Look-up Table Approach Research Articles

High speed data encryption technique with optimized memory based RSA algorithm for communications

PurposeThe privacy of the information is a major challenge in the communication process. In the present modern generation, the cryptography plays a vital role in providing security for data, such as text, images and video while transmitting from source to destination through internet or intranet. The Rivest-Shamir-Adleman (RSA) is an asymmetric key cryptographic system, where the security of the method works on the strength of the key.Design/methodology/approachIn an asymmetric key crypto system, a pair of keys is generated one public key for encryption and one private key for decryption. The major challenge of implementing the RSA is the power function which becomes tedious and time consuming as the exponential value increases. The Chinese remainder theorem proves to be the best for data encryption when it comes to execution time of the algorithm. The proposed novel RSA algorithm with lookup table (LUT) is an extension to the Chinese remainder algorithm, which works better for image and video in terms of time complexity.FindingsThis paper presents a LUT approach for implementing the RSA with a minimal processing time. The proposed algorithm was compared with the standard algorithms like, Chinese remainder theorem, binary approach and squared multiplication approach. As the size of the exponent value increases, the proposed method shows better performance compared to other standard methods.Originality/valueThis paper presents a LUT approach for implementing the RSA with a minimal processing time. The proposed algorithm was compared with the standard algorithms like, Chinese remainder theorem, binary approach and squared multiplication approach. As the size of the exponent value increases, the proposed method shows better performance compared to other standard methods.

Full-Wave Modeling and Inversion of UWB Radar Data for Wave Propagation in Cylindrical Objects

The nondestructive characterization of cylindrical objects is needed in many fields, such as medical diagnostics, tree trunk inspection, or concrete column testing. In this study, the radar equation of Lambot et al. is combined with cylindrical Green’s functions to fully model and invert ultra-wideband (UWB) ground-penetrating radar (GPR) data and retrieve the properties of cylindrical objects. Inversion is carried out using a lookup table (LUT) approach followed by local optimization to ensure retrieval of the global minimum of the objective function. Numerical experiments were conducted to analyze the capabilities of the developed inversion procedure to estimate the radius, permittivity, and conductivity of the cylinders. The full-wave model was validated in laboratory conditions on metallic and plastic pipes of different sizes. The adopted radar system consists of a lightweight vector network analyzer (VNA) connected to a single transmitting and receiving horn antenna. The numerical experiments highlighted the complexity of the inverse problem, mainly originating from the multiple propagation modes within cylindrical objects. The laboratory measurements demonstrated the accuracy of the forward modeling and reconstructions in far-field conditions.

Retrieval of Arctic Vegetation Biophysical and Biochemical Properties from CHRIS/PROBA Multi-Angle Imagery Using Empirical and Physical Modelling

Mapping and monitoring of Arctic vegetation biochemical and biophysical properties is gaining importance as global climate change is disproportionately affecting this region. Previous studies using remote sensing to model Arctic vegetation biochemical and biophysical properties have generally involved empirical modelling with nadir looking broadband sensors and have typically been conducted at the field scale in one study area. Satellite hyperspectral remote sensing has not been previously investigated for retrieving leaf and canopy biochemical and biophysical properties of Arctic vegetation across multiple sites using either empirical or physically-based modelling approaches. Furthermore, multi-angle hyperspectral sensors (CHRIS/PROBA), which can provide insight into vegetation reflectance anisotropy and potentially improve vegetation parameter estimation, have also not been investigated for this purpose. In this study, three modelling approaches previously investigated with field spectroscopy data (Kennedy et al., 2020) were used with CHRIS Mode-1 imagery to predict leaf chlorophyll content, plant area index and canopy chlorophyll content across a bioclimatic gradient in the Western Canadian Arctic. Modelling approaches included: parametric linear regression based on vegetation indices (VI), non-parametric machine learning Gaussian processes regression (GPR) and inversion of the PROSAIL radiative transfer model using a look-up table approach (LUT). CHRIS imagery was acquired with −55°, −36°, 0°, +36°, +55° view zenith angles (VZA) between 2011 and 2014 over three field sites extending from the Richardson Mountains in central Yukon, Canada to the north end of Banks Island, Northwest Territories, Canada. Field measurements were acquired within several weeks of satellite acquisitions. GPR had the best model fit (mean cross-validated (cv) coefficient of determination, r2cv = 0.61 across all vegetation variables, sites and VZAs vs. 0.59 for the simple ratio, SR) and predictive performance (normalized root mean square error, NRMSEcv = 0.13 vs. 0.14 for SR). The revised optimized soil adjusted VI (ROSAVI) performance was slightly poorer (r2cv = 0.51; NRMSEcv = 0.15). The physically-based PROSAIL model performed poorer than all empirical models (r2 = 0.50; NRMSE = 0.18). This ranking of model performance is similar to that found in the previous field spectroscopy study, where empirical model fits and predictive performance were only slightly worse. With respect to view angle performance, NRMSE varied only slightly, indicating no distinct advantage for any one VZA. Overall, strong potential has been demonstrated for empirical modelling of Arctic vegetation chlorophyll and plant area index using hyperspectral data combined with band selection/optimization procedures in the Arctic. Recently launched and future hyperspectral satellites, including next generation airborne sensors, will likely provide improvements to the model performance reported here.

Scaled boundary finite element method for hydrodynamic bearings in rotordynamic simulations

The behavior of rotor systems with hydrodynamic bearings can be affected crucially by the nonlinear bearing forces. These forces are incorporated in rotordynamic simulations by solving the Reynolds equation in every step of a time integration scheme. Since closed-form analytical solutions of the Reynolds equation are known only for simplified boundary conditions, numerical methods and look-up table approaches are applied. To obtain accurate and convergent results, numerical solutions require a fine discretization, while the look-up tables require a fine grid of interpolation points. In both cases, the numerical effort of the rotordynamic simulation is dominated by the computation of the bearing forces.In this study, a semi-analytical solution of the Reynolds equation based on the Scaled Boundary Finite Element Method (SBFEM) is developed to reduce the numerical effort of the overall simulation. In contrast to a purely numerical solution, the two-dimensional pressure field is discretized in only one coordinate direction, whereas in the other direction, an analytical formulation is used. This results in a system of non-homogeneous ordinary differential equations, which is solved under consideration of the boundary conditions. Since the complexity of the SBFEM model and the resulting numerical effort depend on the modeling of the oil supply groove, three different models are analyzed in this paper. The equations are derived, the solutions are verified, and their numerical efficiency is investigated in comparison to an FEM solution, which is one of the standard numerical approaches.

Digital Non-Linearity Calibration for ADCs With Redundancy Using a New LUT Approach

This paper presents a novel Look-up Table (LUT) calibration technique for static non-linearity compensation in analog-to-digital converters (ADCs) with digital redundancy, such as Successive Approximation Register (SAR), Algorithmic, Sub-ranging or Pipeline ADCs. The method compensates the performance limitations of the conventional LUT approach in presence of comparison noise and/or non-monotonicity. In these circumstances, the input-output transfer function of a redundant ADC becomes significantly multivalued - that is, different output codes can be achieved for the same input level at different time instants. This behavior is motivated because from sample to sample, in a design with redundancy, the processing signal path is not unique, causing that the error under calibration becomes time-dependent, something which is not contemplated in the conventional calibration model. To deal with this effect, this work proposes a digital low-cost post-processing of the standardized Integral-Non-linearity (INL), which resolves multivalued situations using a direct access to the internal redundant codes. The method improvements are validated by realistic SAR and Pipeline ADC case studies at behavioral level, and by experimental data from an 11-bit 60Msps Pipeline ADC implemented in a 130nm CMOS process. These experimental results show that the proposed calibration achieves an improvement of approximately 1.6 effective bits at full-scale input amplitude.

Modulation frequency selection and efficient look-up table inversion for frequency domain diffuse optical spectroscopy.

.Significance: Frequency domain diffuse optical spectroscopy (FD-DOS) uses intensity modulated light to measure the absorption and reduced scattering coefficients of turbid media such as biological tissue. Some FD-DOS instruments utilize a single modulation frequency, whereas others use hundreds of frequencies. The effect of modulation frequency choice and measurement bandwidth on optical property (OP) extraction accuracy has not yet been fully characterized.Aim: We aim to assess the effect of modulation frequency selection on OP extraction error and develop a high-speed look-up table (LUT) approach for OP estimation.Approach: We first used noise-free simulations of light transport in homogeneous media to determine optimized iterative inversion model parameters and developed a new multi-frequency LUT method to increase the speed of inversion. We then used experimentally derived noise models for two FD-DOS instruments to generate realistic simulated data for a broad range of OPs and modulation frequencies to test OP extraction accuracy.Results: We found that repeated measurements at a single low-frequency (110 MHz) yielded essentially identical OP errors as a broadband frequency sweep (35 evenly spaced frequencies between 50 and 253 MHz) for these noise models. The inclusion of modulation frequencies diminished overall performance for one of the instruments. Additionally, we developed a LUT inversion algorithm capable of increasing inversion speeds by up to , with and error when a single modulation frequency was used.Conclusion: These results suggest that simpler single-frequency systems are likely sufficient for many applications and pave the way for a new generation of simpler digital FD-DOS systems capable of rapid, large-volume measurements with real-time feedback.

Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms

Gap-filling eddy covariance flux data using quantitative approaches has increased over the past decade. Numerous methods have been proposed previously, including look-up table approaches, parametric methods, process-based models, and machine learning. Particularly, the REddyProc package from the Max Planck Institute for Biogeochemistry and ONEFlux package from AmeriFlux have been widely used in many studies. However, there is no consensus regarding the optimal model and feature selection method that could be used for predicting different flux targets (Net Ecosystem Exchange, NEE; or Evapotranspiration –ET), due to the limited systematic comparative research based on the identical site-data. Here, we compared NEE and ET gap-filling/prediction performance of the least-square-based linear model, artificial neural network, random forest (RF), and support vector machine (SVM) using data obtained from four major row-crop and forage agroecosystems located in the subtropical or the climate-transition zones in the US. Additionally, we tested the impacts of different training-testing data partitioning settings, including a 10-fold time-series sequential (10FTS), a 10-fold cross validation (CV) routine with single data point (10FCV), daily (10FCVD), weekly (10FCVW) and monthly (10FCVM) gap length, and a 7/14-day flanking window (FW) approach; and implemented a novel Sliced Inverse Regression-based Recursive Feature Elimination algorithm (SIRRFE). We benchmarked the model performance against REddyProc and ONEFlux-produced results. Our results indicated that accurate NEE and ET prediction models could be systematically constructed using SVM/RF and only a few top informative features. The gap-filling performance of ONEFlux is generally satisfactory (R2 = 0.39–0.71), but results from REddyProc could be very limited or even unreliable in many cases (R2 = 0.01–0.67). Overall, SIRRFE-refined SVM models yielded excellent results for predicting NEE (R2 = 0.46–0.92) and ET (R2 = 0.74–0.91). Finally, the performance of various models was greatly affected by the types of ecosystem, predicting targets, and training algorithms; but was insensitive towards training-testing partitioning. Our research provided more insights into constructing novel gap-filling models and understanding the underlying drivers affecting boundary layer carbon/water fluxes on an ecosystem level.

A New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data

Clouds are one of the major uncertainties of the climate system. The study of cloud processes requires information on cloud physical properties, in particular liquid water path (LWP). This parameter is commonly retrieved from satellite data using look-up table approaches. However, existing LWP retrievals come with uncertainties related to assumptions inherent in physical retrievals. Here, we present a new retrieval technique for cloud LWP based on a statistical machine learning model. The approach utilizes spectral information from geostationary satellite channels of Meteosat Spinning-Enhanced Visible and Infrared Imager (SEVIRI), as well as satellite viewing geometry. As ground truth, data from CloudNet stations were used to train the model. We found that LWP predicted by the machine-learning model agrees substantially better with CloudNet observations than a current physics-based product, the Climate Monitoring Satellite Application Facility (CM SAF) CLoud property dAtAset using SEVIRI, edition 2 (CLAAS-2), highlighting the potential of such approaches for future retrieval developments.

Combined geometric-radiometric and neural network approach to shallow bathymetric mapping with UAS imagery

There is a pressing need for shallow bathymetry in regions around the world, including in fragile marine ecosystems, such as coral reefs. Unfortunately, mapping these shallow-water areas using in situ or boat-based methods is expensive, time consuming, and potentially dangerous, due to the need to put personnel and/or boats in high-energy nearshore areas, which may contain rocks, reefs, and other submerged hazards. For this reason, passive remote sensing methods of shallow bathymetric mapping have been rapidly growing in interest and usage. Two general categories of approaches using passive imaging can be distinguished as: (1) those based on geometry (stereo-photogrammetry); (2) those based on radiometry (attenuation of light in the water column). These two broad categories of approaches have been tested on imagery collected using aircraft and satellites in numerous previous studies. Recent work within the geometric category includes processing unmanned aircraft system (UAS) imagery using Structure from Motion and Multi-View Stereo (SfM-MVS) photogrammetry, while radiometric methods encompass the broad range of spectral bathymetry retrieval algorithms. Each broad approach category has advantages and disadvantages. Fortunately, the geometric and radiometric approaches are highly complementary. The geometric approaches tend to work best in areas of high bottom texture, which facilitates feature matching in the SfM-MVS software. Meanwhile, the radiometric approaches work best in relatively homogeneous bottom types. To leverage the strengths of each type of approach and overcome their respective weaknesses, this work develops and tests a combined geometric-radiometric bathymetric mapping approach designed for shallow-water mapping from UAS imagery. Four radiometric models of varying complexity are tested, ranging from a color-based lookup table approach to neural networks. Two UAS flights on Buck Island, off St Croix in the U.S. Virgin Islands (USVI), are used to assess the accuracy of the methodology when compared to aerial bathymetric lidar data. The results show that the combined geometric-radiometric approach provides an increase in spatial coverage of up to 61% and improvement (i.e. decrease) in 95th percentile error of up to 49% when compared to traditional refraction-corrected SfM-MVS bathymetry.

Simultaneous Measurements of Wire Diameter and Conductivity Using a Combined Inductive and Capacitive Sensor

Metallic wires are used in many industrial applications, where the diameter and conductivity are key parameters to be measured. In eddy currents testing, the inductance of the sensor is affected by both the diameter and conductivity of sample wire. Consequently, wire diameter and conductivity cannot be determined from a single inductance measurement. In this paper, a novel combined inductive and capacitive sensor is designed, in which the diameter can be inversed from the capacitance measurement of the sensor. Based on the diameter value, the inductance measurement can be used to accurately determine the conductivity of the wires using a look-up table approach. In addition, a model for a wire inside of the capacitive sensor is derived. Measurements have been carried out for 16 samples and the results have verified the proposed approach. This technique could contribute to real-time in-situ quality control of wire + arc additive manufacture processes.

Comparison of Empirical and Physical Modelling for Estimation of Biochemical and Biophysical Vegetation Properties: Field Scale Analysis across an Arctic Bioclimatic Gradient

To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches.

Stochastic modelling of leading-edge noise in time-domain using vortex particles

The interaction of a wing/blade profile with turbulent inflow is one of the main sources of sound generation in turbomachines. There are already several noise prediction methodologies available which either appear not to account for the influence of geometrical and flow parameters on noise generation or have been able to account for a remarkably limited extent due to the requirement of high-performance computing for flow calculations. This leads to the motivation of the current paper, which presents a low-cost and easy-to-use noise prediction methodology based on the statistical modelling of the inflow turbulence and Lookup Table (LUT) approach for aeroacoustic design and optimisation. The development of the statistical method is divided into three parts; namely – i) calculating the background flow, ii) modelling of statistically optimised inflow disturbance, iii) computing the far-field sound pressure for individual vortex passages and superpose them linearly – this step involves repeated computation of identical vortex passages and can be therefore easily sped up using a database approach. In the framework of this work, a new approach to model the inflow turbulence using vortex particles characterised by shape functions, based on waveforms, is presented. The idea is to not conduct a time-dependent unsteady calculation of the flow field in real-time, instead to consider the mean flow around the profile in the computational domain, in which the vortex particles are convected to realise a statistical turbulent signal. The convection of these vortex particles, also, does not take place in the real-time calculation, instead, vortices of every possible size and strength are convected in a similar domain with a specific airfoil, and the acoustic radiation due to their interaction with the airfoil are computed and stored in a database. The far-field noise is predicted using Curle’s formulation. The generated database is accessed using the LUT approach to rapidly extract the acoustic signals. Through this approach, the influence of geometrical as well as flow parameters on the noise generated by airfoils can be quantified without requiring to conduct a numerical simulation every time for a new set of geometrical and flow variables. In the article, the application of the method for different blade profiles is shown, and the results obtained are compared with the standard literature.

An Efficient Implementation and Analysis of Tail-Biting Convolution Coding Algorithm for OFDM Based System in Terms of Speed, Memory and Peak-to-Average Power Ratio Using DSP

Fourth Generation (4G) mobile communication system uses Orthogonal Frequency Division Multiplexing (OFDM). With this technique, high data rate demands are achieved. Tail biting Convolution Coding (TBCC) avoids the data rate loss, hence it is widely used error control coding algorithm in OFDM and other various wireless communication technologies. But, OFDM has increased Peak to Average Power Ratio (PAPR). High PAPR signal consumes more power and makes system power inefficient. To gain supercomputing performance in terms of Speed, Memory and Power on mobile devices, an efficient implementation of algorithms on Digital Signal Processing (DSP) processor is an essential requirement. This requirement becomes more stringent for Fifth Generation (5G) mobile devices. For this, wide scope of knowledge as well as skills are required to understand the algorithm, DSP architecture, instruction set, optimization and performance measurement. In this article, we have implemented TBCC algorithm using Bit by Bit (BYB) and Look Up Table (LUT) approaches on Freescale StarCore SC140 based DSP platform and proposed an efficient algorithm implementation methodology by comparing the machine cycles and memory requirement. We have used coding rate (R) = 1/2, 1/3, 1/4 and constraint length (K) = 5 and K = 9 for implementation of TBCC. Using our proposed LUT approach, we have achieved average 45.82% Computational Complexity Reduction Ratio (CCRR) in machine cycles compared to BYB approach. Proposed LUT approach increases the TBCC execution speed. Our developed fixed point routines can be used for any K ≤ 9. TBCC is also analyzed for PAPR to get an overall profiling results and three dimensional optimization of TBCC algorithm in terms of Speed, Memory and Power.

Retrieving Crop Leaf Chlorophyll Content Using an Improved Look-Up-Table Approach by Combining Multiple Canopy Structures and Soil Backgrounds

Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R2 value of 0.81 and an RMSE value of 10.1 μg cm−2. Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm−2 for wheat and 9.6 μg cm−2 for soybean using canopy spectra and 8.6 μg cm−2 for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale.

The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2

Abstract. The Aerosol Robotic Network (AERONET) Version 3 (V3) aerosol retrieval algorithm is described, which is based on the Version 2 (V2) algorithm with numerous updates. Comparisons of V3 aerosol retrievals to those of V2 are presented, along with a new approach to estimate uncertainties in many of the retrieved aerosol parameters. Changes in the V3 aerosol retrieval algorithm include (1) a new polarized radiative transfer code (RTC), which replaced the scalar RTC of V2, (2) detailed characterization of gas absorption by adding NO2 and H2O to specify total gas absorption in the atmospheric column, specification of vertical profiles of all the atmospheric species, (3) new bidirectional reflectance distribution function (BRDF) parameters for land sites adopted from the MODIS BRDF/Albedo product, (4) a new version of the extraterrestrial solar flux spectrum, and (5) a new temperature correction procedure of both direct Sun and sky radiance measurements. The potential effect of each change in V3 on single scattering albedo (SSA) retrievals was analyzed. The operational almucantar retrievals of V2 versus V3 were compared for four AERONET sites: GSFC, Mezaira, Mongu, and Kanpur. Analysis showed very good agreement in retrieved parameters of the size distributions. Comparisons of SSA retrievals for dust aerosols (Mezaira) showed a good agreement in 440 nm SSA, while for longer wavelengths V3 SSAs are systematically higher than those of V2, with the largest mean difference at 675 nm due to cumulative effects of both extraterrestrial solar flux and BRDF changes. For non-dust aerosols, the largest SSA deviation is at 675 nm due to differences in extraterrestrial solar flux spectrums used in each version. Further, the SSA 675 nm mean differences are very different for weakly (GSFC) and strongly (Mongu) absorbing aerosols, which is explained by the lower sensitivity to a bias in aerosol scattering optical depth by less absorbing aerosols. A new hybrid (HYB) sky radiance measurement scan is introduced and discussed. The HYB combines features of scans in two different planes to maximize the range of scattering angles and achieve scan symmetry, thereby allowing for cloud screening and spatial averaging, which is an advantage over the principal plane scan that lacks robust symmetry. We show that due to an extended range of scattering angles, HYB SSA retrievals for dust aerosols exhibit smaller variability with solar zenith angles (SZAs) than those of almucantar (ALM), which allows extension of HYB SSA retrievals to SZAs less than 50∘ to as small as 25∘. The comparison of SSA retrievals from closely time-matched HYB and ALM scans in the 50 to 75∘ SZA range showed good agreement with the differences below ∼0.005. We also present an approach to estimate retrieval uncertainties which utilizes the variability in retrieved parameters generated by perturbing both measurements and auxiliary input parameters as a proxy for retrieval uncertainty. The perturbations in measurements and auxiliary inputs are assumed as estimated biases in aerosol optical depth (AOD), radiometric calibration of sky radiances combined with solar spectral irradiance, and surface reflectance. For each set of Level 2 Sun/sky radiometer observations, 27 inputs corresponding to 27 combinations of biases were produced and separately inverted to generate the following statistics of the inversion results: average, standard deviation, minimum and maximum values. From these statistics, standard deviation (labeled U27) is used as a proxy for estimated uncertainty, and a lookup table (LUT) approach was implemented to reduce the computational time. The U27 climatological LUT was generated from the entire AERONET almucantar (1993–2018) and hybrid (2014–2018) scan databases by binning U27s in AOD (440 nm), Angström exponent (AE, 440–870 nm), and SSA (440, 675, 870, 1020 nm). Using this LUT approach, the uncertainty estimates U27 for each individual V3 Level 2 retrieval can be obtained by interpolation using the corresponding measured and inverted combination of AOD, AE, and SSA.

Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes

Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.

Modelling of instantaneous emissions from diesel vehicles with a special focus on NOx: Insights from machine learning techniques

Accurate instantaneous vehicle emissions models are vital for evaluating the impacts of road transport on air pollution at high temporal and spatial resolution. In this study, we apply machine learning techniques to a dataset of 70 diesel vehicles tested in real-world driving conditions to: (i) cluster vehicles with similar emissions performance, and (ii) model instantaneous emissions. The application of dynamic time warping and clustering analysis by NOx emissions resulted in 17 clusters capturing 88% of trips in the dataset. We show that clustering effectively groups vehicles with similar emissions profiles, however no significant correlation between emissions and vehicle characteristics (i.e. engine size, vehicle weight) were found. For each cluster, we evaluate three instantaneous emissions models: a look-up table (LT) approach, a non-linear regression (NLR) model and a neural network multi-layer perceptron (MLP) model. The NLR model provides accurate instantaneous NOx predictions, on par with the MLP: relative errors in prediction of emission factors are below 20% for both models, average fractional biases are −0.01 (s.d. 0.02) and −0.0003 (s.d. 0.04), and average normalised mean squared errors are 0.25 (s.d. 0.14) and 0.29 (s.d. 0.16), for the NLR and MLP models respectively. However, neural networks are better able to deal with vehicles not belonging to a specific cluster. The new models that we present rely on simple inputs of vehicle speed and acceleration, which could be extracted from existing sources including traffic cameras and vehicle tracking devices, and can therefore be deployed immediately to enable fast and accurate prediction of vehicle NOx emissions. The speed and the ease of use of these new models make them an ideal operational tool for policy makers aiming to build emission inventories or evaluate emissions mitigation strategies.

Coupled fuel performance calculations in VERA and demonstration on Watts Bar unit 1, cycle 1

As the core simulator capabilities in the Virtual Environment for Reactor Applications (VERA) have become more mature and stable, increased attention has been focused on coupling Bison to provide fuel performance simulations. This technique has been a very important driver for the pellet-clad interaction challenge problem being addressed by the Consortium for Advanced Simulation of Light Water Reactors (CASL). In this article, two coupling approaches are demonstrated on quarter core problems based on Watts Bar Nuclear Plant Unit 1, Cycle 1: (1) an inline approach in which a one-way coupling is used between neutronics/thermal hydraulics (through MPACT/CTF) and fuel performance (through Bison) but no fuel temperature information is passed back to MPACT/CTF, and (2) a two-way approach (coupled) in which the fuel temperature is passed from Bison to MPACT/CTF. In both approaches, power and temperature distributions from MPACT/CTF are used to inform the Bison simulations for each rod in the core.The demonstrations presented here are the first integrated fuel performance simulations in VERA, which opens many possibilities for future work, including applications to accident-tolerant fuel efforts and transient simulations, which are of critical importance to CASL. These demonstrations also highlight the potential to move away from the current Bison-informed fuel temperature lookup table approach, which is the default in MPACT/CTF simulations, if performance improvements are made in the near future.

Complex exponential functions: A high-precision hardware realization

Hardware realization of complex exponential functions (CEF) is not practical for over 16-bit operands. This is due to high cost and delay of the required look-up table (LUT) and arithmetic units. Therefore, we decompose larger (up to 64 bits) operands to three parts (e.g., 11+24+29=64). They are handled by minimax approximation, one-dimensional degree-3 interpolation, and pure LUT approaches, respectively. However, the second one is not used for smaller operands. Taking advantage of rectangular multipliers, we have realized high precision CEF, on FPGA platform. More than 30% speedup and 75% lower cost are achieved in comparison with the previous relevant works.

Simplified and Fast Atmospheric Radiative Transfer model for satellite-based aerosol optical depth retrieval

Satellite-based aerosol optical depth (AOD) retrieval over land remains a considerable challenge when high temporal and spatial resolutions are required. This paper presents the Simplified and Fast Atmospheric Radiative Transfer (SFART) model for direct calculation of satellite AOD via analytical equations rather than the lookup table (LUT) approach. The SFART model considers the impact of both Rayleigh and aerosol multiple scattering. A comprehensive comparison with the Simplified Method for Atmospheric Correction (SMAC) and single scattering approximation method is conducted. In validation with 6S, good atmospheric reflectance (Rayleigh + aerosol) accuracy is achieved by SFART, with approximately 69% of the data falling within the 5% estimated error (EE) envelope at both 440 and 640 nm. This is higher than the SMAC accuracy (within 5% EE: 42.83% and 39.91% at 440 and 640 nm, respectively) and is a tremendous improvement over the single scattering approximation method (within 5% EE: 15.67% and 20.5% at 440 and 640 nm, respectively). SFART is then applied for AOD retrieval to Himawari-8 satellite data over the North China Plain on both normal and hazy days. The retrieved AOD values are validated against collected AERONET data (Version 3, Level 2.0). Approximately 59% of the SFART AOD values fall within the EE bounds of ±(0.05 + 15%) with a root mean squared error of 0.22 (for 339 collocations). The promising results given by SFART indicate that this method can facilitate improved AOD calculation using empirical or real-time aerosol models in an efficient and flexible manner.