502 resultados para Pixels


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Photographic records of dietary intake (PhDRs) are an innovative method for the dietary assessment and may alleviate the burden of recording intake compared to traditional methods of recording intake. While the performance of PhDRs has been evaluated, no investigation into the application of this method had occurre within dietetic practice. This study examined the attitudes of dietitians towards the use of PhDRs in the provision of nutrition care. A web-based survey on the practices and beliefs with regards to technology use among Dietitians Association of Australia members was conducted in August 2011. Of the 87 dietitians who responded, 86% assessed the intakes of clients as part of individualised medical nutrition therapy, with the diet history the most common method used. The majority (91%) of dietitians surveyed believed that a PhDR would be of use in their current practice to estimate intake. Information contained in the PhDR would primarily be used to obtain a qualitative evaluation of diet (84%) or to supplement an existing assessment method (69%), as opposed to deriving an absolute measure of nutrient intake (31%). Most (87%) indicated that a PhDR would also be beneficial in both the delivery of the intervention and to evaluate and monitor goals and outcomes, while only 46% felt that a PhDR would assist in determining the nutrition diagnosis. This survey highlights the potential for the use of PhDRs within practice. Future endeavours lie in establishing resources which support the inclusion of PhDRs within the nutrition care process.

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We propose a computationally efficient image border pixel based watermark embedding scheme for medical images. We considered the border pixels of a medical image as RONI (region of non-interest), since those pixels have no or little interest to doctors and medical professionals irrespective of the image modalities. Although RONI is used for embedding, our proposed scheme still keeps distortion at a minimum level in the embedding region using the optimum number of least significant bit-planes for the border pixels. All these not only ensure that a watermarked image is safe for diagnosis, but also help minimize the legal and ethical concerns of altering all pixels of medical images in any manner (e.g, reversible or irreversible). The proposed scheme avoids the need for RONI segmentation, which incurs capacity and computational overheads. The performance of the proposed scheme has been compared with a relevant scheme in terms of embedding capacity, image perceptual quality (measured by SSIM and PSNR), and computational efficiency. Our experimental results show that the proposed scheme is computationally efficient, offers an image-content-independent embedding capacity, and maintains a good image quality

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In this paper, we propose a new steganalytic method to detect the message hidden in a black and white image using the steganographic technique developed by Liang, Wang and Zhang. Our detection method estimates the length of hidden message embedded in a binary image. Although the hidden message embedded is visually imperceptible, it changes some image statistic (such as inter-pixels correlation). Based on this observation, we first derive the 512 patterns histogram from the boundary pixels as the distinguishing statistic, then we compute the histogram difference to determine the changes of the 512 patterns histogram induced by the embedding operation. Finally we propose histogram quotient to estimate the length of the embedded message. Experimental results confirm that the proposed method can effectively and reliably detect the length of the embedded message.

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Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

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Hybrid integration of GaAs/AlGaAs multiple quantum well self electro-optic effect device (SEED) arrays are demonstrated flip-chip bonded directly onto 1 mu m silicon CMOS circuits. The GaAs/AlGaAs MQW devices are designed for 850 nm operation. Some devices are used as input light detectors and others serve as output light modulators. The measurement results under applied biases show good optoelectronic characteristics of elements in SEED arrays. Nearly the same reflection spectrum is obtained for the different devices at an array and the contrast ratio is more than 1.2:1 after flip-chip bonding and packaging. The transimpedance receiver-transmitter circuit can be operated at a frequency of 300 MHz.

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于2010-11-23批量导入

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The investigations on GaAs/AlGaAs multiple quantum well self electro-optic effect device (SEED) arrays for optoelectronic smart pixels are reported. The hybrid integration of GaAs/AlGaAs multiple quantum well devices flip-chip bonding directly over 1 mu m silicon CMOS circuits are demonstrated. The GaAs/AlGaAs multiple quantum well devices are designed for 850nm operation. The measurement results under applied biases show the good optoelectronic characteristics of elements in SEED arrays. The 4x4 optoelectronic crossbar structure consisting of hybrid CMOS-SEED smart pixels have been designed, which could be potentially used in optical interconnects for multiple processors.

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Hybrid integration of GaAs/AlGaAs multiple quantum well self electro-optic effect device (SEED) arrays are demonstrated flip-chip bonded directly onto 1 mu m silicon CMOS circuits. The GaAs/AlGaAs MQW devices are designed for 850 nm operation. Some devices are used as input light detectors and others serve as output light modulators. The measurement results under applied biases show good optoelectronic characteristics of elements in SEED arrays. Nearly the same reflection spectrum is obtained for the different devices at an array and the contrast ratio is more than 1.2:1 after flip-chip bonding and packaging. The transimpedance receiver-transmitter circuit can be operated at a frequency of 300 MHz.

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An organic integrated pixel consisting of an organic light-emitting diode driven by an organic thin-film field-effect transistor (OTFT) was fabricated by a full evaporation method oil a transparent glass substrate. The OTFT was designed as a top-gate Structure, and the insulator is composed of a double-layer polymer of Nylon 6 and Teflon to lower the operation voltage and the gate-leakage current, and improve the device stability. The field-effect mobility of the OTFT is more than 0.5 cm(2) V-1 s(-1), and the on/off ratio is larger than 10(3). The brightness of the pixel reached as large as 300 cd m(-2) at a driving current of 50 mu A.

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Langstaff, David; Chase, T., (2007) 'A multichannel detector array with 768 pixels developed for electron spectroscopy', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 573(1-2) pp.169-171 RAE2008

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Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

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Les détecteurs à pixels Medipix ont été développés par la collaboration Medipix et permettent de faire de l'imagerie en temps réel. Leur surface active de près de $2\cm^2$ est divisée en 65536~pixels de $55\times 55\um^2$ chacun. Seize de ces détecteurs, les Medipix2, sont installés dans l'expérience ATLAS au CERN afin de mesurer en temps réel les champs de radiation produits par les collisions de hadrons au LHC. Ils seront prochainement remplacés par des Timepix, la plus récente version de ces détecteurs, qui permettent de mesurer directement l'énergie déposée dans chaque pixel en mode \textit{time-over-threshold} (TOT) lors du passage d'une particule dans le semi-conducteur. En vue d'améliorer l'analyse des données recueillies avec ces détecteurs Timepix dans ATLAS, un projet de simulation Geant4 a été amorcé par John Id\'{a}rraga à l'Université de Montréal. Dans le cadre de l'expérience ATLAS, cette simulation pourra être utilisée conjointement avec Athena, le programme d'analyse d'ATLAS, et la simulation complète du détecteur ATLAS. Sous l'effet de leur propre répulsion, les porteurs de charge créés dans le semi-conducteur sont diffusés vers les pixels adjacents causant un dépôt d'énergie dans plusieurs pixels sous l'effet du partage de charges. Un modèle effectif de cette diffusion latérale a été développé pour reproduire ce phénomène sans résoudre d'équation différentielle de transport de charge. Ce modèle, ainsi que le mode TOT du Timepix, qui permet de mesurer l'énergie déposée dans le détecteur, ont été inclus dans la simulation afin de reproduire adéquatement les traces laissées par les particules dans le semi-conducteur. On a d'abord étalonné le détecteur pixel par pixel à l'aide d'une source de $\Am$ et de $\Ba$. Ensuite, on a validé la simulation à l'aide de mesures d'interactions de protons et de particules $\alpha$ produits au générateur Tandem van de Graaff du Laboratoire René-J.-A.-Lévesque de l'Université de Montréal.