bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial–Spectral Squeeze-and-Excitation Residual Network for Hyperspectral Image Classification
Authors: Wang Li , Peng Jiangtao , Sun Weiwei
Published: April 2019

Jointly using spectral and spatial information has become a mainstream strategy in the field of hyperspectral image (HSI) processing, especially for classification. However, due to the existence of noisy or correlated spectral bands in the spectral domain and inhomogeneous pixels in the spatial neighborhood, HSI classification results are often degraded and unsatisfactory. Motivated by the attention mechanism, this paper proposes a spatial–spectral squeeze-and-excitation (SSSE) module to adaptively learn the weights for different spectral bands and for different neighboring pixels. The SSSE structure can suppress or motivate features at a certain position, which can effectively resist noise interference and improve the classification results. Furthermore, we embed several SSSE modules into a residual network architecture and generate an SSSE-based residual network (SSSERN) model for HSI classification. The proposed SSSERN method is compared with several existing deep learning networks on two benchmark hyperspectral data sets. Experimental results demonstrate the effectiveness of our proposed network.
Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France
Authors: Bazzi Hassan , Baghdadi Nicolas , El Hajj Mohammad , Zribi Mehrez , Minh Dinh Ho Tong , Ndikumana Emile , Courault Dominique , Belhouchette Hatem
Published: April 2019

This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas.
Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method
Authors: Du Zhenrong , Yang Jianyu , Ou Cong , Zhang Tingting
Published: April 2019

The growing population in China has led to an increasing importance of crop area (CA) protection. A powerful tool for acquiring accurate and up-to-date CA maps is automatic mapping using information extracted from high spatial resolution remote sensing (RS) images. RS image information extraction includes feature classification, which is a long-standing research issue in the RS community. Emerging deep learning techniques, such as the deep semantic segmentation network technique, are effective methods to automatically discover relevant contextual features and get better image classification results. In this study, we exploited deep semantic segmentation networks to classify and extract CA from high-resolution RS images. WorldView-2 (WV-2) images with only Red-Green-Blue (RGB) bands were used to confirm the effectiveness of the proposed semantic classification framework for information extraction and the CA mapping task. Specifically, we used the deep learning framework TensorFlow to construct a platform for sampling, training, testing, and classifying to extract and map CA on the basis of DeepLabv3+. By leveraging per-pixel and random sample point accuracy evaluation methods, we conclude that the proposed approach can efficiently obtain acceptable accuracy (Overall Accuracy = 95%, Kappa = 0.90) of CA classification in the study area, and the approach performs better than other deep semantic segmentation networks (U-Net/PspNet/SegNet/DeepLabv2) and traditional machine learning methods, such as Maximum Likelihood (ML), Support Vector Machine (SVM), and RF (Random Forest). Furthermore, the proposed approach is highly scalable for the variety of crop types in a crop area. Overall, the proposed approach can train a precise and effective model that is capable of adequately describing the small, irregular fields of smallholder agriculture and handling the great level of details in RGB high spatial resolution images.
UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features
Authors: Jiang Qi , Fang Shenghui , Peng Yi , Gong Yan , Zhu Renshan , Wu Xianting , Ma Yi , Duan Bo , Liu Jian
Published: April 2019

Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R2 = 0.64, RMSE = 286.79 g/m2, MAE = 236.49 g/m2), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R2 = 0.86, RMSE = 178.37 g/m2, MAE = 127.34 g/m2). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.
Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations
Authors: Shi Zheng , Xing Tingyan , Guang Jie , Xue Yong , Che Yahui
Published: April 2019

Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones.
Theoretical Evaluation of Water Cloud Model Vegetation Parameters
Authors: Park Sang-Eun , Jung Yoon Taek , Cho Jae-Hyoung , Moon Hyoi , Han Seung-hoon
Published: April 2019

The advantage of implementing the Water Cloud Model (WCM) is in being able to express complex scattering characteristics in a vegetated area with simple bulk vegetation descriptors. However, there has been a lack of understanding or consensus about the optimal set of vegetation descriptors. In this paper, the original and improved expressions of WCM are evaluated and the optimal vegetation descriptors are presented by examining the relationship between WCM vegetation parameters and the theoretical scattering model predictions. In addition, the condition-specific regression relationship between bulk vegetation descriptors and theoretical scattering and attenuation coefficients, expressed by the A and B parameters in the WCM, is analyzed in relation to the shape, size, and orientation distribution of the scatterer. Furthermore, the influence of radar observation conditions on the parameterization of the WCM is presented. The results show that the particle moisture content and the vegetation water content can be the optimal vegetation descriptors, denoted by the V 1 and V 2 variables in the WCM, respectively.
Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique
Authors: Tsai Ya-Lun , Dietz Andreas , Oppelt Natascha , Kuenzer Claudia
Published: April 2019

Traditional studies on mapping wet snow cover extent (SCE) often feature limitations, especially in vegetated and mountainous areas. The aim of this study is to propose a new total and wet SCE mapping strategy based on freely accessible spaceborne synthetic aperture radar (SAR) data. The approach is transferable on a global scale as well as for different land cover types (including densely vegetated forest and agricultural regions), and is based on the use of backscattering coefficient, interferometric SAR coherence, and polarimetric parameters. Furthermore, four topographical factors were included in the simple tuning of random forest-based land cover type-dependent classification strategy. Results showed the classification accuracy was above 0.75, with an F-measure higher than 0.70, in all five selected regions of interest located around globally distributed mountain ranges. Whilst excluding forest-type land cover classes, the accuracy and F-measure increases to 0.80 and 0.75. In cross-location model set, the accuracy can also be maintained at 0.80 with non-forest accuracy up to 0.85. It has been found that the elevation and polarimetric parameters are the most critical factors, and that the quality of land cover information would also affect the subsequent mapping reliability. In conclusion, through comprehensive validation using optical satellite and in-situ data, our land cover-dependent total SCE mapping approach has been confirmed to be robustly applicable, and the holistic SCE map for different months were eventually derived.
Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image
Authors: Zhai Wei , Huang Chunlin , Pei Wansheng
Published: April 2019

After a destructive earthquake, most of the casualties are brought about by building collapse. Our work is focused on using a single postevent PolSAR (full-polarimetric synthetic aperture radar) imagery to extract the building damage information for effective emergency decision-making. PolSAR data is subject to sunlight and contains richer backscatter information. The undamaged buildings whose orientation is not parallel to the SAR flight pass and the collapsed buildings share similar dominated scattering mechanisms, i.e., volume scattering, so they are easily confused. However, the two kinds of buildings have different textures. For a more accurate classification of damaged buildings and undamaged buildings, the OPCE (optimization of polarimetric contrast enhancement) algorithm is employed to enhance the contrast ratio of the textures for the two kinds of buildings and the precision-weighted multifeature fusion (PWMF) method is proposed to merge the multiple texture features. The experiment results show that the accuracy of the proposed novel method is improved by 8.34% compared to the traditional method. In general, the proposed PWMF method can effectively merge the multiple features and the overestimation of the building collapse rate can be reduced using the proposed method in this study.
A Gold Nanoclusters Film Supported on Polydopamine for Fluorescent Sensing of Free Bilirubin
Authors: Li Zhou , Xiao Wenxiang , Huang Rongen , Shi Yajing , Fang Cheng , Chen Zhencheng
Published: April 2019

Serum bilirubin is an important biomarker for the diagnosis of various types of liver diseases and blood disorders. A polydopamine/gold nanoclusters composite film was fabricated for the fluorescent sensing of free bilirubin. Bovine serum albumin (BSA)-stabilized gold nanoclusters (AuNCs) were used as probes for biorecognition. The polydopamine film was utilized as an adhesion layer for immobilization of AuNCs. When the composite film was exposed to free bilirubin, due to the complex that was formed between BSA and free bilirubin, the fluorescence intensity of the composite film was gradually weakened as the bilirubin concentration increased. The fluorescence quenching ratio (F0/F) was linearly proportional to free bilirubin over the concentration range of 0.8~50 μmol/L with a limit of detection of 0.61 ± 0.12 μmol/L (S/N = 3). The response was quick, the film was recyclable, and common ingredients in human serum did not interfere with the detection of free bilirubin.
Univariate and Multivariate Analysis of Phosphorus Element in Fertilizers Using Laser-Induced Breakdown Spectroscopy
Authors: Zhang Baohua , Ling Pengpeng , Sha Wen , Jiang Yongcheng , Cui Zhifeng
Published: April 2019

Rapid detection of phosphorus (P) element is beneficial to the control of compound fertilizer production process and is of great significance in the fertilizer industry. The aim of this work was to compare the univariate and multivariate analysis of phosphorus element in compound fertilizers and obtain a reliable and accurate method for rapid detection of phosphorus element. A total of 47 fertilizer samples were collected from the production line; 36 samples were used as a calibration set, and 11 samples were used as a prediction set. The univariate calibration curve was constructed by the intensity of characteristic line and the concentration of P. The linear correlation coefficient was 0.854 as the existence of the matrix effect. In order to eliminate the matrix effect, the internal standardization as the appropriate methodology was used to increase the accuracy. Using silicon (Si) element as an internal element, a linear correlation coefficient of 0.932 was obtained. Furthermore, the chemometrics model of partial least-squares regression (PLSR) was used to analysis the concentration of P in fertilizer. The correlation coefficient was 0.977 and 0.976 for the calibration set and prediction set, respectively. The results indicated that the LIBS technique coupled with PLSR could be a reliable and accurate method in the quantitative determination of P element in complex matrices like compound fertilizers.
Front-Vehicle Detection in Video Images Based on Temporal and Spatial Characteristics
Authors: Yang Bo , Zhang Sheng , Tian Yan , Li Bijun
Published: April 2019

Assisted driving and unmanned driving have been areas of focus for both industry and academia. Front-vehicle detection technology, a key component of both types of driving, has also attracted great interest from researchers. In this paper, to achieve front-vehicle detection in unmanned or assisted driving, a vision-based, efficient, and fast front-vehicle detection method based on the spatial and temporal characteristics of the front vehicle is proposed. First, a method to extract the motion vector of the front vehicle is put forward based on Oriented FAST and Rotated BRIEF (ORB) and the spatial position constraint. Then, by analyzing the differences between the motion vectors of the vehicle and those of the background, feature points of the vehicle are extracted. Finally, a feature-point clustering method based on a combination of temporal and spatial characteristics are applied to realize front-vehicle detection. The effectiveness of the proposed algorithm is verified using a large number of videos.
Possibilities for Groundwater Flow Sensing with Fiber Bragg Grating Sensors
Authors: Drusová Sandra , Bakx Wiecher , Wexler Adam D. , Offerhaus Herman L.
Published: April 2019

An understanding of groundwater flow near drinking water extraction wells is crucial when it comes to avoiding well clogging and pollution. A promising new approach to groundwater flow monitoring is the deployment of a network of optical fibers with fiber Bragg grating (FBG) sensors. In preparation for a field experiment, a laboratory scale aquifer was constructed to investigate the feasibility of FBG sensors for this application. Multiparameter FBG sensors were able to detect changes in temperature, pressure, and fiber shape with sensitivities influenced by the packaging. The first results showed that, in a simulated environment with a flow velocity of 2.9 m/d, FBG strain effects were more pronounced than initially expected. FBG sensors of a pressure-induced strain implemented in a spatial array could form a multiplexed sensor for the groundwater flow direction and magnitude. Within the scope of this research, key technical specifications of FBG interrogators for groundwater flow sensing were also identified.
Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques
Authors: Bălan Oana , Moise Gabriela , Moldoveanu Alin , Leordeanu Marius , Moldoveanu Florica
Published: April 2019

There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a system for treating phobias that would automatically determine fear levels and adapt exposure intensity based on the user’s current affective state, we propose a comparative study between various machine and deep learning techniques (four deep neural network models, a stochastic configuration network, Support Vector Machine, Linear Discriminant Analysis, Random Forest and k-Nearest Neighbors), with and without feature selection, for recognizing and classifying fear levels based on the electroencephalogram (EEG) and peripheral data from the DEAP (Database for Emotion Analysis using Physiological signals) database. Fear was considered an emotion eliciting low valence, high arousal and low dominance. By dividing the ratings of valence/arousal/dominance emotion dimensions, we propose two paradigms for fear level estimation—the two-level (0—no fear and 1—fear) and the four-level (0—no fear, 1—low fear, 2—medium fear, 3—high fear) paradigms. Although all the methods provide good classification accuracies, the highest F scores have been obtained using the Random Forest Classifier—89.96% and 85.33% for the two-level and four-level fear evaluation modality.
Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging
Authors: Hsu Shih-Yen , Lin Hsin-Chieh , Chen Tai-Been , Du Wei-Chang , Hsu Yun-Hsuan , Wu Yi-Chen , Tu Po-Wei , Huang Yung-Hui , Chen Huei-Yung
Published: April 2019

The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson’s disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky’s skewness coefficient, Pearson’s median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model.
Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor
Authors: Lazaro Antonio , Boada Marti , Villarino Ramon , Girbau David
Published: April 2019

This paper presents a color-based classification system for grading the ripeness of fruit using a battery-less Near Field Communication (NFC) tag. The tag consists of a color sensor connected to a low-power microcontroller that is connected to an NFC chip. The tag is powered by the energy harvested from the magnetic field generated by a commercial smartphone used as a reader. The raw RGB color data measured by the colorimeter is converted to HSV (hue, saturation, value) color space. The hue angle and saturation are used as features for classification. Different classification algorithms are compared for classifying the ripeness of different fruits in order to show the robustness of the system. The low cost of NFC chips means that tags with sensing capability can be manufactured economically. In addition, nowadays, most commercial smartphones have NFC capability and thus a specific reader is not necessary. The measurement of different samples obtained on different days is used to train the classification algorithms. The results of training the classifiers have been saved to the cloud. A mobile application has been developed for the prediction based on a table-based method, where the boundary decision is downloaded from a cloud service for each product. High accuracy, between 80 and 93%, is obtained depending on the kind of fruit and the algorithm used.
An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping
Authors: Qian Chuang , Zhang Hongjuan , Tang Jian , Li Bijun , Liu Hui
Published: April 2019

An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement Unit (IMU) for 2D indoor mapping. A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans. Scan-to-map matching is utilized to find the optimal rigid-body transformation in order to avoid the accumulation of matching errors. Map generation and update are probabilistically motivated. According to the assumption that the orthogonal is the main feature of indoor environments, we propose a lightweight segment extraction method, based on the orthogonal blurred segments (OBS) method. Instead of calculating the parameters of segments, we give the scan points contained in blurred segments a greater weight during the construction of the grid-based occupancy likelihood map, which we call the orthogonal feature weighted occupancy likelihood map (OWOLM). The OWOLM enhances the occupancy likelihood map by fusing the orthogonal features. It can filter out noise scan points, produced by objects, such as glass cabinets and bookcases. Experiments were carried out in a library, which is a representative indoor environment, consisting of orthogonal features. The experimental result proves that, compared with the general occupancy likelihood map, the OWOLM can effectively reduce accumulated errors and construct a clearer indoor map.
Reliability and Validity of Non-invasive Blood Pressure Measurement System Using Three-Axis Tactile Force Sensor
Authors: Yoo Sun-Young , Ahn Ji-Eun , Cserey György , Lee Hae-Young , Seo Jong-Mo
Published: April 2019

Blood pressure (BP) is a physiological parameter reflecting hemodynamic factors and is crucial in evaluating cardiovascular disease and its prognosis. In the present study, the reliability of a non-invasive and continuous BP measurement using a three-axis tactile force sensor was verified. All the data were collected every 2 min for the short-term experiment, and every 10 min for the long-term experiment. In addition, the effects on the BP measurement of external physical factors such as the tension to the radial artery on applying the device and wrist circumference were evaluated. A high correlation between the measured BP with the proposed system and with the cuff-based non-invasive blood pressure, and reproducibility, were demonstrated. All data satisfied the Association for the Advancement of Medical Instrumentation criteria. The external physical factors did not affect the measurement results. In addition to previous research indicating the high reliability of the arterial pulse waveforms, the present results have demonstrated the reliability of numerical BP values, and this implies that the three-axis force sensor can be used as a patient monitoring device.
Self-Sensing Polymer Composite: White-Light-Illuminated Reinforcing Fibreglass Bundle for Deformation Monitoring
Authors: Hegedus Gergely , Sarkadi Tamas , Czigany Tibor
Published: April 2019

The goal of our research was to develop a continuous glass fibre-reinforced epoxy matrix self-sensing composite. A fibre bundle arbitrarily chosen from the reinforcing glass fabric in the composite was prepared to guide white light. The power of the light transmitted by the fibres changes as a result of tensile loading. In our research, we show that a selected fibre bundle even without any special preparation can be used as a sensor to detect deformation even before the composite structure is damaged (before fibre breaking).
Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology
Authors: Peng Cong , Goswami Prashant
Published: April 2019

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.
Finite Element Analysis for Surface Acoustic Wave Device Characteristic Properties and Sensitivity
Authors: Wang Tao , Green Ryan , Guldiken Rasim , Wang Jing , Mohapatra Subhra , Mohapatra Shyam S.
Published: April 2019

The most vital step in the development of novel and existing surface acoustic wave (SAW)-based sensors and transducers is their design and optimization. Demand for SAW devices has been steadily increasing due to their low cost, portability, and versatility in electronics, telecommunications, and biosensor applications. However, a full characterization of surface acoustic wave biosensors in a three-dimensional (3D) finite element model has not yet been developed. In this study, a novel approach is developed for analyzing shear horizontal Love wave resonator devices. The developed modeling methodology was verified using fabricated devices. A thorough analysis of the 3D model and the experimental device was performed in this study including scattering parameters (S-parameters), reflection coefficient parameters, transmission parameters, and phase velocity. The simulated results will be used as a design guideline for future device design and optimization, which has thus far resulted in close matching between prediction and experimental results. This manuscript is the first to demonstrate a 3D finite element model to correlate the sensitivity of the SAW device with the magnitude of the phase shift, the real and imaginary part of the response, insertion loss, and the frequency shift. The results show that the imaginary part of the response shift has a higher sensitivity compared to other parameters.
Plasmonic Color Filter Array with High Color Purity for CMOS Image Sensors
Authors: Miyamichi Atsutaka , Ono Atsushi , Kagawa Keiichiro , Yasutomi Keita , Kawahito Shoji
Published: April 2019

We demonstrate the multiband color filtering of a standard RGB color and a complementary CMY color by a plasmonic color filter, composed of concentric corrugated metallic thin film rings. The surface plasmon resonance is excited by the periodic corrugation, and the coupled light is transmitted through the central subwavelength aperture. Color selectivity is achieved not only in the visible but also in the near-infrared (NIR) region. Therefore, simultaneous imaging with visible and NIR can be realized by the integration of plasmonic color filters with sensors. We investigate the angle of incidence dependence of the transmission color selectivity and the color purity of the fabricated plasmonic color filter array.
Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
Authors: Kota John S. , Papandreou-Suppappola Antonia
Published: April 2019

We examine a multiple object tracking problem by jointly optimizing the transmit waveforms used in a multimodal system. Coexisting sensors in this system were assumed to share the same spectrum. Depending on the application, a system can include radars tracking multiple targets or multiuser wireless communications and a radar tracking both multiple messages and a target. The proposed spectral coexistence approach was based on designing all transmit waveforms to have the same time-varying phase function while optimizing desirable performance metrics. Considering the scenario of tracking a target with a pulse–Doppler radar and multiple user messages, two signaling schemes were proposed after selecting the waveform parameters to first minimize multiple access interference. The first scheme is based on system interference minimization, whereas the second scheme explores the multiobjective optimization tradeoff between system interference and object parameter estimation error. Simulations are provided to demonstrate the performance tradeoffs due to different system requirements.
A Sensitivity-enhanced Fiber Grating Current Sensor Based on Giant Magnetostrictive Material for Large-Current Measurement
Authors: Wang Shuchao , Wan Fu , Zhao Hong , Chen Weigen , Zhang Weichao , Zhou Quan
Published: April 2019

Currently, in the modern power industry, it is still a great challenge to achieve high sensitivity and uninterrupted-online measurement of large current on the high voltage gridlines. At present, the fiber grating current sensors based on giant magnetostrictive material used in the modern power industry to achieve uninterrupted-online measurement of large currents on high voltage grid lines is a better method, but the sensitivity of this current sensor is relatively low, therefore, it is key to improve the sensitivity of this current sensor. Here we show a sensitivity-enhanced fiber grating current sensor based on giant magnetostrictive material (in the following, simply referred to as the sensitivity-enhanced fiber grating current sensor) that is able to achieve high sensitivity and uninterrupted-online measurement of large currents by means of pressurizing the giant magnetostrictive material. Sampling the power frequency sinusoidal alternating current signals with the amplitudes of 107, 157 and 262 A respectively, based on realistic factors, for the sensitivity-enhanced current sensor, the sensitivities, compared with that of the traditional fiber grating current sensor based on giant magnetostrictive material (in the following, simply referred to as the traditional fiber grating current sensor), were respectively enhanced by 268.96%, 135.72% and 71.57%. Thus the sensitivity-enhanced fiber grating current sensor allows us to solve the issue of high sensitivity and uninterrupted-online measurement of large currents that have been plaguing the power industry in a very simple and low-cost way.
Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole
Authors: Lee Sung-Sin , Choi Sang Tae , Choi Sang-Il
Published: April 2019

In this paper, we proposed a gait type classification method based on deep learning using a smart insole with various sensor arrays. We measured gait data using a pressure sensor array, an acceleration sensor array, and a gyro sensor array built into a smart insole. Features of gait pattern were then extracted using a deep convolution neural network (DCNN). In order to accomplish this, measurement data of continuous gait cycle were divided into unit steps. Pre-processing of data were then performed to remove noise followed by data normalization. A feature map was then extracted by constructing an independent DCNN for data obtained from each sensor array. Each of the feature maps was then combined to form a fully connected network for gait type classification. Experimental results for seven types of gait (walking, fast walking, running, stair climbing, stair descending, hill climbing, and hill descending) showed that the proposed method provided a high classification rate of more than 90%.
Signal Analysis, Signal Demodulation and Numerical Simulation of a Quasi-Distributed Optical Fiber Sensor Based on FDM/WDM Techniques and Fabry-Pérot Interferometers
Authors: Guillen Bonilla José Trinidad , Guillen Bonilla Héctor , Rodríguez Betancourtt Verónica María , Casillas Zamora Antonio , Sánchez Morales María Eugenia , Gildo Ortiz Lorenzo , Guillen Bonilla Alex
Published: April 2019

In civil engineering quasi-distributed optical fiber sensors are used for reinforced concrete monitoring, precast concrete monitoring, temperature monitoring, strain monitoring and temperature/strain monitoring. These quasi-distributed sensors necessarily apply some multiplexing technique. However, on many occasions, two or more multiplexing techniques are combined to increase the number of local sensors and then the cost of each sensing point is reduced. In this work, a signal analysis and a new signal demodulation algorithm are reported for a quasi-distributed optic fiber sensor system based on Frequency Division Multiplexing/Wavelength Division Multiplexing (FDM/WDM) and low-precision Fabry-Pérot interferometers. The mathematical analysis and the new algorithm optimize its design, its implementation, improve its functionality and reduce the cost per sensing point. The analysis was corroborated by simulating a quasi-distributed sensor in operation. Theoretical analysis and numerical simulation are in concordance. The optimization considers multiplexing techniques, signal demodulation, physical parameters, system noise, instrumentation, and detection technique. Based on our analysis and previous results reported, the optical sensing system can have more than 4000 local sensors and it has practical applications in civil engineering.
Magnetoimpedance in Symmetric and Non-Symmetric Nanostructured Multilayers: A Theoretical Study
Authors: Buznikov Nikita A. , Kurlyandskaya Galina V.
Published: April 2019

Intensive studies of the magnetoimpedance (MI) effect in nanostructured multilayers provide a good phenomenological basis and theoretical description for the symmetric case when top and bottom layers of ferromagnet/conductor/ferromagnet structure have the same thickness and consist of one magnetic layer each. At the same time, there is no model to describe the MI response in multilayered films. Here, we propose the corresponding model and analyze the influence of the multilayer parameters on the field and frequency dependences of the MI. The approach is based on the calculation of the field distribution within the multilayer by means of a solution of lineralizied Maxwell equations together with the Landau–Lifshitz equation for the magnetization motion. The theoretical model developed allows one to explain qualitatively the main features of the MI effect in multilayers and could be useful for optimization of the film parameters. It might also be useful as a model case for the development of MI magnetic biosensors for magnetic biomarker detection.
The Spatiotemporal Dynamics of Soybean and Cattle Production in Brazil
Authors: Maranhão Rebecca Lima Albuquerque , de Carvalho Júnior Osmar Abílio , Hermuche Potira Meirelles , Gomes Roberto Arnaldo Trancoso , McManus Pimentel Concepta Margaret , Guimarães Renato Fontes
Published: April 2019

The expansion of agricultural frontiers in Brazil has caused substantial changes in land use and land cover. This research aims to analyze the space-time dynamics of soybeans and cattle production in the Brazilian territory during the period 1991–2015. The spatial analysis adopted the following procedures: (a) The change vector from the annual calculation of the midpoint of production; (b) mapping of the growth and acceleration rates of the two productions, and (c) mapping of the correlation between the time series of soybean and cattle. The results showed high rates of growth and acceleration for soy production in the South, Central-West and Matopiba regions. The growth acceleration rate identified the long-term deviations that characterized the effective soybean and cattle expansion areas. The results demonstrated the effects of Brazil’s soy moratorium contained soybean expansion into the Amazon region. However, as a side effect, the soybean production replaced cattle production in the savanna region, which in turn, migrated to the Amazon rainforest. Therefore, the present study highlights the importance of public policies that comprehensively understand the spatial-temporal dynamics of Brazilian agriculture to promote sustainable land-use practices.
Achieving Sustainable Development Goals (SDG): Implementation of the 2030 Agenda in Romania
Authors: Firoiu Daniela , Ionescu George H. , Băndoi Anca , Florea Nicoleta Mihaela , Jianu Elena
Published: April 2019

Romania needs a change of the current development paradigm to face the challenges of the 21st century. As a member of the European Union, leaders in Romania are is interested in implementing the principles of sustainable development at a national level to reduce development gaps, to increase citizens’ well-being, and to preserve a clean environment. The purpose of this research is to determine the implementation status of the 2030 Agenda sustainable development goals (SDG) in Romania and to explore to what extent Romania will be able to reach, for the 2030 horizon, EU average values for the selected indicators. The research is based on 107 indicators that monitored the Sustainable Development Goals (SDGs). Eurostat database (sustainable development indicators) was the source of data in terms of their availability and integrity. The research results showed that the implementation status of SDG is sub-optimal. In the case of 40 indicators out of the 107 analyzed, forecasts indicate the possibility of reaching the EU average values by 2030. However, the country can remain on the path to sustainable development only by involving all stakeholders and increasing concrete and well-targeted measures to improve SDG indicators.
Spatial Characteristics of Population Activities in Suburban Villages Based on Cellphone Signaling Analysis
Authors: Zhou Jizhe , Hou Quanhua , Dong Wentao
Published: April 2019

There are frequent population flow and complex spatial structures in suburban villages. Understanding the spatial characteristics of population activities in suburban villages helps to coordinate the relationship between urban and rural areas and guide the development of suburban villages and the formulation of sound policies. Taking the rural area of Qin and Han New City as the research object, this paper constructs a population time-space analysis framework of “population attribute-activity characteristics-spatial analysis” based on cellphone signaling data. According to the characteristics of the population activity curve, K-means clustering algorithm was used to classify rural space and analyze their characteristics. This study has shown that migrants, who are showed as young and energetic, account for 49.8% of the local registered population per day. Bidirectional flow of residents and commuters is generally presented in urban and rural areas. The urban-rural relation curve was characterized by “double peaks”. The changes in the population in each village and the intensity of urban-rural relation were affected by location, industry and land use. The village population activity curve was classified into three categories, and nine characteristic villages are formed combined with the activity function. The research results can provide a scientific basis for urban and rural planning, spatial planning, industrial guidance and the facility layout.
Price Elasticity of Production Factors in Beijing’s Picking Gardens
Authors: Du Na , Shao Qianqian , Hu Ruifa
Published: April 2019

Picking agriculture is a form of leisure agriculture based on the concept of traditional garden. Due to their unique layout and construction style, picking gardens have different attractive elements, including sightseeing, leisure, entertainment, crop production, and crop picking. However, despite its increasing importance, there is no systematic research on price elasticity or price substitution elasticity of production factors in picking gardens. To fill this gap, we surveyed 308 farmers in five districts of Beijing and employed a translog cost function to compare the impact of operation patterns on peach and cherry production cost by estimating elasticities of substitution between and among inputs. We found that own-price elasticity of all input factors was negative, while substitution relationships existed between labor and land, labor and fertilizer, fertilizer and manure, and manure and pesticide. This indicates that Beijing’s agricultural sector is labor intensive, while fertilizer and pesticide are scarcely used.
Vaquita Face Extinction from Bycatch. Comment on Manjarrez-Bringas, N. et al., Lessons for Sustainable Development: Marine Mammal Conservation Policies and Its Social and Economic Effects. Sustainability 2018, 10, 2185
Authors: Flessa Karl , Calderon-Aguilera Luis , Cintra-Buenrostro Carlos , Dettman David , Dietl Gregory , Goodwin David , Jacobs David , Kowalewski Michal , Nelson Steven , Rowell Kirsten , Schöne Bernd , Smith Jansen , Zamora-Arroyo Francisco
Published: April 2019

We are among the scientists who have documented the environmental and ecological changes to the Upper Gulf of California following the reduction in the Colorado River’s flow. We object to any suggestion that our research supports Manjarrez-Bringas et al.’s conclusion that the decline in the Colorado River’s flow is the reason for the decline in the population of the endangered vaquita porpoise (Phocoena sinus). Manjarrez-Bringas et al.’s conclusions are incongruent with their own data, their logic is untenable, their analyses fail to consider current illegal fishing practices, and their recommendations are unjustified and misdirected. Vaquita face extinction because of bycatch, not because of the lack of river flow.
Investigation on the Possibility of Increasing the Environmental Safety and Fuel Efficiency of Vehicles by Means of Gasoline Nano-Additive
Authors: Magaril Elena , Magaril Romen , Al-Kayiem Hussain H. , Skvortsova Elena , Anisimov Ilya , Rada Elena Cristina
Published: April 2019

Environmental safety problem originated from vehicles requires development and exploration of integrated and effective solutions, which considers the development level of technologies, the cost of their widespread use, the legislation requirements and other relevant aspects. One improvement method of the petroleum-derived fuels characteristics is the use of additives that complement the refining methods and provide ample opportunities to influence the individual characteristics. The aim of this work is to study the influence of the developed multifunctional surface-active nano-additive on the gasoline characteristics and engine performance. The measurement results confirmed the effective reduction of the surface tension of gasoline at the boundary with air, improving the mixture formation in the engine. On the other hand, the saturated vapor pressure was significantly decreased, which dramatically reduces evaporation losses and air pollution by light hydrocarbons. The use of the additive, due to a combination of its surface-active and catalytic action, significantly increases the fuel efficiency of engines and reduces octane requirements, greenhouse gases emissions, as well as noise level during operation of vehicles, and the environmental safety of vehicle operation increases.
Mapping Methodology of Public Urban Green Spaces Using GIS: An Example of Nagpur City, India
Authors: Lahoti Shruti , Kefi Mohamed , Lahoti Ashish , Saito Osamu
Published: April 2019

Faced with a lack of fine grain data availability, in rapidly emerging urban centers of developing nations, the study explored a mapping methodology to create thematic map of public urban green space (UGS). Using GIS, a thematic map of Nagpur city, India was prepared. The objective was to prepare spatial data that are relevant for planners and policy makers, with detailed UGS typologies and to update the status of overall availability and distribution of hierarchical recreational green spaces in the city. The spatial and non-spatial data with added attributes gathered through fieldwork resulted in a holistic dataset, with high accuracy of thematic map (0.93 kappa coefficient). The recorded status of different typologies as well as the distribution of recreational UGS shows disparity in the distribution of UGS. The eastern part of the city grossly lacks UGS provisions, which is compensated by the western part with larger availability of natural green spaces. The mapping methodology is novel and effective for recording qualitative status, analyzing their spatial distribution and prioritizing the provisions of UGS. Future research integrating these spatial data with more qualitative research can provide a holistic view on benefits of UGS provisions and thus facilitate effective UGS governance aiming towards better green infrastructure and hence broader urban sustainability.