scholarly journals Computationally efficient processing of in situ underwater digital holograms

2021 ◽  
Author(s):  
Emma Cotter ◽  
Erin Fischell ◽  
Andone Lavery
Keyword(s):  
Computationally Efficient ◽  
Digital Holograms ◽  
Efficient Processing

scholarly journals Compressed Domain Computationally Efficient Processing Scheme for JPEG Image Filtering

2013 ◽  
Vol 8-9 ◽  
pp. 480-489 ◽  
Author(s):  
Camelia Florea ◽  
Mihaela Gordan ◽  
Bogdan Orza ◽  
Aurel Vlaicu
Keyword(s):  
Image Filtering ◽  
Input Image ◽  
Compressed Domain ◽  
Digital Cameras ◽  
Computationally Efficient ◽  
Processing Scheme ◽  
Jpeg Image ◽  
Convolution Kernels ◽  
High Resolution Images ◽  
Efficient Processing

Image filtering is one of the principal tools used in computer vision applications. Real systems store and manipulate high resolution images in compressed forms, therefore the implementation of the entire processing chain directly in the compressed domain became essential. This includes almost always linear filtering operations implemented by convolution. Linear image filtering implementation directly on the JPEG images is challenging for several reasons, including the complexity of transposing the pixel level convolution in the compressed domain, which may increase the processing time, despite avoiding the decompression. In this paper we propose a new computationally efficient solution for JPEG image filtering (as a spatial convolution between the input image and a given kernel) directly in the DCT based compressed domain. We propose that the convolution operation to be applied just on the periodical extensions of the DCT basis images, as an off-line processing, obtaining the filtered DCT basis images, which are used in data decompression. While this doesn't solve the near block boundaries filtering artefacts for large convolution kernels, for most practical cases, it provides good quality results at a very low computational complexity. These kind of implementations can run at real-time rates/ speeds and are suitable for developments of applications on digital cameras/ DSP/FPGA.

scholarly journals Deep learning for Chinese NOx emission inversion and the integration of in situ observations: a case study on the COVID-19 pandemic

2021 ◽  
Author(s):  
Tai-Long He ◽  
Dylan Jones ◽  
Kazuyuki Miyazaki ◽  
Kevin Bowman ◽  
Zhe Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reanalysis Data ◽  
Data Sets ◽  
Computationally Efficient ◽  
Transport Models ◽  
The Neural Network ◽  
In Situ Observations ◽  
Typical Variation

<p>The COVID-19 pandemic led to the lockdown of over one-third of Chinese cities in early 2020. Observations have shown significant reductions of atmospheric abundances of NO<sub>2</sub> over China during this period. This change in atmospheric NO<sub>2</sub> implies a dramatic change in emission of NO<sub>x</sub>, which provides a unique opportunity to study the response of the chemistry of the atmospheric to large reductions in anthropogenic emissions. We use a deep learning (DL) model to quantify the change in surface emissions of NO<sub>x</sub> in China that are associated with the observed changes in atmospheric NO<sub>2</sub> during the lockdown period. Compared to conventional data assimilation systems, deep neural networks are free of the potential errors associated with parameterized subgrid-scale processes. Furthermore, they are not susceptible to the chemical errors typically found in atmospheric chemical transport models. The neural-network-based approach also offers a more computationally efficient means of inverse modeling of NO<sub>x</sub> emissions at high spatial resolutions. Our DL model is trained using meteorological predictors and reanalysis data of surface NO<sub>2</sub> from 2005 to 2017. The evaluation is conducted using in-situ measurements of NO<sub>2</sub> in 2019 and 2020. The Baidu 'Qianxi' migration data sets are used to evaluate the model's performance in capturing the typical variation in Chinese NOx emissions during the Chinese New Year holidays. The TROPOMI-derived TCR-2 chemical reanalysis is used to evaluate the DL analysis in 2020. We show that the DL-based approach is able to better reproduce the variation in anthropogenic NO<sub>x</sub> emissions and capture the reduction in Chinese NO<sub>x</sub> emissions during the period of the COVID-19 pandemic.</p>

scholarly journals Ownership Identification of Multimedia Content Prediction on a Large Scale

2020 ◽  
Vol 9 (1) ◽  
pp. 794-796
Keyword(s):  
Large Scale ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Service Architecture ◽  
Web Based ◽  
Limited Space ◽  
Large Size ◽  
Multimedia Object ◽  
Efficient Processing ◽  
Multimedia File

This article prepared with three main objectives, first it is to review the large size of multimedia file protection, second for ownership identification of the multimedia content and finally for implementing this setup for cloud based setup instead of web based service architecture. Therefore many of the infrastructure developed for multimedia content to enable the security against the file handling services for managing the better ownership identification. On private and/or public clouds, the device can be deployed. Our framework has two new components: (i) method of producing 3-D image signatures, and (ii) distributed multimedia object matching engine. The signature method produces stable and accurate 3-D video signatures capturing the depth signals in those videos as well as computationally efficient processing and comparison and having limited space. The corresponding distributed engine is highly scalable and designed to support different multimedia objects. We have introduced and deployed the proposed system on two clouds such as private and amazon based clouds.

scholarly journals Numerical Simulation of CPT Cone Penetration in Sand

2014 ◽  
Vol 553 ◽  
pp. 416-421 ◽  
Author(s):  
George P. Kouretzis ◽  
Dai Chao Sheng ◽  
Dong Wang
Keyword(s):  
Numerical Simulation ◽  
Relative Density ◽  
Back Analysis ◽  
Model Parameters ◽  
Computationally Efficient ◽  
Cone Penetration ◽  
Explicit Integration ◽  
Centrifuge Tests ◽  
Single Set

Numerical simulation of cone penetration in sand is performed by means of a computationally efficient critical state model implemented in an explicit-integration finite element code. Its main advantage, compared to other published studies employing simpler soil models such as the Drucker-Prager, is that sand compressibility can be described with a single set of model parameters, irrespective of the stress level and the sand relative density. Calibration of the constitutive model is based on back-analysis of published centrifuge tests results, and consequently the predictions of the numerical methodology are compared against independent tests. Additional analyses are performed for proposing a new simplified formula to correlate the cone penetration resistance with the in situ sand relative density.

scholarly journals Optimal marker gene selection for cell type discrimination in single cell analyses

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bianca Dumitrascu ◽  
Soledad Villar ◽  
Dustin G. Mixon ◽  
Barbara E. Engelhardt
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Marker Gene ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Computationally Efficient ◽  
Data Set ◽  
Gene Markers

AbstractSingle-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization.

Thermal analysis of in-situ microwave casting for efficient processing

2018 ◽  
Vol 5 (9) ◽  
pp. 17253-17259
Author(s):  
Radha Raman Mishra ◽  
Apurbba Kumar Sharma
Keyword(s):  
Thermal Analysis ◽  
Efficient Processing

Prediction of feasible synthesis gas compositions at three European coal deposits

2021 ◽  
Author(s):  
Christopher Otto ◽  
Thomas Kempka
Keyword(s):  
Synthesis Gas ◽  
Coal Gasification ◽  
Economic Feasibility ◽  
Gas Composition ◽  
Underground Coal Gasification ◽  
Computationally Efficient ◽  
Coal Conversion ◽  
Coal Deposits ◽  
In Situ Conditions

<p>In the present study, we apply our validated stoichiometric equilibrium model [1], based on direct minimisation of Gibbs free energy, to predict the synthesis gas compositions produced by in-situ coal conversion at three European coal deposits. The applied modelling approach is computationally efficient and allows to predict synthesis gas compositions and calorific values under various operating and geological boundary conditions, including varying oxidant and coal compositions. Three European coal deposits are assessed, comprising the South Wales Coalfield (United Kingdom), the Upper Silesian Coal Basin (Poland) and the Ruhr District (Germany). The stoichiometric equilibrium models were first validated on the basis of laboratory experiments undertaken at two different operating pressures by [2] and available literature data [3]. Then, the models were adapted to site-specific hydrostatic pressure conditions to enable an extrapolation of the synthesis gas composition to in-situ pressure conditions. Our simulation results demonstrate that changes in the synthesis gas composition follow the expected trends for preferential production of specific gas components at increased pressures, known from the literature, emphasising that a reliable methodology for estimations of synthesis gas compositions for different in-situ conditions has been established. The presented predictive approach can be integrated with techno-economic models [4] to assess the technical and economic feasibility of in-situ coal conversion at selected study areas as well as of biomass and waste to synthesis gas conversion projects.</p><p><span>[</span><span>1] </span><span>Otto, C.; Kempka, T. Synthesis Gas Composition Prediction for Underground Coal Gasification Using a Thermochemical Equilibrium Modeling Approach. </span><em><span>Energies</span></em> <span><strong>2020</strong></span><span>, </span><em><span>13</span></em><span>, 1171.</span></p><p>[2] Kapusta et al., 2020</p><p>[3] Kempka et al., 2011</p><p>[4] Nakaten and Kempka, 2019</p>

scholarly journals Computationally Efficient Multivariate Calibration and Validation of a Grid-Based Hydrologic Model in Sparsely Gauged West African River Basins

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1418 ◽  
Author(s):  
Thomas Poméon ◽  
Bernd Diekkrüger ◽  
Rohini Kumar
Keyword(s):  
River Basins ◽  
Hydrologic Model ◽  
West African ◽  
Evaluation Framework ◽  
Actual Evapotranspiration ◽  
Computationally Efficient ◽  
Modeling Framework ◽  
Diverse Range ◽  
Grid Based

The prediction of freshwater resources remains a challenging task in West Africa, where the decline of in situ measurements has a detrimental effect on the quality of estimates. In this study, we establish a series of modeling routines for the grid-based mesoscale Hydrologic Model (mHM) using Multiscale Parameter Regionalization (MPR). We provide a computationally efficient application of mHM-MPR across a diverse range of data-scarce basins using in situ observations, remote sensing, and reanalysis inputs. Model performance was first screened for four precipitation datasets and three evapotranspiration calculation methods. Subsequently, we developed a modeling framework in which the pre-screened model is first calibrated using discharge as the observed variable (mHM Q), and next calibrated using a combination of discharge and actual evapotranspiration data (mHM Q/ET). Both model setups were validated in a multi-variable evaluation framework using discharge, actual evapotranspiration, soil moisture and total water storage data. The model performed reasonably well, with mean discharge KGE values of 0.53 (mHM Q) and 0.49 (mHM Q/ET) for the calibration; and 0.23 (mHM Q) and 0.13 (mHM Q/ET) for the validation. Other tested variables were also within a good predictive range. This further confirmed the robustness and well-represented spatial distribution of the hydrologic predictions. Using MPR, the calibrated model can then be scaled to produce outputs at much smaller resolutions. Overall, our analysis highlights the worth of utilizing additional hydrologic variables (together with discharge) for the reliable application of a distributed hydrologic model in sparsely gauged West African river basins.

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