bluuebunny/crossref_metadata_2025 huggingface dataset:
Physical, Mechanical, and Morphological Properties of Hybrid Cyrtostachys renda/Kenaf Fiber Reinforced with Multi-Walled Carbon Nanotubes (MWCNT)-Phenolic Composites
Authors: Loganathan Tamil Moli , Hameed Sultan Mohamed Thariq , Jawaid Mohammad , Ahsan Qumrul , Naveen Jesuarockiam , Shah Ain Umaira Md , Abu Talib Abd. Rahim , Basri Adi Azriff
Published: October 2021

Adequate awareness of sustainable materials and eco-legislation have inspired researchers to identify alternative sustainable and green composites for synthetic fiber-reinforced polymer composites in the automotive and aircraft industries. This research focused on investigating the physical, mechanical, and morphological properties of different hybrid Cyrtostachys renda (CR)/kenaf fiber (K) (10C:0K, 7C:3K, 5C:5K, 3C:7K, 0C:10K) reinforced with 0.5 wt% MWCNT–phenolic composites. We incorporated 0.5 wt% of MWCNT into phenolic resin (powder) using a ball milling process for 25 h to achieve homogeneous distribution. The results revealed that CR fiber composites showed higher voids content (12.23%) than pure kenaf fiber composites (6.57%). CR fiber phenolic composite was more stable to the swelling tendency, resulting in the lowest percentage of swelling rate (4.11%) compared to kenaf composite (5.29%). The addition of kenaf fiber into CR composites had improved the tensile, flexural, and impact properties. The highest tensile and flexural properties were found for weight fraction of CR and kenaf fiber at 5C:5K (47.96 MPa) and 3C:7K (90.89 MPa) composites, respectively. In contrast, the highest impact properties were obtained for 0C:10K composites (9.56 kJ/m2). Based on the FE-SEM image, the CR fiber lumen was larger in comparison to kenaf fiber. The lumen of CR fiber was attributed to higher void and water absorption, lower mechanical properties compared to kenaf fiber. 5C:5K composite was selected as an optimal hybrid composite, based on the TOPSIS method. This hybrid composite can be used as an interior component (non-load-bearing structures) in the aviation and automotive sectors.
High-Sensitivity Flexible Pressure Sensor-Based 3D CNTs Sponge for Human–Computer Interaction
Authors: Cui Jianli , Nan Xueli , Shao Guirong , Sun Huixia
Published: October 2021

Researchers are showing an increasing interest in high-performance flexible pressure sensors owing to their potential uses in wearable electronics, bionic skin, and human–machine interactions, etc. However, the vast majority of these flexible pressure sensors require extensive nano-architectural design, which both complicates their manufacturing and is time-consuming. Thus, a low-cost technology which can be applied on a large scale is highly desirable for the manufacture of flexible pressure-sensitive materials that have a high sensitivity over a wide range of pressures. This work is based on the use of a three-dimensional elastic porous carbon nanotubes (CNTs) sponge as the conductive layer to fabricate a novel flexible piezoresistive sensor. The synthesis of a CNTs sponge was achieved by chemical vapor deposition, the basic underlying principle governing the sensing behavior of the CNTs sponge-based pressure sensor and was illustrated by employing in situ scanning electron microscopy. The CNTs sponge-based sensor has a quick response time of ~105 ms, a high sensitivity extending across a broad pressure range (less than 10 kPa for 809 kPa−1) and possesses an outstanding permanence over 4000 cycles. Furthermore, a 16-pixel wireless sensor system was designed and a series of applications have been demonstrated. Its potential applications in the visualizing pressure distribution and an example of human–machine communication were also demonstrated.
Removal of Toxic Copper Ion from Aqueous Media by Adsorption on Fly Ash-Derived Zeolites: Kinetic and Equilibrium Studies
Authors: Buema Gabriela , Trifas Luisa-Maria , Harja Maria
Published: October 2021

This study investigated the adsorption capacity of one material based on the treatment of fly ash with sodium hydroxide as a novel adsorbent for toxic Cu2+ ion removal from aqueous media. The adsorbent was obtained through direct activation of fly ash with 2M NaOH at 90 °C and 6 h of contact time. The adsorbent was characterized by recognized techniques for solid samples. The influence of adsorption parameters such as adsorbent dose, copper initial concentration and contact time was analyzed in order to establish the best adsorption conditions. The results revealed that the Langmuir model fitted with the copper adsorption data. The maximum copper adsorption capacity was 53.5 mg/g. The adsorption process followed the pseudo-second-order kinetic model. The results indicated that the mechanism of adsorption was chemisorption. The results also showed the copper ion removal efficiencies of the synthesized adsorbents. The proposed procedure is an innovative and economical method, which can be used for toxicity reduction by capitalizing on abundant solid waste and treatment wastewater.
Construction of a TTT-η Diagram of High-Refractive Polyurethane Based on Curing Kinetics
Authors: Huang Shidi , Zhang Guiming , Du Weiping , Chen Huifang
Published: October 2021

A time–temperature–transformation–viscosity (TTT-η) diagram can reflect changes in the physical states of a resin, which take on significance for the study of the curing process of polyurethane resin lenses. Coupling the differential scanning calorimetry (DSC) test, the curing kinetic parameters of 1,4-bis(isocyanatomethyl)cyclohexane (H6XDI)/2,3-bis((2-mercaptoethyl)thio)-1-propanethiol (BES) polyurethane system were obtained. By phenomenological modeling, the relationships between degree, temperature, and time were obtained. An isothermal DSC test was carried out at 423 K. Based on the DiBenedetto equation, the relationships between glass transition temperature, degree of cure, and time were obtained, and the glass transition temperature was thus correlated with temperature and time. The gelation time at different temperatures was measured by rotary rheometry, and the relationship between gelation time and gelation temperature was established. The time–temperature–transformation (TTT) diagram of H6XDI/BES system was constructed accordingly. Subsequently, a six-parameter double Arrhenius equation was used as the basis for the rheological study. The viscosity was examined during the curing process. The TTT-η diagram was obtained, which laid the theoretical foundation for the optimization and setting of processing parameters.
Enhanced Propanol Response Behavior of ZnFe2O4 NP-Based Active Sensing Layer Induced by Film Thickness Optimization
Authors: Nemufulwi Murendeni I. , Swart Hendrik C. , Mhlongo Gugu H.
Published: October 2021

Development of gas sensors displaying improved sensing characteristics including sensitivity, selectivity, and stability is now possible owing to tunable surface chemistry of the sensitive layers as well as favorable transport properties. Herein, zinc ferrite (ZnFe2O4) nanoparticles (NPs) were produced using a microwave-assisted hydrothermal method. ZnFe2O4 NP sensing layer films with different thicknesses deposited on interdigitated alumina substrates were fabricated at volumes of 1.0, 1.5, 2.0, and 2.5 µL using a simple and inexpensive drop-casting technique. Successful deposition of ZnFe2O4 NP-based active sensing layer films onto alumina substrates was confirmed by X-ray diffraction and atomic force microscope analysis. Top view and cross-section observations from the scanning electron microscope revealed inter-agglomerate pores within the sensing layers. The ZnFe2O4 NP sensing layer produced at a volume of 2 μL exhibited a high response of 33 towards 40 ppm of propanol, as well as rapid response and recovery times of 11 and 59 s, respectively, at an operating temperature of 120 °C. Furthermore, all sensors demonstrated a good response towards propanol and the highest response against ethanol, methanol, carbon dioxide, carbon monoxide, and methane. The results indicate that the developed fabrication strategy is an inexpensive way to enhance sensing response without sacrificing other sensing characteristics. The produced ZnFe2O4 NP-based active sensing layers can be used for the detection of volatile organic compounds in alcoholic beverages for quality check in the food sector.
Concordance Probability for Insurance Pricing Models
Authors: Ponnet Jolien , Van Oirbeek Robin , Verdonck Tim
Published: October 2021

The concordance probability, also called the C-index, is a popular measure to capture the discriminatory ability of a predictive model. In this article, the definition of this measure is adapted to the specific needs of the frequency and severity model, typically used during the technical pricing of a non-life insurance product. For the frequency model, the need of two different groups is tackled by defining three new types of the concordance probability. Secondly, these adapted definitions deal with the concept of exposure, which is the duration of a policy or insurance contract. Frequency data typically have a large sample size and therefore we present two fast and accurate estimation procedures for big data. Their good performance is illustrated on two real-life datasets. Upon these examples, we also estimate the concordance probability developed for severity models.
How the COVID-19 Pandemic Affects Bank Risks and Returns: Evidence from EU Members in Central, Eastern, and Northern Europe
Authors: Miklaszewska Ewa , Kil Krzysztof , Idzik Marcin
Published: October 2021

The purpose of this study was to examine banks’ strategic adjustments to the challenges brought about by the COVID-19 pandemic. It examines how deep and pressing the necessary transformations are, based on an analysis of the banking sectors of Central, Eastern, and Northern European countries (CENE): the Czech Republic, Hungary, Poland, Slovakia, Estonia, Latvia, and Lithuania. The main research question posed asks how the pandemic and the subsequent economic crisis have changed banks’ sources of profits and risks, forcing banks to speed up structural transformations. In particular, the study identified and verified the following hypotheses: that the initial impact of the COVID-19 pandemic on banks in the analyzed region was heterogeneous and that the pandemic has intensified the challenges of digitalization and forced banks to speed up the digital transformations of their business models. The methodology employed was the dynamic panel data model—generalized method of moment (GMM-SYS version), using an adjusted dataset from the BankFocus database for unconsolidated bank data for the 2016–2020 period. The econometric analysis was supplemented with a CENE bank survey, researching bank attitudes and the stage of digital transformation. The results of the survey revealed that the majority of the surveyed banks consider themselves digitalization leaders, with a clearly articulated and implemented digitalization strategy. The main finding of the study was that the digital focus may help large banks in CENE to address and offset problems revealed by the panel data model: that traditional sources of incomes, based on intermediation and interest-related incomes, no longer contribute positively to profitability but also to stability.
Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis
Authors: Mirmazloumi S. Mohammad , Moghimi Armin , Ranjgar Babak , Mohseni Farzane , Ghorbanian Arsalan , Ahmadi Seyed Ali , Amani Meisam , Brisco Brian
Published: October 2021

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.
Application of Random Forest Algorithm for Merging Multiple Satellite Precipitation Products across South Korea
Authors: Nguyen Giang V. , Le Xuan-Hien , Van Linh Nguyen , Jung Sungho , Yeon Minho , Lee Giha
Published: October 2021

Precipitation is a crucial component of the water cycle and plays a key role in hydrological processes. Recently, satellite-based precipitation products (SPPs) have provided grid-based precipitation with spatiotemporal variability. However, SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution of these products is still relatively coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation based on a combination of rainfall observation data with multiple SPPs for the period of 2003–2017 across South Korea. A Random Forest (RF) machine-learning algorithm model was applied for producing a new merged precipitation product. In addition, several statistical linear merging methods have been adopted to compare with the results achieved from the RF model. To investigate the efficiency of RF, rainfall data from 64 observed Automated Synoptic Observation System (ASOS) installations were collected to analyze the accuracy of products through several continuous as well as categorical indicators. The new precipitation values produced by the merging procedure generally not only report higher accuracy than a single satellite rainfall product but also indicate that RF is more effective than the statistical merging method. Thus, the achievements from this study point out that the RF model might be applied for merging multiple satellite precipitation products, especially in sparse region areas.
Remote Sensing Applications in Sugarcane Cultivation: A Review
Authors: Som-ard Jaturong , Atzberger Clement , Izquierdo-Verdiguier Emma , Vuolo Francesco , Immitzer Markus
Published: October 2021

A large number of studies have been published addressing sugarcane management and monitoring to increase productivity and production as well as to better understand landscape dynamics and environmental threats. Building on existing reviews which mainly focused on the crop’s spectral behavior, a comprehensive review is provided which considers the progress made using novel data analysis techniques and improved data sources. To complement the available reviews, and to make the large body of research more easily accessible for both researchers and practitioners, in this review (i) we summarized remote sensing applications from 1981 to 2020, (ii) discussed key strengths and weaknesses of remote sensing approaches in the sugarcane context, and (iii) described the challenges and opportunities for future earth observation (EO)-based sugarcane monitoring and management. More than one hundred scientific studies were assessed regarding sugarcane mapping (52 papers), crop growth anomaly detection (11 papers), health monitoring (14 papers), and yield estimation (30 papers). The articles demonstrate that decametric satellite sensors such as Landsat and Sentinel-2 enable a reliable, cost-efficient, and timely mapping and monitoring of sugarcane by overcoming the ground sampling distance (GSD)-related limitations of coarser hectometric resolution data, while offering rich spectral information in the frequently recorded data. The Sentinel-2 constellation in particular provides fine spatial resolution at 10 m and high revisit frequency to support sugarcane management and other applications over large areas. For very small areas, and in particular for up-scaling and calibration purposes, unmanned aerial vehicles (UAV) are also useful. Multi-temporal and multi-source data, together with powerful machine learning approaches such as the random forest (RF) algorithm, are key to providing efficient monitoring and mapping of sugarcane growth, health, and yield. A number of difficulties for sugarcane monitoring and mapping were identified that are also well known for other crops. Those difficulties relate mainly to the often (i) time consuming pre-processing of optical time series to cope with atmospheric perturbations and cloud coverage, (ii) the still important lack of analysis-ready-data (ARD), (iii) the diversity of environmental and growth conditions—even for a given country—under which sugarcane is grown, superimposing non-crop related radiometric information on the observed sugarcane crop, and (iv) the general ill-posedness of retrieval and classification approaches which adds ambiguity to the derived information.
Dynamics of Open Green Areas in Polish and Romanian Cities during 2006–2018: Insights for Spatial Planners
Authors: Petrisor Alexandru-Ionut , Mierzejewska Lidia , Mitrea Andrei , Drachal Krzysztof , Tache Antonio Valentin
Published: October 2021

Studying green urban infrastructure is important because of its ecosystem services, contributing to the welfare and comfort of citizens, mitigation of climate changes, and sustainability goals. Urban planning can increase or diminish the performance of ecosystem services. Despite numerous studies on the green infrastructure–services–planning nexus, there are very few concrete planning recommendations. This study aims to provide such recommendations for a broader audience by analyzing the dynamic of open green areas in Polish and Romanian cities, connected with its drivers. A novel approach including mathematical modeling and geostatistical analyses was applied to Urban Atlas and statistical yearbooks data. The results indicated that open green areas were lost and fragmented in all Romanian and Polish cities during 2006–2018. The drivers included urban built-up areas, population and density, the number of building permits, number of new dwellings completed, number of employees, and total length of roads. The study also revealed a tremendous lack of consistent datasets across the countries using the same statistical indicators. Based on the findings, planners should aim to preserve and develop urban greenery and maintain its continuity. City managers should use more research and decision-making policy developers to develop targeted policies and scientists should develop planning manuals.
Model Specialization for the Use of ESRGAN on Satellite and Airborne Imagery
Authors: Clabaut Étienne , Lemelin Myriam , Germain Mickaël , Bouroubi Yacine , St-Pierre Tony
Published: October 2021

Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interesting to specialize the model with respect to a particular theme. The use of enhanced super-resolution generative adversarial networks (ERSGAN), a specific type of deep learning architecture, allows the spatial resolution of remote sensing images to be increased by “hallucinating” non-existent details. In this study, we show that ESRGAN create better quality images when trained on thematically classified images than when trained on a wide variety of examples. All things being equal, we further show that the algorithm performs better on some themes than it does on others. Texture analysis shows that these performances are correlated with the inverse difference moment and entropy of the images.
Analysis of the Geological Controls and Kinematics of the Chgega Landslide (Mateur, Tunisia) Exploiting Photogrammetry and InSAR Technologies
Authors: Gaidi Seifeddine , Galve Jorge Pedro , Melki Fetheddine , Ruano Patricia , Reyes-Carmona Cristina , Marzougui Wissem , Devoto Stefano , Pérez-Peña José Vicente , Azañón José Miguel , Chouaieb Haifa , Zargouni Fouad , Booth-Rea Guillermo
Published: October 2021

Exploration of territories not previously analyzed by landslide experts provides interesting findings. The Chgega landslide, in northern Tunisia, represents a paradigmatic mass movement. It can be classified as a complex landslide, or more specifically as vast rock spreading that evolved into a block slide. It involves a great block of limestone—about 900 m long and 400 m wide—sliding over ductile clays and marls. The viscoplastic creep of the clays drives the landslide and creates, in its crown, a graben ~800 m long and ~120 m wide that breaks the summit of Chgega Mountain. Using Interferometric Synthetic Aperture Radar (InSAR) technologies, we demonstrate that this complex landslide is currently active and moreover shows progressive movement without clear episodic accelerations. The velocity of the limestone block is just above 2 mm/yr. The occurrence of gravity-induced joints indicates that the movement has an orientation towards 333° of azimuth on average, conditioned by the landscape around Chgega. These results were obtained through the analysis of a 3D model and a high-resolution orthoimage created from photographs acquired by an Uncrewed Aerial Vehicle (UAV). We may conclude that the landslide movement is determined by normal faults with directions N060°E and N140–150°E. This characterization of the Chgega landslide can serve as the basis for future studies about the origin of this slope movement. Furthermore, the data provided here may support the recognition of Chgega as a singular geological point that deserves to be declared a geosite.
Fiber Optic Sensors Based on the Faraday Effect
Authors: Mihailovic Pedja , Petricevic Slobodan
Published: October 2021

Some 175 years ago Michael Faraday discovered magnetic circular birefringence, now commonly known as the Faraday effect. Sensing the magnetic field through the influence that the field has on light within the fiber optic sensor offers several advantages, one of them fundamental. These advantages find application in the measurement of electric current at high voltages by measuring the induced magnetic field, thus warranting application for this kind of fiber optic sensor (FOS) in future smart grids. Difficulties in designing and manufacturing high-performance FOSs were greatly alleviated by developments in optical telecommunication technology, thus giving new impetus to magnetometry based on the Faraday effect. Some of the major problems in the processing of optical signals and temperature dependence have been resolved, yet much effort is still needed to implement all solutions into a single commercial device. Artificial structures with giant Faraday rotation, reported in the literature in the 21st century, will further improve the performance of FOSs based on the Faraday effect. This paper will consider obstacles and limits imposed by the available technology and review solutions proposed so far for fiber optic sensors based on the Faraday effect.
Confidence-Calibrated Human Activity Recognition
Authors: Roy Debaditya , Girdzijauskas Sarunas , Socolovschi Serghei
Published: October 2021

Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sensors. However, most state-of-the-art DL models used for AR are trained to discriminate different activity classes at high accuracy, not considering the confidence calibration of predictive output of those models. This results in probabilistic estimates that might not capture the true likelihood and is thus unreliable. In practice, it tends to produce overconfident estimates. In this paper, the problem is addressed by proposing deep time ensembles, a novel ensembling method capable of producing calibrated confidence estimates from neural network architectures. In particular, the method trains an ensemble of network models with temporal sequences extracted by varying the window size over the input time series and averaging the predictive output. The method is evaluated on four different benchmark HAR datasets and three different neural network architectures. Across all the datasets and architectures, our method shows an improvement in calibration by reducing the expected calibration error (ECE)by at least 40%, thereby providing superior likelihood estimates. In addition to providing reliable predictions our method also outperforms the state-of-the-art classification results in the WISDM, UCI HAR, and PAMAP2 datasets and performs as good as the state-of-the-art in the Skoda dataset.
Motion-Encoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles
Authors: Alanezi Mohammed A. , Bouchekara Houssem R. E. H. , Shahriar Mohammad S. , Sha’aban Yusuf A. , Javaid Muhammad S. , Khodja Mohammed
Published: October 2021

In this paper, a new optimization algorithm called motion-encoded electric charged particles optimization (ECPO-ME) is developed to find moving targets using unmanned aerial vehicles (UAV). The algorithm is based on the combination of the ECPO (i.e., the base algorithm) with the ME mechanism. This study is directly applicable to a real-world scenario, for instance the movement of a misplaced animal can be detected and subsequently its location can be transmitted to its caretaker. Using Bayesian theory, finding the location of a moving target is formulated as an optimization problem wherein the objective function is to maximize the probability of detecting the target. In the proposed ECPO-ME algorithm, the search trajectory is encoded as a series of UAV motion paths. These paths evolve in each iteration of the ECPO-ME algorithm. The performance of the algorithm is tested for six different scenarios with different characteristics. A statistical analysis is carried out to compare the results obtained from ECPO-ME with other well-known metaheuristics, widely used for benchmarking studies. The results found show that the ECPO-ME has great potential in finding moving targets, since it outperforms the base algorithm (i.e., ECPO) by as much as 2.16%, 5.26%, 7.17%, 14.72%, 0.79% and 3.38% for the investigated scenarios, respectively.
Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network
Authors: Awais Muhammad Ahsan , Yusoff Mohd Zuki , Khan Danish M. , Yahya Norashikin , Kamel Nidal , Ebrahim Mansoor
Published: October 2021

Motor imagery (MI)-based brain–computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain’s neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms—an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain–computer interfaces.
Motion Magnification of Vibration Image in Estimation of Technical Object Condition-Review
Authors: Śmieja Michał , Mamala Jarosław , Prażnowski Krzysztof , Ciepliński Tomasz , Szumilas Łukasz
Published: October 2021

One of the most important features of the proper operation of technical objects is monitoring the vibrations of their mechanical components. The currently significant proportion of the research methods in this regard includes a group of research methods based on the conversion of vibrations using sensors providing data from individual locations. In parallel with the continuous improvement of these tools, new methods for acquiring information on the condition of the object have emerged due to the rapid development of visual systems. Their actual effectiveness determined the switch from research laboratories to actual industrial installations. In many cases, the application of the visualization methods can supplement the conventional methods applied and, under particular conditions, can effectively replace them. The decisive factor is their non-contact nature and the possibility for simultaneous observation of multiple points of the selected area. Visual motion magnification (MM) is an image processing method that involves the conscious and deliberate deformation of input images to the form that enables the visual observation of vibration processes which are not visible in their natural form. The first part of the article refers to the basic terms in the field of expressing motion in an image (based on the Lagrangian and Eulerian approaches), the formulation of the term of optical flow (OF), and the interpretation of an image in time and space. The following part of the article reviews the main processing algorithms in the aspect of computational complexity and visual quality and their modification for applications under specific conditions. The comparison of the MM methods presented in the paper and recommendations for their applications across a wide variety of fields were supported with examples originating from recent publications. The effectiveness of visual methods based on motion magnification in machine diagnosis and the identification of malfunctions are illustrated with selected examples of the implementation derived from authors’ workshop practice under industrial conditions.
Deployment Optimization Method of Multistatic Radar for Constructing Circular Barrier Coverage
Authors: Li Hai-Peng , Feng Da-Zheng , Chen Shao-Feng , Zhou Ya-Peng
Published: October 2021

To construct circular barrier coverage (CBC) with multistatic radars, a deployment optimization method based on equipartition strategy is proposed in this paper. In the method, the whole circular area is divided into several sub-circles with equal width, and each sub-circle is blanketed by a sub-CBC that is built based on the multistatic radar deployment patterns. To determine the optimal deployment patterns for each sub-CBC, the optimization conditions are firstly studied. Then, to optimize the deployment of the whole circular area, a model based on minimum deployment cost is proposed, and the proposed model is divided into two sub-models to solve the optimization issue. In the inner model, it is assumed that the width of a sub-circle is given. Based on the optimization conditions of the deployment pattern, integer linear programming (ILP) and exhaustive method (EM) are jointly adopted to determine the types and numbers of deployment patterns. Moreover, a modified formula is introduced to calculate the maximum valid number of receivers in a pattern, thus narrowing the search scope of the EM. In the outer model, the width of a sub-circle is assumed to be a variable, and the EM is adopted to determine the minimum total deployment cost and the optimal deployment patterns on each sub-circle. Moreover, the improved formula is exploited to determine the range of width for a sub-circle barrier and reduce the search scope of the EM. Finally, simulations are conducted in different conditions to verify the effectiveness of the proposed method. The simulation results indicate that the proposed method can spend less deployment cost and deploy fewer transmitters than the state-of-the-artwork.
Electrochemical Impedance Spectroscopy (EIS): Principles, Construction, and Biosensing Applications
Authors: Magar Hend S. , Hassan Rabeay Y. A. , Mulchandani Ashok
Published: October 2021

Electrochemical impedance spectroscopy (EIS) is a powerful technique used for the analysis of interfacial properties related to bio-recognition events occurring at the electrode surface, such as antibody–antigen recognition, substrate–enzyme interaction, or whole cell capturing. Thus, EIS could be exploited in several important biomedical diagnosis and environmental applications. However, the EIS is one of the most complex electrochemical methods, therefore, this review introduced the basic concepts and the theoretical background of the impedimetric technique along with the state of the art of the impedimetric biosensors and the impact of nanomaterials on the EIS performance. The use of nanomaterials such as nanoparticles, nanotubes, nanowires, and nanocomposites provided catalytic activity, enhanced sensing elements immobilization, promoted faster electron transfer, and increased reliability and accuracy of the reported EIS sensors. Thus, the EIS was used for the effective quantitative and qualitative detections of pathogens, DNA, cancer-associated biomarkers, etc. Through this review article, intensive literature review is provided to highlight the impact of nanomaterials on enhancing the analytical features of impedimetric biosensors.
Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection
Authors: Maliuk Andrei S. , Prosvirin Alexander E. , Ahmad Zahoor , Kim Cheol Hong , Kim Jong-Myon
Published: October 2021

This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents.
Determining Surface Shape of Translucent Objects with the Combination of Laser-Beam-Based Structured Light and Polarization Technique
Authors: Chen Bingquan , Shi Peng , Wang Yanhua , Xu Yongze , Ma Hongyang , Wang Ruirong , Zheng Chunhong , Chu Pengcheng
Published: October 2021

In this study, we focus on the 3D surface measurement and reconstruction of translucent objects. The proposed approach of surface-shape determination of translucent objects is based on the combination of the projected laser-beam-based sinusoidal structured light and the polarization technique. The theoretical analyses are rigorously completed in this work, including the formation, propagation, and physical features of the generated sinusoidal signal by the designed optical system, the reflection and transmission of the projected monochromatic fringe pattern on the surface of the translucent object, and the formation and the separation of the direct-reflection and the global components of the surface radiance of the observed object. The results of experimental investigation designed in accordance with our theoretical analyses have confirmed that accurate reconstructions can be obtained using the one-shot measurement based on the proposed approach of this study and Fourier transform profilometry, while the monochromaticity and the linearly-polarized characteristic of the projected sinusoidal signal can be utilized by using a polarizer and an optical filter simultaneously for removing the global component, i.e., the noised signal contributed by multiply-scattered photons and the background illuminance in the frame of our approach. Moreover, this study has also revealed that the developed method is capable of getting accurate measurements and reconstructions of translucent objects when the background illumination exists, which has been considered as a challenging issue for 3D surface measurement and reconstruction of translucent objects.
EEG Monitoring Is Feasible and Reliable during Simultaneous Transcutaneous Electrical Spinal Cord Stimulation
Authors: McGeady Ciarán , Vučković Aleksandra , Zheng Yong-Ping , Alam Monzurul
Published: October 2021

Transcutaneous electrical spinal cord stimulation (tSCS) is a non-invasive neuromodulatory technique that has in recent years been linked to improved volitional limb control in spinal-cord injured individuals. Although the technique is growing in popularity there is still uncertainty regarding the neural mechanisms underpinning sensory and motor recovery. Brain monitoring techniques such as electroencephalography (EEG) may provide further insights to the changes in coritcospinal excitability that have already been demonstrated using other techniques. It is unknown, however, whether intelligible EEG can be extracted while tSCS is being applied, owing to substantial high-amplitude artifacts associated with stimulation-based therapies. Here, for the first time, we characterise the artifacts that manifest in EEG when recorded simultaneously with tSCS. We recorded multi-channel EEG from 21 healthy volunteers as they took part in a resting state and movement task across two sessions: One with tSCS delivered to the cervical region of the neck, and one without tSCS. An offline analysis in the time and frequency domain showed that tSCS manifested as narrow, high-amplitude peaks with a spectral density contained at the stimulation frequency. We quantified the altered signals with descriptive statistics—kurtosis, root-mean-square, complexity, and zero crossings—and applied artifact-suppression techniques—superposition of moving averages, adaptive, median, and notch filtering—to explore whether the effects of tSCS could be suppressed. We found that the superposition of moving averages filter was the most successful technique at returning contaminated EEG to levels statistically similar to that of normal EEG. In the frequency domain, however, notch filtering was more effective at reducing the spectral power contribution of stimulation from frontal and central electrodes. An adaptive filter was more appropriate for channels closer to the stimulation site. Lastly, we found that tSCS posed no detriment the binary classification of upper-limb movements from sensorimotor rhythms, and that adaptive filtering resulted in poorer classification performance. Overall, we showed that, depending on the analysis, EEG monitoring during transcutaneous electrical spinal cord stimulation is feasible. This study supports future investigations using EEG to study the activity of the sensorimotor cortex during tSCS, and potentially paves the way to brain–computer interfaces operating in the presence of spinal stimulation.
Statistical Modelling of Photonic Crystal Fibre Based Surface Plasmon Resonance Sensors Resonant Peak Wavelength for Tolerance Studies
Authors: Osifeso Samuel , Chu Suoda , Nakkeeran K.
Published: October 2021

We report a statistical approach to model the resonant peak wavelength (RPW) equation(s) of a photonic crystal fibre (PCF)-based surface plasmon resonance (SPR) sensors in terms of the PCF structural parameters (air-hole diameter, pitch, core diameter and gold layer thickness) at various tolerance levels. Design of experiments (statistical tool) is used to investigate the role played by the PCF structural parameters for sensing performance evaluation—RPW, across three tolerance levels (±2%, ±5% and ±10%). Pitch of the hollow-core PCF was discovered to be the major influencing parameter for the sensing performance (RPW) of the PCF-based SPR sensor while the inner metal (gold) layer thickness and core diameter are the least contributing parameters. This novel statistical method to derive the sensing performance parameter(s) of the PCF-based SPR sensors can be applied effectively and efficiently in the designing, characterisation, tolerance analysis not only at the research level, but also in optical fibre sensor fabrication industry to improve efficiency and lower cost.
Validation and Assessment of a Posture Measurement System with Magneto-Inertial Measurement Units
Authors: Paloschi Davide , Bravi Marco , Schena Emiliano , Miccinilli Sandra , Morrone Michelangelo , Sterzi Silvia , Saccomandi Paola , Massaroni Carlo
Published: October 2021

Inappropriate posture and the presence of spinal disorders require specific monitoring systems. In clinical settings, posture evaluation is commonly performed with visual observation, electrogoniometers or motion capture systems (MoCaps). Developing a measurement system that can be easily used also in non-structured environments would be highly beneficial for accurate posture monitoring. This work proposes a system based on three magneto-inertial measurement units (MIMU), placed on the backs of seventeen volunteers on the T3, T12 and S1 vertebrae. The reference system used for validation is a stereophotogrammetric motion capture system. The volunteers performed forward bending and sit-to-stand tests. The measured variables for identifying the posture were the kyphosis and the lordosis angles, as well as the range of movement (ROM) of the body segments. The comparison between MIMU and MoCap provided a maximum RMSE of 5.6° for the kyphosis and the lordosis angles. The average lumbo-pelvic contribution during forward bending (41.8 ± 8.6%) and the average lumbar ROM during sit-to-stand (31.8 ± 9.8° for sitting down, 29.6 ± 7.6° for standing up) obtained with the MIMU system agree with the literature. In conclusion, the MIMU system, which is wearable, inexpensive and easy to set up in non-structured environments, has been demonstrated to be effective in posture evaluation.
An Incremental Class-Learning Approach with Acoustic Novelty Detection for Acoustic Event Recognition
Authors: Bayram Barış , İnce Gökhan
Published: October 2021

Acoustic scene analysis (ASA) relies on the dynamic sensing and understanding of stationary and non-stationary sounds from various events, background noises and human actions with objects. However, the spatio-temporal nature of the sound signals may not be stationary, and novel events may exist that eventually deteriorate the performance of the analysis. In this study, a self-learning-based ASA for acoustic event recognition (AER) is presented to detect and incrementally learn novel acoustic events by tackling catastrophic forgetting. The proposed ASA framework comprises six elements: (1) raw acoustic signal pre-processing, (2) low-level and deep audio feature extraction, (3) acoustic novelty detection (AND), (4) acoustic signal augmentations, (5) incremental class-learning (ICL) (of the audio features of the novel events) and (6) AER. The self-learning on different types of audio features extracted from the acoustic signals of various events occurs without human supervision. For the extraction of deep audio representations, in addition to visual geometry group (VGG) and residual neural network (ResNet), time-delay neural network (TDNN) and TDNN based long short-term memory (TDNN–LSTM) networks are pre-trained using a large-scale audio dataset, Google AudioSet. The performances of ICL with AND using Mel-spectrograms, and deep features with TDNNs, VGG, and ResNet from the Mel-spectrograms are validated on benchmark audio datasets such as ESC-10, ESC-50, UrbanSound8K (US8K), and an audio dataset collected by the authors in a real domestic environment.
Investigating the Usability of a Head-Mounted Display Augmented Reality Device in Elementary School Children
Authors: Lauer Luisa , Altmeyer Kristin , Malone Sarah , Barz Michael , Brünken Roland , Sonntag Daniel , Peschel Markus
Published: October 2021

Augmenting reality via head-mounted displays (HMD-AR) is an emerging technology in education. The interactivity provided by HMD-AR devices is particularly promising for learning, but presents a challenge to human activity recognition, especially with children. Recent technological advances regarding speech and gesture recognition concerning Microsoft’s HoloLens 2 may address this prevailing issue. In a within-subjects study with 47 elementary school children (2nd to 6th grade), we examined the usability of the HoloLens 2 using a standardized tutorial on multimodal interaction in AR. The overall system usability was rated “good”. However, several behavioral metrics indicated that specific interaction modes differed in their efficiency. The results are of major importance for the development of learning applications in HMD-AR as they partially deviate from previous findings. In particular, the well-functioning recognition of children’s voice commands that we observed represents a novelty. Furthermore, we found different interaction preferences in HMD-AR among the children. We also found the use of HMD-AR to have a positive effect on children’s activity-related achievement emotions. Overall, our findings can serve as a basis for determining general requirements, possibilities, and limitations of the implementation of educational HMD-AR environments in elementary school classrooms.