bluuebunny/crossref_metadata_2025 huggingface dataset:
Remote Sensing Applications in Monitoring of Protected Areas
Authors: Wang Yeqiao , Lu Zhong , Sheng Yongwei , Zhou Yuyu
Published: April 2020

Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the world’s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue.
Geolocation Accuracy Assessment of Himawari-8/AHI Imagery for Application to Terrestrial Monitoring
Authors: Yamamoto Yuhei , Ichii Kazuhito , Higuchi Atsushi , Takenaka Hideaki
Published: April 2020

Recent advancements in new generation geostationary satellites have facilitated the application of their datasets to terrestrial monitoring. In this application, geolocation accuracy is an essential issue because land surfaces are generally heterogeneous. In the case of the Advanced Himawari Imager (AHI) onboard Himawari-8, geometric correction of the Himawari Standard Data provided by the Japan Meteorological Agency (JMA data) was conducted using thermal infrared band with 2 km spatial resolution. Based on JMA data, the Center for Environmental Remote Sensing (CEReS) at Chiba University applied a further geometric correction using a visible band with 500 m spatial resolution and released a dataset (CEReS data). JMA data target more general users mainly for meteorological observations, whereas CEReS data aim at terrestrial monitoring for more precise geolocation accuracy. The objectives of this study are to clarify the temporal and spatial variations of geolocation errors in these two datasets and assess their stability for unexpected large misalignment. In this study, the temporal tendencies of the relative geolocation difference between the two datasets were analyzed, and temporal fluctuations of band 3 reflectances of JMA data and CEReS data at certain fixed sites were investigated. A change in the geolocation trend and occasional shifts greater than 2 pixels were found in JMA data. With improved image navigation performance, the geolocation difference was decreased in CEReS data, suggesting the high temporal stability of CEReS data. Overall, JMA data showed an accuracy of less than 2 pixels with the spatial resolution of band 3. When large geolocation differences were observed, anomalies were also detected in the reflectance of JMA data. Nevertheless, CEReS data successfully corrected the anomalous errors and achieved higher geolocation accuracy in general. As CEReS data are processed during the daytime due to the availability of visible bands, we suggest the use of CEReS data for effective terrestrial monitoring during the daytime.
CO2 Concentration, A Critical Factor Influencing the Relationship between Solar-induced Chlorophyll Fluorescence and Gross Primary Productivity
Authors: Qiu Ruonan , Han Ge , Ma Xin , Sha Zongyao , Shi Tianqi , Xu Hao , Zhang Miao
Published: April 2020

The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO2 concentration from OCO-2 as critical driven factors simultaneously (SIF-CO2-GPP model). Evidences from all sites show that the introduction of the atmospheric CO2 concentration improves accuracies of estimated GPP. Compared with the SIF-CO2-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO2 concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO2 concentration. As OCO-2 is capable of providing SIF and XCO2 products with identical spatial and temporal scales, the SIF-CO2-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO2-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents.
Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries
Authors: Li Wenzhao , El-Askary Hesham , Lakshmi Venkat , Piechota Thomas , Struppa Daniele
Published: April 2020

In September 2015, the members of United Nations adopted the 2030 Agenda for Sustainable Development with universal applicability of 17 Sustainable Development Goals (SDGs) and 169 targets. The SDGs are consequential for the development of the countries in the Nile watershed, which are affected by water scarcity and experiencing rapid urbanization associated with population growth. Earth Observation (EO) has become an important tool to monitor the progress and implementation of specific SDG targets through its wide accessibility and global coverage. In addition, the advancement of algorithms and tools deployed in cloud computing platforms provide an equal opportunity to use EO for developing countries with limited technological capacity. This study applies EO and cloud computing in support of the SDG 6 “clean water and sanitation” and SDG 11 “sustainable cities and communities” in the seven Nile watershed countries through investigations of EO data related to indicators of water stress (Indicator 6.4.2) and urbanization and living conditions (Indicators 11.3.1 and 11.1.1), respectively. Multiple approaches including harmonic, time series and correlational analysis are used to assess and evaluate these indicators. In addition, a contemporary deep-learning classifier, fully convolution neural networks (FCNN), was trained to classify the percentage of impervious surface areas. The results show the spatial and temporal water recharge pattern among different regions in the Nile watershed, as well as the urbanization in selected cities of the region. It is noted that the classifier trained from the developed countries (i.e., the United States) is effective in identifying modern communities yet limited in monitoring rural and slum regions.
Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data
Authors: Kwan Chiman , Gribben David , Ayhan Bulent , Bernabe Sergio , Plaza Antonio , Selva Massimo
Published: April 2020

Hyperspectral (HS) data have found a wide range of applications in recent years. Researchers observed that more spectral information helps land cover classification performance in many cases. However, in some practical applications, HS data may not be available, due to cost, data storage, or bandwidth issues. Instead, users may only have RGB and near infrared (NIR) bands available for land cover classification. Sometimes, light detection and ranging (LiDAR) data may also be available to assist land cover classification. A natural research problem is to investigate how well land cover classification can be achieved under the aforementioned data constraints. In this paper, we investigate the performance of land cover classification while only using four bands (RGB+NIR) or five bands (RGB+NIR+LiDAR). A number of algorithms have been applied to a well-known dataset (2013 IEEE Geoscience and Remote Sensing Society Data Fusion Contest). One key observation is that some algorithms can achieve better land cover classification performance by using only four bands as compared to that of using all 144 bands in the original hyperspectral data with the help of synthetic bands generated by Extended Multi-attribute Profiles (EMAP). Moreover, LiDAR data do improve the land cover classification performance even further.
Crowd-Based Cognitive Perception of the Physical World: Towards the Internet of Senses
Authors: Pasolini Gianni , Guerra Anna , Guidi Francesco , Decarli Nicolò , Dardari Davide
Published: April 2020

This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept stems from the fact that mobile devices, such as smartphones and wearables, are becoming an outstanding mean for zero-effort world-sensing and digitalization thanks to their pervasive diffusion and the increasing number of embedded sensors. Data collected by such devices provide unprecedented insights into the physical world that can be inferred through cognitive processes, thus originating a digital sixth sense. In this paper, we describe how the Internet can behave like a sensing brain, thus evolving into the Internet of Senses, with network-based cognitive perception and action capabilities built upon mobile crowd-sensing mechanisms. The new concept of hyper-map is envisioned as an efficient geo-referenced repository of knowledge about the physical world. Such knowledge is acquired and augmented through heterogeneous sensors, multi-user cooperation and distributed learning mechanisms. Furthermore, we indicate the possibility to accommodate proactive sensors, in addition to common reactive sensors such as cameras, antennas, thermometers and inertial measurement units, by exploiting massive antenna arrays at millimeter-waves to enhance mobile terminals perception capabilities as well as the range of new applications. Finally, we distillate some insights about the challenges arising in the realization of the CPI, corroborated by preliminary results, and we depict a futuristic scenario where the proposed Internet of Senses becomes true.
An End-to-End Reliability Framework of the Internet of Things
Authors: Azghiou Kamal , El Mouhib Manal , Koulali Mohammed-Amine , Benali Abdelhamid
Published: April 2020

The Internet of Things (IoT) paradigm feeds from many scientific and engineering fields. This involves a diversity and heterogeneity of its underlying systems. When considering End-to-End IoT systems, we can identify the emergence of new classes of problems. The best-known ones are those associated to standardization for better interoperability and compatibility of those systems, and those who gave birth of new paradigms like that of Fog Computing. Predicting the reliability of an End-to-End IoT system is a problem belonging to this category. On one hand, predicting reliability can be mandatory, most times, before the deployment stage. On another hand, it may help engineers at the design and the operational stages to establish effective maintenance policies and may provide the various stakeholders and decision-makers a means to take the relevant actions. We can find in the literature works which consider only fragments of End-to-End IoT systems such as those assessing reliability for Wireless Sensors Networks (WSN) or Cloud subsystems, to cite just a few. Some other works are specific to well-defined industries, like those targeting reliability study of E-health and Smart-Grid infrastructures. Works that aims to assess reliability for an End-to-End IoT system are remarkably rare and particularly restrained in terms of expressiveness, flexibility, and in their implementation time complexity. In this paper, we apply the Reliability Block Diagram (RBD) paradigm to set up a framework for End-to-End IoT system reliability modeling and analysis. Our contribution is four-fold: we propose an IoT network-based layered architecture, we model in depth each layer of the proposed architecture, we suggest a flow chart to deploy the proposed framework, and we perform a numerical investigation of simplified scenarios. We affirm that the proposed framework is expressive, flexible, and scalable. The numerical study reveals mission time intervals which characterize the behavior of an IoT system from the point of view of its reliability.
Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification
Authors: Martínez-Cerveró Jayro , Ardali Majid Khalili , Jaramillo-Gonzalez Andres , Wu Shizhe , Tonin Alessandro , Birbaumer Niels , Chaudhary Ujwal
Published: April 2020

Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions of eye movements employing EOG signals. The system is based on open source ecosystems, the Raspberry Pi single-board computer, the OpenBCI biosignal acquisition device, and an open-source python library. The designed system provides a cheap, compact, and easy to carry system that can be replicated or modified. We used Maximum, Minimum, and Median trial values as features to create a Support Vector Machine (SVM) classifier. A mean of 90% accuracy was obtained from 7 out of 10 subjects for online classification of Up, Down, Left, and Right movements. This classification system can be used as an input for an HCI, i.e., for assisted communication in paralyzed people.
LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things
Authors: Wang Jin , Tang Yangning , He Shiming , Zhao Changqing , Sharma Pradip Kumar , Alfarraj Osama , Tolba Amr
Published: April 2020

Log anomaly detection is an efficient method to manage modern large-scale Internet of Things (IoT) systems. More and more works start to apply natural language processing (NLP) methods, and in particular word2vec, in the log feature extraction. Word2vec can extract the relevance between words and vectorize the words. However, the computing cost of training word2vec is high. Anomalies in logs are dependent on not only an individual log message but also on the log message sequence. Therefore, the vector of words from word2vec can not be used directly, which needs to be transformed into the vector of log events and further transformed into the vector of log sequences. To reduce computational cost and avoid multiple transformations, in this paper, we propose an offline feature extraction model, named LogEvent2vec, which takes the log event as input of word2vec to extract the relevance between log events and vectorize log events directly. LogEvent2vec can work with any coordinate transformation methods and anomaly detection models. After getting the log event vector, we transform log event vector to log sequence vector by bary or tf-idf and three kinds of supervised models (Random Forests, Naive Bayes, and Neural Networks) are trained to detect the anomalies. We have conducted extensive experiments on a real public log dataset from BlueGene/L (BGL). The experimental results demonstrate that LogEvent2vec can significantly reduce computational time by 30 times and improve accuracy, comparing with word2vec. LogEvent2vec with bary and Random Forest can achieve the best F1-score and LogEvent2vec with tf-idf and Naive Bayes needs the least computational time.
Locating Forest Management Units Using Remote Sensing and Geostatistical Tools in North-Central Washington, USA
Authors: Palaiologou Palaiologos , Essen Maureen , Hogland John , Kalabokidis Kostas
Published: April 2020

In this study, we share an approach to locate and map forest management units with high accuracy and with relatively rapid turnaround. Our study area consists of private, state, and federal land holdings that cover four counties in North-Central Washington, USA (Kittitas, Okanogan, Chelan and Douglas). This area has a rich history of landscape change caused by frequent wildfires, insect attacks, disease outbreaks, and forest management practices, which is only partially documented across ownerships in an inconsistent fashion. To consistently quantify forest management activities for the entire study area, we leveraged Sentinel-2 satellite imagery, LANDFIRE existing vegetation types and disturbances, monitoring trends in burn severity fire perimeters, and Landsat 8 Burned Area products. Within our methodology, Sentinel-2 images were collected and transformed to orthogonal land cover change difference and ratio metrics using principal component analyses. In addition, the Normalized Difference Vegetation Index and the Relativized Burn Ratio index were estimated. These variables were used as predictors in Random Forests machine learning classification models. Known locations of forest treatment units were used to create samples to train the Random Forests models to estimate where changes in forest structure occurred between the years of 2016 and 2019. We visually inspected each derived polygon to manually assign one treatment class, either clearcut or thinning. Landsat 8 Burned Area products were used to derive prescribed fire units for the same period. The bulk of analyses were performed using the RMRS Raster Utility toolbar that facilitated spatial, statistical, and machine learning tools, while significantly reducing the required processing time and storage space associated with analyzing these large datasets. The results were combined with existing LANDFIRE vegetation disturbance and forest treatment data to create a 21-year dataset (1999–2019) for the study area.
Reliable Road Scene Interpretation Based on ITOM with the Integrated Fusion of Vehicle and Lane Tracker in Dense Traffic Situation
Authors: Jeong Jinhan , Yoon Yook Hyun , Park Jahng Hyon
Published: April 2020

Lane detection and tracking in a complex road environment is one of the most important research areas in highly automated driving systems. Studies on lane detection cover a variety of difficulties, such as shadowy situations, dimmed lane painting, and obstacles that prohibit lane feature detection. There are several hard cases in which lane candidate features are not easily extracted from image frames captured by a driving vehicle. We have carefully selected typical scenarios in which the extraction of lane candidate features can be easily corrupted by road vehicles and road markers that lead to degradations in the understanding of road scenes, resulting in difficult decision making. We have introduced two main contributions to the interpretation of road scenes in dense traffic environments. First, to obtain robust road scene understanding, we have designed a novel framework combining a lane tracker method integrated with a camera and a radar forward vehicle tracker system, which is especially useful in dense traffic situations. We have introduced an image template occupancy matching method with the integrated vehicle tracker that makes it possible to avoid extracting irrelevant lane features caused by forward target vehicles and road markers. Second, we present a robust multi-lane detection by a tracking algorithm that incudes adjacent lanes as well as ego lanes. We verify a comprehensive experimental evaluation with a real dataset comprised of problematic road scenarios. Experimental result shows that the proposed method is very reliable for multi-lane detection at the presented difficult situations.
A Novel Fault Diagnosis Approach for Chillers Based on 1-D Convolutional Neural Network and Gated Recurrent Unit
Authors: Wang Zhuozheng , Dong Yingjie , Liu Wei , Ma Zhuo
Published: April 2020

The safety of an Internet Data Center (IDC) is directly determined by the reliability and stability of its chiller system. Thus, combined with deep learning technology, an innovative hybrid fault diagnosis approach (1D-CNN_GRU) based on the time-series sequences is proposed in this study for the chiller system using 1-Dimensional Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU). Firstly, 1D-CNN is applied to automatically extract the local abstract features of the sensor sequence data. Secondly, GRU with long and short term memory characteristics is applied to capture the global features, as well as the dynamic information of the sequence. Moreover, batch normalization and dropout are introduced to accelerate network training and address the overfitting issue. The effectiveness and reliability of the proposed hybrid algorithm are assessed on the RP-1043 dataset; based on the experimental results, 1D-CNN_GRU displays the best performance compared with the other state-of-the-art algorithms. Further, the experimental results reveal that 1D-CNN_GRU has a superior identification rate for minor faults.
Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods
Authors: Zhang Yangyang , Yang Jian , Liu Xiuguo , Du Lin , Shi Shuo , Sun Jia , Chen Biwu
Published: April 2020

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.
Polarization-Sensitive Surface-Enhanced In Situ Photoluminescence Spectroscopy of S. aureus Bacteria on Gold Nanospikes
Authors: Saraeva Irina , Kudryashov Sergey I. , Danilov Pavel , Busleev Nikolay , Tolordava Eteri R. , Rudenko Andrey A. , Zayarny Dmitriy , Ionin Andrey , Romanova Yulia M.
Published: April 2020

We report the possibility of a time-resolved bacterial live/dead dynamics observation with the use of plasmonic nanospikes. Sharp nanospikes, fabricated on a 500-nm thick gold film by laser ablation with the use of 1030-nm femtosecond pulses, were tested as potential elements for antibacterial surfaces and plasmonic luminescence sensors. Staphylococcus aureus bacteria were stained by a live/dead viability kit, with the dead microorganisms acquiring the red colour, caused by the penetration of the luminescent dye propidium iodide through the damaged cell membrane. Photoluminescence was pumped by 515-nm femtosecond laser pulses with linear (Gaussian beam), circular, azimuthal and radial (Laguerre–Gaussian beam) polarizations, exciting the transverse plasmon resonance of the nanospikes and their apex lightning-rod near-field. According to the numerical electrodynamic modeling, the observed strong increase in the photoluminescence yield for radial polarization, while slightly lower for circular and azimuthal polarizations, compared with the low luminescence intensities for the linear laser polarization, was related to their different laser–nanospike coupling efficiencies.
Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review
Authors: Jaramillo-Yánez Andrés , Benalcázar Marco E. , Mena-Maldonado Elisa
Published: April 2020

Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human–Computer Interaction (HCI) has been developed to overcome the communication barriers between humans and computers. One form of HCI is Hand Gesture Recognition (HGR), which predicts the class and the instant of execution of a given movement of the hand. One possible input for these models is surface electromyography (EMG), which records the electrical activity of skeletal muscles. EMG signals contain information about the intention of movement generated by the human brain. This systematic literature review analyses the state-of-the-art of real-time hand gesture recognition models using EMG data and machine learning. We selected and assessed 65 primary studies following the Kitchenham methodology. Based on a common structure of machine learning-based systems, we analyzed the structure of the proposed models and standardized concepts in regard to the types of models, data acquisition, segmentation, preprocessing, feature extraction, classification, postprocessing, real-time processing, types of gestures, and evaluation metrics. Finally, we also identified trends and gaps that could open new directions of work for future research in the area of gesture recognition using EMG.
Tunable Chemical Grafting of Three-Dimensional Poly (3, 4-ethylenedioxythiophene)/Poly (4-styrenesulfonate)-Multiwalled Carbon Nanotubes Composite with Faster Charge-Carrier Transport for Enhanced Gas Sensing Performance
Authors: Kim Hyojae , Jang Yeongseok , Lee Gyeong Won , Yang Seung Yun , Jung Jinmu , Oh Jonghyun
Published: April 2020

The three-dimensional volumetric application of conductive poly (3,4-ethylenedioxythiophene)/poly (4-styrenesulfonate) (PEDOT:PSS) to multiwalled carbon nanotubes (MWCNTs) has not been widely reported. In this study, the applicability of the 3D PEDOT:PSS-MWCNT composite for a gas sensor was investigated with different PEDOT:PSS concentrations. The gas-sensing performance of the 3D PEDOT:PSS-MWCNT composites was investigated using ethanol and carbon monoxide (CO) gas. Overall, in comparison with the pristine MWCNTs, as the PEDOT:PSS concentration increased, the 3D PEDOT:PSS-MWCNT composites exhibited increased conductivity and enhanced gas sensing performances (fast response and recovery times) to both ethanol and CO gases. Importantly, although the PEDOT:PSS coating layer reduced the number of sites for the adsorption and desorption of gas molecules, the charge-carrier transport between the gas molecules and MWCNTs was significantly enhanced. Thus, PEDOT:PSS can be chemically grafted to MWCNTs to enhance the connectivity and conductivity of a 3D network, leading to possible applications in gas sensors.
An Efficient and Secure Alert System for VANETs to Improve Crosswalks’ Security in Smart Cities
Authors: Branquinho João , Senna Carlos , Zúquete André
Published: April 2020

A key characteristic of Smart Cities is the ability to reduce conflicts between different agents coexisting in a dynamic system, such as the interaction between vehicles and pedestrians. This paper presents a system to augment the awareness of vehicle drivers regarding the presence of pedestrians in nearby crosswalks. The proposed system interconnects Road Side Units (RSUs), which are informed about the state of the crosswalks, and vehicles, in order to spread to vehicles, the information about the presence of pedestrians in crosswalks. To prevent false information spreading, RSUs sign the alert messages they broadcast and all vehicles can validate the signatures. This poses strong security requirements, such as non-repudiation of alert messages, as well as strong real-time requirements, such as minimum message validation delays among vehicles approaching a crosswalk of interest. To manage the signed alert messages, we are proposing Nimble Asymmetric Cryptography (NAC), which authenticates implicit broadcast messages. NAC minimizes the usage of asymmetric ciphers, which are fundamental to assure non-repudiation but increase performance penalties and uses hash chaining for source authentication of implicit messages.
Design of Airport Obstacle-Free Zone Monitoring UAV System Based on Computer Vision
Authors: Wang Liang , Ai Jianliang , Zhang Li , Xing Zhenlin
Published: April 2020

In recent years, a rising number of incidents between Unmanned Aerial Vehicles (UAVs) and planes have been reported at airports and airfields. A design scheme for an airport obstacle-free zone monitoring UAV system based on computer vision is proposed. The system integrates the functions of identification, tracking, and expelling and is mainly used for low-cost control of balloon airborne objects and small aircrafts. First, a quadcopter dynamic model and 2-Degrees of Freedom (2-DOF) Pan/Tilt/Zoom (PTZ) model are analyzed, and an attitude back-stepping controller based on disturbance compensation is designed. Second, a low and slow small-target self-identification and tracking technology is constructed against a complex environment. Based on the You Only Look Once (YOLO) and Kernel Correlation Filter (KCF) algorithms, an autonomous target recognition and high-speed tracking plan with great robustness and high reliability is designed. Third, a PTZ controller and automatic aiming strategy based on Anti-Windup Proportional Integral Derivative (PID) algorithm is designed, and a simplified, automatic-aiming expelling device, the environmentally friendly gel ball blaster, which features high speed and high accuracy, is built. The feasibility and stability of the project can be verified through prototype experiments.
Accurately Shaping Supercontinuum Spectrum via Cascaded PCF
Authors: Rong Jifang , Yang Hua , Xiao Yuzhe
Published: April 2020

Shaping is very necessary in order to obtain a wide and flat supercontinuum (SC). Via numerical simulations, we accurately demonstrated shaping the SC using the fiber cascading method to significantly increase the width as well as the flatness of the spectrum in silica photonic crystal fiber (PCF). The cascaded PCF contains two segments, each of which has dual zero-dispersion frequencies (ZDFs). The spectral range of the SC can be expanded tremendously by tuning the spacing between the two ZDFs of the first segmented cascaded PCF. Increasing the pump power generates more solitons at the red edge, which accelerates solitons trapping and improves the spectral flatness of the blue edge. Furthermore, cascading the second segmented PCF by choosing appropriate fiber parameters ensures the flatness of the red end of SC. Therefore, a cost-effective alternative method for broad and flat supercontinuum generation in the near-infrared range is proposed here, which can be implemented easily in any photonics laboratory, where dual ZDFs PCFs are commonly found.
Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
Authors: Hervás Marcos , Bergadà Pau , Alsina-Pagès Rosa Ma
Published: April 2020

High Frequency (HF) communications through ionospheric reflection is a widely used technique specifically for maritime, aeronautical, and emergency services communication with remote areas due to economic and management reasons, and also as backup system. Although long distance radio links can be established beyond line-of-sight, the availability, the usable frequencies and the capacity of the channel depends on the state of the ionosphere. The main factors that affect the ionosphere are day-night, season, sunspot number, polar aurora and earth magnetic field. These effects impair the transmitted wave, which suffers attenuation, time and frequency dispersion. In order to increase the knowledge of this channel, the ionosphere has been sounded by means of narrowband and wideband waveforms by the research community all over the world in several research initiatives. This work intends to be a review of remarkable projects for vertical sounding with a world wide network and for oblique sounding for high latitude, mid latitude, and trans-equatorial latitude.
The Effect of a Rotating Medium on Bulk Acoustic Wave Polarization: From Theoretical Considerations to Perspective Angular Motion Sensor Design
Authors: Durukan Yasemin , Shevelko Michail , Peregudov Aleksandr , Popkova Ekaterina , Shevchenko Sergey
Published: April 2020

We study the effects of medium rotation on bulk acoustic wave (BAW) propagation. For a theoretical analysis of the BAW propagation characteristics, a motion equation for the plane harmonic waves propagating orthogonal to the rotation axis of the propagation medium was analytically resolved. We found that during medium rotation, the polarization of the waves becomes elliptical with the ratio of the polarization ellipse axes explicitly proportional to the angular velocity of the medium rotation, thereby opening the way for the design of sensitive elements (SE) for perspective angular motion sensors (AMS). Next, an analytical dependence of the SE informative parameter on the Poisson’s ratio of the acoustic duct material was obtained. The rotation effect on the dispersion of BAW propagation velocity was studied. Two approaches to the perspective SE design were proposed. An experimental study of a specially designed test assembly and SE model demonstrated high correlation with theoretical predictions and provided an estimate of a potential SE. Therefore, we believe that the study of acoustic wave propagation under nonclassical conditions is a promising direction for prospective solid-state AMS on based on BAW polarization effects design.
Experimental Application of Methods to Compute Solar Irradiance and Cell Temperature of Photovoltaic Modules
Authors: Abe Caio Felippe , Dias João Batista , Notton Gilles , Faggianelli Ghjuvan Antone
Published: April 2020

Solar irradiance and cell temperature are the most significant aspects when assessing the production of a photovoltaic system. To avoid the need of specific sensors for quantifying such parameters, recent literature presents methods to estimate them through electrical measurements, using the photovoltaic module itself as a sensor. This work presents an application of such methods to data recorded using a research platform at University of Corsica, in France. The methods and the platform are briefly presented and the results are shown and discussed in terms of normalized mean absolute errors (nMAE) and root mean square errors (nRMSE) for various irradiance and cell temperature levels. The nMAE (and nRMSE) for solar irradiance are respectively between 3.5% and 3.9% (4.2% and 4.7%). Such errors on computed irradiance are in the same order of magnitude as those found in the literature, with a simple implementation. For cell temperatures estimation, the nMAE and nRMSE were found to be in the range 3.4%–8.2% and 4.3%–10.7%. These results show that using such methods could provide an estimation for the values of irradiance and cell temperature, even if the modules are not new and are not regularly cleaned, but of course not partially shaded.
Miniaturized Continuous-Flow Digital PCR for Clinical-Level Serum Sample Based on the 3D Microfluidics and CMOS Imaging Device
Authors: Li Bin , Li Yuanming , Manz Andreas , Wu Wenming
Published: April 2020

In recent years, the development of polymerase chain reaction (PCR) technology has focused on digital PCR, which depends on the microfluidics. Based on continuous-flow microfluidic technology, this paper designed a miniaturized digital PCR amplification system, and greatly reduced the area required for microdroplet generation and reaction. The core rod. made of polydimethylsiloxane (PDMS), was combined with the Teflon tube to form 3D microfluidics, which requires only one heating source to form the temperature difference required for gene amplification. Only two 34 g needles can form and transmit micro-droplets in a 4-fold tapered Teflon tube, which is the simplest method to generate digital PCR droplets as far as we know, which allows the microdroplet generation device to be free from dependence on expensive chips. A complementary metal oxide semiconductor (CMOS) camera was used as a detection tool to obtain fluorescence video for the entire loop area or a specified loop area. In addition, we developed a homebrew for automatic image acquisition and processing to realize the function of digital PCR. This technique realizes the analysis of clinical serum samples of hepatitis B virus (HBV) and obtained the same results as real-time quantitative PCR. This system has greatly reduced the size and cost of the entire system, while maintaining a stable response.
FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification
Authors: Chambers Robert D. , Yoder Nathanael C.
Published: April 2020

In this paper, we present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular convolutional neural network (CNN) and long short-term memory (LSTM) motifs which have excelled in activity recognition benchmarks, implementing them in a many-to-many architecture to markedly improve frame-by-frame accuracy, event segmentation accuracy, model size, and computational efficiency. We propose several model variants, evaluate them alongside other published models using the Opportunity benchmark dataset, demonstrate the effect of model ensembling and of altering key parameters, and quantify the quality of the models’ segmentation of discrete events. We also offer recommendations for use and suggest potential model extensions. FilterNet advances the state of the art in all measured accuracy and speed metrics when applied to the benchmarked dataset, and it can be extensively customized for other applications.
Mobile Robot Self-Localization with 2D Push-Broom LIDAR in a 2D Map
Authors: Palacín Jordi , Martínez David , Rubies Elena , Clotet Eduard
Published: April 2020

This paper proposes mobile robot self-localization based on an onboard 2D push-broom (or tilted-down) LIDAR using a reference 2D map previously obtained with a 2D horizontal LIDAR. The hypothesis of this paper is that a 2D reference map created with a 2D horizontal LIDAR mounted on a mobile robot or in another mobile device can be used by another mobile robot to locate its location using the same 2D LIDAR tilted-down. The motivation to tilt-down a 2D LIDAR is the direct detection of holes or small objects placed on the ground that remain undetected for a fixed horizontal 2D LIDAR. The experimental evaluation of this hypothesis has demonstrated that self-localization with a 2D push-broom LIDAR is possible by detecting and deleting the ground and ceiling points from the scan data, and projecting the remaining scan points in the horizontal plane of the 2D reference map before applying a 2D self-location algorithm. Therefore, an onboard 2D push-broom LIDAR offers self-location and accurate ground supervision without requiring an additional motorized device to change the tilt of the LIDAR in order to get these two combined characteristics in a mobile robot.
Thermal Infrared Imaging to Evaluate Emotional Competences in Nursing Students: A First Approach through a Case Study
Authors: Marqués-Sánchez Pilar , Liébana-Presa Cristina , Benítez-Andrades José Alberto , Gundín-Gallego Raquel , Álvarez-Barrio Lorena , Rodríguez-Gonzálvez Pablo
Published: April 2020

During university studies of nursing, it is important to develop emotional skills for their impact on academic performance and the quality of patient care. Thermography is a technology that could be applied during nursing training to evaluate emotional skills. The objective is to evaluate the effect of thermography as the tool for monitoring and improving emotional skills in student nurses through a case study. The student was subjected to different emotions. The stimuli applied were video and music. The process consisted of measuring the facial temperatures during each emotion and stimulus in three phases: acclimatization, stimulus, and response. Thermographic data acquisition was performed with an FLIR E6 camera. The analysis was complemented with the environmental data (temperature and humidity). With the video stimulus, the start and final forehead temperature from testing phases, showed a different behavior between the positive (joy: 34.5 °C–34.5 °C) and negative (anger: 36.1 °C–35.1 °C) emotions during the acclimatization phase, different from the increase experienced in the stimulus (joy: 34.7 °C–35.0 °C and anger: 35.0 °C–35.0 °C) and response phases (joy: 35.0 °C–35.0 °C and anger: 34.8 °C–35.0 °C). With the music stimulus, the emotions showed different patterns in each phase (joy: 34.2 °C–33.9 °C–33.4 °C and anger: 33.8 °C–33.4 °C–33.8 °C). Whenever the subject is exposed to a stimulus, there is a thermal bodily response. All of the facial areas follow a common thermal pattern in response to the stimulus, with the exception of the nose. Thermography is a technique suitable for the stimulation practices in emotional skills, given that it is non-invasive, it is quantifiable, and easy to access.
Shaping Beam Profiles Using Plastic Optical Fiber Tapers with Application to Ice Sensors
Authors: Amoiropoulos Kostas , Kioselaki Georgia , Kourkoumelis Nikolaos , Ikiades Aris
Published: April 2020

Using either bulk or fiber optics the profile of laser beams can be altered from Gaussian to top-hat or hollow beams allowing enhanced performance in applications like laser cooling, optical trapping, and fiber sensing. Here, we report a method based on multimode Plastic Optical Fibers (POF) long-tapers, to tweak the beam profile from near Gaussian to a hollow beam, by generating surface irregularities on the conical sections of the taper with a heat-and-pull technique. Furthermore, a cutback technique applied on long tapers expanded the output beam profile by more than twice the numerical aperture (NA) of the fiber. The enhanced sensitivity and detection efficiency of the extended profile was tested on a fiber optical ice sensor related to aviation safety.
Social Networks Use Patterns among University Youth: The Validity and Reliability of an Updated Measurement Instrument
Authors: Gómez-García Melchor , Matosas-López Luis , Ruiz-Palmero Julio
Published: April 2020

This article addresses the design and validation of an updated questionnaire that makes it possible to understand the use patterns and attitudes of university youth on social networks. The authors utilized a panel of 20 judges who were social media experts and a sample of 640 university students. The exploratory factor analysis (EFA) explained 66.523% of the total variance. The confirmatory factor analysis (CFA), carried out to verify the dimensional structure of the instrument, reflected the appropriate parameters. The reliability study showed a Cronbach’s alpha of 0.864. These data corroborated the development of a robust and reliable questionnaire. The resulting instrument did not contain items alluding to specific social networks (Facebook, Twitter, Instagram, or LinkedIn), but rather students’ usage patterns of them. The exclusion of items that referred to particular social networks during the research demonstrated a convergence in behavior on social media regardless of the nuances of each platform. This fact suggested that the platform was of secondary importance in the context of a new paradigm in which the type of use (viewing, posting, participating, or interacting) took precedence over the name of the network itself.
The Use of Life Cycle-Based Approaches in the Food Service Sector to Improve Sustainability: A Systematic Review
Authors: Takacs Berill , Borrion Aiduan
Published: April 2020

With the prevalence of eating out increasing, the food service sector has an increasing role in accelerating the transition towards more sustainable and healthy food systems. While life cycle-based approaches are recommended to be used as reference methods for assessing the environmental sustainability of food systems and supply chains, their application in the food service sector is still relatively scarce. In this study, a systematic review was conducted to examine the use and effectiveness of life-cycle based interventions in improving the sustainability of food services. This review found that life-cycle based approaches are not only useful for identifying hotspots for impact reduction, but also for comparing the performance of different sustainability interventions. In particular, interventions targeting the production phase, such as promoting dietary change through menu planning in which high-impact ingredients (e.g., animal products) are replaced with low-impact ingredients (e.g., plant foods), had the highest improvement potential. Interventions targeting other phases of the catering supply chain (e.g., food storage, meal preparation, waste management) had considerably lower improvement potentials. This review article provides valuable insights on how the sustainability of the food service sector can be improved without the burden shifting of impacts, which interventions to prioritise, and where knowledge gaps in research exist. A key recommendation for future research is to focus on combined life cycle thinking approaches that are capable of addressing sustainability holistically in the food service sector by integrating and assessing the environmental, social and economic dimensions of interventions.
Dog Walkers’ Views of Urban Biodiversity across Five European Cities
Authors: Fischer Leonie K. , Kowarik Ingo
Published: April 2020

Contact with nature makes people feel better, live healthier and act more environmentally-friendly. We hypothesized that dog walking, an omnipresent people–nature interaction in cities, translates to a more positive view of urban nature and, subsequently, to more support for conservation initiatives. Insights into such positive side-effects of dog walking are relevant for dog-related urban policies that often focus on negative impacts of dogs (e.g., health risks, disturbance of wildlife). Based on a field survey in five European cities (N = 3717), we analyzed if people who walked dogs regularly valued four urban ecosystem types (park meadows, wastelands, streetscapes, forests), and the plant species diversity within, differently from other people. Opposite to our hypothesis, participants from both groups valued urban ecosystems and their biodiversity very similarly across the cities. Thus, our study does not confirm that regular dog walkers value natural elements more than other people. It thus remains an important challenge for urban planners to balance services and disservices of dog walking in urban greenspaces.