bluuebunny/crossref_metadata_2025 huggingface dataset:
Actinobaculum massiliense Proteome Profiled in Polymicrobial Urethral Catheter Biofilms
Authors: Yu Yanbao , Tsitrin Tamara , Singh Harinder , Doerfert Sebastian N. , Sizova Maria V. , Epstein Slava S. , Pieper Rembert
Published: December 2018

Actinobaculum massiliense, a Gram-positive anaerobic coccoid rod colonizing the human urinary tract, belongs to the taxonomic class of Actinobacteria. We identified A. massiliense as a cohabitant of urethral catheter biofilms (CB). The CBs also harbored more common uropathogens, such as Proteus mirabilis and Aerococcus urinae, supporting the notion that A. massiliense is adapted to a life style in polymicrobial biofilms. We isolated a clinical strain from a blood agar colony and used 16S rRNA gene sequencing and shotgun proteomics to confirm its identity as A. massiliense. We characterized this species by quantitatively comparing the bacterial proteome derived from in vitro growth with that of four clinical samples. The functional relevance of proteins with emphasis on nutrient import and the response to hostile host conditions, showing evidence of neutrophil infiltration, was analyzed. Two putative subtilisin-like proteases and a heme/oligopeptide transporter were abundant in vivo and are likely important for survival and fitness in the biofilm. Proteins facilitating uptake of xylose/glucuronate and oligopeptides, also highly expressed in vivo, may feed metabolites into mixed acid fermentation and peptidolysis pathways, respectively, to generate energy. A polyketide synthase predicted to generate a secondary metabolite that interacts with either the human host or co-colonizing microbes was also identified. The product of the PKS enzyme may contribute to A. massiliense fitness and persistence in the CBs.
The Potential of Speleothems from Western Europe as Recorders of Regional Climate: A Critical Assessment of the SISAL Database
Authors: Lechleitner Franziska , Amirnezhad-Mozhdehi Sahar , Columbu Andrea , Comas-Bru Laia , Labuhn Inga , Pérez-Mejías Carlos , Rehfeld Kira
Published: December 2018

Western Europe is the region with the highest density of published speleothem δ18O (δ18Ospel) records worldwide. Here, we review these records in light of the recent publication of the Speleothem Isotopes Synthesis and AnaLysis (SISAL) database. We investigate how representative the spatial and temporal distribution of the available records is for climate in Western Europe and review potential sites and strategies for future studies. We show that spatial trends in precipitation δ18O are mirrored in the speleothems, providing means to better constrain the factors influencing δ18Ospel at a specific location. Coherent regional δ18Ospel trends are found over stadial-interstadial transitions of the last glacial, especially in high altitude Alpine records, where this has been attributed to a strong temperature control of δ18Ospel. During the Holocene, regional trends are less clearly expressed, due to lower signal-to-noise ratios in δ18Ospel, but can potentially be extracted with the use of statistical methods. This first assessment highlights the potential of the European region for speleothem palaeoclimate reconstruction, while underpinning the importance of knowing local factors for a correct interpretation of δ18Ospel.
New Structural Insight into Interface-Controlled α–σ Phase Transformation in Fe-Cr Alloys
Authors: Al Khoury Wael , Tamura Nobumichi , Geandier Guillaume , Goudeau Philippe
Published: December 2018

Synchrotron Laue microdiffraction scanning is used for the ex situ study of the body-centered, cubic-to-tetragonal phase transformation that occurs in equiatomic polycrystalline Fe-Cr alloys at temperatures between 550 and 800 °C. Grain orientation and grain strains were scanned with a micron step resolution after annealing at 700 °C for 12 h. Further microstructural details on the early stage of the transformation, and more particularly on the cubic-to-tetragonal phase interface, were achieved. Only the α and ordered σ phases were detected. The crystallographic relationships at the interface between the two phases did not follow the predicted rules; this result is discussed in relation to the measured microstrains.
Heidegger, Heterotopic Dwelling and Prehistoric Art: An Initial Indication of a Field of Research
Authors: Tonner Philip
Published: December 2018

This paper begins to develop an interpretation of European cave art based on Martin Heidegger’s account of artistic production and ‘dwelling’ so as to indicate a potentially rich area for future research. The paper will also draw on Foucault’s account of heterotopic space and will engage with one of the key researchers on the archaeology of cave art, Randall White. The role of a work of art for Heidegger is to hold open a world. Art enables a decision to be made by a group regarding how things are going to matter for, and to, them as dwellers in their world. Works of art, on Heidegger’s account, put up for decision what will count as the highest values (the gods) for a group while determining what will prove essential for human dwelling in a world. With reference to Foucault, it will be suggested that caves are a good candidate for a heterotopic space. Caves are uncanny, numinous spaces and because of this, I suggest, they enable human beings to produce art as a world-opening event. I suggest that there is something significant about human experience in caves and I attempt to make a connection between heterotopic space, dwelling, and the art of the last Ice Age in Europe in order to point towards a novel field of research: dwelling and prehistoric art.
Remembering for our Future: Affirming the Religious Education Tradition as a Guide for the Religious Education Movement
Authors: Horell Harold D.
Published: December 2018

This article addresses doubts about the viability, and hence future, of religious education. The researcher utilized structural analysis based on the questions: What central concepts and commitments have provided structure for the field of religious education as it has developed over time? How have social and cultural factors and changes in social and cultural context shaped the ways the structuring concepts and commitments of religious education have been embraced? To what extent can an understanding of the structuring concepts and commitments of the field enable us to make sense of the contemporary doubts about religious education? Additionally, the methodology of field mapping was used to map the models and approaches to religious education that have developed over time. The researcher found, and these findings are presented in this article, that structural analysis informed by field mapping can enable us to understand both the strengths and limitations of contemporary religious education. The researcher concluded that, based on a structural analysis of the field, religious educators can and should respond to the present crisis in religious education by defining the purpose and scope of religious education more clearly. The analysis in the final section of this article is based on that conclusion.
Food Waste Recovery with Takakura Portable Compost Boxes in Offices and Working Places
Authors: Jiménez-Antillón Joaquín , Calleja-Amador Carlos , Romero-Esquivel Luis G.
Published: December 2018

The Takakura technique converts food waste into compost. This project explored the potential use of composting in offices and workplaces. The method involves using small boxes containing a fermentation substrate where food waste is added. Two Takakura composting boxes (TCBs) were placed in the kitchens of the Chemistry Department and the Environmental Engineering buildings at the Instituto Tecnológico de Costa Rica, Costa Rica. Food waste from both buildings, comprising the food waste of 45 staff members, were composted from June to December 2015. All types of food, except grease, oil, raw meat, and bones, were processed. The mass and volume of food added to the boxes were quantified daily. A chemical analysis of the compost was also performed. A speech was given to educate the staff members about the system. A survey, before and after the speech, was developed to evaluate the knowledge and tolerance towards composting in closed spaces. A total mass of 88.29 kg, equivalent to 232 L of residues, was processed, from which 17.37 kg (37 L) of compost was obtained. This represented a mass and volume reduction of 80% and 84%, respectively. Compost analysis gave a C/N ratio of 14.7/1, indicative of maturity. Most of the staff members (92%) agreed to the separation of waste at the source. However, 37% suggested allocating the box outside the building. The survey, after the speech, showed that educating staff is necessary before installing boxes at a workplace.
Risk Assessment in a Materials Recycling Facility: Perspectives for Reducing Operational Issues
Authors: Cioca Lucian Ionel , Ferronato Navarro , Viotti Paolo , Magaril Elena , Ragazzi Marco , Torretta Vincenzo , Rada Elena Cristina
Published: December 2018

Mechanical separation of light packaging waste is a useful practice for improving the quality of the recyclable waste flows and its exploitation in a frame of the circular economy. Materials Recovery Facilities can treat from 3000 to 5000 tons per year of light packaging waste. Concerning the plastic content, this is divided in four flows: PET, HDPE, other plastics, and waste rejects. The last two are generally used for energy recovery. For improving the quality of the recyclable plastic waste, a manual separation is required for reducing the impurities detectable in the final products. However, this practice could enhance the risk at work of the operators, which should be constantly monitored. This article explores the main differences of a manual separation and of a mechanical separation, assessing the costs and the health risk for the workers. The analysis started from the situation in an Italian Materials Recovery Facility, generalizing the context; a future scenario with the application of a mechanical separation is theoretically introduced. The main results obtained suggest that the manual separation plant improves the quality of the material, though increasing the risk of the operators due to the possible contact with sharp waste, sanitary danger, and risk of injuries for the mismanagement of machines, among others. The mechanical separation can be considered a real advantage from an economic point of view, since the operating costs are lower and the investment could be recovered in around 10 years, in an Italian-like context. On balance, on the one hand, the article provides indications for the private sector for improving the management of a Materials Recovery Facility, while, on the other hand, it detects the main pros and cons of both methodologies.
Macroeconomic News Sentiment: Enhanced Risk Assessment for Sovereign Bonds
Authors: Erlwein-Sayer Christina
Published: December 2018

We enhance the modelling and risk assessment of sovereign bond spreads by taking into account quantitative information gained from macro-economic news sentiment. We investigate sovereign bonds spreads of five European countries and improve the prediction of spread changes by incorporating news sentiment from relevant entities and macro-economic topics. In particular, we create daily news sentiment series from sentiment scores as well as positive and negative news volume and investigate their effects on yield spreads and spread volatility. We conduct a correlation and rolling correlation analysis between sovereign bond spreads and accumulated sentiment series and analyse changing correlation patterns over time. Market regimes are detected through correlation series and the impact of news sentiment on sovereign bonds in different market circumstances is investigated. We find best-suited external variables for forecasts in an ARIMAX model set-up. Error measures for forecasts of spread changes and volatility proxies are improved when sentiment is considered. These findings are then utilised to monitor sovereign bonds from European countries and detect changing risks through time.
Firm’s Risk-Return Association Facets and Prospect Theory Findings—An Emerging versus Developed Country Context
Authors: Gupta Ranjan Das , Pathak Rajesh
Published: December 2018

A risk-return association under normal market conditions can be conventional positive (risk-averse) or “paradoxical” negative (risk seeking). This study has the objective to investigate whether such an association is stable across market trends (i.e., bull and bear) and for overall, industry-classified and partitions sub-samples after controlling for a firm’s age, size, leverage and liquidity using operating performance risk-return measures. In total, this study analyses 2666 firms (1199 firms from 15 developed countries and 1467 firms from 12 emerging countries) for the period of 1999–2015. Results show that in the overall and bull sub-periods, firms across countries are showing conventional positive (superior firms) and “paradoxical” negative (poor firms) in most cases. However, in the bear sub-periods all firms from emerging countries are risk seeking in order to maintain their position in the pecking order.
Mapping Root-Zone Soil Moisture Using a Temperature–Vegetation Triangle Approach with an Unmanned Aerial System: Incorporating Surface Roughness from Structure from Motion
Authors: Wang Sheng , Garcia Monica , Ibrom Andreas , Jakobsen Jakob , Josef Köppl Christian , Mallick Kaniska , Looms Majken C. , Bauer-Gottwein Peter
Published: December 2018

High resolution root-zone soil moisture (SM) maps are important for understanding the spatial variability of water availability in agriculture, ecosystems research and water resources management. Unmanned Aerial Systems (UAS) can flexibly monitor land surfaces with thermal and optical imagery at very high spatial resolution (meter level, VHR) for most weather conditions. We modified the temperature–vegetation triangle approach to transfer it from satellite to UAS remote sensing. To consider the effects of the limited coverage of UAS mapping, theoretical dry/wet edges were introduced. The new method was tested on a bioenergy willow short rotation coppice site during growing seasons of 2016 and 2017. We demonstrated that by incorporating surface roughness parameters from the structure-from-motion in the interpretation of the measured land surface-atmosphere temperature gradients, the estimates of SM significantly improved. The correlation coefficient between estimated and measured SM increased from not significant to 0.69 and the root mean square deviation decreased from 0.045 m3∙m−3 to 0.025 m3∙m−3 when considering temporal dynamics of surface roughness in the approach. The estimated SM correlated better with in-situ root-zone SM (15–30 cm) than with surface SM (0–5 cm) which is an important advantage over alternative remote sensing methods to estimate SM. The optimal spatial resolution of the triangle approach was found to be around 1.5 m, i.e. similar to the length scale of tree-crowns. This study highlights the importance of considering the 3-D fine scale canopy structure, when addressing the links between surface temperature and SM patterns via surface energy balances. Our methodology can be applied to operationally monitor VHR root-zone SM from UAS in agricultural and natural ecosystems.
Comparison of Persistent Scatterer Interferometry and SAR Tomography Using Sentinel-1 in Urban Environment
Authors: Budillon Alessandra , Crosetto Michele , Johnsy Angel Caroline , Monserrat Oriol , Krishnakumar Vrinda , Schirinzi Gilda
Published: December 2018

In this paper, persistent scatterer interferometry and Synthetic Aperture Radar (SAR) tomography have been applied to Sentinel-1 data for urban monitoring. The paper analyses the applicability of SAR tomography to Sentinel-1 data, which is not granted, due to the reduced range and azimuth resolutions and the low resolution in elevation. In a first part of the paper, two implementations of the two techniques are described. In the experimental part, the two techniques are used in parallel to process the same Sentinel-1 data over two test areas. An intercomparison of the results from persistent scatterer interferometry and SAR tomography is carried out, comparing the main parameters estimated by the two techniques. Finally, the paper addresses the complementarity of the two techniques, and in particular it assesses the increase of measurement density that can be achieved by adding the double scatterers from SAR tomography to the persistent scatterer interferometry measurements.
Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery
Authors: Ramos-Bernal Rocío N. , Vázquez-Jiménez René , Romero-Calcerrada Raúl , Arrogante-Funes Patricia , Novillo Carlos J.
Published: December 2018

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.
MsRi-CCF: Multi-Scale and Rotation-Insensitive Convolutional Channel Features for Geospatial Object Detection
Authors: Wu Xin , Hong Danfeng , Ghamisi Pedram , Li Wei , Tao Ran
Published: December 2018

Geospatial object detection is a fundamental but challenging problem in the remote sensing community. Although deep learning has shown its power in extracting discriminative features, there is still room for improvement in its detection performance, particularly for objects with large ranges of variations in scale and direction. To this end, a novel approach, entitled multi-scale and rotation-insensitive convolutional channel features (MsRi-CCF), is proposed for geospatial object detection by integrating robust low-level feature generation, classifier generation with outlier removal, and detection with a power law. The low-level feature generation step consists of rotation-insensitive and multi-scale convolutional channel features, which were obtained by learning a regularized convolutional neural network (CNN) and integrating multi-scaled convolutional feature maps, followed by the fine-tuning of high-level connections in the CNN, respectively. Then, these generated features were fed into AdaBoost (chosen due to its lower computation and storage costs) with outlier removal to construct an object detection framework that facilitates robust classifier training. In the test phase, we adopted a log-space sampling approach instead of fine-scale sampling by using the fast feature pyramid strategy based on a computable power law. Extensive experimental results demonstrate that compared with several state-of-the-art baselines, the proposed MsRi-CCF approach yields better detection results, with 90.19% precision with the satellite dataset and 81.44% average precision with the NWPU VHR-10 datasets. Importantly, MsRi-CCF incurs no additional computational cost, which is only 0.92 s and 0.7 s per test image on the two datasets. Furthermore, we determined that most previous methods fail to gain an acceptable detection performance, particularly when they face several obstacles, such as deformations in objects (e.g., rotation, illumination, and scaling). Yet, these factors are effectively addressed by MsRi-CCF, yielding a robust geospatial object detection method.
Hierarchical Regularization of Building Boundaries in Noisy Aerial Laser Scanning and Photogrammetric Point Clouds
Authors: Xie Linfu , Zhu Qing , Hu Han , Wu Bo , Li Yuan , Zhang Yeting , Zhong Ruofei
Published: December 2018

Aerial laser scanning or photogrammetric point clouds are often noisy at building boundaries. In order to produce regularized polygons from such noisy point clouds, this study proposes a hierarchical regularization method for the boundary points. Beginning with detected planar structures from raw point clouds, two stages of regularization are employed. In the first stage, the boundary points of an individual plane are consolidated locally by shifting them along their refined normal vector to resist noise, and then grouped into piecewise smooth segments. In the second stage, global regularities among different segments from different planes are softly enforced through a labeling process, in which the same label represents parallel or orthogonal segments. This is formulated as a Markov random field and solved efficiently via graph cut. The performance of the proposed method is evaluated for extracting 2D footprints and 3D polygons of buildings in metropolitan area. The results reveal that the proposed method is superior to the state-of-art methods both qualitatively and quantitatively in compactness. The simplified polygons could fit the original boundary points with an average residuals of 0.2 m, and in the meantime reduce up to 90% complexities of the edges. The satisfactory performances of the proposed method show a promising potential for 3D reconstruction of polygonal models from noisy point clouds.
A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data
Authors: Lou Yidong , Zhang Tian , Tang Jian , Song Weiwei , Zhang Yi , Chen Liang
Published: December 2018

Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck/onboard GNSS receiver/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required.
High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction
Authors: Kanning Martin , Kühling Insa , Trautz Dieter , Jarmer Thomas
Published: December 2018

The efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400–1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R2LAI = 0.79, RMSELAI [m2m−2] = 0.18, R2CHL = 0.77, RMSECHL [µg cm−2] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R2yield = 0.88, RMSEyield [dt ha−1] = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield.
Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm
Authors: Ke Xiaoquan , Yuan Fei , Cheng En
Published: December 2018

For the purpose of improving the accuracy of underwater acoustic target recognition with only a small number of labeled data, we proposed a novel recognition method, including 4 steps: pre-processing, pre-training, fine-tuning and recognition. The 4 steps can be explained as follows: (1) Pre-processing with Resonance-based Sparsity Signal Decomposition (RSSD): RSSD was firstly utilized to extract high-resonance components from ship-radiated noise. The high-resonance components contain the major information for target recognition. (2) Pre-training with unsupervised feature-extraction: we proposed a one-dimensional convolution autoencoder-decoder model and then we pre-trained the model to extract features from the high-resonance components. (3) Fine-tuning with supervised feature-separation: a supervised feature-separation algorithm was proposed to fine-tune the model and separate the extracted features. (4) Recognition: classifiers were trained to recognize the separated features and complete the recognition mission. The unsupervised pre-training autoencoder-decoder can make good use of a large number of unlabeled data, so that only a small number of labeled data are required in the following supervised fine-tuning and recognition, which is quite effective when it is difficult to collect enough labeled data. The recognition experiments were all conducted on ship-radiated noise data recorded using a sensory hydrophone. By combining the 4 steps above, the proposed recognition method can achieve recognition accuracy of 93.28%, which sufficiently surpasses other traditional state-of-art feature-extraction methods.
Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities
Authors: Zhang Hongsheng , Wang Ting , Zhang Yuhan , Dai Yiru , Jia Jiangjie , Yu Chang , Li Gang , Lin Yinyi , Lin Hui , Cao Yang
Published: December 2018

Short-term characteristics of urban land cover change have been observed and reported from satellite images, although urban landscapes are mainly influenced by anthropogenic factors. These short-term changes in urban areas are caused by rapid urbanization, seasonal climate changes, and phenological ecological changes. Quantifying and understanding these short-term characteristics of changes in various land cover types is important for numerous urban studies, such as urbanization assessments and management. Many previous studies mainly investigated one study area with insufficient datasets. To more reliably and confidently investigate temporal variation patterns, this study employed Fourier series to quantify the seasonal changes in different urban land cover types using all available Landsat images over four different cities, Melbourne, Sao Paulo, Hamburg, and Chicago, within a five-year period (2011–2015). The overall accuracy was greater than 86% and the kappa coefficient was greater than 0.80. The R-squared value was greater than 0.80 and the root mean square error was less than 7.2% for each city. The results indicated that (1) the changing periods for water classes were generally from half a year to one and a half years in different areas; and, (2) urban impervious surfaces changed over periods of approximately 700 days in Melbourne, Sao Paulo, and Hamburg, and a period of approximately 215 days in Chicago, which was actually caused by the unavoidable misclassification from confusions between various land cover types using satellite data. Finally, the uncertainties of these quantification results were analyzed and discussed. These short-term characteristics provided important information for the monitoring and assessment of urban areas using satellite remote sensing technology.
Experimental Phantom-Based Security Analysis for Next-Generation Leadless Cardiac Pacemakers
Authors: Awan Muhammad Faheem , Perez-Simbor Sofia , Garcia-Pardo Concepcion , Kansanen Kimmo , Cardona Narcis
Published: December 2018

With technological advancement, implanted medical devices can treat a wide range of chronic diseases such as cardiac arrhythmia, deafness, diabetes, etc. Cardiac pacemakers are used to maintain normal heart rhythms. The next generation of these pacemakers is expected to be completely wireless, providing new security threats. Thus, it is critical to secure pacemaker transmissions between legitimate nodes from a third party or an eavesdropper. This work estimates the eavesdropping risk and explores the potential of securing transmissions between leadless capsules inside the heart and the subcutaneous implant under the skin against external eavesdroppers by using physical-layer security methods. In this work, we perform phantom experiments to replicate the dielectric properties of the human heart, blood, and fat for channel modeling between in-body-to-in-body devices and from in-body-to-off-body scenario. These scenarios reflect the channel between legitimate nodes and that between a legitimate node and an eavesdropper. In our case, a legitimate node is a leadless cardiac pacemaker implanted in the right ventricle of a human heart transmitting to a legitimate receiver, which is a subcutaneous implant beneath the collar bone under the skin. In addition, a third party outside the body is trying to eavesdrop the communication. The measurements are performed for ultrawide band (UWB) and industrial, scientific, and medical (ISM) frequency bands. By using these channel models, we analyzed the risk of using the concept of outage probability and determine the eavesdropping range in the case of using UWB and ISM frequency bands. Furthermore, the probability of positive secrecy capacity is also determined, along with outage probability of a secrecy rate, which are the fundamental parameters in depicting the physical-layer security methods. Here, we show that path loss follows a log-normal distribution. In addition, for the ISM frequency band, the probability of successful eavesdropping for a data rate of 600 kbps (Electromyogram (EMG)) is about 97.68% at an eavesdropper distance of 1.3 m and approaches 28.13% at an eavesdropper distance of 4.2 m, whereas for UWB frequency band the eavesdropping risk approaches 0.2847% at an eavesdropper distance of 0.22 m. Furthermore, the probability of positive secrecy capacity is about 44.88% at eavesdropper distance of 0.12 m and approaches approximately 97% at an eavesdropper distance of 0.4 m for ISM frequency band, whereas for UWB, the same statistics are 96.84% at 0.12 m and 100% at 0.4 m. Moreover, the outage probability of secrecy capacity is also determined by using a fixed secrecy rate.
Variational Pansharpening for Hyperspectral Imagery Constrained by Spectral Shape and Gram–Schmidt Transformation
Authors: Huang Zehua , Chen Qi , Chen Qihao , Liu Xiuguo
Published: December 2018

Image pansharpening can generate a high-resolution hyperspectral (HS) image by combining a high-resolution panchromatic image and a HS image. In this paper, we propose a variational pansharpening method for HS imagery constrained by spectral shape and Gram–Schmidt (GS) transformation. The main novelties of the proposed method are the additional spectral and correlation fidelity terms. First, we design the spectral fidelity term, which utilizes the spectral shape feature of the neighboring pixels with a new weight distribution strategy to reduce spectral distortion caused by the change in spatial resolution. Second, we consider that the correlation fidelity term uses the result of GS adaptive (GSA) to constrain the correlation, thereby preventing the low correlation between the pansharpened image and the reference image. Then, the pansharpening is formulized as the minimization of a new energy function, whose solution is the pansharpened image. In comparative trials, the proposed method outperforms GSA, guided filter principal component analysis, modulation transfer function, smoothing filter-based intensity modulation, the classic and the band-decoupled variational methods. Compared with the classic variation pansharpening, our method decreases the spectral angle from 3.9795 to 3.2789, decreases the root-mean-square error from 309.6987 to 228.6753, and also increases the correlation coefficient from 0.9040 to 0.9367.
Reinforcement Learning-Based Satellite Attitude Stabilization Method for Non-Cooperative Target Capturing
Authors: Ma Zhong , Wang Yuejiao , Yang Yidai , Wang Zhuping , Tang Lei , Ackland Stephen
Published: December 2018

When a satellite performs complex tasks such as discarding a payload or capturing a non-cooperative target, it will encounter sudden changes in the attitude and mass parameters, causing unstable flying and rolling of the satellite. In such circumstances, the change of the movement and mass characteristics are unpredictable. Thus, the traditional attitude control methods are unable to stabilize the satellite since they are dependent on the mass parameters of the controlled object. In this paper, we proposed a reinforcement learning method to re-stabilize the attitude of a satellite under such circumstances. Specifically, we discretize the continuous control torque, and build a neural network model that can output the discretized control torque to control the satellite. A dynamics simulation environment of the satellite is built, and the deep Q Network algorithm is then performed to train the neural network in this simulation environment. The reward of the training is the stabilization of the satellite. Simulation experiments illustrate that, with the iteration of training progresses, the neural network model gradually learned to re-stabilize the attitude of a satellite after unknown disturbance. As a contrast, the traditional PD (Proportion Differential) controller was unable to re-stabilize the satellite due to its dependence on the mass parameters. The proposed method adopts self-learning to control satellite attitudes, shows considerable intelligence and certain universality, and has a strong application potential for future intelligent control of satellites performing complex space tasks.
Deformation Analysis of a Composite Bridge during Proof Loading Using Point Cloud Processing
Authors: Ziolkowski Patryk , Szulwic Jakub , Miskiewicz Mikolaj
Published: December 2018

Remote sensing in structural diagnostics has recently been gaining attention. These techniques allow the creation of three-dimensional projections of the measured objects, and are relatively easy to use. One of the most popular branches of remote sensing is terrestrial laser scanning. Laser scanners are fast and efficient, gathering up to one million points per second. However, the weakness of terrestrial laser scanning is the troublesome processing of point clouds. Currently, many studies deal with the subject of point cloud processing in various areas, but it seems that there are not many clear procedures that we can use in practice, which indicates that point cloud processing is one of the biggest challenges of this issue. To tackle that challenge we propose a general framework for studying the structural deformations of bridges. We performed an advanced object shape analysis of a composite foot-bridge, which is subject to spatial deformations during the proof loading process. The added value of this work is the comprehensive procedure for bridge evaluation, and adaptation of the spheres translation method procedure for use in bridge engineering. The aforementioned method is accurate for the study of structural element deformation under monotonic load. The study also includes a comparative analysis between results from the spheres translation method, a total station, and a deflectometer. The results are characterized by a high degree of convergence and reveal the highly complex state of deformation more clearly than can be concluded from other measurement methods, proving that laser scanning is a good method for examining bridge structures with several competitive advantages over mainstream measurement methods.
Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images
Authors: Vinayaraj Poliyapram , Imamoglu Nevrez , Nakamura Ryosuke , Oda Atsushi
Published: December 2018

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.
A Novel Instantaneous Phase Detection Approach and Its Application in SSVEP-Based Brain-Computer Interfaces
Authors: Huang Xiangdong , Xu Jingwen , Wang Zheng
Published: December 2018

This paper proposes a novel phase estimator based on fully-traversed Discrete Fourier Transform (DFT) which takes all possible truncated DFT spectra into account such that it possesses two merits of `direct phase extraction’ (namely accurate instantaneous phase information can be extracted without any correction) and suppressing spectral leakage. This paper also proves that the proposed phase estimator complies with the 2-parameter joint estimation model rather than the conventional 3-parameter joint model. Numerical results verify the above two merits and demonstrate that the proposed estimator can extract phase information from noisy multi-tone signals. Finally, real data analysis shows that fully-traversed DFT can achieve a better classification on the phase of steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) than the conventional DFT estimator does. Besides, the proposed phase estimator imposes no restrictions on the relationship between the sampling rates and the stimulus frequencies, thus it is capable of wider applications in phase-coded SSVEP BCIs, when compared with the existing estimators.
Crack Assessment of Wheel Hubs via an Ultrasonic Transducer and Industrial Robot
Authors: Zhang Hanming , Xu Chunguang , Xiao Dingguo
Published: December 2018

Crack assessment when making fitness-for-service decisions requires a thorough examination of crack location and size in critical areas. An ultrasonic transducer is used for such assessments, but traditional methods cannot cope with complex rotators, such as wheel hubs. We present a model of robot-assisted crack growth assessment in wheel hubs. We integrate a six-degrees-of-freedom (DOF) industrial robot and a turntable to form a robot-assisted ultrasonic testing (UT) system that does not use traditional UT equipment. Ultrasonic beams are focused at certain depths appropriate for achieving maximum sensitivity. We quantitatively analysed wheel hubs with longitudinal and transverse series of pre-cracks, and concluded that our system autonomously detected cracks.
A Fast Response−Recovery 3D Graphene Foam Humidity Sensor for User Interaction
Authors: Yu Yu , Zhang Yating , Jin Lufan , Chen Zhiliang , Li Yifan , Li Qingyan , Cao Mingxuan , Che Yongli , Yang Junbo , Yao Jianquan
Published: December 2018

Humidity sensors allow electronic devices to convert the water content in the environment into electronical signals by utilizing material properties and transduction techniques. Three-dimensional graphene foam (3DGF) can be exploited in humidity sensors due to its convenient features including low-mass density, large specific surface area, and excellent electrical. In this paper, 3DGF with super permeability to water enables humidity sensors to exhibit a broad relative humidities (RH) range, from 0% to 85.9%, with a fast response speed (response time: ~89 ms, recovery time: ~189 ms). To interpret the physical mechanism behind this, we constructed a 3DGF model decorated with water to calculate the energy structure and we carried out the CASTEP as implemented in Materials Studio 8.0. This can be ascribed to the donor effect, namely, the electronic donation of chemically adsorbed water molecules to the 3DGF surface. Furthermore, this device can be used for user interaction (UI) with unprecedented performance. These high performances support 3DGF as a promising material for humidity sensitive material.
A Wireless Gas Sensor Network to Monitor Indoor Environmental Quality in Schools
Authors: Ortiz Perez Alvaro , Bierer Benedikt , Scholz Louisa , Wöllenstein Jürgen , Palzer Stefan
Published: December 2018

Schools are amongst the most densely occupied indoor areas and at the same time children and young adults are the most vulnerable group with respect to adverse health effects as a result of poor environmental conditions. Health, performance and well-being of pupils crucially depend on indoor environmental quality (IEQ) of which air quality and thermal comfort are central pillars. This makes the monitoring and control of environmental parameters in classes important. At the same time most school buildings do neither feature automated, intelligent heating, ventilation, and air conditioning (HVAC) systems nor suitable IEQ monitoring systems. In this contribution, we therefore investigate the capabilities of a novel wireless gas sensor network to determine carbon dioxide concentrations, along with temperature and humidity. The use of a photoacoustic detector enables the construction of long-term stable, miniaturized, LED-based non-dispersive infrared absorption spectrometers without the use of a reference channel. The data of the sensor nodes is transmitted via a Z-Wave protocol to a central gateway, which in turn sends the data to a web-based platform for online analysis. The results show that it is difficult to maintain adequate IEQ levels in class rooms even when ventilating frequently and that individual monitoring and control of rooms is necessary to combine energy savings and good IEQ.
A Randomized Watermarking Technique for Detecting Malicious Data Injection Attacks in Heterogeneous Wireless Sensor Networks for Internet of Things Applications
Authors: Alromih Arwa , Al-Rodhaan Mznah , Tian Yuan
Published: December 2018

Using Internet of Things (IoT) applications has been a growing trend in the last few years. They have been deployed in several areas of life, including secure and sensitive sectors, such as the military and health. In these sectors, sensory data is the main factor in any decision-making process. This introduces the need to ensure the integrity of data. Secure techniques are needed to detect any data injection attempt before catastrophic effects happen. Sensors have limited computational and power resources. This limitation creates a challenge to design a security mechanism that is both secure and energy-efficient. This work presents a Randomized Watermarking Filtering Scheme (RWFS) for IoT applications that provides en-route filtering to remove any injected data at an early stage of the communication. Filtering injected data is based on a watermark that is generated from the original data and embedded directly in random places throughout the packet’s payload. The scheme uses homomorphic encryption techniques to conceal the report’s measurement from any adversary. The advantage of homomorphic encryption is that it allows the data to be aggregated and, thus, decreases the packet’s size. The results of our proposed scheme prove that it improves the security and energy consumption of the system as it mitigates some of the limitations in the existing works.
Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)
Authors: Choi Ho Seon , Lee Chang Hee , Shim Myounghoon , Han Jong In , Baek Yoon Su
Published: December 2018

As an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reaction force (GRF) were not sufficiently accurate because of the fundamental limitations, fixed coordinates and narrow sensing areas, which cannot cover the whole insole. To address these issues, in this paper, we describe an algorithm of virtual forces and corresponding coordinates with an artificial neural network (ANN) for low-cost flexible insole pressure measurement sensors. The proposed algorithm estimates the magnitude of the GRF and the location of the foot plantar CoP. To compose the algorithm, we divided the insole area into six areas and created six virtual forces and the corresponding coordinates. We used the ANN algorithm with the input of magnitudes of FSR sensors, 1st and 2nd derivatives of them to estimate the virtual forces and coordinates. Eight healthy males were selected for data acquisition. They performed an experiment composed of the following motions: standing with weight shifting, walking with 1 km/h and 2 km/h, squatting and getting up from a sitting position to a standing position. The ANN for estimating virtual forces and corresponding coordinates was fitted according to those data, converted to c script, and downloaded to a microcontroller for validation experiments in real time. The results showed an average RMSE the whole experiment of 31.154 N for GRF estimation and 8.07 mm for CoP calibration. The correlation coefficients of the algorithm were 0.94 for GRF, 0.92 and 0.76 for the X and Y coordinate respectively.