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Synchrotron X-ray diffraction experiments with a prototype hybrid pixel detector

Synchrotron X-ray diffraction experiments with a prototype hybrid pixel detector

The first experiment, referred to in the text as experiment A, concerns the mechanical characterization of metallic thin films deposited on a compliant substrate using in situ biaxial tensile tests coupled to synchrotron X-ray diffraction (XRD). The mechanical properties of thin crystalline films are inves- tigated with respect to their microstructure and often depart from their bulk counterparts (Geandier et al., 2009; Faurie et al., 2009, 2010). Thin films can exhibit high residual stress states, strong texture components, a high density of defects, small grain size and a high ratio of interfaces. X-ray diffraction is well suited to the measurement of small elastic strains with high accuracy and the examination of strain partitioning between different crystallographic phases and/or different texture components. The samples investigated are W/Cu nanocomposite thin films deposited on polyimide cruciform substrates (Geandier et al., 2010; Djaziri et al., 2010). The small diffraction volume (film thickness of 200 nm) combined with the nanometric size of the grains yields very broad and low- intensity diffraction peaks. Improving the data statistics by adopting a long exposure time may imply material creep and substrate relaxation, leading to inaccurate measurements. Hence the main reasons for using the XPAD3.1 detector for this experiment were its fast acquisition time and its two- dimensional geometry, taking advantage also of the intense synchrotron radiation. Data acquired using area detectors can be reduced to 2 line scans by performing data integration along the azimuthal direction, allowing one to improve statistics.
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Synchrotron X-ray diffraction experiments with a prototype hybrid pixel detector

Synchrotron X-ray diffraction experiments with a prototype hybrid pixel detector

The first experiment, referred to in the text as experiment A, concerns the mechanical characterization of metallic thin films deposited on a compliant substrate using in situ biaxial tensile tests coupled to synchrotron X-ray diffraction (XRD). The mechanical properties of thin crystalline films are inves- tigated with respect to their microstructure and often depart from their bulk counterparts (Geandier et al., 2009; Faurie et al., 2009, 2010). Thin films can exhibit high residual stress states, strong texture components, a high density of defects, small grain size and a high ratio of interfaces. X-ray diffraction is well suited to the measurement of small elastic strains with high accuracy and the examination of strain partitioning between different crystallographic phases and/or different texture components. The samples investigated are W/Cu nanocomposite thin films deposited on polyimide cruciform substrates (Geandier et al., 2010; Djaziri et al., 2010). The small diffraction volume (film thickness of 200 nm) combined with the nanometric size of the grains yields very broad and low- intensity diffraction peaks. Improving the data statistics by adopting a long exposure time may imply material creep and substrate relaxation, leading to inaccurate measurements. Hence the main reasons for using the XPAD3.1 detector for this experiment were its fast acquisition time and its two- dimensional geometry, taking advantage also of the intense synchrotron radiation. Data acquired using area detectors can be reduced to 2 line scans by performing data integration along the azimuthal direction, allowing one to improve statistics.
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In-house time-resolved photocrystallography on the millisecond timescale using a gated X-ray hybrid pixel area detector

In-house time-resolved photocrystallography on the millisecond timescale using a gated X-ray hybrid pixel area detector

Abstract With the remarkable progresses of accelerator-based X-ray sources in terms of intensity and brightness, the investigation of structural dynamics from time-resolved X-ray diffraction methods is becoming widespread in chemistry, biochemistry, and materials science applications. Diffraction patterns can now be measured down to the femtosecond time-scale using X-ray free electron lasers or table-top laser plasma X-ray sources. On the other hand, the recent developments in photon counting X-ray area detectors offer new opportunities for time-resolved crystallography. Taking advantage of the fast read-out, the internal stacking of recorded images, and the gating possibilities (electronic shutter) of the XPAD hybrid pixel detector, we implemented a laboratory X-ray diffractometer for time- resolved single crystal X-ray diffraction after pulsed laser excitation, combined with transient optical absorption measurement. The experimental method and instrumental setup are described in details, and validated using the photoinduced nitrosyl linkage isomerism of sodium nitroprusside Na 2 [Fe(CN) 5 NO].2H 2 O as a proof of principle. Light induced Bragg intensity relative variations
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Evaluation of Timepix3 Si and CdTe hybrid-pixel detectors spectral performances on X and gamma-rays

Evaluation of Timepix3 Si and CdTe hybrid-pixel detectors spectral performances on X and gamma-rays

Evaluation of Timepix3 Si and CdTe hybrid-pixel detectors spectral performances on X and gamma-rays The Timepix3 [1] is a hybrid-pixel detector readout chip developed, in 2013, in the framework of the Medipix3 international collaboration [2]. It is the successor of the Timepix [3] chip, and can record Time-Of-Arrival (ToA) and Time-Over-Threshold (ToT) simultaneously in each pixel. The chip is designed to work in data driven mode, and can process a throughput of upto 40 Mhits/cm²/s. The Timepix3 hybrid-pixel readout chip consists in a matrix composed of 256 × 256 square-shaped pixels with 55 µm pitch. For each detected event, the Time-of-Arrival (ToA), with a time resolution of 1.5 ns, and the Time-over-Threshold (ToT) that gives access to deposited energy, are recorded. The Timepix3 chip can be hybridized to several semi-conductors, such as silicon (Si) or cadmium telluride (CdTe) with thicknesses up to 5 mm. For this study, a Timepix3 hybridized with a 300 µm thick p-on-n Si sensor, and a Timepix3 hybridized with a 1 mm thick n-on-p CdTe sensor were used. The Timepix3 Si operates in hole collection and a 100 V bias, whereas the Timepix3 CdTe operates in electron collection and a -300 V bias. The chips are controlled by the Katherine readout from RICE [4]. The per pixel energy calibration in Time-over-Threshold calibration mode is obtained following the method proposed by J. Jakubek in [5].
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Fast X-ray reflectivity measurements using an X-ray pixel area detector at the DiffAbs beamline, Synchrotron SOLEIL

Fast X-ray reflectivity measurements using an X-ray pixel area detector at the DiffAbs beamline, Synchrotron SOLEIL

The experiments described in this paper were all performed at the French synchrotron source (Synchrotron SOLEIL, DiffAbs beamline, https://www.synchrotron-soleil.fr/en/beam lines/diffabs). In all the cases depicted here, an X-ray energy in the 7.0 to 19.0 keV range was used; the precise value will be detailed for each measurement. The beam size was about 200  250 mm [full width at half-maximum (FWHM), vertical  horizontal]. The beam divergence in vertical (V) and hori- zontal (H) directions amounted to 0.01  and 0.09  , respec- tively. The XRR experiments were carried out with a horizontal sample surface; thus the scattering plane is vertical. The detector used for this study was the XPAD S-140 hybrid pixel detector. The family of XPAD3 photon-counting readout chips has been extensively described elsewhere (Basolo et al., 2007; Pangaud et al., 2007, 2008); therefore only its main characteristics are presented in this paper. The circuit comprises 9600 square pixels with a pitch of 130 mm, organized in a matrix of 80 rows and 120 columns. Each pixel is equipped with a 12 bit counter with an extra bit that can be accessed during exposure; this allows the dynamic range to be extended up to 32 bits. The response of the detector is linear up to 3  10 5 photons s 1 pixel 1 and photon selection is done with a single threshold discriminator. The minimum threshold that can be set corresponds to an energy of 4 keV (Basolo et al., 2008). The XPAD S-140 detector is made up of 14 XPAD3.2 chips bump-bonded to a single 500 mm-thick silicon sensor. The XPAD circuits are organized in two rows of seven chips giving about 75  32 mm of sensitive surface (240  560 pixels). The sensor is pixelated with the same pitch as the readout circuits except for the zones that correspond to the gaps between adjacent chips that are 3 pixels wide. Further- more, the detector offers fast readout (depending slightly on the chosen dynamic range of 16 and 32 bits, in the range of a few ms) and low noise (not influenced by the background
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Pixel Classification using General Adaptive Neighborhood-based Features

Pixel Classification using General Adaptive Neighborhood-based Features

The so-called General Adaptive Neighborhood Image Pro- cessing (GANIP) approach has been developed by Debayle and Pinoli [1] for the adaptive processing and analysis of gray- level images. In this framework an image is represented as a set of adaptive neighborhoods (i.e. a surrounding region for each pixel, fitting its local spatial structures). Mathematically, a General Adaptive Neighborhood (GAN) is a connected component whose point intensity values (measured in relation to a certain criterion such as luminance, contrast, etc.) fit within a specific range of homogeneity tolerance. It makes GAN adaptive with respect to the spatial structures and defined from the gray-level image itself. Thereafter, GANs can be used as operational windows for adaptive image processing and analysis. For example, these GANs have been used as struc- turing elements for adaptive mathematical morphology [2]. In addition, geometrical and morphometrical measurements of GANs have also been used for characterizing gray-level images [3], [4].
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TEXTURE REMOVAL BY PIXEL CLASSIFICATION USING A ROTATING FILTER

TEXTURE REMOVAL BY PIXEL CLASSIFICATION USING A ROTATING FILTER

I t is the diffused image at time t, F (I 0 ) is our classification function. But unlike [6], our classification function F (I 0 ) does not provide us with a precise control on image boundaries, then the MCM scheme here takes an important part and moves cor- ner points according to the curvature of iso-intensity lines. As a consequence, this scheme behaves as the MCM scheme then for example a square is transformed into a circle after some iterations. For minimizing this effect we are going to consider the two directions ξ 1 and ξ 2 provided by our pixel classifica- tion process on image boundaries.
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More surface detail with One-Two-Pixel Matching

More surface detail with One-Two-Pixel Matching

III. R ESULTS A. Datasets a) Aerial acquistion – Vaihingen dataset: A set of four aerial images acquired with the UltraCam-X camera at 0.2m GSD is used in this experiment. The images are part of the EuroSDR Matching benchmark [38]. The camera calibration and orientation parameters were also furnished with the bench- mark. The available ground truth (GT) data is a surface that is a median of several DSMs furnished by all of the participants of the benchmark. As a result, many surface details are discarded and the GT is considered unsuitable to evaluate the One-Two- Pixel matching algorithm. The evaluation was carried out on an alternative GT (cf. Section III-B) over an area of ≈ 5km x 3km (cf. Fig. 7a and Fig. 8a).
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Pixel Classification using General Adaptive Neighborhood-based Features

Pixel Classification using General Adaptive Neighborhood-based Features

IEEE Computer Society - CPS Conference Publishing Services, Proceedings : 22nd International Conference on Pattern Recognition, 2014.. Pixel Classification using General Adaptive Neighb[r]

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A fast monolithic active pixel sensor with pixel-level reset noise suppression and binary outputs for charged particle detection

A fast monolithic active pixel sensor with pixel-level reset noise suppression and binary outputs for charged particle detection

II. P IXEL D ESIGNS A. DC-Coupling Pixel Design The pixel design with DC-coupling of the amplifier to the charge sensing diode is the basis of the chip design described in this paper. This pixel architecture, derived from [3] and proposed in [4], uses a common-source (CS) preamplifying stage placed closest to the detector, and double sampling circuitry with a serial capacitor, a switch, and a source follower (SF) combination to store the reset level of the detector (Fig. 1a). This reset level is memorized until the next readout of the pixel during which it is eliminated by subtracting from the signal. The short interval between readouts in this application give us the opportunity to store easily the reset level of the detector in the pixel. Offset mismatches of the amplifiers are also suppressed as a result of the double sampling process. The threshold voltage mismatches of the SFs are successively corrected by a second double sampling process performed at the column level.
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Two-pixel polarimetric camera by compressive sensing

Two-pixel polarimetric camera by compressive sensing

For this first imaging scenario, we consider a square object with high total intensity value over a dark background, forming an image of N = 128 × 128 pixels. This intensity image x T , plotted in Fig. 7 is supposed to be the sum of two polarimetric image components xS and xP , yielding a true OSC map also plotted in Fig. 7. In this scenario, we assume that a first object (smaller square) cannot be distinguished from the second object (bigger square) on the intensity image xT , while OSC map allows the two objects to be clearly identified. The background is assumed totally depolarizing (OSC = 0). The smaller square object is always supposed slightly depolarizing (OSC = 0.8) whereas the OSC of the second object (bigger square) is varied between 0 and 1 in the following numerical experiments. Fig. 7 shows an example of reconstruction of the total intensity and the OSC map with the combined-rFISTA algorithm (with Haar wavelets) for SNR = 40 dB, compression rate 40%, incidence angle θ = 50 ◦ and without bias or uncertainty on the tilt directions. The reconstruction quality is visually very good (PSNR = 52.8 dB), as evidenced by the reconstruction error map of the total intensity and OSC shown in Fig. 7. This simple example demonstrates the possibility of providing relevant polarimetric information from the proposed concept of 2-pixel polarimetric CS camera.
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Combining Pixel and Depth Information in Image-Based Visual Servoing

Combining Pixel and Depth Information in Image-Based Visual Servoing

Jaume-I University Blaise Pascal University of Clermont-Ferrand 12071 Castello, Spain 63177 Aubiere - Cedex, France Abstract Image-based visual servoing has been found gener- ally satisfactory and robust in the presence of camera and hand-eye calibration errors. However, in some cases, singularities and local minima may arise. We propose a modication of the feature vector which alle- viates this problem without need of introducing further information, i.e., only pixel coordinates and depth es- timations are used. Using the task-function approach, we demonstrate the relationship between the velocity screw of the camera and the current and desired poses of the object in the camera frame. Experimental re- sults on a real robotic platform illustrate the presented approaches.
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Pixel: a content management platform for quantitative omics data

Pixel: a content management platform for quantitative omics data

We developed the Pixel web application (Pixel Web App) with these ideas in mind. It is a content management platform to help the researchers involved in a multi-omics biological project, to collaboratively work with their HT data. The Pixel Web App does not store the primary data. It is rather focused on annotation, storage and exploration of secondary data (see Fig. 1 ). These explorations represent critical steps to answer biological questions and need to be carefully annotated and recorded to be further exploited in the context of new biological questions. The Pixel Web App helps the researcher to specify necessary information required to replicate multi-omics results. We added an original hierarchical system of tags, which allows to easily explore and select multi-omics results stored in the system and to use them for new interpretations. The Pixel Web App can be installed on any individual computer (for a single researcher for instance), or on a web server for collaborative work between several researchers or research teams. The entire software has been developed with high quality programming standards and complies to major rules of open-source development ( Taschuk & Wilson, 2017 ). The Pixel project is available on GitHub at https://github.com/Candihub/pixel , where full source code and detailed documentation are provided. We present in this article the Pixel Web App design and implementation. We provide a simple case study, emblematic of our daily use of the Pixel Web App, with the exploration of results issued from transcriptomics and proteomics experiments performed in the pathogenic yeast Candida glabrata.
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Control and application of a pixel-parallel image processing system

Control and application of a pixel-parallel image processing system

The Write Data Register (WDR) is a write-only 32-bit register, used to program the Array Instruc- tion Memory (AIM) portion of the control store.. The data to be written to mem[r]

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QUALITÉ DE VIE DU MALADE DÉMENT À DOMICILE ET QUALITÉ DE VIE DE L'AIDANT. L'ÉTUDE PIXEL

QUALITÉ DE VIE DU MALADE DÉMENT À DOMICILE ET QUALITÉ DE VIE DE L'AIDANT. L'ÉTUDE PIXEL

RÉSUMÉ/ABSTRACT > Au-delà des effets neuropsychiatriques des thérapeutiques spécifiques de la démence, l’un des enjeux aujourd’hui est la qualité de vie (QdV) des malades comme de leurs aidants informels. Objectifs. Cette étude vise à rechercher d’éventuels lien entre QdV des malades et QdV des aidants, à déterminer les paramètres les influençant, et à mesurer les conséquen- ces de leur altération. Méthodes. Deux indexs de QdV ont été utilisés : la grille ADRQL (Alzheimer Disease Related Quality of Life) de Rabins pour la QdV des malades, une grille de QdV des aidants développée à partir des données des études PIXEL précédentes. Cette dernière se compose de 20 items et est passée sous la forme d’un auto questionnaire. Les deux grilles ont été mises en relation avec des données socio-démographiques sur le malade et sur son aidant principal, avec les données médicales et thérapeutiques du malade, avec un inventaire neuropsychologique : test de Folstein pour la cogni- tion, échelle de Cornell pour la dépression, batterie rapide d’évaluation frontale, groupe iso-ressources et index de Katz pour la dépendance, inventaire neuropsychiatrique de Cummings pour les symptômes comportementaux et psychologi- ques de la démence. Le médecin évaluait de plus la dépression de l’aidant. Résultats. 82 patients déments vivant à domi- cile et leur aidant principal ont été recrutés pour cette étude. Les malades étaient âgés de 80.1 ± 6.6 ans et les aidants de 64.4 ± 12.9 ans. La QdV des malades était corrélée à celle de leurs aidants, à leur atteinte cognitive, à leur perte d’autono- mie, à l’importance de leur dépression ou de leurs troubles du comportement. Ces deux derniers éléments sont corrélés à la QdV de l’aidant. Les femmes aidantes ont une plus mauvaise QdV et sont plus dépressives que les hommes. Les QdV des malades et des aidants sont favorablement influencées par la thérapeutique spécifique de la démence, ce qui réduit le risque d’institutionnalisation. Discussion. QdV des aidants et des malades sont liés et les deux personnes partagent une communauté de détresse. La thérapeutique de la démence est bénéfique aux deux. On peut penser que ce bénéfice sera potentialisé par une bonne prise en charge de la dépression chez l’un ou/et l’autre. MOTS CLÉS : QUALITÉ DE VIE, DÉMENCE, ALZHEIMER, DÉPRESSION, AIDANTS, FAMILLES, THÉRAPEUTIQUE.
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Sub-pixel matching method for low-resolution thermal stereo images

Sub-pixel matching method for low-resolution thermal stereo images

regularizing the matched features spatially. In [2, 20], among several variants, the authors propose a relatively similar framework for pedestrian detection: after hav- ing extracted features with the Canny edge detector, they perform matching using cross-correlation and specialized methods adapted to a human silhouette. Features matching is also important for thermal image analysis in medical applications [27], [28]. Most of the time, Thermal images are used to analyze the variation of the temperature of the skin or an organ. So in such a condition, the extracted features must be very robust. In [29], the authors propose a framework to correct motion ar- tifact due to body movement when recovering breast images. While the amplitude of the Fourier transforms gives the pixel intensity, the phase components represent the spatial information. Given three consecutive frames I 1 , I 3 and I 3 , they combine
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From the Grain to the Pixel, Aesthetic and Political Choices

From the Grain to the Pixel, Aesthetic and Political Choices

and Lisa Jevbratt illustrated it perfectly when she worked on the data visualization as an indexical trace of the reality, an imprint. She conceived “Mapping the Web Infome” (see Figure 11.3) project which consisted of software that she made in 2001 with some help of students and friends, and the “crawler” could visualize several types of information activities on the internet. You could search by name, by webpage or by acronym and have some indexical information resulting from this search engine. She collaborated with ten artists, one of whom was Lev Manovich, who decided to aestheticize the software activity in itself, and to interpret the visualization of the mapping as paintings created by information space in the software. When you clicked on each pixel, the IP address appeared as well as the country where the information came from. The person using this crawler could decide which colors, which shape, which formal representation could be used to represent this reality. For Jevbratt 27 these lines, dots,
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Antenna-Coupled LEKIDs for Multi-Band CMB Polarization Sensitive Pixel

Antenna-Coupled LEKIDs for Multi-Band CMB Polarization Sensitive Pixel

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r]

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A data path for a pixel-parallel image processing system

A data path for a pixel-parallel image processing system

Since the focus of this research is on the design and implementation of the format converters, a Raytheon video digitizer chip containing an A/D converter and all th[r]

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Pixel characterization for the MFT upgrade of the ALICE experiment

Pixel characterization for the MFT upgrade of the ALICE experiment

1 Introduction The LHC The Large Hadron Collider (LHC) is the world’s largest and most powerful particle accelerator ever built. Based at the European particle physics laboratory dedicated to the pursuit of fundamental science CERN, it lies in a 100m depth tunnel beneath the Franco-Swiss border near Geneva in Switzerland. The accelerator consists in a 27-kilometre ring of superconducting magnets where particles are accelerated at close to the speed of light and collide at four different locations around the ring where detector experiments (ALICE, ATLAS, CMS and LHCb) stand. LHC works by firing two beams of particles in opposite directions and forced to collide, releasing massive amounts of energy (proton-proton collisions at 7TeV for run I and 13TeV for run II). It uses some of the most powerful dipoles and radio-frequency cavities in existence. Figure 1 shows the LHC facility and the tunnel with two separate vacuum tubes channelling particles.
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