Non-diagonal solutions of the reflection equation for the trigonometric A((1)) (n-1) vertex model

We obtain a class of non-diagonal solutions of the reflection equation for the trigonometric A(n-1)((1)) vertex model. The solutions can be expressed in terms of intertwinner matrix and its inverse, which intertwine two trigonometric R-matrices. In addition to a discrete (positive integer) parameter l, 1 less than or equal to l less than or equal to n, the solution contains n + 2 continuous boundary parameters.

Vertex component analysis: a fast algorithm to extract endmembers spectra from hyperspectral data

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.

Parallel implementation of vertex component analysis for hyperspectral endmember extraction

International Conference with Peer Review 2012 IEEE International Conference in Geoscience and Remote Sensing Symposium (IGARSS), 22-27 July 2012, Munich, Germany

Vertex component analysis GPU-based implementation for hyperspectral unmixing

Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis ( VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Many Hyperspectral imagery applications require a response in real time or near-real time. Thus, to met this requirement this paper proposes a parallel implementation of VCA developed for graphics processing units. The impact on the complexity and on the accuracy of the proposed parallel implementation of VCA is examined using both simulated and real hyperspectral datasets.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

vertex effect in polycrystalline materials : simulation, a macroscopic model, and structural application

Polycrystal Plasticity, Yield-Vertex, Corner, Vertex-Effect, Microscale, Macroscale, Multiaxial, Torsional Buckling, Cruciform Column

State Fair 2008 Coloring Book, 2008

The Secretary of the State Office produced a coloring book all any or all children that go to the State Fair of Iowa.

Abduction Prevention Coloring Book

Coloring book for the prevention of abduction made by Division of Criminal Investigation

Contribution to the Development of the LHCb Vertex Locator Readout Electronics

L'expérience LHCb sera installée sur le futur accélérateur LHC du CERN. LHCb est un spectromètre à un bras consacré aux mesures de précision de la violation CP et à l'étude des désintégrations rares des particules qui contiennent un quark b. Actuellement LHCb se trouve dans la phase finale de recherche et développement et de conception. La construction a déjà commencé pour l'aimant et les calorimètres. Dans le Modèle Standard, la violation CP est causée par une phase complexe dans la matrice 3x3 CKM (Cabibbo-Kobayashi-Maskawa) de mélange des quarks. L'expérience LHCb compte utiliser les mesons B pour tester l'unitarité de cette matrice, en mesurant de diverses manières indépendantes tous les angles et côtés du "triangle d'unitarité". Cela permettra de surdéterminer le modèle et, peut-être, de mettre en évidence des incohérences qui seraient le signal de l'existence d'une physique au-delà du Modèle Standard. La reconstruction du vertex de désintégration des particules est une condition fondamentale pour l'expérience LHCb. La présence d'un vertex secondaire déplacé est une signature de la désintégration de particules avec un quark b. Cette signature est utilisée dans le trigger topologique du LHCb. Le Vertex Locator (VeLo) doit fournir des mesures précises de coordonnées de passage des traces près de la région d'interaction. Ces points sont ensuite utilisés pour reconstruire les trajectoires des particules et l'identification des vertices secondaires et la mesure des temps de vie des hadrons avec quark b. L'électronique du VeLo est une partie essentielle du système d'acquisition de données et doit se conformer aux spécifications de l'électronique de LHCb. La conception des circuits doit maximiser le rapport signal/bruit pour obtenir la meilleure performance de reconstruction des traces dans le détecteur. L'électronique, conçue en parallèle avec le développement du détecteur de silicium, a parcouru plusieurs phases de "prototyping" décrites dans cette thèse.<br/><br/>The LHCb experiment is being built at the future LHC accelerator at CERN. It is a forward single-arm spectrometer dedicated to precision measurements of CP violation and rare decays in the b quark sector. Presently it is finishing its R&D and final design stage. The construction already started for the magnet and calorimeters. In the Standard Model, CP violation arises via the complex phase of the 3 x 3 CKM (Cabibbo-Kobayashi-Maskawa) quark mixing matrix. The LHCb experiment will test the unitarity of this matrix by measuring in several theoretically unrelated ways all angles and sides of the so-called "unitary triangle". This will allow to over-constrain the model and - hopefully - to exhibit inconsistencies which will be a signal of physics beyond the Standard Model. The Vertex reconstruction is a fundamental requirement for the LHCb experiment. Displaced secondary vertices are a distinctive feature of b-hadron decays. This signature is used in the LHCb topology trigger. The Vertex Locator (VeLo) has to provide precise measurements of track coordinates close to the interaction region. These are used to reconstruct production and decay vertices of beauty-hadrons and to provide accurate measurements of their decay lifetimes. The Vertex Locator electronics is an essential part of the data acquisition system and must conform to the overall LHCb electronics specification. The design of the electronics must maximise the signal to noise ratio in order to achieve the best tracking reconstruction performance in the detector. The electronics is being designed in parallel with the silicon detector development and went trough several prototyping phases, which are described in this thesis.

The 5alpha-reductase type 1, but not type 2, gene is expressed in anagen hairs plucked from the vertex area of the scalp of hirsute women and normal individuals

The aim of the present study was to determine the expression of the genes for type 1 (SDR5A1) and type 2 (SDR5A2) 5alpha-reductase isoenzymes in scalp hairs plucked from 33 hirsute patients (20 with polycystic ovary syndrome and 13 with idiopathic hirsutism) and compare it with that of 10 men and 15 normal women. SDR5A1 and SDR5A2 expression was estimated by RT-PCR using the gene of the ubiquitously expressed protein ß2-microglobulin as an internal control. The results are expressed as arbitrary units in relation to ß2-microglobulin absorbance (mean ± SEM). SDR5A2 expression was not detected in any hair samples analyzed in this study. No differences were found in SDR5A1 mRNA levels between men and normal women (0.78 ± 0.05 vs 0.74 ± 0.06, respectively). SDR5A1 gene expression in the cells of hair plucked from the scalp of normal women (0.85 ± 0.04) and of women with polycystic ovary syndrome (0.78 ± 0.05) and idiopathic hirsutism (0.80 ± 0.06) was also similar. These results indicate that SDR5A1 gene expression in the follicular keratinocytes from the vertex area of the scalp seems not to be related to the differences in hair growth observed between normal men and women and hirsute patients. Further studies are needed to investigate the expression of the 5alpha-reductase genes in other scalp follicular compartments such as dermal papillae, and also in hair follicles from other body sites, in order to elucidate the mechanism of androgen action on the hair growth process and related diseases.

Clique Irreducibility and Clique Vertex Irreducibility of Graphs

A graphs G is clique irreducible if every clique in G of size at least two,has an edge which does not lie in any other clique of G and is clique reducible if it is not clique irreducible. A graph G is clique vertex irreducible if every clique in G has a vertex which does not lie in any other clique of G and clique vertex reducible if it is not clique vertex irreducible. The clique vertex irreducibility and clique irreducibility of graphs which are non-complete extended p-sums (NEPS) of two graphs are studied. We prove that if G(c) has at least two non-trivial components then G is clique vertex reducible and if it has at least three non-trivial components then G is clique reducible. The cographs and the distance hereditary graphs which are clique vertex irreducible and clique irreducible are also recursively characterized.

Mathematical Morphology on Hypergraphs Using Vertex-Hyperedge Correspondence

The focus of this paper is to develop computationally efficient mathematical morphology operators on hypergraphs. To this aim we consider lattice structures on hypergraphs on which we build morphological operators. We develop a pair of dual adjunctions between the vertex set and the hyperedge set of a hypergraph , by defining a vertex-hyperedge correspondence. This allows us to recover the classical notion of a dilation/erosion of a subset of vertices and to extend it to subhypergraphs of . This paper also studies the concept of morphological adjunction on hypergraphs for which both the input and the output are hypergraphs