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  • 1
    Electronic Resource
    Electronic Resource
    Electroluminescence of the oxide thin film phosphors Zn2SiO4 and Y2SiO5 (1996)
    [S.l.] : American Institute of Physics (AIP)
    Journal of Applied Physics 79 (1996), S. 3229-3234 
    Details
    ISSN: 1089-7550
    Source: AIP Digital Archive
    Topics: Physics
    Notes: Mn-activated Zn2SiO4 and Ce-activated Y2SiO5 multilayer thin film electroluminescent (EL) devices were prepared by rf magnetron sputtering. The EL response of the devices under different voltages, frequencies, and pulse widths, as well as the transferred charge and decay characteristics were studied. The EL device using Zn2SiO4:Mn as the phosphor layer is shown to achieve a brightness of over 200 cd/m2 at 400 Hz and a field of 3×106 V/cm (twice the threshold field) and a maximum efficiency of 0.78 lm/W, with a decay time of 0.6 ms. The main characteristics of the oxide phosphors include (a) strong trailing edge excitation in nonsymmetrical EL devices, (b) a narrow transferred charge loop, and (c) a time response sensitive to device structures. Compared with the nonsymmetrical structure, the more symmetrical double-insulated structure is shown to increase the efficiency of the EL device by generating strong EL excitation from both positive and negative voltage pulses. © 1996 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.361269
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  • 2
    Electronic Resource
    Electronic Resource
    Thin film electroluminescence in highly anisotropic oxide materials (1998)
    Woodbury, NY : American Institute of Physics (AIP)
    Applied Physics Letters 72 (1998), S. 3356-3358 
    Details
    ISSN: 1077-3118
    Source: AIP Digital Archive
    Topics: Physics
    Notes: A criterion for high performance electroluminescent (EL) phosphor based on the crystal structure of the phosphor host has been developed. The performance of some best-performing EL oxide phosphors is correlated to the fundamental atomic arrangements of the phosphor host and to the microstructural characteristics of their thin films. It is shown that, contrary to popular beliefs, oxide phosphors with certain crystallographic features do have the capability of transporting significant current densities of hot electrons. Examples of newly developed high brightness and efficiency oxide EL phosphors are presented. © 1998 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.121602
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  • 3
    Electronic Resource
    Electronic Resource
    Synthesis of Si(N,C) nanostructured powders from an organometallic aerosol using a hot-wall reactor (1993)
    Springer
    Journal of materials science 28 (1993), S. 1334-1340 
    Details
    ISSN: 1573-4803
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Current studies show that nanostructured Si(N,C) powders are readily synthesized by rapid condensation of a pyrolytically decomposed silazane precursor, namely [CH3SiHNH] n ,n−3 or 4. Basically, the process involves ultrasonic conversion of the liquid-phase precursor to an aerosol, followed by thermal decomposition in a hot reactor. This was followed by the rapid condensation of the gaseous product exiting the reactor, to form ceramic particles of nanoscale dimension. Thermal decomposition was performed at a temperature of 1000 °C, near ambient pressure with a flow rate of ∼ 150 standard cm3 min−1 for NH3. One critical feature examined in this process was the rapidity of the powder synthesis, in a reaction which involves (i) elimination of ligand groups, (ii) formation of ceramic species, and (iii) condensation of ceramic species into ultrafine ceramic particles. These features have been studied using Fourier transform infrared spectroscopy, transmission electron microscopy, X-ray photo-electron and nuclear magnetic resonance spectroscopies. Additionally, a model is formulated to determine the effect of process parameters on particle size.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01191974
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  • 4
    Electronic Resource
    Electronic Resource
    Synthesis of Si(C, N) nanoparticles by rapid laser polycondensation/crosslinking reactions of an organosilazane precursor (1992)
    Springer
    Journal of materials science 27 (1992), S. 3231-3238 
    Details
    ISSN: 1573-4803
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract A study has been made of basic mechanisms involved in the rapid synthesis of pre-ceramic and ceramic nanoparticle powders. In this process an aerosol, formed from an ultrasonically atomized liquid organosilazane monomer, [CH3SiHNH] n , is injected into the beam of an industrial CW CO2 laser. One critical feature examined was the rapid condensation of molecular species from the laser plume, in a process involving three-dimensional crosslinking polycondensation reactions. In accompanying studies, a model has been formulated to determine the laser plume temperature, the cooling rate of condensing species and the particle diameter. These are obtained by analytical solution of heat conduction, momentum and mass conservation equations that consider heat loss by gas conduction, radiation, evaporation and convection.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01116018
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  • 5
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    Artificial Intelligence for flood analysis - first results from the 'AI4Flood project' (2021)
    Details
    Publication Date: 2021-06-11
    Language: English
    Type: info:eu-repo/semantics/lecture
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  • 6
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    Investigating the role of globally available labeled dataset for flood detection using Sentinel-1 imagery and deep learning (2021)
    In:  Abstract Book
    Details
    Publication Date: 2021-06-11
    Description: Flood is one of the most widespread and frequent natural disasters. Deriving accurate and rapid cartographic information on flood extent is essential to help manage the situation. Satellite remote sensing is now widely used for near real-time flood monitoring as it provides large scale detection in a time- and cost-efficient manner. Optical satellite imagery is employed as important tools for flood mapping due to easier interpretability and high spatial resolution. However, cloudy weather associated with floods are a great obstacle to optical sensors for flood monitoring. In contrast, Synthetic Aperture Radar (SAR) allows observation of wide areas across near all-weather conditions and plays a significant role in operational services for flood management. Although in many cases smooth water surfaces can be easily extracted from SAR imagery, it is subjected to overestimation of flooded areas especially in the arid and semi-arid regions since the complex interactions between SAR characteristics and environmental conditions. Advanced machine learning and deep learning approaches have demonstrated large potential to overcome the problem by learning features directly from images which requires a large number of labeled samples for training and validation. Therefore, some public georeferenced dataset to train and test deep learning flood algorithms are being produced. To investigate the role of globally available label datasets in obtaining reliable flood maps using SAR data and deep learning approaches, we tried one of the open access dataset, Sen1Floods11, which is a surface water dataset. We trained, validated and tested a ResNet50 model to segment flood water using a subset of this dataset.The classification results of flood water have obtained an overall accuracy of 89.5% for the test dataset in India and 78.9% for the test dataset in Pakistan. Results show the potential of the flood water dataset to better detect the flooded area.
    Type: info:eu-repo/semantics/conferenceObject
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  • 7
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    GNSS Remote Sensing: Recent Results from GFZ (Oral presentation) (2022)
    In:  Presentations
    Details
    Publication Date: 2023-01-20
    Type: info:eu-repo/semantics/conferenceObject
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  • 8
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    GNSS Remote Sensing: Recent Results from GFZ (2022)
    In:  Abstracts
    Details
    Publication Date: 2023-06-09
    Type: info:eu-repo/semantics/conferenceObject
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  • 9
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    DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation (2023)
    In:  Remote Sensing of Environment
    Details
    Publication Date: 2023-07-24
    Description: Global navigation satellite system reflectometry (GNSS-R) has shown a capability in recent years to be applied as a novel remote sensing technique to retrieve ocean wind speeds. The combination of GNSS-R observable delay-Doppler maps (DDMs) and deep learning algorithms provides the possibility to build an end-to-end pipeline for improving wind speed estimations. Recent studies have proven that data-driven approaches can be applied to generate enhanced estimation products. However, these are usually trained with convolutional neural networks (CNNs), which include inductive bias throughout the models. The inbuilt translation equivariance in CNNs represents an inexactitude for the feature extraction on DDMs. To address this issue, we propose Transformer-based models, named DDM-Former and DDM-Sequence-Former (DDM-Seq-Former), to exploit delay-Doppler correlation within and between DDMs, respectively. The advantages of our methods over conventional retrieval algorithms and other deep learning-based approaches are presented based on the Cyclone GNSS (CYGNSS) version 3.0 dataset. In addition, a comprehensive study on the attention mechanism for our models is demonstrated. The proposed DDM-Former yields the best overall performance with a root mean square error (RMSE) of and a bias of over the nine months test period. Moreover, with an RMSE of and a bias of , the proposed DDM-Seq-Former can promisingly improve the estimations in the cases with wind speeds higher than . There are still opportunities for further enhancements in creating more robust models that could perform well in all wind regimes given a non-uniform wind distribution. We will make our code publicly available.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 10
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    Deep learning in spaceborne GNSS Reflectometry for Ocean Remote Sensing (2023)
    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Details
    Publication Date: 2023-12-11
    Description: GNSS Reflectometry (GNSS-R), referring to exploiting the GNSS signal of opportunity reflected off the Earth surface, has emerged as a novel remote sensing technique for monitoring geophysical parameters. The Cyclone GNSS (CYGNSS), launched on December 15th, 2016, is a constellation of eight microsatellites with cost-effected receivers, fully dedicated to the GNSS-R applications, and can track reflected signals from multiple GNSS satellites. Compared with traditional optical and radar remote sensing, GNSS-R can provide massive datasets with global coverage and improved temporal resolution, which offers unique potential for characterizing the complex Earth system.With the increase of GNSS-R observation data volume, deep learning techniques show their strong capability in retrieving ocean surface wind speed by extracting features from the Delay-Doppler Maps (DDMs). Furthermore, it is shown that deep learning models significantly improve the quality of existing GNSS-R wind speed products. The model achieves an overall RMSE of 1.31 m/s compared with the ERA5 reanalysis data and leads to an improvement of 28% in comparison to the operational retrieval algorithm based on the empirical geophysical model functions (GMFs).However, some known geophysical parameters, such as precipitation, are theorized to be impacting the reflected signals, altering the pattern of the DDMs, and consequently biasing the retrievals. The correction of such bias is not trivial because of its nonlinear dependency on various environmental and technical parameters. Therefore, we explore how deep learning-based fusion on additional precipitation data can correct the bias and further investigate the potential of deep learning models to retrieve precipitation.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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