bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements
Authors: Řezník Tomáš , Pavelka Tomáš , Herman Lukáš , Lukas Vojtěch , Širůček Petr , Leitgeb Šimon , Leitner Filip
Published: June 2020

Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones—that is to say, areas with the same yield level within fields over the long-term—are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rostěnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations.
Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques
Authors: Masoom Akriti , Kosmopoulos Panagiotis , Kashyap Yashwant , Kumar Shashi , Bansal Ankit
Published: June 2020

This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations.
Fine-Tuning Self-Organizing Maps for Sentinel-2 Imagery: Separating Clouds from Bright Surfaces
Authors: Kristollari Viktoria , Karathanassi Vassilia
Published: June 2020

Removal of cloud interference is a crucial step for the exploitation of the spectral information stored in optical satellite images. Several cloud masking approaches have been developed through time, based on direct interpretation of the spectral and temporal properties of clouds through thresholds. The problem has also been tackled by machine learning methods with artificial neural networks being among the most recent ones. Detection of bright non-cloud objects is one of the most difficult tasks in cloud masking applications since spectral information alone often proves inadequate for their separation from clouds. Scientific attention has recently been redrawn on self-organizing maps (SOMs) because of their unique ability to preserve topologic relations, added to the advantage of faster training time and more interpretative behavior compared to other types of artificial neural networks. This study evaluated a SOM for cloud masking Sentinel-2 images and proposed a fine-tuning methodology to separate clouds from bright land areas. The fine-tuning process which is based on the output of the non-fine-tuned network, at first directly locates the neurons that correspond to the misclassified pixels. Then, the incorrect labels of the neurons are altered without applying further training. The fine-tuning method follows a general procedure, thus its applicability is broad and not confined only in the field of cloud-masking. The network was trained on the largest publicly available spectral database for Sentinel-2 cloud masking applications and was tested on a truly independent database of Sentinel-2 cloud masks. It was evaluated both qualitatively and quantitatively with the interpretation of its behavior through multiple visualization techniques being a main part of the evaluation. It was shown that the fine-tuned SOM successfully recognized the bright non-cloud areas and outperformed the state-of-the-art algorithms: Sen2Cor and Fmask, as well as the version that was not fine-tuned.
Deep Learning-Based Identification of Collapsed, Non-Collapsed and Blue Tarp-Covered Buildings from Post-Disaster Aerial Images
Authors: Miura Hiroyuki , Aridome Tomohiro , Matsuoka Masashi
Published: June 2020

A methodology for the automated identification of building damage from post-disaster aerial images was developed based on convolutional neural network (CNN) and building damage inventories. The aerial images and the building damage data obtained in the 2016 Kumamoto, and the 1995 Kobe, Japan earthquakes were analyzed. Since the roofs of many moderately damaged houses are covered with blue tarps immediately after disasters, not only collapsed and non-collapsed buildings but also the buildings covered with blue tarps were identified by the proposed method. The CNN architecture developed in this study correctly classifies the building damage with the accuracy of approximately 95 % in both earthquake data. We applied the developed CNN model to aerial images in Chiba, Japan, damaged by the typhoon in September 2019. The result shows that more than 90 % of the building damage are correctly classified by the CNN model.
Geo-Registering Consecutive Construction Site Recordings Using a Pre-Registered Reference Module
Authors: Vincke Stan , Vergauwen Maarten
Published: June 2020

The monitoring of construction sites and the achieved progress is a vital aspect of construction works in the Architecture, Engineering and Construction (AEC) industry. Only if three-dimensional reconstructions require a limited time, the creation of consecutive datasets, portraying the site in subsequent phases, becomes feasible. Moreover, a shared coordinate system between all datasets is essential for monitoring purposes. In this work, a new photogrammetric framework is presented to shift from the current error-prone and tedious manual geo-referencing process to a semi-automated one. The fundament of the method is an accurately processed reference module that repeatedly serves as the starting point for processing subsequent image datasets. By means of overlap between pictures in both datasets, the coordinate system, incorporated in the reference module, is inherited by the subsequent datasets, hence bypassing the indication process. The proposed procedure is able to outperform current methods, while requiring less time considered over all consecutive datasets. In our experiments, we compared an unaltered part of two subsequent datasets, each of them processed via the traditional and our proposed method. The obtained mean disparity was 9 mm, while for the manual approach it was 16 mm. Especially for comparative analyses, the proposed approach yields excellent results as every dataset is registered exactly the same, whereas results diverge more when following manual methods. In conclusion, our approach is favourable over the current one, especially for a multitude of consecutive site reconstructions, as no ground control points (GCPs) must be indicated in each separate subsequent dataset, while yielding similar to even better results.
Investigating the Patterns and Dynamics of Urban Green Space in China’s 70 Major Cities Using Satellite Remote Sensing
Authors: Kuang Wenhui , Dou Yinyin
Published: June 2020

Urban green space (UGS) plays a pivotal role in improving urban ecosystem services and building a livable environment for urban dwellers. However, remotely sensed investigation of UGS at city scale is facing a challenge due to the pixels’ mosaics of buildings, squares, roads and green spaces in cities. Here we developed a new algorithm to unmix the fraction of UGS derived from Landsat TM/ETM/8 OLI using a big-data platform. The spatiotemporal patterns and dynamics of UGSs were examined for 70 major cities in China between 2000 and 2018. The results showed that the total area of UGS in these cities grew from 2780.66 km2 in 2000 to 6764.75 km2 in 2018, which more than doubled its area. As a result, the UGS area per inhabitant rose from 15.01 m2 in 2000 to 18.09 m2 in 2018. However, an uneven layout of UGS occurred among the coastal, western, northeastern and central zones. For example, the UGS percentage in newly expanded urban areas in the coastal zone rose significantly in 2000–2018, with an increase of 2.51%, compared to the decline in UGS in cities in the western zone. Therefore, the effective strategies we have developed should be adopted to show disparities and promote green infrastructure capacity building in those cities with less green space, especially in western China.
Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2
Authors: Derkacheva Anna , Mouginot Jeremie , Millan Romain , Maier Nathan , Gillet-Chaulet Fabien
Published: June 2020

During the last decade, the number of available satellite observations has increased significantly, allowing for far more frequent measurements of the glacier speed. Appropriate methods of post-processing need to be developed to efficiently deal with the large volumes of data generated and relatively large intrinsic errors associated with the measurements. Here, we process and combine together measurements of ice velocity of Russell Gletscher in Greenland from three satellites—Sentinel-1, Sentinel-2, and Landsat-8, creating a multi-year velocity database with high temporal and spatial resolution. We then investigate post-processing methodologies with the aim of generating corrected, ordered, and simplified time series. We tested rolling mean and median, cubic spline regression, and linear non-parametric local regression (LOWESS) smoothing algorithms to reduce data noise, evaluated the results against ground-based GPS in one location, and compared the results between two locations with different characteristics. We found that LOWESS provides the best solution for noisy measurements that are unevenly distributed in time. Using this methodology with these sensors, we can robustly derive time series with temporal resolution of 2–3 weeks and improve the accuracy on the ice velocity to about 10 m/yr, or a factor of three compared to the initial measurements. The presented methodology could be applied to the entire Greenland ice sheet with an aim of reconstructing comprehensive sub-seasonal ice flow dynamics and mass balance.
First Experiences with the Landsat-8 Aquatic Reflectance Product: Evaluation of the Regional and Ocean Color Algorithms in a Coastal Environment
Authors: Nazeer Majid , Bilal Muhammad , Nichol Janet Elizabeth , Wu Weicheng , Alsahli Mohammad M. M. , Shahzad Muhammad Imran , Gayen Bijoy Krishna
Published: June 2020

Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there has been a continuous effort to produce reliable ocean color products by taking the advantages of its medium spatial resolution (30 m) and higher Signal to Noise Ratio (SNR). A Provisional Aquatic Reflectance product for the L8 OLI (L8PAR) has been recently released to the public to explore its potential for ocean color applications. This study used a six-year data record of L8 for development of a regionally tuned algorithm (RTA20) for estimating Chlorophyll-a (Chl-a) concentrations around the complex coastal environment of Hong Kong, and is the first to report the usability of the L8PAR product for coastal areas. Furthermore, this study validated three previously developed algorithms, namely RTA16, RTA17 and RTA19, and two ocean color algorithms (OC2 and OC3) modified for L8 OLI by NASA’s Ocean Color group. Results indicate that the newly released L8PAR product has a high potential for estimating the coastal water Chl-a concentrations with higher detail and higher accuracy than previously. The RTA20 algorithm developed in this study outperformed the previous algorithms (RTA16, RTA17, RTA19, OC2 and OC3), e.g., with lower values for Root Mean Square Error (RMSE; 0.92 mg/m3), bias (−0.26 mg/m3) and mean ratio (1.29). Although inferior to the RTA20, the OC2 algorithm also performed well in terms of Pearson’s correlation coefficient (r; 0.84), slope (6.87) and intercept (−8.44) while for RTA20 the values for r, slope and intercept were 0.96, 0.77 and 0.27, respectively. This preliminary evaluation reveals that the OC2 algorithm can be used as an operational algorithm for L8 Chl-a product generation for global coastal areas while RTA20 can be used as a regional algorithm for the routine monitoring of Chl-a concentrations around the coastal areas of Hong Kong or for coastal areas with similar water quality elsewhere in the world.
Evaluation of a Novel Ear Pulse Oximeter: Towards Automated Oxygen Titration in Eyeglass Frames
Authors: Braun Fabian , Verjus Christophe , Solà Josep , Marienfeld Marcus , Funke-Chambour Manuela , Krauss Jens , Geiser Thomas , Guler Sabina A.
Published: June 2020

Current oxygen delivery modes lack monitoring and can be cumbersome for patients with chronic respiratory diseases. Integrating a pulse oximeter and nasal oxygen cannulas into eyeglasses would reduce the burden of current solutions. An ear pulse oximeter (OxyFrame) was evaluated on 16 healthy volunteers and 20 hypoxemic patients with chronic respiratory diseases undergoing a prespecified protocol simulating daily activities. Correlation, error, and accuracy root mean square error (ARMS) were calculated to compare SpO2 measured by OxyFrame, a standard pulse oximeter (MASIMO), and arterial blood gas analysis (aBGA). SpO2 measured by OxyFrame and MASIMO correlated strongly in volunteers, with low error and high accuracy (r = 0.85, error = 0.2 ± 2.9%, ARMS = 2.88%). Performances were similar in patients (r = 0.87, error 0 ± 2.5%, ARMS = 2.49% compared with MASIMO; and r = 0.93, error = 0.4 ± 1.9%, ARMS = 1.94% compared with aBGA). However, the percentage of rejected measurements was high (volunteers 77.2%, patients 46.9%). The OxyFrame cavum conchae pulse oximeter was successfully evaluated, and demonstrated accurate SpO2 measurements, compliant with ISO 80601-2-61:2017. Several reasons for the high rejection rate were identified, and potential solutions were proposed, which might be valuable for optimization of the sensor hardware.
A Comprehensive Study of the Microclimate-Induced Conservation Risks in Hypogeal Sites: The Mithraeum of the Baths of Caracalla (Rome)
Authors: Frasca Francesca , Verticchio Elena , Caratelli Alessia , Bertolin Chiara , Camuffo Dario , Siani Anna Maria
Published: June 2020

The peculiar microclimate inside cultural hypogeal sites needs to be carefully investigated. This study presents a methodology that aimed at providing a user-friendly assessment of the frequently occurring hazards in such sites. A Risk Index was specifically defined as the percentage of time for which the hygrothermal values lie in ranges that are considered to be hazardous for conservation. An environmental monitoring campaign that was conducted over the past ten years inside the Mithraeum of the Baths of Caracalla (Rome) allowed for us to study the deterioration before and after a maintenance intervention. The general microclimate assessment and the specific conservation risk assessment were both carried out. The former made it possible to investigate the influence of the outdoor weather conditions on the indoor climate and estimate condensation and evaporation responsible for salts crystallisation/dissolution and bio-colonisation. The latter took hygrothermal conditions that were close to wall surfaces to analyse the data distribution on diagrams with critical curves of deliquescence salts, mould germination, and growth. The intervention mitigated the risk of efflorescence thanks to reduced evaporation, while promoting the risk of bioproliferation due to increased condensation. The Risk Index provided a quantitative measure of the individual risks and their synergism towards a more comprehensive understanding of the microclimate-induced risks.
Quick and Sensitive Detection of Water Using Galvanic-Coupled Arrays with a Submicron Gap for the Advanced Prediction of Dew Condensation
Authors: Shrestha Rekha Goswami , Kubota Yusuke , Sakamoto Yukihiro , Kawakita Jin
Published: June 2020

We have demonstrated a highly sensitive moisture sensor that can detect water molecules, in addition to water droplets, and therefore, can predict dew condensation with high accuracy and high speed before the formation of water droplets, showing a better performance than a commercial hygrometer. Additionally, the dependence of the output response from the sensor on factors, such as the cooling rate of the sensor’s surface and the vapor pressure in the chamber, that affect the performance of the moisture sensor has been clarified. The output response showed a clear dependence on the variation in cooling rate, as well as the vapor pressure. The higher the cooling rate and vapor pressure, the higher the output response. The output response showed a linear response to the change in the above-mentioned parameters. The higher sensitivity and accuracy of the moisture sensor, as a function of the physical parameters, such as cooling rates, vapor pressure, enables the sensor to perform in advanced detection applications. The sensor can be modified to the actual target regarding the surface nature and the heat capacity of the target object, making it more suitable for wide applications.
A Genetic Algorithm Procedure for the Automatic Updating of FEM Based on Ambient Vibration Tests
Authors: Bianconi Francesca , Salachoris Georgios Panagiotis , Clementi Francesco , Lenci Stefano
Published: June 2020

The dynamic identification of the modal parameters of a structure, in order to gain control of its functionality under operating conditions, is currently under discussion from a scientific and technical point of views. The experimental observations obtained through structural health monitoring (SHM) are a useful calibration reference of numerical models (NMs). In this paper, the procedures for the identification of modal parameters in historical bell towers using a stochastic subspace identification (SSI) algorithm are presented. Then, NMs are manually calibrated on the identification’s results. Finally, the applicability of a genetic algorithm for the automatic calibration of the elastic parameters is considered with the aim of searching for the properties of the autochthonous material, in order to reduce modelling error following the model assurance criterion (MAC). In this regard, several material values on the same model are examined to see how to approach the evolution and the distribution of these features, comparing the characterization proposed by the genetic algorithm with the results considered by the manual iterative procedure.
A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments
Authors: Aragon Bruno , Johansen Kasper , Parkes Stephen , Malbeteau Yoann , Al-Mashharawi Samir , Al-Amoudi Talal , Andrade Cristhian F. , Turner Darren , Lucieer Arko , McCabe Matthew F.
Published: June 2020

Thermal infrared cameras provide unique information on surface temperature that can benefit a range of environmental, industrial and agricultural applications. However, the use of uncooled thermal cameras for field and unmanned aerial vehicle (UAV) based data collection is often hampered by vignette effects, sensor drift, ambient temperature influences and measurement bias. Here, we develop and apply an ambient temperature-dependent radiometric calibration function that is evaluated against three thermal infrared sensors (Apogee SI-11(Apogee Electronics, Santa Monica, CA, USA), FLIR A655sc (FLIR Systems, Wilsonville, OR, USA), TeAx 640 (TeAx Technology, Wilnsdorf, Germany)). Upon calibration, all systems demonstrated significant improvement in measured surface temperatures when compared against a temperature modulated black body target. The laboratory calibration process used a series of calibrated resistance temperature detectors to measure the temperature of a black body at different ambient temperatures to derive calibration equations for the thermal data acquired by the three sensors. As a point-collecting device, the Apogee sensor was corrected for sensor bias and ambient temperature influences. For the 2D thermal cameras, each pixel was calibrated independently, with results showing that measurement bias and vignette effects were greatly reduced for the FLIR A655sc (from a root mean squared error (RMSE) of 6.219 to 0.815 degrees Celsius (℃)) and TeAx 640 (from an RMSE of 3.438 to 1.013 ℃) cameras. This relatively straightforward approach for the radiometric calibration of infrared thermal sensors can enable more accurate surface temperature retrievals to support field and UAV-based data collection efforts.
A Multi-Module Fixed Inclinometer for Continuous Monitoring of Landslides: Design, Development, and Laboratory Testing
Authors: Ruzza Giuseppe , Guerriero Luigi , Revellino Paola , Guadagno Francesco M.
Published: June 2020

Continuous monitoring of landslides is of basic importance for understanding their behavior, defining their 3D geometry, and providing a basis for early warning purposes. While a number of instrumentations can be used for tracking surface displacement, only automatic or fixed multi-module inclinometers can be used for continuous monitoring of displacement at depth, providing valuable information for landslide geometry reconstruction. Since these instruments are very expensive, thus rarely used, a low-cost and multi-module fixed inclinometer for continuous landslide monitoring has been developed. In this paper, the electronics of the system, including sensor characteristics and optimization, controlling software, and structure are presented. For system development, a single module prototype was first developed and tested in the field to ensure sufficient measuring performance. Subsequently, the multi-module system was designed, assembled, and tested in controlled conditions. Test results indicate the good performance of the system with a displacement measuring accuracy of 0.37% of the length of the inclinometer chain. The linearity test indicates the high linearity of the measures, especially in the range ±20°, which is the typical operating range of such kinds of instrumentations. The thermal efficiency test indicates the high efficiency of the system in preventing measuring errors caused by thermal drifting.
Data Description Technique-Based Islanding Classification for Single-Phase Grid-Connected Photovoltaic System
Authors: Haque Ahteshamul , Alshareef Abdulaziz , Khan Asif Irshad , Alam Md Mottahir , Kurukuru Varaha Satya Bharath , Irshad Kashif
Published: June 2020

This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a 5   kW grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%.
Fluorescence Anisotropy Sensor Comprising a Dual Hollow-Core Antiresonant Fiber Polarization Beam Splitter
Authors: Stawska Hanna Izabela , Popenda Maciej Andrzej
Published: June 2020

Fluorescence anisotropy imaging and sensing is a widely recognized method for studying molecular orientation and mobility. However, introducing this technique to in vivo systems is a challenging task, especially when one considers multiphoton excitation methods. Past two decades have brought a possible solution to this issue in the form of hollow-core antiresonant fibers (HC-ARFs). The continuous development of their fabrication technology has resulted in the appearance of more and more sophisticated structures. One of the most promising concepts concerns dual hollow-core antiresonant fibers (DHC-ARFs), which can be used to split and combine optical signals, effectively working as optical fiber couplers. In this paper, the design of a fluorescence anisotropy sensor based on a DHC-ARF structure is presented. The main purpose of the proposed DHC-ARF is multiphoton-excited fluorescence spectroscopy; however, other applications are also possible.
Trust Based Multipath QoS Routing Protocol for Mission-Critical Data Transmission in Tactical Ad-Hoc Networks
Authors: Keum DooHo , Lim Jihun , Ko Young-Bae
Published: June 2020

In tactical ad-hoc networks, the importance of various tactical sensors and mission-critical data is increasing owing to their role in determining a tactical situation and ensuring the viability of soldiers. In particular, the reliability of mission-critical data has to be ensured for accurate situation determination and decision making. However, managing the network and trustworthiness in an environment where malicious nodes exist and a large amount of mission-critical data occur is a challenging issue. To solve these issues, a routing protocol is needed that can effectively detect malicious nodes and ensure the reliability and quality of service (QoS) of mission-critical data. In this paper, we propose a trust-based multipath QoS routing protocol (called MC_TQR) for tactical ad-hoc networks that can detect malicious nodes and satisfy the requirements of mission-critical data. The proposed scheme is verified using an OPNET simulator, and the results confirm the improved network performance when compared with existing schemes.
Estimation of the Intrinsic Power Efficiency in Magnetoelectric Laminates Using Temperature Measurements
Authors: Zhuang Xin , Leung Chung-Ming , Li Jiefang , Viehland Dwight
Published: June 2020

Magnetoelectric (ME) power efficiency is a more important property than the ME voltage or the current coefficients for power conversion applications. This paper introduces an analytical model that describes the relation between the external magnetic field and the power efficiency in layered ME composites. It is a two-phase model. The first fragment establishes the expression between the magnetic field strength and the temperature increase within an operating period. It uses a magneto-elasto-electric equivalent circuit model that was developed by Dong et al. Following previous investigations; the main loss source is the mechanical power dissipation. The second fragment links the power efficiency and the temperature increase in a heat-balanced system. This method is generally used by researchers in the piezoelectric field. The analytical model and the experimental data shows that the decrease of the power efficiency in a laminated composite is between 5% and 10% for a power density of 10 W/in3 (0.61 W/cm3) to 30 W/in3 (1.83 W/cm3). The failure mechanism/process of ME composites under high power density can be estimated/monitored by the proposed method for ME composites in practical applications.
Magnetic Bioreactor for Magneto-, Mechano- and Electroactive Tissue Engineering Strategies
Authors: Castro Nelson , Fernandes Margarida M. , Ribeiro Clarisse , Correia Vítor , Minguez Rikardo , Lanceros-Méndez Senentxu
Published: June 2020

Biomimetic bioreactor systems are increasingly being developed for tissue engineering applications, due to their ability to recreate the native cell/tissue microenvironment. Regarding bone-related diseases and considering the piezoelectric nature of bone, piezoelectric scaffolds electromechanically stimulated by a bioreactor, providing the stimuli to the cells, allows a biomimetic approach and thus, mimicking the required microenvironment for effective growth and differentiation of bone cells. In this work, a bioreactor has been designed and built allowing to magnetically stimulate magnetoelectric scaffolds and therefore provide mechanical and electrical stimuli to the cells through magnetomechanical or magnetoelectrical effects, depending on the piezoelectric nature of the scaffold. While mechanical bioreactors need direct application of the stimuli on the scaffolds, the herein proposed magnetic bioreactors allow for a remote stimulation without direct contact with the material. Thus, the stimuli application (23 mT at a frequency of 0.3 Hz) to cells seeded on the magnetoelectric, leads to an increase in cell viability of almost 30% with respect to cell culture under static conditions. This could be valuable to mimic what occurs in the human body and for application in immobilized patients. Thus, special emphasis has been placed on the control, design and modeling parameters governing the bioreactor as well as its functional mechanism.
Monitoring for Rare Events in a Wireless Powered Communication mmWave Sensor Network
Authors: Koutsioumpos Michael , Zervas Evangelos , Hadjiefthymiades Efstathios , Merakos Lazaros
Published: June 2020

The use of a wireless sensor network to monitor an area of interest for possible hazardous events has become a common practice. The difficulty of replacing or recharging sensor batteries dictates the use of energy harvesting as a means to extend the network’s lifetime. To this end, energy beamforming is used in a millimeter wave wireless power sensor network with randomly deployed nodes. A simple protocol is proposed that allows nodes to report their charging conditions in an effort to select efficient energy-beamforming strategies. Analytical expressions for the probability of successful information reception and successful reporting are provided for two benchmark schemes: the random and the circular energy-beamforming scheme. A Markov chain is used for the former to model the energy level of sensor nodes. Simple sector selection strategies are presented and their performance, in terms of delay and failure information delivery, is assessed through simulations.
Capacitive and Infrared Gas Sensors for the Assessment of the Methane Number of LNG Fuels
Authors: Sweelssen Jörgen , Blokland Huib , Rajamäki Timo , Boersma Arjen
Published: June 2020

Liquid Natural Gas (LNG) is an energy source that is becoming more important in energy transition, as the world is facing lower the CO2 emissions and backup sources for wind and solar energy are needed. LNG is becoming a major player not only as fuel for power plants, but also in transport and mobility. However, the composition of LNG varies significantly between the various production locations around the world, and the layering of hydrocarbons with different molecular weights takes place even in LNG containers. This is especially critical for LNG engines, in which the ignition properties of the gas depend heavily on the fuel quality or Methane Number (MN) of the gas. For optimized engine operation and motor management, this fuel quality should be measured regularly, preferably online and by a small and low-cost sensor. This paper presents two sensor solutions for the assessment of the full gas composition. For both sensors, the standard deviation in the composition of the relevant hydrocarbons was low enough to calculate the Methane Number with an accuracy of approximately 1 MN unit. It was demonstrated that the electronic capacitive sensor was better suited to assess the higher hydrocarbons, whereas the infrared sensor showed higher selectivity for the lower hydrocarbons.
Inkjet Printing of Plate Acoustic Wave Devices
Authors: Kuznetsova Iren , Smirnov Andrey , Anisimkin Vladimir , Gubin Sergey , Signore Maria Assunta , Francioso Luca , Kondoh Jun , Kolesov Vladimir
Published: June 2020

In the paper, the results of production of Ag inkjet printed interdigital transducers to the acoustic delay line based on Y-cut X-propagation direction of lithium niobate plate for the frequency range from 1 to 14 MHz are presented. Additionally, morphological, structural, and electro-physical characteristics of the obtained electrodes were investigated. Mathematical modeling of the excitation of acoustic waves by these electrode structures was carried out. Comparison of the theoretical results with experimental ones showed their qualitative and quantitative coincidences. It was shown that conventional inkjet printing can replace the complex photolithographic method for production of interdigital transducers for acoustic delay lines working up to 14 MHz. The resulting electrode structures make it possible to efficiently excite acoustic waves with a high value of electromechanical coupling coefficient in piezoelectric plates.
An Analytical Framework for the IEEE 802.15.4 MAC Layer Protocol under Periodic Traffic
Authors: Wang Yipeng , Yang Wei , Han Ruisong , Xu Linsen , Zhao Haojiang
Published: June 2020

As the reference communication standard of wireless sensor networks (WSNs), the IEEE 802.15.4 standard has been adopted in various WSN-based applications. In many of these applications, one of the most common traffic pattern types is a periodic traffic patterns, however, the majority of existing analytical models target either saturated or unsaturated network traffic patterns. Furthermore, few of them can be directly extended to the periodic traffic scenario, since periodic traffic brings unstable load status to sensor nodes. To better characterize the WSNs with periodic traffic, we propose an accurate and scalable analytical framework for the IEEE 802.15.4 MAC protocol. By formulating the relationship between clear channel assessment (CCA) and its successful probability from the perspective of channel state and node state, single node’s behavior and whole network’s performance under different network scales and traffic loads can be derived. Extensive simulations are conducted to validate the proposed framework in terms of both local statistics and overall statistics, and the results show that the model can represent the actual behavior and the real performance of both single node and whole network. Besides, as the simplified version of double CCAs mode (DS mode), single CCA mode (SS mode), is also analyzed with simple modifications on the proposed analytical framework. Combining the analytical framework with simulation results, the applicable network scenarios of two modes are also demonstrated respectively. Finally, an approximate distribution of one data packet’s backoff duration is proposed. With this approximate distribution, a conservative estimation of data packet’s average transmission latency in networks with given configurations can be easily carried out.
Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys
Authors: Wu Jianqing , Wu Qiang , Shen Jun , Cai Chen
Published: June 2020

Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.
Sensor Data Fusion in Multi-Sensor Weigh-In-Motion Systems
Authors: Gajda Janusz , Sroka Ryszard , Burnos Piotr
Published: June 2020

In this paper, we present the results of a comparison of two estimators of the gross vehicle weight (GVW) and the static load of individual axles of vehicles. The estimators were used to process measurement data derived from Multi-Sensor Weigh-In-Motion systems (MS-WIM). The term estimator is understood as an algorithm according to which the dynamic axle load measurement results are processed in order to determine the static load. The result obtained is called static load estimate. As a measure of measurement uncertainty, we adopted the standard deviation of the static load estimate. The mean value and the maximum likelihood estimators were compared. Studies were conducted using simulation methods based on synthetic data and experimental data obtained from a WIM system equipped with 16 lines of polymer axle load sensors. We have shown a substantially lower uncertainty of estimates determined using the maximum likelihood estimator. The results obtained have considerable practical significance, particularly during long-term usage of multi-sensor WIM systems.
Smart Characterization of Rogowski Coils by Using a Synthetized Signal
Authors: Mingotti Alessandro , Peretto Lorenzo , Tinarelli Roberto
Published: June 2020

With the spread of new Low-Power Instrument Transformers (LPITs), it is fundamental to provide models and characterization procedures to estimate and even predict the LPITs’ behavior. In fact, distribution system operators and designers of network models are looking for all forms of information which may help the management and the control of power networks. For this purpose, the paper wants to contribute to the scientific community presenting a smart characterization procedure which easily provides sufficient information to predict the output signal of a Low-Power Current Transformer (LPCT), the Rogowski coil. The presented procedure is based on a synthetized signal applied to the Rogowski coil. Afterwards, the validity of the procedure is assessed within the Matlab environment and then by applying it on three off-the-shelf Rogowski coils. Simulations and experimental tests and results involving a variety of distorted signals in the power quality frequency range and by adopting a quite simple measurement setup demonstrated the effectiveness and the capability of the procedure to correctly estimate the output of the tested device.
An Energy Efficient Design of Computation Offloading Enabled by UAV
Authors: Li Linpei , Wen Xiangming , Lu Zhaoming , Jing Wenpeng
Published: June 2020

The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different parameters is discussed.
Enhanced Electrochemical Characteristics of the Glucose Oxidase Bioelectrode Constructed by Carboxyl-Functionalized Mesoporous Carbon
Authors: Lv Chuhan , Li Shuangfei , Liu Liangxu , Zhu Xingyu , Yang Xuewei
Published: June 2020

This research revealed the effect of carboxyl-functionalization on the mesoporous carbon (MC)-fixed glucose oxidase (GOx) for promoting the properties of bioelectrodes. It showed that the oxidation time, temperature and concentration, can significantly affect MC carboxylation. The condition of 2 M ammonium persulfate, 50 °C and 24 h was applied in the study for the successful addition of carboxyl groups to MC, analyzed by FTIR. The nitrogen adsorption isotherms, and X-ray diffraction analysis showed that the carboxylation process slightly changed the physical properties of MC and that the specific surface area and pore size were all well-maintained in MC-COOH. Electrochemical characteristics analysis showed that Nafion/GOx/MC-COOH presented better electrocatalytic activity with greater peak current intensity (1.13-fold of oxidation peak current and 4.98-fold of reduction peak current) compared to Nafion/GOx/MC. Anodic charge-transfer coefficients (α) of GOx/MC-COOH increased to 0.77, implying the favored anodic reaction. Furthermore, the GOx immobilization and enzyme activity in MC-COOH increased 140.72% and 252.74%, leading to the enhanced electroactive GOx surface coverage of Nafion/GOx/MC-COOH electrode (22.92% higher, 1.29 × 10−8 mol cm−2) than the control electrode. Results showed that carboxyl functionalization could increase the amount and activity of immobilized GOx, thereby improving the electrode properties.
Visual Tracking via Deep Feature Fusion and Correlation Filters
Authors: Xia Haoran , Zhang Yuanping , Yang Ming , Zhao Yufang
Published: June 2020

Visual tracking is a fundamental vision task that tries to figure out instances of several object classes from videos and images. It has attracted much attention for providing the basic semantic information for numerous applications. Over the past 10 years, visual tracking has made a great progress, but huge challenges still exist in many real-world applications. The facade of a target can be transformed significantly by pose changing, occlusion, and sudden movement, which possibly leads to a sudden target loss. This paper builds a hybrid tracker combining the deep feature method and correlation filter to solve this challenge, and verifies its powerful characteristics. Specifically, an effective visual tracking method is proposed to address the problem of low tracking accuracy due to the limitations of traditional artificial feature models, then rich hiearchical features of Convolutional Neural Networks are used to make the multi-layer features fusion improve the tracker learning accuracy. Finally, a large number of experiments are conducted on benchmark data sets OBT-100 and OBT-50, and show that our proposed algorithm is effective.
Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition
Authors: Chen Kai , Xie Kai , Wen Chang , Tang Xin-Gong
Published: June 2020

In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.
Towards Fingerprint Spoofing Detection in the Terahertz Range
Authors: Pałka Norbert , Kowalski Marcin
Published: June 2020

Spoofing attacks using imitations of fingerprints of legal users constitute a serious threat. In this study, a terahertz time domain spectroscopy (TDS) setup in a reflection configuration was used for the non-intrusive detection of fingerprint spoofing. Herein, the skin structure of the finger pad is described with a focus on the outermost stratum corneum. We identified and characterized five representative spoofing materials and prepared thin and thick finger imitations. The complex refractive index of the materials was determined in TDS in the transmission configuration. For dataset collection, we selected a group of 16 adults of various ages and genders. The reflection results were analyzed both in the time (reflected signal) and frequency (reflectivity) domains. The measured signals were positively verified with the theoretical calculations. The signals corresponding to samples differ from the finger-related signals, which facilitates spoofing detection. Thanks to deconvolution, we provide a basic explanation of the observed phenomena. We propose two spoofing detection methods, predefined time–frequency features and deep learning based. The methods achieved high true detection rates of 87.9% and 98.8%. Our results show that the terahertz technology can be successfully applied for spoofing detection with high detection probability.