bluuebunny/crossref_metadata_2025 huggingface dataset:
Isolation, Identification, and Optimization of γ-Aminobutyric Acid (GABA)-Producing Bacillus cereus Strain KBC from a Commercial Soy Sauce moromi in Submerged-Liquid Fermentation
Authors: Wan-Mohtar Wan Abd Al Qadr Imad , Sohedein Mohamad Nor Azzimi , Ibrahim Mohamad Faizal , Ab Kadir Safuan , Suan Ooi Poh , Weng Loen Alan Wong , Sassi Soumaya , Ilham Zul
Published: June 2020

A new high γ-aminobutyric acid (GABA) producing strain of Bacillus cereus was successfully isolated from soy sauce moromi. This B. cereus strain named KBC shared similar morphological characteristics (Gram-positive, rod-shaped) with the reference B. cereus. 16S rRNA sequence of B. cereus KBC was found to be 99% similar with B. cereus strain OPWW1 under phylogenetic tree analysis. B. cereus KBC cultivated in unoptimized conditions using De Man, Rogosa, Sharpe (MRS) broth was capable of producing 523.74 mg L−1 of GABA within five days of the cultivation period. By using response surface methodology (RSM), pH level, monosodium glutamate (MSG) concentration and temperature were optimized for a high concentration of GABA production. The pH level significantly influenced the GABA production by B. cereus KBC with p-value = 0.0023. GABA production by B. cereus KBC under the optimized condition of pH 7, MSG concentration of 5 g L−1 and temperature of 40 °C resulted in GABA production of 3393.02 mg L−1, which is 6.37-fold higher than under unoptimized conditions. Overall, this study has shown that B. cereus KBC isolated from soy sauce moromi is capable of producing a high concentration of GABA together with the optimal fermentation conditions that have been statistically analysed using RSM.
“On Golden Tablets”: The Cleveland Museum of Art’s Aṣṭasāhasrikā Prajñāpāramitā Manuscript as a Self-Referential Icon
Authors: O’Mara Reed
Published: June 2020

This article examines the paintings on the five surviving illuminated palm-leaf folios and the interiors of the two wooden covers of the Cleveland Museum of Art’s almost complete Perfection of Wisdom in Eight Thousand Lines, or the Aṣṭasāhasrikā Prajñāpāramitā-sūtra, from the early twelfth century (CMA, Acc. No. 1938.301). Earlier scholarship on the CMA manuscript has overlooked the importance of the first folio, which depicts centrally a female personification of the Prajñāpāramitā text itself. Focusing on the details of the image and comparing it to the other instances of the figure in the manuscript, I argue that the golden image of Prajñāpāramitā on folio 1v serves as the core self-referential icon of the manuscript, alluding to not only the content of the text itself, but also to the very manuscript the image resides in. This essay shows the ways in which South Asian palm-leaf manuscripts can be understood from the purview of materiality, already well established in the scholarship of western European medieval parchment manuscripts.
Copula Model Selection for Vehicle Component Failures Based on Warranty Claims
Authors: Wifvat Kathryn , Kumerow John , Shemyakin Arkady
Published: June 2020

In the automotive industry, it is important to know whether the failure of some car parts may be related to the failure of others. This project studies warranty claims for five engine components obtained from a major car manufacturer with the purpose of modeling the joint distributions of the failure of two parts. The one-dimensional distributions of components are combined to construct a bivariate copula model for the joint distribution that makes it possible to estimate the probabilities of two components failing before a given time. Ultimately, the influence of the failure of one part on the operation of another related part can be described, predicted, and addressed. The performance of several families of one-parameter Archimedean copula models (Clayton, Gumbel–Hougaard, survival copulas) is analyzed, and Bayesian model selection is performed. Both right censoring and conditional approaches are considered with the emphasis on conditioning to the warranty period.
A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images
Authors: Ahmadi Salman , Homayouni Saeid
Published: June 2020

In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods.
GPR Spectra for Monitoring Asphalt Pavements
Authors: Pedret Rodés Josep , Martínez Reguero Adriana , Pérez-Gracia Vega
Published: June 2020

Ground Penetrating Radar (GPR) is a prospecting method frequently used in monitoring asphalt pavements, especially as an optimal complement to the defection test that is commonly used for determining the structural condition of the pavements. Its application is supported by studies that demonstrate the existence of a relationship between the parameters determined in GPR data (usually travel time and wave amplitude) and the preservation conditions of the structure. However, the analysis of frequencies is rarely applied in pavement assessment. Nevertheless, spectral analysis is widespread in other fields such as medicine or dynamic analysis, being one the most common analytical methods in wave processing through use of the Fourier transform. Nevertheless, spectral analysis has not been thoroughly applied and evaluated in GPR surveys, specifically in the field of pavement structures. This work is focused on analyzing the behavior of the GPR data spectra as a consequence of different problems affecting the pavement. The study focuses on the determination of areas with failures in bituminous pavement structures. Results epitomize the sensitivity of frequencies to the materials and, in some cases, to the damage.
Machine Learning-Based and 3D Kinematic Models for Rockfall Hazard Assessment Using LiDAR Data and GIS
Authors: Fanos Ali Mutar , Pradhan Biswajeet , Alamri Abdullah , Lee Chang-Wook
Published: June 2020

Rockfall is one of the most hazardous phenomena in mountainous and hilly regions with high and steep terrain. Such incidents can cause massive damage to people, properties, and infrastructure. Therefore, proper rockfall hazard assessment methods are required to save lives and provide a guide for the development of an area. The aim of this research is to develop a method for rockfall hazard assessment at two different scales (regional and local). A high-resolution airborne laser scanning (ALS) technique was utilized to derive an accurate digital terrain model (DTM); next, a terrestrial laser scanner (TLS) was used to capture the topography of the two most critical areas within the study area. A staking machine-learning model based on different classifiers, namely logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (KNN), was optimized and employed to determine rockfall probability by utilizing various rockfall conditioning factors. A developed 3D rockfall kinematic model was used to obtain rockfall trajectories, velocity, frequency, bouncing height, kinetic energy, and impact location. Next, a spatial model combined with a fuzzy analytical hierarchy process (fuzzy-AHP) integrated in the Geographic Information System (GIS) was developed to assess rockfall hazard in two different areas in Ipoh, Malaysia. Additionally, mitigation processes were suggested and assessed to provide a comprehensive information for urban planning management. The results show that, the stacking random forest–k-nearest neighbor (RF-KNN) model is the best hybrid model compared to other tested models with an accuracy of 89%, 86%, and 87% based on training, validation, and cross-validation datasets, respectively. The three-dimension rockfall kinematic model was calibrated with an accuracy of 93% and 95% for the two study areas and subsequently the rockfall trajectories and their characteristics were derived. The assessment of the suggested mitigation processes proves that the proposed methods can reduce or eliminate rockfall hazard in these areas. According to the results, the proposed method can be generalized and applied in other geographical places to provide decision-makers with a comprehensive rockfall hazard assessment.
Multi-Scale and Occlusion Aware Network for Vehicle Detection and Segmentation on UAV Aerial Images
Authors: Zhang Wang , Liu Chunsheng , Chang Faliang , Song Ye
Published: June 2020

With the advantage of high maneuverability, Unmanned Aerial Vehicles (UAVs) have been widely deployed in vehicle monitoring and controlling. However, processing the images captured by UAV for the extracting vehicle information is hindered by some challenges including arbitrary orientations, huge scale variations and partial occlusion. In seeking to address these challenges, we propose a novel Multi-Scale and Occlusion Aware Network (MSOA-Net) for UAV based vehicle segmentation, which consists of two parts including a Multi-Scale Feature Adaptive Fusion Network (MSFAF-Net) and a Regional Attention based Triple Head Network (RATH-Net). In MSFAF-Net, a self-adaptive feature fusion module is proposed, which can adaptively aggregate hierarchical feature maps from multiple levels to help Feature Pyramid Network (FPN) deal with the scale change of vehicles. The RATH-Net with a self-attention mechanism is proposed to guide the location-sensitive sub-networks to enhance the vehicle of interest and suppress background noise caused by occlusions. In this study, we release a large comprehensive UAV based vehicle segmentation dataset (UVSD), which is the first public dataset for UAV based vehicle detection and segmentation. Experiments are conducted on the challenging UVSD dataset. Experimental results show that the proposed method is efficient in detecting and segmenting vehicles, and outperforms the compared state-of-the-art works.
A Novel Dynamical Filter Based on Multi-Epochs Least-Squares to Integrate the Carrier Phase and Pseudorange Observation for GNSS Measurement
Authors: Li Fangchao , Gao Jingxiang , Psimoulis Panos , Meng Xiaolin , Ke Fuyang
Published: June 2020

The high noise of pseudorange and the ambiguity of carrier phase observation restrain the GNSS (Global Navigation Satellite System) application in military, industrial, and agricultural, to name a few. Thus, it is crucial for GNSS technology to integrate the pseudorange and carrier phase observations. However, the traditional method proposed by Hatch has obtained only a low convergence speed and precision. For higher convergence speed and precision of the smoothed pseudorange, aiming to improve positioning accuracy and expand the application of GNSS, we introduced a new method named MELS (Multi-Epochs Least-Squares) that considered the cross-correlation of the estimating parameters inspired by DELS (Double-Epochs Least-Square). In this study, the ionospheric delay was compensated, and so its impact was limited to the performance of the filters, and then exploited the various filters to integrate carrier phase observation and pseudorange. We compared the various types of Hatch’s filter and LS (Least-Square) methods using simulation datasets, which confirmed that the types of LS method provided a smaller residual error and a faster convergence speed than Hatch’s method under various precisions of raw pseudorange. The experimental results from the measured GNSS data showed that LS methods provided better performance than Hatch’s methods at E and U directions and a lower accuracy at N direction. Nevertheless, the types of LS method and Hatch’s methods improved about 12% and 9–10% at the 3D direction, respectively, which illustrated the accumulating improvement at the enhanced directions was more than the decreased direction, proving that the types of LS method resulted to better performance than the Hatch’s filters. Additionally, the curve of residual and precision based on various LS methods illustrated that the MELS only provided a millimeter accuracy difference compared with DELS, which was proved by the simulated and measured GNSS datasets.
Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral Imagery with Customized Image Alignment and Stitching Algorithms
Authors: Feng Aijing , Zhou Jianfeng , Vories Earl , Sudduth Kenneth A.
Published: June 2020

Crop stand count and uniformity are important measures for making proper field management decisions to improve crop production. Conventional methods for evaluating stand count based on visual observation are time consuming and labor intensive, making it difficult to adequately cover a large field. The overall goal of this study was to evaluate cotton emergence at two weeks after planting using unmanned aerial vehicle (UAV)-based high-resolution narrow-band spectral indices that were collected using a pushbroom hyperspectral imager flying at 50 m above ground. A customized image alignment and stitching algorithm was developed to process hyperspectral cubes efficiently and build panoramas for each narrow band. The normalized difference vegetation index (NDVI) was calculated to segment cotton seedlings from soil background. A Hough transform was used for crop row identification and weed removal. Individual seedlings were identified based on customized geometric features and used to calculate stand count. Results show that the developed alignment and stitching algorithm had an average alignment error of 2.8 pixels, which was much smaller than that of 181 pixels from the associated commercial software. The system was able to count the number of seedlings in seedling clusters with an accuracy of 84.1%. Mean absolute percentage error (MAPE) in estimation of crop density at the meter level was 9.0%. For seedling uniformity evaluation, the MAPE of seedling spacing was 9.1% and seedling spacing standard deviation was 6.8%. Results showed that UAV-based high-resolution narrow-band spectral images had the potential to evaluate cotton emergence.
HY-1C Observations of the Impacts of Islands on Suspended Sediment Distribution in Zhoushan Coastal Waters, China
Authors: Cai Lina , Zhou Minrui , Liu Jianqiang , Tang Danling , Zuo Juncheng
Published: June 2020

We analyzed the impacts of islands on suspended sediment concentration (SSC) in Zhoushan Coastal waters based on data from HY-1C, which was launched in September 2018 in China, carrying Coastal Zone Imager (CZI) and Chinese Ocean Color and Temperature Scanner (COCTS) on it for offshore observation. A new SSC retrieved model was established based on the relationship between in situ SSC and the reflectance in red and near infrared bands of CZI image. Fifteen CZI images obtained from October to December 2019 were applied to retrieve SSC in Zhoushan coastal waters. The results show that SSC in study area is 100–1600 mg·L−1. The SSC near islands changes obviously. Upstream of the islands, SSC is lower than downstream. During the flood and ebb, when the current passes through the islands, circumfluence will appear, under certain geophysical factors, generating Karman vortex streets downstream of the islands. The sediments were stirred by the fast speed current at the outer side of vortex street to the sea surface inducing higher SSC at the outer side of the vortex street, while the central sediments of the vortex street were lower. In the direction of ocean currents, the SSC of the vortex street downstream of islands is changing regularly, i.e., increasing, then decreasing and increasing again and then decreasing in a snaking vortex street whose length downstream is between 1000 and 8000 m long.
A Review of the Sustainability Concept and the State of SDG Monitoring Using Remote Sensing
Authors: Estoque Ronald
Published: June 2020

The formulation of the 17 sustainable development goals (SDGs) was a major leap forward in humankind’s quest for a sustainable future, which likely began in the 17th century, when declining forest resources in Europe led to proposals for the re-establishment and conservation of forests, a strategy that embodies the great idea that the current generation bears responsibility for future generations. Global progress toward SDG fulfillment is monitored by 231 unique social-ecological indicators spread across 169 targets, and remote sensing (RS) provides Earth observation data, directly or indirectly, for 30 (18%) of these indicators. Unfortunately, the UN Global Sustainable Development Report 2019—The Future is Now: Science for Achieving Sustainable Development concluded that, despite initial efforts, the world is not yet on track for achieving most of the SDG targets. Meanwhile, through the EO4SDG initiative by the Group on Earth Observations, the full potential of RS for SDG monitoring is now being explored at a global scale. As of April 2020, preliminary statistical data were available for 21 (70%) of the 30 RS-based SDG indicators, according to the Global SDG Indicators Database. Ten (33%) of the RS-based SDG indicators have also been included in the SDG Index and Dashboards found in the Sustainable Development Report 2019—Transformations to Achieve the Sustainable Development Goals. These statistics, however, do not necessarily reflect the actual status and availability of raw and processed geospatial data for the RS-based indicators, which remains an important issue. Nevertheless, various initiatives have been started to address the need for open access data. RS data can also help in the development of other potentially relevant complementary indicators or sub-indicators. By doing so, they can help meet one of the current challenges of SDG monitoring, which is how best to operationalize the SDG indicators.
Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities
Authors: Johnson Brian Alan , Ma Lei
Published: June 2020

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.
Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery
Authors: Islam Md Nazrul , Tahtali Murat , Pickering Mark
Published: June 2020

Multispectral Polarimetric Imagery (MSPI) contains significant information about an object’s distribution, shape, shading, texture and roughness features which can distinguish between foreground and background in a complex scene. Due to spectral signatures being limited to material properties, Background Segmentation (BS) is a difficult task when there are shadows, illumination and clutter in a scene. In this work, we propose a two-fold BS approach: multiband image fusion and polarimetric BS. Firstly, considering that the background in a scene is polarized by nature, the spectral reflectance and correlations and the textural features of MSPI are calculated and analyzed to demonstrate the fusion significance. After that, integrating Principal Component Analysis (PCA) with Fast Fourier Transform (FFT), a hybrid fusion technique is proposed to show the multiband fusion effectiveness. Secondly, utilizing the Stokes vector, polarimetric components are calculated to separate a complex scene’s background from its foreground by constructing four significant foreground masks. An intensity-invariant mask is built by differentiating between the median filtering versions of unpolarized and polarized images. A strongly unpolarized foreground mask is also constructed in two different ways, through analyzing the Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP). Moreover, a strongly polarized mask and a strong light intensity mask are also calculated based on the azimuth angle and the total light intensity. Finally, all these masks are combined, and a morphological operation is applied to segment the final background area of a scene. The proposed two-fold BS algorithm is evaluated using distinct statistical measurements and compared with well-known fusion methods and BS methods highlighted in this paper. The experimental results demonstrate that the proposed hybrid fusion method significantly improves multiband fusion quality. Furthermore, the proposed polarimetric BS approach also improves the mean accuracy, geometric mean and F1-score to 0.95, 0.93 and 0.97, respectively, for scenes in the MSPI dataset compared with those obtained from the methods in the literature considered in this paper. Future work will investigate mixed polarized and unpolarized BS in the MSPI dataset with specular reflection.
Locating Seismo-Conductivity Anomaly before the 2017 MW 6.5 Jiuzhaigou Earthquake in China Using Far Magnetic Stations
Authors: Mao Zhiqiang , Chen Chieh-Hung , Zhang Suqin , Yisimayili Aisa , Yu Huaizhong , Yu Chen , Liu Jann-Yenq
Published: June 2020

Changes in the underlying conductivity around hypocenters are generally considered one of the promising mechanisms of seismo-electromagnetic anomaly generation. Parkinson vectors are indicators of high-conductivity materials and were utilized to remotely monitor conductivity changes during the MW 6.5 Jiuzhaigou earthquake (103.82°E, 33.20°N) on 8 August 2017. Three-component geomagnetic data recorded in 2017 at nine magnetic stations with epicenter distances of 63–770 km were utilized to compute the azimuths of the Parkinson vectors based on the magnetic transfer function. The monitoring and background distributions at each station were constructed by using the azimuths within a 15-day moving window and over the entire study period, respectively. The background distribution was subtracted from the monitoring distribution to mitigate the effects of underlying inhomogeneous electric conductivity structures. The differences obtained at nine stations were superimposed and the intersection of a seismo-conductivity anomaly was located about 70 km away from the epicenter about 17 days before the earthquake. The anomaly disappeared about 7 days before and remained insignificant after the earthquake. Analytical results suggested that the underlying conductivity close to the hypocenter changed before the Jiuzhaigou earthquake. These changes can be detected simultaneously by using multiple magnetometers located far from the epicenter. The disappearance of the seismo-conductivity anomaly after the earthquake sheds light on a promising candidate of the pre-earthquake anomalous phenomena.
Satellite Monitoring of Mass Changes and Ground Subsidence in Sudan’s Oil Fields Using GRACE and Sentinel-1 Data
Authors: Gido Nureldin A.A. , Amin Hadi , Bagherbandi Mohammad , Nilfouroushan Faramarz
Published: June 2020

Monitoring environmental hazards, owing to natural and anthropogenic causes, is an important issue, which requires proper data, models, and cross-validation of the results. The geodetic satellite missions, for example, the Gravity Recovery and Climate Experiment (GRACE) and Sentinel-1, are very useful in this respect. GRACE missions are dedicated to modeling the temporal variations of the Earth’s gravity field and mass transportation in the Earth’s surface, whereas Sentinel-1 collects synthetic aperture radar (SAR) data, which enables us to measure the ground movements accurately. Extraction of large volumes of water and oil decreases the reservoir pressure and form compaction and, consequently, land subsidence occurs, which can be analyzed by both GRACE and Sentinel-1 data. In this paper, large-scale groundwater storage (GWS) changes are studied using the GRACE monthly gravity field models together with different hydrological models over the major oil reservoirs in Sudan, that is, Heglig, Bamboo, Neem, Diffra, and Unity-area oil fields. Then, we correlate the results with the available oil wells production data for the period of 2003–2012. In addition, using the only freely available Sentinel-1 data, collected between November 2015 and April 2019, the ground surface deformation associated with this oil and water depletion is studied. Owing to the lack of terrestrial geodetic monitoring data in Sudan, the use of GRACE and Sentinel-1 satellite data is very valuable to monitor water and oil storage changes and their associated land subsidence over our region of interest. Our results show that there is a significant correlation between the GRACE-based GWS anomalies (ΔGWS) and extracted oil and water volumes. The trend of ΔGWS changes due to water and oil depletion ranged from –18.5 ± 6.3 to –6.2 ± 1.3 mm/year using the CSR GRACE monthly solutions and the best tested hydrological model in this study. Moreover, our Sentinel-1 SAR data analysis using the persistent scatterer interferometry (PSI) method shows a high rate of subsidence, that is, –24.5 ± 0.85, –23.8 ± 0.96, –14.2 ± 0.85, and –6 ± 0.88 mm/year over Heglig, Neem, Diffra, and Unity-area oil fields, respectively. The results of this study can help us to control the integrity and safety of operations and infrastructure in that region, as well as to study the groundwater/oil storage behavior.
Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow
Authors: Feo Giuseppe , Sharma Jyotsna , Cunningham Stephen
Published: June 2020

A novel workflow is presented for integrating fiber optic Distributed Temperature Sensor (DTS) data in numerical simulation model for the Cyclic Steam Stimulation (CSS) process, using an intelligent optimization routine that automatically learns and improves from experience. As the steam–oil relationship is the main driver for forecasting and decision-making in thermal recovery operations, knowledge of downhole steam distribution across the well over time can optimize injection and production. This study uses actual field data from a CSS operation in a heavy oil field in California, and the value of integrating DTS in the history matching process is illustrated as it allows the steam distribution to be accurately estimated along the entire length of the well. The workflow enables the simultaneous history match of water, oil, and temperature profiles, while capturing the reservoir heterogeneity and the actual physics of the injection process, and ultimately reducing the uncertainty in the predictive models. A novel stepwise grid-refinement approach coupled with an evolutionary optimization algorithm was implemented to improve computational efficiency and predictive accuracy. DTS surveillance also made it possible to detect a thermal communication event due to steam channeling in real-time, and even assess the effectiveness of the remedial workover to resolve it, demonstrating the value of continuous fiber optic monitoring.
Hybrid Temperature and Stress Monitoring of Woven Fabric Thermoplastic Composite Using Fiber Bragg Grating Based Sensing Technique
Authors: Chen Changhao , Wu Qi , Xiong Ke , Zhai Hongzhou , Yoshikawa Nobuhiro , Wang Rong
Published: June 2020

Process monitoring of woven fabric thermoplastic composite is crucial to enhance the quality of composite products. In this work, a new fiber Bragg grating based technique was proposed to achieve hybrid temperature and stress monitoring according to the changes of wavelength and reflectivity, respectively. The sensor head consisting of a pre-annealed fiber Bragg grating and a steel capillary was properly designed to overcome the challenge of high forming temperatures up to 332 °C, complex woven structure, and high forming pressure of 2 MPa, which hinder the use of the conventional fiber Bragg grating sensor during the forming process. The forming temperature changes of thermoplastic composite in the heating, dwelling, and cooling phases can be precisely measured by the proposed sensor head after using a curve-reconstruction algorithm based on cubic polynomial fitting. The measured difference from the reference thermocouple is 2.92 °C, averaged from three sets of repeated experiments. Meanwhile, the change of the residual stresses of the composite can be illustrated by using the micro-bending-caused optical power loss of the fiber pigtail commencing at the glass-transition temperature in the cooling phase. The decrease of grating reflectivity that was equivalent to the optical loss was discussed by comparing to strain change detected by strain gauges and a calculated theoretical curve. These results are beneficial for developing an advanced in situ monitoring technique and understanding the forming process of the woven fabric thermoplastic composite.
Simple Shading Correction Method for Brightfield Whole Slide Imaging
Authors: Tak Yoon-Oh , Park Anjin , Choi Janghoon , Eom Jonghyun , Kwon Hyuk-Sang , Eom Joo Beom
Published: June 2020

Whole slide imaging (WSI) refers to the process of creating a high-resolution digital image of a whole slide. Since digital images are typically produced by stitching image sequences acquired from different fields of view, the visual quality of the images can be degraded owing to shading distortion, which produces black plaid patterns on the images. A shading correction method for brightfield WSI is presented, which is simple but robust not only against typical image artifacts caused by specks of dust and bubbles, but also against fixed-pattern noise, or spatial variations in pixel values under uniform illumination. The proposed method comprises primarily of two steps. The first step constructs candidates of a shading distortion model from a stack of input image sequences. The second step selects the optimal model from the candidates. The proposed method was compared experimentally with two previous state-of-the-art methods, regularized energy minimization (CIDRE) and background and shading correction (BaSiC) and showed better correction scores, as smooth operations and constraints were not imposed when estimating the shading distortion. The correction scores, averaged over 40 image collections, were as follows: proposed method, 0.39 ± 0.099; CIDRE method, 0.67 ± 0.047; BaSiC method, 0.55 ± 0.038. Based on the quantitative evaluations, we can confirm that the proposed method can correct not only shading distortion, but also fixed-pattern noise, compared with the two previous state-of-the-art methods.
A Decentralized Low-Chattering Sliding Mode Formation Flight Controller for a Swarm of UAVs
Authors: Cordeiro Thiago F. K. , Ishihara João Y. , Ferreira Henrique C.
Published: June 2020

In this paper, a nonlinear robust formation flight controller for a swarm of unmanned aerial vehicles (UAVs) is presented. It is based on the virtual leader approach and is capable of achieving and maintaining a formation with time-varying shape. By using a decentralized architecture, the local controller in each UAV uses information only from the UAV itself, its neighbors, and from the virtual leader. Also, a synchronization control objective provides a mechanism to weight between the fleet achieving the desired formation shape, that is, achieving the desired relative position between the UAVs, and each UAV achieving its desired absolute position. The use of a combination of a sliding mode controller and a low pass filter reduces the usual chattering effect, providing a smooth control signal while maintaining robustness. Simulation results show the effectiveness of the proposed decentralized controller.
Moving Object Detection under a Moving Camera via Background Orientation Reconstruction
Authors: Zhang Wenlong , Sun Xiaoliang , Yu Qifeng
Published: June 2020

Moving object detection under a moving camera is a challenging question, especially in a complex background. This paper proposes a background orientation field reconstruction method based on Poisson fusion for detecting moving objects under a moving camera. As enlightening by the optical flow orientation of a background is not dependent on the scene depth, this paper reconstructs the background orientation through Poisson fusion based on the modified gradient. Then, the motion saliency map is calculated by the difference between the original and the reconstructed orientation field. Based on the similarity in appearance and motion, the paper also proposes a weighted accumulation enhancement method. It can highlight the motion saliency of the moving objects and improve the consistency within the object and background region simultaneously. Furthermore, the proposed method incorporates the motion continuity to reject the false positives. The experimental results obtained by employing publicly available datasets indicate that the proposed method can achieve excellent performance compared with current state-of-the-art methods.
A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds
Authors: Nguyen Cong Dai , Prosvirin Alexander , Kim Jong-Myon
Published: June 2020

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.
A Robust Quasi-Quantum Walks-based Steganography Protocol for Secure Transmission of Images on Cloud-based E-healthcare Platforms
Authors: Abd-El-Atty Bassem , Iliyasu Abdullah M. , Alaskar Haya , Abd El-Latif Ahmed A.
Published: June 2020

Traditionally, tamper-proof steganography involves using efficient protocols to encrypt the stego cover image and/or hidden message prior to embedding it into the carrier object. However, as the inevitable transition to the quantum computing paradigm beckons, its immense computing power will be exploited to violate even the best non-quantum, i.e., classical, stego protocol. On its part, quantum walks can be tailored to utilise their astounding ‘quantumness’ to propagate nonlinear chaotic behaviours as well as its sufficient sensitivity to alterations in primary key parameters both important properties for efficient information security. Our study explores using a classical (i.e., quantum-inspired) rendition of the controlled alternate quantum walks (i.e., CAQWs) model to fabricate a robust image steganography protocol for cloud-based E-healthcare platforms by locating content that overlays the secret (or hidden) bits. The design employed in our technique precludes the need for pre and/or post encryption of the carrier and secret images. Furthermore, our design simplifies the process to extract the confidential (hidden) information since only the stego image and primary states to run the CAQWs are required. We validate our proposed protocol on a dataset of medical images, which exhibited remarkable outcomes in terms of their security, good visual quality, high resistance to data loss attacks, high embedding capacity, etc., making the proposed scheme a veritable strategy for efficient medical image steganography.
Tracking by Risky Particle Filtering over Sensor Networks
Authors: Lim Jaechan , Park Hyung-Min
Published: June 2020

The system of wireless sensor networks is high of interest due to a large number of demanded applications, such as the Internet of Things (IoT). The positioning of targets is one of crucial problems in wireless sensor networks. Particularly, in this paper, we propose minimax particle filtering (PF) for tracking a target in wireless sensor networks where multiple-RSS-measurements of received signal strength (RSS) are available at networked-sensors. The minimax PF adopts the maximum risk when computing the weights of particles, which results in the decreased variance of the weights and the immunity against the degeneracy problem of generic PF. Via the proposed approach, we can obtain improved tracking performance beyond the asymptotic-optimal performance of PF from a probabilistic perspective. We show the validity of the employed strategy in the applications of various PF variants, such as auxiliary-PF (APF), regularized-PF (RPF), Kullback–Leibler divergence-PF (KLDPF), and Gaussian-PF (GPF), besides the standard PF (SPF) in the problem of tracking a target in wireless sensor networks.
Transition Activity Recognition System Based on Standard Deviation Trend Analysis
Authors: Shi Junhao , Zuo Decheng , Zhang Zhan
Published: June 2020

With the development and popularity of micro-electromechanical systems (MEMS) and smartphones, sensor-based human activity recognition (HAR) has been widely applied. Although various kinds of HAR systems have achieved outstanding results, there are still issues to be solved in this field, such as transition activities, which means the transitional process between two different basic activities, discussed in this paper. In this paper, we design an algorithm based on standard deviation trend analysis (STD-TA) for recognizing transition activity. Compared with other methods, which directly take them as basic activities, our method achieves a better overall performance: the accuracy is over 80% on real data.
MapGAN: An Intelligent Generation Model for Network Tile Maps
Authors: Li Jingtao , Chen Zhanlong , Zhao Xiaozhen , Shao Lijia
Published: June 2020

In recent years, the generative adversarial network (GAN)-based image translation model has achieved great success in image synthesis, image inpainting, image super-resolution, and other tasks. However, the images generated by these models often have problems such as insufficient details and low quality. Especially for the task of map generation, the generated electronic map cannot achieve effects comparable to industrial production in terms of accuracy and aesthetics. This paper proposes a model called Map Generative Adversarial Networks (MapGAN) for generating multitype electronic maps accurately and quickly based on both remote sensing images and render matrices. MapGAN improves the generator architecture of Pix2pixHD and adds a classifier to enhance the model, enabling it to learn the characteristics and style differences of different types of maps. Using the datasets of Google Maps, Baidu maps, and Map World maps, we compare MapGAN with some recent image translation models in the fields of one-to-one map generation and one-to-many domain map generation. The results show that the quality of the electronic maps generated by MapGAN is optimal in terms of both intuitive vision and classic evaluation indicators.
Aptamer-Conjugated Polydiacetylene Colorimetric Paper Chip for the Detection of Bacillus thuringiensis Spores
Authors: Zhou Chaoge , You Taeyeong , Jang Huisoo , Ryu Hyunil , Lee Eun-Seon , Oh Mi-Hwa , Huh Yun Suk , Kim Sun Min , Jeon Tae-Joon
Published: June 2020

A colorimetric polydiacetylene (PDA) paper strip sensor that can specifically recognize Bacillus thuringiensis (BT) HD-73 spores is described in this work. The target-specific aptamer was combined with PDA, and the aptamer-conjugated PDA vesicles were then coated on polyvinylidene fluoride (PVDF) paper strips by a simple solvent evaporation method. The PDA-aptamer paper strips can be used to detect the target without any pre-treatment. Using the paper strip, the presence of BT spores is directly observable by the naked eye based on the unique blue-to-red color transition of the PDA. Quantitative studies using the paper strip were also carried out by analyzing the color transitions of the PDA. The specificity of this PDA sensor was verified with a high concentration of Escherichia coli, and no discernable change was observed. The observable color change in the paper strip occurs in less than 1 h, and the limit of detection is 3 × 107 CFU/mL, much below the level harmful to humans. The PDA-based paper sensor, developed in this work, does not require a separate power or detection device, making the sensor strip highly transportable and suitable for spore analysis anytime and anywhere. Moreover, this paper sensor platform is easily fabricated, can be adapted to other targets, is highly portable, and is highly specific for the detection of BT spores.
Combinatorial Subset Difference—IoT-Friendly Subset Representation and Broadcast Encryption
Authors: Lee Jiwon , Lee Seunghwa , Kim Jihye , Oh Hyunok
Published: June 2020

In the Internet of Things (IoT) systems, it is often required to deliver a secure message to a group of devices. The public key broadcast encryption is an efficient primitive to handle IoT broadcasts, by allowing a user (or a device) to broadcast encrypted messages to a group of legitimate devices. This paper proposes an IoT-friendly subset representation called Combinatorial Subset Difference (CSD), which generalizes the existing subset difference (SD) method by allowing wildcards (*) in any position of the bitstring. Based on the CSD representation, we first propose an algorithm to construct the CSD subset, and a CSD-based public key broadcast encryption scheme. By providing the most general subset representation, the proposed CSD-based construction achieves a minimal header size among the existing broadcast encryption. The experimental result shows that our CSD saves the header size by 17% on average and more than 1000 times when assuming a specific IoT example of IP address with 20 wildcards and 2 20 total users, compared to the SD-based broadcast encryption. We prove the semantic security of CSD-based broadcast encryption under the standard l-BDHE assumption, and extend the construction to a chosen-ciphertext-attack (CCA)-secure version.
Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm
Authors: Said Sherif , Boulkaibet Ilyes , Sheikh Murtaza , Karar Abdullah S. , Alkork Samer , Nait-ali Amine
Published: June 2020

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.
Social, Psychological, and Philosophical Reflections on Pandemics and Beyond
Authors: Rudnick Abraham
Published: June 2020

This conceptual paper presents social, psychological and philosophical (ethical and epistemological) reflections regarding the current (COVID-19) pandemic and beyond, using an analytic and comparative approach. For example, Taiwan and Canada are compared, addressing Taiwan’s learning from SARS. Suggestions are made in relation to current and future relevant practice, policy, research and education. For example, highly exposed individuals and particularly vulnerable populations, such as health care providers and socially disadvantaged (homeless and other) people, respectively, are addressed as requiring special attention. In conclusion, more reflection on and study of social and psychological challenges as well as underlying philosophical issues related to the current pandemic and more generally to global crises is needed.
The Motherhood Pension Gap in a Defined Contribution Pension Scheme—the Case of Poland
Authors: Jędrzychowska Anna , Kwiecień Ilona , Poprawska Ewa
Published: June 2020

A gender gap in pensions has recently been discussed in the context of non-discrimination and the sustainability of pension systems. Such systems in Europe are evolving towards strengthening the role of individual contributions from periods of paid work. Among other factors, the women’s pension gap is affected by interruptions in employment arising from care responsibilities. The purpose of this article is to measure the pension gap associated with having children in defined contribution pension systems. Using financial mathematics, the retirement capital of a childless woman (without breaks in work) was determined and compared with mothers of 1–4 children. The results indicate that the motherhood pension gap is approximately 4.5%–9.5%, 7.5%–15%, 9%–20%, and 12.5%–25% for mothers of 1, 2, 3, and 4 children, respectively. Measuring these individual gaps allows the cost of investing in children to be estimated. Significant for systemic and individual decisions is that the gap size is highest by the first and the second child, however the decision about the third child—relevant to the demography as ensuring the generational replacement—means the whole pension gap could rise to 20%. This could help support a policy of counteracting adverse demographic trends in fertility rates through the building of socially sustainable pensions schemes. In terms of future research, it forms the basis for building a gap measurement model that takes into account various drivers of the gender gap.