bluuebunny/crossref_metadata_2025 huggingface dataset:
SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure
Authors: Dehzangi Abdollah , López Yosvany , Taherzadeh Ghazaleh , Sharma Alok , Tsunoda Tatsuhiko
Published: December 2018

Post Translational Modification (PTM) is defined as the modification of amino acids along the protein sequences after the translation process. These modifications significantly impact on the functioning of proteins. Therefore, having a comprehensive understanding of the underlying mechanism of PTMs turns out to be critical in studying the biological roles of proteins. Among a wide range of PTMs, sumoylation is one of the most important modifications due to its known cellular functions which include transcriptional regulation, protein stability, and protein subcellular localization. Despite its importance, determining sumoylation sites via experimental methods is time-consuming and costly. This has led to a great demand for the development of fast computational methods able to accurately determine sumoylation sites in proteins. In this study, we present a new machine learning-based method for predicting sumoylation sites called SumSec. To do this, we employed the predicted secondary structure of amino acids to extract two types of structural features from neighboring amino acids along the protein sequence which has never been used for this task. As a result, our proposed method is able to enhance the sumoylation site prediction task, outperforming previously proposed methods in the literature. SumSec demonstrated high sensitivity (0.91), accuracy (0.94) and MCC (0.88). The prediction accuracy achieved in this study is 21% better than those reported in previous studies. The script and extracted features are publicly available at: https://github.com/YosvanyLopez/SumSec.
New Protocol for Cell Culture to Obtain Mitotic Chromosomes in Fishes
Authors: Paim Fabilene G. , Maia Leandro , Landim-Alvarenga Fernanda da Cruz , Foresti Fausto , Oliveira Claudio
Published: December 2018

Cell culture is an excellent alternative for the maintenance of cell lines and to obtain quality chromosome preparations of fishes. However, this methodology is still little employed, mainly because of the difficulty of standardization of cell cultures. In this study, we describe a methodology for the rapid acquisition of cell lineages and mitotic chromosomes for cytogenetic studies of fish species from muscle tissue cells. Our methodology is based on the use of a gelatin film, which provides better adhesion of a large number of cells and appropriate conditions for multiplication. The cells of Astyanax altiparanae, used as an experimental model, with fibroblast-like morphology, showed rapid cellular proliferation, resulting in a great number of cells. Chromosomal preparations of cultured cells showed the diploid number of the species, 2n = 50 chromosomes, in 80% of the cells examined, with chromosomes intact and distended. Cell populations were cryopreserved and after being recovered, these cells maintained their proliferative capacity. The development of this methodology represents an innovation for the fish cytogenetics area and it may bring a significant contribution to the conservation and study of several groups due to the difficulty of obtaining good-quality chromosome preparations.
Egg Intake in Chronic Kidney Disease
Authors: Tallman Dina A. , Sahathevan Sharmela , Karupaiah Tilakavati , Khosla Pramod
Published: December 2018

Patients with chronic kidney disease (CKD) are often instructed to adhere to a renal-specific diet depending on the severity and stage of their kidney disease. The prescribed diet may limit certain nutrients, such as phosphorus and potassium, or encourage the consumption of others, such as high biological value (HBV) proteins. Eggs are an inexpensive, easily available and high-quality source of protein, as well as a rich source of leucine, an essential amino acid that plays a role in muscle protein synthesis. However, egg yolk is a concentrated source of both phosphorus and the trimethylamine N-oxide precursor, choline, both of which may have potentially harmful effects in CKD. The yolk is also an abundant source of cholesterol which has been extensively studied for its effects on lipoprotein cholesterol and the risk of cardiovascular disease. Efforts to reduce dietary cholesterol to manage dyslipidemia in dialysis patients (already following a renal diet) have not been shown to offer additional benefit. There is a paucity of data regarding the impact of egg consumption on lipid profiles of CKD patients. Additionally, egg consumption has not been associated with the risk of developing CKD based on epidemiological studies. The egg yolk also contains bioactive compounds, including lutein, zeaxanthin, and vitamin D, which may confer health benefits in CKD patients. Here we review research on egg intake and CKD, discuss both potential contraindications and favorable effects of egg consumption, and describe the need for further research examining egg intake and outcomes in the CKD and end-stage renal disease population.
Impaired Glucose Tolerance in Adults with Duchenne and Becker Muscular Dystrophy
Authors: Bostock Emma L. , Edwards Bryn T. , Jacques Matthew F. , Pogson Jake T.S. , Reeves Neil D. , Onambele-Pearson Gladys L. , Morse Christopher I.
Published: December 2018

The aim of this study was to determine the response to an oral glucose tolerance test (OGTT) in adult males with Becker muscular dystrophy (BMD) and Duchenne muscular dystrophy (DMD), and to investigate whether body composition contributes to any variance in the glucose response. Twenty-eight adult males with dystrophinopathy (BMD, n = 13; DMD, n = 15) and 12 non-dystrophic controls, ingested 75 g oral anhydrous glucose solution. Fingertip capillary samples were assessed for glucose at 30-min intervals over 2-h post glucose ingestion. Fat free mass relative to body mass (FFM/BM) and body fat (BF%) was assessed using bioelectrical impedance. Vastus lateralis muscle anatomical cross sectional area (VL ACSA) was measured using B-mode ultrasonography. Blood glucose was higher in MD groups than control at 60, 90 and 120 min post ingestion of glucose. Compared to controls, FFM/BM and VL ACSA were lower in MD groups compared to controls (p < 0.001). Glucose tolerance values at 120 min were correlated with FFM/BM and BF% in the BMD group only. Our results suggest that glucose tolerance is impaired following OGTT in adult males with BMD and DMD. It is recommended that adults with BMD and DMD undertake routine glucose tolerance assessments to allow early detection of impaired glucose tolerance.
Investigation of Different Iontophoretic Currents Profiles for Short-Term Applications in Cosmetics
Authors: Cázares-Delgadillo Jennyfer , Planard-Luong Lien , Gregoire Sébastian , Serna-Jiménez César E. , Singhal Mayank , Kalia Yogeshvar N. , Merino Virginia , Merino-Sanjuán Matilde , Nácher Amparo , Martínez-Gómez Ma. Amparo , Burnier-Yalaoui Veronique , Barbarat Philippe
Published: December 2018

This study aimed at investigating the effect of electrical current profile upon the iontophoretic transport of (i) ascorbic acid (AA) and (ii) ellagic acid (EA), into porcine skin in vitro, and the impact of the physicochemical properties of both actives on their mechanism of transport when formulated in cosmetic compositions. The experiments were performed using a proprietary iontophoretic device containing a roller to apply the formulation. Three current profiles were tested: (i) galvanic direct current (DC), (ii) square unipolar pulse current (SPC), and (iii) galvanic direct current (DC) + pulse current (PC). The skin samples were collected at different sampling points, extracted and analyzed by HPLC. Results suggested that the DC + PC mode for only 5 min was able to significantly increase the delivery of AA from o/w cosmetic compositions. The use of this current profile might improve the skin penetration of AA due to electromigration and passive diffusion, the latter being facilitated by the physical enhancement method. The SPC mode significantly improved the passage of EA in its neutral form from cosmetic o/w formulations by electroosmosis. Tailoring specific electrical current modes considering the ionization state of active ingredients would allow the design of short and personalized cosmetic treatments that significantly improve the penetration efficiency of the active ingredients and possibly reduce the doses applied.
United States Physician Preferences Regarding Healthcare Financing Options: A Multistate Survey
Authors: Khan Shamima , Spooner Joshua J. , Spotts Harlan E.
Published: December 2018

Background: Not much is currently known about United States (US) physicians’ opinions about healthcare financing, specifically subsequent to the creation and implementation of the Affordable Care Act (ACA). Objectives: A four state survey of practicing US based physicians’ opinions about healthcare financing following ACA passage and implementation. Methods: Physician leaders practicing in the state of New York, Texas, Colorado and Mississippi were surveyed. Two factor analyses (FA) were conducted to understand the underlying constructs. Results: We determined the final response rate to be 26.7% after adjusting it for a variety of factors. Most physicians favored either a single payer system (43.8%) or individualized insurance coverage using health savings accounts (33.2%). For the single-payer system, FA revealed two underlying constructs: System orientation (how the physicians perceived the impact on the healthcare system or patients) and individual orientation (how the physicians perceived the impact on individual physicians). Subsequently, we found that physicians who were perceived neutral in their attitudes towards physician-patient relationship and patient conflict were also neutral in reference to system orientation and individual orientation. Physicians who were perceived as stronger on the physician-patient relationship were more supportive of a single-payer system. Conclusion: This study brings attention to the paradox of social responsibility (to provide quality healthcare) and professional autonomy (the potential impact of a healthcare financing structure to negatively affect income and workload). Efforts to further reform healthcare financing and delivery in the US may encounter resistance from healthcare providers (physicians, mid-level prescribers, pharmacists, or nurses) if the proposed reform interferes with their professional autonomy.
Research Support Services in Spanish Academic Libraries: An Analysis of Their Strategic Plans and of an Opinion Survey Administered to Their Directors
Authors: Borrego Ángel , Anglada Lluís
Published: December 2018

The transition from print to digital information has transformed the role of academic libraries, which have had to redefine themselves as intermediaries and partners in the learning and research processes. This study analyzes the evolution and current provision of research support services in Spanish academic libraries through an analysis of the three strategic plans published by the Spanish association of academic libraries (REBIUN) and a survey administered to the library directors. Results show that academic libraries are firmly embedded within universities’ research activities, and that most of them depend on vice-rectorates for research or scientific policy. There is a relationship between the size of the library and its provision of research support services, although no correlation is observed between the size of the library and the staff devoted to research support. Library directors stress the value of institutional repositories, a vision also reflected in the importance assigned to open access in the strategic plans. Other current hot topics, such as data management, do not seem to be among the priorities of Spanish library directors as yet.
Karst as Important Resource for Geopark-Based Tourism: Current State and Biases
Authors: Ruban Dmitry A.
Published: December 2018

The United Nations Educational, Scientific and Cultural Organization (UNESCO) Global Geoparks initiative balances the conservation of geological heritage with its use in purposes of tourism industry. However, the resources of geoparks and the current state of their use are yet to be fully understood. The phenomenon of karst (caves, sinkholes, etc.) appears to be a valuable geopark resource because it attracts numerous visitors interested in geo-, eco-, and speleotourism. Of 140 global geoparks, 37% exploit karst resources. These are located chiefly in Europe and Southeast Asia. Just a few geoparks are fully based on karst features and the others use this resource together with other geological heritage resources. Global geoparks tend to emphasize either particular karst elements or entire karst landscapes. Many interesting features (e.g., gypsum and salt karst) are underrepresented in global geoparks. The UNESCO Global Geoparks initiative should become more phenomenon-focused to offer full representation of karst resources. For some countries like Russia and the United States, where geoparks have not been created yet, the consideration of karst resources may provide significant advantages in the strategic development of geopark-based geotourism.
Stakeholder Management: An Approach in CCS Projects
Authors: Ilinova Alina , Cherepovitsyn Alexey , Evseeva Olga
Published: December 2018

Currently, a prime position in the global emission mitigation and power transformation system is taken up by CCS technology (carbon capture and storage), proven by the successful realization of a number of CCS projects around the world, not only for CO2 storage, but also for its deployment in industry. CO2-utilizing technologies are circular business models connected with the usage of emissions to provide added benefits throughout the value chain, with their value being part of a response to end the era of cheap mineral resources and materials, as well as their ability to contribute to the transition to a low carbon economy. At the same time, one of the main problems they face lies in the engagement and interaction of stakeholders, as well as public perception of these projects. The purpose of this research is to explore the main stakeholder groups in CCS projects and offer an approach for their management. The methodology of this study is based on case studies, stakeholder management tools, and a checklist method, which were adapted to the specific area of CCS projects. The results of this study amounted to the identification of stakeholder groups with interests and respective roles in CCS projects, as well as proposing a new approach for their assessment through the created checklist method.
A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations
Authors: Fischer Matthias , Moser Thorsten , Pfeuffer Marius
Published: December 2018

In both financial theory and practice, Value-at-risk (VaR) has become the predominant risk measure in the last two decades. Nevertheless, there is a lively and controverse on-going discussion about possible alternatives. Against this background, our first objective is to provide a current overview of related competitors with the focus on credit risk management which includes definition, references, striking properties and classification. The second part is dedicated to the measurement of risk concentrations of credit portfolios. Typically, credit portfolio models are used to calculate the overall risk (measure) of a portfolio. Subsequently, Euler’s allocation scheme is applied to break the portfolio risk down to single counterparties (or different subportfolios) in order to identify risk concentrations. We first carry together the Euler formulae for the risk measures under consideration. In two cases (Median Shortfall and Range-VaR), explicit formulae are presented for the first time. Afterwards, we present a comprehensive study for a benchmark portfolio according to Duellmann and Masschelein (2007) and nine different risk measures in conjunction with the Euler allocation. It is empirically shown that—in principle—all risk measures are capable of identifying both sectoral and single-name concentration. However, both complexity of IT implementation and sensitivity of the risk figures w.r.t. changes of portfolio quality vary across the specific risk measures.
Nearshore Benthic Habitat Mapping Based on Multi-Frequency, Multibeam Echosounder Data Using a Combined Object-Based Approach: A Case Study from the Rowy Site in the Southern Baltic Sea
Authors: Janowski Lukasz , Trzcinska Karolina , Tegowski Jaroslaw , Kruss Aleksandra , Rucinska-Zjadacz Maria , Pocwiardowski Pawel
Published: December 2018

Recently, the rapid development of the seabed mapping industry has allowed researchers to collect hydroacoustic data in shallow, nearshore environments. Progress in marine habitat mapping has also helped to distinguish the seafloor areas of varied acoustic properties. As a result of these new developments, we have collected a multi-frequency, multibeam echosounder dataset from the valuable nearshore environment of the southern Baltic Sea using two frequencies: 150 kHz and 400 kHz. Despite its small size, the Rowy area is characterized by diverse habitat conditions and the presence of red algae, unique on the Polish coast of the Baltic Sea. This study focused on the utilization of multibeam bathymetry and multi-frequency backscatter data to create reliable maps of the seafloor. Our approach consisted of the extraction of 70 secondary features of bathymetric and backscatter data, including statistic and textural attributes of different scales. Based on ground-truth samples, we have identified six habitat classes and selected the most relevant features of the bathymetric and backscatter data. Additionally, five types of image processing pixel-based and object-based classifiers were tested. We also evaluated the performance of algorithms using an accuracy assessment based on the validation subset of the ground-truth samples. Our best results reached 93% overall accuracy and a kappa coefficient of 0.90, confirming that nearshore seabed habitats can be accurately distinguished based on multi-frequency, multibeam echosounder measurements. Our predictive habitat mapping of shallow euphotic zones creates a new scientific perspective and provides relevant data for the management of natural resources. Object-based approaches previously used in various environments and areas suggest that methodology presented in this study may be scalable.
A Novel Deep Fully Convolutional Network for PolSAR Image Classification
Authors: Li Yangyang , Chen Yanqiao , Liu Guangyuan , Jiao Licheng
Published: December 2018

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR image classification. However, there are some problems to be solved in PolSAR image classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR image classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the image integrity can be maintained in the maximum extent. We use three PolSAR images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR image classification.
Partitioning of Terrain Features Based on Roughness
Authors: Wu Jiang , Yang Qinke , Li Yuru
Published: December 2018

Surface roughness is a key parameter that reflects topographic characteristics and influences surface processes, and characterization of surface roughness is a fundamental problem in geoscience. In recent years, although there have been basic studies on roughness, few studies have compared the concept and quantification of roughness, and there have been few studies that have evaluated the ability of partition terrain features. Based on 1″ resolution Shuttle Radar Topography Mission (SRTM) data and previous studies, we selected the Qinba Mountain region of China and its adjacent areas as our study area, and used 13 different roughness algorithms to extract roughness in this study. Using spatial patterns and statistical distributions, the results were analyzed, and the best algorithm suited to partitioning terrain features was selected. We then evaluated the ability of the algorithm to distinguish the terrain morphology. The results showed the following: (1) The 13 algorithms were able to be classified into four types, that is, gradient (SLOPE), relief (root mean squared height, RMSH), local vector (directional cosine eigenvalue, DCE) and power-spectral (two-dimensional continuous wavelet transform, 2D CWT). (2) The SLOPE and RMSH algorithms were better able to express and distinguish terrain, as they were able to macroscopically distinguish between four types of terrain in the study areas. Based on power-spectral methods, 2D CWT had the same discrimination ability as the first two methods following a normalization transform, whereas the DCE method had a general effect and could only distinguish two types of terrain. (3) Different roughness algorithms had their own applicability for different terrain areas and application directions.
The Outlining of Agricultural Plots Based on Spatiotemporal Consensus Segmentation
Authors: Garcia-Pedrero Angel , Gonzalo-Martín Consuelo , Lillo-Saavedra Mario , Rodríguez-Esparragón Dionisio
Published: December 2018

The outlining of agricultural land is an important task for obtaining primary information used to create agricultural policies, estimate subsidies and agricultural insurance, and update agricultural geographical databases, among others. Most of the automatic and semi-automatic methods used for outlining agricultural plots using remotely sensed imagery are based on image segmentation. However, these approaches are usually sensitive to intra-plot variability and depend on the selection of the correct parameters, resulting in a poor performance due to the variability in the shape, size, and texture of the agricultural landscapes. In this work, a new methodology based on consensus image segmentation for outlining agricultural plots is presented. The proposed methodology combines segmentation at different scales—carried out using a superpixel (SP) method—and different dates from the same growing season to obtain a single segmentation of the agricultural plots. A visual and numerical comparison of the results provided by the proposed methodology with field-based data (ground truth) shows that the use of segmentation consensus is promising for outlining agricultural plots in a semi-supervised manner.
Evaluation of the Weak Constraint Data Assimilation Approach for Estimating Turbulent Heat Fluxes at Six Sites
Authors: He Xinlei , Xu Tongren , Bateni Sayed M. , Neale Christopher M. U. , Auligne Thomas , Liu Shaomin , Wang Kaicun , Mao Kebiao , Yao Yunjun
Published: December 2018

A number of studies have estimated turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into the strong constraint-variational data assimilation (SC-VDA) approaches. The SC-VDA approaches do not account for the structural model errors and uncertainties in the micrometeorological variables. In contrast to the SC-VDA approaches, the WC-VDA approach (the so-called weak constraint-VDA) accounts for the effects of structural and model errors by adding a model error term. In this study, the WC-VDA approach is tested at six study sites with different climatic and vegetative conditions. Its performance is also compared with that of SC-VDA at the six study sites. The results show that the WC-VDA produces 10.16% and 10.15% lower root mean square errors (RMSEs) for sensible and latent heat flux estimates compared with the SC-VDA approach. The model error term can capture errors in the turbulent heat flux estimates due to errors in LST and micrometeorological measurements, as well as structural model errors, and does not allow those errors to adversely affect the turbulent heat flux estimates. The findings also indicate that the estimated model error term varies reasonably well, so as to capture the misfit between predicted and observed net radiation in different hydrological and vegetative conditions. Finally, synthetically generated positive (negative) noises are added to the hydrological input variables (e.g., LST, air temperature, air humidity, incoming solar radiation, and wind speed) to examine whether the WC-VDA approach can capture those errors. It was found that the WC-VDA approach accounts for the errors in the input data and reduces their effect on the turbulent heat flux estimates.
An Automated Hierarchical Approach for Three-Dimensional Segmentation of Single Trees Using UAV LiDAR Data
Authors: Yan Wanqian , Guan Haiyan , Cao Lin , Yu Yongtao , Gao Sha , Lu JianYong
Published: December 2018

Forests play a key role in terrestrial ecosystems, and the variables extracted from single trees can be used in various fields and applications for evaluating forest production and assessing forest ecosystem services. In this study, we developed an automated hierarchical single-tree segmentation approach based on the high density three-dimensional (3D) Unmanned Aerial Vehicle (UAV) point clouds. First, this approach obtains normalized non-ground UAV points in data preprocessing; then, a voxel-based mean shift algorithm is used to roughly classify the non-ground UAV points into well-detected and under-segmentation clusters. Moreover, potential tree apices for each under-segmentation cluster are obtained with regard to profile shape curves and finally input to the normalized cut segmentation (NCut) algorithm to segment iteratively the under-segmentation cluster into single trees. We evaluated the proposed method using datasets acquired by a Velodyne 16E LiDAR system mounted on a multi-rotor UAV. The results showed that the proposed method achieves the average correctness, completeness, and overall accuracy of 0.90, 0.88, and 0.89, respectively, in delineating single trees. Comparative analysis demonstrated that our method provided a promising solution to reliable and robust segmentation of single trees from UAV LiDAR data with high point cloud density.
Monitoring Crop Evapotranspiration and Crop Coefficients over an Almond and Pistachio Orchard Throughout Remote Sensing
Authors: Bellvert Joaquim , Adeline Karine , Baram Shahar , Pierce Lars , Sanden Blake , Smart David
Published: December 2018

In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (Kcb), or potential crop water use. The soil water evaporation coefficient (Ke) was measured from microlysimeters. The water stress coefficient (Ks) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ψstem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum Kcb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an R2 ≥ 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm·day−1 for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions.
Robust Switched Tracking Control for Wheeled Mobile Robots Considering the Actuators and Drivers
Authors: García-Sánchez José Rafael , Tavera-Mosqueda Salvador , Silva-Ortigoza Ramón , Hernández-Guzmán Victor Manuel , Sandoval-Gutiérrez Jacobo , Marcelino-Aranda Mariana , Taud Hind , Marciano-Melchor Magdalena
Published: December 2018

By using the hierarchical controller approach, a new solution for the control problem related to trajectory tracking in a differential drive wheeled mobile robot (DDWMR) is presented in this paper. For this aim, the dynamics of the three subsystems composing a DDWMR, i.e., the mechanical structure (differential drive type), the actuators (DC motors), and the power stage (DC/DC Buck power converters), are taken into account. The proposed hierarchical switched controller has three levels: the high level corresponds to a kinematic control for the mechanical structure; the medium level includes two controls based on differential flatness for the actuators; and the low level is linked to two cascade switched controls based on sliding modes and PI control for the power stage. The hierarchical switched controller was experimentally implemented on a DDWMR prototype via MATLAB-Simulink along with a DS1104 board. With the intention of assessing the performance of the switched controller, experimental results associated with a hierarchical average controller recently reported in literature are also presented here. The experimental results show the robustness of both controllers when parametric uncertainties are applied. However, the performance achieved with the switched controller introduced in the present paper is better than, or at least similar to, performance achieved with the average controller reported in literature.
Tensor Approach to DOA Estimation of Coherent Signals with Electromagnetic Vector-Sensor Array
Authors: Cao Ming-Yang , Mao Xingpeng , Long Xiaozhuan , Huang Lei
Published: December 2018

This paper addresses the direction-of-arrival (DOA) estimation problem using a uniform rectangular array with electromagnetic vector-sensors in correlated/coherent signal environments. The polarization information is separated from the steering matrix to decorrelate the signals. By developing a tensorial structured received measurements of the array, we propose a tensor-based eigenvector DOA estimation method. Then we apply the forward-backward averaging to the tensor since it obeys the centro-Hermitian structure. In addition, a tensor-based polarization parameters estimation method is presented. The proposed algorithms are superior to the state-of-the-art algorithms in terms of estimation accuracy of coherent signals while only demand a modest computation burden comparing with the latter ones. Simulation results are given to demonstrate the effectiveness of the proposed methods under different scenarios.
Experimental Demonstration of Temperature Sensing with Packaged Glass Bottle Microresonators
Authors: Herter Jonas , Wunderlich Valentin , Janeczka Christian , Zamora Vanessa
Published: December 2018

Whispering gallery mode (WGM) glass bottle microresonators are potential highly sensitive structures for a variety of physical and bio-chemical sensing applications. In this paper, we experimentally demonstrate the practical use of glass bottle resonators as temperature sensors. The basic parameters, such as WGM resonance wavelengths, free spectral ranges, and Q factors, have been investigated by coupling light from a tapered fiber to the bottle structure. We show the spectral characteristics of the WGMs by choosing different bottle dimensions and taper diameters. For practical measurements, a robust 3D-printed package that includes the bottle resonator and the tapered fiber has been proposed. The packaged bottle has a central diameter Dc = 207 µm and a length L = 300 µm. Temperature sensing experiments were also performed. A linear response of the WGM shifts as a function of the temperature is confirmed. The fitted experimental data indicate a temperature sensitivity of 10.5 pm/K at λ ~ 1550 nm, resulting in a limit of detection of 0.06 K. These values can be compared with values reported for other WGM resonators. Additionally, bottle resonators are made with simple splicing methods and their assembly method can be easily defined due to large coupling tolerances.
Research on the Temperature Characteristics of the Photoacoustic Sensor of Glucose Solution
Authors: Tao Wei , Lu Zhiqian , He Qiaozhi , Lv Pengfei , Wang Qian , Zhao Hui
Published: December 2018

In order to weaken the influence of temperature on photoacoustic (PA) measurements and compensate PA signals with a proposed theoretical model, the relationship of PA signal amplitude with temperature, under the condition of different glucose concentrations and different light intensities, was studied in this paper. First, the theoretical model was derived from the theory of the PA effect. Then, the temperature characteristics of the PA signals were investigated, based on the analyses of the temperature-dependent Grüneisen parameter in glucose solution. Next, the concept of a PA temperature coefficient was proposed in this paper. The result of the theoretical analysis shows that this coefficient is linear to light intensity and irrelevant to the concentration of glucose solution. Furthermore, a new concept of a PA temperature coefficient of unit light intensity was proposed in this paper. This coefficient is approximately constant, with different light intensities and solution concentrations, which is similar to the thermal expansion coefficient. After calculation, the PA temperature coefficient by the unit light intensity of glucose solution is about 0.936 bar/K. Finally, relevant experiments were carried out to verify the theoretical analysis, and the PA temperature coefficient of the unit light intensity of glucose solution is about 0.04/°C. This method can also be used in sensors measuring concentrations in other aqueous solutions.
Optical Fiber Magnetic Field Sensors Based on Magnetic Fluid: A Review
Authors: Alberto Nélia , Domingues Maria Fátima , Marques Carlos , André Paulo , Antunes Paulo
Published: December 2018

Magnetic field sensing is an important issue for many application areas, such as in the military, industry and navigation. The current sensors used to monitor this parameter can be susceptible to electromagnetic interferences, however due to their advantages over the traditional sensors, the optical fiber devices could be an excellent alternative. Furthermore, magnetic fluid (MF) is a new type of functional material which possesses outstanding properties, including Faraday effect, birefringence, tunable refractive index and field dependent transmission. In this paper, the optical fiber magnetic field sensors using MF as sensing element are reviewed. Due to the extensive literature, only the most used sensing configurations are addressed and discussed, which include optical fiber grating, interferometry, surface plasmon resonance (SPR) and other schemes involving tailored (etched, tapered and U-shaped) fibers.
Fluorescent Aptamer Immobilization on Inverse Colloidal Crystals
Authors: Chiappini Andrea , Pasquardini Laura , Nodehi Somayeh , Armellini Cristina , Bazzanella Nicola , Lunelli Lorenzo , Pelli Stefano , Ferrari Maurizio , Pietralunga Silvia M.
Published: December 2018

In this paper, we described a versatile two steps approach for the realization of silica inverse opals functionalized with DNA-aptamers labelled with Cy3 fluorophore. The co-assembly method was successfully employed for the realization of high quality inverse silica opal, whilst the inverse network was functionalized via epoxy chemistry. Morphological and optical assessment revealed the presence of large ordered domains with a transmission band gap depth of 32%, after the functionalization procedure. Finite Difference Time-Domain (FDTD) simulations confirmed the high optical quality of the inverse opal realized. Photoluminescence measurements evidenced the effective immobilization of DNA-aptamer molecules labelled with Cy3 throughout the entire sample thickness. This assumption was verified by the inhibition of the fluorescence of Cy3 fluorophore tailoring the position of the photonic band gap of the inverse opal. The modification of the fluorescence could be justified by a variation in the density of states (DOS) calculated by the Plane Wave Expansion (PWE) method. Finally, the development of the aforementioned approach could be seen as proof of the concept experiment, suggesting that this type of system may act as a suitable platform for the realization of fluorescence-based bio-sensors.
Outlier Detection in Wireless Sensor Networks Using Model Selection-Based Support Vector Data Descriptions
Authors: Huan Zhan , Wei Chang , Li Guang-Hui
Published: December 2018

Wireless sensor networks (WSNs) are often deployed in harsh and unattended environments, which may cause the generation of abnormal or low quality data. The inaccurate and unreliable sensor data may increase generation of false alarms and erroneous decisions, so it’s very important to detect outliers in sensor data efficiently and accurately to ensure sound scientific decision-making. In this paper, an outlier detection algorithm (TSVDD) using model selection-based support vector data description (SVDD) is proposed. Firstly, the Toeplitz matrix random feature mapping is used to reduce the time and space complexity of outlier detection. Secondly, a novel model selection strategy is realized to keep the algorithm stable under the low feature dimensions, this strategy can select a relatively optimal decision model and avoid both under-fitting and overfitting phenomena. The simulation results on SensorScope and IBRL datasets demonstrate that, TSVDD achieves higher accuracy and lower time complexity for outlier detection in WSNs compared with existing methods.
A Revised Hilbert–Huang Transform and Its Application to Fault Diagnosis in a Rotor System
Authors: Wang Hongjun , Ji Yongjian
Published: December 2018

As a classical method to deal with nonlinear and nonstationary signals, the Hilbert–Huang transform (HHT) is widely used in various fields. In order to overcome the drawbacks of the Hilbert–Huang transform (such as end effects and mode mixing) during the process of empirical mode decomposition (EMD), a revised Hilbert–Huang transform is proposed in this article. A method called local linear extrapolation is introduced to suppress end effects, and the combination of adding a high-frequency sinusoidal signal to, and embedding a decorrelation operator in, the process of EMD is introduced to eliminate mode mixing. In addition, the correlation coefficients between the analyzed signal and the intrinsic mode functions (IMFs) are introduced to eliminate the undesired IMFs. Simulation results show that the improved HHT can effectively suppress end effects and mode mixing. To verify the effectiveness of the new HHT method with respect to fault diagnosis, the revised HHT is applied to analyze the vibration displacement signals in a rotor system collected under normal, rubbing, and misalignment conditions. The simulation and experimental results indicate that the revised HHT method is more reliable than the original with respect to fault diagnosis in a rotor system.
Background Light Rejection in SPAD-Based LiDAR Sensors by Adaptive Photon Coincidence Detection
Authors: Beer Maik , Haase Jan F. , Ruskowski Jennifer , Kokozinski Rainer
Published: December 2018

Light detection and ranging (LiDAR) systems based on silicon single-photon avalanche diodes (SPAD) offer several advantages, like the fabrication of system-on-chips with a co-integrated detector and dedicated electronics, as well as low cost and high durability due to well-established CMOS technology. On the other hand, silicon-based detectors suffer from high background light in outdoor applications, like advanced driver assistance systems or autonomous driving, due to the limited wavelength range in the infrared spectrum. In this paper we present a novel method based on the adaptive adjustment of photon coincidence detection to suppress the background light and simultaneously improve the dynamic range. A major disadvantage of fixed parameter coincidence detection is the increased dynamic range of the resulting event rate, allowing good measurement performance only at a specific target reflectance. To overcome this limitation we have implemented adaptive photon coincidence detection. In this technique the parameters of the photon coincidence detection are adjusted to the actual measured background light intensity, giving a reduction of the event rate dynamic range and allowing the perception of high dynamic scenes. We present a 192 × 2 pixel CMOS SPAD-based LiDAR sensor utilizing this technique and accompanying outdoor measurements showing the capability of it. In this sensor adaptive photon coincidence detection improves the dynamic range of the measureable target reflectance by over 40 dB.
Adaptive Kalman Filter-Based Single-Beacon Underwater Tracking with Unknown Effective Sound Velocity
Authors: Deng Zhong-Chao , Yu Xiang , Qin Hong-De , Zhu Zhong-Ben
Published: December 2018

In the single-beacon underwater tracking system, vehicles rely on slant range measurements from an acoustic beacon to bound errors accumulated by dead reckoning. Ranges are usually obtained based on a presumed known effective sound velocity (ESV). Since the ESV is difficult to determine accurately, traditional methods suffer from large positioning error. By treating the unknown ESV as a state variable, a novel single-beacon tracking model (the so called “5-sv” model) and an extended Kalman filter (EKF)-based solution method have been discussed to solve the problem of ESV estimation. However, due to the uncertainty of underwater acoustic propagation, the probabilistic characteristics of the ESV uncertainty and acoustic measurement noise are unknown and varying both with time and location. EKF, which runs with presupposed noise parameters, cannot describe the practical noise specifications. To overcome the divergence issue of EKF-based single-beacon tracking methods, this paper proposes an adaptive Kalman filter-based single-beacon tracking algorithm which employs the “5-sv” model as the baseline model. Through numerical examples using simulated and field data, both the filter and smoother results show that while implementing the proposed algorithm, the tracking accuracy can be significantly improved, and the estimated noise parameter agrees well with its true value.
Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection
Authors: Park Jongwon , Min Kyushik , Kim Hayoung , Lee Woosung , Cho Gaehwan , Huh Kunsoo
Published: December 2018

Deep learning is a fast-growing field of research, in particular, for autonomous application. In this study, a deep learning network based on various sensor data is proposed for identifying the roads where the vehicle is driving. Long-Short Term Memory (LSTM) unit and ensemble learning are utilized for network design and a feature selection technique is applied such that unnecessary sensor data could be excluded without a loss of performance. Real vehicle experiments were carried out for the learning and verification of the proposed deep learning structure. The classification performance was verified through four different test roads. The proposed network shows the classification accuracy of 94.6% in the test data.
A Novel Method of Frequency Band Selection for Squared Envelope Analysis for Fault Diagnosing of Rolling Element Bearings in a Locomotive Powertrain
Authors: Xu Lang , Chatterton Steven , Pennacchi Paolo
Published: December 2018

The development of diagnostics for rolling element bearings (REBs) in recent years makes it possible to detect faults in bearings in real-time. Squared envelope analysis (SEA), in which the statistical characteristics of the squared envelope (SE) or the squared envelope spectrum (SES) are analysed, is widely recognized as both an effective and the simplest method. The most critical step of SEA is to find an optimal frequency band that contains the maximum defect information. The most commonly used approaches for selecting the optimal frequency band are derived from measuring the kurtosis of the SE or the SES. However, most methods fail to cope with the interference of a single or a few impulses in the corresponding domain. A new method is proposed in this paper called “PMFSgram”, which just calculates the kurtosis of the SES in the range centred at the first two harmonics with a span of three times the modulation frequency rather than the entire SES of the band filtered signals. It is possible to avoid most of the interference from a small number of undesired impulses in the SES. PMFSgram uses several bandwidths from 1.5 times to 4.5 times the fault frequency and for each bandwidth has the same number of central frequencies. The frequency setting method is able to select an optimal frequency band containing most of the useful information with less noise. The performance of the new method is verified using synthesized signals and actual vibration data.
A Novel Vision-Based Pose Measurement Method Considering the Refraction of Light
Authors: Liu Wei , Ma Xin , Li Xiao , Pan Yi , Wang Fuji , Jia Zhenyuan
Published: December 2018

Nowadays, due to the advantages of non-contact and high-speed, vision-based pose measurements have been widely used for aircraft performance testing in a wind tunnel. However, usually glass ports are used to protect cameras against the high-speed airflow influence, which will lead to a big measurement error. In this paper, to further improve the vision-based pose measurement accuracy, an imaging model which considers the refraction light of the observation window was proposed. In this method, a nonlinear camera calibration model considering the refraction brought by the wind tunnel observation window, was established first. What’s more, a new method for the linear calibration of the normal vector of the glass observation window was presented. Then, combining with the proposed matching method based on coplanarity constraint, the six pose parameters of the falling target could be calculated. Finally, the experimental setup was established to conduct the pose measurement study in the laboratory, and the results satisfied the application requirements. Besides, experiments for verifying the vision measurement accuracy were also performed, and the results indicated that the displacement and angle measurement accuracy approximately increased by 57% and 33.6%, respectively, which showed the high accuracy of the proposed method.