bluuebunny/crossref_metadata_2025 huggingface dataset:
Geodetic Mass Balance of the South Shetland Islands Ice Caps, Antarctica, from Differencing TanDEM-X DEMs
Authors: Shahateet Kaian , Seehaus Thorsten , Navarro Francisco , Sommer Christian , Braun Matthias
Published: September 2021

Although the glaciers in the Antarctic periphery currently modestly contribute to sea level rise, their contribution is projected to increase substantially until the end of the 21st century. The South Shetland Islands (SSI), located to the north of the Antarctic Peninsula, are lacking a geodetic mass balance calculation for the entire archipelago. We estimated its geodetic mass balance over a 3–4-year period within 2013–2017. Our estimation is based on remotely sensed multispectral and interferometric SAR data covering 96% of the glacierized areas of the islands considered in our study and 73% of the total glacierized area of the SSI archipelago (Elephant, Clarence, and Smith Islands were excluded due to data limitations). Our results show a close to balance, slightly negative average specific mass balance for the whole area of −0.106 ± 0.007 m w.e. a−1, representing a mass change of −238 ± 12 Mt a−1. These results are consistent with a wider scale geodetic mass balance estimation and with glaciological mass balance measurements at SSI locations for the same study period. They are also consistent with the cooling trend observed in the region between 1998 and the mid-2010s.
Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China
Authors: Li Suosuo , Liu Yuanpu , Pan Yongjie , Li Zhe , Lyu Shihua
Published: September 2021

Land-surface characteristics (LSCs) and land-soil moisture conditions can modulate energy partition at the land surface, impact near-surface atmosphere conditions, and further affect land–atmosphere interactions. This study investigates the effect of land-surface-characteristic parameters (LSCPs) including albedo, leaf-area index (LAI), and soil moisture (SM) on hot weather by in East China using the numerical model. Simulations using the Weather Research and Forecasting (WRF) Model were conducted for a hot weather event with a high spatial resolution of 1 km in domain 3 by using ERA-Interim forcing fields on 20 July 2017 until 16:00 UTC on 25 July 2017. The satellite-based albedo and LAI, and assimilation-based soil-moisture data of high temporal–spatial resolution, which are more accurate to match fine weather forecasts and high-resolution simulations, were used to update the default LSCPs. A control simulation with the default LSCPs (WRF_CTL), a main sensitivity simulation with the updated LSCP albedo, LAI and SM (WRF_CHAR), and a series of other sensitivity simulations with one or two updated LSCPs were performed. Results show that WRF_CTL could reproduce the spatial distribution of hot weather, but overestimated air temperature (Ta) and maximal air temperature (Tamax) with a warming bias of 1.05 and 1.32 °C, respectively. However, the WRF_CHAR simulation reduced the warming bias, and improved the simulated Ta and Tamax with reducing relative biases of 33.08% and 29.24%, respectively. Compared to the WRF_CTL, WRF_CHAR presented a negative sensible heat-flux difference, positive latent heat flux, and net radiation difference of the area average. LSCPs modulated the partition of available land-surface energy and then changed the air temperature. On the basis of statistical-correlation analysis, the soil moisture of the top 10 cm is the main factor to improve warming bias on hot weather in East China.
Multi-Stage Convolutional Broad Learning with Block Diagonal Constraint for Hyperspectral Image Classification
Authors: Kong Yi , Wang Xuesong , Cheng Yuhu , Chen C. L. Philip
Published: September 2021

By combining the broad learning and a convolutional neural network (CNN), a block-diagonal constrained multi-stage convolutional broad learning (MSCBL-BD) method is proposed for hyperspectral image (HSI) classification. Firstly, as the linear sparse feature extracted by the conventional broad learning method cannot fully characterize the complex spatial-spectral features of HSIs, we replace the linear sparse features in the mapped feature (MF) with the features extracted by the CNN to achieve more complex nonlinear mapping. Then, in the multi-layer mapping process of the CNN, information loss occurs to a certain degree. To this end, the multi-stage convolutional features (MSCFs) extracted by the CNN are expanded to obtain the multi-stage broad features (MSBFs). MSCFs and MSBFs are further spliced to obtain multi-stage convolutional broad features (MSCBFs). Additionally, in order to enhance the mutual independence between MSCBFs, a block diagonal constraint is introduced, and MSCBFs are mapped by a block diagonal matrix, so that each feature is represented linearly only by features of the same stage. Finally, the output layer weights of MSCBL-BD and the desired block-diagonal matrix are solved by the alternating direction method of multipliers. Experimental results on three popular HSI datasets demonstrate the superiority of MSCBL-BD.
MUSSOL: A Micro-Uas to Survey Ship Cargo hOLds
Authors: Bonnin-Pascual Francisco , Garcia-Fidalgo Emilio , Company-Corcoles Joan P. , Ortiz Alberto
Published: September 2021

Because of their high maneuverability and fast deployment times, aerial robots have recently gained popularity for automating inspection tasks. In this paper, we address the visual inspection of vessel cargo holds, aiming at safer, cost-efficient and more intensive visual inspections of ships by means of a multirotor-type platform. To this end, the vehicle is equipped with a sensor suite able to supply the surveyor with imagery from relevant areas, while the control software is supporting the operator during flight with enhanced functionalities and reliable autonomy. All this has been accomplished in the context of the supervised autonomy (SA) paradigm, by means of extensive use of behaviour-based high-level control (including obstacle detection and collision prevention), all specifically devised for visual inspection. The full system has been evaluated both in laboratory and in real environments, on-board two different vessels. Results show the vehicle effective for the referred application, in particular due to the inspection-oriented capabilities it has been fitted with.
Deposits’ Morphology of the 2018 Hokkaido Iburi-Tobu Earthquake Mass Movements from LiDAR & Aerial Photographs
Authors: Gomez Christopher , Hotta Norifumi
Published: September 2021

On 6 September at 03:08 a.m. local time, a 33 km deep earthquake underneath the Iburi mountains triggered more than 7000 co-seismic mass movements within 25 km of the epicenter. Most of the mass movements occurred in complex terrain and became coalescent. However, a total of 59 mass movements occurred as discrete events and stopped on the semi-horizontal valley floor. Using this case study, the authors aimed to define planar and vertical parameters to (1) compare the geometrical parameters with rain-triggered mass movements and (2) to extend existing datasets used for hazards and disaster risk purposes. To reach these objectives, the methodology relies on LiDAR data flown in the aftermath of the earthquake as well as aerial photographs. Using a Geographical Information System (GIS), planform and vertical parameters were extracted from the DEM in order to calculate the relationship between areas and volume, between the Fahrböschung and the volume of the deposits, and to discuss the relationship between the deposit slope surface and the effective stress of the deposit. Results have shown that the relation S=k[Vd]2/3 (where S is the surface area of a deposit and Vd the volume, and k a scalar that is function of S) is k = 2.1842ln(S) − 10.167 with a R2 of 0.52, with less variability in deposits left by valley-confined processes compared to open-slope processes. The Fahrböschung for events that started as valley-confined mass-movements was Fc = −0.043ln(D) + 0.7082, with a R2 of 0.5, while for open-slope mass-movements, the Fo = −0.046ln(D) + 0.7088 with a R2 of 0.52. The “T-values”, as defined by Takahashi (2014), are displaying values as high as nine times that of the values for experimental rainfall debris-flow, signifying that the effective stress is higher than in rain-triggered counterparts, which have an increased pore pressure due to the need for further water in the material to be moving. For co-seismic debris-flows and other co-seismic mass movements it is the ground acceleration that “fluidizes” the material. The maxima found in this study are as high as 3.75.
Integrated Analysis of the Combined Risk of Ground Subsidence, Sea Level Rise, and Natural Hazards in Coastal and Delta River Regions
Authors: Zhao Qing , Pan Jiayi , Devlin Adam , Xu Qing , Tang Maochuan , Li Zhengjie , Zamparelli Virginia , Falabella Francesco , Mastro Pietro , Pepe Antonio
Published: September 2021

Non-climate-related anthropogenic processes and frequently encountered natural hazards exacerbate the risk in coastal zones and megacities and amplify local vulnerability. Coastal risk is amplified by the combination of sea level rise (SLR) resulting from climate change, associated tidal evolution, and the local sinking of land resulting from anthropogenic and natural hazards. In this framework, the authors of this investigation have actively contributed to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon IV initiative through a project (ID. 32294) that was explicitly designed to address the issue of monitoring coastal and delta river regions through Earth Observation (EO) technologies. The project’s primary goals were to provide a complete characterization of the changes in target scenes over time and provide estimates of future regional sea level changes to derive submerged coastal areas and wave fields. Suggestions are also provided for implementing coastal protection measures in order to adapt and mitigate the multifactor coastal vulnerability. In order to achieve these tasks, well-established remote sensing technologies based on the joint exploitation of multi-spectral information gathered at different spectral wavelengths, the exploitation of advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques for the retrieval of ground deformations, the realization of geophysical analyses, and the use of satellite altimeters and tide gauge data have effectively been employed. The achieved results, which mainly focus on selected sensitive regions including the city of Shanghai, the Pearl River Delta in China, and the coastal city of Saint Petersburg in Europe, provide essential assets for planning present and future scientific activities devoted to monitoring such fragile environments. These analyses are crucial for assessing the factors that will amplify the vulnerability of low-elevation coastal zones.
A Review of Voxel-Based Computerized Ionospheric Tomography with GNSS Ground Receivers
Authors: Lu Weijun , Ma Guanyi , Wan Qingtao
Published: September 2021

Ionized by solar radiation, the ionosphere causes a phase rotation or time delay to trans-ionospheric radio waves. Reconstruction of ionospheric electron density profiles with global navigation satellite system (GNSS) observations has become an indispensable technique for various purposes ranging from space physics studies to radio applications. This paper conducts a comprehensive review on the development of voxel-based computerized ionospheric tomography (CIT) in the last 30 years. A brief introduction is given in chronological order starting from the first report of CIT with simulation to the newly proposed voxel-based algorithms for ionospheric event analysis. The statement of the tomographic geometry and voxel models are outlined with the ill-posed and ill-conditioned nature of CIT addressed. With the additional information from other instrumental observations or initial models supplemented to make the coefficient matrix less ill-conditioned, equation constructions are categorized into constraints, virtual data assimilation and multi-source observation fusion. Then, the paper classifies and assesses the voxel-based CIT algorithms of the algebraic method, statistical approach and artificial neural networks for equation solving or electron density estimation. The advantages and limitations of the algorithms are also pointed out. Moreover, the paper illustrates the representative height profiles and two-dimensional images of ionospheric electron densities from CIT. Ionospheric disturbances studied with CIT are presented. It also demonstrates how the CIT benefits ionospheric correction and ionospheric monitoring. Finally, some suggestions are provided for further research about voxel-based CIT.
Observations of Surface Currents and Tidal Variability Off of Northeastern Taiwan from Shore-Based High Frequency Radar
Authors: Chen Yu-Ru , Paduan Jeffrey D. , Cook Michael S. , Chuang Laurence Zsu-Hsin , Chung Yu-Jen
Published: September 2021

A network of high-frequency radars (HFRs) has been deployed around Taiwan. The wide-area data coverage is dedicated to revealing near real-time sea-surface current information. This paper investigates three primary objectives: (1) describing the seasonal current synoptic variability; (2) determining the influence of wind forcing; (3) describing the tidal current field pattern and variability. Sea surface currents derived from HFR data include both geostrophic components and wind-driven components. This study explored vector complex correlations between the HFR time series and wind, which was sufficient to identify high-frequency components, including an Ekman balance among the surface currents and wind. Regarding the characteristics of mesoscale events and the tidal field, a year-long high-resolution surface dataset was utilized to observe the current–eddy–tide interactions over four seasons. The harmonic analysis results derived from surface currents off of northeastern Taiwan during 2013 are presented. The results agree well with the tidal parameters estimated from tide-gauge station observations. The analysis shows that this region features a strong, mixed, mainly semidiurnal tide. Continued monitoring by a variety of sensors (e.g., satellite and HFR) would improve the understanding of the circulation in the region.
Thermospheric Parameters during Ionospheric G-Conditions
Authors: Perrone Loredana , Mikhailov Andrey V. , Sabbagh Dario
Published: September 2021

For the first time thermospheric parameters (neutral composition, exospheric temperature and vertical plasma drift related to thermospheric winds) have been inferred for ionospheric G-conditions observed with Millstone Hill ISR on 11–13 September 2005; 13 June 2005, and 15 July 2012. The earlier developed method to extract a consistent set of thermospheric parameters from ionospheric observations has been revised to solve the problem in question. In particular CHAMP/STAR and GOCE neutral gas density observations were included into the retrieval process. It was found that G-condition days were distinguished by enhanced exospheric temperature and decreased by ~2 times of the column atomic oxygen abundance in a comparison to quiet reference days, the molecular nitrogen column abundance being practically unchanged. The inferred upward plasma drift corresponds to strong ~90 m/s equatorward thermospheric wind presumably related to strong auroral heating on G-condition days.
A Two-Stage Deep Learning Registration Method for Remote Sensing Images Based on Sub-Image Matching
Authors: Chen Yuan , Jiang Jie
Published: September 2021

The registration of multi-temporal remote sensing images with abundant information and complex changes is an important preprocessing step for subsequent applications. This paper presents a novel two-stage deep learning registration method based on sub-image matching. Unlike the conventional registration framework, the proposed network learns the mapping between matched sub-images and the geometric transformation parameters directly. In the first stage, the matching of sub-images (MSI), sub-images cropped from the images are matched through the corresponding heatmaps, which are made of the predicted similarity of each sub-image pairs. The second stage, the estimation of transformation parameters (ETP), a network with weight structure and position embedding estimates the global transformation parameters from the matched pairs. The network can deal with an uncertain number of matched sub-image inputs and reduce the impact of outliers. Furthermore, the sample sharing training strategy and the augmentation based on the bounding rectangle are introduced. We evaluated our method by comparing the conventional and deep learning methods qualitatively and quantitatively on Google Earth, ISPRS, and WHU Building Datasets. The experiments showed that our method obtained the probability of correct keypoints (PCK) of over 99% at α = 0.05 (α: the normalized distance threshold) and achieved a maximum increase of 16.8% at α = 0.01, compared with the latest method. The results demonstrated that our method has good robustness and improved the precision in the registration of optical remote sensing images with great variation.
A Novel Ensemble Architecture of Residual Attention-Based Deep Metric Learning for Remote Sensing Image Retrieval
Authors: Cheng Qimin , Gan Deqiao , Fu Peng , Huang Haiyan , Zhou Yuzhuo
Published: September 2021

Recently, deep metric learning (DML) has received widespread attention in the field of remote sensing image retrieval (RSIR), owing to its ability to extract discriminative features to represent images and then to measure the similarity between images via learning a distance function among feature vectors. However, the distinguishability of features extracted by the most current DML-based methods for RSIR is still not sufficient, and the retrieval efficiency needs to be further improved. To this end, we propose a novel ensemble architecture of residual attention-based deep metric learning (EARA) for RSIR. In our proposed architecture, residual attention is introduced and ameliorated to increase feature discriminability, maintain global features, and concatenate feature vectors of different weights. Then, descriptor ensemble rather than embedding ensemble is chosen to further boost the performance of RSIR with reduced time cost and memory consumption. Furthermore, our proposed architecture can be flexibly extended with different types of deep neural networks, loss functions, and feature descriptors. To evaluate the performance and efficiency of our architecture, we conduct exhaustive experiments on three benchmark remote sensing datasets, including UCMD, SIRI-WHU, and AID. The experimental results demonstrate that the proposed architecture outperforms the four state-of-the-art methods, including BIER, A-BIER, DCES, and ABE, by 15.45%, 13.04%, 10.31%, and 6.62% in the mean Average Precision (mAP), respectively. As for the retrieval execution complexity, the retrieval time and floating point of operations (FLOPs), needed by the proposed architecture on AID, reduce by 92% and 80% compared to those needed by ABE, albeit with the same Recall@1 between the two methods.
Disentangling LiDAR Contribution in Modelling Species–Habitat Structure Relationships in Terrestrial Ecosystems Worldwide. A Systematic Review and Future Directions
Authors: Acebes Pablo , Lillo Paula , Jaime-González Carlos
Published: September 2021

Global biodiversity is threatened by unprecedented and increasing anthropogenic pressures, including habitat loss and fragmentation. LiDAR can become a decisive technology by providing accurate information about the linkages between biodiversity and ecosystem structure. Here, we review the current use of LiDAR metrics in ecological studies regarding birds, mammals, reptiles, amphibians, invertebrates, bryophytes, lichens, and fungi (BLF). We quantify the types of research (ecosystem and LiDAR sources) and describe the LiDAR platforms and data that are currently available. We also categorize and harmonize LiDAR metrics into five LiDAR morphological traits (canopy cover, height and vertical distribution, understory and shrubland, and topographic traits) and quantify their current use and effectiveness across taxonomic groups and ecosystems. The literature review returned 173 papers that met our criteria. Europe and North America held most of the studies, and birds were the most studied group, whereas temperate forest was by far the most represented ecosystem. Globally, canopy height was the most used LiDAR trait, especially in forest ecosystems, whereas canopy cover and terrain topography traits performed better in those ecosystems where they were mapped. Understory structure and shrubland traits together with terrain topography showed high effectiveness for less studied groups such as BLF and invertebrates and in open landscapes. Our results show how LiDAR technology has greatly contributed to habitat mapping, including organisms poorly studied until recently, such as BLF. Finally, we discuss the forthcoming opportunities for biodiversity mapping with different LiDAR platforms in combination with spectral information. We advocate (i) for the integration of spaceborne LiDAR data with the already available airborne (airplane, drones) and terrestrial technology, and (ii) the coupling of it with multispectral/hyperspectral information, which will allow for the exploration and analyses of new species and ecosystems.
Determining Temporal Uncertainty of a Global Inland Surface Water Time Series
Authors: Mayr Stefan , Klein Igor , Rutzinger Martin , Kuenzer Claudia
Published: September 2021

Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product’s quality with respect to its use for modeling and the geoscientific community.
Image Generation for 2D-CNN Using Time-Series Signal Features from Foot Gesture Applied to Select Cobot Operating Mode
Authors: Aswad Fadwa El , Djogdom Gilde Vanel Tchane , Otis Martin J.-D. , Ayena Johannes C. , Meziane Ramy
Published: September 2021

Advances in robotics are part of reducing the burden associated with manufacturing tasks in workers. For example, the cobot could be used as a “third-arm” during the assembling task. Thus, the necessity of designing new intuitive control modalities arises. This paper presents a foot gesture approach centered on robot control constraints to switch between four operating modalities. This control scheme is based on raw data acquired by an instrumented insole located at a human’s foot. It is composed of an inertial measurement unit (IMU) and four force sensors. Firstly, a gesture dictionary was proposed and, from data acquired, a set of 78 features was computed with a statistical approach, and later reduced to 3 via variance analysis ANOVA. Then, the time series collected data were converted into a 2D image and provided as an input for a 2D convolutional neural network (CNN) for the recognition of foot gestures. Every gesture was assimilated to a predefined cobot operating mode. The offline recognition rate appears to be highly dependent on the features to be considered and their spatial representation in 2D image. We achieve a higher recognition rate for a specific representation of features by sets of triangular and rectangular forms. These results were encouraging in the use of CNN to recognize foot gestures, which then will be associated with a command to control an industrial robot.
Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case
Authors: Fraga-Lamas Paula , Lopes Sérgio Ivan , Fernández-Caramés Tiago M.
Published: September 2021

Internet of Things (IoT) can help to pave the way to the circular economy and to a more sustainable world by enabling the digitalization of many operations and processes, such as water distribution, preventive maintenance, or smart manufacturing. Paradoxically, IoT technologies and paradigms such as edge computing, although they have a huge potential for the digital transition towards sustainability, they are not yet contributing to the sustainable development of the IoT sector itself. In fact, such a sector has a significant carbon footprint due to the use of scarce raw materials and its energy consumption in manufacturing, operating, and recycling processes. To tackle these issues, the Green IoT (G-IoT) paradigm has emerged as a research area to reduce such carbon footprint; however, its sustainable vision collides directly with the advent of Edge Artificial Intelligence (Edge AI), which imposes the consumption of additional energy. This article deals with this problem by exploring the different aspects that impact the design and development of Edge-AI G-IoT systems. Moreover, it presents a practical Industry 5.0 use case that illustrates the different concepts analyzed throughout the article. Specifically, the proposed scenario consists in an Industry 5.0 smart workshop that looks for improving operator safety and operation tracking. Such an application case makes use of a mist computing architecture composed of AI-enabled IoT nodes. After describing the application case, it is evaluated its energy consumption and it is analyzed the impact on the carbon footprint that it may have on different countries. Overall, this article provides guidelines that will help future developers to face the challenges that will arise when creating the next generation of Edge-AI G-IoT systems.
Intuitive Spatial Tactile Feedback for Better Awareness about Robot Trajectory during Human–Robot Collaboration
Authors: Grushko Stefan , Vysocký Aleš , Heczko Dominik , Bobovský Zdenko
Published: September 2021

In this work, we extend the previously proposed approach of improving mutual perception during human–robot collaboration by communicating the robot’s motion intentions and status to a human worker using hand-worn haptic feedback devices. The improvement is presented by introducing spatial tactile feedback, which provides the human worker with more intuitive information about the currently planned robot’s trajectory, given its spatial configuration. The enhanced feedback devices communicate directional information through activation of six tactors spatially organised to represent an orthogonal coordinate frame: the vibration activates on the side of the feedback device that is closest to the future path of the robot. To test the effectiveness of the improved human–machine interface, two user studies were prepared and conducted. The first study aimed to quantitatively evaluate the ease of differentiating activation of individual tactors of the notification devices. The second user study aimed to assess the overall usability of the enhanced notification mode for improving human awareness about the planned trajectory of a robot. The results of the first experiment allowed to identify the tactors for which vibration intensity was most often confused by users. The results of the second experiment showed that the enhanced notification system allowed the participants to complete the task faster and, in general, improved user awareness of the robot’s movement plan, according to both objective and subjective data. Moreover, the majority of participants (82%) favoured the improved notification system over its previous non-directional version and vision-based inspection.
Physical Training In-Game Metrics for Cognitive Assessment: Evidence from Extended Trials with the Fitforall Exergaming Platform
Authors: Konstantinidis Evdokimos I. , Bamidis Panagiotis D. , Billis Antonis , Kartsidis Panagiotis , Petsani Despoina , Papageorgiou Sokratis G.
Published: September 2021

Conventional clinical cognitive assessment has its limitations, as evidenced by the environmental shortcomings of various neuropsychological tests conducted away from an older person’s everyday environment. Recent research activities have focused on transferring screening tests to computerized forms, as well as on developing short screening tests for screening large populations for cognitive impairment. The purpose of this study was to present an exergaming platform, which was widely trialed (116 participants) to collect in-game metrics (built-in game performance measures). The potential correlation between in-game metrics and cognition was investigated in-depth by scrutinizing different in-game metrics. The predictive value of high-resolution monitoring games was assessed by correlating it with classical neuropsychological tests; the area under the curve (AUC) in the receiver operating characteristic (ROC) analysis was calculated to determine the sensitivity and specificity of the method for detecting mild cognitive impairment (MCI). Classification accuracy was calculated to be 73.53% when distinguishing between MCI and normal subjects, and 70.69% when subjects with mild dementia were also involved. The results revealed evidence that careful design of serious games, with respect to in-game metrics, could potentially contribute to the early and unobtrusive detection of cognitive decline.
A Flexible Strain Sensor Based on Embedded Ionic Liquid
Authors: Zhang Huiyang , Lowe Andrew , Kalra Anubha , Yu Yang
Published: September 2021

We present a simple-structured strain sensor based on a low-cost ionic liquid. The ionic liquid was made of sodium chloride/propylene glycol solution and was embedded in a linear microfluidic channel fabricated using Ecoflex. The proposed sensor is capable of measuring strain up to 100% with excellent repeatability. The highest gauge factor is obtained as 6.19 under direct current excitation and 3.40 under alternating current excitation at 1 kHz. The sensor shows negligible hysteresis and overshoot, and survived 10,000 rapid stretch-release cycles of a 100% peak strain with a minor deviation in the response signal. The sensor can be mounted to different locations on the human body and suits a variety of applications in the field of motion detection, human–machine interface and healthcare monitoring.
Computer Game-Based Telerehabilitation Platform Targeting Manual Dexterity: Exercise Is Fun. “You Are Kidding—Right?”
Authors: Parmar Sanjay Tejraj , Kanitkar Anuprita , Sepehri Nariman , Bhairannawar Satish , Szturm Tony
Published: September 2021

There is a need for innovation to improve the engagement and accessibility of rehabilitation programs for children and adults with upper extremity motor impairments due to neurodevelopmental disorders, acquired brain injuries, or spinal cord injuries. For this purpose, a computer game-based telerehabilitation platform (GTP) was developed to address this need. Through the application of a miniature inertial-based computer mouse and the wide variety of commercial computer games, the developed GTP can provide engaging task-specific exercises for the rehabilitation of manual dexterity (object handling and manipulation). A purpose-built repetitive task practice software (RTP) was also developed to gather event data and synchronize it with patient movements during gameplays. This provides automated monitoring and quantification of patients’ motor skills, while they practice a range of game-based exercises with their hand and/or arm. The GTP would initially be used in a supervised clinical setting followed by a transition to function at home and be monitored by clinician specialists. Clinical support for home and rural communities, with protocols that can be easily updated, will help increase accessibility to targeted and personalized solutions for patients and achieve the desired training effect.
A Robust UWSN Handover Prediction System Using Ensemble Learning
Authors: Eldesouky Esraa , Bekhit Mahmoud , Fathalla Ahmed , Salah Ahmad , Ali Ahmed
Published: September 2021

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.
A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts
Authors: Wang Runqiong , Song Qinghua , Liu Zhanqiang , Ma Haifeng , Gupta Munish Kumar , Liu Zhaojun
Published: September 2021

Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.
New Sensor to Measure the Microencapsulated Active Compounds Released in an Aqueous Liquid Media Based in Dielectric Properties in Radiofrequency Range
Authors: Tomas-Egea Juan Angel , Fito Pedro J. , Colom Ricardo J. , Castro-Giraldez Marta
Published: September 2021

In recent years, the general and scientific interest in nutrition, digestion, and what role they play in our body has increased, and there is still much work to be carried out in the field of developing sensors and techniques that are capable of identifying and quantifying the chemical species involved in these processes. Iron deficiency is the most common and widespread nutritional disorder that mainly affects the health of children and women. Iron from the diet may be available as heme or organic iron, or as non-heme or inorganic iron. The absorption of non-heme iron requires its solubilization and reduction in the ferric state to ferrous that begins in the gastric acid environment, because iron in the ferric state is very poorly absorbable. There are chemical species with reducing capacity (antioxidants) that also have the ability to reduce iron, such as ascorbic acid. This paper aims to develop a sensor for measuring the release of encapsulated active compounds, in different media, based on dielectric properties measurement in the radio frequency range. An impedance sensor able to measure the release of microencapsulated active compounds was developed. The sensor was tested with calcium alginate beads encapsulating iron ions and ascorbic acid as active compounds. The prediction and measurement potential of this sensor was improved by developing a thermodynamic model that allows obtaining kinetic parameters that will allow suitable encapsulation design for subsequent release.
Emerging Optical Materials in Sensing and Discovery of Bioactive Compounds
Authors: Vaz Raquel , Valpradinhos Beatriz , Frasco Manuela F. , Sales Maria Goreti F.
Published: September 2021

Optical biosensors are used in numerous applications and analytical fields. Advances in these sensor platforms offer high sensitivity, selectivity, miniaturization, and real-time analysis, among many other advantages. Research into bioactive natural products serves both to protect against potentially dangerous toxic compounds and to promote pharmacological innovation in drug discovery, as these compounds have unique chemical compositions that may be characterized by greater safety and efficacy. However, conventional methods for detecting these biomolecules have drawbacks, as they are time-consuming and expensive. As an alternative, optical biosensors offer a faster, simpler, and less expensive means of detecting various biomolecules of clinical interest. In this review, an overview of recent developments in optical biosensors for the detection and monitoring of aquatic biotoxins to prevent public health risks is first provided. In addition, the advantages and applicability of these biosensors in the field of drug discovery, including high-throughput screening, are discussed. The contribution of the investigated technological advances in the timely and sensitive detection of biotoxins while deciphering the pathways to discover bioactive compounds with great health-promoting prospects is envisaged to meet the increasing demands of healthcare systems.
Two-Stage Calibration Scheme for Magnetic Measurement System on Guided Munition
Authors: Xue Yuyang , Zhang Xiaoming
Published: September 2021

In order to calibrate the magnetic measurement system used in guided munition on site, a two-stage calibration (TSC) scheme without reference is proposed in this paper. Analyzing the interfering magnetic field in the projectile and misalignment angles between the projectile coordinate system and measurement coordinate system establishes a proper mathematical equivalent model and derives a calibration method. The first stage is ellipsoid fitting to obtain the equivalent zero-offset, equivalent sensitivity and equivalent non-orthogonal angles of the sensor; the second stage is to calibrate the misalignment angles between the projectile coordinate system and the measurement coordinate system with the three-position calibration (TPC) method. Complete calibration is convenient to operate and does not need an additional reference, which has wide applicability. The simulation results show that the deviation in the measured value after compensation is within 100 nT. The experiment proves that the error of compensated magnetic value is about 150 nT, which meets the accuracy of requirements in guided munitions.
Towards a Modular On-Premise Approach for Data Sharing
Authors: Resende João S. , Magalhães Luís , Brandão André , Martins Rolando , Antunes Luís
Published: September 2021

The growing demand for everyday data insights drives the pursuit of more sophisticated infrastructures and artificial intelligence algorithms. When combined with the growing number of interconnected devices, this originates concerns about scalability and privacy. The main problem is that devices can detect the environment and generate large volumes of possibly identifiable data. Public cloud-based technologies have been proposed as a solution, due to their high availability and low entry costs. However, there are growing concerns regarding data privacy, especially with the introduction of the new General Data Protection Regulation, due to the inherent lack of control caused by using off-premise computational resources on which public cloud belongs. Users have no control over the data uploaded to such services as the cloud, which increases the uncontrolled distribution of information to third parties. This work aims to provide a modular approach that uses cloud-of-clouds to store persistent data and reduce upfront costs while allowing information to remain private and under users’ control. In addition to storage, this work also extends focus on usability modules that enable data sharing. Any user can securely share and analyze/compute the uploaded data using private computing without revealing private data. This private computation can be training machine learning (ML) models. To achieve this, we use a combination of state-of-the-art technologies, such as MultiParty Computation (MPC) and K-anonymization to produce a complete system with intrinsic privacy properties.
A Preliminary Investigation of the Effects of Obstacle Negotiation and Turning on Gait Variability in Adults with Multiple Sclerosis
Authors: Weed Lara , Little Casey , Kasser Susan L. , McGinnis Ryan S.
Published: September 2021

Many falls in persons with multiple sclerosis (PwMS) occur during daily activities such as negotiating obstacles or changing direction. While increased gait variability is a robust biomarker of fall risk in PwMS, gait variability in more ecologically related tasks is unclear. Here, the effects of turning and negotiating an obstacle on gait variability in PwMS were investigated. PwMS and matched healthy controls were instrumented with inertial measurement units on the feet, lumbar, and torso. Subjects completed a walk and turn (WT) with and without an obstacle crossing (OW). Each task was partitioned into pre-turn, post-turn, pre-obstacle, and post-obstacle phases for analysis. Spatial and temporal gait measures and measures of trunk rotation were captured for each phase of each task. In the WT condition, PwMS demonstrated significantly more variability in lumbar and trunk yaw range of motion and rate, lateral foot deviation, cadence, and step time after turning than before. In the OW condition, PwMS demonstrated significantly more variability in both spatial and temporal gait parameters in obstacle approach after turning compared to before turning. No significant differences in gait variability were observed after negotiating an obstacle, regardless of turning or not. Results suggest that the context of gait variability measurement is important. The increased number of variables impacted from turning and the influence of turning on obstacle negotiation suggest that varying tasks must be considered together rather than in isolation to obtain an informed understanding of gait variability that more closely resembles everyday walking.
A CNN Deep Local and Global ASD Classification Approach with Continuous Wavelet Transform Using Task-Based FMRI
Authors: Haweel Reem , Seada Noha , Ghoniemy Said , Alghamdi Norah Saleh , El-Baz Ayman
Published: September 2021

Autism spectrum disorder (ASD) is a neurodegenerative disorder characterized by lingual and social disabilities. The autism diagnostic observation schedule is the current gold standard for ASD diagnosis. Developing objective computer aided technologies for ASD diagnosis with the utilization of brain imaging modalities and machine learning is one of main tracks in current studies to understand autism. Task-based fMRI demonstrates the functional activation in the brain by measuring blood oxygen level-dependent (BOLD) variations in response to certain tasks. It is believed to hold discriminant features for autism. A novel computer aided diagnosis (CAD) framework is proposed to classify 50 ASD and 50 typically developed toddlers with the adoption of CNN deep networks. The CAD system includes both local and global diagnosis in a response to speech task. Spatial dimensionality reduction with region of interest selection and clustering has been utilized. In addition, the proposed framework performs discriminant feature extraction with continuous wavelet transform. Local diagnosis on cingulate gyri, superior temporal gyrus, primary auditory cortex and angular gyrus achieves accuracies ranging between 71% and 80% with a four-fold cross validation technique. The fused global diagnosis achieves an accuracy of 86% with 82% sensitivity, 92% specificity. A brain map indicating ASD severity level for each brain area is created, which contributes to personalized diagnosis and treatment plans.