bluuebunny/crossref_metadata_2025 huggingface dataset:
Urethane Formation with an Excess of Isocyanate or Alcohol: Experimental and Ab Initio Study
Authors: Cheikh Wafaa , Rózsa Zsófia Borbála , Camacho López Christian Orlando , Mizsey Péter , Viskolcz Béla , Szőri Milán , Fejes Zsolt
Published: September 2019

A kinetic and mechanistic investigation of the alcoholysis of phenyl isocyanate using 1-propanol as the alcohol was undertaken. A molecular mechanism of urethane formation in both alcohol and isocyanate excess is explored using a combination of an accurate fourth generation Gaussian thermochemistry (G4MP2) with the Solvent Model Density (SMD) implicit solvent model. These mechanisms were analyzed from an energetic point of view. According to the newly proposed two-step mechanism for isocyanate excess, allophanate is an intermediate towards urethane formation via six-centered transition state (TS) with a reaction barrier of 62.6 kJ/mol in the THF model. In the next step, synchronous 1,3-H shift between the nitrogens of allophanate and the cleavage of the C–N bond resulted in the release of the isocyanate and the formation of a urethane bond via a low-lying TS with 49.0 kJ/mol energy relative to the reactants. Arrhenius activation energies of the stoichiometric, alcohol excess and the isocyanate excess reactions were experimentally determined by means of HPLC technique. The activation energies for both the alcohol (measured in our recent work) and the isocyanate excess reactions were lower compared to that of the stoichiometric ratio, in agreement with the theoretical calculations.
Synthesis, Thermal Properties and Curing Kinetics of Hyperbranched BPA/PEG Epoxy Resin
Authors: Boonlert-uthai Tossapol , Samthong Chavakorn , Somwangthanaroj Anongnat
Published: September 2019

The hyperbranched epoxy resins (HBE) composed of bisphenol A (BPA) and polyethylene glycol (PEG) as reactants and pentaerythritol as branching point were successfully synthesized via A2 + B4 polycondensation reaction at various BPA/PEG ratios. The 13C NMR spectra revealed that the synthesized HBE mainly had a dendritic structure as confirmed by the high degree of branching (DB). The addition of PEG in the resin enhanced degree of branching (DB) (from 0.82 to 0.90), epoxy equivalent weight (EEW) (from 697 g eq−1 to 468 g eq−1) as well as curing reaction. Adding 5–10 wt.% PEG in the resin decreased the onset and peak curing temperatures and glass transition temperature; however, adding 15 wt.% PEG in the resin have increased these thermal properties due to the lowest EEW. The curing kinetics were evaluated by fitting the experimental data of the curing behavior of all resins with the Šesták–Berggren equation. The activation energy increased with the increase of PEG in the resins due to HBE’s steric hindrance, whereas the activation energy of HBE15P decreased due to a large amount of equivalent active epoxy group per mass sample. The curing behavior and thermal properties of obtained hyperbranched BPA/PEG epoxy resin would be suitable for using in electronics application.
Structural and Mechanical Properties of Ionic Di-block Copolymers via a Molecular Dynamics Approach
Authors: Ma Mengze , Fu Yao
Published: September 2019

Polymerized ionic copolymers have recently evolved as a new class of materials to overcome the limited range of mechanical properties of ionic homopolymers. In this paper, we investigate the structural and mechanical properties of charged ionic homopolymers and di-block copolymers, while using coarse-grained molecular dynamics simulation. Tensile and compressive deformation are applied to the homopolymers and copolymers in the glassy state. The effect of charge ratio and loading direction on the stress-strain behavior are studied. It is found that the electrostatic interactions among charged pairs play major roles, as evidenced by increased Young’s modulus and yield strength with charge ratio. Increased charge ratio lead to enhanced stress contribution from both bonding and pairwise (Van der Waals + coulombic) interaction. The increase in the gyration of the radius is observed with increasing charge ratio in homopolymers, yet a reversed tendency is observed in copolymers. Introduced charge pairs leads to an increased randomness in the segmental orientation in copolymers.
Mechanical, Thermal, and Electrical Properties of BN–Epoxy Composites Modified with Carboxyl-Terminated Butadiene Nitrile Liquid Rubber
Authors: Bian Xingming , Tuo Rui , Yang Wei , Zhang Yiran , Xie Qing , Zha Junwei , Lin Jun , He Shaojian
Published: September 2019

Filled high thermal conductivity epoxy composite solves the problem of the low thermal conductivity of the epoxy resin itself, but the addition of the thermal conductive filler reduces the mechanical properties of the composite, which limits its application in the field of high voltage insulation. In this work, carboxyl-terminated butadiene nitrile liquid rubber (CTBN) was used to toughen the boron nitride-epoxy hybrid system, and the effects of different contents of CTBN on the mechanical properties, thermal conductivity, glass transition temperature, thermal stability, and dielectric properties of the composites were investigated. The results showed that when the content of CTBN was 5–15 wt.%, the CTBN formed a dispersed island structure in the epoxy resin matrix. The toughness of the composite increased by about 32%, the breakdown strength was improved, and the thermal conductivity was about 160% higher than that of pure epoxy resin. As the CTBN content increased, the glass transition temperature and thermal stability of the composite decreased and the dielectric constant and the dielectric loss increased. When the CTBN content is 10–15 wt.%, a toughened epoxy composite material with better comprehensive properties is obtained.
Fabrication and Characterization of Wrapped Metal Yarns-based Fabric Temperature Sensors
Authors: Yang Qian , Wang Xi , Ding Xin , Li Qiao
Published: September 2019

Textile temperature sensors are highly in demanded keep a real-time and accurate track of human body temperature for identification of healthy conditions or clinical diagnosis. Among various materials for textile temperature sensors, temperature-sensitive metal fibers have highest precision. However, those metal fibers are mechanically too weak, and break constantly during the weaving process. To enhance the mechanical strength of the metal fibers, this paper proposes to make wrapped metal fibers using wrapping technology, and characterize the effect of wrapped metal yarns on both mechanical properties and sensing behaviors. The wrapped yarns were woven into fabrics, forming the fabric temperature sensors. Results show that strength and maximum strain of the wrapped yarns are 2.69 and 1.82 times of pure Pt fibers. The response time of fabric temperature sensors using wrapped yarns was observed as 0.78 s and 1.1 s longer compared to that using Pt fibers when front and back sides contacted heat source, respectively. It is recommended that the wrapping method should be implemented for the protection of Pt fibers in fabric temperature sensors.
One Religion, Two Tales: Religion and Happiness in Urban and Rural Areas of China
Authors: Zhang Jing Hua , Zou Wen Chi , Jiang Xiao Yang
Published: September 2019

Most previous studies performed in Western social contexts have revealed that religion can influence an individual’s sense of happiness. Few studies have sought to clarify the influence of religion in a Chinese social context, however, and there has been no study specifically about the potential differences in the dichotomous social environments of urban and rural areas in China. Via the nationwide survey data of the 2007 Spiritual Life Study of Chinese Residents (SLSC), this study examines the association between religion and happiness among urban and rural residents of China. The results reveal that there is a generally positive association between religion and happiness among those with religious affiliations in China. Regardless of affiliation with Buddhism or Protestantism, there is a strong positive association among rural respondents, an insignificant association among urban respondents, and mixed results among town residents.
“Not Your Grandmother’s Bible”—A Comparative Study of the Biblical Deluge Myth in Film
Authors: Ruah-Midbar Shapiro Marianna , Moore Lila
Published: September 2019

The biblical story of the Flood, which portrays a humanity worthy of annihilation, is the fundamental myth used by generations of interpreters as they radically criticized the society and culture in which they lived. Accordingly, the Deluge myth’s various versions and interpretations over the years mirror ever-changing cultural dilemmas and values. Our comparative–historic study observes this mirror and how changes in modern Western culture are reflected in it through the analysis of selected films. It exemplifies the representation and evolution of the Deluge myth in cinema from its inception to the present day, and demonstrates that the myth’s cinematic adaptation and its infusion with current interpretations turn it into an imminent apocalyptic threat. Apparently, the Flood becomes a relevant concern involving a wide scope of cultural, theological, and ethical issues, e.g., the problem of evil, ecology, the treatment of animals, family values, feminism, the Other, and deification of science and technology.
Family History in an Old Genre: The Strange Tales of Lü Meisun and Guo Zeyun
Authors: Huntington Rania
Published: September 2019

Recording personal and family history has been a secondary purpose of the zhiguai (tales of the strange) genre from its inception. As there is no proven female author of a surviving collection before the 20th century, these family histories were shaped by male collectors recording tales told by both female and male informants. Yet in the Republican period, when the practice of recording strange incidents from memory or hearsay had become a marginal practice, Lü Meisun 呂美蓀 (1881–1945) published two collections. Lü’s work stands at a fascinating intersection of gender, genre, and cultural change. She presents a family history centered on the female side of her family and her personal spiritual autobiography against the larger backdrop of cultural transformation from the late Qing through the Republican period. In this paper, I consider a male author of zhiguai during the same years, Guo Zeyun 郭則澐 (1882–1946), for comparison. With their differing conceptions of family, both writers strive to convert familial memory and strange experience into meaning relevant for a wider audience in the present moment.
Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements
Authors: Wu Ying , Qian Bo , Bao Yansong , Petropoulos George P. , Liu Xulin , Li Lin
Published: September 2019

The Qinghai-Tibetan plateau plays an important role in climate change with its unique characteristics, and the surface emissivity is an important parameter to describe the surface characteristics. It is also very important for the accurate retrieval of surface and atmospheric parameters. Different types of surface features have their own radiation characteristics due to their differences in structure, water content and roughness. In this study, the microwave land surface emissivity (10.65, 18.7, 23.8, 36.5 and 89 GHz) of the Qinghai-Tibetan Plateau was calculated using the simplified microwave radiation transmission equation under clear atmospheric conditions based on Level 1 brightness temperatures from the Microwave Radiation Imager onboard the FY-3B meteorological satellite (FY-3B/MWRI) and the National Centers for Environmental Prediction Final (NCEP-FNL) Global Operational Analysis dataset. Furthermore, according to the IGBP (International Geosphere-Biosphere Program) classified data, the spectrum and spatial distribution characteristics of microwave surface emittance in Qinghai-Tibetan plateau were further analyzed. The results show that almost all 16 types of emissivity from IGBP at dual-polarization (vertical and horizontal) increase with the increase of frequency. The spatial distribution of the retrieving results is in line with the changes of surface cover types on the Qinghai-Tibetan plateau, showing the distribution characteristics of large polarization difference of surface emissivity in the northwest and small polarization difference in the southeast, and diverse vegetation can be clearly seen in the retrieving results. In addition, the emissivity is closely related to the type of land surface. Since the emissivity of vegetation is higher than that of bare soil, the contribution of bare soil increases and the surface emissivity decreases as the density of vegetation decreases. Finally, the source of retrieval error was analyzed. The errors in calculating the surface emissivity might mainly come from spatiotemporal collocation of reanalysis data with satellite measurements, the quality of these auxiliary datasets and cloud and precipitation pixel discrimination scheme. Further quantitative analysis of these errors is required, and even standard procedures may need to be improved as well to improve the accuracy of the calculation.
Experimental Dynamic Impact Factor Assessment of Railway Bridges through a Radar Interferometer
Authors: Pieraccini Massimiliano , Miccinesi Lapo , Abdorazzagh Nejad Ali , Naderi Nejad Fard Azadeh
Published: September 2019

The dynamic impact factor (IM) is a widely accepted parameter to account for the effect of vehicles on bridges. An accurate evaluation of this IM is of paramount importance in bridge engineering, both for designing safe and economical new bridges, and for the assessment of the existing ones. At the state of the art, the current procedure for the experimental assessment of the IM of a bridge relies upon the deployment of a sensor network. The aim of this article is to propose the use of a remote sensor, the interferometric radar, for assessing the IM without the deployment of any sensor on the bridge, with evident advantages in terms of cost, time and safety of the workers. Two different case studies of bridges in Northern Iran are reported. In both cases the interferometric radar has been demonstrated an effective and reliable measurement equipment for this kind of in-field assessment.
Understanding Lateral Marsh Edge Erosion with Terrestrial Laser Scanning (TLS)
Authors: Huff Thomas P. , Feagin Rusty A. , Delgado Arturo
Published: September 2019

Coastal wetlands are a crucial buffer zone between land and sea but lateral erosion threatens their long-term sustainability. Better understanding of the forces leading to lateral marsh retreat will benefit the assessment of management options applied to mitigate the erosion. Terrestrial Laser Scanning (TLS), Light Detection and Ranging (lidar), and associated technologies are increasingly being used to assess this erosion. The central objective of this study was to identify a methodology for measuring marsh edge erosion with a TLS and correlate that erosion with exposed roots and incident wave energy. We quantified edge erosion across multiple temporal and spatial scales using a TLS, showing greater than one meter of lateral erosion over a 318-day period. We then evaluated the relationships between the erosion and incident wave energy along with erosion and vegetation roots. Wave height and erosion was strongly related (r2 = 0.99), while vegetation roots did not show an apparent effect. We discuss the challenges that arise from using TLS equipment, TLS data sets, and the use of voxels to measure marsh edge erosion.
Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning
Authors: Stewart Ethan L. , Wiesner-Hanks Tyr , Kaczmar Nicholas , DeChant Chad , Wu Harvey , Lipson Hod , Nelson Rebecca J. , Gore Michael A.
Published: September 2019

Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease.
Introducing WIW for Detecting the Presence of Water in Wetlands with Landsat and Sentinel Satellites
Authors: Lefebvre Gaëtan , Davranche Aurélie , Willm Loïc , Campagna Julie , Redmond Lauren , Merle Clément , Guelmami Anis , Poulin Brigitte
Published: September 2019

Many wetlands are characterized by a vegetation cover of variable height and density over time. Tracking spatio-temporal changes in inundation patterns of these wetlands remains a challenge for remote sensing. Water In Wetlands (WIW) was predicted using a dichotomous partitioning of reflectance values encoded based on ground-truth (n = 4038) and optical-space derived (n = 7016) data covering all land cover types (n = 17) found in the Rhône delta, southern France. The models were developed with spectral data from Sentinel 2, Landsat 7, and Landsat 8 sensors, hence providing a monitoring tool that covers a 35-year period (same sensor for Landsat 5 and 7). A single model combining the near infrared (NIR ≤ 0.1558 to 0.1804, depending on sensors) and short-wave infrared (SWIR2 ≤ 0.0871 to 0.1131) wavelengths was identified by three independent analyses, each one using a different satellite. Overall accuracy of water maps ranged from 89% to 94% for the training samples and from 90% to 94% for the validation samples, encompassing standard water indices that systematically underestimate flooding duration under vegetation cover. Sentinel 2 provided the highest performance with a kappa coefficient of 0.82 for both samples. Such tool will be most useful for monitoring the water dynamics of seasonal wetlands, which are particularly sensitive to climate change while providing multiple services to humankind. Considering the high temporal resolution of Sentinel 2 (every 5 days), cumulative water maps built with the WIW logical rule could further be used for mapping a wide range of wetlands which are either periodically or permanently flooded.
Efficient Algorithm for SAR Refocusing of Ground Fast-Maneuvering Targets
Authors: Wan Jun , Zhou Yu , Zhang Linrang , Chen Zhanye , Yu Hengli
Published: September 2019

The synthetic aperture radar (SAR) image of moving targets will defocus due to the unknown motion parameters. For fast-maneuvering targets, the range cell migration (RCM), Doppler frequency migration and Doppler ambiguity are complex problems. As a result, focusing of fast-maneuvering targets is difficult. In this work, an efficient SAR refocusing algorithm is proposed for fast-maneuvering targets. The proposed algorithm mainly contains three steps. Firstly, the RCM is corrected using sequence reversing, matrix complex multiplication and an improved second-order RCM correction function. Secondly, a 1D scaled Fourier transform is introduced to estimate the remaining chirp rate. Thirdly, a matched filter based on the estimated chirp rate is proposed to focus the maneuvering target in the range–azimuth time domain. The proposed method is computationally efficient because it can be implemented by the fast Fourier transform (FFT), inverse FFT and non-uniform FFT. A new deramp function is proposed to further address the serious problem of Doppler ambiguity. A spurious peak recognition procedure is proposed on the basis of the cross-term analysis. Simulated and real data processing results demonstrate the validity of the proposed target focusing algorithm and spurious peak recognition procedure.
Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong–Hong Kong–Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data
Authors: Yang Chao , Li Qingquan , Zhao Tianhong , Liu Huizeng , Gao Wenxiu , Shi Tiezhu , Guan Minglei , Wu Guofeng
Published: September 2019

The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the major bay areas in the world. However, the spatiotemporal characteristics and rationalities of urban expansions within this region over a relatively long period of time are not well-understood. This study explored the spatiotemporal evolution of 11 cities within the GBA in 1987–2017 by integrating remote sensing, landscape analysis, and geographic information system (GIS) techniques, and further evaluated the rationalities of their expansion using the urban area population elastic coefficient (UPEC) and the urban area gross domestic product (GDP) elastic coefficient (UGEC). The results showed the following: (1) Guangzhou, Shenzhen, Foshan, Dongguan, Zhongshan, and Zhuhai experienced unprecedented urbanization compared with the other cities, and from 1987 to 2017, their urban areas expanded by 10.12, 11.48, 14.21, 24.90, 37.07, and 30.15 times, respectively; (2) several expansion patterns were observed in the 11 cities, including a mononuclear polygon radiation pattern (Guangzhou and Foshan), a double-nucleated polygon pattern (Macau and Zhongshan), and a multi-nuclear urbanization pattern (Shenzhen, Hong Kong, Dongguan, Jiangmen, Huizhou, Zhaoqing, and Zhuhai); (3) with regard to the proportion of area, the edge-expansion and outlying growth types were the predominant types for all 11 cities, and the infilling growth type was the one of the important types during 2007–2017 for Shenzhen, Hong Kong, Dongguan, Zhongshan, and Foshan; (4) the expansion of most cities took on an urban-to-rural landscape gradient, especially for Guangzhou, Shenzhen, Foshan, Zhongshan, Dongguan, and Zhuhai; and (5) the rationalities of expansion in several time periods were rational for Guangzhou (1997–2007), Hong Kong (2007–2017), Foshan (1987–2007), Huizhou (1987–1997), and Dongguan (1997–2007), and the rationalities of expansion in the other cities and time periods were found to be irrational. These findings may help policy- and decision-makers to maintain the sustainable development of the Guangdong–Hong Kong–Macau Greater Bay Area.
Special Section Guest Editorial: Change Detection Using Multi-Source Remotely Sensed Imagery
Authors: Huang Xin , Li Jiayi , Bovolo Francesca , Wang Qi
Published: September 2019

This special issue hosts papers on change detection technologies and analysis in remote sensing, including multi-source sensors, advanced machine learning technologies for change information mining, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multi-source remote sensed data was used in change detection.
Change Vector Analysis, Tasseled Cap, and NDVI-NDMI for Measuring Land Use/Cover Changes Caused by a Sudden Short-Term Severe Drought: 2011 Texas Event
Authors: Rahman Shoumik , Mesev Victor
Published: September 2019

Sudden short-term severe droughts have major impacts on ecosystem balance. Synoptic and replicable measurements from remotely sensed data are essential for calculating changes to land use/cover caused by severe drought conditions. In the US, Texas experienced a particularly severe drought in 2011, which adversely affected forest and grassland ecosystems in addition to $7.62 billion of agricultural loss. To assess the extent and severity of the drought we use satellite sensor data and image processing techniques to measure changes in land use/cover. Our methodology uses change vector analysis (CVA), the normalized difference vegetation index, the normalized difference moisture index, and three variables-brightness, greenness, and wetness-extracted from tasseled cap transforms (TCT). All are established techniques in remote sensing but have as yet been applied in combination to measure land use/cover changes affected by intense short-term drought conditions. Our objective is to calculate not only vegetation and bare soil indices, but also the intensity of change (magnitude) and the type of change (direction). For CVA direction, we include an improved methodology using the arctangent function based on two arguments, ATAN2 which produces results in all four possible quadrants, and complete characterization of all possible change directions. The three variables of TCT are applied to CVA magnitude and direction using vectors in three dimensions, resulting in eight change categories. Our results are based on Landsat TM sensor data for the years 2009, 2010 and 2011, which represent a short period of severe drought, above average precipitation, and severe drought respectively, for two study sites in Texas. Results indicate that land use/cover changes were affected by both an increase in precipitation in 2010 as well as a considerable decrease of precipitation in 2011 resulting in the devastating sudden drought.
Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter
Authors: Jin Shikuan , Ma Yingying , Zhang Ming , Gong Wei , Dubovik Oleg , Liu Boming , Shi Yifan , Yang Changlan
Published: September 2019

Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products are used worldwide for their reliable accuracy. However, the aerosol optical depth (AOD) usually retrieved by the operational dark target (DT) algorithm of MODIS has been missing for most of the urban regions in Central China. This was due to a high surface reflectance and heavy aerosol loading, especially in winter, when a high cloud cover fraction and the frequent occurrence of haze events reduce the number of effective satellite observations. The retrieval of the AOD from limited satellite data is much needed and important for further aerosol investigations. In this paper, we propose an improved AOD retrieval method for 500 m MODIS data, which is based on an extended surface reflectance estimation scheme and dynamic aerosol models derived from ground-based sun-photometric observations. This improved method was applied to retrieve AOD during heavy aerosol loading and effectively complements the scarcity of AOD in correspondence with urban surface of a higher spatial resolution. The validation results showed that the retrieved AOD was consistent with MODIS DT AOD (R = ~0.87; RMSE = ~0.11) and ground measurements (R = ~0.89; RMSE = ~0.15) from both the Terra and the Aqua satellite. The method can be easily applied to different urban environments affected by air pollution and contributes to the research on aerosol.
2D Image-To-3D Model: Knowledge-Based 3D Building Reconstruction (3DBR) Using Single Aerial Images and Convolutional Neural Networks (CNNs)
Authors: Alidoost Fatemeh , Arefi Hossein , Tombari Federico
Published: September 2019

In this study, a deep learning (DL)-based approach is proposed for the detection and reconstruction of buildings from a single aerial image. The pre-required knowledge to reconstruct the 3D shapes of buildings, including the height data as well as the linear elements of individual roofs, is derived from the RGB image using an optimized multi-scale convolutional–deconvolutional network (MSCDN). The proposed network is composed of two feature extraction levels to first predict the coarse features, and then automatically refine them. The predicted features include the normalized digital surface models (nDSMs) and linear elements of roofs in three classes of eave, ridge, and hip lines. Then, the prismatic models of buildings are generated by analyzing the eave lines. The parametric models of individual roofs are also reconstructed using the predicted ridge and hip lines. The experiments show that, even in the presence of noises in height values, the proposed method performs well on 3D reconstruction of buildings with different shapes and complexities. The average root mean square error (RMSE) and normalized median absolute deviation (NMAD) metrics are about 3.43 m and 1.13 m, respectively for the predicted nDSM. Moreover, the quality of the extracted linear elements is about 91.31% and 83.69% for the Potsdam and Zeebrugge test data, respectively. Unlike the state-of-the-art methods, the proposed approach does not need any additional or auxiliary data and employs a single image to reconstruct the 3D models of buildings with the competitive precision of about 1.2 m and 0.8 m for the horizontal and vertical RMSEs over the Potsdam data and about 3.9 m and 2.4 m over the Zeebrugge test data.
Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification
Authors: Cui Ximin , Zheng Ke , Gao Lianru , Zhang Bing , Yang Dong , Ren Jinchang
Published: September 2019

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification.
A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking
Authors: Griffith Henry , Shi Yan , Biswas Subir
Published: September 2019

Various sensors have been proposed to address the negative health ramifications of inadequate fluid consumption. Amongst these solutions, motion-based sensors estimate fluid intake using the characteristics of drinking kinematics. This sensing approach is complicated due to the mutual influence of both the drink volume and the current fill level on the resulting motion pattern, along with differences in biomechanics across individuals. While motion-based strategies are a promising approach due to the proliferation of inertial sensors, previous studies have been characterized by limited accuracy and substantial variability in performance across subjects. This research seeks to address these limitations for a container-attachable triaxial accelerometer sensor. Drink volume is computed using support vector machine regression models with hand-engineered features describing the container’s estimated inclination. Results are presented for a large-scale data collection consisting of 1908 drinks consumed from a refillable bottle by 84 individuals. Per-drink mean absolute percentage error is reduced by 11.05% versus previous state-of-the-art results for a single wrist-wearable inertial measurement unit (IMU) sensor assessed using a similar experimental protocol. Estimates of aggregate consumption are also improved versus previously reported results for an attachable sensor architecture. An alternative tracking approach using the fill level from which a drink is consumed is also explored herein. Fill level regression models are shown to exhibit improved accuracy and reduced inter-subject variability versus volume estimators. A technique for segmenting the entire drink motion sequence into transport and sip phases is also assessed, along with a multi-target framework for addressing the known interdependence of volume and fill level on the resulting drink motion signature.
Optimization of Modulation Methods for Solenoid Valves to Realize an Odor Generation System
Authors: Aleixandre Manuel , Nakazawa Kaoru , Nakamoto Takamichi
Published: September 2019

An artificial olfactory system coupled with an odor generation system is herein reported. The artificial olfactory system is composed of four chemical sensors consisting of quartz crystal microbalances (QCMs) coated with room temperature ionic liquids (RTILs). The sensors are interrogated by four vector network analyzers, which are used to measure the series resonant frequency and motional resistance. The odor generation system can generate eight different odors and mix them in any composition. Solenoid valves are used to switch the path and control the concentration of the different odors before blending. Two algorithms to control the solenoid valves, delta-sigma modulator, and simple pulse width modulation (PWM) are studied, optimized, and compared. Finally, the uncertainty of the odor generating system is calculated.
Radio Frequency Identification and Sensing Techniques and Their Applications—A Review of the State-of-the-Art
Authors: Cui Lei , Zhang Zonghua , Gao Nan , Meng Zhaozong , Li Zhen
Published: September 2019

Radio Frequency Identification (RFID) sensors, integrating the features of Wireless Information and Power Transfer (WIPT), object identification and energy efficient sensing capabilities, have been considered a new paradigm of sensing and communication for the futuristic information systems. RFID sensor tags featuring contactless sensing, wireless information transfer, wireless powered, light weight, non-line-of-sight transmission, flexible and pasteable are a critical enabling technology for future Internet-of-Things (IoT) applications, such as manufacturing, logistics, healthcare, agriculture and food. They have attracted numerous research efforts due to their innovative potential in the various application fields. However, there has been a gap between the in-lab investigations and the practical IoT application scenarios, which has motivated this survey of this research to identify the promising enabling techniques and the underlying challenges. This study aims to provide an exhaustive review on the state-of-art RFID sensor technologies from the system implementation perspective by focusing on the fundamental RF energy harvesting theories, the recent technical progresses and commercial solutions, innovative applications and some RFID sensor based IoT solutions, identify the underlying technological challenges at the time being, and give the future research trends and promising application fields in the rich sensing applications of the forthcoming IoT era.
Wearable-Based Affect Recognition—A Review
Authors: Schmidt Philip , Reiss Attila , Dürichen Robert , Laerhoven Kristof Van
Published: September 2019

Affect recognition is an interdisciplinary research field bringing together researchers from natural and social sciences. Affect recognition research aims to detect the affective state of a person based on observables, with the goal to, for example, provide reasoning for the person’s decision making or to support mental wellbeing (e.g., stress monitoring). Recently, beside of approaches based on audio, visual or text information, solutions relying on wearable sensors as observables, recording mainly physiological and inertial parameters, have received increasing attention. Wearable systems enable an ideal platform for long-term affect recognition applications due to their rich functionality and form factor, while providing valuable insights during everyday life through integrated sensors. However, existing literature surveys lack a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods and best practices of wearable affect and stress recognition. Following a summary of different psychological models, we detail the influence of affective states on the human physiology and the sensors commonly employed to measure physiological changes. Then, we outline lab protocols eliciting affective states and provide guidelines for ground truth generation in field studies. We also describe the standard data processing chain and review common approaches related to the preprocessing, feature extraction and classification steps. By providing a comprehensive summary of the state-of-the-art and guidelines to various aspects, we would like to enable other researchers in the field to conduct and evaluate user studies and develop wearable systems.
Design and Implementation of a Novel Measuring Scheme for Fiber Interferometer Based Sensors
Authors: Ma Chao-Tsung , Lee Cheng-Ling , You Yan-Wun
Published: September 2019

This paper presents a novel measuring scheme for fiber interferometer (FI) based sensors. With the advantages of being small sizes, having high sensitivity, a simple structure, good durability, being easy to integrate fiber optic communication and having immunity to electromagnetic interference (EMI), FI based sensing devices are suitable for monitoring remote system states or variations in physical parameters. However, the sensing mechanism for the interference spectrum shift of FI based sensors requires expensive equipment, such as a broadband light source (BLS) and an optical spectrum analyzer (OSA). This has strongly handicapped their wide application in practice. To solve this problem, we have, for the first time, proposed a smart measuring scheme, in which a commercial laser diode (LD) and a photodetector (PD) are used to detect the equivalent changes of optical power corresponding to the variation in measuring parameters, and a signal processing system is used to analyze the optical power changes and to determine the spectrum shifts. To demonstrate the proposed scheme, a sensing device on polymer microcavity fiber Fizeau interferometer (PMCFFI) is taken as an example for constructing a measuring system capable of long-distance monitoring of the temperature and relative humidity. In this paper, theoretical analysis and fundamental tests have been carried out. Typical results are presented to verify the feasibility and effectiveness of the proposed measuring scheme, smartly converting the interference spectrum shifts of an FI sensing device into the corresponding variations of voltage signals. With many attractive features, e.g., simplicity, low cost, and reliable remote-monitoring, the proposed scheme is very suitable for practical applications.
SERS-Based Immunoassays for the Detection of Botulinum Toxins A and B Using Magnetic Beads
Authors: Kim Kihyun , Choi Namhyun , Jeon Jun Ho , Rhie Gi-eun , Choo Jaebum
Published: September 2019

Rapid and sensitive detection of botulinum neurotoxins (BoNTs) is important for immediate treatment with proper antitoxins. However, it is difficult to detect BoNTs at the acute phase of infection, owing to its rarity and ambiguous symptoms. To resolve this problem, we developed a surface-enhanced Raman scattering (SERS)-based immunoassay technique for the rapid and sensitive detection of BoNTs. Magnetic beads and SERS nanotags as capture substrates and detection probes, respectively, and Nile Blue A (NBA) and malachite green isothiocyanate (MGITC) as Raman reporter molecules were used for the detection of two different types of BoNTs (types A and B), respectively. The corresponding limits of detection (LODs) were determined as 5.7 ng/mL (type A) and 1.3 ng/mL (type B). Total assay time, including that for immunoreaction, washing, and detection, was less than 2 h.
Measurement of Three-Dimensional Structural Displacement Using a Hybrid Inertial Vision-Based System
Authors: Zhang Xinxiang , Zeinali Yasha , Story Brett A. , Rajan Dinesh
Published: September 2019

Accurate three-dimensional displacement measurements of bridges and other structures have received significant attention in recent years. The main challenges of such measurements include the cost and the need for a scalable array of instrumentation. This paper presents a novel Hybrid Inertial Vision-Based Displacement Measurement (HIVBDM) system that can measure three-dimensional structural displacements by using a monocular charge-coupled device (CCD) camera, a stationary calibration target, and an attached tilt sensor. The HIVBDM system does not require the camera to be stationary during the measurements, while the camera movements, i.e., rotations and translations, during the measurement process are compensated by using a stationary calibration target in the field of view (FOV) of the camera. An attached tilt sensor is further used to refine the camera movement compensation, and better infers the global three-dimensional structural displacements. This HIVBDM system is evaluated on both short-term and long-term synthetic static structural displacements, which are conducted in an indoor simulated experimental environment. In the experiments, at a 9.75 m operating distance between the monitoring camera and the structure that is being monitored, the proposed HIVBDM system achieves an average of 1.440 mm Root Mean Square Error (RMSE) on the in-plane structural translations and an average of 2.904 mm RMSE on the out-of-plane structural translations.
Alternative Approaches to Vibration Measurement Due to the Blasting Operation: A Pilot Study
Authors: Kepak Stanislav , Stolarik Martin , Nedoma Jan , Martinek Radek , Kolarik Jakub , Pinka Miroslav
Published: September 2019

As the infrastructure grows, space on the surface in the urban area is diminishing, and the view of the builders is increasingly moving underground. Implementation of underground structures, however, presents a number of problems during construction. One of the primary side effects of tunnel excavation is vibration. These vibrations need to be monitored for potential damage to structures on the surface, and this monitoring is an integral part of any such structure. This paper brings an original pilot comparative study of standard seismic instrumentation with experimentally developed fiber-optic interferometric and acoustic systems for the purpose of monitoring vibration caused by the blasting operation. The results presented show that systems operating on physical principles (other than those previously used) have the potential to be an alternative that will replace the existing costly seismic equipment. The paper presents waveform images and frequency spectra from experimental measurements of the dynamic response of the rock environment, due to blasting operation performed shallowly during the tunnel excavation of a sewer collector. In the time and frequency domain, there is, by comparison, significant agreement both in the character of the waveform images (recording length, blasting operation timing) and in the spectra (bandwidth, dominant maxima).
Lychee Fruit Detection Based on Monocular Machine Vision in Orchard Environment
Authors: Guo Qiwei , Chen Yayong , Tang Yu , Zhuang Jiajun , He Yong , Hou Chaojun , Chu Xuan , Zhong Zhenyu , Luo Shaoming
Published: September 2019

Due to the change of illumination environment and overlapping conditions caused by the neighboring fruits and other background objects, the simple application of the traditional machine vision method limits the detection accuracy of lychee fruits in natural orchard environments. Therefore, this research presented a detection method based on monocular machine vision to detect lychee fruits growing in overlapped conditions. Specifically, a combination of contrast limited adaptive histogram equalization (CLAHE), red/blue chromatic mapping, Otsu thresholding and morphology operations were adopted to segment the foreground regions of the lychees. A stepwise method was proposed for extracting individual lychee fruit from the lychee foreground region. The first step in this process was based on the relative position relation of the Hough circle and an equivalent area circle (equal to the area of the potential lychee foreground region) and was designed to distinguish lychee fruits growing in isolated or overlapped states. Then, a process based on the three-point definite circle theorem was performed to extract individual lychee fruits from the foreground regions of overlapped lychee fruit clusters. Finally, to enhance the robustness of the detection method, a local binary pattern support vector machine (LBP-SVM) was adopted to filter out the false positive detections generated by background chaff interferences. The performance of the presented method was evaluated using 485 images captured in a natural lychee orchard in Conghua (Area), Guangzhou. The detection results showed that the recall rate was 86.66%, the precision rate was greater than 87% and the F1-score was 87.07%.
Design of Transmission-Type Refractive Index Sensor, Based on Silica Planar Lightwave Circuit Using Combination of Refractive Angle and Phase Measurements
Authors: Maru Koichi
Published: September 2019

A transmission-type refractive index sensor, based on planar lightwave circuit (PLC) technology is proposed. In the proposed structure, we introduce a combination of coarse measurements, using the dependence of the angle of refraction and fine measurement, and the dependence of the phase on the refractive index to measure the absolute refractive index precisely, without expensive optical measurement equipment. The theoretical model of the proposed refractive index sensor is derived based on Fourier optics and transfer function to simulate its performance. The simulation results for the use of the 2.5%-Δ silica-based PLC technology indicate that the proposed structure has the potential to achieve a refractive index error of approximately 1 × 10−6 RIU or less when a monitored power deviation of ±0.05 dB is accepted.
Non-Invasive Forehead Segmentation in Thermographic Imaging
Authors: Rodriguez-Lozano Francisco J. , León-García Fernando , Ruiz de Adana M. , Palomares Jose M. , Olivares J.
Published: September 2019

The temperature of the forehead is known to be highly correlated with the internal body temperature. This area is widely used in thermal comfort systems, lie-detection systems, etc. However, there is a lack of tools to achieve the segmentation of the forehead using thermographic images and non-intrusive methods. In fact, this is usually segmented manually. This work proposes a simple and novel method to segment the forehead region and to extract the average temperature from this area solving this lack of non-user interaction tools. Our method is invariant to the position of the face, and other different morphologies even with the presence of external objects. The results provide an accuracy of 90% compared to the manual segmentation using the coefficient of Jaccard as a metric of similitude. Moreover, due to the simplicity of the proposed method, it can work with real-time constraints at 83 frames per second in embedded systems with low computational resources. Finally, a new dataset of thermal face images is presented, which includes some features which are difficult to find in other sets, such as glasses, beards, moustaches, breathing masks, and different neck rotations and flexions.