bluuebunny/crossref_metadata_2025 huggingface dataset:
Assimilation of Deep Learning and Machine Learning Schemes into a Remote Sensing-Incorporated Crop Model to Simulate Barley and Wheat Productivities
Authors: Shin Taehwan , Ko Jonghan , Jeong Seungtaek , Kang Jiwoo , Lee Kyungdo , Shim Sangin
Published: October 2022

Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches. We investigated the most appropriate ML regressors for exploring LAI estimations of barley and wheat through the relationships between the LAI values and four vegetation indices. After analysing ten ML regression models, we concluded that the gradient boost (GB) regressor most effectively estimated the LAI for both barley and wheat. Furthermore, the GB regressor outperformed the DNN regressor, with model efficiencies of 0.89 for barley and 0.45 for wheat. Additionally, we verified that it would be possible to simulate LAI using proximal and remote sensing data based on assimilating the DNN and ML regressors into a process-based mathematical crop model. In summary, we have demonstrated that if DNN and ML schemes are integrated into a crop model, they can facilitate crop growth and boost productivity monitoring.
Remote Sensing Imaging as a Tool to Support Mulberry Cultivation for Silk Production
Authors: Giora Domenico , Assirelli Alberto , Cappellozza Silvia , Sartori Luigi , Saviane Alessio , Marinello Francesco , Martínez-Casasnovas José A.
Published: October 2022

In recent decades there has been an increasing use of remotely sensed data for precision agricultural purposes. Sericulture, the activity of rearing silkworm (Bombyx mori L.) larvae to produce silk in the form of cocoons, is an agricultural practice that has rarely used remote sensing techniques but that could benefit from them. The aim of this work was to investigate the possibility of using satellite imaging in order to monitor leaf harvesting in mulberry (Morus alba L.) plants cultivated for feeding silkworms; additionally, quantitative parameters on silk cocoon production were related to the analyses on vegetation indices. Adopting PlanetScope satellite images, four M. alba fields were monitored from the beginning of the silkworm rearing season until its end in 2020 and 2021. The results of our work showed that a decrease in the multispectral vegetation indices in the mulberry plots due to leaf harvesting was correlated with the different parameters of silk cocoons spun by silkworm larvae; in particular, a decrease in the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) had high correlations with quantitative silk cocoon production parameters (R2 values up to 0.56, p < 0.05). These results led us to the conclusion that precision agriculture can improve sericultural practice, offering interesting solutions for estimating the quantity of produced silk cocoons through the remote analysis of mulberry fields.
VirtuaLot—A Case Study on Combining UAS Imagery and Terrestrial Video with Photogrammetry and Deep Learning to Track Vehicle Movement in Parking Lots
Authors: Koskowich Bradley , Starek Michael , King Scott A.
Published: October 2022

This study investigates the feasibility of applying monoplotting to video data from a security camera and image data from an uncrewed aircraft system (UAS) survey to create a mapping product which overlays traffic flow in a university parking lot onto an aerial orthomosaic. The framework, titled VirtuaLot, employs a previously defined computer-vision pipeline which leverages Darknet for vehicle detection and tests the performance of various object tracking algorithms. Algorithmic object tracking is sensitive to occlusion, and monoplotting is applied in a novel way to efficiently extract occluding features from the video using a digital surface model (DSM) derived from the UAS survey. The security camera is also a low fidelity model not intended for photogrammetry with unstable interior parameters. As monoplotting relies on static camera parameters, this creates a challenging environment for testing its effectiveness. Preliminary results indicate that it is possible to manually monoplot between aerial and perspective views with high degrees of transition tilt, achieving coordinate transformations between viewpoints within one deviation of vehicle short and long axis measurements throughout 70.5% and 99.6% of the study area, respectively. Attempted automation of monoplotting on video was met with limited success, though this study offers insight as to why and directions for future work on the subject.
Frequency Spectrum Intensity Attention Network for Building Detection from High-Resolution Imagery
Authors: Feng Dan , Chu Hongyun , Zheng Ling
Published: October 2022

Computational intelligence techniques have been widely used for automatic building detection from high-resolution remote sensing imagery and especially the methods based on neural networks. However, existing methods do not pay attention to the value of high-frequency and low-frequency information in the frequency domain for feature extraction of buildings in remote sensing images. To overcome these limitations, this paper proposes a frequency spectrum intensity attention network (FSIANet) with an encoder–decoder structure for automatic building detection. The proposed FSIANet mainly involves two innovations. One, a novel and plug-and-play frequency spectrum intensity attention (FSIA) mechanism is devised to enhance feature representation by evaluating the informative abundance of the feature maps. The FSIA is deployed after each convolutional block in the proposed FSIANet. Two, an atrous frequency spectrum attention pyramid (AFSAP) is constructed by introducing FSIA in widely used atrous spatial pyramid pooling. The AFSAP is able to select the features with high response to building semantic features at each scale and weaken the features with low response, thus enhancing the feature representation of buildings. The proposed FSIANet is evaluated on two large public datasets (East Asia and Inria Aerial Image Dataset), which demonstrates that the proposed method can achieve the state-of-the-art performance in terms of F1-score and intersection-over-union.
A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine
Authors: Guo Linghui , Zhao Sha , Gao Jiangbo , Zhang Hebing , Zou Youfeng , Xiao Xiangming
Published: October 2022

High-resolution crop type mapping is of importance for site-specific agricultural management and food security in smallholder farming regions, but is challenging due to limited data availability and the need for image-based algorithms. In this paper, we developed an efficient object- and pixel-based mapping algorithm to generate a 10 m resolution crop type map over large spatial domains by integrating time series optical images (Sentinel-2) and synthetic aperture radar (SAR) images (Sentinel-1) using the Google Earth Engine (GEE) platform. The results showed that the proposed method was reliable for crop type mapping in the study area with an overall accuracy (OA) of 93.22% and a kappa coefficient (KC) of 0.89. Through experiments, we also found that the monthly median values of the vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH) bands were insensitive to crop type mapping itself, but adding this information to supplement the optical images improved the classification accuracy, with an OA increase of 0.09–2.98%. Adding the slope of vegetation index change (VIslope) at the critical period to crop type classification was obviously better than that of relative change ratio of vegetation index (VIratio), both of which could make an OA improvement of 2.58%. These findings not only highlighted the potential of the VIslope and VIratio indices during the critical period for crop type mapping in small plots, but suggested that SAR images could be included to supplement optical images for crop type classification.
GPS-Derived Slant Water Vapor for Cloud Monitoring in Singapore
Authors: Heh Ding Yu , Lee Yee Hui , Biswas Anik Naha , Koh Liang Mong
Published: October 2022

This paper presents a GPS-derived slant water vapor technique for cloud monitoring in Singapore. The normalized slant wet delay (SWD) and slant water vapor (SWV) are introduced. The suitability of the normalized SWV over SWV for cloud monitoring is demonstrated, as it is not very sensitive to the satellite elevation angle. For better illustration and representation of the spatial distribution of the normalized SWV, the skyplot is discretized into different cells based on the azimuth and elevation angles to produce the spatial plot. The spatial plots are analyzed for cloud monitoring and compared alongside the sky images. The results show that the spatial plots of normalized SWV are generally consistent with the cloud formation observed in the sky images, hence demonstrating their usefulness for cloud monitoring. The probability distribution of the normalized SWV associated with cloudy and clear sky conditions is also analyzed, which shows that the mean values of normalized SWV associated with the former are higher. Finally, the time series of the normalized SWV is explored in relation to the solar irradiance. It is shown that the time series and spatial plots of normalized SWV are also consistent with the ratio of clear sky to measured irradiance.
Differences in Urban Morphology between 77 Cities in China and Europe
Authors: Guo Fengxiang , Schlink Uwe , Wu Wanben , Mohamdeen Abdelrhman
Published: October 2022

Urban morphology refers to the physical form of a city that is constantly transformed and updated in the process of urbanization. A valuable source of data on ‘built forms’ is modern remote sensing technology, which provides a variety of products on building footprints and heights at national, continental, and global levels. A large-scale comparison of urban morphologies is important for assessing urban development as well as its influence on urban ecology; however, this has not been well documented so far. This study includes 41 cities in China and 36 in Europe with various city sizes, population densities, and climate features. We applied 3D landscape metrics and principal component analysis (PCA) to compare the spatial aspects of the urban morphology of these cities. We found: (1) measurements of the building height, surface fluctuation, and texture directionality of urban building layouts in China are higher than those of European cities, while the latter are high-density and compact built landscapes; (2) a significant clustering phenomenon for Chinese and European cities revealed by PCA, with the former showing a much more aggregated pattern, indicating a relatively uniform morphology of urban buildings in China; (3) distinctions between cities in China and Europe are suggested by the first principal component, to which building height, surface fluctuation, building complexity, and spatial distance among buildings contribute significantly; and (4) the second principal component (mainly represented by maximum building height, surface area, volume, and shape metrics) can separate large metropolitan cities and provincial capitals from cities with lower urban population, smaller size, and slower economic development. Our results demonstrate the potential of 3D landscape metrics for measuring urban morphology. Together with a temporal analysis, these metrics are useful for quantifying how urban morphology varies in space and time on a large scale, as well as evaluating the process of urbanization.
Satellite-Observed Four-Dimensional Spatiotemporal Characteristics of Maritime Aerosol Types over the Coastal Waters of the Guangdong–Hong Kong–Macao Greater Bay Area and the Northern South China Sea
Authors: Ma Qihan , Liu Yingying , Qiu Ting , Huang Tingxuan , Deng Tao , Hu Zhiyuan , Cui Tingwei
Published: October 2022

Aerosol is important to climate and air pollution, and different aerosol types have a non-negligible impact on the environment and climate system. Based on long-term satellite lidar profiles from 2006 to 2020, the four-dimensional (x-y-z-t) spatiotemporal characteristics of different aerosol types, including clean marine (CM), dust (DU), polluted continental/smoke (PC), clean continental (CC), polluted dust (PD), elevated smoke (ES), and dusty marine (DM), over the coastal waters of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) were revealed for the first time and compared to the surrounding northern South China Sea (NSCS). (1) The dominant aerosol types in both study areas were found to be CM, ES, and DM, whose proportions summed up to more than 85%. In spring, ES was the dominant aerosol type (>40%); in other seasons, CM dominated (>34%). The proportions of anthropogenic aerosols (PC, PD, and ES) and dust-related aerosols (DU, PD, and DM) were higher in spring and winter than in summer and autumn. (2) Vertically, the number of all aerosol types declined with increasing altitude, with the exception of abnormal increase at the heights of approximately 1.5–2.8 km in spring, which was probably attributed to the effect of local and regional anthropogenic pollutants. Below the height of 2 km, the main aerosol types were CM and DM, whereas ES, PD, and DU aerosols were dominant above 2 km. (3) Horizontally, the dominant aerosol types were spatially uniform in the lower atmosphere (<2 km), while higher altitudes (especially > 4 km) showed significant horizontal heterogeneity in space. The proportion of anthropogenic aerosols over the coastal waters of the GBA was higher than that over the NSCS, due to terrestrial pollution transportation. (4) In terms of the long-term trend, the proportion of CM aerosols was found to be steadily increasing, with the anthropogenic aerosols and dust-related aerosols showing a fluctuating and decreasing trend, which resulted from the enforcement of effective air pollution control policies. Overall, the terrestrial aerosol influence tended to decrease in the study areas. The insight into aerosol types and its variation will facilitate the understanding of the aerosol climate effects and pollutant control in the coastal waters of the GBA and the NSCS.
Comparative Analysis between Sea Surface Salinity Derived from SMOS Satellite Retrievals and in Situ Measurements
Authors: Wang Haodi , Han Kaifeng , Bao Senliang , Chen Wen , Ren Kaijun
Published: October 2022

Validating Sea Surface Salinity (SSS) data has become a key component of the Soil Moisture Ocean Salinity (SMOS) satellite mission. In this study, the gridded SMOS SSS products are compared with in situ SSS data from analyzed products, a ship-based thermosalinograph and a tropical moored buoy array. The comparison was conducted at different spatial and temporal scales. A regional comparison in the Baltic Sea shows that SMOS slightly underestimates the mean SSS values. The influence of river discharge overrides the temperature in the Baltic Sea, bringing larger biases near river mouths in warm seasons. The global comparison with two Optimal Interpolated (OI) gridded in situ products shows consistent large-scale structures. Excluding regions with large SSS biases, the mean ΔSSS between monthly gridded SMOS data and OI in situ data is −0.01 PSU in most open sea areas between 60°S and 60°N, with a mean Root Mean Square Deviation (RMSD) of 0.2 PSU and a mean correlation coefficient of 0.50. An interannual tendency of mean ΔSSS shifting from negative to positive between satellite SSS and in situ SSS has been identified in tropical to mid-latitude seas, especially across the tropical eastern Pacific Ocean. A comparison with collocated buoy salinity shows that on weekly and interannual scales, the SMOS Level 3 (L3) product well captures the SSS variations at the locations of tropical moored buoy arrays and shows similar performance with in situ gridded products. Excluding suspicious buoys, the synergetic analysis of SMOS, SMAP and gridded in situ products is capable of identifying the erroneous data, implying that satellite SSS has the potential to act as a real-time 27 Quality Control (QC) for buoy data.
A Comparison of Analytical Approaches for the Spectral Discrimination and Characterisation of Mite Infestations on Banana Plants
Authors: Aeberli Aaron , Robson Andrew , Phinn Stuart , Lamb David W. , Johansen Kasper
Published: October 2022

This research investigates the capability of field-based spectroscopy (350–2500 nm) for discriminating banana plants (Cavendish subgroup Williams) infested with spider mites from those unaffected. Spider mites are considered a major threat to agricultural production, as they occur on over 1000 plant species, including banana plant varieties. Plants were grown under a controlled glasshouse environment to remove any influence other than the imposed treatment (presence or absence of spider mites). The spectroradiometer measurements were undertaken with a leaf clip over three infestation events. From the resultant spectral data, various classification models were evaluated including partial least squares discriminant analysis (PLSDA), K-nearest neighbour, support vector machines and back propagation neural network. Wavelengths found to have a significant response to the presence of spider mites were extracted using competitive adaptive reweighted sampling (CARS), sub-window permutation analysis (SPA) and random frog (RF) and benchmarked using the classification models. CARS and SPA provided high detection success (86% prediction accuracy), with the wavelengths found to be significant corresponding with the red edge and near-infrared portions of the spectrum. As there is limited access to operational commercial hyperspectral imaging and additional complexity, a multispectral camera (Sequoia) was assessed for detecting spider mite impacts on banana plants. Simulated multispectral bands were able to provide a high level of detection accuracy (prediction accuracy of 82%) based on a PLSDA model, with the near-infrared band being most important, followed by the red edge, green and red bands. Multispectral vegetation indices were trialled using a simple threshold-based classification method using the green normalised difference vegetation index (GNDVI), which achieved 82% accuracy. This investigation determined that remote sensing approaches can provide an accurate method of detecting mite infestations, with multispectral sensors having the potential to provide a more commercially accessible means of detecting outbreaks.
Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin
Authors: Zhou Zhilan , Fan Lei , De Lannoy Gabrielle , Liu Xiangzhuo , Peng Jian , Bai Xiaojing , Frappart Frédéric , Baghdadi Nicolas , Xing Zanpin , Li Xiaojun , Ma Mingguo , Li Xin , Che Tao , Geng Liying , Wigneron Jean-Pierre
Published: October 2022

Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland.
Block-Adaptive Rényi Entropy-Based Denoising for Non-Stationary Signals
Authors: Saulig Nicoletta , Lerga Jonatan , Miličić Siniša , Tomasović Željka
Published: October 2022

This paper approaches the problem of signal denoising in time-variable noise conditions. Non-stationary noise results in variable degradation of the signal’s useful information content over time. In order to maximize the correct recovery of the useful part of the signal, this paper proposes a denoising method that uses a criterion based on amplitude segmentation and local Rényi entropy estimation which are limited over short time blocks of the signal spectrogram. Local estimation of the signal features reduces the denoising problem to the stationary noise case. Results, presented for synthetic and real data, show consistently better performance gained by the proposed adaptive method compared to denoising driven by global criteria.
MixNN: A Design for Protecting Deep Learning Models
Authors: Liu Chao , Chen Hao , Wu Yusen , Jin Rui
Published: October 2022

In this paper, we propose a novel design, called MixNN, for protecting deep learning model structure and parameters since the model consists of several layers and each layer contains its own structure and parameters. The layers in a deep learning model of MixNN are fully decentralized. It hides communication address, layer parameters and operations, and forward as well as backward message flows among non-adjacent layers using the ideas from mix networks. MixNN has the following advantages: (i) an adversary cannot fully control all layers of a model, including the structure and parameters; (ii) even some layers may collude but they cannot tamper with other honest layers; (iii) model privacy is preserved in the training phase. We provide detailed descriptions for deployment. In one classification experiment, we compared a neural network deployed in a virtual machine with the same one using the MixNN design on the AWS EC2. The result shows that our MixNN retains less than 0.001 difference in terms of classification accuracy, while the whole running time of MixNN is about 7.5 times slower than the one running on a single virtual machine.
Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data
Authors: Diaz Claudio , Caillaud Corinne , Yacef Kalina
Published: October 2022

Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant’s needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively.
Construction of Structured Random Measurement Matrices in Semi-Tensor Product Compressed Sensing Based on Combinatorial Designs
Authors: Liang Junying , Peng Haipeng , Li Lixiang , Tong Fenghua
Published: October 2022

A random matrix needs large storage space and is difficult to be implemented in hardware, and a deterministic matrix has large reconstruction error. Aiming at these shortcomings, the objective of this paper is to find an effective method to balance these performances. Combining the advantages of the incidence matrix of combinatorial designs and a random matrix, this paper constructs a structured random matrix by the embedding operation of two seed matrices in which one is the incidence matrix of combinatorial designs, and the other is obtained by Gram–Schmidt orthonormalization of the random matrix. Meanwhile, we provide a new model that applies the structured random matrices to semi-tensor product compressed sensing. Finally, compared with the reconstruction effect of several famous matrices, our matrices are more suitable for the reconstruction of one-dimensional signals and two-dimensional images by experimental methods.
DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data
Authors: Lee Jungjae , Kim Wooseong
Published: October 2022

Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.
Class-Aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset
Authors: Alaba Simegnew Yihunie , Nabi M M , Shah Chiranjibi , Prior Jack , Campbell Matthew D. , Wallace Farron , Ball John E. , Moorhead Robert
Published: October 2022

Fish species recognition is crucial to identifying the abundance of fish species in a specific area, controlling production management, and monitoring the ecosystem, especially identifying the endangered species, which makes accurate fish species recognition essential. In this work, the fish species recognition problem is formulated as an object detection model to handle multiple fish in a single image, which is challenging to classify using a simple classification network. The proposed model consists of MobileNetv3-large and VGG16 backbone networks and an SSD detection head. Moreover, a class-aware loss function is proposed to solve the class imbalance problem of our dataset. The class-aware loss takes the number of instances in each species into account and gives more weight to those species with a smaller number of instances. This loss function can be applied to any classification or object detection task with an imbalanced dataset. The experimental result on the large-scale reef fish dataset, SEAMAPD21, shows that the class-aware loss improves the model over the original loss by up to 79.7%. The experimental result on the Pascal VOC dataset also shows the model outperforms the original SSD object detection model.
Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors
Authors: Miklusis Donatas , Markevicius Vytautas , Navikas Dangirutis , Ambraziunas Mantas , Cepenas Mindaugas , Valinevicius Algimantas , Zilys Mindaugas , Okarma Krzysztof , Cuinas Inigo , Andriukaitis Darius
Published: October 2022

Magnetic field sensors installed in the road infrastructure can be used for autonomous traffic flow parametrization. Although the main goal of such a measuring system is the recognition of the class of vehicle and classification, velocity is the essential parameter for further calculation and it must be estimated with high reliability. In-field test campaigns, during actual traffic conditions, showed that commonly accepted velocity estimation methods occasionally produce highly erroneous results. For anomaly detection, we propose a criterion and two different correction algorithms. Non-linear signal rescaling and time-based segmentation algorithms are presented and compared for faulty result mitigation. The first one consists of suppressing the highly distorted signal peaks and looking for the best match with cross-correlation. The second approach relies on signals segmentation according to the feature points and multiple cross-correlation comparisons. The proposed two algorithms are evaluated with a dataset of over 300 magnetic signatures of a vehicle from unconstraint traffic conditions. Results show that the proposed criteria highlight all greatly faulty results and that the correction algorithms reduce the maximum error by twofold, but due to the increased mean error, mitigation technics shall be used explicitly with distorted signals.
Indicators and Instruments to Assess Components of Disability in Community-Dwelling Older Adults: A Systematic Review
Authors: Moreira Juliana Santos , Melo Ana , Santos Rubim , Sousa Andreia S. P.
Published: October 2022

The epidemiological demands of aging point to the need for characterizing older adults regarding health and disability. This systematic review aims to summarize the indicators (instruments) identifying different components of disability as a result of aging exposition in community-dwelling older adults, considering the International Classification of Functioning, Disability, and Health framework. Taking the PRISMA 2020 recommendations as a reference, studies with community-dwelling older adults, reporting the development and/or age disability modifications were included. Two reviewers analyzed the observational studies searched in the MEDLINE, CINAHL, Web of Science, Scopus, and Embase databases. Of the 137 potentially eligible studies, 49 were included in this review. Several indicators (instruments) demonstrated older adults’ disabilities according to the different domains of the ICF. Objective measures assessed Body Structures, Body Functions, and Environmental Factors and included handgrip strength (dynamometry, n = 8), cognitive function (Mini-Mental State examination, n = 7), gait speed (walk test, n = 6), and endurance (Chair stand-test, n = 4). Self-reported measures assessed Activities and Participation, but not the Body Structures, and included the basic and instrumental activities of daily living (ADL) (the Katz Index of ADL, n = 4 studies, the Lawton and Brody Instrumental ADL, n = 4 studies). The summary of the measures gathered can guide researchers and health professionals to select indicators (instruments) to assess and monitor older adults’ disabilities resulting from aging exposition, to support the development of new wearables, and to provide improvements to the existing ones, allowing the tailored assessment of different health and disability dimensions.
IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security
Authors: Na Dongjun , Park Sejin
Published: October 2022

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.
Wearable Inertial Sensor Approach for Postural Adjustment Assessments during Predictable Perturbations in Sport
Authors: Duarte Manuela Brito , da Costa Moraes Anderson Antunes , Ferreira Eduardo Veloso , da Silva Almeida Gizele Cristina , Cabral André dos Santos , de Athayde Costa e Silva Anselmo , Garcez Daniela Rosa , da Silva Souza Givago , Callegari Bianca
Published: October 2022

Introduction: Evidence supports the importance of efficient postural control to improve performance in sports. This involves the use of strategies such as anticipatory posture adjustments and compensatory adjustments. Technology makes analysis and assessments in sports cheaper, while being valid and reliable compared to the gold-standard assessment equipment. Objectives: This article aimed to test the validity and reliability of signals extracted from the sensor’s accelerometer (Metamotion C), by comparing it to the data obtained from the gold-standard equipment (a three-dimensional video-motion-capture system). Design: Observational, cross-sectional study. Methods: We exposed 20 healthy young standing people to the pendulum impact paradigm, which consisted of predictable anteroposterior disturbances applied at the shoulder level. In order to measure this, we observed the acceleration of the center of mass in the anticipatory and compensatory phase of the disturbance and compared the signals of the two devices (Metamotion C and a motion-capture system). Results: The validation results showed the significant linear correlation of all variables with a moderate to large correlation of r ≥ 0.5 between the devices. In contrast, the reliability results between sessions obtained by filming were all significant and above 0.75, indicating excellent reliability. The APAonset variable had a reasonable to high intra-class correlation in the anticipatory phase. In the compensatory phase, the CPAtime variable showed an excellent correlation. Conclusions: Metamotion C proved reasonably valid and highly reliable in measuring the center of mass acceleration compared to the camera system in both the anticipatory and compensatory phases.
Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller
Authors: Sharif Hajar , Eslaminia Ahmadreza , Chembrammel Pramod , Kesavadas Thenkurussi
Published: October 2022

Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved.
A Hierarchical Spatial–Temporal Cross-Attention Scheme for Video Summarization Using Contrastive Learning
Authors: Teng Xiaoyu , Gui Xiaolin , Xu Pan , Tong Jianglei , An Jian , Liu Yang , Jiang Huilan
Published: October 2022

Video summarization (VS) is a widely used technique for facilitating the effective reading, fast comprehension, and effective retrieval of video content. Certain properties of the new video data, such as a lack of prominent emphasis and a fuzzy theme development border, disturb the original thinking mode based on video feature information. Moreover, it introduces new challenges to the extraction of video depth and breadth features. In addition, the diversity of user requirements creates additional complications for more accurate keyframe screening issues. To overcome these challenges, this paper proposes a hierarchical spatial–temporal cross-attention scheme for video summarization based on comparative learning. Graph attention networks (GAT) and the multi-head convolutional attention cell are used to extract local and depth features, while the GAT-adjusted bidirection ConvLSTM (DB-ConvLSTM) is used to extract global and breadth features. Furthermore, a spatial–temporal cross-attention-based ConvLSTM is developed for merging hierarchical characteristics and achieving more accurate screening in similar keyframes clusters. Verification experiments and comparative analysis demonstrate that our method outperforms state-of-the-art methods.
Automatic Measurement of External Thread at the End of Sucker Rod Based on Machine Vision
Authors: Li Xianyou , Wang Shun , Xu Ke
Published: October 2022

Aiming at the low efficiency of manual measurement of threads and the lack of practicability in machine vision measurement before, online size measurement of threads at the end of sucker rods based on machine vision was studied. A robotic arm is used to carry an optical device to achieve high-quality image acquisition of threads. Based on the prior knowledge of the thread profile angle, the directional edge detection operator is customized to achieve the accurate detection of the left and right edges of the thread. Noise filtering, sorting, and left and right edge-matching algorithms based on connected domains are developed to eliminate the interference effects of electrostatic dust and oil pollution in online measurement, and the dimension of thread profile angles, pitches, major diameters, and minor diameters can be precisely calculated. The experimental results show that the screw thread parameter measurement time is about 0.13 s; the maximum and minimum average errors of the thread angles are 0.011° and 0.632°, respectively; and the total average deviation is less than 0.08°. For the screw thread pitch, major diameter, minor diameter, and pitch diameter parameter measurement, the deviation of the measurement results between the proposed method and the universal tool microscope (UTM) method is less than 10 μm. It fully proves the effectiveness and accuracy of the method in this paper and, at the same time, shows that the method has good real-time performance and high application significance, which lays a good foundation for the subsequent online thread measurement.
Wireless Body Area Sensor Networks: Survey of MAC and Routing Protocols for Patient Monitoring under IEEE 802.15.4 and IEEE 802.15.6
Authors: Akbar Muhammad Sajjad , Hussain Zawar , Sheng Michael , Shankaran Rajan
Published: October 2022

Wireless body area sensor networks (WBASNs) have received growing attention from industry and academia due to their exceptional potential for patient monitoring systems that are equipped with low-power wearable and implantable biomedical sensors under communications standards such as IEEE 802.15.4-2015 and IEEE 802.15.6-2012. The goal of WBASNs is to enhance the capabilities of wireless patient monitoring systems in terms of data accuracy, reliability, routing, channel access, and the data communication of sensors within, on and around the human body. The huge scope of challenges related to WBASNs has led to various research publications and industrial experiments. In this paper, a survey is conducted for the recent state-of-art in the context of medium access control (MAC) and routing protocols by considering the application requirements of patient monitoring systems. Moreover, we discuss the open issues, lessons learned, and challenges for these layers to provide a source of motivation for the upcoming design and development in the domain of WBASNs. This survey will be highly useful for the 6th generation (6G) networks; it is expected that 6G will provide efficient and ubiquitous connectivity to a huge number of IoT devices, and most of them will be sensor-based. This survey will further clarify the QoS requirement part of the 6G networks in terms of sensor-based IoT.
Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System
Authors: Zubair Mohammed , Ghubaish Ali , Unal Devrim , Al-Ali Abdulla , Reimann Thomas , Alinier Guillaume , Hammoudeh Mohammad , Qadir Junaid
Published: October 2022

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores.
Multi-Strategy Learning Boosted Colony Predation Algorithm for Photovoltaic Model Parameter Identification
Authors: Wang Mingjing , Chen Long , Chen Huiling
Published: October 2022

Modeling solar systems necessitates the effective identification of unknown and variable photovoltaic parameters. To efficiently convert solar energy into electricity, these parameters must be precise. The research introduces the multi-strategy learning boosted colony predation algorithm (MLCPA) for optimizing photovoltaic parameters and boosting the efficiency of solar power conversion. In MLCPA, opposition-based learning can be used to investigate each individual’s opposing position, thereby accelerating convergence and preserving population diversity. Level-based learning categorizes individuals according to their fitness levels and treats them differently, allowing for a more optimal balance between variation and intensity during optimization. On a variety of benchmark functions, the MLCPA’s performance is compared to the performance of the best algorithms currently in use. On a variety of benchmark functions, the MLCPA’s performance is compared to that of existing methods. MLCPA is then used to estimate the parameters of the single, double, and photovoltaic modules. Last but not least, the stability of the proposed MLCPA algorithm is evaluated on the datasheets of many manufacturers at varying temperatures and irradiance levels. Statistics have demonstrated that the MLCPA is more precise and dependable in predicting photovoltaic mode critical parameters, making it a viable tool for solar system parameter identification issues.
A Multi-Tier Trust-Based Security Mechanism for Vehicular Ad-Hoc Network Communications
Authors: Akwirry Brian , Bessis Nik , Malik Hassan , McHale Sarah
Published: October 2022

Securing communications in vehicle ad hoc networks is crucial for operations. Messages exchanged in vehicle ad hoc network communications hold critical information such as road safety information, or road accident information and it is essential these packets reach their intended destination without any modification. A significant concern for vehicle ad hoc network communications is that malicious vehicles can intercept or modify messages before reaching their intended destination. This can hamper vehicle ad hoc network operations and create safety concerns. The multi-tier trust management system proposed in this paper addresses the concern of malicious vehicles in the vehicle ad hoc network using three security tiers. The first tier of the proposed system assigns vehicles in the vehicle ad hoc network a trust value based on behaviour such as processing delay, packet loss and prior vehicle behavioural history. This will be done by selecting vehicles as watchdogs to observe the behaviour of neighbouring vehicles and evaluate the trust value. The second tier is to protect the watchdogs, which is done by watchdogs’ behaviour history. The third security tier is to protect the integrity of data used for trust value calculation. Results show that the proposed system is successful in identifying malicious vehicles in the VANET. It also improves the packet delivery ratio and end-to-end delay of the vehicle ad hoc network in the presence of malicious vehicles.
rSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks
Authors: Xiong Xiaoxiong , Dong Chao , Niu Kai
Published: October 2022

In this paper, a new system entropy measure is used to optimize the routing algorithm in power consumption. We introduce the system entropy measure into the problem of industrial wireless sensor networks (iWSNs) routing and propose a high-performance routing algorithm guided by the system entropy measure (rSEM). Based on the cluster iWSNs architecture, the rSEM selects the cluster heads and cluster member nodes successively, according to the system entropy measure, and constructs the iWSNs with the minimum system entropy. The method of the cluster head selection is traversal, while the method of the cluster member selection is a greedy algorithm to reduce the complexity. The experiments show that the power consumption of the iWSNs generated by the rSEM is in the same order of magnitude as that of Dijkstra in both 2D and 3D scenarios. In addition, the delay of the rSEM is slightly higher than that of LEACH. Therefore, the rSEM is suitable for networks that are sensitive to both the delay and power consumption. The rSEM puts forward a new idea for the design of routing for the next-generation iWSNs, which improves the overall network performance according to the network topology, instead of relying on the power consumption or delay performance only.