bluuebunny/crossref_metadata_2025 huggingface dataset:
Bibliometric Analysis of the Literature on Measuring Techniques for Manipulating Financial Statements
Authors: Safta Ioana Lavinia , Sabău (Popa) Andrada-Ioana , Muntean Neli
Published: July 2021

Creative accounting has its background since early studies in 1975, until the present time. It continues to be a subject of great interest for the companies and interested parties. Thus, the current paper will aim to answer the following proposed research questions: 1. Which are the most used methods for detecting the manipulation of financial statements in the literature? 2. Which are the terms that are most frequently encountered in the literature associated with “creative accounting? 3. Which are the journals that have the highest frequency of articles written on the topic “creative accounting”? 4. Over time, how did research evolve in the field of creative accounting? 5. Which countries are most preoccupied in publishing regarding this topic? To answer the research question 1, the models published in the literature for measuring manipulation techniques through creative accounting were reviewed and analyzed. For the remaining research questions, a bibliometric analysis for the publications in this area was performed. For collecting the sample, articles on this topic were selected from the international Web of Science database. Following this, a bibliometric analysis of the articles was performed, using the VOSviewer program. A total of 4045 publications on creative accounting were identified. Through the bibliometric analysis we have answered research question 2, by identifying the key words that have the closest proximity to creative accounting. To answer the remaining research questions, we identified the journals with the highest frequency of publication and the countries with the highest interest on the topic. It is especially important to evaluate the quality of this many research papers and to obtain valuable information.
Using Hybrid Artificial Intelligence and Evolutionary Optimization Algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices
Authors: Yoosefzadeh-Najafabadi Mohsen , Tulpan Dan , Eskandari Milad
Published: July 2021

Recent advanced high-throughput field phenotyping combined with sophisticated big data analysis methods have provided plant breeders with unprecedented tools for a better prediction of important agronomic traits, such as yield and fresh biomass (FBIO), at early growth stages. This study aimed to demonstrate the potential use of 35 selected hyperspectral vegetation indices (HVI), collected at the R5 growth stage, for predicting soybean seed yield and FBIO. Two artificial intelligence algorithms, ensemble-bagging (EB) and deep neural network (DNN), were used to predict soybean seed yield and FBIO using HVI. Considering HVI as input variables, the coefficients of determination (R2) of 0.76 and 0.77 for yield and 0.91 and 0.89 for FBIO were obtained using DNN and EB, respectively. In this study, we also used hybrid DNN-SPEA2 to estimate the optimum HVI values in soybeans with maximized yield and FBIO productions. In addition, to identify the most informative HVI in predicting yield and FBIO, the feature recursive elimination wrapper method was used and the top ranking HVI were determined to be associated with red, 670 nm and near-infrared, 800 nm, regions. Overall, this study introduced hybrid DNN-SPEA2 as a robust mathematical tool for optimizing and using informative HVI for estimating soybean seed yield and FBIO at early growth stages, which can be employed by soybean breeders for discriminating superior genotypes in large breeding populations.
MAMOTH: An Earth Observational Data-Driven Model for Mosquitoes Abundance Prediction
Authors: Tsantalidou Argyro , Parselia Elisavet , Arvanitakis George , Kyratzi Katerina , Gewehr Sandra , Vakali Athena , Kontoes Charalampos
Published: July 2021

Mosquito-Borne Diseases (MBDs) are known to be more prevalent in the tropics, and yet, in the last two decades, they are spreading to many other countries, especially in Europe. The set (volume) of environmental, meteorological and other spatio-temporally variable parameters affecting mosquito abundance makes the modeling and prediction tasks quite challenging. Up to now, mosquito abundance prediction problems were addressed with ad-hoc area-specific and genus-tailored approaches. We propose and develop MAMOTH, a generic and accurate Machine Learning model that predicts mosquito abundances for the upcoming period (the Mean Absolute Error of the predictions do not deviate more than 14%). The designed model relies on satellite Earth Observation and other in-situ geo-spatial data to tackle the problem. MAMOTH is not site- nor mosquito genus-dependent; thus, it can be easily replicated and applied to multiple cases without any special parametrization. The model was applied to different mosquito genus and species Culex spp. as potential vectors for West Nile Virus, Anopheles spp. for Malaria and Aedes albopictus for Zika/Chikungunya/Dengue) and in different areas of interest (Italy, Serbia, France, Germany). The results show that the model performs accurately and consistently for all case studies. Additionally, the evaluation of different cases, with the model using the same principles, provides an opportunity for multi-case and multi-scope comparative studies.
TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping
Authors: Yu Lei , Wu Haoyu , Zhong Zhi , Zheng Liying , Deng Qiuyue , Hu Haicheng
Published: July 2021

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.
DLR HySU—A Benchmark Dataset for Spectral Unmixing
Authors: Cerra Daniele , Pato Miguel , Alonso Kevin , Köhler Claas , Schneider Mathias , de los Reyes Raquel , Carmona Emiliano , Richter Rudolf , Kurz Franz , Reinartz Peter , Müller Rupert
Published: July 2021

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.
Signal Subspace Reconstruction for DOA Detection Using Quantum-Behaved Particle Swarm Optimization
Authors: Zhang Rui , Xu Kaijie , Quan Yinghui , Zhu Shengqi , Xing Mengdao
Published: July 2021

Spatial spectrum estimation, also known as direction of arrival (DOA) detection, is a popular issue in many fields, including remote sensing, radar, communication, sonar, seismic exploration, radio astronomy, and biomedical engineering. MUltiple SIgnal Classification (MUSIC) and Estimation Signal Parameter via Rotational Invariance Technique (ESPRIT), which are well-known for their high-resolution capability for detecting DOA, are two examples of an eigen-subspace algorithm. However, missed detection and estimation accuracy reduction often occur due to the low signal-to-noise ratio (SNR) and snapshot deficiency (small time-domain samples of the observed signal), especially for sources with different SNRs. To avoid the above problems, in this study, we develop a DOA detection approach through signal subspace reconstruction using Quantum-Behaved Particle Swarm Optimization (QPSO). In the developed scheme, according to received data, a noise subspace is established through performing an eigen-decomposition operation on a sampling covariance matrix. Then, a collection of angles randomly selected from the observation space are used to build a potential signal subspace on the basis of the steering matrix of the array. Afterwards, making use of the fact that the signal space is orthogonal to the noise subspace, a cost function, which contains the desired DOA information, is designed. Thus, the problem of capturing the DOA information can be transformed into the optimization of the already constructed cost function. In this respect, the DOA finding of multiple signal sources—that is, the multi-objective optimization problem—can be regarded as a single objective optimization problem, which can effectively reduce the probability of missed detection of the signals. Subsequently, the QPSO is employed to determine an optimal signal subspace by minimizing the orthogonality error so as to obtain the DOA. Ultimately, the performance of DOA detection is improved. An explicit analysis and derivation of the developed scheme are provided. The results of computer simulation show that the proposed scheme has superior estimation performance when detecting signals with very different SNR levels and small snapshots.
Erratum: Schrader-Patton, C.C.; Underwood, E.C. New Biomass Estimates for Chaparral-Dominated Southern California Landscapes. Remote Sens. 2021, 13, 1581
Authors: Schrader-Patton Charlie C. , Underwood Emma C.
Published: July 2021

The authors wish to make the following corrections to this paper [...]
Tar Spot Disease Quantification Using Unmanned Aircraft Systems (UAS) Data
Authors: Oh Sungchan , Lee Da-Young , Gongora-Canul Carlos , Ashapure Akash , Carpenter Joshua , Cruz A. P. , Fernandez-Campos Mariela , Lane Brenden Z. , Telenko Darcy E. P. , Jung Jinha , Cruz C. D.
Published: July 2021

Tar spot is a foliar disease of corn characterized by fungal fruiting bodies that resemble tar spots. The disease emerged in the U.S. in 2015, and severe outbreaks in 2018 caused an economic impact on corn yields throughout the Midwest. Adequate epidemiological surveillance and disease quantification are necessary to develop immediate and long-term management strategies. This study presents a measurement framework that evaluates the disease severity of tar spot using unmanned aircraft systems (UAS)-based plant phenotyping and regression techniques. UAS-based plant phenotypic information, such as canopy cover, canopy volume, and vegetation indices, were used as explanatory variables. Visual estimations of disease severity were performed by expert plant pathologists per experiment plot basis and used as response variables. Three regression methods, namely ordinary least squares (OLS), support vector regression (SVR), and multilayer perceptron (MLP), were used to determine an optimal regression method for UAS-based tar spot measurement. The cross-validation results showed that the regression model based on MLP provides the highest accuracy of disease measurements. By training and testing the model with spatially separated datasets, the proposed regression model achieved a Lin’s concordance correlation coefficient (ρc) of 0.82 and a root mean square error (RMSE) of 6.42. This study demonstrated that we could use the proposed UAS-based method for the disease quantification of tar spot, which shows a gradual spectral response as the disease develops.
Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem
Authors: Azevedo Olivia , Parker Thomas C. , Siewert Matthias B. , Subke Jens-Arne
Published: July 2021

Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e3.508 x, p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis.
Assessing Repeatability and Reproducibility of Structure-from-Motion Photogrammetry for 3D Terrain Mapping of Riverbeds
Authors: De Marco Jessica , Maset Eleonora , Cucchiaro Sara , Beinat Alberto , Cazorzi Federico
Published: July 2021

Structure-from-Motion (SfM) photogrammetry is increasingly employed in geomorphological applications for change detection, but repeatability and reproducibility of this methodology are still insufficiently documented. This work aims to evaluate the influence of different survey acquisition and processing conditions, including the camera used for image collection, the number of Ground Control Points (GCPs) employed during Bundle Adjustment, GCP coordinate precision and Unmanned Aerial Vehicle flight mode. The investigation was carried out over three fluvial study areas characterized by distinct morphology, performing multiple flights consecutively and assessing possible differences among the resulting 3D models. We evaluated both residuals on check points and discrepancies between dense point clouds. Analyzing these metrics, we noticed high repeatability (Root Mean Square of signed cloud-to-cloud distances less than 2.1 cm) for surveys carried out under the same conditions. By varying the camera used, instead, contrasting results were obtained that appear to depend on the study site characteristics. In particular, lower reproducibility was highlighted for the surveys involving an area characterized by flat topography and homogeneous texturing. Moreover, this study confirms the importance of the number of GCPs entering in the processing workflow, with different impact depending on the camera used for the survey.
Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints
Authors: Touzani Samir , Granderson Jessica
Published: July 2021

Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.
Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve
Authors: Zagajewski Bogdan , Kluczek Marcin , Raczko Edwin , Njegovec Ajda , Dabija Anca , Kycko Marlena
Published: July 2021

Mountain forests are exposed to extreme conditions (e.g., strong winds and intense solar radiation) and various types of damage by insects such as bark beetles, which makes them very sensitive to climatic changes. Therefore, continuous monitoring is crucial, and remote-sensing techniques allow the monitoring of transboundary areas where a common policy is needed to protect and monitor the environment. In this study, we used Sentinel-2 and Landsat 8 open data to assess the forest stands classification of the UNESCO Krkonoše/Karkonosze Transboundary Biosphere Reserve, which is undergoing dynamic changes in recovering woodland vegetation due to an ecological disaster that led to damage and death of a large portion of the forests. Currently, in this protected area, dry big trunks and branches coexist with naturally occurring young forests. This heterogeneity generates mixes, which hinders the automation of classification. Thus, we used three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—to classify dominant tree species (birch, beech, larch and spruce). The best results were obtained for the SVM RBF classifier, which offered an average median F1-score that oscillated around 67.2–91.5% depending on the species. The obtained maps, which were based on multispectral satellite images, were also compared with classifications made for the same area on the basis of hyperspectral APEX imagery (288 spectral bands with three-meter resolution), indicating high convergence in the recognition of woody species.
An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data
Authors: Bazzi Hassan , Baghdadi Nicolas , Amin Ghaith , Fayad Ibrahim , Zribi Mehrez , Demarez Valérie , Belhouchette Hatem
Published: July 2021

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).
Secure Content Distribution with Access Control Enforcement in Named Data Networking
Authors: Htet Hlaing Htet , Funamoto Yuki , Mambo Masahiro
Published: July 2021

NDN is one of the new emerging future internet architectures which brings up new solutions over today’s internet architecture, facilitating content distribution, in-network caching, mobility support, and multicast forwarding. NDNs ubiquitous in-network caching allows consumers to access data directly from the intermediate router’s cache. However, it opens content privacy problems since data packets replicated in the router are always accessible by every consumer. Sensitive contents in the routers should be protected and accessed only by authorized consumers. Although the content protection problem can be solved by applying an encryption-based access control policy, it still needs an efficient content distribution scheme with lower computational overhead and content retrieval time. We propose an efficient and secure content distribution (ES_CD), by combining symmetric encryption and identity-based proxy re-encryption. The analysis shows that our proposed scheme achieves content retrieval time reduction up to 20% for the cached contents in our network simulation environment and a slight computational overhead of less than 19 ms at the content producer and 9 ms at the consumer for 2 KB content. ES_CD provides content confidentiality and ensures only legitimate consumers can access the contents during a predefined time without requiring a trusted third party and keeping the content producer always online.
Pose Estimation of Excavator Manipulator Based on Monocular Vision Marker System
Authors: Zhao Jiangying , Hu Yongbiao , Tian Mingrui
Published: July 2021

Excavation is one of the broadest activities in the construction industry, often affected by safety and productivity. To address these problems, it is necessary for construction sites to automatically monitor the poses of excavator manipulators in real time. Based on computer vision (CV) technology, an approach, through a monocular camera and marker, was proposed to estimate the pose parameters (including orientation and position) of the excavator manipulator. To simulate the pose estimation process, a measurement system was established with a common camera and marker. Through comprehensive experiments and error analysis, this approach showed that the maximum detectable depth of the system is greater than 11 m, the orientation error is less than 8.5°, and the position error is less than 22 mm. A prototype of the system that proved the feasibility of the proposed method was tested. Furthermore, this study provides an alternative CV technology for monitoring construction machines.
Numerical Analysis of Protection Method of Metallic Sub-Wavelength Concentric Arrays for Radially Polarized Light Selection and Its Applications
Authors: Kim Hyuntai
Published: July 2021

Radially polarized light has various advantages on sensing, thanks for its symmetric field distribution. To select radial component, metallic sub-wavelength concentric arrays are widely used. To increase the stability of the metallic nanostructure from mechanical or chemical hazards, a method to apply an additional protective layer has been proposed. The structure was numerically calculated, and optimized structure showed ~97.4% of transmittance for radially polarized component with ~20 dB of polarization extinction ratio compared to the azimuthally polarized component. This result is a 22% increase compared to the case without the protective layer. In addition, the utility the protective layer applied to metallic sub-wavelength concentric arrays is also discussed. The structure has been applied to a binary, concentric optical plate, and showed the same function with radially polarized input, but prohibited azimuthally polarized input. The proposed structure is expected to be applied on numerous centrosymmetric flat optical components.
A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
Authors: Huo Lihuan , Bai Rulong , Jiang Man , Chen Bing , Chen Jianfeng , Huang Penghui , Liao Guisheng
Published: July 2021

With the increase in satellite communication interference, the tri-satellite time difference of arrival (TDOA) localization technique, which is an effective method to determine the location of the interference using sensors or antennas, has been developed rapidly. The location of the interference source is determined through the intersection of the TDOA lines of position (LOP). However, when the two TDOA LOP have two mirrored intersection points, it is theoretically difficult to determine the real location. Aiming at this problem, a method for eliminating mirrored location based on multiple moment TDOA is proposed in this paper. First, the TDOA results are measured at multiple moments using the cross-ambiguity function (CAF), and the localization equation set is established based on the World Geodetic System (WGS)-84 earth ellipsoid model. Then, the initial location result can be obtained by solving the equation set through the Newton iteration method. Finally, the high-precision location result after eliminating the mirrored location is obtained after the single moment localization based on the initial location. Simulation experiments and real measured data processing results verify the effectiveness of the proposed method. It still has good robustness under the condition of large measurement errors and deviations from the prior initial values.
Validation of a Low-Cost Electrocardiography (ECG) System for Psychophysiological Research
Authors: Wagner Ruth Erna , Plácido da Silva Hugo , Gramann Klaus
Published: July 2021

Background and Objective: The reliability of low-cost mobile systems for recording Electrocardiographic (ECG) data is mostly unknown, posing questions regarding the quality of the recorded data and the validity of the extracted physiological parameters. The present study compared the BITalino toolkit with an established medical-grade ECG system (BrainAmp-ExG). Methods: Participants underwent simultaneous ECG recordings with the two instruments while watching pleasant and unpleasant pictures of the “International Affective Picture System” (IAPS). Common ECG parameters were extracted and compared between the two systems. The Intraclass Correlation Coefficients (ICCs) and the Bland–Altman Limits of Agreement (LoA) method served as criteria for measurement agreement. Results: All but one parameter showed an excellent agreement (>80%) between both devices in the ICC analysis. No criteria for Bland–Altman LoA and bias were found in the literature regarding ECG parameters. Conclusion: The results of the ICC and Bland–Altman methods demonstrate that the BITalino system can be considered as an equivalent recording device for stationary ECG recordings in psychophysiological experiments.
Sensitive Planar Microwave Diode on the Base of Ternary AlxGa1-xAs Semiconductor Compound
Authors: Anbinderis Maksimas , Ašmontas Steponas , Čerškus Aurimas , Gradauskas Jonas , Lučun Andžej , Šilėnas Aldis , Sužiedėlis Algirdas
Published: July 2021

The article presents the results of experimental studies of the dc and high-frequency electrical characteristics of planar microwave diodes that are fabricated on the base of the n-AlxGa1-xAs layer (x = 0, 0.15 or 0.3), epitaxially grown on a semi-insulating GaAs substrate. The diodes can serve as reliable and inexpensive sensors of microwave radiation in the millimeter wavelength range; they sense electromagnetic radiation directly, without any external bias voltage at room temperature. The investigation revealed a strong dependence of the detection properties of the microwave diodes on AlAs mole fraction x: in the Ka microwave frequency range, the median value of voltage responsivity is several volts per watt in the case of GaAs-based diodes (x = 0), and it substantially increases, reaching hundreds of volts per watt at higher x values. Also, a model enabling us to forecast the responsivity of the sensor in other frequency ranges is proposed.
A Low-Cost, Point-of-Care Test for Confirmation of Nasogastric Tube Placement via Magnetic Field Tracking
Authors: Miyasaka Muneaki , Li Hao , Tay Kon Voi , Phee Soo Jay
Published: July 2021

In this work, we aim to achieve low-cost real-time tracking for nasogastric tube (NGT) insertion by using a tracking method based on two magnetic sensors. Currently, some electromagnetic (EM) tracking systems used to detect the misinsertion of the NGT are commercially available. While the EM tracking systems can be advantageous over the other conventional methods to confirm the NGT position, their high costs are a factor hindering such systems from wider acceptance in the clinical community. In our approach, a pair of magnetic sensors are used to estimate the location of a permanent magnet embedded at the tip of the NGT. As the cost of the magnet and magnetic sensors is low, the total cost of the system can be less than one-tenth of that of the EM tracking systems. The experimental results exhibited that tracking can be achieved with a root mean square error (RMSE) of 2–5 mm and indicated a great potential for use as a point-of-care test for NGT insertion, to avoid misplacement into the lung and ensure correct placement in the stomach.
A Discrete-Time Extended Kalman Filter Approach Tailored for Multibody Models: State-Input Estimation
Authors: Adduci Rocco , Vermaut Martijn , Naets Frank , Croes Jan , Desmet Wim
Published: July 2021

Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors.
Reasons and Strategies for Privacy Features in Tracking and Tracing Systems—A Systematic Literature Review
Authors: Jandl Christian , Wagner Markus , Moser Thomas , Schlund Sebastian
Published: July 2021

In the course of the digitization of production facilities, tracking and tracing of assets in the supply chain is becoming increasingly relevant for the manufacturing industry. The collection and use of real-time position data of logistics, tools and load carriers are already standard procedure in entire branches of the industry today. In addition to asset tracking, the technologies used also offer new possibilities for collecting and evaluating position and biometric data of employees. Thus, these technologies can be used for monitoring performance or for tracking worker behaviour, which can lead to additional burdens and stress for employees. In this context, the collection and evaluation of employee data can influence the workplace of the affected employee in the company to his or her disadvantage. The approach of Privacy by Design can help to benefit from all the advantages of these systems, while ensuring that the impact on employee privacy is kept to a minimum. Currently, there is no survey available that reviews tracking and tracing systems supporting this important and emerging field. This work provides a systematic overview from the perspective of the impact on employee privacy. Additionally, this paper identifies and evaluates the techniques used with regard to employee privacy in industrial tracking and tracing systems. This helps to reveal new privacy preserving techniques that are currently underrepresented, therefore enabling new research opportunities in the industrial community.
Data Augmentation of Automotive LIDAR Point Clouds under Adverse Weather Situations
Authors: Vargas Rivero Jose Roberto , Gerbich Thiemo , Buschardt Boris , Chen Jia
Published: July 2021

In contrast to previous works on data augmentation using LIDAR (Light Detection and Ranging), which mostly consider point clouds under good weather conditions, this paper uses point clouds which are affected by spray. Spray water can be a cause of phantom braking and understanding how to handle the extra detections caused by it is an important step in the development of ADAS (Advanced Driver Assistance Systems)/AV (Autonomous Vehicles) functions. The extra detections caused by spray cannot be safely removed without considering cases in which real solid objects may be present in the same region in which the detections caused by spray take place. As collecting real examples would be extremely difficult, the use of synthetic data is proposed. Real scenes are reconstructed virtually with an added extra object in the spray region, in a way that the detections caused by this obstacle match the characteristics a real object in the same position would have regarding intensity, echo number and occlusion. The detections generated by the obstacle are then used to augment the real data, obtaining, after occlusion effects are added, a good approximation of the desired training data. This data is used to train a classifier achieving an average F-Score of 92. The performance of the classifier is analyzed in detail based on the characteristics of the synthetic object: size, position, reflection, duration. The proposed method can be easily expanded to different kinds of obstacles and classifier types.
A Combined Model and Data-Driven Approach for the Determination of Rotor Temperature in an Induction Machine
Authors: Mocanu Razvan , Onea Alexandru , Dosoftei Constantin Catalin
Published: July 2021

The need for protection of electrical machines comes as a demand of safety regulations in the automotive industry as well as a result of the general desire to obtain a robust and reliable electric powertrain. This paper introduces a hybrid method for estimating the temperature of the rotor of an Induction Machine (IM) based on a Nonlinear Autoregressive Network with Exogenous inputs (NARX) used as a prediction function within a particle filter. The temperature of the stator case is measured, and the information is used as an input to a NARX network and as a variable to a thermal process with first-order dynamics which serves as an observation function. Uncertainties of the NARX and thermal model are determined and used to correct the posterior estimate. Experimental data are used from a real IM test-bench and the results prove the applicability and good performance.
Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks
Authors: Cai Meng , Luo Han , Meng Xiao , Cui Ying
Published: July 2021

The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective of network, this study aims to explore the topic and emotion propagation mechanism, as well as the interaction and communication relations of the public in social networks under four types of emergencies, including public health events, accidents and disasters, social security events, and natural disasters. Event topics were identified by Word2vec and K-means clustering. The biLSTM model was used to identify emotion in posts. The propagation maps of topic and emotion were presented visually on the network, and the synergistic relationship between topic and emotion propagation as well as the communication characteristics of multiple subjects were analyzed. The results show that there were similarities and differences in the propagation mechanism of topic and emotion in different types of emergencies. There was a positive correlation between topic and emotion of different types of users in social networks in emergencies. Users with a high level of topic influence were often accompanied by a high level of emotion appeal.
Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting
Authors: Aliansyah Zulhaj , Shimasaki Kohei , Senoo Taku , Ishii Idaku , Umemoto Shuji
Published: July 2021

Vision-based structural displacement methods allow convenient monitoring of civil structures such as bridges, though they are often limited due to the small number of measurement points, constrained spatial resolution, and inability to identify the acting forces of the measured displacement. To increase the number of measurement points in vision-based bridge displacement measurement, this study introduces a front-view tandem marker motion capture system with side-view traffic counting to identify the force-inducing passing vehicles on the bridge’s deck. The proposed system was able to measure structural displacement at submillimeter resolution on eight measurement points at once at a distance of 40.8–64.2 m from a front-view camera. The traffic counting system with a side-view camera recorded the passing vehicles from two opposing lanes. We conducted a 35-min experiment for a 25 m-span steel road bridge with hundreds of cars passing over it and confirmed dynamic displacement distributions with amplitudes of several millimeters when large vehicles passed.
Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units
Authors: Chambino Luis Lopes , Silva José Silvestre , Bernardino Alexandre
Published: July 2021

Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed. A domain-specific transfer-learning methodology applied to a deep neural network pre-trained in RGB images is shown to generalize well to the multispectral domain. We also propose a skin detector module for forgery detection. Several experiments were planned to assess the performance of our methods. First, we evaluate the performance of the forgery detection module using face masks and coverings of different materials. A second study was carried out with the objective of tuning the parameters of our domain-specific transfer-learning methodology, in particular which layers of the pre-trained network should be retrained to obtain good adaptation to multispectral images. A third study was conducted to evaluate the performance of support vector machines (SVM) and k-nearest neighbor classifiers using the embeddings obtained from the trained neural network. Finally, we compare the proposed method with other state-of-the-art approaches. The experimental results show performance improvements in the Tufts and CASIA NIR-VIS 2.0 multispectral databases, with a rank-1 score of 99.7% and 99.8%, respectively.
Infrared Small Target Detection Method with Trajectory Correction Fuze Based on Infrared Image Sensor
Authors: Zhang Cong , Li Dongguang , Qi Jiashuo , Liu Jingtao , Wang Yu
Published: July 2021

Due to the complexity of background and diversity of small targets, robust detection of infrared small targets for the trajectory correction fuze has become a challenge. To solve this problem, different from the traditional method, a state-of-the-art detection method based on density-distance space is proposed to apply to the trajectory correction fuze. First, parameters of the infrared image sensor on the fuze are calculated to set the boundary limitations for the target detection method. Second, the density-distance space method is proposed to detect the candidate targets. Finally, the adaptive pixel growth (APG) algorithm is used to suppress the clutter so as to detect the real targets. Three experiments, including equivalent detection, simulation and hardware-in-loop, were implemented to verify the effectiveness of this method. Results illustrated that the infrared image sensor on the fuze has a stable field of view under rotation of the projectile, and could clearly observe the infrared small target. The proposed method has superior anti-noise, different size target detection, multi-target detection and various clutter suppression capability. Compared with six novel algorithms, our algorithm shows a perfect detection performance and acceptable time consumption.
An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing
Authors: Ali Abid , Iqbal Muhammad Munawar , Jamil Harun , Qayyum Faiza , Jabbar Sohail , Cheikhrouhou Omar , Baz Mohammed , Jamil Faisal
Published: July 2021

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.
Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester
Authors: Lima Jeovano de Jesus Alves de , Maldaner Leonardo Felipe , Molin José Paulo
Published: July 2021

Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy. A harvester was experimentally instrumented, and neural network models integrated sensor data along the harvester to perform the self-calibration of these sensors and estimate the mass flow. Nonlinear autoregressive networks with exogenous input (NARX) and multiple linear regression (MLR) models were compared to predict the mass flow. The prediction with the NARX showed a significant superiority over MLR. MLR decreases the estimated mass flow variability in the harvester. NARX with multi-sensor data has an RMSE of 0.3 kg s−1, representing a MAPE of 0.7%. The fusion of sensor signals improves prediction accuracy, with higher performance than studies with approaches that used a single sensor. The mass flow approach with multiple sensors is a potential approach to replace conventional yield monitors. The system generates accurate data with high sample density within sugarcane rows.