962 resultados para lightning detectors


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The Large Hadron Collider (LHC) in The European Organization for Nuclear Research (CERN) will have a Long Shutdown sometime during 2017 or 2018. During this time there will be maintenance and a possibility to install new detectors. After the shutdown the LHC will have a higher luminosity. A promising new type of detector for this high luminosity phase is a Triple-GEM detector. During the shutdown these detectors will be installed at the Compact Muon Solenoid (CMS) experiment. The Triple-GEM detectors are now being developed at CERN and alongside also a readout ASIC chip for the detector. In this thesis a simulation model was developed for the ASICs analog front end. The model will help to carry out more extensive simulations and also simulate the whole chip before the whole design is finished. The proper functioning of the model was tested with simulations, which are also presented in the thesis.

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The single photon emission microscope (SPEM) is an instrument developed to obtain high spatial resolution single photon emission computed tomography (SPECT) images of small structures inside the mouse brain. SPEM consists of two independent imaging devices, which combine a multipinhole collimator, a high-resolution, thallium-doped cesium iodide [CsI(Tl)] columnar scintillator, a demagnifying/intensifier tube, and an electron-multiplying charge-coupling device (CCD). Collimators have 300- and 450-µm diameter pinholes on tungsten slabs, in hexagonal arrays of 19 and 7 holes. Projection data are acquired in a photon-counting strategy, where CCD frames are stored at 50 frames per second, with a radius of rotation of 35 mm and magnification factor of one. The image reconstruction software tool is based on the maximum likelihood algorithm. Our aim was to evaluate the spatial resolution and sensitivity attainable with the seven-pinhole imaging device, together with the linearity for quantification on the tomographic images, and to test the instrument in obtaining tomographic images of different mouse organs. A spatial resolution better than 500 µm and a sensitivity of 21.6 counts·s-1·MBq-1 were reached, as well as a correlation coefficient between activity and intensity better than 0.99, when imaging 99mTc sources. Images of the thyroid, heart, lungs, and bones of mice were registered using 99mTc-labeled radiopharmaceuticals in times appropriate for routine preclinical experimentation of <1 h per projection data set. Detailed experimental protocols and images of the aforementioned organs are shown. We plan to extend the instrument's field of view to fix larger animals and to combine data from both detectors to reduce the acquisition time or applied activity.

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Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

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Highlights from the creative presentation made to Lightning Fastener for the 1965 Womens Wear campaign.

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Lightning Fastener ad featuring the Lightning Zephyr nylon zipper from the 1965 Women’s Wear campaign. See the 1965 Womens Wear campaign creative rationale for a rough draft of this advertisement.

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An ad extolling the benefits of the Lightning Zephyr nylon zipper. This ad was directed towards the garment industry.

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Highlights from the creative presentation made to Lightning Fastener for the 1969 campaign.

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We’re having a really great run on our nylons. In the 1960's and 1970's Lightning Fastener's market share was being challenged by cheaper, but less dependable imports.

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As long as you've got metal zippers in your garments you're selling an old line. Today's fashions are much more demanding. They need what a Lightning Zephyr can give them. Part of the tag line for an ad aimed at the garment industry.

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Memo from Bruce Scott to Terry O'Malley regarding Lightning Fastener Company Limited and Time Magazine.

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A method using L-cysteine for the determination of arsenous acid (As(III)), arsenic acid (As(V)), monomethylarsonic acid (MMAA), and dimethylarsinic acid (DMAA) by hydride generation was demonstrated. The instrument used was a d.c. plasma atomic emission spectrometer (OCP-AES). Complete recovery was reported for As(III), As(V), and DMAA while 86% recovery was reported for MMAA. Detection limits were determined, as arsenic for the species listed previously, to be 1.2, 0.8, 1.1, and 1.0 ngemL-l, respectively. Precision values, at 50 ngemL-1 arsenic concentration, were f.80/0, 2.50/0, 2.6% and 2.6% relative standard deviation, respectively. The L-cysteine reagent was compared directly with the conventional hydride generation technique which uses a potassium iodide-hydrochloric acid medium. Recoveries using L-cysteine when compared with the conventional method provided the following results: similar recoveries were obtained for As(III), slightly better recoveries were obtained for As(V) and MMAA, and significantly better recoveries for DMAA. In addition, tall and sharp peak shapes were observed for all four species when using L-cysteine. The arsenic speciation method involved separation by ion exchange .. high perfonnance liquid chromatography (HPLC) with on-line hydride generation using the L.. cysteine reagent and measurement byOCP-AES. Total analysis time per sample was 12 min while the time between the start of subsequent runs was approximately 20 min. A binary . gradient elution program, which incorporated the following two eluents: 0.01 and 0.5 mM tri.. sodium citrate both containing 5% methanol (v/v) and both at a pH of approximately 9, was used during the separation by HPLC. Recoveries of the four species which were measured as peak area, and were normalized against As(III), were 880/0, 290/0, and 40% for DMAA, MMAA and As(V), respectively. Resolution factors between adjacent analyte peaks of As(III) and DMAA was 1.1; DMAA and MMAA was 1.3; and MMAA and As(V) was 8.6. During the arsenic speciation study, signals from the d.c. plasma optical system were measured using a new photon-signal integrating device. The_new photon integrator developed and built in this laboratory was based on a previously published design which was further modified to reflect current available hardware. This photon integrator was interfaced to a personal computer through an AID convertor. The .photon integrator has adjustable threshold settings and an adjustable post-gain device.

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The Beckman Helium Discharge Detector has been found to be sensitive to the fixed gases oxygen, nitrogen, and hydrogen at detection levels 10-100 times more sensitive than possible with a Bow-Mac Thermal Conductivity Detector. Detection levels o~ approximately 1.9 E-4 ~ v/v oxygen, 3.1 E-4 ~ v/v nitrogen, and 3.0 E-3 ~ v/v hydrogen are estimated. Response of the Helium Discharge Detector was not linear, but is useable for quantitation over limited ranges of concentration using suitably prepared working standards. Cleanliness of the detector discharge electrodes and purity of the helium carrier and discharge gas were found to be critical to the operation of the detector. Higher sensitivities of the Helium Discharge Detector may be possible by the design and installation of a sensitive, solid-state electrometer.

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A new Ultra-High Vacuum (UHV) reflectance spectrometer was successfully designed, making use of a Janis Industries ST-400 sample cryostat, IR Labs bolometer, and Briiker IFS 66 v/S spectrometer. Two of the noteworthy features include an in situ gold evaporator and internal reference path, both of which allow for the experiment to progress with a completely undisturbed sample position. As tested, the system was designed to operate between 4.2 K and 325 K over a frequency range of 60 - 670 cm~^. This frequency range can easily be extended through the addition of appUcable detectors. Tests were performed on SrTiOa, a highly ionic incipient ferroelectric insulator with a well known reflectance. The presence and temperatmre dependence of the lowest frequency "soft" mode were measured, as was the presence of the other two infrared modes. During the structural phase transition from cubic to tetragonal perovskite, the splitting of the second phonon mode was also observed. All of the collected data indicate good agreement with previous measurements, with a minor discrepency between the actual and recorded sample temperatures.

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Gideon Sundback was born in Stockholm, Sweden in 1880. He was educated as an engineer and settled in the United States in 1905. While working for the Universal Fastener Company, New Jersey in 1913 he developed and patented a “separable fastener”, which improved on an earlier version of what today is known as the zipper. He later moved his family to Meadville, Pennsylvania and sought a Canadian location for the production of his new invention. He settled on St. Catharines as it was an easy commute from his Pennsylvania home and opened The Lightning Fastener Company on Niagara Street. Sundback died on June 21, 1954 and is interred in Meadville, Pennsylvania. The plant continued to operate, but with increased foreign competition the manufacture of the zipper declined. The plant closed in 1981. Source: The St. Catharines Standard, July 3, 2004 Harold Fox was a noted lawyer, academic, businessman, author and a leading authority on intellectual property. He was engaged by Gideon Sunback and the Lightening Fastening Company to combat patent infringements by Colonial Fastener in the 1930s. The relationship continued when Fox was asked to become the managing director of the company, which he did until 1949. Fox lived in St. Catharines at his home “Foxcroft” until his death in 1969. Source: http://thefoxfund.com/harold.htm (November 2, 2009)

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Les tâches de vision artificielle telles que la reconnaissance d’objets demeurent irrésolues à ce jour. Les algorithmes d’apprentissage tels que les Réseaux de Neurones Artificiels (RNA), représentent une approche prometteuse permettant d’apprendre des caractéristiques utiles pour ces tâches. Ce processus d’optimisation est néanmoins difficile. Les réseaux profonds à base de Machine de Boltzmann Restreintes (RBM) ont récemment été proposés afin de guider l’extraction de représentations intermédiaires, grâce à un algorithme d’apprentissage non-supervisé. Ce mémoire présente, par l’entremise de trois articles, des contributions à ce domaine de recherche. Le premier article traite de la RBM convolutionelle. L’usage de champs réceptifs locaux ainsi que le regroupement d’unités cachées en couches partageant les même paramètres, réduit considérablement le nombre de paramètres à apprendre et engendre des détecteurs de caractéristiques locaux et équivariant aux translations. Ceci mène à des modèles ayant une meilleure vraisemblance, comparativement aux RBMs entraînées sur des segments d’images. Le deuxième article est motivé par des découvertes récentes en neurosciences. Il analyse l’impact d’unités quadratiques sur des tâches de classification visuelles, ainsi que celui d’une nouvelle fonction d’activation. Nous observons que les RNAs à base d’unités quadratiques utilisant la fonction softsign, donnent de meilleures performances de généralisation. Le dernière article quand à lui, offre une vision critique des algorithmes populaires d’entraînement de RBMs. Nous montrons que l’algorithme de Divergence Contrastive (CD) et la CD Persistente ne sont pas robustes : tous deux nécessitent une surface d’énergie relativement plate afin que leur chaîne négative puisse mixer. La PCD à "poids rapides" contourne ce problème en perturbant légèrement le modèle, cependant, ceci génère des échantillons bruités. L’usage de chaînes tempérées dans la phase négative est une façon robuste d’adresser ces problèmes et mène à de meilleurs modèles génératifs.