bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Analysis of a Novel, Magnetic-Driven Tactile Feedback Device
Authors: Alshaibani Fahad , Thompson Mark S , Bergmann Jeroen HM
Published: March 2020

The study presented in this paper details the development and experimental testing of a novel, magnetic, tactile feedback device that is able to deliver a stimulus to a patch of skin on the lower arm of a user. The device utilizes magnets to deliver a sensation that is not dependent on controlling specific frequency bands to stimulate the mechanoreceptors, as is the case with vibro-tactile methods. The device was tested on human volunteers to evaluate its ability to induce a response from the user through the magnetic interface. The study aims to quantify the ability of the user to sense the stimulus by analysis of the receiver operating characteristic (ROC) and delay in response under different experimental conditions. Three different speeds and two different distances were explored for the magnetic interface. A two-way, repeated-measures ANOVA with post-hoc analysis was performed for the percentage of correct responses, delay in response time, and area under the curve (AUC) of the obtained ROCs. The results showed that the different conditions had a significant effect on the number of correct responses and the AUC, but not on the delay. The magnetic interface thus needs to be optimized across different parameters to deliver the best detectable stimulus to the user. Future work includes further development of the device and working towards a comparative trial with other tactile feedback approaches.
Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir
Authors: Rodrigues Gonçalo , Potes Miguel , Costa Maria João , Novais Maria Helena , Penha Alexandra Marchã , Salgado Rui , Morais Maria Manuela
Published: March 2020

The Alqueva reservoir (South of Portugal) in the Guadiana river basin constitutes the most important water resource in southern Portugal for domestic and agricultural consumption. We present a methodology developed to characterize spatial and temporal variations of Secchi depth and diffuse attenuation coefficient (both related to dissolved/suspended particles and to water transparency), using high spatial resolution satellite images from Sentinel-2 Multi-Spectral Instrument (MSI). Empirical relations between satellite retrievals of surface reflectances and in situ measurements of water parameters were defined and applied to the entire reservoir for spatial and temporal analysis in the period July 2017–June 2019, useful in the identification of microalgae blooms and rapid variations in water characteristics, which allowed us to differentiate five zones. Water estimates with lower transparency and higher attenuation of radiation were found in the northern area of Alqueva reservoir during the months characterized by higher water temperatures, with Secchi depth monthly averages near 1.0 m and diffuse attenuation coefficient near or above 1.5 m-1. Satellite retrievals of water with greater transparency in the reservoir were obtained in the southern area in months with low water temperature and atmospheric stability, presenting some monthly Secchi depth averages above 3 m, and diffuse attenuation coefficient below 0.8 m-1. January 2018 presented great transparency of water with a Secchi depth of 7.5 m for pixels representing the 95th percentile and diffuse attenuation coefficient of 0.36 m for pixels representing the 5th percentile in the Southern region.
Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement over Lakes and Reservoirs in China
Authors: Yuan Cui , Gong Peng , Bai Yuqi
Published: March 2020

Although the Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was primarily designed for glacier and sea-ice measurement, it can also be applied to monitor lake surface height (LSH). However, its performance in monitoring lakes/reservoirs has rarely been assessed. Here, we report an accuracy evaluation of the ICESat-2 laser altimetry data over 30 reservoirs in China using gauge data. To show its characteristics in large-scale lake monitoring, we also applied an advanced radar altimeter SARAL (Satellite for ARgos and ALtika) and the first laser altimeter ICESat (Ice, Cloud and land Elevation Satellite) to investigate all lakes and reservoirs (>10 km2) in China. We found that the ICESat-2 has a greatly improved altimetric capability, and the relative altimetric error was 0.06 m, while the relative altimetric error was 0.25 m for SARAL. Compared with SARAL and ICESat data, ICESat-2 data had the lowest measurement uncertainty (the standard deviation of along-track heights; 0.02 m vs. 0.17 m and 0.07 m), the greatest temporal frequency (3.43 vs. 1.35 and 1.48 times per year), and the second greatest lake coverage (636 vs. 814 and 311 lakes). The precise LSH profiles derived from the ICESat-2 data showed that most lakes (90% of 636 lakes) had a quasi-horizontal LSH profile (measurement uncertainty <0.05 m), and special methods are needed for mountainous lakes or shallow lakes to extract precise LSHs.
Small Multicopter-UAV-Based Radar Imaging: Performance Assessment for a Single Flight Track
Authors: Catapano Ilaria , Gennarelli Gianluca , Ludeno Giovanni , Noviello Carlo , Esposito Giuseppe , Renga Alfredo , Fasano Giancarmine , Soldovieri Francesco
Published: March 2020

This paper deals with a feasibility study assessing the reconstruction capabilities of a small Multicopter-Unmanned Aerial Vehicle (M-UAV) based radar system, whose flight positions are determined by using the Carrier-Phase Differential GPS (CDGPS) technique. The paper describes the overall radar imaging system in terms of both hardware devices and data processing strategy for the case of a single flight track. The data processing is cast as the solution of an inverse scattering problem and is able to provide focused images of on surface targets. In particular, the reconstruction is approached through the adjoint of the functional operator linking the unknown contrast function to the scattered field data, which is computed by taking into account the actual flight positions provided by the CDGPS technique. For this inverse problem, we provide an analysis of the reconstruction capabilities by showing the effect of the radar parameters, the flight altitude and the spatial offset between target and flight path on the resolution limits. A measurement campaign is carried out to demonstrate the imaging capabilities in controlled conditions. Experimental results referred to two surveys performed on the same scene but at two different UAV altitudes verify the consistency of these results with the theoretical resolution analysis.
Using MODIS LAI Data to Monitor Spatio-Temporal Changes of Winter Wheat Phenology in Response to Climate Warming
Authors: Song Yang , Wang Jing , Yu Qiang , Huang Jianxi
Published: March 2020

Understanding spatio-temporal changes in winter wheat (Triticum aestivum L) phenology and its response to temperature will be vital for adapting to climate change in the coming years. For this purpose, the heading date (HD), maturity date (MD), and length of the reproductive growth period (LRGP) were detected from the remotely sensed leaf area index (LAI) data by a threshold-based method during the harvest year 2003 to 2018 across the North China Plain. The results show that there was high spatial heterogeneity of winter wheat phenology in pixel scale across the whole area, which could not be detected in previous site-based studies. The results also verified that climate warming could explain part of the change in the HD. However, for the LRGP, the potential impact of non-climate effects should be further investigated. This study presents the spatio-temporal changes both in winter wheat phenology and corresponding mean temperature and then analyzes their relationships in pixel scale. Additionally, this study further discusses the potential impact of non-climate effects on the LRGP.
Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval
Authors: Yang Jingjing , Duan Si-Bo , Zhang Xiaoyu , Wu Penghai , Huang Cheng , Leng Pei , Gao Maofang
Published: March 2020

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.
Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks
Authors: Wang Guojie , Wu Mengjuan , Wei Xikun , Song Huihui
Published: March 2020

The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment. However, there are significant limitations in the traditionally used index for water body identification. In this study, we have proposed a deep convolutional neural network (CNN), based on the multidimensional densely connected convolutional neural network (DenseNet), for identifying water in the Poyang Lake area. The results from DenseNet were compared with the classical convolutional neural networks (CNNs): ResNet, VGG, SegNet and DeepLab v3+, and also compared with the Normalized Difference Water Index (NDWI). Results have indicated that CNNs are superior to the water index method. Among the five CNNs, the proposed DenseNet requires the shortest training time for model convergence, besides DeepLab v3+. The identification accuracies are evaluated through several error metrics. It is shown that the DenseNet performs much better than the other CNNs and the NDWI method considering the precision of identification results; among those, the NDWI performance is by far the poorest. It is suggested that the DenseNet is much better in distinguishing water from clouds and mountain shadows than other CNNs.
Machine-Learning Applications for the Retrieval of Forest Biomass from Airborne P-Band SAR Data
Authors: Santi Emanuele , Paloscia Simonetta , Pettinato Simone , Cuozzo Giovanni , Padovano Antonio , Notarnicola Claudia , Albinet Clement
Published: March 2020

This study aimed at evaluating the potential of machine learning (ML) for estimating forest biomass from polarimetric Synthetic Aperture Radar (SAR) data. Retrieval algorithms based on two different machine-learning methods, namely Artificial Neural Networks (ANNs) and Supported Vector Regressions (SVRs), were implemented and validated using the airborne polarimetric SAR data derived from the AfriSAR, BioSAR, and TropiSAR campaigns. These datasets, composed of polarimetric airborne SAR data at P-band and corresponding biomass values from in situ and LiDAR measurements, were made available by the European Space Agency (ESA) in the framework of the Biomass Retrieval Algorithm Inter-Comparison Exercise (BRIX). The sensitivity of the SAR measurements at all polarizations to the target biomass was evaluated on the entire set of data from all the campaigns, and separately on the dataset of each campaign. Based on the results of the sensitivity analysis, the retrieval was attempted by implementing general algorithms, using the entire dataset, and specific algorithms, using data of each campaign. Algorithm inputs are the SAR data and the corresponding local incidence angles, and output is the estimated biomass. To allow the comparison, both ANN and SVR were trained using the same subset of data, composed of 50% of the available dataset, and validated on the remaining part of the dataset. The validation of the algorithms demonstrated that both machine-learning methods were able to estimate the forest biomass with comparable accuracies. In detail, the validation of the general ANN algorithm resulted in a correlation coefficient R = 0.88, RMSE = 60 t/ha, and negligible BIAS, while the specific ANN for data obtained R from 0.78 to 0.94 and RMSE between 15 and 50 t/ha, depending on the dataset. Similarly, the general SVR was able to estimate the target parameter with R = 0.84, RMSE = 69 t/ha, and BIAS negligible, while the specific algorithms obtained 0.22 ≤ R ≤ 0.92 and 19 ≤ RMSE ≤ 70 (t/ha). The study also pointed out that the computational cost is similar for both methods. In this respect, the training is the only time-demanding part, while applying the trained algorithm to the validation set or to any other dataset occurs in near real time. As a final step of the study, the ANN and SVR algorithms were applied to the available SAR images for obtaining biomass maps from the available SAR images.
Raman Spectroscopy from Laboratory and Proximal to Remote Sensing: A Tool for the Volcanological Sciences
Authors: Giordano Daniele , Russell James K. , González-García Diego , Bersani Danilo , Dingwell Donald B. , Del Negro Ciro
Published: March 2020

Here we explore and review some of the latest ideas and applications of Raman spectroscopy to the volcanological sciences. Firstly, we provide a brief overview of how Raman spectral analysis works and how spectra from silicate glasses are interpreted. We then look at specific applications of Raman spectral analysis to the volcanological sciences based on measurements on and studies of natural materials in the laboratory. We conclude by examining the potential for Raman spectral analysis to be used as a field based aid to volcano monitoring via in situ studies of proximal deposits and; perhaps; in remote sensing campaigns
Radiometric Cross Calibration and Validation Using 4 Angle BRDF Model between Landsat 8 and Sentinel 2A
Authors: Farhad M M , Kaewmanee Morakot , Leigh Larry , Helder Dennis
Published: March 2020

This work describes a proposed radiometric cross calibration between the Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) sensors. The cross-calibration procedure involves (i) correction of the MSI data to account for spectral band differences with OLI and (ii) normalization of Bidirectional Reflectance Distribution Function (BRDF) effects in the data from both sensors using a new model accounting for the view zenith/azimuth angles in addition to the solar zenith/view angles. Following application of the spectral and BRDF normalization, standard least-squares linear regression is used to determine the cross-calibration gain and offset in each band. Uncertainties related to each step in the proposed process are determined, as is the overall uncertainty associated with the complete processing sequence. Validation of the proposed cross-calibration gains and offsets is performed on image data acquired over the Algodones Dunes site. The results of this work indicate that the blue band has the most significant offset, requiring use of the estimated cross-calibration offset in addition to the estimated gain. The highest difference was observed in the blue and red bands, which are 2.6% and 1.4%, respectively, while other bands shows no significant difference. Overall, the net uncertainty in the proposed process was estimated to be on the order of 6.76%, with the largest uncertainty component due to each sensor’s calibration uncertainty on the order of 5% and 3% for the MSI and OLI, respectively. Other significant contributions to the uncertainty include seasonal changes in solar zenith and azimuth angles, target site nonuniformity, variability in atmospheric water vapor, and/or aerosol concentration.
Differential Kalman Filter Design for GNSS Open Loop Tracking
Authors: Jin Tian , Yuan Heliang , Ling Keck-Voon , Qin Honglei , Kang Jianrong
Published: March 2020

Global navigation satellite system (GNSS) positioning in an urban environment is in need for accurate, reliable and robust positioning. Unfortunately, conventional closed-loop tracking fails to meet the demand. The open loop tracking shows improved robustness, however, the precision is unsatisfactory. We propose a differential Kalman filter for open loop, of which the measurement vector contains the differential values of open loop navigation results between adjacent epochs. The differential Kalman filter makes use of the satellite geometry (i.e., spatial domain) and motion relationship (i.e., temporal domain) to filter frequency and code phase estimations of conventional open loop tracking. The improved performances of this architecture have been analyzed theoretically and demonstrated by road tests in an urban environment. The proposed architecture shows more than 50% accuracy improvement than the conventional open-loop tracking architecture.
Woody Cover Fractions in African Savannas From Landsat and High-Resolution Imagery
Authors: Nagelkirk Ryan L. , Dahlin Kyla M.
Published: March 2020

The challenge of mapping savanna vegetation has limited our understanding of the factors that shape these ecosystems at large scales. We tested seven methods for mapping savanna woody cover (trees and shrubs; WC) across 12 protected areas (PAs) in eastern Africa using Landsat 8 imagery. Because we wanted a method viable for mapping across the often-limited Landsat historical archive, we limited ourselves to three images: one each from the wet, dry, and transition (halfway between wet and dry) seasons. Models were trained and tested using 1,330 WC reference points and the variance explained by cross validation (VEcv) accuracy metric. Of the methods we tested, RF significantly (p < 0.001) outperformed the others with the best models in nine PAs scoring over 75% (range of 34.5%–91.1%). RF models trained using data from all the PAs and tested in the individual PAs significantly (p < 0.001) outperformed their single-PA-derived counterparts (67.7 ± 23.3% versus 30.5 ± 27.4%). We also found that while the transition image appears to be critical to mapping WC and the wet season image should be avoided, no single season or seasonal combination significantly outperformed all the others, allowing some flexibility in image selection. Our findings show that with proper sampling of landscape heterogeneity, even with limited imagery, accurate maps of savanna WC are possible and could catalyze discoveries in this crucial biome.
An Improved Descalloping Method Combined With Imaging Parameters for GaoFen-3 ScanSAR
Authors: Zhong Lihua , Qiu Xiaolan , Han Bing , Hu Yuxin
Published: March 2020

Due to the specific working mechanism of alternately transmitting and receiving signals between multiple adjacent swaths, scanning synthetic aperture radar (ScanSAR) will cause periodical wavelike modulation of the intensity image along azimuth direction, which is known as scalloping. Conventional descalloping methods are achieved by proper azimuth antenna pattern (AAP) correction and multi-looking techniques but are limited by the accuracy of Doppler centroid estimating and the thermal noise. Another type of method extracts and suppresses the scalloping texture on the image, but the scanning parameters of ScanSAR are insufficiently considered. The period of scalloping on the image is related to the period of switching between subswathes. While the harmonics can be calculated by the period of a periodic signal, an improved frequency filtering method combined with imaging parameters is proposed. The scalloping modulation model of ScanSAR combined with imaging parameters is constructed, and the harmonics of scalloping texture are accurately calculated and filtered. For the low signal to noise ratio (SNR) image, the antenna pattern is modified according to SNR to avoid scalloping caused by noise. For non-uniform scenes, scalloping suppression is achieved by using the scalloping features acquired by uniform scenes. To separate the non-uniform scenes from uniform scenes, we still use the characteristics of harmonics caused by scalloping. Our descalloping method achieves accurate suppression of scalloping without sea-land segmentation and ship mask and the residual scalloping is reduced from 1.0 to 0.3–0.5 dB. The residual scalloping and statistical characteristics of the image are analyzed to demonstrate the effectiveness of the proposed method.
Climatology Perspective of Sensitive Regimes and Active Regions of Aerosol Indirect Effect for Cirrus Clouds over the Global Oceans
Authors: Zhao Xuepeng , Liu Yangang , Yu Fangqun , Heidinger Andrew K. , Saha Korak
Published: March 2020

Long-term satellite climate data records (CDRs) of clouds and aerosols are used to investigate the aerosol indirect effect (AIE) of cirrus clouds over the global oceans from a climatology perspective. Our study focuses on identifying the sensitive regimes and active regions where AIE signatures easily manifest themselves in the sense of the long-term average of cloud and aerosol variables. The aerosol index (AIX) regimes of AIX < 0.18 and 0.18 < AIX < 0.46 are respectively identified as the sensitive regimes for negative and positive aerosol albedos and lifetime effects of cirrus clouds. Relative humidity first decreases (along with upward motions) and then reverses to increase (along with downward motions) in the first regime of negative aerosol albedo and lifetime effects. Relatively wet and strong upward motions are the favorable meteorological conditions for the second regime of positive aerosol albedo and lifetime effects. Two swath regions extending from 15°N to 30°N over the east coastal oceans of China and the USA are the active regions of positive aerosol albedo effects. Positive aerosol lifetime effects are only active or easy to manifest in the regions where a positive aerosol albedo effect is active. The results based on the long-term averaged satellite observations are valuable for the evaluation and improvement of aerosol-cloud interactions for cirrus clouds in global climate models.
Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model
Authors: Lewis-Beck Colin , Walker Victoria A. , Niemi Jarad , Caragea Petruţa , Hornbuckle Brian K.
Published: March 2020

Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of corn (R3) as well as other new agronomic signals from SMOS VOD. We compare our estimates of the timing of R3 to United States Department of Agriculture (USDA) survey data for the years 2011, 2012, and 2013. We find that 87%, 70%, and 37%, respectively, of our model estimates of R3 timing agree with USDA district-level observations. We postulate that since the satellite estimates can be directly linked to a physiological state (the maximum amount of plant water, or water contained within plant tissue per ground area) it is more accurate than the USDA data which is based upon visual observations from roadways. Consequently, SMOS VOD could be used to replace, at a finer resolution than the district-level USDA reports, the R3 data that has not been reported by the USDA since 2013. We hypothesize the other model parameters contain new information about soil and crop management and crop productivity that are not routinely collected by any federal or state agency in the Corn Belt.
Comparative Study of Two Pose Measuring Systems Used to Reduce Robot Localization Error
Authors: Franaszek Marek , Cheok Geraldine S. , Marvel Jeremy A.
Published: March 2020

The performance of marker-based, six degrees of freedom (6DOF) pose measuring systems is investigated. For instruments in this class, the pose is derived from locations of a few three-dimensional (3D) points. For such configurations to be used, the rigid-body condition—which requires that the distance between any two points must be fixed, regardless of orientation and position of the configuration—must be satisfied. This report introduces metrics that gauge the deviation from the rigid-body condition. The use of these metrics is demonstrated on the problem of reducing robot localization error in assembly applications. Experiments with two different systems used to reduce the localization error of the same industrial robot yielded two conflicting outcomes. The data acquired with one system led to substantial reduction in both position and orientation error of the robot, while the data acquired with a second system led to comparable reduction in the position error only. The difference is attributed to differences between metrics used to characterize the two systems.
Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations over the Amhara Region, Ethiopia
Authors: Alemu Woubet G. , Wimberly Michael C.
Published: March 2020

Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from satellite remote sensing or spatial interpolation of ground-based observations in relation to data from 22 meteorological stations in Amhara Region, Ethiopia, for 2003–2016. Famine Early Warning Systems Network (FEWS-Net) Land Data Assimilation System (FLDAS) interpolated temperature showed the lowest bias (mean error (ME) ≈ 1–3 °C), and error (mean absolute error (MAE) ≈ 1–3 °C), and the highest correlation with day-to-day variability of station temperature (COR ≈ 0.7–0.8). In contrast, temperature retrievals from the blended Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave and Moderate-resolution Imaging Spectroradiometer (MODIS) land-surface temperature data had higher bias and error. Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) rainfall showed the least bias and error (ME ≈ −0.2–0.2 mm, MAE ≈ 0.5–2 mm), and the best agreement (COR ≈ 0.8), with station rainfall data. In contrast FLDAS had the higher bias and error and the lowest agreement and Global Precipitation Mission/Tropical Rainfall Measurement Mission (GPM/TRMM) data were intermediate. This information can inform the selection of geospatial data products for use in climate and disease research and applications.
Determination of Trace Lead and Cadmium in Decorative Material Using Disposable Screen-Printed Electrode Electrically Modified with Reduced Graphene Oxide/L-Cysteine/Bi-Film
Authors: Hou Xiaopeng , Xiong Benhai , Wang Yue , Wang Li , Wang Hui
Published: March 2020

Cadmium (Cd) and lead (Pb) in decorative materials threaten human health. To determine the content of Cd(II) and Pb(II), a disposable screen-printed electrode (DSPE) electrically modified with reduced graphene oxide (rGO) and L-cysteine (LC) was fabricated, which was further electroplated with bismuth film (Bi/LC-rGO/DSPE) in situ. The electrochemical properties of this electrode were studied using cyclic voltammetry, electrochemical impedance spectroscopy, linear sweep voltammetry and differential pulse voltammetry. The results indicated that the Bi/LC-rGO/DSPE had excellent sensitivity, selectivity and stability with low cost and easy production. After optimizing the detection parameters, the linear range of the Bi/LC-rGO/DSPE was from 1.0 to 30.0 μg/L for Cd(II) and Pb(II), and the detection limits were 0.10 μg/L for Cd(II) and 0.08 μg/L for Pb(II). Finally, the Bi/LC-rGO/DSPE was applied to determine the concentrations of Cd(II) and Pb(II) in different decorative materials where the recoveries were in the range from 95.86% to 106.64%.
AMARO—An On-Board Ship Detection and Real-Time Information System
Authors: Willburger Katharina , Schwenk Kurt , Brauchle Jörg
Published: March 2020

The monitoring of worldwide ship traffic is a field of high topicality. Activities like piracy, ocean dumping, and refugee transportation are in the news every day. The detection of ships in remotely sensed data from airplanes, drones, or spacecraft contributes to maritime situational awareness. However, the crucial factor is the up-to-dateness of the extracted information. With ground-based processing, the time between image acquisition and delivery of the extracted product data is in the range of several hours, mainly due to the time consumed by storing and transmission of the large image data. By processing and analyzing them on-board and transmitting the product data directly as ship position, heading, and velocity, the delay can be shortened to some minutes. Real-time connections via satellite telecommunication services allow small packets of information to be sent directly to the user without significant delay. The AMARO (Autonomous Real-Time Detection of Moving Maritime Objects) project at DLR is a feasibility study of an on-board ship detection system involving on-board processing and real-time communication. The operation of a prototype system was successfully demonstrated on an airborne platform in spring 2018. The on-ground user could be informed about detected vessels within minutes after sighting without a direct communication link. In this article, the scope, aim, and design of the AMARO system are described, and the results of the flight experiment are presented in detail.
Chaotic Image Encryption Using Hopfield and Hindmarsh–Rose Neurons Implemented on FPGA
Authors: Tlelo-Cuautle Esteban , Díaz-Muñoz Jonathan Daniel , González-Zapata Astrid Maritza , Li Rui , León-Salas Walter Daniel , Fernández Francisco V. , Guillén-Fernández Omar , Cruz-Vega Israel
Published: March 2020

Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh–Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan–Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh–Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests.
Coupling of Anodic Stripping Voltammetry with Sampled-Current Voltammetry on an Electrode Array: Application to Lead Detection
Authors: Mazerie Isabelle , Geneste Florence
Published: March 2020

Electrochemical detection systems are very promising for pollution monitoring owing to their easy miniaturization and low cost. For this purpose, we have recently developed a new concept of device based on Electrodes Array for Sampled-Current Voltammetry (EASCV), which is compatible with miniaturization and portability. In this work, to improve the sensitivity of the analytical method, we added a preconcentration step before EASCV analysis, combining sampled-current voltammetry with anodic stripping voltammetry. Lead was chosen as analyte for this probe of concept owing to its high toxicity. The conditions for electrodeposition of lead on gold were optimized by means of under potential deposition. Current intensities 300 times higher than with linear sweep anodic stripping voltammetry were obtained, showing the interest in the method. The value of the sampling time directly affected the sensitivity of the sensor given by the slope of the linear calibration curve. The sensor exhibited a limit of detection of 1.16 mg L−1, similar to those obtained with linear sweep anodic stripping voltammetry.
A Real-Time Wearable Assist System for Upper Extremity Throwing Action Based on Accelerometers
Authors: Lian Kuang-Yow , Hsu Wei-Hsiu , Balram Deepak , Lee Chen-Yi
Published: March 2020

This paper focuses on the development of a real-time wearable assist system for upper extremity throwing action based on the accelerometers of inertial measurement unit (IMU) sensors. This real-time assist system can be utilized to the learning, rectification, and rehabilitation for the upper extremity throwing action of players in the field of baseball, where incorrect throwing phases are recognized by a delicate action analysis. The throwing action includes not only the posture characteristics of each phase, but also the transition of continuous posture movements, which is more complex when compared to general action recognition with no continuous phase change. In this work, we have considered six serial phases including wind-up, stride, arm cocking, arm acceleration, arm deceleration, and follow-through in the throwing action recognition process. The continuous movement of each phase of the throwing action is represented by a one-dimensional data sequence after the three-axial acceleration signals are processed by efficient noise filtering based on Kalman filter followed by conversion processes such as leveling and labeling techniques. The longest common subsequence (LCS) method is then used to determine the six serial phases of the throwing action by verifying the sequence data with a sample sequence. We have incorporated various intelligent action recognition functions including automatic recognition for getting ready status, starting movement, handle interrupt situation, and detailed posture transition in the proposed assist system. Moreover, a liquid crystal display (LCD) panel and mobile interface are incorporated into the developed assist system to make it more user-friendly. The real-time system provides precise comments to assist players to attain improved throwing action by analyzing their posture during throwing action. Various experiments were conducted to analyze the efficiency and practicality of the developed assist system as part of this work. We have obtained an average percentage accuracy of 95.14%, 91.42%, and 95.14%, respectively, for all the three users considered in this study. We were able to successfully recognize the throwing action with good precision and the high percentage accuracy exhibited by the proposed assist system indicates its excellent performance.
Designing Laboratory for IoT Communication Infrastructure Environment for Remote Maritime Surveillance in Equatorial Areas Based on the Gulf of Guinea Field Experiences
Authors: Petrovic Ranko , Simic Dejan , Drajic Dejan , Cica Zoran , Nikolic Dejan , Peric Miroslav
Published: March 2020

The steady increase of the world population and economy leads to an increase in both types and amounts of goods transported over seas, which further inevitably leads to an increase of criminal activities in the maritime arena. In order to stifle criminal activities nations are forced to develop sophisticated sensor networks. The backbone of any sensor network is a communication network which connects all sensors with the command centers, most often located hundreds of kilometers away from the sensors. In developing countries, communication networks are very often poorly developed, leaving only satellite links as somewhat reliable means of communication. Henceforth, in this paper, a laboratory for the Internet of Things (IoT) communication infrastructure environment designed to facilitate maritime sensor network design process in areas where communication network is dependent on data transfer over satellite links is presented. In order to successfully describe and develop a laboratory for IoT communication infrastructure environment, necessary data are collected during the design and deployment of a maritime surveillance network in the Gulf of Guinea. The main advantage of the proposed laboratory environment is the inclusion of satellite link simulation in the IoT laboratory environment. This feature provides an opportunity to cover a much broader scope of IoT solutions compared to other IoT laboratories.
ANN-Based Airflow Control for an Oscillating Water Column Using Surface Elevation Measurements
Authors: M'zoughi Fares , Garrido Izaskun , Garrido Aitor J. , De La Sen Manuel
Published: March 2020

Oscillating water column (OWC) plants face power generation limitations due to the stalling phenomenon. This behavior can be avoided by an airflow control strategy that can anticipate the incoming peak waves and reduce its airflow velocity within the turbine duct. In this sense, this work aims to use the power of artificial neural networks (ANN) to recognize the different incoming waves in order to distinguish the strong waves that provoke the stalling behavior and generate a suitable airflow speed reference for the airflow control scheme. The ANN is, therefore, trained using real surface elevation measurements of the waves. The ANN-based airflow control will control an air valve in the capture chamber to adjust the airflow speed as required. A comparative study has been carried out to compare the ANN-based airflow control to the uncontrolled OWC system in different sea conditions. Also, another study has been carried out using real measured wave input data and generated power of the NEREIDA wave power plant. Results show the effectiveness of the proposed ANN airflow control against the uncontrolled case ensuring power generation improvement.
A Rail Fastener Tightness Detection Approach Using Multi-source Visual Sensor
Authors: Han Qiang , Wang Shengchun , Fang Yue , Wang Le , Du Xinyu , Li Hailang , He QiXin , Feng Qibo
Published: March 2020

At present, the method of two-dimensional image recognition is mainly used to detect the abnormal fastener in the rail-track inspection system. However, the too-tight-or-too-loose fastener condition may cause the clip of the fastener to break or loose due to the high frequency vibration shock, which is difficult to detect from the two-dimensional image. In this practical application background, 3D visual detection technology provides a feasible solution. In this paper, we propose a fundamental multi-source visual data detection method, as well as an accurate and robust fastener location and nut or bolt segmentation algorithm. By combining two-dimensional intensity information and three-dimensional depth information generated by the projection of line structural light, the locating of nut or bolt position and accurate perception of height information can be realized in the dynamic running environment of railway. The experimental results show that the static measurement accuracy in the vertical direction using the structural light vision sensor is 0.1 mm under the laboratory condition, and the dynamic measurement accuracy is 0.5 mm under the dynamic train running environment. We use dynamic template matching algorithm to locate fasteners from 2D intensity map, which achieves 99.4% accuracy, then use the watershed algorithm to segment the nut and bolt from the corresponding depth image of located fastener. Finally, the 3D shape of the nut and bolt is analyzed to determine whether the nut or bolt height meets the local statistical threshold requirements, so as to detect the hidden danger of railway transportation caused by too loose or too tight fasteners.
Experimental Research on Shear Failure Monitoring of Composite Rocks Using Piezoelectric Active Sensing Approach
Authors: Liu Yang , Ye Yicheng , Wang Qihu , Wang Weiqi
Published: March 2020

Underground space engineering structures are generally subject to extensive damages and significant deformation. Given that composite rocks are prone to shear failure, which cannot be accurately monitored, the piezoelectric active sensing method and wavelet packet analysis method were employed to conduct a shear failure monitoring test on composite rocks in this study. For the experiment, specimens were prepared for the simulation of the composite rocks using cement. Two pairs of piezoelectric smart aggregates (SAs) were embedded in the composite specimens. When the specimens were tested using the direct shear apparatus, an active sensing-based monitoring test was conducted using the embedded SAs. Moreover, a wavelet packet analysis was conducted to compute the energy of the monitoring signal; thus allowing for the determination of the shear damage index of the composite specimens and the quantitative characterization of the shear failure process. The results indicated that upon the shear failure of the composite specimens, the amplitudes and peak values of the monitoring signals decreased significantly, and the shear failure and damage indices of the composite specimens increased abruptly and approached a value of 1. The feasibility and reliability of the piezoelectric active sensing method, with respect to the monitoring of the shear failure of composite rocks, was therefore experimentally demonstrated in this study.
Recent Advances in Indoor Localization via Visible Lights: A Survey
Authors: Rahman A B M Mohaimenur , Li Ting , Wang Yu
Published: March 2020

Because of the limitations of the Global Positioning System (GPS) in indoor scenarios, various types of indoor positioning or localization technologies have been proposed and deployed. Wireless radio signals have been widely used for both communication and localization purposes due to their popular availability in indoor spaces. However, the accuracy of indoor localization based purely on radio signals is still not perfect. Recently, visible light communication (VLC) has made use of electromagnetic radiation from light sources for transmitting data. The potential for deploying visible light communication for indoor localization has been investigated in recent years. Visible-light-based localization enjoys low deployment cost, high throughput, and high security. In this article, the most recent advances in visible-light-based indoor localization systems have been reviewed. We strongly believe that visible-light-based localization will become a low-cost and feasible complementary solution for indoor localization and other smart building applications.
Large-Area Resistive Strain Sensing Sheet for Structural Health Monitoring
Authors: Aygun Levent E. , Kumar Vivek , Weaver Campbell , Gerber Matthew , Wagner Sigurd , Verma Naveen , Glisic Branko , Sturm James C.
Published: March 2020

Damage significantly influences response of a strain sensor only if it occurs in the proximity of the sensor. Thus, two-dimensional (2D) sensing sheets covering large areas offer reliable early-stage damage detection for structural health monitoring (SHM) applications. This paper presents a scalable sensing sheet design consisting of a dense array of thin-film resistive strain sensors. The sensing sheet is fabricated using flexible printed circuit board (Flex-PCB) manufacturing process which enables low-cost and high-volume sensors that can cover large areas. The lab tests on an aluminum beam showed the sheet has a gauge factor of 2.1 and has a low drift of 1.5 μ ϵ / d a y . The field test on a pedestrian bridge showed the sheet is sensitive enough to track strain induced by the bridge’s temperature variations. The strain measured by the sheet had a root-mean-square (RMS) error of 7 μ ϵ r m s compared to a reference strain on the surface, extrapolated from fiber-optic sensors embedded within the bridge structure. The field tests on an existing crack showed that the sensing sheet can track the early-stage damage growth, where it sensed 600 μ ϵ peak strain, whereas the nearby sensors on a damage-free surface did not observe significant strain change.
Static and Dynamic Evaluation of an UWB Localization System for Industrial Applications
Authors: Delamare Mickaël , Boutteau Remi , Savatier Xavier , Iriart Nicolas
Published: March 2020

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.
Development of a BIM-Based Web Tool as a Material and Component Bank for a Sustainable Construction Industry
Authors: Jayasinghe Laddu Bhagya , Waldmann Daniele
Published: March 2020

The construction industry consumes an enormous amount of global resources and produces more waste than any other sector. The need to move toward sustainable development in construction requires significant changes in construction and demolition (C&D) waste management. The estimation of waste, recycling materials and reusable components could be vital in waste management, achieving huge efficiency in the construction industry. Moreover, a typical building comprises of an extensive amount of materials and components with various characteristics. This study proposes a Building Information Modelling (BIM)-based system to allow the circular economy by storing information of the materials and components of buildings and by effectively managing the recycling of materials and reuse of components. A tool which serves as a Material and Component (M&C) bank was developed with PHP and MYSQL by making use of a web browser able to extract the materials and component information of a building through the BIM model. This information is vital for several uses such as quantification of C&D waste and assessing for the design for deconstruction. It can also be used to obtain the information of the reusable condition of the components and instructions for the reconstruction.