bluuebunny/crossref_metadata_2025 huggingface dataset:
Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters
Authors: Román Alejandro , Tovar-Sánchez Antonio , Gauci Adam , Deidun Alan , Caballero Isabel , Colica Emanuele , D’Amico Sebastiano , Navarro Gabriel
Published: January 2023

Remote-sensing ocean colour studies have already been used to determine coastal water quality, coastal biodiversity, and nutrient availability. In recent years, Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors, originally designed for agriculture applications, have also enabled water-quality studies of coastal waters. However, since the sea surface is constantly changing, commonly used photogrammetric methods fail when applied to UAV images captured over water areas. In this work, we evaluate the applicability of a five-band multispectral sensor mounted on a UAV to derive scientifically valuable water parameters such as chlorophyll-a (Chl-a) concentration and total suspended solids (TSS), including a new Python workflow for the manual generation of an orthomosaic in aquatic areas exclusively based on the sensor’s metadata. We show water-quality details in two different sites along the Maltese coastline on the centimetre-scale, improving the existing approximations that are available for the region through Sentinel-3 OLCI imagery at a much lower spatial resolution of 300 m. The Chl-a and TSS values derived for the studied regions were within the expected ranges and varied between 0 to 3 mg/m3 and 10 to 20 mg/m3, respectively. Spectral comparisons were also carried out along with some statistics calculations such as RMSE, MAE, or bias in order to validate the obtained results.
Integrated Geophysical Imaging and Remote Sensing for Enhancing Geological Interpretation of Landslides with Uncertainty Estimation—A Case Study from Cisiec, Poland
Authors: Wróbel Małgorzata , Stan-Kłeczek Iwona , Marciniak Artur , Majdański Mariusz , Kowalczyk Sebastian , Nawrot Adam , Cader Justyna
Published: January 2023

Landslides, as one of the main problems in mountainous areas, are a challenging issue for modern geophysics. The triggers that cause these phenomena are diverse (including geological, geomorphological, and hydrological conditions, climatic factors, and earthquakes) and can occur in conjunction with each other. Human activity is also relevant, undoubtedly contributing to the intensification of landslide phenomena. One of these is the production of artificial snow on ski slopes. This paper presents a multimethod approach for imaging the landslide structure in Cisiec, in southwestern Poland, where such a situation occurs. In the presented work, the integration of remote sensing with multi-method geophysical imaging was used to visualize landslide zones, and to estimate ground motion. To verify the uncertainty of the obtained data, the combination of electrical resistivity tomography (ERT), multi-channel analysis of surface waves (MASW), and seismic refraction method (SRT) was supported by synthetic modeling. Using geophysical data with accurate GPS-based topography and a terrestrial laser scanning-based digital terrain model (DTM), it was possible to model the spatial variability and surface area of the landslide more precisely, as well as to estimate the velocity field in the nearest surface more accurately. The final result shows displacement up to 1 m on the ground surface visible on the DTM models, while the geophysical methods confirm the change in internal structure. The proposed methodology is fast, cost-effective, and can be used to image the structure of landslides, where the shallowest parts are usually complex and thus difficult to observe seismically.
A New Method for Identifying the Central Business Districts with Nighttime Light Radiance and Angular Effects
Authors: Jie Na , Cao Xin , Chen Jin , Chen Xuehong
Published: January 2023

Central business districts (CBDs) play a crucial role in urban economic activities. Thus, the location and boundaries of CBDs identified by the unified standards are essential for comparative analyses in urban geography. However, past research mainly applied specific data or sensitive methods to delimitate CBDs within local knowledge in the case study, there remains no automated standardization technique for identifying and delimitating CBDs across the globe. This paper proposed a new method for identifying CBDs based on nighttime lights (NTL) to overcome the above limitations. The main advantages of this method include (1) the use of available high-quality global Black Marble products, which are the basis of a standardized delineation of CBDs and (2) the use of more characteristics of CBD (i.e., the brightness) and NTL negative angular effects that can reflect high-rise building. The proposed method was employed in 14 cities in China and the U.S., and the results showed that China cities needed five NTL indexes and U.S. cities needed two NTL indexes to distinguish CBD and non-CBD successfully. Therefore, our approach is recommended for CBD detection and delineation over large areas.
Whitecap Fraction Parameterization and Understanding with Deep Neural Network
Authors: Zhou Shuyi , Xu Fanghua , Shi Ruizi
Published: January 2023

Accurate calculation of the whitecap fraction is of great importance for the estimation of air-sea momentum flux, heat flux and sea-salt aerosol flux in Earth system models. Past whitecap fraction parameterizations were mostly power functions of wind speed, lacking consideration of other factors, while the single wind speed dependence makes it difficult to explain the variability of the whitecap fraction. In this work, we constructed a novel multivariate whitecap fraction parameterization using a deep neural network, which is diagnosed and interpreted. Compared with a recent developed parameterization by Albert and coworkers, the new parameterization can reduce the computational error of the whitecap fraction by about 15%, and it can better characterize the variability of the whitecap fraction, which provides a reference for the uncertainty study of sea-salt aerosol estimation. Through a permutation test, we ranked the importance of different input variables and revealed the indispensable role of variables such as significant wave height, sea surface temperature, etc., in the whitecap fraction parameterization.
Multispectral LiDAR Point Cloud Segmentation for Land Cover Leveraging Semantic Fusion in Deep Learning Network
Authors: Xiao Kai , Qian Jia , Li Teng , Peng Yuanxi
Published: January 2023

Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral wavelength intensity information, which can provide richer attribute features for semantic segmentation of point cloud scenes. However, due to the disordered distribution and huge number of point clouds, it is still a challenging task to accomplish fine-grained semantic segmentation of point clouds from large-scale multispectral LiDAR data. To deal with this situation, we propose a deep learning network that can leverage contextual semantic information to complete the semantic segmentation of large-scale point clouds. In our network, we work on fusing local geometry and feature content based on 3D spatial geometric associativity and embed it into a backbone network. In addition, to cope with the problem of redundant point cloud feature distribution found in the experiment, we designed a data preprocessing with principal component extraction to improve the processing capability of the proposed network on the applied multispectral LiDAR data. Finally, we conduct a series of comparative experiments using multispectral LiDAR point clouds of real land cover in order to objectively evaluate the performance of the proposed method compared with other advanced methods. With the obtained results, we confirm that the proposed method achieves satisfactory results in real point cloud semantic segmentation. Moreover, the quantitative evaluation metrics show that it reaches state-of-the-art.
Evaluation of Fairness of Urban Park Green Space Based on an Improved Supply Model of Green Space: A Case Study of Beijing Central City
Authors: Wang Xinke , Meng Qingyan , Liu Xingzhao , Allam Mona , Zhang Linlin , Hu Xinli , Bi Yaxin , Jancsó Tamás
Published: January 2023

Urban park green space (UPGS) plays an important role in providing ecological and social benefits. However, in many large cities with rapid economic development, the supply of UPGS is unfairly distributed, and there is a severe mismatch between its supply and residents’ demand. Taking the Beijing central city as an example, this study aims to develop a fairness assessment model to quantify the fairness of UPGS distribution and the matching relationships between supply and demand for UPGS. To achieve the aims of the study, we improved the supply model of UPGS by integrating three factors: the number of UPGS, the service capacity of UPGS, and the quality of UPGS in the Beijing central city. Subsequently, we evaluated the spatial fairness and social fairness of the supply of UPGS using the Gini coefficient. Then, we used the number of residents in the sub-district to characterize the intensity of residents’ needs and quantitatively analyzed the spatial matching relationship between the supply of UPGS and residents’ demand. The results show that: (a) The improved supply model of UPGS can measure the supply of UPGS of different types in a more detailed way. (b) The per capita supply of UPGS is unevenly distributed among the six urban districts of Beijing, which may lead to a sense of unfairness among residents. While residents in Haidian District (Gini = 0.649) may have the highest sense of unfairness, followed by those in Fengtai (Gini = 0.505), Dongcheng (Gini = 0.410), Xicheng (Gini = 0.392), and Chaoyang District (Gini = 0.225). (c) The matching relationship between the supply of UPGS and the needs of different social groups is not ideal, especially the spatial matching relationship between the needs of the elderly and the supply of UPGS. This study can be used as a reference for supporting decision making in optimizing UPGS and providing a reference for fine urban management.
A Block-Scale FFT Filter Based on Spatial Autocorrelation Features of Speckle Noise in SAR Image
Authors: Wang Xigang , Meng Zhiguo , Chen Si , Feng Zhuangzhuang , Li Xinbiao , Guo Tianhao , Wang Chunmei , Zheng Xingming
Published: January 2023

In order to reduce the impact of noise on the accuracy of inversion products based on SAR images, many filtering algorithms have been developed for noise reduction of SAR images. This paper proposes a filtering method based on the spatial autocorrelation feature of the block fast Fourier transform (BFFT). The method statistically analyses the autocorrelation length of speckle noise on Sentinel-1B images for different features and then constructs a relationship between autocorrelation length and noise period. After that, the size of the optimal FFT filtering window radius was determined based on the relationship between the noise period and the components in the image frequency domain. Finally, we filtered the SAR image within the parcels. We compared BFFT with six commonly used filtering methods. The results show that: (1) The noise periods of the soybean, corn, paddy, and water objects on the SAR image have little difference, with noise periods of 3.36, 3.17, 3.13, and 3.14 pixels on the VV polarization and 3.49, 3.17, 2.94, and 2.42 pixels on the VH polarization; (2) after the BFFT filtering in the land parcel area, the mean value of the backscattering coefficient (BC) kept constant, whilst at the same time, the standard deviation (STD) was reduced to half of that before the filtering and (3) the BFFT and NLM filtering methods have a better effect on noise reduction inside the block. The BFFT filtering method retains the variation trend between different regions within the block and preserves the block boundary’s clarity. This study provides a new idea for refined image processing.
The Transmission Effect and Influencing Factors of Land Pressure in the Yangtze River Delta Region from 1995–2020
Authors: Yu Ziqi , Chen Longqian , Zhang Ting , Li Long , Yuan Lina , Hu Sai , Cheng Liang , Shi Shuai , Xiao Jianying
Published: January 2023

Human societal growth has greatly pressured available land resources. The key to reducing land pressure and fostering regional synergistic development is revealing the transmission effect of land pressure. We used a modified gravity model to construct a spatial correlation network (SCN) of the land pressure in the Yangtze River Delta region (YRDR) for the years 1995, 2000, 2005, 2010, 2015 and 2020. To examine how the land pressure is transmitted throughout the cities in the YRDR, we used a social network analysis to examine the overall network structure, individual network characteristics and spatial clustering characteristics. Finally, the center of gravity-GTWR model that coupled the inter-city interactions and the temporal non-smoothness further revealed the spatiotemporal evolution and the different patterns of the influencing factors. The results revealed that (1) the spatial correlation structure of the land pressure in the YRDR was relatively stable. Nanjing, Shanghai, Suzhou, Hangzhou and Changzhou played a significant role as linkages. (2) The YRDR was beyond the geographical limit for the land pressure transmission effect and each block had a considerable and mostly steady transmission impact. (3) The center of gravity-GTWR model that coupled the inter-city interactions and the temporal non-stationarity was a viable method for analyzing the factors that influence the land pressure. (4) There were significant regional and temporal variations in the factors influencing land pressure. The influencing factors differed in intensity and direction from city to city. Our results can provide a new perspective on relieving land pressure from the perspective of urban agglomerations and help accomplish the sustainable development of regional land resources.
Hyperspectral Inversion Model of Relative Heavy Metal Content in Pennisetum sinese Roxb via EEMD-db3 Algorithm
Authors: Tang Ting , Chen Canming , Wu Weibin , Zhang Ying , Han Chongyang , Li Jie , Gao Ting , Li Jiehao
Published: January 2023

Detection rapidity and model accuracy are the keys to hyperspectral nondestructive testing technology, especially for Pennisetum sinese Roxb (PsR) due to its extremely high adsorptive heavy metal content. The study of the resolution of PsR is conducive to the analysis of the accumulated heavy metal content in its different parts. In this paper, the contents of Cd, Cu and Zn accumulated in the old leaves, young leaves, upper stem, middle stem and lower stem, as well as the hyperspectral data of the corresponding parts, were measured simultaneously in both fresh and dry states. To begin, the spectral data of PsR were preprocessed by using Ensemble Empirical Mode Decomposition-Daubechies3 (EEMD-db3), Savitzky–Golay (SG), Symlet3 (sym3), Symlet5 (sym5), and multiplicative scatter correction (MSC). The 40 samples were divided into 32 training sets and 8 validation sets. The preprocessed spectral data were transformed by the first derivative (FD) and reciprocal logarithm (log(1/R)) to highlight the singularities using binary wavelet decomposition. After screening the significant bands from the correlation curve, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were applied to extract the spectral characteristic variables, which were used to establish the partial least-squares (PLS) regression and multiple stepwise linear regression (MSLR) inversion models of Cd, Cu, and Zn contents. Based on EEMD-db3 pretreatment, the inversion model of Zn in the dry (fresh) state had R2 values of 0.884 (0.880), NRMSE values of 0.179 (0.253) and RPD values of 3.191 (3.221), indicating excellent stability and predictive performance. The findings of this study can not only aid in the rapid nondestructive detection of heavy metal adsorption in various parts of PsR, but can also be applied to guide the development and use of animal feed.
Mapping of Greenhouse Gas Concentration in Peninsular Malaysia Industrial Areas Using Unmanned Aerial Vehicle-Based Sniffer Sensor
Authors: Hashim Mazlan , Ng Hui Lin , Zakari Dahiru Mohammed , Sani Dalhatu Aliyu , Chindo Musa Muhammad , Hassan Noordyana , Azmy Muna Maryam , Pour Amin Beiranvand
Published: January 2023

The increasing concentration of greenhouse gas (GHG) emissions due to increased fossil fuel consumption for manufacturing activities to support population growth is worrisome. Carbon dioxide (CO2) and methane (CH4) remain the two GHGs that contribute to the impact of global warming, and inventorying their concentrations is important for monitoring their changes, which can be used to infer their emissions over time. Hence, this article highlights sniffer4D, an unmanned aerial vehicle (UAV)-based air pollutant mapping system that visualise and analyse three-dimensional (3D) air pollution data in real time, for mapping GHGs concentrations within industrial areas. Consequently, GHGs concentrations for two industrial and adjacent residential areas in Johor, Peninsular Malaysia were mapped. The GHGs concentrations were validated using a ground-based portable gas detector. The results revealed that CO2 has the highest concentration mean of 625.235 mg/m3, followed by CH4 with a mean of 249.239 mg/m3. The mapped UAV GHG concentration also reported good agreement with the in situ observations with an RMSE of 7 and 6 mg/m3 for CO2 and CH4 concentration, respectively. Ozone and nitrogen dioxide mixture (O3 + NO2) with a mean concentration of 249 μg/m3 and an RMSE of 9 μg/m3 are the remaining significant concentrations reported. This approach shall assist in fast-tracking the United Nations climate change mitigation agenda.
Hyperspectral Image Classification Based on a 3D Octave Convolution and 3D Multiscale Spatial Attention Network
Authors: Shi Cuiping , Sun Jingwei , Wang Tianyi , Wang Liguo
Published: January 2023

Convolutional neural networks are widely used in the field of hyperspectral image classification. After continuous exploration and research in recent years, convolutional neural networks have achieved good classification performance in the field of hyperspectral image classification. However, we have to face two main challenges that restrict the improvement of hyperspectral classification accuracy, namely, the high dimension of hyperspectral images and the small number of training samples. In order to solve these problems, in this paper, a new hyperspectral classification method is proposed. First, a three-dimensional octave convolution (3D-OCONV) is proposed. Subsequently, a dense connection structure of three-dimensional asymmetric convolution (DC-TAC) is designed. In the spectral branch, the spectral features are extracted through a combination of the 3D-OCONV and spectral attention modules, followed by the DC-TAC. In the spatial branch, a three-dimensional, multiscale spatial attention module (3D-MSSAM) is presented. The spatial information is fully extracted using the 3D-OCONV, 3D-MSSAM, and DC-TAC. Finally, the spectral and spatial information extracted from the two branches is fully fused with an interactive information fusion module. Compared to some state-of-the-art classification methods, the proposed method shows superior classification performance with a small number of training samples on four public datasets.
An Assessment of the Influences of Clouds on the Solar Photovoltaic Potential over China
Authors: Jiang Yuhui , Yi Bingqi
Published: January 2023

Clouds are important modulators of the solar radiation reaching the earth’s surface. However, the impacts of cloud properties other than cloud cover are seldom mentioned. By combining the satellite-retrieved cloud properties, the latest radiative transfer model, and an advanced PVLIB-python software for solar photovoltaic (PV) estimation, the impacts of different types of clouds on the maximum available solar PV potential (measured with the plane-of-array-irradiance, POAI) are quantified. The impacts of ice and liquid water clouds are found to be the highest on Tibetan Plateau over western China in spring, and central and southern China in winter, respectively. The reduction of POAI by liquid water clouds is almost twice of that by ice clouds except for spring. It is found that the POAI can be reduced by 27–34% by all clouds (ice + liquid water clouds) in different seasons. The sensitivities of the solar PV potential to the changes in cloud properties including the cloud fraction, cloud top pressure, cloud effective radius, and cloud water path are also analyzed. Three kinds of settings of PV panel tilting, namely fixed tilt, one-axis tracking, and two-axis tracking, are considered. It is found that the cloud properties are essential to estimate the solar PV potentials, especially for the cloud fraction. The attenuation of solar radiation by clouds are growingly larger as the solar plane tilting settings get more complicated. The outlook of solar PV potential is quite variable as the changes in cloud properties are highly uncertain in the future climate scenarios.
Quantitative Evaluation of Bathymetric LiDAR Sensors and Acquisition Approaches in Lærdal River in Norway
Authors: Awadallah Mahmoud Omer Mahmoud , Malmquist Christian , Stickler Morten , Alfredsen Knut
Published: January 2023

The development of bathymetric LiDAR technology has contributed significantly to both the quality and quantity of river bathymetry data. Although several bathymetric LiDAR sensors are available today, studies that evaluate the performance of the different bathymetric LiDAR sensors comparatively are still lacking. This study evaluates the performance of three bathymetric LiDAR sensors, CZMIL Supernova, Riegl VQ880-G, and Riegl VQ840-G, used with different acquisition approaches, in mapping Lærdal River bathymetry in Norway. The performance was evaluated based on comparing the sensors against a multibeam echosounder (MBES), a terrestrial laser scanner (TLS), and by an intercomparison between the individual sensors. The comparison was completed by comparing point clouds from the instruments and through the comparison of DEMs created from the point clouds. For the comparison against the MBES, the results show that the median residuals range between 3 to 13 cm, while against the TLS the median residuals range between 0 to 5 cm. The comparison of the CZMIL sensor against the two Riegl sensors shows median residuals of around 12 cm where the CZMIL map is shallower against the VQ880-G and deeper against the VQ840-G sensor. For the two Riegl sensors, the results show a median difference of 2 cm with the VQ880-G map deeper. We do observe that areas with high residuals are linked to river features such as large substrate variability, steep banks, and whitewater/turbulent flow. The study shows that all the LiDAR instruments provide high-quality representations of the river geometry and create a solid foundation for planning, modelling, or other work in rivers where detailed bathymetry is needed.
MC-UNet: Martian Crater Segmentation at Semantic and Instance Levels Using U-Net-Based Convolutional Neural Network
Authors: Chen Dong , Hu Fan , Mathiopoulos P. Takis , Zhang Zhenxin , Peethambaran Jiju
Published: January 2023

Crater recognition on Mars is of paramount importance for many space science applications, such as accurate planetary surface age dating and geological mapping. Such recognition is achieved by means of various image-processing techniques employing traditional CNNs (convolutional neural networks), which typically suffer from slow convergence and relatively low accuracy. In this paper, we propose a novel CNN, referred to as MC-UNet (Martian Crater U-Net), wherein classical U-Net is employed as the backbone for accurate identification of Martian craters at semantic and instance levels from thermal-emission-imaging-system (THEMIS) daytime infrared images. Compared with classical U-Net, the depth of the layers of MC-UNet is expanded to six, while the maximum number of channels is decreased to one-fourth, thereby making the proposed CNN-based architecture computationally efficient while maintaining a high recognition rate of impact craters on Mars. For enhancing the operation of MC-UNet, we adopt average pooling and embed channel attention into the skip-connection process between the encoder and decoder layers at the same network depth so that large-sized Martian craters can be more accurately recognized. The proposed MC-UNet is adequately trained using 2∼32 km radii Martian craters from THEMIS daytime infrared annotated images. For the predicted Martian crater rim pixels, template matching is subsequently used to recognize Martian craters at the instance level. The experimental results indicate that MC-UNet has the potential to recognize Martian craters with a maximum radius of 31.28 km (136 pixels) with a recall of 0.7916 and F1-score of 0.8355. The promising performance shows that the proposed MC-UNet is on par with or even better than other classical CNN architectures, such as U-Net and Crater U-Net.
Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles
Authors: Oh Kwangseok , Seo Jaho
Published: January 2023

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles.
Semi-Global Matching Assisted Absolute Phase Unwrapping
Authors: Liao Yi-Hong , Zhang Song
Published: January 2023

Measuring speed is a critical factor to reduce motion artifacts for dynamic scene capture. Phase-shifting methods have the advantage of providing high-accuracy and dense 3D point clouds, but the phase unwrapping process affects the measurement speed. This paper presents an absolute phase unwrapping method capable of using only three speckle-embedded phase-shifted patterns for high-speed three-dimensional (3D) shape measurement on a single-camera, single-projector structured light system. The proposed method obtains the wrapped phase of the object from the speckle-embedded three-step phase-shifted patterns. Next, it utilizes the Semi-Global Matching (SGM) algorithm to establish the coarse correspondence between the image of the object with the embedded speckle pattern and the pre-obtained image of a flat surface with the same embedded speckle pattern. Then, a computational framework uses the coarse correspondence information to determine the fringe order pixel by pixel. The experimental results demonstrated that the proposed method can achieve high-speed and high-quality 3D measurements of complex scenes.
Real-Time Remaining Useful Life Prediction of Cutting Tools Using Sparse Augmented Lagrangian Analysis and Gaussian Process Regression
Authors: Qin Xiao , Huang Weizhi , Wang Xuefei , Tang Zezhi , Liu Zepeng
Published: January 2023

Remaining useful life (RUL) of cutting tools is concerned with cutting tool operational status prediction and damage prognosis. Most RUL prediction methods utilized different features collected from different sensors to predict the life of the tool. To increase the prediction accuracy, it is often necessary to mount a great deal of sensors on the machine in order to collect more types of signals, which can heavily increase the cost in industrial applications. To deal with this issue, this study, for the first time, proposed a new feature network dictionary, which can enlarge the number of candidate features under limited sensor conditions, and the developed dictionary can potentially contain as much useful information as possible. This process can replace the installation of more sensors and incorporate more information. Then, the sparse augmented Lagrangian (SAL) feature selection method is proposed to reduce the number of candidate features and select the most significant features. Finally, the selected features are input to the Gaussian Process Regression (GPR) model for the RUL estimation. Extensive experiments demonstrate that our proposed RUL estimation framework output performs traditional methods, especially for the cost savings for on-line RUL estimation.
Robust Mesh Segmentation Using Feature-Aware Region Fusion
Authors: Wu Lulu , Hou Yu , Xu Junli , Zhao Yong
Published: January 2023

This paper introduces a simple but powerful segmentation algorithm for 3D meshes. Our algorithm consists of two stages: over-segmentation and region fusion. In the first stage, adaptive space partition is applied to perform over-segmentation, which is very efficient. In the second stage, we define a new intra-region difference, inter-region difference, and fusion condition with the help of various shape features and propose an iterative region fusion method. As the region fusion process is feature aware, our algorithm can deal with complex 3D meshes robustly. Massive qualitative and quantitative experiments also validate the advantages of the proposed algorithm.
Validation of SuPerSense, a Sensorized Surface for the Evaluation of Posture Perception in Supine Position
Authors: De Bartolo Daniela , D’amico Ilaria , Iosa Marco , Aloise Fabio , Morone Giovanni , Marinozzi Franco , Bini Fabiano , Paolucci Stefano , Spadini Ennio
Published: January 2023

This study aimed to validate a sensorized version of a perceptive surface that may be used for the early assessment of misperception of body midline representation in subjects with right stroke, even when they are not yet able to stand in an upright posture. This device, called SuPerSense, allows testing of the load distribution of the body weight on the back in a supine position. The device was tested in 15 patients with stroke, 15 age-matched healthy subjects, and 15 young healthy adults, assessing three parameters analogous to those conventionally extracted by a baropodometric platform in a standing posture. Subjects were hence tested on SuPerSense in a supine position and on a baropodometric platform in an upright posture in two different conditions: with open eyes and with closed eyes. Significant correlations were found between the lengths of the center of pressure path with the two devices in the open-eyes condition (R = 0.44, p = 0.002). The parameters extracted by SuPerSense were significantly different among groups only when patients were divided into those with right versus left brain damage. This last result is conceivably related to the role of the right hemisphere of the brain in the analysis of spatial information.
Development of a Wearable Haptic Glove Presenting Haptic Sensation by Electrical Stimulation
Authors: Zhou Dongbo , Hayakawa Wataru , Nakajima Yoshikazu , Tadano Kotaro
Published: January 2023

Most haptic devices generate haptic sensation using mechanical actuators. However, the workload and limited workspace handicap the operator from operating freely. Electrical stimulation is an alternative approach to generate haptic sensations without using mechanical actuators. The light weight of the electrodes adhering to the body brings no limitations to free motion. Because a real haptic sensation consists of feelings from several areas, mounting the electrodes to several different body areas can make the sensations more realistic. However, simultaneously stimulating multiple electrodes may result in “noise” sensations. Moreover, the operators may feel tingling because of unstable stimulus signals when using the dry electrodes to help develop an easily mounted haptic device using electrical stimulation. In this study, we first determine the appropriate stimulation areas and stimulus signals to generate a real touch sensation on the forearm. Then, we propose a circuit design guideline for generating stable electrical stimulus signals using a voltage divider resistor. Finally, based on the aforementioned results, we develop a wearable haptic glove prototype. This haptic glove allows the user to experience the haptic sensations of touching objects with five different degrees of stiffness.
An Integrated Fusion Engine for Early Threat Detection Demonstrated in Public-Space Trials
Authors: Bouma Henri , Villani Maria Luisa , van Rooijen Arthur , Räsänen Pauli , Peltola Johannes , Toivonen Sirra , De Nicola Antonio , Guarneri Massimiliano , Stifini Cristiano , De Dominicis Luigi
Published: January 2023

Counter terrorism is a huge challenge for public spaces. Therefore, it is essential to support early detection of threats, such as weapons or explosives. An integrated fusion engine was developed for the management of a plurality of sensors to detect threats without disrupting the flow of commuters. The system improves security of soft targets (such as airports, undergrounds and railway stations) by providing security operators with real-time information of the threat combined with image and position data of each person passing the monitored area. This paper describes the results of the fusion engine in a public-space trial in a metro station in Rome. The system consists of 2D-video tracking, person re-identification, 3D-video tracking, and command and control (C&C) formulating two co-existing data pipelines: one for visualization on smart glasses and another for hand-over to another sensor. Over multiple days, 586 commuters participated in the trial. The results of the trial show overall accuracy scores of 97.4% and 97.6% for the visualization and hand-over pipelines, respectively, and each component reached high accuracy values (2D Video = 98.0%, Re-identification = 100.0%, 3D Video = 99.7% and C&C = 99.5%).
Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information
Authors: Kim Kangjun , Park Hyunjae , Kim Taewung
Published: January 2023

Excessive tire wear can affect vehicle driving safety. While there are various methods for predicting the tire wear amount in real-time, it is unclear which method is the most effective in terms of the difficulty of sensing and prediction accuracy. The current study aims to develop prediction algorithms of tire wear and compare their performances. A finite element tire model was developed and validated against experimental data. Parametric tire rolling simulations were conducted using various driving and tire wear conditions to obtain tire internal accelerations. Machine-learning-based algorithms for tire wear prediction utilizing various sensing options were developed, and their performances were compared. A wheel translational and rotational speed-based (V and ω) method resulted in an average prediction error of 1.2 mm. Utilizing the internal pressure and vertical load of the tire with the V and ω improved the prediction accuracy to 0.34 mm. Acceleration-based methods resulted in an average prediction error of 0.6 mm. An algorithm using both the vehicle and tire information showed the best performance with a prediction error of 0.21 mm. When accounting for sensing cost, the V and ω-based method seems to be promising option. This finding needs to be experimentally verified.
Enhanced Frequency Stability of SAW Yarn Tension Sensor by Using the Dual Differential Channel Surface Acoustic Wave Oscillator
Authors: Feng Yang , Liu Wenbo , Wang Ben
Published: January 2023

This paper presents a 60 MHz surface acoustic wave (SAW) yarn tension sensor incorporating a novel SAW oscillator with high-frequency stability. A SAW delay line was fabricated on ST-X quartz substrate using the unbalanced-split electrode and bi-directional engraving slots. The dual differential channel delay linear acoustic surface wave oscillator is designed and implemented to test yarn tension, which can effectively remove the interference of temperature, humidity, and other peripheral factors through differential design. The yarn tension sensor using the surface acoustic wave has high-precision characteristics, and the SAW delay line oscillator is designed to ensure the test system’s stable operation. The effect of time and tension on oscillator frequency stability is studied in detail, and the single oscillator and the dual differential channel system were tested, respectively. After using the dual differential channel system, the short-term frequency stability from is reduced from 1.0163 ppm to 0.17726 ppm, the frequency accuracy of the tension sensor is improved from 134 Hz to 27 Hz, and the max frequency jump steady is reduced from 2.2395 ppm to 0.45123 ppm.
Investigation of GOx Stability in a Chitosan Matrix: Applications for Enzymatic Electrodes
Authors: Chmayssem Ayman , Shalayel Ibrahim , Marinesco Stéphane , Zebda Abdelkader
Published: January 2023

In this study, we designed a new biosensing membrane for the development of an electrochemical glucose biosensor. To proceed, we used a chitosan-based hydrogel that entraps glucose oxidase enzyme (GOx), and we crosslinked the whole matrix using glutaraldehyde, which is known for its quick and reactive crosslinking behavior. Then, the stability of the designed biosensors was investigated over time, according to different storage conditions (in PBS solution at temperatures of 4 °C and 37 °C and in the presence or absence of glucose). In some specific conditions, we found that our biosensor is capable of maintaining its stability for more than six months of storage. We also included catalase to protect the biosensing membranes from the enzymatic reaction by-products (e.g., hydrogen peroxide). This design protects the biocatalytic activity of GOx and enhances the lifetime of the biosensor.
Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins
Authors: Lehmann Joel , Schorz Stefan , Rache Alessa , Häußermann Tim , Rädle Matthias , Reichwald Julian
Published: January 2023

One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.
Sensor Equipped UAS for Non-Contact Bridge Inspections: Field Application
Authors: Nasimi Roya , Moreu Fernando , Fricke G. Matthew
Published: January 2023

In the future, sensors mounted on uncrewed aerial systems (UASs) will play a critical role in increasing both the speed and safety of structural inspections. Environmental and safety concerns make structural inspections and maintenance challenging when conducted using traditional methods, especially for large structures. The methods developed and tested in the laboratory need to be tested in the field on real-size structures to identify their potential for full implementation. This paper presents results from a full-scale field implementation of a novel sensor equipped with UAS to measure non-contact transverse displacement from a pedestrian bridge. To this end, the authors modified and upgraded a low-cost system that previously showed promise in laboratory and small-scale outdoor settings so that it could be tested on an in-service bridge. The upgraded UAS system uses a commodity drone platform, low-cost sensors including a laser range-finder, and a computer vision-based algorithm with the aim of measuring bridge displacements under load indicative of structural problems. The aim of this research is to alleviate the costs and challenges associated with sensor attachment in bridge inspections and deliver the first prototype of a UAS-based non-contact out-of-plane displacement measurement. This work helps to define the capabilities and limitations of the proposed low-cost system in obtaining non-contact transverse displacement in outdoor experiments.
Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing
Authors: Morales Giorgio , Sheppard John W. , Hegedus Paul B. , Maxwell Bruce D.
Published: January 2023

In recent years, the use of remotely sensed and on-ground observations of crop fields, in conjunction with machine learning techniques, has led to highly accurate crop yield estimations. In this work, we propose to further improve the yield prediction task by using Convolutional Neural Networks (CNNs) given their unique ability to exploit the spatial information of small regions of the field. We present a novel CNN architecture called Hyper3DNetReg that takes in a multi-channel input raster and, unlike previous approaches, outputs a two-dimensional raster, where each output pixel represents the predicted yield value of the corresponding input pixel. Our proposed method then generates a yield prediction map by aggregating the overlapping yield prediction patches obtained throughout the field. Our data consist of a set of eight rasterized remotely-sensed features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), aspect, and two radar backscatter coefficients acquired from the Sentinel-1 satellites. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present leave-one-out cross-validation experiments for rain-fed winter wheat over four fields and show that our proposed methodology produces better predictions than five compared methods, including Bayesian multiple linear regression, standard multiple linear regression, random forest, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.
Assessing the Role of AI-Based Smart Sensors in Smart Cities Using AHP and MOORA
Authors: Khan Habib Ullah , Nazir Shah
Published: January 2023

We know that in today’s advanced world, artificial intelligence (AI) and machine learning (ML)-grounded methodologies are playing a very optimistic role in performing difficult and time-consuming activities very conveniently and quickly. However, for the training and testing of these procedures, the main factor is the availability of a huge amount of data, called big data. With the emerging techniques of the Internet of Everything (IoE) and the Internet of Things (IoT), it is very feasible to collect a large volume of data with the help of smart and intelligent sensors. Based on these smart sensing devices, very innovative and intelligent hardware components can be made for prediction and recognition purposes. A detailed discussion was carried out on the development and employment of various detectors for providing people with effective services, especially in the case of smart cities. With these devices, a very healthy and intelligent environment can be created for people to live in safely and happily. With the use of modern technologies in integration with smart sensors, it is possible to use energy resources very productively. Smart vehicles can be developed to sense any emergency, to avoid injuries and fatal accidents. These sensors can be very helpful in management and monitoring activities for the enhancement of productivity. Several significant aspects are obtained from the available literature, and significant articles are selected from the literature to properly examine the uses of sensor technology for the development of smart infrastructure. The analytical hierarchy process (AHP) is used to give these attributes weights. Finally, the weights are used with the multi-objective optimization on the basis of ratio analysis (MOORA) technique to provide the different options in their order of importance.
Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing
Authors: Chan Hsiao-Lung , Ouyang Yuan , Chen Rou-Shayn , Lai Yen-Hung , Kuo Cheng-Chung , Liao Guo-Sheng , Hsu Wen-Yen , Chang Ya-Ju
Published: January 2023

Fall detection and physical activity (PA) classification are important health maintenance issues for the elderly and people with mobility dysfunctions. The literature review showed that most studies concerning fall detection and PA classification addressed these issues individually, and many were based on inertial sensing from the trunk and upper extremities. While shoes are common footwear in daily off-bed activities, most of the aforementioned studies did not focus much on shoe-based measurements. In this paper, we propose a novel footwear approach to detect falls and classify various types of PAs based on a convolutional neural network and recurrent neural network hybrid. The footwear-based detections using deep-learning technology were demonstrated to be efficient based on the data collected from 32 participants, each performing simulated falls and various types of PAs: fall detection with inertial measures had a higher F1-score than detection using foot pressures; the detections of dynamic PAs (jump, jog, walks) had higher F1-scores while using inertial measures, whereas the detections of static PAs (sit, stand) had higher F1-scores while using foot pressures; the combination of foot pressures and inertial measures was most efficient in detecting fall, static, and dynamic PAs.