bluuebunny/crossref_metadata_2025 huggingface dataset:
Dentistry 4.0 Concept in the Design and Manufacturing of Prosthetic Dental Restorations
Authors: Dobrzański Leszek A. , Dobrzański Lech B.
Published: April 2020

The paper is a comprehensive but compact review of the literature on the state of illnesses of the human stomatognathic system, related consequences in the form of dental deficiencies, and the resulting need for prosthetic treatment. Types of prosthetic restorations, including implants, as well as new classes of implantable devices called implant-scaffolds with a porous part integrated with a solid core, as well as biological engineering materials with the use of living cells, have been characterized. A review of works on current trends in the technical development of dental prosthetics aiding, called Dentistry 4.0, analogous to the concept of the highest stage of Industry 4.0 of the industrial revolution, has been presented. Authors’ own augmented holistic model of Industry 4.0 has been developed and presented. The studies on the significance of cone-beam computed tomography (CBCT) in planning prosthetic treatment, as well as in the design and manufacture of prosthetic restorations, have been described. The presented and fully digital approach is a radical turnaround in both clinical procedures and the technologies of implant preparation using computer-aided design and manufacturing methods (CAD/CAM) and additive manufacturing (AM) technologies, including selective laser sintering (SLS). The authors’ research illustrates the practical application of the Dentistry 4.0 approach for several types of prosthetic restorations. The development process of the modern approach is being observed all over the world. The use of the principles of the augmented holistic model of Industry 4.0 in advanced dental engineering indicates a change in the traditional relationship between a dentist and a dental engineer. The overall conclusion demonstrates that it is inevitable and extremely beneficial to implement the idea of Dentistry 4.0 following the assumptions of the authors’ own, holistic Industry 4.0 model.
A Lightweight Spectral–Spatial Feature Extraction and Fusion Network for Hyperspectral Image Classification
Authors: Chen Linlin , Wei Zhihui , Xu Yang
Published: April 2020

Hyperspectral image (HSI) classification accuracy has been greatly improved by employing deep learning. The current research mainly focuses on how to build a deep network to improve the accuracy. However, these networks tend to be more complex and have more parameters, which makes the model difficult to train and easy to overfit. Therefore, we present a lightweight deep convolutional neural network (CNN) model called S2FEF-CNN. In this model, three S2FEF blocks are used for the joint spectral–spatial features extraction. Each S2FEF block uses 1D spectral convolution to extract spectral features and 2D spatial convolution to extract spatial features, respectively, and then fuses spectral and spatial features by multiplication. Instead of using the full connected layer, two pooling layers follow three blocks for dimension reduction, which further reduces the training parameters. We compared our method with some state-of-the-art HSI classification methods based on deep network on three commonly used hyperspectral datasets. The results show that our network can achieve a comparable classification accuracy with significantly reduced parameters compared to the above deep networks, which reflects its potential advantages in HSI classification.
Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days
Authors: Yoo Cheolhee , Im Jungho , Cho Dongjin , Yokoya Naoto , Xia Junshi , Bechtel Benjamin
Published: April 2020

Land surface temperature (LST) is used as a critical indicator for various environmental issues because it links land surface fluxes with the surface atmosphere. Moderate-resolution imaging spectroradiometers (MODIS) 1 km LSTs have been widely utilized but have the serious limitation of not being provided under cloudy weather conditions. In this study, we propose two schemes to estimate all-weather 1 km Aqua MODIS daytime (1:30 p.m.) and nighttime (1:30 a.m.) LSTs in South Korea for humid summer days. Scheme 1 (S1) is a two-step approach that first estimates 10 km LSTs and then conducts the spatial downscaling of LSTs from 10 km to 1 km. Scheme 2 (S2), a one-step algorithm, directly estimates the 1 km all-weather LSTs. Eight advanced microwave scanning radiometer 2 (AMSR2) brightness temperatures, three MODIS-based annual cycle parameters, and six auxiliary variables were used for the LST estimation based on random forest machine learning. To confirm the effectiveness of each scheme, we have performed different validation experiments using clear-sky MODIS LSTs. Moreover, we have validated all-weather LSTs using bias-corrected LSTs from 10 in situ stations. In clear-sky daytime, the performance of S2 was better than S1. However, in cloudy sky daytime, S1 simulated low LSTs better than S2, with an average root mean squared error (RMSE) of 2.6 °C compared to an average RMSE of 3.8 °C over 10 stations. At nighttime, S1 and S2 demonstrated no significant difference in performance both under clear and cloudy sky conditions. When the two schemes were combined, the proposed all-weather LSTs resulted in an average R2 of 0.82 and 0.74 and with RMSE of 2.5 °C and 1.4 °C for daytime and nighttime, respectively, compared to the in situ data. This paper demonstrates the ability of the two different schemes to produce all-weather dynamic LSTs. The strategy proposed in this study can improve the applicability of LSTs in a variety of research and practical fields, particularly for areas that are very frequently covered with clouds.
The Quantitative Estimation of Grazing Intensity on the Zoige Plateau Based on the Space-Air-Ground Integrated Monitoring Technology
Authors: Lei Guangbin , Li Ainong , Zhang Zhengjian , Bian Jinhu , Hu Guyue , Wang Changbo , Nan Xi , Wang Jiyan , Tan Jianbo , Liao Xiaohan
Published: April 2020

Grazing intensity (GI) is an important indicator for grazing situations in pastoral areas. However, it has been difficult to be observed directly in the field, due to the randomness and dynamics of the grazing behavior of livestock. Consequently, the lack of actual GI information has become a common issue in studies on quantitatively estimating GI. In this paper, a novel quantitative estimation method is proposed based on the Space-Air-Ground integrated monitoring technology. It systematically integrates GPS tracking technology, Unmanned Aerial Vehicle (UAV) observation technology, and satellite remote sensing technology. Taking Xiangdong Village on the Zoige Plateau as a study area, the trajectory data and UAV images were acquired by the GPS tracking experiments and UAV observation experiments, respectively. The GI at paddock scale (PGI) was then generated with the Kernel Density Estimation (KDE) algorithm and the above data. Taking the generated PGI as training data, an estimation model of GI at region scale (RGI) was constructed by using the time-series satellite remote sensing images and random forest regression algorithm. Finally, the time-series RGI data with a spatial resolution of 10 m in Xiangdong Village were produced by the above model. The accuracy assessment demonstrated that the generated time-series RGI data could reflect the spatial-temporal heterogeneity of actual GI, with a mean absolute error of 0.9301 and r2 of 0. 8573. The proposed method provides a new idea for generating the actual GI on the ground and the time-series RGI data. This study also highlights the feasibility and potential of using the Space-Air-Ground integrated monitoring technology to generate time-series RGI data with high spatial resolution. The generated time-series RGI data would provide data support for the formulation of policies and plans related to the sustainable development of animal husbandry.
Building Extraction Based on U-Net with an Attention Block and Multiple Losses
Authors: Guo Mingqiang , Liu Heng , Xu Yongyang , Huang Ying
Published: April 2020

Semantic segmentation of high-resolution remote sensing images plays an important role in applications for building extraction. However, the current algorithms have some semantic information extraction limitations, and these can lead to poor segmentation results. To extract buildings with high accuracy, we propose a multiloss neural network based on attention. The designed network, based on U-Net, can improve the sensitivity of the model by the attention block and suppress the background influence of irrelevant feature areas. To improve the ability of the model, a multiloss approach is proposed during training the network. The experimental results show that the proposed model offers great improvement over other state-of-the-art methods. For the public Inria Aerial Image Labeling dataset, the F1 score reached 76.96% and showed good performance on the Aerial Imagery for Roof Segmentation dataset.
City-Scale Distance Sensing via Bispectral Light Extinction in Bad Weather
Authors: Zhao Dong , Asano Yuta , Gu Lin , Sato Imari , Zhou Huixin
Published: April 2020

In this paper, we propose a novel city-scale distance sensing algorithm based on atmosphere optics. The suspended particles, especially in bad weather, would attenuate the light at almost all wavelengths. Observing this fact and starting from the light scattering mechanism, we derive a bispectral distance sensing algorithm by leveraging the difference of extinction coefficient between two specifically selected near infrared wavelengths. The extinction coefficient of the atmosphere is related to both wavelength and meteorological conditions, also known as visibility, such as the fog and haze day. To account for different bad weather conditions, we explicitly introduce visibility into our algorithm by incorporating it into the calculation of extinction coefficient, making our algorithm simple yet effective. To capture the data, we build a bispectral imaging system that is able to take a pair of images with a monochrome camera and two narrow band-pass filters. We also present a wavelength selection strategy that allows us to accurately sense distance regardless of material reflectance and texture. Specifically, this strategy determines two distinct near infrared wavelengths by maximising the extinction coefficient difference while minimizing the influence of building’s reflectance variance. The experiments empirically validate our model and its practical performance on the distance sensing for the city-scale buildings.
Long-Term Analysis of Sea Ice Drift in the Western Ross Sea, Antarctica, at High and Low Spatial Resolution
Authors: Farooq Usama , Rack Wolfgang , McDonald Adrian , Howell Stephen
Published: April 2020

The Ross Sea region, including three main polynya areas in McMurdo Sound, Terra Nova Bay, and in front of the Ross Ice Shelf, has experienced a significant increase in sea ice extent in the first four decades of satellite observations. Here, we use Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) to estimate 894 high-resolution sea ice motion fields of the Western Ross Sea in order to explore ice-atmosphere interactions based on sequential high-resolution Advanced Synthetic Aperture Radar (ASAR) images from the Envisat satellite acquired between 2002–2012. Validation of output motion vectors with manually drawn vectors for 24 image pairs show Pearson correlation coefficients of 0.92 ± 0.09 with a mean deviation in direction of −3.17 ± 6.48 degrees. The high-resolution vectors were also validated against the Environment and Climate Change Canada sea ice motion tracking algorithm, resulting in correlation coefficients of 0.84 ± 0.20 and the mean deviation in the direction of −0.04 ± 17.39 degrees. A total of 480 one-day separated velocity vector fields have been compared to an available NSIDC low-resolution sea ice motion vector product, showing much lower correlations and high directional differences. The high-resolution product is able to better identify short-term and spatial variations, whereas the low-resolution product underestimates the actual sea ice velocities by 47% in this important near-coastal region. The large-scale pattern of sea ice drift over the full time period is similar in both products. Improved image coverage is still desired to capture drift variations shorter than 24 h.
Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection
Authors: Zhang Jian , Xie Tianjin , Yang Chenghai , Song Huaibo , Jiang Zhao , Zhou Guangsheng , Zhang Dongyan , Feng Hui , Xie Jing
Published: April 2020

Crop leaf purpling is a common phenotypic change when plants are subject to some biotic and abiotic stresses during their growth. The extraction of purple leaves can monitor crop stresses as an apparent trait and meanwhile contributes to crop phenotype analysis, monitoring, and yield estimation. Due to the complexity of the field environment as well as differences in size, shape, texture, and color gradation among the leaves, purple leaf segmentation is difficult. In this study, we used a U-Net model for segmenting purple rapeseed leaves during the seedling stage based on unmanned aerial vehicle (UAV) RGB imagery at the pixel level. With the limited spatial resolution of rapeseed images acquired by UAV and small object size, the input patch size was carefully selected. Experiments showed that the U-Net model with the patch size of 256 × 256 pixels obtained better and more stable results with a F-measure of 90.29% and an Intersection of Union (IoU) of 82.41%. To further explore the influence of image spatial resolution, we evaluated the performance of the U-Net model with different image resolutions and patch sizes. The U-Net model performed better compared with four other commonly used image segmentation approaches comprising support vector machine, random forest, HSeg, and SegNet. Moreover, regression analysis was performed between the purple rapeseed leaf ratios and the measured N content. The negative exponential model had a coefficient of determination (R²) of 0.858, thereby explaining much of the rapeseed leaf purpling in this study. This purple leaf phenotype could be an auxiliary means for monitoring crop growth status so that crops could be managed in a timely and effective manner when nitrogen stress occurs. Results demonstrate that the U-Net model is a robust method for purple rapeseed leaf segmentation and that the accurate segmentation of purple leaves provides a new method for crop nitrogen stress monitoring.
EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques
Authors: Alturki Fahd A. , AlSharabi Khalil , Abdurraqeeb Akram M. , Aljalal Majid
Published: April 2020

Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Two different modes, single-channel and multi-channel, of EEG signals are analyzed for epilepsy and ASD. The independent components analysis (ICA) technique is used to remove the artifacts from EEG dataset. Then, the EEG dataset is segmented and filtered to remove noise and interference using an elliptic band-pass filter. Next, the EEG signal features are extracted from the filtered signal using a discrete wavelet transform (DWT) to decompose the filtered signal to its sub-bands delta, theta, alpha, beta and gamma. Subsequently, five statistical methods are used to extract features from the EEG sub-bands: the logarithmic band power (LBP), standard deviation, variance, kurtosis, and Shannon entropy (SE). Further, the features are fed into four different classifiers, linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural networks (ANNs), to classify the features corresponding to their classes. The combination of DWT with SE and LBP produces the highest accuracy among all the classifiers. The overall classification accuracy approaches 99.9% using SVM and 97% using ANN for the three-class single-channel and multi-channel modes, respectively.
A Path-Length Efficient, Low-Overhead, Load-Balanced Routing Protocol for Maximum Network Lifetime in Wireless Sensor Networks with Holes
Authors: Nguyen Phi Le , Nguyen Thanh Hung , Nguyen Kien
Published: April 2020

In wireless sensor networks (WSNs) with holes, designing efficient routing protocols, which prolong the network lifetime, is one of the most critical issues. To this end, this paper proposes a new geographic routing protocol for WSNs named the load Balanced and constant Stretch protocol for bypassing Multiple Holes (i.e., BSMH). In contrast to the existing works in the literature, the design of BSMH simultaneously takes into account the three factors that impacted the network lifetime, including routing path length, control packet overhead, and load balance among the nodes. Moreover, BSMH aims at minimizing the routing path length and the control overhead, while maximizing the load balance. We theoretically prove the efficiency of BSMH and extensively evaluate BSMH against the state-of-the-art protocols. The evaluation results show that the proposed protocol outperforms the others in various investigated metrics, not only network lifetime, but also routing path stretch, load balance, and control overhead. Specifically, BSMH prolongs the network lifetime by 30 % compared to the existing protocols while guaranteeing that the routing path stretch is under 1.3 .
Three-Factor Fast Authentication Scheme with Time Bound and User Anonymity for Multi-Server E-Health Systems in 5G-Based Wireless Sensor Networks
Authors: Wong Alice May-Kuen , Hsu Chien-Lung , Le Tuan-Vinh , Hsieh Mei-Chen , Lin Tzu-Wei
Published: April 2020

The fifth generation (5G) mobile network delivers high peak data rates with ultra-low latency and massive network capacity. Wireless sensor network (WSN) in Internet of Thing (IoT) architecture is of prominent use in 5G-enabled applications. The electronic healthcare (e-health) system has gained a lot of research attention since it allows e-health users to store and share data in a convenient way. By the support of 5G technology, healthcare data produced by sensor nodes are transited in the e-health system with high efficiency and reliability. It helps in reducing the treatment cost, providing efficient services, better analysis reports, and faster access to treatment. However, security and privacy issues become big concerns when the number of sensors and mobile devices is increasing. Moreover, existing single-server architecture requires to store a massive number of identities and passwords, which causes a significant database cost. In this paper, we propose a three-factor fast authentication scheme with time bound and user anonymity for multi-server e-health systems in 5G-based wireless sensor networks. In our work, the three-factor authentication scheme integrating biometrics, password, and smart card ensures a high-security sensor-enabled environment for communicating parties. User anonymity is preserved during communication process. Besides, time bound authentication can be applied to various healthcare scenarios to enhance security. The proposed protocol includes fast authentication, which can provide a fast communication for participating parties. Our protocol is also designed with multi-server architecture to simplify network load and significantly save database cost. Furthermore, security proof and performance analysis results show that our proposed protocol can resist various attacks and bear a rational communication cost.
Wireless Measuring System for Monitoring the Condition of Devices Designed to Protect Line Structures
Authors: Pieš Martin , Hájovský Radovan , Velička Jan
Published: April 2020

A large number of rock formations in the Czech Republic and abroad directly threaten to damage objects or traffic along the roads located beneath these formations. For this reason, many such rock formations are stabilized using protective fences or dynamic barriers. There are several special sensors available on the market. However, there is no comprehensive monitoring system, including remote threshold settings, data processing, and alarm conditions. This statement is supported by extensive research in this area as well as information from major geotechnical companies that are interested in such a system and want to include it in their portfolio. The aim of the article is to describe the unique wireless monitoring system used to measure the geotechnical quantities we have developed. The design and implementation of systems used to measure protective fence states with accelerometers and slope shift with load anchor cells are presented. Wireless accelerometric sensors and load anchor cell sensors are proposed for both systems. To transfer data from the accelerometer sensor to a superior system, IQRF® technology is applied for the communication between the wireless nodes and the network coordinator under the IQMESH topology. The article includes a detailed description of the development of the accelerometric wireless sensor node and load anchor cell wireless sensor node. Three case studies are also discussed. The first case study focuses on the data implementation and assessment at a testing polygon at the village of Málkov. The second case study describes the data implementation and an assessment of the measuring system under operating conditions in Zbraslav, a municipality near Prague. The third case study describes the implementation and assessment of data from load anchor cell wireless nodes installed in realistic conditions on a supporting gabion wall next to a road. All communication between the sensors and with the IQMESH network coordinator and database was executed wirelessly. The data were archived in a MySQL database and it provides a data source for the assessment and visualizations using the Grafana SW system.
Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments
Authors: Xiong Chengke , Zhou Hexiong , Lu Di , Zeng Zheng , Lian Lian , Yu Caoyang
Published: April 2020

This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfying constraints on collision avoidance and pre-specified mission time. The proposed rapidly-exploring adaptive sampling tree star (RAST*) algorithm combines inspirations from rapidly-exploring random tree star (RRT*) with a tournament selection method and informative heuristics to achieve efficient searching of informative data in continuous space. Results of numerical experiments and proof-of-concept field experiments demonstrate the effectiveness and superiority of the proposed RAST* over rapidly-exploring random sampling tree star (RRST*), rapidly-exploring adaptive sampling tree (RAST), and particle swarm optimization (PSO).
Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network
Authors: Ju Chunhua , Gu Qiuyang , Wu Gongxing , Zhang Shuangzhu
Published: April 2020

Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various privacy protection methods based on differential privacy have been proposed, but most of them cannot simultaneously solve the complex attribute association problem between high-dimensional perception data and the privacy threat problems from untrustworthy servers. To address this problem, we put forward a local privacy protection based on Bayes network for high-dimensional perceptual data in this paper. This mechanism realizes the local data protection of the users at the very beginning, eliminates the possibility of other parties directly accessing the user’s original data, and fundamentally protects the user’s data privacy. During this process, after receiving the data of the user’s local privacy protection, the perception server recognizes the dimensional correlation of the high-dimensional data based on the Bayes network, divides the high-dimensional data attribute set into multiple relatively independent low-dimensional attribute sets, and then sequentially synthesizes the new dataset. It can effectively retain the attribute dimension correlation of the original perception data, and ensure that the synthetic dataset and the original dataset have as similar statistical characteristics as possible. To verify its effectiveness, we conduct a multitude of simulation experiments. Results have shown that the synthetic data of this mechanism under the effective local privacy protection has relatively high data utility.
Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System
Authors: Chen Hao , Mao Yingchi , Wang Longbao , Qi Hai
Published: April 2020

Many various types of sensors have been installed to monitor the deformation and stress in the dam structure. It is difficult to directly evaluate the operation status of the dam structure based on the massive monitoring data. The sensor network is divided into multiple regions according to the design specifications, simulation data, and engineering experiences. The local results from sub-regions are integrated to achieve overall evaluation. However, it ignores the spatial distribution of sensors and the variation of time series, which cannot meet the real-time evaluation for the dam safety monitoring. If the network partitions can provide the preliminary foundation for analyzing the dynamic change laws of the dam’s working conditions in a real-way, we should consider the similarity of structure and stresses in the local region of the dam and the correlation among the monitoring data. A time-series denoising autoencoder (TSDA) is proposed to represent the spatial and temporal features of the nodes by compressing high-dimensional monitoring data. Then, a network partitioning algorithm (NPA) based on spatial-temporal features based on the TSDA is presented. The NPA ensures that the partition results can support the analysis of the physical change laws by introducing the auxiliary objective variable to optimize the network partition objective function. Experimental results on the public datasets and a real dataset from an arch dam demonstrate that the proposed network partition algorithm NPA can achieve better partition performance than TSDA+K-Means and TSDA+GMM. The NPA can improve the silhouette coefficient by 45.1% and 58.4% higher than the TSDA+K-Means and TSDA+GMM, respectively. The NPA can increase the Calinski-Harabaz Index by 30.8% and 61.6%, respectively.
Multispectral LIF-Based Standoff Detection System for the Classification of CBE Hazards by Spectral and Temporal Features
Authors: Fellner Lea , Kraus Marian , Gebert Florian , Walter Arne , Duschek Frank
Published: April 2020

Laser-induced fluorescence (LIF) is a well-established technique for monitoring chemical processes and for the standoff detection of biological substances because of its simple technical implementation and high sensitivity. Frequently, standoff LIF spectra from large molecules and bio-agents are only slightly structured and a gain of deeper information, such as classification, let alone identification, might become challenging. Improving the LIF technology by recording spectral and additionally time-resolved fluorescence emission, a significant gain of information can be achieved. This work presents results from a LIF based detection system and an analysis of the influence of time-resolved data on the classification accuracy. A multi-wavelength sub-nanosecond laser source is used to acquire spectral and time-resolved data from a standoff distance of 3.5 m. The data set contains data from seven different bacterial species and six types of oil. Classification is performed with a decision tree algorithm separately for spectral data, time-resolved data and the combination of both. The first findings show a valuable contribution of time-resolved fluorescence data to the classification of the investigated chemical and biological agents to their species level. Temporal and spectral data have been proven as partly complementary. The classification accuracy is increased from 86% for spectral data only to more than 92%.
Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things
Authors: Miah Md. Sipon , Ahmed Kazi Mowdud , Islam Md. Khairul , Mahmud Md. Ashek Raihan , Rahman Md. Mahbubur , Yu Heejung
Published: April 2020

Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback–Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels.
Automatic Irrigation Scheduling on a Hedgerow Olive Orchard Using an Algorithm of Water Balance Readjusted with Soil Moisture Sensors
Authors: Millán Sandra , Campillo Carlos , Casadesús Jaume , Pérez-Rodríguez Juan Manuel , Prieto Maria Henar
Published: April 2020

Recent technological advances have made possible automated irrigation scheduling using decision-support tools. These tools help farmers to make better decisions in the management of their irrigation system, thus increasing yields while preserving water resources. The aim of this study is to evaluate in a commercial plot an automated irrigation system combined with remote-sensing techniques and soil mapping that allows the establishment of regulated deficit irrigation (RDI) strategies. The study was carried out over 3 years (2015–2017) in a commercial hedgerow olive orchard of the variety ‘Arbequina’ located in Alvarado (Extremadura, Spain). An apparent electrical conductivity (ECa) map and a normalized difference vegetation index (NDVI) map were generated to characterize the spatial variability of the plot and classify the zones in homogeneous areas. Then, reference points were selected to monitor the different irrigation sectors. In 2015, the plot was irrigated according to the farmer’s technical criteria throughout the plot. In 2016 and 2017, two different areas of the plot were irrigated applying an RDI strategy, one under expert supervision and the other automatically. The results show that in a heterogeneous plot the use of new technologies can be useful to establish the ideal location for an automatic irrigation system. Furthermore, automatic irrigation scheduling made it possible to establish an RDI strategy recommended by an expert, resulting in the homogenization of production throughout the plot without the need for human intervention.
Inertial Sensor-Based Motion Tracking in Football with Movement Intensity Quantification
Authors: Wilmes Erik , de Ruiter Cornelis J. , Bastiaansen Bram J. C. , Zon Jasper F. J. A. van , Vegter Riemer J. K. , Brink Michel S. , Goedhart Edwin A. , Lemmink Koen A. P. M. , Savelsbergh Geert J. P.
Published: April 2020

Inertial sensor-based measurements of lower body kinematics in football players may improve physical load estimates during training sessions and matches. However, the validity of inertial-based motion analysis systems is specific to both the type of movement and the intensity at which movements are executed. Importantly, such a system should be relatively simple, so it can easily be used in daily practice. This paper introduces an easy-to-use inertial-based motion analysis system and evaluates its validity using an optoelectronic motion analysis system as a gold standard. The system was validated in 11 football players for six different football specific movements that were executed at low, medium, and maximal intensity. Across all movements and intensities, the root mean square differences (means ± SD) for knee and hip flexion/extension angles were 5.3° ± 3.4° and 8.0° ± 3.5°, respectively, illustrating good validity with the gold standard. In addition, mean absolute flexion/extension angular velocities significantly differed between the three movement intensities. These results show the potential to use the inertial based motion analysis system in football practice to obtain lower body kinematics and to quantify movement intensity, which both may improve currently used physical load estimates of the players.
Design and Integration of a Wireless Stretchable Multimodal Sensor Network in a Composite Wing
Authors: Chen Xiyuan , Maxwell Loic , Li Franklin , Kumar Amrita , Ransom Elliot , Topac Tanay , Lee Sera , Faisal Haider Mohammad , Dardona Sameh , Chang Fu-Kuo
Published: April 2020

This article presents the development of a stretchable sensor network with high signal-to-noise ratio and measurement accuracy for real-time distributed sensing and remote monitoring. The described sensor network was designed as an island-and-serpentine type network comprising a grid of sensor “islands” connected by interconnecting “serpentines.” A novel high-yield manufacturing process was developed to fabricate networks on recyclable 4-inch wafers at a low cost. The resulting stretched sensor network has 17 distributed and functionalized sensing nodes with low tolerance and high resolution. The sensor network includes Piezoelectric (PZT), Strain Gauge (SG), and Resistive Temperature Detector (RTD) sensors. The design and development of a flexible frame with signal conditioning, data acquisition, and wireless data transmission electronics for the stretchable sensor network are also presented. The primary purpose of the frame subsystem is to convert sensor signals into meaningful data, which are displayed in real-time for an end-user to view and analyze. The challenges and demonstrated successes in developing this new system are demonstrated, including (a) developing separate signal conditioning circuitry and components for all three sensor types (b) enabling simultaneous sampling for PZT sensors for impact detection and (c) configuration of firmware/software for correct system operation. The network was expanded with an in-house developed automated stretch machine to expand it to cover the desired area. The released and stretched network was laminated into an aerospace composite wing with edge-mount electronics for signal conditioning, processing, power, and wireless communication.
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture
Authors: Mazzia Vittorio , Comba Lorenzo , Khaliq Aleem , Chiaberge Marcello , Gay Paolo
Published: April 2020

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d’Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers.
Art through the Colors of Graffiti: From the Perspective of the Chromatic Structure
Authors: Feitosa-Santana Claudia , Gaddi Carlo M. , Gomes Andreia E. , Nascimento Sérgio M. C.
Published: April 2020

Graffiti is a general term that describes inscriptions on a wall, a practice with ancient origins, ranging from simple drawings and writings to elaborate pictorial representations. Nowadays, the term graffiti commonly describes the street art dedicated to wall paintings, which raises complex questions, including sociological, legal, political and aesthetic issues. Here we examine the aesthetics of graffiti colors by quantitatively characterizing and comparing their chromatic structure to that of traditional paintings in museums and natural scenes obtained by hyperspectral imaging. Two hundred twenty-eight photos of graffiti were taken in the city of São Paulo, Brazil. The colors of graffiti were represented in a color space and characterized by several statistical parameters. We found that graffiti have chromatic structures similar to those of traditional paintings, namely their preferred colors, distribution, and balance. In particular, they have color gamuts with the same degree of elongation, revealing a tendency for combining similar colors in the same proportions. Like more traditional artists, the preferred colors are close to the yellow–blue axis of color space, suggesting that graffiti artists’ color choices also mimic those of the natural world. Even so, graffiti tend to have larger color gamuts due to the availability of a new generation of synthetic pigments, resulting in a greater freedom in color choice. A complementary analysis of graffiti from other countries supports the global generalization of these findings. By sharing their color structures with those of paintings, graffiti contribute to bringing art to the cities.
Operational Load Monitoring of a Composite Panel Using Artificial Neural Networks
Authors: Mucha Waldemar , Kuś Wacław , Viana Júlio C. , Nunes João Pedro
Published: April 2020

Operational Load Monitoring consists of the real-time reading and recording of the number and level of strains and stresses during load cycles withstood by a structure in its normal operating environment, in order to make more reliable predictions about its remaining lifetime in service. This is particularly important in aeronautical and aerospace industries, where it is very relevant to extend the components useful life without compromising flight safety. Sensors, like strain gauges, should be mounted on points of the structure where highest strains or stresses are expected. However, if the structure in its normal operating environment is subjected to variable exciting forces acting in different points over time, the number of places where data will have be acquired largely increases. The main idea presented in this paper is that instead of mounting a high number of sensors, an artificial neural network can be trained on the base of finite element simulations in order to estimate the state of the structure in its most stressed points based on data acquired just by a few sensors. The model should also be validated using experimental data to confirm proper predictions of the artificial neural network. An example with an omega-stiffened composite structural panel (a typical part used in aerospace applications) is provided. Artificial neural network was trained using a high-accuracy finite element model of the structure to process data from six strain gauges and return information about the state of the panel during different load cases. The trained neural network was tested in an experimental stand and the measurements confirmed the usefulness of presented approach.
Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images
Authors: He Jian , Guo Yongfei , Yuan Hangfei
Published: April 2020

Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.
A Night at the OPERA: A Conceptual Framework for an Integrated Distributed Sensor Network-Based System to Figure out Safety Protocols for Animals under Risk of Fire
Authors: Tamburis Oscar , Giannino Francesco , D’Arco Mauro , Tocchi Alessandro , Esposito Christian , Di Fiore Giorgio , Piscopo Nadia , Esposito Luigi
Published: April 2020

Large scale wildfire events that occurred around the world involved a massive loss of animal lives, with a consequent economic impact on agricultural holdings and damages to ecosystems. Preparing animals for a wildfire evacuation requires an extra level of planning, preparedness and coordination, which is missing in the current practice. This paper describes a conceptual framework of an ICT system implemented to support the activities of the Regional Veterinary referral Center for non-epidemic emergencies (CeRVEnE) in the Campania Region for the twofold objectives. On the one hand, it realizes the monitoring of the wooded areas under risk of fire in the so-called “Mount Vesuvius’ red zone”. On the other hand, it determines the OPtimal Evacuation Route for Animals (OPERA) in case of fire, for each of the reported animal species living in the mentioned red zone. The main innovation of the proposed system lies in its software architecture that aims at integrating a Distributed Sensor Network (DSN), an ad-hoc software to generate timely simulations for fire risk modeling, and a GIS (Geographic Information System) for both the activities of web mapping and OPERA definition. This paper shows some effective preliminary results of the system implementation. The importance of the system mainly lies in its accordance with the so-called “Foresight approach” perspective, that provides models and tools to guarantee the prevention of systematic failure in disaster risk management, and becomes moreover critical in the case of Mount Vesuvius, which hosts a unique combination of both animal and anthropic elements within a delicate natural ecosystem.
The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data
Authors: Luong Tu T.N. , Kim Jong-Myon
Published: April 2020

Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak detection method based on a model using statistical parameters extracted from acoustic emission (AE) signals. Since leak signals depend on many operation conditions, the training data in real-life situations usually has a small size. To solve the problem of a small sample size, a data improving method based on enhancing the generalization ability of the data was proposed. To evaluate the effectiveness of the proposed method, this study used the datasets obtained from two artificial leak cases which were generated by pinholes with diameters of 0.3 mm and 0.2 mm. Experimental results show that the employment of the additional data improving block in the leak detection scheme enhances the quality of leak detection in both terms of accuracy and stability.
Mind Wandering in a Multimodal Reading Setting: Behavior Analysis & Automatic Detection Using Eye-Tracking and an EDA Sensor
Authors: Brishtel Iuliia , Khan Anam Ahmad , Schmidt Thomas , Dingler Tilman , Ishimaru Shoya , Dengel Andreas
Published: April 2020

Mind wandering is a drift of attention away from the physical world and towards our thoughts and concerns. Mind wandering affects our cognitive state in ways that can foster creativity but hinder productivity. In the context of learning, mind wandering is primarily associated with lower performance. This study has two goals. First, we investigate the effects of text semantics and music on the frequency and type of mind wandering. Second, using eye-tracking and electrodermal features, we propose a novel technique for automatic, user-independent detection of mind wandering. We find that mind wandering was most frequent in texts for which readers had high expertise and that were combined with sad music. Furthermore, a significant increase in task-related thoughts was observed for texts for which readers had little prior knowledge. A Random Forest classification model yielded an F 1 -Score of 0.78 when using only electrodermal features to detect mind wandering, of 0.80 when using only eye-movement features, and of 0.83 when using both. Our findings pave the way for building applications which automatically detect events of mind wandering during reading.