Calibration Information Research Articles

Magnetic resonance imaging of enhanced mobility of light non aqueous phase liquid (LNAPL) during drying of water wet porous media

Visualization of NAPLs in multiphase systems in porous media is important for determining contaminant transport in the environment. In this study, magnetic resonance imaging (MRI) was used to confirm the recent observations of mobilisation of a light non aqueous phase liquid (LNAPL) trapped in wet sand under natural drying conditions of the wet porous medium. Visualization of LNAPL (motor oil) and water mobility during the drying of wet glass beads (0.5 mm) in a cylindrical glass column (15 mm ID, 45 mm long) was obtained using spin echo-based NMR microimaging performed at 500 MHz, corresponding to a field of ca. 11.75 T. Sagittal and axial images of LNAPL and water in the porous medium were obtained at a spatial resolution of 59 μm/pixel at different time intervals. A rise of 15–20 mm was observed in the presence of evaporation of water as compared to a 2–3 mm rise in the absence of evaporation in a time span of about 1400 min. The spatio-temporal MRI scans of the water and LNAPL in the glass column reveals that LNAPL rise occurs when the water evaporation front reaches the LNAPL layer. This implied that the enhanced LNAPL rise was strongly linked to the process of water evaporation. A linear correlation of the MRI signal intensities of LNAPL and water with reference to different saturation levels of LNAPL and water in the porous media was obtained. This calibration information was used to quantify the saturation levels of the LNAPL and water during the drying process. These findings show the application of non-invasive techniques such as MRI in quantifying and understanding the mechanism of fate and transport of LNAPLs in porous media, towards effective environmental quality assessment.

A Closed Loop Detection Method for Lidar Simultaneous Localization and Mapping with Light Calibration Information

A Closed Loop Detection Method for Lidar Simultaneous Localization and Mapping with Light Calibration Information

Study on Dimension Measurement for Gas Guide Groove of Gun Barrel

Abstract An automatic measurement system based on the stationary wavelet transform (SWT) of vibration signal is developed to obtain the depth and width of gas guide grooves in the gun barrel. The system consists of the programmable logic controller module used to control the motions of system components, a mechanical apparatus used to fix the gun barrel and position the transducer, and a measurement device used to capture and analyze the measured data. With the SWT, the interference noise, baseline wander, and interference harmonics are successively eliminated from the captured displacement signal. Afterwards, the peak and valley points are detected out, which, respectively, represent the information of groove depth and of groove width. Finally, the dimensions of the gas guide groove are determined according to the points detected out and the calibration information. In the experiment, the steps of data processing are provided in detail, and the analysis results show that the measuring error is less than 3 %, which can meet the actual requirements.

Machine Learning Approaches For Improved Continuous, Non-occlusive Arterial Pressure Monitoring Using Photoplethysmography.

Arterial pressure (AP) is a crucial biomarker for cardiovascular disease prevention and management. Photoplethysmography (PPG) could provide a novel, paradigm-shifting approach for continuous, non-obtrusive AP monitoring, comfortably integrated in wearable and mobile devices; yet, it still faces challenges in accuracy and robustness. In this work, we sought to integrate machine learning (ML) techniques into a previously established, clinically-validated classical approach (oBPM®) to develop new accurate AP estimation tools based on PPG, and at the same time improve our understanding of the underlying physiological parameters. In this novel approach, oBPM® was used to pre-process PPG signals and robustly extract physiological features, and ML models were trained on these features to estimate systolic AP (SAP). A feature relevance analysis showed that reference (calibration) information, followed by various morphological parameters of the PPG pulse wave, comprised the most important features for SAP estimation. A performance analysis then revealed that LASSO-regularized linear regression, Gaussian process regression and support vector regression are effective for SAP estimation, particularly when operating on reduced feature sets previously obtained with e.g. LASSO. These approaches yielded substantial reductions in error standard deviation of 9-15% relative to conventional oBPM®. Altogether, these results indicate that ML approaches are well-suited, and promising tools to help overcoming the challenges of ubiquitous AP monitoring.

A Calculation Method for Vehicle Movement Reconstruction from Videos

This paper proposes a new enhanced method based on one-dimensional direct linear transformation for estimating vehicle movement states in video sequences. The proposed method utilizes a contoured structure of target vehicles, and the data collection procedure is found to be relatively stable and effective, providing a better applicability. The movements of vehicles in the video are captured by active calibration regions while the spatial consistency between the vehicle’s driving track and the calibration information are in sync. The vehicle movement states in the verification phase are estimated using the proposed method first, and then the estimated states are compared with the actual movement states recorded in the experimental test. The results show that, in the case of camera perspective of 90 degrees, in all driving states of low speed, high speed, or deceleration, the error between estimated speed and recorded speed is less than 1.5%, the error of accelerations is less than 7%, and the error of distances is less than 2%; similarly, in the case of camera perspective of 30 degrees, the errors of speeds, distances, and accelerations are less than 4%, 5%, and 10%, respectively. It is found that the proposed method is superior to other existing methods.

Fine-scale infestation pattern of Bactrocera dorsalis (Diptera: Tephritidae) in a mango orchard in Central Mozambique

The production of mango in Mozambique is under threat due to fruit fly infestation, and most of the farmers cannot afford the cost of control. Spatio-temporal dynamic of Bactrocera dorsalis was assessed at farm level in Manica Province to provide information for better calibration of pest management strategies and thereby minimize the use of pesticides and other costs associated to pest control. 64 Chempac Bucket traps baited with torula yeast were monitored weekly at a 10 ha mango orchard. From the species captured on the traps only B. dorsalis were considered for the study due to its importance on mango. A universal spatio-temporal kriging model was fitted to investigate the spatio-temporal pattern of the fly. Results of the analysis showed high spatial heterogeneity of the fly over time, with the occurrence spreading from the margins of the mango orchard and infesting the whole orchard during the period of peak of abundances reaching densities of more than 500 fly per trap per week. These results shows that the flies that infest this particular orchard may come from the marginal area, especially from local varieties which get matured earlier. The findings of this study provides information to be taken into consideration by farmers about when and where apply the control measures.

Consumer Behavior Analysis in the Offline Retail Stores based on convolutional neural network

Pedestrian attributes recognizing (PAR) is an important task in computer vision area due to it plays an important role in video surveillance. On the other hand, pedestrian visual attributes are treated as middle-level semantic features which can provide calibration information for high- level human related visual tasks in order to improve the discriminative ability of the models, such as pedestrian detection, people tracking, person re-identification, action recognition and scene understanding. However, the most study of PAR is based on single person. In this work, I implements a multiple pedestrian attributes recognition model based on the offline retail scenes. This model combines object detection and multitask classification techniques and can be trained end-to-end directly for back propagation. This paper also demonstrates the performance of the final model through a series of experiment.

DOA Estimation and Mutual Coupling Calibration Algorithm for Array in Plasma Environment

There is a multipath effect caused by multireflections between the plasma and the vehicle surface, and there are coupling characteristics between array antennas. These two errors will seriously affect the quality of navigation signal. To solve the mutual coupling of uniform linear array (ULA) of plasma, a direction-of-arrival (DOA) estimation algorithm based on mutual coupling correction is proposed. The algorithm is based on the special structure of mutual coupling matrix (MCM) of ULA and combines spatial smoothing algorithms. It solves a quadratic optimization problem under the linear constraint and utilizes peak search technology to obtain DOA. The effectiveness of the proposed algorithm is verified by the simulation results. Numerical simulation is applied in this article to analyze the influence of array antenna from the plasma. In addition, array antenna is calibrated with the mutual coupling calibration information estimated by the algorithm. The result of antenna calibration validates the correctness of the simulation conclusion.

An Introduction to the Geostationary-NASA Earth Exchange (GeoNEX) Products: 1. Top-of-Atmosphere Reflectance and Brightness Temperature

GeoNEX is a collaborative project led by scientists from NASA, NOAA, and many other institutes around the world to generate Earth monitoring products using data streams from the latest Geostationary (GEO) sensors including the GOES-16/17 Advanced Baseline Imager (ABI), the Himawari-8/9 Advanced Himawari Imager (AHI), and more. An accurate and consistent product of the Top-Of-Atmosphere (TOA) reflectance and brightness temperature is the starting point in the scientific processing pipeline and has significant influences on the downstream products. This paper describes the main steps and the algorithms in generating the GeoNEX TOA products, starting from the conversion of digital numbers to physical quantities with the latest radiometric calibration information. We implement algorithms to detect and remove residual georegistration uncertainties automatically in both GOES and Himawari L1bdata, adjust the data for topographic relief, estimate the pixelwise data-acquisition time, and accurately calculate the solar illumination angles for each pixel in the domain at every time step. Finally, we reproject the TOA products to a globally tiled common grid in geographic coordinates in order to facilitate intercomparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites.

Comparison of auditory spatial bisection and minimum audible angle in front, lateral, and back space

Although vision is important for calibrating auditory spatial perception, it only provides information about frontal sound sources. Previous studies of blind and sighted people support the idea that azimuthal spatial bisection in frontal space requires visual calibration, while detection of a change in azimuth (minimum audible angle, MAA) does not. The influence of vision on the ability to map frontal, lateral and back space has not been investigated. Performance in spatial bisection and MAA tasks was assessed for normally sighted blindfolded subjects using bursts of white noise presented frontally, laterally, or from the back relative to the subjects. Thresholds for both tasks were similar in frontal space, lower for the MAA task than for the bisection task in back space, and higher for the MAA task in lateral space. Two interpretations of the results are discussed, one in terms of visual calibration and the use of internal representations of source location and the other based on comparison of the magnitude or direction of change of the available binaural cues. That bisection thresholds were increased in back space relative to front space, where visual calibration information is unavailable, suggests that an internal representation of source location was used for the bisection task.

Atmospheric correction for satellite-derived bathymetry in the Caribbean waters: from a single image to multi-temporal approaches using Sentinel-2A/B.

Different atmospheric correction (AC) procedures for Sentinel-2 satellites are evaluated for their effectiveness in retrieving consistent satellite-derived bathymetry (SDB) over two islands in the Caribbean (Buck and Culebra). The log-ratio method for SDB, which allows use of minimal calibration information from lidar surveys (25 points in this study), is applied to several Sentinel-2A/B scenes at 10 m spatial resolution. The overall performance during a one-year study period depends on the image quality and AC. Three AC processors were evaluated: ACOLITE Exponential model (EXP), ACOLITE Dark Spectrum Fitting model (DSF), and C2RCC model. ACOLITE EXP and ACOLITE DSF produce greater consistency and repeatability with accurate results in a scene-by-scene analysis (mean errors ∼1.1 m) for depths up to 23 m (limit of lidar surveys). In contrast, C2RCC produces lower accuracy and noisier results with generally higher (>50%) errors (mean errors ∼2.2 m), but it is able to retrieve depth for scenes in Buck Island that have moderately severe sunglint. Furthermore, we demonstrate that a multi-temporal compositing model for SDB mapping, using ACOLITE for the input scenes, could achieve overall median errors <1 m for depths ranging 0-23 m. The simple and effective compositing model can considerably enhance coastal SDB estimates with high reliability and no missing data, outperforming the traditional single image approaches and thus eliminating the need to evaluate individual scenes. The consistency in the output from the AC correction indicates the potential for automated application of the multi-scene compositing technique, which can apply the open and free Sentinel-2 data set for the benefit of operational and scientific investigations.

On the application of non-standard rainbow schlieren technique upon supersonic jets

A quantitative rainbow schlieren study was conducted on an over-expanded jet at nozzle pressure ratio of 2.8, based on two different schlieren set-ups: the standard z-type and a single-mirror schlieren set-up. The technique used a single, weak focal-length lens placed in the field of view of the system to provide the calibration information required for the extraction of the quantitative data. In the case of the single-mirror set-up, the calibration image required further post-processing procedures to take into account the double refraction experienced by the light. Density gradients were calculated using Abel transform and compared to validated reference data. Results indicate that the single-mirror set-up is able to improve prediction of the density gradient field as compared to the standard z-type schlieren, due to its inherent property of higher sensitivity. The study has shown that the single-mirror set-up performs on average better than the standard z-type system, yielding an overall averaged error of ± 20%, with localized values as low as ± 5% where the shock cell structure is clearly defined, with respect to the validated reference data. At the same time, both systems perform poorly in regions where the flow structure displays poor image contrast.

Life Satisfaction and International Migration: a Field Theoretic Measurement Model

This article explores the use of migration data for the measurement of life satisfaction as an alternative to data collection by standardized interviews. It starts with the development of a field theoretic model of the impact of life satisfaction on international migration, which is subsequently reversed such that it explains life satisfaction by means of migration data. Empirical tests with data about the migration from Poland to other European countries confirm the main hypotheses of the model. However, the accuracy of ex-post predictions about the quality of life of a target country depends on the number of earlier (Polish) immigrants, who are a source of information for others considering to emigrate. Thus, under certain conditions the model may be used for imputing the life satisfaction of countries for which the respective information is missing. The model is even useful if the information for model calibration is completely missing: the article shows how in this situation migration data may be used for ranking countries with regard to their not directly measurable life satisfaction.

Use of Global Sensitivity and Data-Worth Analysis for an Efficient Estimation of Soil Hydraulic Properties

Accurate simulation of flow and contaminant transport processes through unsaturated soils requires adequate knowledge of the soil parameters. This study deals with the hydraulic characterization of soils using laboratory experiments. A new strategy is developed by combining global sensitivity analysis (GSA) and Bayesian data-worth analysis (DWA) to obtain efficient data that ensure a good estimation of the soil properties. The strategy is applied for the estimation of soil properties from a laboratory infiltration experiment. Results of this study show that GSA allows identification of regions and periods of high sensitivity of each parameter and thereby, the observations prone to contain information for a successful calibration. Further, the sensitivity depicts a nonlinear behavior with regions of high influence and regions of weak influence inside the parameter space. Bayesian DWA, performed a priori, allows to quantify the improvement of the posterior uncertainty of the estimated parameters when adding a type of measurement. The results reveal that an accurate estimation of the soil properties can be obtained if the target parameter values are located in the regions of high influence in the parameter space.

Equilibrium States and Their Stability in the Head-Direction Ring Network.

Head-direction cells have been found in several areas in the mammalian brains. The firing rate of an ideal head-direction cell reaches its peak value only when the animal's head points in a specific direction, and this preferred direction stays the same regardless of spatial location. In this paper we combine mathematical analytical techniques and numerical simulations to fully analyze the equilibrium states of a generic ring attractor network, which is a widely used modeling framework for the head-direction system. Under specific conditions, all solutions of the ring network are bounded, and there exists a Lyapunov function that guarantees the stability of the network for any given inputs, which may come from multiple sources in the biological system, including self-motion information for inertially based updating and landmark information for calibration. We focus on the first few terms of the Fourier series of the ring network to explicitly solve for all possible equilibrium states, followed by a stability analysis based on small perturbations. In particular, these equilibrium states include the standard single-peaked activity pattern as well as double-peaked activity pattern, whose existence is unknown but has testable experimental implications. To our surprise, we have also found an asymmetric equilibrium activity profile even when the network connectivity is strictly symmetric. Finally we examine how these different equilibrium solutions depend on the network parameters and obtain the phase diagrams in the parameter space of the ring network.

Effect of altitude on the stable carbon and oxygen isotopic compositions of land snails at the margin of the East Asian monsoon

The carbon and oxygen isotopic composition of land snail shells (δ13Cshell and δ18Oshell) has been used to reconstruct paleoenvironmental changes worldwide. Adding to the limited calibration information available for the monsoon marginal regions, we present δ13C and δ18O results for modern snails, plants and soils, collected from the Helan Mountains, in North China, spanning an altitudinal gradient of 1733–2851 m a.s.l. Linear regression analysis show that δ13Cshell is positively correlated with altitude on both slopes, with similar rates of altitudinal variation; in contrast, δ18Oshell is negatively correlated with altitude on the western slopes, but this effect is absent on the eastern slopes. The variations in the integrated δ13C signals of living vegetation and the flux of absorbed atmospheric CO2 attributed to changes in metabolic rates are likely responsible for the altitudinal effect on δ13Cshell above 2000 m a.s.l., while changes in vegetation type and/or carbonate ingestion by snails may be important in inducing the positive shift in δ13Cshell below 2000 m a.s.l. δ18Oshell is determined by the rainwater δ18O and by the fractionation processes between shell carbonate and snail body water, and both are controlled by the temperature of the alpine environment. In addition, evaporative enrichment of the body fluid or of the environmental water prior to ingestion by snails exerts an important influence on δ18Oshell. Notably, both δ13Cshell and δ18Oshell in the study region respond more sensitively to climate than in the monsoon core area. The rate of change of δ13Cshell in response to temperature and precipitation above the altitude of 2000 m a.s.l. are −0.87‰/°C and +2.76‰/100 mm, while the corresponding rates for δ18Oshell on the western slopes are +0.41‰/°C and −1.29‰/100 mm. Our results expand the range of application of δ13Cshell and δ18Oshell as climatic indicators, and emphasize the differences in the climatic sensitivity of these proxies between the monsoon margin and core areas. In addition, the differences in shell isotope composition in response to environment changes between adjacent locations (i.e. on different slopes) in mountainous terrain need to be considered in future studies.

Visualizing and labeling dense multi-sensor earth observation time series: The EO Time Series Viewer

Multi-spectral spaceborne sensors with different spatial resolutions produce Earth observation (EO) time series (TS) with global coverage. The interactive visualization and interpretation of TS is essential to better understand changes in land-use and land-cover and to extract reference information for model calibration and validation. However, available software tools are often limited to specific sensors or optimized for application-specific visualizations. To overcome these limitations, we developed the EO Time Series Viewer, a free and open source QGIS plugin for user-friendly visualization, interpretation and labeling of multi-sensor TS data. The EO Time Series Viewer (i) combines advantages of spatial, spectral and temporal data visualization concepts that are so far not available in a single tool, (ii) provides maximum flexibility in terms of supported data formats, (iii) minimizes the user-interactions required to load and visualize multi-sensor TS data and (iv) speeds-up labeling of TS data based on enhanced GIS vector tools and formats.

高光谱成像激光雷达系统辐射定标和地物信息获取

高光谱成像激光雷达系统辐射定标和地物信息获取

条纹投射技术中的投影机非线性自校正方法研究进展

In fringe projection profilometry, the luminance nonlinearity of the projector has been recognized as one of the most crucial factors decreasing the measurement accuracy. It induces the ripple-like artifacts on the measured phase map. The self-adaptive correcting algorithms, i.e., self-correcting algorithms, allow us to suppress the effect of the projector nonlinearity without a prior calibration for the projector intensities or phase errors. This paper introduces the research progress in the self-correcting algorithms. Among them, the first algorithm is to determine a nonlinear curve representing the projector nonlinearity, directly from the captured fringe patterns, thus correcting the phase errors using this curve. The second one is to recognize and remove the nonlinearity-induced errors, directly from a calculated phase map. With the last one, error function coefficients are estimated from a couple of phase maps having different frequencies. Measurement results demonstrate these self-correcting algorithms to be effective in suppressing influences of the projector nonlinearity in the absence of any calibration information.

Historical Photograph Orthorectification Using SfM for Land Cover Change Analysis

Historical photographs acquired early 1900s provide valuable baseline data for earth surface change analysis. However, orthorectifying such data faces great challenges due to relatively low radiometric quality, the difficulty in finding control points due to surface changes, and the lack of camera calibration information. This study developed a method to standardize the scanned photographs into a common camera model, and a workflow to perform block bundle adjustment in aerial triangulation using the structure from motion technology that calibrates cameras within the aerial triangulation solution. Orthophotograph and DSM were generated from the historical photographs. This could potentially push the quantitative analysis of land surface change several decades back before the modern remote sensing era. The stability of aerial triangulation solutions using structure from motion and the accuracy of the produced DSM and orthophotographs were evaluated.