bluuebunny/crossref_metadata_2025 huggingface dataset:
Studies on Glass Fiber-Reinforced Poly(Ethylene-Grafted-Styrene)-Based Cation Exchange Membrane Composite
Authors: Huang Di , Chen Zhichao , Hwang Jiann-Yang
Published: December 2020

To improve interfacial adhesion between glass fiber (GF) and poly(ethylene-grafted-styrene)-based cation exchange membranes (CEM), GF was modified by four coupling agents: [3-(Methacryloxy)propyl] trimethoxy silane (3-MPS), 1,6-bis (trimethoxysilyl) hexane (1,6 bis), Poly(propylene-graft-maleic anhydride) (PP-g-MA) and Triethoxyvinylsilane (TES). The results indicated the addition of modified GF increased tensile strength, tensile modulus, storage modulus and interfacial adhesion of GF/CEM composite but degraded the strains. The composite with 3-MPS modified GF obtained superior mechanical properties and interfacial adhesion, whereas the modified effect of TES was inconspicuous. The addition of unmodified GF even had negative effects on GF/CEM mechanical properties. The field emission scanning electron microscopes (FE-SEM) showed that the GF treated by 3-MPS and PP-g-MA have better compatibility with the CEM matrix than 1,6 bis and TES-treated GF. The Fourier-transform infrared spectroscopy (FT-IR) verified that the strengthening effects from modified GF were attributed to the formation of Si-O-Si and Si-O-C bonds. The additions of modified GF in CEM positively influence water uptake ability but negatively influence ion exchange capacity (IEC). This research provided a way of strengthening GF/CEM composite and pointed out which functional groups included in coupling agents could be useful to GF-reinforced composite.
SAC-NMF-Driven Graphical Feature Analysis and Applications
Authors: Li Nannan , Wang Shengfa , Li Haohao , Li Zhiyang
Published: December 2020

Feature analysis is a fundamental research area in computer graphics; meanwhile, meaningful and part-aware feature bases are always demanding. This paper proposes a framework for conducting feature analysis on a three-dimensional (3D) model by introducing modified Non-negative Matrix Factorization (NMF) model into the graphical feature space and push forward further applications. By analyzing and utilizing the intrinsic ideas behind NMF, we propose conducting the factorization on feature matrices constructed based on descriptors or graphs, which provides a simple but effective way to raise compressed and scale-aware descriptors. In order to enable part-aware model analysis, we modify the NMF model to be sparse and constrained regarding to both bases and encodings, which gives rise to Sparse and Constrained Non-negative Matrix Factorization (SAC-NMF). Subsequently, by adapting the analytical components (including hidden variables, bases, and encodings) to design descriptors, several applications have been easily but effectively realized. The extensive experimental results demonstrate that the proposed framework has many attractive advantages, such as being efficient, extendable, and so forth.
The Impact of Polymer Size and Cleavability on the Intravenous Pharmacokinetics of PEG-Based Hyperbranched Polymers in Rats
Authors: Marasini Nirmal , Fu Changkui , Fletcher Nicholas L. , Subasic Christopher , Er Gerald , Mardon Karine , Thurecht Kristofer J. , Whittaker Andrew K. , Kaminskas Lisa M.
Published: December 2020

A better understanding of the impact of molecular size and linkers is important for PEG-based hyperbranched polymers (HBPs) intended as tailored drug delivery vehicles. This study aimed to evaluate the effects of crosslinker chemistry (cleavable disulphide versus non-cleavable ethylene glycol methacrylate (EGDMA) linkers) and molecular weight within the expected size range for efficient renal elimination (22 vs. 48 kDa) on the intravenous pharmacokinetic and biodistribution properties of 89Zr-labelled HBPs in rats. All HBPs showed similar plasma pharmacokinetics over 72 h, despite differences in linker chemistry and size. A larger proportion of HBP with the cleavable linker was eliminated via the urine and faeces compared to a similar-sized HBP with the non-cleavable linker, while size had no impact on the proportion of the dose excreted. The higher molecular weight HBPs accumulated in organs of the mononuclear phagocyte system (liver and spleen) more avidly than the smaller HBP. These results suggest that HBPs within the 22 to 48 kDa size range show no differences in plasma pharmacokinetics, but distinct patterns of organ biodistribution and elimination are evident.
FRET-Based Analysis of AgInS2/ZnAgInS/ZnS Quantum Dot Recombination Dynamics
Authors: Miropoltsev Maksim , Kuznetsova Vera , Tkach Anton , Cherevkov Sergei , Sokolova Anastasiia , Osipova Viktoria , Gromova Yulia , Baranov Mikhail , Fedorov Anatoly , Gun'ko Yurii , Baranov Alexander
Published: December 2020

Ternary quantum dots (QDs) are very promising nanomaterials with a range of potential applications in photovoltaics, light-emitting devices, and biomedicine. Despite quite intensive studies of ternary QDs over the last years, the specific relaxation channels involved in their emission mechanisms are still poorly understood, particularly in the corresponding core-shell nanostructures. In the present work, we have studied the recombination pathways of AgInS2 QDs stabilized with the ZnAgInS alloy layer and the ZnS shell (AIS/ZAIS/ZnS QDs) using time-resolved fluorescence spectroscopy. We have also investigated FRET in complexes of AIS/ZAIS/ZnS QDs and cyanine dyes with the absorption bands overlapping in the different regions of the QD emission spectrum, which allowed us to selectively quench the radiative transitions of the QDs. Our studies have demonstrated that FRET from QDs to dyes results in decreasing of all QD PL decay components with the shortest lifetime decreasing the most and the longest one decreasing the least. This research presents important approaches for the investigation of ternary QD luminescence mechanisms by the selective quenching of recombination pathways. These studies are also essential for potential applications of ternary QDs in photodynamic therapy, multiplex analysis, and time-resolved FRET sensing.
Expression of Telomeric Repeat–Containing RNA Decreases in Sarcopenia and Increases after Exercise and Nutrition Intervention
Authors: Chang Ke-Vin , Chen Yu-Chen , Wu Wei-Ting , Shen Hong-Jhin , Huang Kuo-Chin , Chu Hsueh-Ping , Han Der-Sheng
Published: December 2020

Sarcopenia is defined as aging-related loss of muscle mass and function. Telomere length in chromosomes shortens with age and is modulated by telomeric repeat-containing RNA (TERRA). This study aimed to explore the impact of aging and sarcopenia on telomere length and TERRA expression, and changes following strengthening exercise and nutrition intervention (supplement of branched-chain amino acids, calcium and vitamin D3) for 12 weeks in the sarcopenic population. Older adults (≥65 years old) were divided into non-sarcopenic controls (n = 36) and sarcopenic individuals (n = 36) after measurement of grip strength and body composition. The relative telomere length of leukocytes in all research participants was evaluated using the T/S ratio (telomere/single copy gene), and relative TERRA expression of leukocytes was determined by reverse-transcription qPCR (RT-qPCR). Generalized estimating equation (GEE) was used to analyze the influence of sarcopenia and intervention on the outcomes. There was no significant difference in telomere length between control subjects and participants with sarcopenia. TERRA expression was lower in sarcopenic participants compared to that in non-sarcopenic controls (5.18 ± 2.98 vs. 2.51 ± 1.89; p < 0.001). In the sarcopenic group, intervention significantly increased TERRA expression, but not telomere length. The GEE analysis demonstrated that TERRA expression was negatively associated with sarcopenia (β coefficient = −2.705, p < 0.001) but positively associated with intervention (β coefficient = 1.599, p = 0.023). Sarcopenia is associated with a decrease in TERRA expression in leukocytes. Rebound TERRA expression (returning to the level similar to the non-sarcopenic controls) was observed in the sarcopenic group after exercise and nutrition intervention. Future studies are warranted to examine the potential of TERRA as a biomarker for sarcopenia and its subsequent responses to intervention.
Virulence Characterization of Listeria monocytogenes, Listeria innocua, and Listeria welshimeri Isolated from Fish and Shrimp Using In Vivo Early Zebrafish Larvae Models and Molecular Study
Authors: Zakrzewski Arkadiusz Józef , Chajęcka-Wierzchowska Wioleta , Zadernowska Anna , Podlasz Piotr
Published: December 2020

Listeriosis is one of the most notable foodborne diseases and is characterized by high rates of mortality. L. monocytogenes is the main cause of human listeriosis outbreaks, however, there are isolated cases of disease caused by other species of the genus Listeria. The aim of this study was to evaluate strains of L. monocytogenes (n = 7), L. innocua (n = 6), and L. welshimeri (n = 2) isolated from fish and shrimps for their virulence based on the presence of virulence genes and the in vivo Danio rerio (zebrafish) larvae models. A total of 15 strains were analyzed. The zebrafish larvae model showed that the larvae injected with L. monocytogenes strains were characterized by the lowest survival rate (46.5%), followed by L. innocua strains (64.2%) and L. welshimeri (83.0%) strains. Multiplex PCRs were used for detection of selected virulence genes (luxS, actA2, prfA, inlB, rrn, iap, sigB, plcB, actA, hlyA), the majority of which were present in L. monocytogenes. Only a few virulence-related genes were found in L. welshimeri, however, no correlation between the occurrence of these genes and larval survival was confirmed. This research highlights the importance of the potential impact that Listeria spp. strains isolated from fish and shrimps may have on consumers.
Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product
Authors: Go Sujung , Kim Jhoon , Park Sang Seo , Kim Mijin , Lim Hyunkwang , Kim Ji-Young , Lee Dong-Won , Im Jungho
Published: December 2020

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet (UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of >~0.4 and GEMS SSA values of <~0.95. Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy.
Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models
Authors: Nevavuori Petteri , Narra Nathaniel , Linna Petri , Lipping Tarmo
Published: December 2020

Unmanned aerial vehicle (UAV) based remote sensing is gaining momentum worldwide in a variety of agricultural and environmental monitoring and modelling applications. At the same time, the increasing availability of yield monitoring devices in harvesters enables input-target mapping of in-season RGB and crop yield data in a resolution otherwise unattainable by openly availabe satellite sensor systems. Using time series UAV RGB and weather data collected from nine crop fields in Pori, Finland, we evaluated the feasibility of spatio-temporal deep learning architectures in crop yield time series modelling and prediction with RGB time series data. Using Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks as spatial and temporal base architectures, we developed and trained CNN-LSTM, convolutional LSTM and 3D-CNN architectures with full 15 week image frame sequences from the whole growing season of 2018. The best performing architecture, the 3D-CNN, was then evaluated with several shorter frame sequence configurations from the beginning of the season. With 3D-CNN, we were able to achieve 218.9 kg/ha mean absolute error (MAE) and 5.51% mean absolute percentage error (MAPE) performance with full length sequences. The best shorter length sequence performance with the same model was 292.8 kg/ha MAE and 7.17% MAPE with four weekly frames from the beginning of the season.
Semiautomated Mapping of Benthic Habitats and Seagrass Species Using a Convolutional Neural Network Framework in Shallow Water Environments
Authors: Mohamed Hassan , Nadaoka Kazuo , Nakamura Takashi
Published: December 2020

Benthic habitats are structurally complex and ecologically diverse ecosystems that are severely vulnerable to human stressors. Consequently, marine habitats must be mapped and monitored to provide the information necessary to understand ecological processes and lead management actions. In this study, we propose a semiautomated framework for the detection and mapping of benthic habitats and seagrass species using convolutional neural networks (CNNs). Benthic habitat field data from a geo-located towed camera and high-resolution satellite images were integrated to evaluate the proposed framework. Features extracted from pre-trained CNNs and a “bagging of features” (BOF) algorithm was used for benthic habitat and seagrass species detection. Furthermore, the resultant correctly detected images were used as ground truth samples for training and validating CNNs with simple architectures. These CNNs were evaluated for their accuracy in benthic habitat and seagrass species mapping using high-resolution satellite images. Two study areas, Shiraho and Fukido (located on Ishigaki Island, Japan), were used to evaluate the proposed model because seven benthic habitats were classified in the Shiraho area and four seagrass species were mapped in Fukido cove. Analysis showed that the overall accuracy of benthic habitat detection in Shiraho and seagrass species detection in Fukido was 91.5% (7 classes) and 90.4% (4 species), respectively, while the overall accuracy of benthic habitat and seagrass mapping in Shiraho and Fukido was 89.9% and 91.2%, respectively.
Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network
Authors: Li Yansheng , Chen Ruixian , Zhang Yongjun , Zhang Mi , Chen Ling
Published: December 2020

As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest. Human beings can easily perform MLRSSC by examining the visual elements contained in the scene and the spatio-topological relationships of these visual elements. However, most of existing methods are limited by only perceiving visual elements but disregarding the spatio-topological relationships of visual elements. With this consideration, this paper proposes a novel deep learning-based MLRSSC framework by combining convolutional neural network (CNN) and graph neural network (GNN), which is termed the MLRSSC-CNN-GNN. Specifically, the CNN is employed to learn the perception ability of visual elements in the scene and generate the high-level appearance features. Based on the trained CNN, one scene graph for each scene is further constructed, where nodes of the graph are represented by superpixel regions of the scene. To fully mine the spatio-topological relationships of the scene graph, the multi-layer-integration graph attention network (GAT) model is proposed to address MLRSSC, where the GAT is one of the latest developments in GNN. Extensive experiments on two public MLRSSC datasets show that the proposed MLRSSC-CNN-GNN can obtain superior performance compared with the state-of-the-art methods.
Reply to Lanari, R., et al. Comment on “Pre-Collapse Space Geodetic Observations of Critical Infrastructure: The Morandi Bridge, Genoa, Italy” by Milillo et al. (2019)
Authors: Milillo Pietro , Giardina Giorgia , Perissin Daniele , Milillo Giovanni , Coletta Alessandro , Terranova Carlo
Published: December 2020

We would like to thank our colleagues for their comment, as we believe that this discussion further highlights the importance of innovative research in the emerging field of InSAR applications to civil engineering structures. We discuss the statement from Lanari et al. (2020): “Our analysis shows that, although both the SBAS and the TomoSAR analyses allow achieving denser coherent pixel maps relevant to the Morandi bridge, nothing of the pre-collapse large displacements reported in Milillo et al. (2019) appears in our results”. In this reply we argue that (1) they cannot detect the pre-collapse movements because they use standard approaches and (2) the signals of interest become observable by changing the point of view.
Structural Crack Detection Using DPP-BOTDA and Crack-Induced Features of the Brillouin Gain Spectrum
Authors: Zhang Dongyu , Yang Yang , Xu Jinlong , Ni Li , Li Hui
Published: December 2020

Structural damage generally initiates in the form of structural cracks. Thus, developing efficient crack detection techniques is of great importance for the structural health monitoring. In this paper, a new crack identification method is proposed, which is based on the differential pulse-width pair Brillouin optical time domain analysis (DPP-BOTDA) technology and the irregular features of Brillouin gain spectrum (BGS) in the fiber due to structural cracks. The proposed method provides a new way to detect and quantify structural cracks without knowing the strain in the structure. First, the working mechanism of DPP-BOTDA is introduced to illustrate the reason that the DPP-BOTDA, compared to traditional BOTDA technique, can significantly improve the spatial resolution of distributed strain sensing, which is critical for structural crack detection. Then, the BGSs in the fiber with the presence of structural cracks, measured by the DPP-BOTDA, are numerically simulated, from which the crack-induced irregular features of the BGS are summarized. Based these irregular features, new structural crack detection and quantification methods are proposed, which are found to be independent of structural stain. Finally, an experiment is conducted on a simple supported reinforced concrete (RC) beam. The results demonstrate that by using the BGS measured by the DPP-BOTDA, the proposed structural crack identification method successfully detects the occurrence of structural cracks and relatively accurately predicts the crack widths.
Vector Hydrophone Array Design Based on Off-Grid Compressed Sensing
Authors: Shi Zhibo , Liang Guolong , Qiu Longhao , Wan Guangming , Zhao Lei
Published: December 2020

Array design is the primary consideration for array signal processing, and sparse array design is an important and challenging task. In underwater acoustic environments, the vector hydrophone array contains more information than the scalar hydrophone array, but there are few articles focused on the design of the vector hydrophone array. The difference between the vector hydrophone array and the scalar hydrophone array is that each vector hydrophone has three or four channels. When designing a sparse vector hydrophone array, these channels need to be optimized at the same time to ensure the sparsity of the array elements’ number. To solve this problem, this paper introduced the compressed sensing (CS) theory into the vector hydrophone array design, constructed the vector hydrophone array design problem into a globally solvable optimization problem, proposed a CS-based algorithm with the L1 norm suitable for vector hydrophone array, and realized the simultaneous optimization of multiple channels from the same vector hydrophone. At the same time, the off-grid algorithm was added to obtain higher design accuracy. Two design examples verify the effectiveness of the proposed method. The theoretical analysis and simulation results show that compared with the conventional compressed sensing algorithm with the same aperture, the algorithm proposed in this paper used fewer vector hydrophone elements to obtain better fitting of the desired beam pattern.
Correlation between Microstructure and Chemical Composition of Zinc Oxide Gas Sensor Layers and Their Gas-Sensitive Properties in Chlorine Atmosphere
Authors: Fiedot-Toboła Marta , Suchorska-Woźniak Patrycja , Startek Kamila , Rac-Rumijowska Olga , Szukiewicz Rafał , Kwoka Monika , Teterycz Helena
Published: December 2020

In this article, we present results concerning the impact of structural and chemical properties of zinc oxide in various morphological forms and its gas-sensitive properties, tested in an atmosphere containing a very aggressive gas such as chlorine. The aim of this research was to understand the mechanism of chlorine detection using a resistive gas sensor with an active layer made of zinc oxide with a different structure and morphology. Two types of ZnO sensor layers obtained by two different technological methods were used in sensor construction. Their morphology, crystal structure, specific surface area, porosity, surface chemistry and structural defects were characterized, and then compared with gas-sensitive properties in a chlorine-containing atmosphere. To achieve this goal, scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and photoluminescence spectroscopy (PL) methods were used. The sensing properties of obtained active layers were tested by the temperature stimulated conductance method (TSC). We have noticed that their response in a chlorine atmosphere is not determined by the size of the specific surface or porosity. The obtained results showed that the structural defects of ZnO crystals play the most important role in chlorine detection. We demonstrated that Cl2 adsorption is a concurrent process to oxygen adsorption. Both of them occur on the same active species (oxygen vacancies). Their concentration is higher on the side planes of the zinc oxide crystal than the others. Additionally, ZnO sublimation process plays an important role in the chlorine detection mechanism.
Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm
Authors: Mondal Sabyasachi , Tsourdos Antonios
Published: December 2020

This paper presents an idea of how new agents can be added autonomously to a group of existing agents without changing the existing communication topology among them. Autonomous agent addition to existing Multi-Agent Systems (MASs) can give a strategic advantage during the execution of a critical beyond visual line-of-sight (BVLOS) mission. The addition of the agent essentially means that new connections with existing agents are established. It is obvious that the consensus control energy increases as the number of agent increases considering a specific consensus protocol. The objective of this work is to establish the new connections in a way such that the consensus energy increase due to the new agents is minimal. The updated topology, including new connections, must contain a spanning tree to maintain the stability of the MASs network. The updated optimal topology is obtained by solving minimum additional consensus control energy using the Two-Dimensional Genetic Algorithm. The results obtained are convincing.
Gathering Big Data in Wireless Sensor Networks by Drone
Authors: Rezende Josiane da Costa Vieira , Silva Rone Ilídio da , Souza Marcone Jamilson Freitas
Published: December 2020

The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several works. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to transmit. This paper considers each sensor node having a relatively larger volume of data stored in its memory. That is, they have several data packets to send to sink. We also consider a drone with hovering capability, such as a quad-copter, as a mobile sink to gather this data. Hence, the mobile collector eventually has to hover to guarantee that all data will be received. Drones, however, have a limited power supply that restricts their flying time. Hence, the drone’s energy cost must also be considered to increase the amount of collected data from the WSN. This work investigates the problem of determining the best drone tour for big data gathering in a WSN. We focus on minimizing the overall drone flight time needed to collect all data from the WSN. We propose an algorithm to create a subset of sensor nodes to send data to the drone during its movement and, consequently, reduce its hovering time. The proposed algorithm guarantees that the drone will stay a minimum time inside every sensor node’s radio range. Our experimental results showed that the proposed algorithm surpasses, by up to 30%, the state-of-the-art heuristics’ performance in finding drone tours in this type of scenario.
Trust-Based Model for the Assessment of the Uncertainty of Measurements in Hybrid IoT Networks
Authors: Cofta Piotr , Orłowski Cezary , Lebiedź Jacek
Published: December 2020

The aim of this paper is to introduce a NUT model (NUT: network-uncertainty-trust) that aids the decrease of the uncertainty of measurements in autonomous hybrid Internet of Things sensor networks. The problem of uncertainty in such networks is a consequence of various operating conditions and varied quality of measurement nodes, making statistical approach less successful. This paper presents a model for decreasing the uncertainty through the use of socially inspired metaphors of reputation, trust, and confidence that are the untapped latent information. The model described in the paper shows how the individual reputation of each node can be assessed on the basis of opinions provided by other nodes of the hybrid measurement network, and that this method allows to assess the extent of uncertainty the node introduces to the network. This, in turn, allows nodes of low uncertainty to have a greater impact on the reconstruction of values. The verification of the model, as well as examples of its applicability to air quality measurements are presented as well. Simulations demonstrate that the use of the model can decrease the uncertainty by up to 55% while using the EWMA (exponentially weighted moving average) algorithm, as compared to the reference one.
A Geometry-Based Beamforming Channel Model for UAV mmWave Communications
Authors: Mao Kai , Zhu Qiuming , Song Maozhong , Hua Boyu , Zhong Weizhi , Ye Xijuan
Published: December 2020

Considering the three-dimensional (3D) trajectory, 3D antenna array, and 3D beamforming of unmanned aerial vehicle (UAV), a novel non-stationary millimeter wave (mmWave) geometry-based stochastic model for UAV to vehicle communication channels is proposed. Based on the analysis results of measured and ray tracing simulation data of UAV mmWave communication links, the proposed parametric channel model is constructed by a line-of-sight path, a ground specular path, and two strongest single-bounce paths. Meanwhile, a new parameter computation method is also developed, which is divided into the deterministic (or geometry-based) part and the random (or empirical) part. The simulated power delay profile and power angle profile demonstrate that the statistical properties of proposed channel model are time-variant with respect to the scattering scenarios, positions and beam direction. Moreover, the simulation results of autocorrelation functions fit well with the theoretical ones as well as the measured ones.
MLCA—A Machine Learning Framework for INS Coarse Alignment
Authors: Zak Idan , Katz Reuven , Klein Itzik
Published: December 2020

Inertial navigation systems provides the platform’s position, velocity, and attitude during its operation. As a dead-reckoning system, it requires initial conditions to calculate the navigation solution. While initial position and velocity vectors are provided by external means, the initial attitude can be determined using the system’s inertial sensors in a process known as coarse alignment. When considering low-cost inertial sensors, only the initial roll and pitch angles can be determined using the accelerometers measurements. The accuracy, as well as time required for the for the coarse alignment process are critical for the navigation solution accuracy, particularly for pure-inertial scenarios, because of the navigation solution drift. In this paper, a machine learning framework for the stationary coarse alignment stage is proposed. To that end, classical machine learning approaches are used in a two-stage approach to regress the roll and pitch angles. Alignment results obtained both in simulations and field experiments, using a smartphone, shows the benefits of using the proposed approach instead of the commonly used analytical coarse alignment procedure.
ISSD: Improved SSD for Insulator and Spacer Online Detection Based on UAV System
Authors: Liu Xuan , Li Yong , Shuang Feng , Gao Fang , Zhou Xiang , Chen Xingzhi
Published: December 2020

In power inspection tasks, the insulator and spacer are important inspection objects. UAV (unmanned aerial vehicle) power inspection is becoming more and more popular. However, due to the limited computing resources carried by a UAV, a lighter model with small model size, high detection accuracy, and fast detection speed is needed to achieve online detection. In order to realize the online detection of power inspection objects, we propose an improved SSD (single shot multibox detector) insulator and spacer detection algorithm using the power inspection images collected by a UAV. In the proposed algorithm, the lightweight network MnasNet is used as the feature extraction network to generate feature maps. Then, two multiscale feature fusion methods are used to fuse multiple feature maps. Lastly, a power inspection object dataset containing insulators and spacers based on aerial images is built, and the performance of the proposed algorithm is tested on real aerial images and videos. Experimental results show that the proposed algorithm can efficiently detect insulators and spacers. Compared with existing algorithms, the proposed algorithm has the advantages of small model size and fast detection speed. The detection accuracy can achieve 93.8%. The detection time of a single image on TX2 (NVIDIA Jetson TX2) is 154 ms and the capture rate on TX2 is 8.27 fps, which allows realizing online detection.
Reflective Properties of a Polymer Micro-Transducer for an Optical Fiber Refractive Index Sensor
Authors: Marć Paweł , Żuchowska Monika , Jaroszewicz Leszek R.
Published: December 2020

A polymer microtip manufactured at the end of a multi-mode optical fiber by using the photopolymerization process offers good reflective properties, therefore, it is applicable as an optical fiber sensor micro-transducer. The reflective properties of this microelement depend on the monomer mixture used, optical fiber type, and light source initiating polymerization. Experimental results have shown that a proper selection of these parameters has allowed the design of a new class of sensing structure which is sensitive to the refractive index (RI) changes of a liquid medium surrounding the microtip. An optical backscatter reflectometer was applied to test a group of micro-transducers. They were manufactured from two monomer mixtures on three different types of multi-mode optical fibers. They were polymerized by means of three optical light sources. Selected micro-transducers with optimal geometries were immersed in reference liquids with a known RI within the range of 1.3–1.7. For a few sensors, the linear dependences of return loss and RI have been found. The highest sensitivity was of around 208 dB/RIU with dynamic 32 dB within the range of 1.35–1.48. Sensing characteristics have minima close to RI of a polymer microelement, therefore, changing its RI can give the possibility to tune sensing properties of this type of sensor.
A Two-Stage Fall Recognition Algorithm Based on Human Posture Features
Authors: Han Kun , Yang Qiongqian , Huang Zefan
Published: December 2020

Falls are seriously threatening the health of elderly. In order to reduce the potential danger caused by falls, this paper proposes a two-stage fall recognition algorithm based on human posture features. For preprocessing, we construct the new key features: deflection angles and spine ratio to describe the changes of human posture based on the human skeleton extracted by OpenPose. In the first stage, based on the variables: tendency symbol and steady symbol integrated by the scattered key features, we divide the human body state into three states: stable state, fluctuating state, and disordered state. By analyzing whether the body is in a stable state, the ADL (activities of daily living) actions with high stability can be preliminarily excluded. In the second stage: to further identify the confusing ADL actions and the fall actions, we innovatively design a time-continuous recognition algorithm. When human body is constantly in an unstable state, the human posture features: compare value γ, energy value ε, state score τ are proposed to form a feature vector, and support vector machine (SVM), K nearest neighbors (KNN), decision tree (DT), random forest (RF) are utilized for classification. Experiment results demonstrate that SVM with linear kernel function can distinguish falling actions best and our approach achieved a detection accuracy of 97.34%, precision of 98.50%, and the recall, F1 score are 97.33%, 97.91% respectively. Compared with previous state-of-art algorithms, our algorithm can achieve the highest recognition accuracy. It proves that our fall detection method is effective.
Towards the Monitoring of Functional Status in a Free-Living Environment for People with Hip or Knee Osteoarthritis: Design and Evaluation of the JOLO Blended Care App
Authors: Emmerzaal Jill , De Brabandere Arne , Vanrompay Yves , Vranken Julie , Storms Valerie , De Baets Liesbet , Corten Kristoff , Davis Jesse , Jonkers Ilse , Vanwanseele Benedicte , Timmermans Annick
Published: December 2020

(1) Background: Joint loading is an important parameter in patients with osteoarthritis (OA). However, calculating joint loading relies on the performance of an extensive biomechanical analysis, which is not possible to do in a free-living situation. We propose the concept and design of a novel blended-care app called JOLO (Joint Load) that combines free-living information on activity with lab-based measures of joint loading in order to estimate a subject’s functional status. (2) Method: We used an iterative design process to evaluate the usability of the JOLO app through questionnaires. The user interfaces that resulted from the iterations are described and provide a concept for feedback on functional status. (3) Results: In total, 44 people (20 people with OA and 24 health-care providers) participated in the testing of the JOLO app. OA patients rated the latest version of the JOLO app as moderately useful. Therapists were predominantly positive; however, their intention to use JOLO was low due to technological issues. (4) Conclusion: We can conclude that JOLO is promising, but further technological improvements concerning activity recognition, the development of personalized joint loading predictions and a more comfortable means to carry the device are needed to facilitate its integration as a blended-care program.
ALS Point Cloud Classification by Integrating an Improved Fully Convolutional Network into Transfer Learning with Multi-Scale and Multi-View Deep Features
Authors: Lei Xiangda , Wang Hongtao , Wang Cheng , Zhao Zongze , Miao Jianqi , Tian Puguang
Published: December 2020

Airborne laser scanning (ALS) point cloud has been widely used in various fields, for it can acquire three-dimensional data with a high accuracy on a large scale. However, due to the fact that ALS data are discretely, irregularly distributed and contain noise, it is still a challenge to accurately identify various typical surface objects from 3D point cloud. In recent years, many researchers proved better results in classifying 3D point cloud by using different deep learning methods. However, most of these methods require a large number of training samples and cannot be widely used in complex scenarios. In this paper, we propose an ALS point cloud classification method to integrate an improved fully convolutional network into transfer learning with multi-scale and multi-view deep features. First, the shallow features of the airborne laser scanning point cloud such as height, intensity and change of curvature are extracted to generate feature maps by multi-scale voxel and multi-view projection. Second, these feature maps are fed into the pre-trained DenseNet201 model to derive deep features, which are used as input for a fully convolutional neural network with convolutional and pooling layers. By using this network, the local and global features are integrated to classify the ALS point cloud. Finally, a graph-cuts algorithm considering context information is used to refine the classification results. We tested our method on the semantic 3D labeling dataset of the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that overall accuracy and the average F1 score obtained by the proposed method is 89.84% and 83.62%, respectively, when only 16,000 points of the original data are used for training.
Application of a Small Unmanned Aerial System to Measure Ammonia Emissions from a Pilot Amine-CO2 Capture System
Authors: Schuyler Travis J. , Irvin Bradley , Abad Keemia , Thompson Jesse G. , Liu Kunlei , Guzman Marcelo I.
Published: December 2020

The quantification of atmospheric gases with small unmanned aerial systems (sUAS) is expanding the ability to safely perform environmental monitoring tasks and quickly evaluate the impact of technologies. In this work, a calibrated sUAS is used to quantify the emissions of ammonia (NH3) gas from the exit stack a 0.1 MWth pilot-scale carbon capture system (CCS) employing a 5 M monoethanolamine (MEA) solvent to scrub CO2 from coal combustion flue gas. A comparison of the results using the sUAS against the ion chromatography technique with the EPA CTM-027 method for the standard emission sampling of NH3 shows good agreement. Therefore, the work demonstrates the usefulness of sUAS as an alternative method of emission measurement, supporting its application in lieu of traditional sampling techniques to collect real time emission data.
Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms
Authors: Wei Yanlin , Li Xiaofeng , Pan Xin , Li Lei
Published: December 2020

During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry and high-performance liquid chromatography) are destructive and time-consuming. In this paper, a robust and accurate method for nondestructive soybean classification is developed through hyperspectral imaging and ensemble machine learning algorithms. Image acquisition, preprocessing, and feature selection are used to obtain different types of soybean hyperspectral features. Based on these features, one of ensemble classifiers-random subspace linear discriminant (RSLD) algorithm is used to classify soybean seeds. Compared with the linear discrimination (LD) and linear support vector machine (LSVM) methods, the results show that the RSLD algorithm in this paper is more stable and reliable. In classifying soybeans in 10, 15, 20, and 25 categories, the RSLD method achieves the highest classification accuracy. When 155 features are used to classify 15 types of soybeans, the classification accuracy of the RSLD method reaches 99.2%, while the classification accuracies of the LD and LSVM methods are only 98.6% and 69.7%, respectively. Therefore, the ensemble classification algorithm RSLD can maintain high classification accuracy when different types and different classification features are used.
Optimization of Cost-Effective and Reproducible Flexible Humidity Sensors Based on Metal-Organic Frameworks
Authors: Toral Victor , Loghin Florin C. , Rodríguez-Diéguez Antonio , Lapresta-Fernández Alejandro , Morales Diego P. , Rivadeneyra Almudena , Salinas-Castillo Alfonso
Published: December 2020

In this letter, we present the extension of a previous work on a cost-effective method for fabricating highly sensitive humidity sensors on flexible substrates with a reversible response, allowing precise monitoring of the humidity threshold. In that work we demonstrated the use of three-dimensional metal-organic framework (MOF) film deposition based on the perylene-3,4,9,10-tetracarboxylate linker, potassium as metallic center and the interspacing of silver interdigitated electrodes (IDEs) as humidity sensors. In this work, we study one of the most important issues in efficient and reproducible mass production, which is to optimize the most important processes’ parameters in their fabrication, such as controlling the thickness of the sensor’s layers. We demonstrate this method not only allows for the creation of humidity sensors, but it also is possible to change the humidity value that changes the actuator state.
QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs
Authors: Bwambok David K. , Siraj Noureen , Macchi Samantha , Larm Nathaniel E. , Baker Gary A. , Pérez Rocío L. , Ayala Caitlan E. , Walgama Charuksha , Pollard David , Rodriguez Jason D. , Banerjee Souvik , Elzey Brianda , Warner Isiah M. , Fakayode Sayo O.
Published: December 2020

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.
Identifying Fatigue Indicators Using Gait Variability Measures: A Longitudinal Study on Elderly Brisk Walking
Authors: Zhang Guoxin , Wong Ivy Kwan-Kei , Chen Tony Lin-Wei , Hong Tommy Tung-Ho , Wong Duo Wai-Chi , Peng Yinghu , Yan Fei , Wang Yan , Tan Qitao , Zhang Ming
Published: December 2020

Real-time detection of fatigue in the elderly during physical exercises can help identify the stability and thus falling risks which are commonly achieved by the investigation of kinematic parameters. In this study, we aimed to identify the change in gait variability parameters from inertial measurement units (IMU) during a course of 60 min brisk walking which could lay the foundation for the development of fatigue-detecting wearable sensors. Eighteen elderly people were invited to participate in the brisk walking trials for 60 min with a single IMU attached to the posterior heel region of the dominant side. Nine sets of signals, including the accelerations, angular velocities, and rotation angles of the heel in three anatomical axes, were measured and extracted at the three walking times (baseline, 30th min, and 60th min) of the trial for analysis. Sixteen of eighteen participants reported fatigue after walking, and there were significant differences in the median acceleration (p = 0.001), variability of angular velocity (p = 0.025), and range of angle rotation (p = 0.0011), in the medial–lateral direction. In addition, there were also significant differences in the heel pronation angle (p = 0.005) and variability and energy consumption of the angles in the anterior–posterior axis (p = 0.028, p = 0.028), medial–lateral axis (p = 0.014, p = 0.014), and vertical axis (p = 0.002, p < 0.001). Our study demonstrated that a single IMU on the posterior heel of the dominant side can address the variability of kinematics parameters for elderly performing prolonged brisk walking and could serve as an indicator for walking instability, and thus fatigue.