computationally efficient distance measure. Using this approach, an approximate set of nearest neighbors can be retrieved efficiently - often orders of magnitude faster than retrieval using the exact distance measure in the original space. The BoostMap algorithm has two key distinguishing features with respect to existing embedding methods. First, embedding construction explicitly maximizes the amount of nearest neighbor information preserved by the embedding. Second, embedding construction is treated as a machine learning problem, in contrast to existing methods that are based on geometric considerations. The second contribution is a method for constructing query-sensitive distance measures for the purposes of nearest neighbor retrieval and classification. In high-dimensional spaces, query-sensitive distance measures allow for automatic selection of the dimensions that are the most informative for each specific query object. It is shown theoretically and experimentally that query-sensitivity increases the modeling power of embeddings, allowing embeddings to capture a larger amount of the nearest neighbor structure of the original space. The third contribution is a method for speeding up nearest neighbor classification by combining multiple embedding-based nearest neighbor classifiers in a cascade. In a cascade, computationally efficient classifiers are used to quickly classify easy cases, and classifiers that are more computationally expensive and also more accurate are only applied to objects that are harder to classify. An interesting property of the proposed cascade method is that, under certain conditions, classification time actually decreases as the size of the database increases, a behavior that is in stark contrast to the behavior of typical nearest neighbor classification systems. The proposed methods are evaluated experimentally in several different applications: hand shape recognition, off-line character recognition, online character recognition, and efficient retrieval of time series. In all datasets, the proposed methods lead to significant improvements in accuracy and efficiency compared to existing state-of-the-art methods. In some datasets, the general-purpose methods introduced in this thesis even outperform domain-specific methods that have been custom-designed for such datasets.

Temporal extension of Laplacian Eigenmaps for unsupervised dimensionality reduction of time series

A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach, temporal information is intrinsic to the objective function, which produces description of low dimensional spaces with time coherence between data points. Since the proposed scheme also includes bidirectional mapping between data and embedded spaces and automatic tuning of key parameters, it offers the same benefits as mapping-based approaches. Experiments on a couple of computer vision applications demonstrate the superiority of the new approach to other dimensionality reduction method in term of accuracy. Moreover, its lower computational cost and generalisation abilities suggest it is scalable to larger datasets. © 2010 IEEE.

Hypercyclic tuples of operators on $C^n$ and $R^n$

A tuple $(T_1,\dots,T_n)$ of continuous linear operators on a topological vector space $X$ is called hypercyclic if there is $x\in X$ such that the the orbit of $x$ under the action of the semigroup generated by $T_1,\dots,T_n$ is dense in $X$. This concept was introduced by N.~Feldman, who have raised 7 questions on hypercyclic tuples. We answer those 4 of them, which can be dealt with on the level of operators on finite dimensional spaces. In
particular, we prove that the minimal cardinality of a hypercyclic tuple of operators on $\C^n$ (respectively, on $\R^n$) is $n+1$ (respectively, $\frac n2+\frac{5+(-1)^n}{4}$), that there are non-diagonalizable tuples of operators on $\R^2$ which possess an orbit being neither dense nor nowhere dense and construct a hypercyclic 6-tuple of operators on $\C^3$ such that every operator commuting with each member of the tuple is non-cyclic.

Interacting kinetics of neutral and ionic species in an atmospheric-pressure helium-oxygen plasma with humid air impurities

We unravel the complex chemistry in both the neutral and ionic systems of a radio-frequency-driven atmospheric-pressure plasma in a helium-oxygen mixture (He-0.5% O) with air impurity levels from 0 to 500 ppm of relative humidity from 0% to 100% using a zero-dimensional, time-dependent global model. Effects of humid air impurity on absolute densities and the dominant production and destruction pathways of biologically relevant reactive neutral species are clarified. A few hundred ppm of air impurity crucially changes the plasma from a simple oxygen-dependent plasma to a complex oxygen-nitrogen-hydrogen plasma. The density of reactive oxygen species decreases from 10 to 10 cm, which in turn results in a decrease in the overall chemical reactivity. Reactive nitrogen species (10 cm ), atomic hydrogen and hydroxyl radicals (10-10 cm) are generated in the plasma. With 500 ppm of humid air impurity, the densities of positively charged ions and negatively charged ions slightly increase and the electron density slightly decreases (to the order of 10 cm). The electronegativity increases up to 2.3 compared with 1.5 without air admixture. Atomic hydrogen, hydroxyl radicals and oxygen ions significantly contribute to the production and destruction of reactive oxygen and reactive nitrogen species. © 2013 IOP Publishing Ltd.

Chemical kinetics and reactive species in atmospheric pressure helium-oxygen plasmas with humid-air impurities

In most applications helium-based plasma jets operate in an open-air environment. The presence of humid air in the plasma jet will influence the plasma chemistry and can lead to the production of a broader range of reactive species. We explore the influence of humid air on the reactive species in radio frequency (rf)-driven atmospheric-pressure helium-oxygen mixture plasmas (He-O, helium with 5000 ppm admixture of oxygen) for wide air impurity levels of 0-500 ppm with relative humidities of from 0% to 100% using a zero-dimensional, time-dependent global model. Comparisons are made with experimental measurements in an rf-driven micro-scale atmospheric pressure plasma jet and with one-dimensional semi-kinetic simulations of the same plasma jet. These suggest that the plausible air impurity level is not more than hundreds of ppm in such systems. The evolution of species concentration is described for reactive oxygen species, metastable species, radical species and positively and negatively charged ions (and their clusters). Effects of the air impurity containing water humidity on electronegativity and overall plasma reactivity are clarified with particular emphasis on reactive oxygen species. © 2013 IOP Publishing Ltd.

Efficient tracking of human poses using a manifold hierarchy

In this paper a 3D human pose tracking framework is presented. A new dimensionality reduction method (Hierarchical Temporal Laplacian Eigenmaps) is introduced to represent activities in hierarchies of low dimensional spaces. Such a hierarchy provides increasing independence between limbs, allowing higher flexibility and adaptability that result in improved accuracy. Moreover, a novel deterministic optimisation method (Hierarchical Manifold Search) is applied to estimate efficiently the position of the corresponding body parts. Finally, evaluation on public datasets such as HumanEva demonstrates that our approach achieves a 62.5mm-65mm average joint error for the walking activity and outperforms state-of-the-art methods in terms of accuracy and computational cost.

Investigating the dynamics of laser induced sparks in atmospheric helium using Rayleigh and Thomson scattering

We have used optical Rayleigh and Thomson scattering to investigate the expansion dynamics of laser induced plasma in atmospheric helium and to map its electron parameters both in time and space. The plasma is created using 9 ns duration, 140 mJ pulses from a Nd:YAG laser operating at 1064 nm, focused with a 10 cm focal length lens, and probed with 7 ns, 80 mJ, and 532 nm Nd:YAG laser pulses. Between 0.4 μs and 22.5 μs after breakdown, the electron density decreases from 3.3 × 1017 cm−3 to 9 × 1013 cm−3, while the temperature drops from 3.2 eV to 0.1 eV. Spatially resolved Thomson scattering data recorded up to 17.5 μs reveal that during this time the laser induced plasma expands at a rate given by R ∼ t0.4 consistent with a non-radiative spherical blast wave. This data also indicate the development of a toroidal structure in the lateral profile of both electron temperature and density. Rayleigh scattering data show that the gas density decreases in the center of the expanding plasma with a central scattering peak reemerging after about 12 μs. We have utilized a zero dimensional kinetic global model to identify the dominant particle species versus delay time and this indicates that metastable helium and the He2 + molecular ion play an important role.

Universalité, variables complexes et réarrangement

Nous allons exposer dans ce mémoire divers résultats sur l’universalité en analyse complexe. Nous énoncerons d’abord des résultats généraux sur les séries universelles, puis sur un type d’universalité dû à Fournier et Nestoridis qui établit un lien nouveau entre l’universalité et la non-normalité d’une famille de fonctions. Par la suite, nous introduirons un type différent de séries universelles obtenues en réarrangeant les termes de séries arbitraires. Nous prouverons dans ce mémoire la généricité algébrique de ce type de séries universelles pour tout espace de Banach et la généricité topologique dans les espaces de dimension finie. Aussi, nous démontrerons que pour toute série universelle par réarrangement il existe un réarrangement de ses termes pour lequel cette série devient universelle au sens usuel.

Biological Routes to Gold Nanoplates

Much effort has been devoted to the synthesis of gold nanoparticles with different shapes, including the zero-dimensional nanospheres, one dimensional nanorods, and two-dimensional nanoplates. Compared to zero or one dimensional nanostructures, the synthesis of two-dimensional nanostructures in high yield has always been more involved, often requiring complex and time-consuming steps such as morphology transformation from the nanospheres, or the seeded growth process. Herein we report a high yield method for gold nanoplate synthesis using the extract of unicellular green alga Chlorella vulgaris, which can be carried out under ambient conditions. More than 90% of the total nanoparticle population is of the platelet morphology, surpassing the previously reported value of 45%. The control of the anisotropic growth of different planes; as well as the lateral size, has also been partially optimized.

Good-visibility computation using graphics hardware

Aquesta tesi tracta del disseny, implementació i discussió d'algoritmes per resoldre problemes de visibilitat i bona-visibilitat utilitzant el hardware gràfic de l'ordinador. Concretament, s'obté una discretització dels mapes de multi-visibilitat i bona-visibilitat a partir d'un conjunt d'objectes de visió i un conjunt d'obstacles. Aquests algoritmes són útils tant per fer càlculs en dues dimensions com en tres dimensions. Fins i tot ens permeten calcular-los sobre terrenys.

Space partitioning for scalable K-means

K-Means is a popular clustering algorithm which adopts an iterative refinement procedure to determine data partitions and to compute their associated centres of mass, called centroids. The straightforward implementation of the algorithm is often referred to as `brute force' since it computes a proximity measure from each data point to each centroid at every iteration of the K-Means process. Efficient implementations of the K-Means algorithm have been predominantly based on multi-dimensional binary search trees (KD-Trees). A combination of an efficient data structure and geometrical constraints allow to reduce the number of distance computations required at each iteration. In this work we present a general space partitioning approach for improving the efficiency and the scalability of the K-Means algorithm. We propose to adopt approximate hierarchical clustering methods to generate binary space partitioning trees in contrast to KD-Trees. In the experimental analysis, we have tested the performance of the proposed Binary Space Partitioning K-Means (BSP-KM) when a divisive clustering algorithm is used. We have carried out extensive experimental tests to compare the proposed approach to the one based on KD-Trees (KD-KM) in a wide range of the parameters space. BSP-KM is more scalable than KDKM, while keeping the deterministic nature of the `brute force' algorithm. In particular, the proposed space partitioning approach has shown to overcome the well-known limitation of KD-Trees in high-dimensional spaces and can also be adopted to improve the efficiency of other algorithms in which KD-Trees have been used.