AlGaInP LED with surface structure of two-dimensional photonic crystal

Some progress in the research of GaN based LED with photonic crystal structure has been made recently. Based on the photonic crystal's photonic band gap effect and photon grating diffraction principle, the extraction efficiency of LED with photonic crystal can be improved. In this paper, the restriction on AlGaInP LED's extraction efficiency is analyzed, and the photonic crystal is introduced in to the AlGaInP LED to improve the extraction efficiency. The theoretical analyses and the experiment results show that the output luminous intensity of LED with photonic crystal is improved by 16%, which results from some effect of the GaN based LED with photonic crystal.

Analysis of sulfur-containing compounds in crude oils by comprehensive two-dimensional gas chromatography with sulfur chemiluminescence detection

This paper reports an analytical method for separating, identifying, and quantifying sulfur-containing compounds in crude oil fraction (IBP-360degreesC) samples based on comprehensive two-dimensional gas chromatography coupled with a sulfur chemiluminescence detector. Various sulfur-containing compounds and their groups were analyzed with one direct injection. 3620 peaks were detected including 1722 thiols/thioethers/ disulfides/1-ring thiophenes, 953 benzothiophenes, 704 dibenzothiophenes, and 241 benzonaphthothiophenes. The target sulfur compounds and their groups were identified based on the group separation feature and structured retention of comprehensive two-dimensional gas chromatography as well as standard substances. The quantitative analysis of major sulfur-containing compounds and total sulfur was based on the linear response of the sulfur chemiluminescence detector using the internal standard method. The sulfur contents of target sulfur compounds and their groups in 4 crude oil fractions were also determined. The recoveries for standard sulfur-containing compounds were in the range of 90-102%. The quantitative result of total sulfur in the Oman crude oil fraction sample was compared with those from ASTM D 4294 standard method (total S by X-ray fluorescence spectrometry), the relative deviation (RD%) was 4.2% and the precision of the method satisfactory.

Determination of sulfur-containing compounds in diesel oils by comprehensive two-dimensional gas chromatography with a sulfur chemiluminescence detector

This article reports an analytical method for separating, identifying and quantitating sulfur-containing compounds and their groups in diesel oils (170-400degreesC) using comprehensive two-dimensional gas chromatography coupled with a sulfur chemiluminescence detector. The identification of target compounds and their groups was based on standard substances, the group separation feature and the-effect of comprehensive two-dimensional gas chromatography. The quantitative analysis on major sulfur compounds and total sulfur was carried out based on the linear response of sulfur chemiluminescence detector and the internal standards method. The results of total sulfur determination in the samples were compared with those from ASTM D 4294 standard method, the R.S.D. percentage were <6.02%, correctness of this method can meet the industrial requirement. To the end, the method developed was used to investigate the sulfur-containing compounds in different diesel oils, the result shows that the distribution of sulfur-containing compounds in diesel oils from different process units are apparently different. The sulfur compounds in fluid catalytic cracking (FCC), residuum fluid catalytic cracking (RFCC) diesel oils mainly exist in the form of alkyl-substituted dibenzothiophenes that add up to about 40-50% of the total sulfur, while this number is only 6-8 and 20-28% in visbreaking (VB) and delayed-coking (DC) diesel oils, respectively. (C) 2003 Elsevier B.V. All rights reserved.

Self-Assembly from Two-Dimensional Layered Networks to Tetranuclear Structures: Syntheses, Structures, and Properties of Four Copper-Thiacalix[4]arene Compounds

Two new copper-thiacalix[4]arene compounds, [Cu-2(1)-Cl-2(H(4)TC4A)](CH3OH) (1) and [Cu(I)2Cl(2)(H(4)PTC4A)](CH3OH)(CHCl3)(0.5) (2) (where H(4)TC4A = p-tert-butylthiacalix[4]arene and H(4)PTC4A = p-phenylthiacalix[4]arene), were synthesized by the solvothermal method in the mixed CH3OH/CHCl3 (1: 1) solvent and reassembled in air at room temperature to two other structures, [(Cu4Cl3)-Cl-II(HCO2)(TC4A)(CH3-OH)(2)(H2O)](CHCl3)(CH3OH)(2.7) (3) and [(Cu4Cl4)-Cl-II(PTC4A)(CH3OH)(4)] (4), respectively. All these four compounds were characterized by TG analyses, FTIR spectroscopy, and singlecrystal X-ray diffraction analyses. Compounds 1 and 2 feature two-dimensional layered networks, while compounds 3 and 4 are assembled by some tetranuclear units.

Three-Dimensional Recognition of Solid Objects from a Two-Dimensional Image

This thesis addresses the problem of recognizing solid objects in the three-dimensional world, using two-dimensional shape information extracted from a single image. Objects can be partly occluded and can occur in cluttered scenes. A model based approach is taken, where stored models are matched to an image. The matching problem is separated into two stages, which employ different representations of objects. The first stage uses the smallest possible number of local features to find transformations from a model to an image. This minimizes the amount of search required in recognition. The second stage uses the entire edge contour of an object to verify each transformation. This reduces the chance of finding false matches.

Improved performance of comprehensive two-dimensional HPLC separation of traditional Chinese medicines by using a silica monolithic column and normalization of peak heights

Performance of comprehensive two-dimensional liquid chromatography system is greatly improved than we reported previously by using a silica monolithic column as for the second dimensional separation. Due to the increase of the elution speed on the second dimensional monolithic column, the first dimensional column efficiency and analysis rate can be greatly improved as comparing with conventionally second dimensional column. The developed system was applied to analysis of methanol extraction of two umbelliferae herbs Ligusticum chuanxiong Hort. and Angelica sinensis (Oliv.) Diels by using CN column as for the first dimensional separation and a silica monolithic ODS column for the second dimensional separation, and the obtained three-dimensional chromatograms were treated by normalization of peak heights with the value of the highest peak or setting a certain value using a software written in-house. It was observed that much more peaks for low-abundant components in TCM extract can clearly be detected here than we reported before, due to the large difference for the amount of components in TCMs' extract. With the above improvements in separation performance and data treatment, totally about 120 components in methanol extraction of Rhizoma chuanxiong and 100 in A. sinensis were separated with UV detection within 130 min. This result meant that both the number of peaks detected increase twice but the analysis time decease twice if comparing with the previously reported result. (c) 2005 Published by Elsevier B.V.

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.

Fiber-optic interferometric two-dimensional scattering-measurement system.

We present a fiber-optic interferometric system for measuring depth-resolved scattering in two angular dimensions using Fourier-domain low-coherence interferometry. The system is a unique hybrid of the Michelson and Sagnac interferometer topologies. The collection arm of the interferometer is scanned in two dimensions to detect angular scattering from the sample, which can then be analyzed to determine the structure of the scatterers. A key feature of the system is the full control of polarization of both the illumination and the collection fields, allowing for polarization-sensitive detection, which is essential for two-dimensional angular measurements. System performance is demonstrated using a double-layer microsphere phantom. Experimental data from samples with different sizes and acquired with different polarizations show excellent agreement with Mie theory, producing structural measurements with subwavelength accuracy.

Max separation clustering for feature extraction from Optical Emission Spectroscopy Data

This paper proposes max separation clustering (MSC), a new non-hierarchical clustering method used for feature extraction from optical emission spectroscopy (OES) data for plasma etch process control applications. OES data is high dimensional and inherently highly redundant with the result that it is difficult if not impossible to recognize useful features and key variables by direct visualization. MSC is developed for clustering variables with distinctive patterns and providing effective pattern representation by a small number of representative variables. The relationship between signal-to-noise ratio (SNR) and clustering performance is highlighted, leading to a requirement that low SNR signals be removed before applying MSC. Experimental results on industrial OES data show that MSC with low SNR signal removal produces effective summarization of the dominant patterns in the data.

Toward Unconstrained Ear Recognition From Two-Dimensional Images

Ear recognition, as a biometric, has several advantages. In particular, ears can be measured remotely and are also relatively static in size and structure for each individual. Unfortunately, at present, good recognition rates require controlled conditions. For commercial use, these systems need to be much more robust. In particular, ears have to be recognized from different angles ( poses), under different lighting conditions, and with different cameras. It must also be possible to distinguish ears from background clutter and identify them when partly occluded by hair, hats, or other objects. The purpose of this paper is to suggest how progress toward such robustness might be achieved through a technique that improves ear registration. The approach focuses on 2-D images, treating the ear as a planar surface that is registered to a gallery using a homography transform calculated from scale-invariant feature-transform feature matches. The feature matches reduce the gallery size and enable a precise ranking using a simple 2-D distance algorithm. Analysis on a range of data sets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees, and up to 18% occlusion. In addition, recognition remains accurate with masked ear images as small as 20 x 35 pixels.

Improving sampling, optimization and feature extraction in Boltzmann machines

L’apprentissage supervisé de réseaux hiérarchiques à grande échelle connaît présentement un succès fulgurant. Malgré cette effervescence, l’apprentissage non-supervisé représente toujours, selon plusieurs chercheurs, un élément clé de l’Intelligence Artificielle, où les agents doivent apprendre à partir d’un nombre potentiellement limité de données. Cette thèse s’inscrit dans cette pensée et aborde divers sujets de recherche liés au problème d’estimation de densité par l’entremise des machines de Boltzmann (BM), modèles graphiques probabilistes au coeur de l’apprentissage profond. Nos contributions touchent les domaines de l’échantillonnage, l’estimation de fonctions de partition, l’optimisation ainsi que l’apprentissage de représentations invariantes. Cette thèse débute par l’exposition d’un nouvel algorithme d'échantillonnage adaptatif, qui ajuste (de fa ̧con automatique) la température des chaînes de Markov sous simulation, afin de maintenir une vitesse de convergence élevée tout au long de l’apprentissage. Lorsqu’utilisé dans le contexte de l’apprentissage par maximum de vraisemblance stochastique (SML), notre algorithme engendre une robustesse accrue face à la sélection du taux d’apprentissage, ainsi qu’une meilleure vitesse de convergence. Nos résultats sont présent ́es dans le domaine des BMs, mais la méthode est générale et applicable à l’apprentissage de tout modèle probabiliste exploitant l’échantillonnage par chaînes de Markov. Tandis que le gradient du maximum de vraisemblance peut-être approximé par échantillonnage, l’évaluation de la log-vraisemblance nécessite un estimé de la fonction de partition. Contrairement aux approches traditionnelles qui considèrent un modèle donné comme une boîte noire, nous proposons plutôt d’exploiter la dynamique de l’apprentissage en estimant les changements successifs de log-partition encourus à chaque mise à jour des paramètres. Le problème d’estimation est reformulé comme un problème d’inférence similaire au filtre de Kalman, mais sur un graphe bi-dimensionnel, où les dimensions correspondent aux axes du temps et au paramètre de température. Sur le thème de l’optimisation, nous présentons également un algorithme permettant d’appliquer, de manière efficace, le gradient naturel à des machines de Boltzmann comportant des milliers d’unités. Jusqu’à présent, son adoption était limitée par son haut coût computationel ainsi que sa demande en mémoire. Notre algorithme, Metric-Free Natural Gradient (MFNG), permet d’éviter le calcul explicite de la matrice d’information de Fisher (et son inverse) en exploitant un solveur linéaire combiné à un produit matrice-vecteur efficace. L’algorithme est prometteur: en terme du nombre d’évaluations de fonctions, MFNG converge plus rapidement que SML. Son implémentation demeure malheureusement inefficace en temps de calcul. Ces travaux explorent également les mécanismes sous-jacents à l’apprentissage de représentations invariantes. À cette fin, nous utilisons la famille de machines de Boltzmann restreintes “spike & slab” (ssRBM), que nous modifions afin de pouvoir modéliser des distributions binaires et parcimonieuses. Les variables latentes binaires de la ssRBM peuvent être rendues invariantes à un sous-espace vectoriel, en associant à chacune d’elles, un vecteur de variables latentes continues (dénommées “slabs”). Ceci se traduit par une invariance accrue au niveau de la représentation et un meilleur taux de classification lorsque peu de données étiquetées sont disponibles. Nous terminons cette thèse sur un sujet ambitieux: l’apprentissage de représentations pouvant séparer les facteurs de variations présents dans le signal d’entrée. Nous proposons une solution à base de ssRBM bilinéaire (avec deux groupes de facteurs latents) et formulons le problème comme l’un de “pooling” dans des sous-espaces vectoriels complémentaires.

Two-dimensional clusters of liquid 4He

The binding energies of two-dimensional clusters (puddles) of¿4He are calculated in the framework of the diffusion Monte Carlo method. The results are well fitted by a mass formula in powers of x=N-1/2, where N is the number of particles. The analysis of the mass formula allows for the extraction of the line tension, which turns out to be 0.121 K/Å. Sizes and density profiles of the puddles are also reported.

Optical response of two-dimensional few-electron concentric double quantum rings: A local-spin-density-functional theory study

We have investigated the dipole charge- and spin-density response of few-electron two-dimensional concentric nanorings as a function of the intensity of a erpendicularly applied magnetic field. We show that the dipole response displays signatures associated with the localization of electron states in the inner and outer ring favored by the perpendicularly applied magnetic field. Electron localization produces a more fragmented spectrum due to the appearance of additional edge excitations in the inner and outer ring.

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH RECOGNITION

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.