bluuebunny/crossref_metadata_2025 huggingface dataset:
Modification of the Bacterial Cell Wall—Is the Bioavailability Important in Creosote Biodegradation?
Authors: Smułek Wojciech , Pacholak Amanda , Kaczorek Ewa
Published: January 2020

Creosote oil, widely used as a wood preservative, is a complex mixture of different polycyclic aromatic compounds. The soil contamination result in the presence of a specific microcosm. The presented study focuses on the most active strains involved in bioremediation of long-term creosote-contaminated soil. In three soil samples from different boreholes, two Sphingomonas maltophilia (S. maltophilia) and one Paenibacillus ulginis (P. ulginis) strain were isolated. The conducted experiments showed the differences and similarities between the bacteria strains capable of degrading creosote from the same contaminated area. Both S. maltophilia strains exhibit higher biodegradation efficiency (over 50% after 28 days) and greater increase in glutathione S-transferase activity than P. ulginis ODW 5.9. However, S. maltophilia ODW 3.7 and P. ulginis ODW 5.9 were different from the third of the tested strains. The growth of the former two on creosote resulted in an increase in cell adhesion to Congo red and in the total membrane permeability. Nevertheless, all three strains have shown a decrease in the permeability of the inner cell membrane. That suggests the complex relationship between the cell surface modifications and bioavailability of the creosote to microorganisms. The conducted research allowed us to broaden the current knowledge about the creosote bioremediation and the properties of microorganisms involved in the process.
Designing Hydrogen and Oxygen Flow Rate Control on a Solid Oxide Fuel Cell Simulator Using the Fuzzy Logic Control Method
Authors: Darjat  , Sulistyo  , Triwiyatno Aris , Sudjadi  , Kurniahadi Andra
Published: January 2020

A solid oxide fuel cell (SOFC) is an electrochemical cell that converts chemical energy into electrical energy by oxidizing fuel. SOFC has high efficiency and cleans oxidation residues. Research has shown the importance of SOFC control. Voltage output control is needed because of nonlinearity, slow dynamics, and proper SOFC operating restrictions. This study aims to design an SOFC simulator with output voltage control to optimize the flow rate of fuel (hydrogen) and air (oxygen). This SOFC simulator is designed based on a microcontroller model. The controller is designed using the fuzzy logic method. Tests show that the output voltage can approach the set point with an average of 340.6 volts. The pressure difference (∆Pressure) between the two gases averaged 4428 Pa, and the fuel/gas flow rate was in the range of 0.7 mol/s. The controller can correct both the output voltage of the SOFC simulator and the difference in gas pressure under 8106 Pa (0.08 atm).
Assessing the Performance of Methods for Monitoring Ice Phenology of the World’s Largest High Arctic Lake Using High-Density Time Series Analysis of Sentinel-1 Data
Authors: Murfitt Justin , Duguay Claude R.
Published: January 2020

Lake ice is a dominant component of Canada’s landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015–2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3–10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2–10 days. Mean errors for comparisons of different sections of the lake ranged 0–15 days for the first difference method and 2–17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring.
Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece
Authors: Giannaros Christos , Kotroni Vassiliki , Lagouvardos Konstantinos , Giannaros Theodore M. , Pikridas Christos
Published: January 2020

The derivation of global navigation satellite systems (GNSSs) tropospheric products is nowadays a state-of-the-art technique that serves both research and operational needs in a broad range of applications in meteorology. In particular, GNSS zenith tropospheric delay (ZTD) data assimilation is widely applied in Europe to enhance numerical weather predictions (NWPs). The current study presents the first attempt at introducing assimilation of ZTDs, derived from more than 48 stations of the Hellenic GNSS network, into the operational NWP system of the National Observatory of Athens (NOA) in Greece, which is based on the mesoscale Weather Research and Forecasting (WRF) model. WRF was applied during seven high-impact precipitation events covering the dry and wet season of 2018. The simulation employing the ZTD data assimilation reproduces more accurately, compared to the control experiment, the observed heavy rainfall (especially for high precipitation events, exceeding 20 mm in 24h) during both dry and wet periods. Assimilating ZTDs also improves the simulation of intense (>20 mm) convective precipitation during the time window of its occurrence in the dry season, and provides a beneficial influence during synoptic-scale events in the wet period. The above results, which are statistically significant, highlight an important positive impact of ZTD assimilation on the model’s precipitation forecast skill over Greece. Overall, the modelling system’s configuration, including the assimilation of ZTD observations, satisfactorily captures the spatial and temporal distribution of the observed rainfall and can therefore be used as the basis for examining further improvements in the future.
Drone-Based Optical Measurements of Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower Dams
Authors: Strelnikova Dariia , Paulus Gernot , Käfer Sabine , Anders Karl-Heinrich , Mayr Peter , Mader Helmut , Scherling Ulf , Schneeberger Rudi
Published: January 2020

In Austria, more than a half of all electricity is produced with the help of hydropower plants. To reduce their ecological impact, dams are being equipped with fish passages that support connectivity of habitats of riverine fish species, contributing to hydropower sustainability. The efficiency of fish passages is being constantly monitored and improved. Since the likelihood of fish passages to be discovered by fish depends, inter alia, on flow conditions near their entrances, these conditions have to be monitored as well. In this study, we employ large-scale particle image velocimetry (LSPIV) in seeded flow conditions to analyse images of the area near a fish passage entrance, captured with the help of a ready-to-fly consumer drone. We apply LSPIV to short image sequences and test different LSPIV interrogation area sizes and correlation methods. The study demonstrates that LSPIV based on ensemble correlation yields velocities that are in good agreement with the reference values regarding both magnitude and flow direction. Therefore, this non-intrusive methodology has a potential to be used for flow monitoring near fish passages on a regular basis, enabling timely reaction to undesired changes in flow conditions when possible.
Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images
Authors: Wróżyński Rafał , Pyszny Krzysztof , Sojka Mariusz
Published: January 2020

The study presents a new method for quantitative landscape assessment. The method uses LiDAR data and combines the potential of GIS (ArcGIS) and 3D graphics software (Blender). The developed method allows one to create Classified Digital Surface Models (CDSM), which are then used to create 360° panoramic images from the point of view of the observer. In order to quantify the landscape, 360° panoramic images were transformed to the Interrupted Sinusoidal Projection using G.Projector software. A quantitative landscape assessment is carried out automatically with the following landscape classes: ground, low, medium, and high vegetation, buildings, water, and sky according to the LiDAR 1.2 standard. The results of the analysis are presented quantitatively—the percentage distribution of landscape classes in the 360° field of view. In order to fully describe the landscape around the observer, graphs of little planets have been proposed to interpret the obtained results. The usefulness of the developed methodology, together with examples of its application and the way of presenting the results, is described. The proposed Quantitative Landscape Assessment method (QLA360) allows quantitative landscape assessment to be performed in the 360° field of view without the need to carry out field surveys. The QLA360 uses LiDAR American Society of Photogrammetry and Remote Sensing (ASPRS) classification standards, which allows one to avoid differences resulting from the use of different algorithms for classifying images in semantic segmentation. The most important advantages of the method are as follows: observer-independent, 360° field of view which simulates human perspective, automatic operation, scalability, and easy presentation and interpretation of results.
Urban Land-Cover Classification Using Side-View Information from Oblique Images
Authors: Xiao Changlin , Qin Rongjun , Ling Xiao
Published: January 2020

Land-cover classification on very high resolution data (decimetre-level) is a well-studied yet challenging problem in remote sensing data processing. Most of the existing works focus on using images with orthographic view or orthophotos with the associated digital surface models (DSMs). However, the use of the nowadays widely-available oblique images to support such a task is not sufficiently investigated. In the effort of identifying different land-cover classes, it is intuitive that information of side-views obtained from the oblique can be of great help, yet how this can be technically achieved is challenging due to the complex geometric association between the side and top views. We aim to address these challenges in this paper by proposing a framework with enhanced classification results, leveraging the use of orthophoto, digital surface models and oblique images. The proposed method contains a classic two-step of (1) feature extraction and (2) a classification approach, in which the key contribution is a feature extraction algorithm that performs simplified geometric association between top-view segments (from orthophoto) and side-view planes (from projected oblique images), and joint statistical feature extraction. Our experiment on five test sites showed that the side-view information could steadily improve the classification accuracy with both kinds of training samples (1.1% and 5.6% for evenly distributed and non-evenly distributed samples, separately). Additionally, by testing the classifier at a large and untrained site, adding side-view information showed a total of 26.2% accuracy improvement of the above-ground objects, which demonstrates the strong generalization ability of the side-view features.
Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants
Authors: Luan Xiali , Yu Zhaowu , Zhang Yuting , Wei Sheng , Miao Xinyu , Huang Zheng Y. X. , Teng Shuqing N. , Xu Chi
Published: January 2020

Urban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-specificity remain largely lacking. Here, we selected 32 major Chinese cities as cases and used Landsat 8 images to retrieve land surface temperature (LST) and quantify natural surface variables using point of interest (POI) data as a measure of the human activity variable and using multiple regression and relative weight analysis to study the contribution and relative importance of these factors to LST at a range of grain sizes (0.25–5 km) and spatial extents (20–60 km). We revealed that the contributions and relative importance of natural surfaces and human activities are largely scale-dependent and system-specific. Natural surfaces, especially vegetation cover, are often the most important UHI determinants for a majority of scales, but the importance of non-surface human activities is increasingly pronounced at a coarser spatial scale with respect to both grain and spatial extent. The scaling relations of the UHI determinants and their relative importance were mostly linear-like at the city-collective level, but highly diverse across individual cities, so reducing non-surface heat emissions could be the most effective measure in particular cases, especially at relatively large spatial scales. This study advances the understanding of UHI formation mechanisms and highlights the complexity of the scale issue underpinning the UHI effect.
Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China
Authors: Wang Shuai , Gao Jinhu , Zhuang Qianlai , Lu Yuanyuan , Gu Hanlong , Jin Xinxin
Published: January 2020

Accurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study, based on 12 environmental variables of multispectral remote sensing, topography and climate and 236 soil sampling data, three different boosted regression tree (BRT) models were compared to obtain the most accurate map of SOC stocks covering the forest area of Lvshun District in the Northeast China. Four validation indexes, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Lin’s concordance correlation coefficient (LCCC) were calculated to evaluate the performance of the three models. The results showed that the full variable model performed the best, except the model using multispectral remote sensing variables. In the full variable model, the regional SOC stocks are primarily determined by multispectral remote sensing variables, followed by topographic and climatic variables, with the relative importance of variables in the model being 63%, 28%, and 9%, respectively. The average prediction results of full variables model and only multispectral remote sensing variables model were 8.99 and 9.32 kg m−2, respectively. Our results indicated that there is a strong dependence of SOC stocks on multispectral remote sensing data when forest ecosystems have dense natural vegetation. Our study suggests that the multispectral remote sensing variables should be used to map SOC stocks of forest ecosystems in our study region.
PolSAR Image Classification with Lightweight 3D Convolutional Networks
Authors: Dong Hongwei , Zhang Lamei , Zou Bin
Published: January 2020

Convolutional neural networks (CNNs) have become the state-of-the-art in optical image processing. Recently, CNNs have been used in polarimetric synthetic aperture radar (PolSAR) image classification and obtained promising results. Unlike optical images, the unique phase information of PolSAR data expresses the structure information of objects. This special data representation makes 3D convolution which explicitly modeling the relationship between polarimetric channels perform better in the task of PolSAR image classification. However, the development of deep 3D-CNNs will cause a huge number of model parameters and expensive computational costs, which not only leads to the decrease of the interpretation speed during testing, but also greatly increases the risk of over-fitting. To alleviate this problem, a lightweight 3D-CNN framework that compresses 3D-CNNs from two aspects is proposed in this paper. Lightweight convolution operations, i.e., pseudo-3D and 3D-depthwise separable convolutions, are considered as low-latency replacements for vanilla 3D convolution. Further, fully connected layers are replaced by global average pooling to reduce the number of model parameters so as to save the memory. Under the specific classification task, the proposed methods can reduce up to 69.83% of the model parameters in convolution layers of the 3D-CNN as well as almost all the model parameters in fully connected layers, which ensures the fast PolSAR interpretation. Experiments on three PolSAR benchmark datasets, i.e., AIRSAR Flevoland, ESAR Oberpfaffenhofen, EMISAR Foulum, show that the proposed lightweight architectures can not only maintain but also slightly improve the accuracy under various criteria.
The Development of a Two-Step Merging and Downscaling Method for Satellite Precipitation Products
Authors: Lu Xinyu , Tang Guoqiang , Wang Xiuqin , Liu Yan , Wei Ming , Zhang Yingxin
Published: January 2020

Low accuracy and coarse spatial resolution are the two main drawbacks of satellite precipitation products. Therefore, calibration and downscaling are necessary before these products are applied. This study proposes a two-step framework to improve the accuracy of satellite precipitation estimates. The first step is data merging based on optimum interpolation (OI), and the second step is downscaling based on geographically weighted regression (GWR); therefore, the framework is called OI-GWR. An Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) product is used to demonstrate the effectiveness of OI-GWR in the Tianshan Mountains, China. First, the original IMERG precipitation data (OIMERG) are merged with rain gauge data using the OI method to produce corrected IMERG precipitation data (CIMERG). Then, using CIMERG as the first guess and the normalized difference vegetation index (NDVI) as the auxiliary variable, GWR is utilized for spatial downscaling. The two-step OI-GWR method is compared with several traditional methods, including GWR downscaling (Ori_GWR) and spline interpolation. The cross-validation results show that (1) the OI method noticeably improves the accuracy of OIMERG, and (2) the 1-km downscaled data obtained using OI-GWR are much better than those obtained from Ori_GWR, spline interpolation, and OIMERG. The proposed OI-GWR method can contribute to the development of high-resolution and high-accuracy regional precipitation datasets. However, it should be noted that the method proposed in this study cannot be applied in regions without any meteorological stations. In addition, further efforts will be needed to achieve daily- or hourly-scale downscaling of precipitation.
Dynamic Inversion of Inland Aquaculture Water Quality Based on UAVs-WSN Spectral Analysis
Authors: Wang Linhui , Yue Xuejun , Wang Huihui , Ling Kangjie , Liu Yongxin , Wang Jian , Hong Jinbao , Pen Wen , Song Houbing
Published: January 2020

The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and susceptible to pollution, with poor self-purification capacity. Considering its low cost and large-scale monitoring ability, many researches have developed spectrum sensor on-board satellite platforms to allow remote monitoring of inland water surface. However, there remain many problems, such as low image resolution, poor flexible data acquisition, and anti-interference. Apart from that, the conventional forecasting model is of weak generalization ability and low accuracy. In our study, we combine unmanned aerial vehicles system (UAVs) with the wireless sensor network (WSN) to design a new ground water quality parameter and drone spectrum information acquisition approach, and to propose a novel dynamic network surgery-deep neural networks (DNS-DNNs) model based on multi-source feature fusion to forecast the distribution of dissolved oxygen (DO) and turbidity (TUB) in inland aquaculture areas. The result of using fused features, including characteristic spectrum, Gray-level co-occurrence matrix (GLCM) texture feature, and convolutional neural network (CNN) texture feature to build a model is that the characteristic spectrum+ CNN texture fusion features were the best input items for DNS-DNNs when forecasting DO, with the determination coefficient R 2 of the vertical set arriving at 0.8741, while the characteristic spectrum+ GLCM texture+ CNN texture fusion features were the best for TUB, with the R 2 reaching 0.8531. Compared with a variety of conventional models, our model had a better performance in the inversion of DO and TUB, and there was a strong correlation between predicted and real values: R 2 reached 0.8042 and 0.8346, whereas the root mean square error (RMSE) were only 0.1907 and 0.1794, separately. Our study provides a new insight about using remote sensing to rapidly monitor water quality in inland aquaculture regions.
Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes
Authors: Jurjević Luka , Gašparović Mateo , Milas Anita Simic , Balenović Ivan
Published: January 2020

The quality and accuracy of Unmanned Aerial System (UAS) products greatly depend on the methods used to define image orientations before they are used to create 3D point clouds. While most studies were conducted in non- or partially-forested areas, a limited number of studies have evaluated the spatial accuracy of UAS products derived by using different image block orientation methods in forested areas. In this study, three image orientation methods were used and compared: (a) the Indirect Sensor Orientation (InSO) method with five irregularly distributed Ground Control Points (GCPs); (b) the Global Navigation Satellite System supported Sensor Orientation (GNSS-SO) method using non-Post-Processed Kinematic (PPK) single-frequency carrier-phase GNSS data (GNSS-SO1); and (c) using PPK dual-frequency carrier-phase GNSS data (GNSS-SO2). The effect of the three methods on the accuracy of plot-level estimates of Lorey’s mean height (HL) was tested over the mixed, even-aged pedunculate oak forests of Pokupsko basin located in Central Croatia, and validated using field validation across independent sample plots (HV), and leave-one-out cross-validation (LOOCV). The GNSS-SO2 method produced the HL estimates of the highest accuracy (RMSE%: HV = 5.18%, LOOCV = 4.06%), followed by the GNSS-SO1 method (RMSE%: HV = 5.34%, LOOCV = 4.37%), while the lowest accuracy was achieved by the InSO method (RMSE%: HV = 5.55%, LOOCV = 4.84%). The negligible differences in the performances of the regression models suggested that the selected image orientation methods had no considerable effect on the estimation of HL. The GCPs, as well as the high image overlaps, contributed considerably to the block stability and accuracy of image orientation in the InSO method. Additional slight improvements were achieved by replacing single-frequency GNSS measurements with dual-frequency GNSS measurements and by incorporating PPK into the GNSS-SO2 method.
TextRS: Deep Bidirectional Triplet Network for Matching Text to Remote Sensing Images
Authors: Abdullah Taghreed , Bazi Yakoub , Al Rahhal Mohamad M. , Mekhalfi Mohamed L. , Rangarajan Lalitha , Zuair Mansour
Published: January 2020

Exploring the relevance between images and their respective natural language descriptions, due to its paramount importance, is regarded as the next frontier in the general computer vision literature. Thus, recently several works have attempted to map visual attributes onto their corresponding textual tenor with certain success. However, this line of research has not been widespread in the remote sensing community. On this point, our contribution is three-pronged. First, we construct a new dataset for text-image matching tasks, termed TextRS, by collecting images from four well-known different scene datasets, namely AID, Merced, PatternNet, and NWPU datasets. Each image is annotated by five different sentences. All the five sentences were allocated by five people to evidence the diversity. Second, we put forth a novel Deep Bidirectional Triplet Network (DBTN) for text to image matching. Unlike traditional remote sensing image-to-image retrieval, our paradigm seeks to carry out the retrieval by matching text to image representations. To achieve that, we propose to learn a bidirectional triplet network, which is composed of Long Short Term Memory network (LSTM) and pre-trained Convolutional Neural Networks (CNNs) based on (EfficientNet-B2, ResNet-50, Inception-v3, and VGG16). Third, we top the proposed architecture with an average fusion strategy to fuse the features pertaining to the five image sentences, which enables learning of more robust embedding. The performances of the method expressed in terms Recall@K representing the presence of the relevant image among the top K retrieved images to the query text shows promising results as it yields 17.20%, 51.39%, and 73.02% for K = 1, 5, and 10, respectively.
Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars
Authors: Berry Paul , Nguyen Ngoc Hung , Tran Hai-Tan
Published: January 2020

The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth and consequently enhances resolution. The case of unknown range offsets occurring between the radars’ range profiles due to incorrect temporal and spatial synchronisation between the radars was considered, and the use of both pruned orthogonal matching pursuit and refined l 1 -norm regularisation solvers was explored to estimate the offsets between the radars’ channels so as to attain the necessary coherence for combining their data. The proposed techniques were demonstrated and compared using simulated radar data.
A Distributed Image Compression Scheme for Energy Harvesting Wireless Multimedia Sensor Networks
Authors: Han Chong , Zhang Songtao , Zhang Biao , Zhou Jian , Sun Lijuan
Published: January 2020

As an emerging technology, edge computing will enable traditional sensor networks to be effective and motivate a series of new applications. Meanwhile, limited battery power directly affects the performance and survival time of sensor networks. As an extension application for traditional sensor networks, the energy consumption of Wireless Multimedia Sensor Networks (WMSNs) is more prominent. For the image compression and transmission in WMSNs, consider using solar energy as the replenishment of node energy; a distributed image compression scheme based on solar energy harvesting is proposed. Two level clustering management is adopted. The camera node-normal node cluster enables camera nodes to gather and send collected raw images to the corresponding normal nodes for compression, and the normal node cluster enables the normal nodes to send the compressed images to the corresponding cluster head node. The re-clustering and dynamic adjustment methods for normal nodes are proposed to adjust adaptively the operation mode in the working chain. Simulation results show that the proposed distributed image compression scheme can effectively balance the energy consumption of the network. Compared with the existing image transmission schemes, the proposed scheme can transmit more and higher quality images and ensure the survival of the network.
Helmert Variance Component Estimation for Multi-GNSS Relative Positioning
Authors: Li Mowen , Nie Wenfeng , Xu Tianhe , Rovira-Garcia Adria , Fang Zhenlong , Xu Guochang
Published: January 2020

The Multi-constellation Global Navigation Satellite System (Multi-GNSS) has become the standard implementation of high accuracy positioning and navigation applications. It is well known that the noise of code and phase measurements depend on GNSS constellation. Then, Helmert variance component estimation (HVCE) is usually used to adjust the contributions of different GNSS constellations by determining their individual variances of unit weight. However, HVCE requires a heavy computation load. In this study, the HVCE posterior weighting was employed to carry out a kinematic relative Multi-GNSS positioning experiment with six short-baselines from day of year (DoY) 171 to 200 in 2019. As a result, the HVCE posterior weighting strategy improved Multi-GNSS positioning accuracy by 20.5%, 15.7% and 13.2% in east-north-up (ENU) components, compared to an elevation-dependent (ED) priori weighting strategy. We observed that the weight proportion of both code and phase observations for each GNSS constellation were consistent during the entire 30 days, which indicates that the weight proportions of both code and phase observations are stable over a long period of time. It was also found that the quality of a phase observation is almost equivalent in each baseline and GNSS constellation, whereas that of a code observation is different. In order to reduce the time consumption of the HVCE method without sacrificing positioning accuracy, the stable variances of unit weights of both phase and code observations obtained over 30 days were averaged and then frozen as a priori information in the positioning experiment. The result demonstrated similar ENU improvements of 20.0%, 14.1% and 11.1% with respect to the ED method but saving 88% of the computation time of the HCVE strategy. Our study concludes with the observations that the frozen variances of unit weight (FVUW) could be applied to the positioning experiment for the next 30 days, that is, from DoY 201 to 230 in 2019, improving the positioning ENU accuracy of the ED method by 18.1%, 13.2% and 10.6%, indicating the effectiveness of the FVUW.
Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review
Authors: Weygers Ive , Kok Manon , Konings Marco , Hallez Hans , De Vroey Henri , Claeys Kurt
Published: January 2020

The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be applicable in a clinical setting, and to suggest future research directions. Studies within the PubMed, Web Of Science and EMBASE databases were screened for eligibility, based on the following inclusion criteria: (1) studies must include a methodological description of how kinematic variables were obtained for the lower limb, (2) kinematic data must have been acquired by means of IMUs, (3) studies must have validated the implemented method against a golden standard reference system. Information on study characteristics, signal processing characteristics and study results was assessed and discussed. This review shows that methods for lower limb joint kinematics are inherently application dependent. Sensor restrictions are generally compensated with biomechanically inspired assumptions and prior information. Awareness of the possible adaptations in the IMU-based kinematic estimates by incorporating such prior information and assumptions is necessary, before drawing clinical decisions. Future research should focus on alternative validation methods, subject-specific IMU-based biomechanical joint models and disturbed movement patterns in real-world settings.
Quick and Cost-Effective Estimation of Vitamin C in Multifruit Juices Using Voltammetric Methods
Authors: López-Pastor Jose-Antonio , Martínez-Sánchez Ascensión , Aznar-Poveda Juan , García-Sánchez Antonio-Javier , García-Haro Joan , Aguayo Encarnación
Published: January 2020

Ascorbic Acid (AA) is a natural and powerful water-soluble antioxidant associated with long-lasting food products. As time passes, the AA content in products sharply decreases, and they become increasingly degraded. There are several techniques to precisely quantify AA concentrations. However, most of them employ costly laboratory instruments, such as High-Performance Liquid Chromatography (HPLC) or complex electrochemical methods, which make unfeasible recurrent AA measurements along the entire supply chain. To address this issue, we contribute with an in-field and real-time voltammetric method, carried out with a low-cost, easy-to-use, and portable device. An unmodified Screen-Printed Electrode (SPE) is used together with the device to achieve short reading times. Our method has been extensively tested in two multifruit juices using three different SPEs. Calibration curves and Limit of Detection were derived for each SPE. Furthermore, periodic experiments were conducted to study the shelf life of juices under consideration. During the analysis, a set of assays for each SPE were implemented to determine the remaining AA amount per juice and compare it with that obtained using HPLC under the same conditions. Results revealed that our cost-effective device is fully comparable to the HPLC equipment, as long as the juice does not include certain interferents; a scenario also contemplated in this article.
FDIPP: False Data Injection Prevention Protocol for Smart Grid Distribution Systems
Authors: Hittini Hosam , Abdrabou Atef , Zhang Liren
Published: January 2020

In this paper, a false data injection prevention protocol (FDIPP) for smart grid distribution systems is proposed. The protocol is designed to work over a novel hierarchical communication network architecture that matches the distribution system hierarchy and its vast number of entities. The proposed protocol guarantees both system and data integrity via preventing packet injection, duplication, alteration, and rogue node access. Therefore, it prevents service disruption or damaging power network assets due to drawing the wrong conclusions about the current operating status of the power grid. Moreover, the impact of the FDIPP protocol on communication network performance is studied using intensive computer simulations. The simulation study shows that the proposed communication architecture is scalable and meets the packet delay requirements of inter-substation communication as mandated by IEC 61850-90-1 with a minimal packet loss while the security overhead of FDIPP is taken into account.
External Corrosion Detection of Oil Pipelines Using Fiber Optics
Authors: Vahdati Nader , Wang Xueting , Shiryayev Oleg , Rostron Paul , Yap Fook Fah
Published: January 2020

Oil flowlines, the first “pipeline” system connected to the wellhead, are pipelines that are 5 to 30.5 cm (two to twelve inches) in diameter, most susceptible to corrosion, and very difficult to inspect. Herein, an external corrosion detection sensor for oil and gas pipelines, consisting of a semicircular plastic strip, a flat dog-bone-shaped sacrificial metal plate made out of the same pipeline material, and an optical fiber with Fiber Bragg Grating (FBG) sensors, is described. In the actual application, multiple FBG optical fibers are attached to an oil and gas pipeline using straps or strips or very large hose clamps, and, every few meters, our proposed corrosion detection sensor will be glued to the FBG sensors. When the plastic parts are attached to the sacrificial metals, the plastic parts will be deformed and stressed; thus, placing the FBG sensors in tension. When corrosion is severe at any given pipeline location, the sacrificial metal at that location will corrode till failure and the tension strain is relieved at that FBG Sensor location, and therefore, a signal is detected at the interrogator. Herein, the external corrosion detection sensor and its design equations are described, and experimental results, verifying our theory, are presented.
Laser Ranging-Assisted Binocular Visual Sensor Tracking System
Authors: Wang Qilong , Zhang Yu , Shi Weichao , Nie Meng
Published: January 2020

Aimed at improving the low measurement accuracy of the binocular vision sensor along the optical axis in the process of target tracking, we proposed a method for auxiliary correction using a laser-ranging sensor in this paper. In the process of system measurement, limited to the mechanical performance of the two-dimensional turntable, the measurement value of a laser-ranging sensor is lagged. In this paper, the lag information is updated directly to solve the time delay. Moreover, in order to give full play to the advantages of binocular vision sensors and laser-ranging sensors in target tracking, federated filtering is used to improve the information utilization and measurement accuracy and to solve the estimated correlation. The experimental results show that the real-time and measurement accuracy of the laser ranging-assisted binocular visual-tracking system is improved by the direct update algorithm and the federal filtering algorithm. The results of this paper are significant for binocular vision sensors and laser-ranging sensors in engineering applications involving target tracking systems.
Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring
Authors: Zhang Ruicheng , Gao Chengfa , Pan Shuguo , Shang Rui
Published: January 2020

Real-time dynamic displacement and spectral response on the midspan of Jiangyin Bridge were calculated using Global Navigation Satellite System (GNSS) and a speedometer for the purpose of understanding the dynamic behavior and the temporal evolution of the bridge structure. Considering that the GNSS measurement noise is large and the velocity/acceleration sensors cannot measure the low-frequency displacement, the Variational Mode Decomposition (VMD) algorithm was used to extract the low-frequency displacement of GNSS. Then, the low-frequency displacement extracted from the GNSS time series and the high-frequency vibration calculated by speedometer were combined in this paper in order to obtain the high precision three-dimensional dynamic displacement of the bridge in real time. Simulation experiment and measured data show that the VMD algorithm could effectively resist the modal aliasing caused by noise and discontinuous signals compared with the commonly used Empirical Mode Decomposition (EMD) algorithm, which is guaranteed to get high-precision fusion data. Finally, the fused displacement results can identify high-frequency vibrations and low-frequency displacements of a mm level, which can be used to calculate the spectral characteristics of the bridge and provide reference to evaluate the dynamic and static loads, and the health status of the bridge in the full frequency domain and the full time domain.
A Gradient-Based Method for Robust Sensor Selection in Hypothesis Testing
Authors: Ma Ting , Qian Bo , Niu Dunbiao , Song Enbin , Shi Qingjiang
Published: January 2020

This paper considers the binary Gaussian distribution robust hypothesis testing under a Bayesian optimal criterion in the wireless sensor network (WSN). The distribution covariance matrix under each hypothesis is known, while the distribution mean vector under each hypothesis drifts in an ellipsoidal uncertainty set. Because of the limited bandwidth and energy, we aim at seeking a subset of p out of m sensors such that the best detection performance is achieved. In this setup, the minimax robust sensor selection problem is proposed to deal with the uncertainties of distribution means. Following a popular method, minimizing the maximum overall error probability with respect to the selection matrix can be approximated by maximizing the minimum Chernoff distance between the distributions of the selected measurements under null hypothesis and alternative hypothesis to be detected. Then, we utilize Danskin’s theorem to compute the gradient of the objective function of the converted maximization problem, and apply the orthogonal constraint-preserving gradient algorithm (OCPGA) to solve the relaxed maximization problem without 0/1 constraints. It is shown that the OCPGA can obtain a stationary point of the relaxed problem. Meanwhile, we provide the computational complexity of the OCPGA, which is much lower than that of the existing greedy algorithm. Finally, numerical simulations illustrate that, after the same projection and refinement phases, the OCPGA-based method can obtain better solutions than the greedy algorithm-based method but with up to 48.72 % shorter runtimes. Particularly, for small-scale problems, the OCPGA -based method is able to attain the globally optimal solution.
Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
Authors: Lwin May Thura , Yim Jinhyuk , Ko Young-Bae
Published: January 2020

As a trending and interesting research topic, in recent years, researchers have been adopting the blockchain in the wireless ad-hoc environment. Owing to its strong characteristics, such as consensus, immutability, finality, and provenance, the blockchain is utilized not only as a secure data storage for critical data but also as a platform that facilitates the trustless exchange of data between independent parties. However, the main challenge of blockchain application in an ad-hoc network is which kind of nodes should be involved in the validation process and how to adopt the heavy computational complexity of block validation appropriately while maintaining the genuine characteristics of a blockchain. In this paper, we propose the blockchain-based trust management system with a lightweight consensus algorithm in a mobile ad-hoc network (MANET). The proposed scheme provides the distributed trust framework for routing nodes in MANETs that is tamper-proof via blockchain. The optimized link state routing protocol (OLSR) is exploited as a representative protocol to embed the blockchain concept in MANETs. As a securely distributed and trusted platform, blockchain solves most of the security issues in the OLSR, in which every node is performing the security operation individually and in a repetitive manner. Additionally, using predefined principles, the routing nodes in the proposed scheme can collaborate to defend themselves from the attackers in the network. The experimental results show that the proposed consensus algorithm is suitable to be used in the resource-hungry MANET with reduced validation time and less overhead. Meanwhile, the attack detection overhead and time also decrease because the repetitivity of the process is reduced while providing a scalable and distributed trust among the routing nodes.
Food Security Sensing System Using a Waveguide Antenna Microwave Imaging through an Example of an Egg
Authors: Tai Tzu-Chun , Wu Hung-Wei , Hung Cheng-Yuan , Wang Yeong-Her
Published: January 2020

In this paper, we present a form of food security sensing using a waveguide antenna microwave imaging system through an example of an egg. A waveguide antenna system with a frequency range of 7–13 GHz and a maximum gain of 17.37 dBi was proposed. The maximum scanning area of the waveguide antenna microwave imaging sensing system is 30 × 30 cm2. In order to study the resolution and sensitivity of the waveguide antenna microwave imaging sensing system, the circular and triangular high-k materials (with the same thickness but with different dielectric constants of the materials) were used as the testing sample for observing the microwave images. By using the proposed waveguide antenna microwave imaging sensing system, the high-k materials with different dielectric constants and shapes could be easily sensed. Therefore, the waveguide antenna microwave imaging sensing system could be potentially used for applications in rapid, non-destructive food security sensing. Regarding the example of an egg, the proposed waveguide antenna microwave imaging sensing system could effectively identify the health status of many eggs very quickly. The proposed waveguide antenna microwave imaging sensing system provides a simple, non-destructive, effective, and rapid method for food security applications.
Framework for Structural Health Monitoring of Steel Bridges by Computer Vision
Authors: Marchewka Adam , Ziółkowski Patryk , Aguilar-Vidal Victor
Published: January 2020

The monitoring of a structural condition of steel bridges is an important issue. Good condition of infrastructure facilities ensures the safety and economic well-being of society. At the same time, due to the continuous development, rising wealth of the society and socio-economic integration of countries, the number of infrastructural objects is growing. Therefore, there is a need to introduce an easy-to-use and relatively low-cost method of bridge diagnostics. We can achieve these benefits by the use of Unmanned Aerial Vehicle-Based Remote Sensing and Digital Image Processing. In our study, we present a state-of-the-art framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability. The presented framework of image processing procedure is suitable for diagnostics of steel truss riveted bridges. In our considerations, we used photographic documentation of the Fitzpatrick Bridge located in Tallassee, Alabama, USA.
EAgLE: Equivalent Acoustic Level Estimator Proposal
Authors: Guarnaccia Claudio
Published: January 2020

Road infrastructures represent a key point in the development of smart cities. In any case, the environmental impact of road traffic should be carefully assessed. Acoustic noise is one of the most important issues to be monitored by means of sound level measurements. When a large measurement campaign is not possible, road traffic noise predictive models (RTNMs) can be used. Standard RTNMs present in literature usually require in input several information about the traffic, such as flows of vehicles, percentage of heavy vehicles, average speed, etc. Many times, the lack of information about this large set of inputs is a limitation to the application of predictive models on a large scale. In this paper, a new methodology, easy to be implemented in a sensor concept, based on video processing and object detection tools, is proposed: the Equivalent Acoustic Level Estimator (EAgLE). The input parameters of EAgLE are detected analyzing video images of the area under study. Once the number of vehicles, the typology (light or heavy vehicle), and the speeds are recorded, the sound power level of each vehicle is computed, according to the EU recommended standard model (CNOSSOS-EU), and the Sound Exposure Level (SEL) of each transit is estimated at the receiver. Finally, summing up the contributions of all the vehicles, the continuous equivalent level, Leq, on a given time range can be assessed. A preliminary test of the EAgLE technique is proposed in this paper on two sample measurements performed in proximity of an Italian highway. The results will show excellent performances in terms of agreement with the measured Leq and comparing with other RTNMs. These satisfying results, once confirmed by a larger validation test, will open the way to the development of a dedicated sensor, embedding the EAgLE model, with possible interesting applications in smart cities and road infrastructures monitoring. These sites, in fact, are often equipped (or can be equipped) with a network of monitoring video cameras for safety purposes or for fining/tolling, that, once the model is properly calibrated and validated, can be turned in a large scale network of noise estimators.