Look-up Table Generation Research Articles

AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach

Abstract. Soil organic carbon storage is a well-identified climate change mitigation solution. Quantification of the soil carbon storage in cropland for agricultural policy and offset carbon markets using in situ sampling would be excessively costly, especially at the intrafield scale. For this reason, comprehensive monitoring, reporting, and verification (MRV) of soil carbon and its explanatory variables at a large scale need to rely on hybrid approaches that combine remote sensing and modelling tools to provide the carbon budget components with their associated uncertainties at intrafield scale. Here, we present AgriCarbon-EO v1.0.1: an end-to-end processing chain that enables the estimation of carbon budget components for major and cover crops at intrafield resolution (10 m) and regional extents (e.g. 10 000 km2) by assimilating remote sensing data (e.g. Sentinel-2 and Landsat8) in a physically based radiative transfer (PROSAIL) and agronomic models (SAFYE-CO2). The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines normalized importance sampling and look-up table generation. This approach propagates the uncertainties across the processing chain from the reflectances to the output variables. After a presentation of the chain, we demonstrate the accuracy of the estimates of AgriCarbon-EO through an application over winter wheat in the southwest of France during the cropping seasons from 2017 to 2019. We validate the outputs with flux tower data for net ecosystem exchange, biomass destructive samples, and combined harvester yield maps. Our results show that the scalability and uncertainty estimates proposed by the approach do not hinder the accuracy of the estimates (net ecosystem exchange, NEE: RMSE =1.68–2.38 gC m−2, R2=0.87–0.77; biomass: RMSE =11.34 g m−2, R2=0.94). We also show the added value of intrafield simulations for the carbon components through scenario testing of pixel and field simulations (biomass: bias =-47 g m−2, −39 % variability). Our overall analysis shows satisfying accuracy, but it also points out the need to represent more soil processes and include synthetic aperture radar data that would enable a larger coverage of AgriCarbon-EO. The paper's findings confirm the suitability of the choices made in building AgriCarbon-EO as a hybrid solution for an MRV scheme to diagnose agro-ecosystem carbon fluxes.

Prescriptive Lifetime Management for PEM fuel cell systems in transportation applications, Part II: On-board operando feature extraction, condition assessment and lifetime prediction

The 2030 and 2050 targets for lifetime requirements of proton exchange membrane (PEM) fuel cell (FC) systems in heavy-duty vehicle (HDV) applications are set to 25,000h and 30,000h, respectively. The need to reduce PEM fuel cell system (FCS) costs is expected to be accompanied by greater uncertainty in lifetime. This will require robust and reliable approaches and algorithms for feature extraction, condition assessment and lifetime prediction to obtain the Remaining Useful Life (RUL) and to prevent premature failure, downtime and lower system availability leading to higher total cost of ownership (TCO).Current approaches to feature extraction and component-based condition assessment typically focus on in/ex situ measurement methods performed on test benches, rather than operando measurement methods that can be performed on-board commercial vehicles. Methods developed on test benches are often difficult or even impossible to transfer to commercial vehicle applications. As a result, the lifetime prediction in commercial vehicle applications often focuses on data-driven approaches to predict voltage or power decay. Since these data-driven approaches do not contain sufficient information about the component-based State of Health (SoH), a component-based lifetime prediction and subsequent optimization of operating strategy to control RUL and meet the lifetime requirements is significantly limited.Prescriptive Lifetime Management is a technique that involves developing and implementing on-board strategies and algorithms for feature extraction, condition assessment and lifetime prediction to mitigate degradation, extend the RUL and reduce the overall TCO, e.g., of the PEM fuel cell system through global optimization-based post-prognostic decision-making. With this in mind, this paper presents approaches for on-board operando feature extraction and condition assessment of the PEM integrity and the electrochemical surface area (ECSA). The calibration of macroscopic degradation models allows for lifetime prediction and RUL control to meet the lifetime requirements of PEM fuel cells. The results show that the operating parameters have a high effect on PEM fuel cell degradation. The complexity of degradation prevents the generation of reliable degradation lookup tables. Therefore, the direct implementation of calibratable macroscopic degradation models in the Prescriptive Lifetime Management framework is recommended.

Technical Note: Standalone application to generate custom reflectance Look-Up Table for advanced optical Monte Carlo simulation in GATE/Geant4.

The need for high-fidelity modeling of radiation detectors to perform reliable detector performance optimization using Monte Carlo simulations requires to accurately simulate the light transport in the scintillator and the light collection by the photodetector. In this work, we implement our well-validated crystal reflectance model computed from three-dimensional (3D) crystal surface measurement in a standalone open-source application to allow researchers to generate fully customized crystal reflectance look-up-tables (LUTs) to be used in optical Monte Carlo simulation. The LUTDavisModel application can be installed in a few minutes on Windows, macOS, and Linux, using 26MB of space. MATLAB Runtime is required and is automatically installed with the application. The core algorithm has been previously validated experimentally and implemented in GATE v8.0. The standalone is divided into five panels, each of which performing a specific task: generate LUTs from a combination of surface type, scintillator, and coupling medium available in the database (such as LSO or BGO) or custom; compute LUTs with the reflectors available and custom coupling thickness; create a mixture of coupling media to account for possible defects in the optical coupling; plot precomputed LUTs for visual comparison. Tooltips and errors/warnings facilitate the navigation. The reported computational times were obtained with an Intel Core i7 MacBook Pro. LUTs can be generated with computational time ranging from a few minutes to several hours depending on the selected surface, sampling, and computational power. A longer time is needed when using rough surfaces and thick coupling media (hundreds of ) due to increased photon tracking. We developed a user-friendly standalone application to generate LUTs that can be used inside GATE Monte Carlo simulations. It can be easily downloaded, installed, and used. Future optimizations will expand the database, decrease the computational time through greater parallelization, and include the generation of LUTs to study Cerenkov photons transport.

Efficient Implementation of ARX-Based Block Ciphers on 8-Bit AVR Microcontrollers

As the development of Internet of Things (IoT), the data exchanged through the network has significantly increased. To secure the sensitive data with user’s personal information, it is necessary to encrypt the transmitted data. Since resource-constrained wireless devices are typically used for IoT services, it is required to optimize the performance of cryptographic algorithms which are computation-intensive tasks. In this paper, we present efficient implementations of ARX-based Korean Block Ciphers (HIGHT and LEA) with CounTeR (CTR) mode of operation, and CTR_DRBG, one of the most widely used DRBGs (Deterministic Random Bit Generators), on 8-bit AVR Microcontrollers (MCUs). Since 8-bit AVR MCUs are widely used for various types of IoT devices, we select it as the target platform in this paper. We present an efficient implementation of HIGHT and LEA by making full use of the property of CTR mode, where the nonce value is fixed, and only the counter value changes during the encryption. On our implementation, the cost of additional function calls occurred by the generation of look-up table can be reduced. With respect to CTR_DRBG, we identified several parts that do not need to be computed. Thus, precomputing those parts in offline and using them online can result in performance improvements for CTR_DRBG. Furthermore, we applied several optimization techniques by making full use of target devices’ characteristics with AVR assembly codes on 8-bit AVR MCUs. Our proposed table generation way can reduce the cost for building a precomputation table by around 6.7% and 9.1% in the case of LEA and HIGHT, respectively. Proposed implementations of LEA and HIGHT with CTR mode on 8-bit AVR MCUs provide 6.3% and 3.8% of improved performance, compared with the previous best results, respectively. Our implementations are the fastest compared to previous LEA and HIGHT implementations on 8-bit AVR MCUs. In addition, the proposed CTR_DRBG implementations on AVR provide better performance by 37.2% and 8.7% when the underlying block cipher is LEA and HIGHT, respectively.

Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0).

Atmospheric radiative transfer models (RTMs) are software tools that help researchers in understanding the radiative processes occurring in the Earth’s atmosphere. Given their importance in remote sensing applications, the intercomparison of atmospheric RTMs is therefore one of the main tasks used to evaluate model performance and identify the characteristics that differ between models. This can be a tedious tasks that requires good knowledge of the model inputs/outputs and the generation of large databases of consistent simulations. With the evolution of these software tools, their increase in complexity bears implications for their use in practical applications and model intercomparison. Existing RTM-specific graphical user interfaces are not optimized for performing intercomparison studies of a wide variety of atmospheric RTMs. In this paper, we present the Atmospheric Look-up table Generator (ALG) version 2.0, a new software tool that facilitates generating large databases for a variety of atmospheric RTMs. ALG facilitates consistent and intuitive user interaction to enable the running of model executions and storing of RTM data for any spectral configuration in the optical domain. We demonstrate the utility of ALG in performing intercomparison studies of radiance simulations from broadly used atmospheric RTMs (6SV, MODTRAN, and libRadtran) through global sensitivity analysis. We expect that providing ALG to the research community will facilitate the usage of atmospheric RTMs to a wide range of applications in Earth observation.

A machine learning methodology for improving the accuracy of laminar flame simulations with reduced chemical kinetics mechanisms

The focus of the present work is to investigate a new methodology for the rapid generation of laminar flame speed lookup tables, to be used replacing correlation laws in internal combustion engine simulations. Current production engines run mostly under the thickened wrinkled flame combustion regime, which allows the application of a flamelet modelling approach, which requires the a-priori evaluation of the laminar flame speed and thickness. The use of correlation laws, derived from experimental data, has the advantage to be extremely fast to compute, but displays a lack of precision in conditions far from the experimental reference, as it usually happens for ICE applications. On the other hand, the detailed chemical simulation of a freely propagating adiabatic flame, performed for a sufficiently refined grid of reference points, to be interpolated during runtime, might require hundreds of hours of computing. The use of a reduced chemical mechanism can potentially cut by orders of magnitude the required time, but on the other hand it will decrease accuracy. In the present work, the potential of integrating machine learning algorithms and neural networks in the workflow with different approaches was valuated, leveraging the potential of new and optimized software libraries, to reduce simulation times while maintaining a high level of accuracy, with respect to the results obtained with the complete scheme.

Retrieval of Fine-Resolution Aerosol Optical Depth (AOD) in Semiarid Urban Areas Using Landsat Data: A Case Study in Urumqi, NW China

The aerosol optical depth (AOD) represents the light attenuation by aerosols and is an important threat to urban air quality, production activities, human health, and sustainable urban development in arid and semiarid regions. To some extent, the AOD reflects the extent of regional air pollution and is often characterized by significant spatiotemporal dynamics. However, detailed local AOD information is ambiguous at best due to limited monitoring techniques. Currently, the availability of abundant satellite data and constantly updated AOD extraction algorithms offer unprecedented perspectives for high-resolution AOD extraction and long-time series analysis. This study, based on the long-term sequence MOD09A1 data from 2010 to 2018 and lookup table generation, uses the improved deep blue algorithm (DB) to conduct fine-resolution (500 m) AOD (at 550 nm wavelength) remote sensing (RS) estimation on Landsat TM/OLI data from the Urumqi region, analyzes the spatiotemporal AOD variation characteristics in Urumqi and combines gray relational analysis (GRA) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to analyze AOD influence factors and simulate pollutant propagation trajectories in representative periods. The results demonstrate that the improved DB algorithm has a high inversion accuracy for continuous AOD inversion at a high spatial resolution in urban areas. The spatial AOD distribution in Urumqi declines from urban to suburban areas, and higher AODs are concentrated in cities and along roads. Among these areas, Xinshi District has the highest AOD, and Urumqi County has the lowest AOD. The seasonal AOD variation characteristics are distinct, and the AOD order is spring (0.411) > summer (0.285) > autumn (0.203), with the largest variation in spring. The average AOD in Urumqi is 0.187, and the interannual variation generally shows an upward trend. However, from 2010 to 2018, AOD first declined gradually and then declined significantly. Thereafter, AOD reached its lowest value in 2015 (0.076), followed by a significant AOD increase, reaching a peak in 2016 (0.354). This shows that coal to natural gas (NG) project implementation in Urumqi promoted the improvement of Urumqi’s atmospheric environment. According to GRA, the temperature has the largest impact on the AOD in Urumqi (0.699). Combined with the HYSPLIT model, it was found that the aerosols observed over Urumqi were associated with long-range transport from Central Asia, and these aerosols can affect the entire northern part of China through long-distance transport.

On the generation of enhanced lookup tables for wheel-rail contact models

In railway dynamics, the interpolation of lookup tables (LUTs) is a procedure utilized to reduce the computational effort when computing the wheel-rail contact forces. However, the generation of LUTs with multiple inputs and multiple outputs is a challenging task for which aspects such as their minimal size and uniform accuracy over the LUT domain have not been systematically addressed in the literature. Thus, this work presents a comprehensive methodology for a detailed analysis of general LUTs, and identifies ways to improve them. For that, an analysis of the variation of the input parameters is made and the interpolation error is assessed on the cells and edges of the original table, then, based on this analysis, two enhanced LUTs are proposed. The first one is approximately 5 times smaller than the original but holds similar accuracy. The second table exhibits half of the maximum interpolation error of the original LUT but holds an identical size. The methodology is demonstrated here using the recently published Kalker Book of Tables for Non-Hertzian contact (KBTNH) but it can be used by any other LUT approach in order to improve accuracy and/or to reduce size.

Optimization and Database Management in Smart Modeling of Aviation Heat Exchangers

Analytical tools for modern aviation heat exchangers (HEXs), such as those that are additively manufactured, have to be advanced and smart. To develop such tools, procedures are presented in this paper that obviate the need for the standard -number-of-transfer-units method of analysis, enable multiple-rating capabilities, and support the generation and exporting of lookup tables that could be readily inserted into a system-level multidisciplinary design optimization platform. Three practical optimization procedures are evaluated and compared for their ability to efficiently handle the design variables and the performance rating, constraints, and objective functions in the optimization of aviation thermal management components and subsystems. Modern database management techniques are also implemented to enable the generation of the training data that are required for building intelligence into the developed HEX software. The mathematical and qualitative details of the heat transfer aspects of a few of these capabilities are presented in this paper, as are two case studies pertaining to optimization where we compare two of the developed approaches. To the knowledge of the authors, no previous studies have addressed the issues resolved in this paper.

Inductive localization accuracy of a passive 3-D coil in an Industry 4.0 environment

Abstract. In this paper a localization system of a passive 3-D coil is proposed and signal uncertainties due to the 3-D coil's arbitrary orientation are analyzed. The 3-D coil is excited by an alternating primary magnetic field. Geometrically distributed pick-up coils measure the 3-D coil's secondary field. By means of a simulated look-up table that assigns expected voltages from the pick-up coils to the positions of the 3-D coil, the position of the 3-D coil is deduced by a least-squares approach. A basic assumption is that the secondary field is invariant to the orientation of the 3-D coil. This allows a reduction of the computational effort for the look-up table generation and the table search during the localization phase since for each position the field distribution for only one orientation has to be calculated. However, the assumption of invariance to rotation is only valid for a dipole model. In this paper we investigate the localization error introduced by this assumption when using 3-D coils with a geometric extent in an inhomogeneous primary field. Optimized localization methods that decrease the statistical error are proposed. The theoretical results are verified with measurements conducted on a laboratory system.

Adaptive chemistry lookup tables for combustion simulations using optimal B-spline interpolants

Flamelet models, which enable the storing of precomputed detailed chemistry into lookup tables, are widely used in combustion simulations. They allow the computation of accurate results at low computational cost, but standard implementations can lead to numerical problems due to a non-smooth representation, and their applicability is limited by memory requirements. Here, the methods used by a newly developed and optimised lookup table generator based on B-spline interpolation are presented. The creation of smooth representations of flamelet solutions requiring less than one fifth of the number of points in each direction compared to the non-smooth representations of standard lookup tables based on linear interpolation is shown to be possible. The new B-spline interpolation based tables are also applied within a large-eddy simulation of the Swirling Methane/Hydrogen Flame 1 and the results are compared to simulations using lookup tables based on linear interpolation or optimised artificial neural networks. Better performance of the B-spline interpolation based tables with respect to physical accuracy and numerical performance is demonstrated.

Implementation of the Weighted L1-Norm Scatter Correction Scheme in Dual-Energy Radiography

Dual-energy radiography (DER) based on basis material decomposition (BMD) is a wellestablished X-ray technique that uses low- and high-kV radiographs to separate soft and dense tissues. Conventional DER methods often lead to reduced image contrast in resulting dual-energy images when extensive X-ray scatter is present in the images. In this study, we applied the weighted l1-norm scatter correction algorithm in conventional DER to create a scatter correction scheme able to overcome the reduced image contrast associated with dual-energy images. The proposed DER process consists of two main steps: (1) the generation of a scatter-corrected pairwise lookup table for equivalent acryl and aluminum thicknesses and (2) the separation of soft and dense tissues based on BMD. We performed a computational simulation and an experiment to investigate the image quality and evaluate the effectiveness of the proposed DER method. Polychromatic X-ray images were emulated at two different tube voltage settings (80 kVp and 140 kVp); then, the images were corrected for scatter prior to BMD. Our results indicate that the proposed scatter correction algorithm implemented in conventional DER effectively reduces X-ray scatter in radiography and results in improved DER image quality.

A Masked White-Box Cryptographic Implementation for Protecting Against Differential Computation Analysis

Recently, gray-box attacks on white-box cryptographic implementations have succeeded. These attacks are more efficient than white-box attacks because they can be performed without detailed knowledge of the target implementation. The success of the gray-box attack is reportedly due to the unbalanced encodings used to generate the white-box lookup table. In this paper, we propose a method to protect the gray-box attack against white-box implementations. The basic idea is to apply the masking technique before encoding intermediate values during the white-box lookup table generation. Because we do not require any random source in runtime, it is possible to perform efficient encryption and decryption using our method. The security and performance analysis shows that the proposed method can be a reliable and efficient countermeasure.

Ultra-fast basic geometrical transformations on linear image data structure

Abstract This paper presents a general, ultra-fast approach for geometrical image transformations, based on the usage of linear lookup hash tables. The new method is developed to fix distortions on document images as part of a real-time optical character recognition (OCR) system. The approach is generalized and uses linear image representation combined with pre-computed lookup tables. Backward mapping is used for generation of lookup tables, while forward mapping is presented as an alternative and more efficient mapping model for specific cases. Also, a theoretical space and time complexity analysis of the proposed method is provided. To achieve maximal computational performance, pointer arithmetic and highly-optimized low-level machine code implementations are provided, including the specialized implementations for horizontal mirror, vertical mirror, and 90° rotation. Also, a modified variant of the approach, based on auto-generated machine code is presented. Very high computational performances are achieved at the expense of memory usage. The performances from the perspective of time complexity are analyzed and compared with classical implementation, FPGA implementation, and other implementations of the image rotation. Numerical results are given for a set of different PC specifications to provide full insight into the implementation performances. The processing time for very large images are below 200 ms for backward mapping and below 100 ms for forward mapping for most machines, which is 30–60 times faster than the classical implementation, 5–20 times faster than the FPGA implementation, and up to 6 times faster than other implementations of image rotation. Original documents belonging to Nikola Tesla are used for visual demonstration of performance.

Visual surveillance using wide‐angle camera and laser range finder

In this Letter, a new algorithm to reduce false positive in video surveillance by integrating depth using laser range finder with conventional camera is proposed. Typical video surveillance detects foreground objects by background model using only image. Background model is updated using brightness value on image so that it has difficulty in modelling diverse variations caused by illumination changes or scene structure variations. Inherently, there could be many false positives. Hypothesis generation and verification paradigm is adopted by integrating laser range finder with camera on video surveillance. Conventional background updating algorithm is used for generating foreground objects. They are verified by depth value provided by laser range finder. Rotating 2D laser range finder for targeting and generation of look-up table is used. Experimental results show that proposed algorithm can reduce false positives in video surveillance.

Generation of Look-Up Tables for Dynamic Job Shop Scheduling Decision Support Tool

Majority of existing scheduling techniques are based on static demand and deterministic processing time, while most job shop scheduling problem are concerned with dynamic demand and stochastic processing time. As a consequence, the solutions obtained from the traditional scheduling technique are ineffective wherever changes occur to the system. Therefore, this research intends to develop a decision support tool (DST) based on promising artificial intelligent that is able to accommodate the dynamics that regularly occur in job shop scheduling problem. The DST was designed through three phases, i.e. (i) the look-up table generation, (ii) inverse model development and (iii) integration of DST components. This paper reports the generation of look-up tables for various scenarios as a part in development of the DST. A discrete event simulation model was used to compare the performance among SPT, EDD, FCFS, S/OPN and Slack rules; the best performances measures (mean flow time, mean tardiness and mean lateness) and the job order requirement (inter-arrival time, due dates tightness and setup time ratio) which were compiled into look-up tables. The well-known 6/6/J/Cmax Problem from Muth and Thompson (1963) was used as a case study. In the future, the performance measure of various scheduling scenarios and the job order requirement will be mapped using ANN inverse model.

Automatic generation of LUTs for Hardware Neural Networks

One very important component of Hardware implementations of Artificial Neural Networks (HNN) is the activation function. A common solution for activation function design in HNN is the Look-Up Table (LUT) representation that has acceptable area constraints and usually achieves acceptable execution time. Tools and techniques for automatic generation of LUTs have been developed but have some problems as high complexity, high dependency of external tools or languages and the optimization techniques that are not efficient enough for critical applications. In order to solve some of the presented problems this paper describes a method for automatic LUT generation that overcomes some deficiencies of existing methods using a simple approach. This method was evaluated initially considering the xor function and the sine function for evaluation purposes. After these initial tests, two other ANN based robotic systems were tested. These robotic systems will be implemented in hardware in the near future and they demonstrate a different behavior using different domains of the same activation function. Proposed method achieved good results that are directly related to how neural networks define the activation function domain.

Ziria

Software-defined radio (SDR) brings the flexibility of software to wireless protocol design, promising an ideal platform for innovation and rapid protocol deployment. However, implementing modern wireless protocols on existing SDR platforms often requires careful hand-tuning of low-level code, which can undermine the advantages of software. Ziria is a new domain-specific language (DSL) that offers programming abstractions suitable for wireless physical (PHY) layer tasks while emphasizing the pipeline reconfiguration aspects of PHY programming. The Ziria compiler implements a rich set of specialized optimizations, such as lookup table generation and pipeline fusion. We also offer a novel -- due to pipeline reconfiguration -- algorithm to optimize the data widths of computations in Ziria pipelines. We demonstrate the programming flexibility of Ziria and the performance of the generated code through a detailed evaluation of a line-rate Ziria WiFi 802.11a/g implementation that is on par and in many cases outperforms a hand-tuned state-of-the-art C++ implementation on commodity CPUs.

Ziria

Software-defined radio (SDR) brings the flexibility of software to wireless protocol design, promising an ideal platform for innovation and rapid protocol deployment. However, implementing modern wireless protocols on existing SDR platforms often requires careful hand-tuning of low-level code, which can undermine the advantages of software. Ziria is a new domain-specific language (DSL) that offers programming abstractions suitable for wireless physical (PHY) layer tasks while emphasizing the pipeline reconfiguration aspects of PHY programming. The Ziria compiler implements a rich set of specialized optimizations, such as lookup table generation and pipeline fusion. We also offer a novel -- due to pipeline reconfiguration -- algorithm to optimize the data widths of computations in Ziria pipelines. We demonstrate the programming flexibility of Ziria and the performance of the generated code through a detailed evaluation of a line-rate Ziria WiFi 802.11a/g implementation that is on par and in many cases outperforms a hand-tuned state-of-the-art C++ implementation on commodity CPUs.

Novel calibration and color adaptation schemes in three-fringe RGB photoelasticity

Isochromatic demodulation in digital photoelasticity using RGB calibration is a two step process. The first step involves the construction of a look-up table (LUT) from a calibration experiment. In the second step, isochromatic data is demodulated by matching the colors of an analysis image with the colors existing in the LUT. As actual test and calibration experiment tint conditions vary due to different sources, color adaptation techniques for modifying an existing primary LUT are employed. However, the primary LUT is still generated from bending experiments. In this paper, RGB demodulation based on a theoretically constructed LUT has been attempted to exploit the advantages of color adaptation schemes. Thereby, the experimental mode of LUT generation and some uncertainties therein can be minimized. Additionally, a new color adaptation algorithm is proposed using quadratic Lagrangian interpolation polynomials, which is numerically better than the two-point linear interpolations available in the literature. The new calibration and color adaptation schemes are validated and applied to demodulate fringe orders in live models and stress frozen slices.