bluuebunny/crossref_metadata_2025 huggingface dataset:
Moisture Sorption and Degradation of Polymer Filaments Used in 3D Printing
Authors: Aniskevich Andrey , Bulderberga Olga , Stankevics Leons
Published: June 2023

Experimental research of the moisture sorption process of 12 typical filaments used for FFF was performed in atmospheres with a relative humidity from 16 to 97% at room temperature. Materials with high moisture sorption capacity were revealed. Fick’s diffusion model was applied to all tested materials, and a set of sorption parameters was found. The solution of Fick’s second equation for the two-dimensional cylinder was obtained in series form. Moisture sorption isotherms were obtained and classified. Moisture diffusivity dependence on relative humidity was evaluated. The diffusion coefficient was independent of the relative humidity of the atmosphere for six materials. It essentially decreased for four materials and grew for the other two. Swelling strain changed linearly with the moisture content of the materials and reached up to 0.5% for some of them. The degree of degradation of the elastic modulus and the strength of the filaments due to moisture absorption were estimated. All tested materials were classified as having a low (changes ca. 2–4% or less), moderate (5–9%), or high sensitivity to water (more than 10%) by their reduction in mechanical properties. This reduction in stiffness and strength with absorbed moisture should be considered for responsible applications.
Corrosion Resistance of CeO2-GO/Epoxy Nanocomposite Coating in Simulated Seawater and Concrete Pore Solutions
Authors: Liu Xiaoyan , Wu Zitao , Lyu Yaoyao , Li Tianyu , Yang Heng , Liu Yanqi , Liu Ruidan , Xie Xian , Lyu Kai , Shah Surendra P.
Published: June 2023

Reinforced concrete structures in the marine environment face serious corrosion risks. Coating protection and adding corrosion inhibitors are the most economical and effective methods. In this study, a nano-composite anti-corrosion filler with a mass ratio of CeO2:GO = 4:1 was prepared by hydrothermally growing cerium oxide on the surface of graphene oxide. The filler was mixed with pure epoxy resin at a mass fraction of 0.5% to prepare a nano-composite epoxy coating. The basic properties of the prepared coating were evaluated from the aspects of surface hardness, adhesion grade, and anti-corrosion performance on Q235 low carbon steel subjected to simulated seawater and simulated concrete pore solutions. Results showed that after 90 days of service, the corrosion current density of the nanocomposite coating mixed with corrosion inhibitor was the lowest (Icorr = 1.001 × 10−9 A/cm2), and the protection efficiency was up to 99.92%. This study provides a theoretical foundation for solving the corrosion problem of Q235 low carbon steel in the marine environment.
System Design and Mechanism Study of Ultrasonic-Assisted Electrochemical Grinding for Hard and Tough Materials
Authors: Hu Chen , Zhu Yongwei
Published: June 2023

In this study, an ultrasonic-assisted electrochemical grinding (UAECG) system was designed to improve the low efficiency and tool wear in conventional grinding of hard and tough materials. In this system, multiple-field energy consisting of ultrasonic, electrochemical and mechanical grinding was used. The processing mechanism was investigated to determine the interaction mechanism between ultrasonic, grinding and electrochemical processing. The established theoretical model showed that the processing efficiency was affected by the ultrasonic amplitude, ultrasonic frequency, electrolyte conductivity and other parameters. In verifying the feasibility of UAECG machining and the effect of machining elements on machining, a series of corresponding machining experiments was conducted. Experiments showed that the machining efficiency can be improved by machining through the UAECG system. The material removal rate of W18Cr4V machining was 2.7 times higher than that of conventional grinding and 1.7 times higher than UAG. The processing efficiency of YT15 was increased by 3.2 times when the processing voltage increased from 2 to 6 V. The surface shape and roughness were also affected by these parameters. The surface roughness of the SiCp/Al workpiece reached the best level at 4 V as the machining voltage increased from 2 to 6 V. However, the surface roughness increased significantly when the voltage increased to 6 V. Thus, parameters such as machining voltage must be optimised for efficient and precise machining in practice.
From Inter-Religious Dialogue to Intra-Religious Dialogue: An Original Perspective of André Scrima’s Thought
Authors: Danca Mihai-Iulian
Published: June 2023

André Scrima was confronted from the very beginning with the multifaceted reality of the religious phenomenon. To help us understand this reality, his theological approach can be compared to that of the reverse perspective in Byzantine art: the further away an object is, the larger it becomes, and the closer it is, the smaller it becomes. From this perspective, the usual landmarks of knowledge are reversed, since it is the viewer who decides on the validity and truth of knowledge, and not the objective reality that would force adherence without any right to object. In theological terms, we could say that the ultimate significance of religious pluralism is not its outward manifestation nor the objectivity of its material data. In the final analysis, the person of faith brings their own experience into play as they decide on the truth of religions in general. The vanishing line no longer lies in the phenomenal exteriority of data but within each one. We will call this perspective intra-religious dialogue because, in addition to starting from the inner dimension of the experience of faith, it allows each of these experiences to reflect the “seeds of truth” that the Spirit scatters where it wills.
DDL-MVS: Depth Discontinuity Learning for Multi-View Stereo Networks
Authors: Ibrahimli Nail , Ledoux Hugo , Kooij Julian F. P. , Nan Liangliang
Published: June 2023

We propose an enhancement module called depth discontinuity learning (DDL) for learning-based multi-view stereo (MVS) methods. Traditional methods are known for their accuracy but struggle with completeness. While recent learning-based methods have improved completeness at the cost of accuracy, our DDL approach aims to improve accuracy while retaining completeness in the reconstruction process. To achieve this, we introduce the joint estimation of depth and boundary maps, where the boundary maps are explicitly utilized for further refinement of the depth maps. We validate our idea by integrating it into an existing learning-based MVS pipeline where the reconstruction depends on high-quality depth map estimation. Extensive experiments on various datasets, namely DTU, ETH3D, “Tanks and Temples”, and BlendedMVS, show that our method improves reconstruction quality compared to our baseline, Patchmatchnet. Our ablation study demonstrates that incorporating the proposed DDL significantly reduces the depth map error, for instance, by more than 30% on the DTU dataset, and leads to improved depth map quality in both smooth and boundary regions. Additionally, our qualitative analysis has shown that the reconstructed point cloud exhibits enhanced quality without any significant compromise on completeness. Finally, the experiments reveal that our proposed model and strategies exhibit strong generalization capabilities across the various datasets.
Analysis and Validation of the Aerosol Optical Depth of MODIS Products in Gansu Province, Northwest China
Authors: Huang Fangfang , Ma Weiqiang , Wang Suichan , Feng Chao , Kong Xiaoyi , Liu Hao
Published: June 2023

The accurate determination of aerosol optical depth (AOD) is of great importance for climate change research and environmental monitoring. To understand the applicability of the MODIS aerosol product inversion algorithm in Gansu Province, this work uses ground-based solar photometer AOD observation data to validate the MODIS C6 version of the AOD product. Additionally, the retrieval accuracy of MODIS C6 Deep Blue (DB) algorithm AOD products and Deep Blue and Dark Target Fusion (DB–DT combined) algorithm AOD products for Gansu Province when setting different spatial sampling windows is compared and analyzed. Meanwhile, the monitoring effects of these two AOD algorithms in typical polluted atmospheric conditions in Gansu Province are compared. The results show that (1) the correlation between the MODIS AOD products of the two algorithms and the ground-based observation data decreases with an increasing spatial sampling window size. When the spatial sampling window of the two algorithms is set at 30 km × 30 km, it is more representative of the AOD value in Gansu Province, thus reflecting local characteristics. (2) When the spatial sampling window is set at 30 km × 30 km, the inversion effect of the DB algorithm AOD is better than that of the DB–DT combined algorithm AOD on different underlying surfaces. (3) The seasonal variability in the inversion accuracy of the DB algorithm AOD is less than that of the DB–DT combined algorithm, and it has inversion advantages in spring, autumn and winter, while the DB–DT combined algorithm outperforms the DB algorithm only in winter. The inversion effect of the two algorithms on AOD is influenced by the spatial sampling window setting. (4) Both the DB algorithm AOD and the DB–DT combined algorithm AOD can monitor the distribution of AOD in the central and western regions of Gansu, especially for high values of AOD under polluted atmospheric conditions, which represents a good monitoring effect. However, the two algorithms perform poorly in monitoring the southeast region of Gansu, while there is a discontinuous AOD distribution in the northwest region of Gansu. Overall, the MODIS DB algorithm AOD product has higher applicability in Gansu Province. This work provides a good reference for local air pollution and climate prediction.
Real-World Urban Light Emission Functions and Quantitative Comparison with Spacecraft Measurements
Authors: Espey Brian R. , Yan Xinhang , Patrascu Kevin
Published: June 2023

We provide quantitative results from GIS-based modelling of urban emission functions for a range of representative low- and mid-rise locations, ranging from individual streets to residential communities within cities, as well as entire towns and city regions. Our general aim is to determine whether lantern photometry or built environment has the dominant effect on light pollution and whether it is possible to derive a common emission function applicable to regions of similar type. We demonstrate the scalability of our work by providing results for the largest urban area modelled to date, comprising the central 117 km2 area of Dublin City and containing nearly 42,000 public lights. Our results show a general similarity in the shape of the azimuthally averaged emission function for all areas examined, with differences in the angular distribution of total light output depending primarily on the nature of the lighting and, to a smaller extent, on the obscuring environment, including seasonal foliage effects. Our results are also consistent with the emission function derived from the inversion of worldwide skyglow data, supporting our general results by an independent method. Additionally, a comparison with global satellite observations shows that our results are consistent with the deduced angular emission function for other low-rise areas worldwide. Finally, we validate our approach by demonstrating very good agreement between our results and calibrated imagery taken from the International Space Station of a range of residential locations. To our knowledge, this is the first such detailed quantitative verification of light loss calculations and supports the underlying assumptions of the emission function model. Based on our findings, we conclude that it should be possible to apply our approach more generally to produce estimates of the energy and environmental impact of urban areas, which can be applied in a statistical sense. However, more accurate values will depend on the details of the particular locations and require treatment of atmospheric scattering, as well as differences in the spectral nature of the sources.
Modified Auto-Focusing Algorithm for High Squint Diving SAR Imaging Based on the Back-Projection Algorithm with Spectrum Alignment and Truncation
Authors: Gao Anqi , Sun Bing , Yan Mengyuan , Xue Chen , Li Jingwen
Published: June 2023

The study focuses on addressing the image defocusing issue caused by motion errors in highly squinted Synthetic Aperture Radar (SAR). The traditional auto-focusing algorithm, Phase Gradient Autofocus (PGA), is not effective in this mode due to difficulties in estimating the phase gradient accurately from strong point targets. Two main reasons contribute to this problem. Firstly, the direction of the energy-distributed lines in the Point Spread Function (PSF) does not align with the image’s azimuth direction in highly squinted mode. Secondly, the wavenumber spectrum of high squint SAR images obtained using the Back-Projection Algorithm (BPA) varies spatially, causing aliasing in the azimuth spectrum of all targets. In this paper, a new auto-focusing method is proposed for highly squinted SAR imaging. The modifications to the BP imaging grids have been implemented to address the first problem, while a novel wavenumber spectrum shifting and truncation method is proposed to accurately extract the phase gradient and tackle the spatial variation issue. The feasibility of the proposed algorithm is verified through simulations with point targets and processing of real data. The evaluation of the image shows an average improvement of four times in PSLR (Peak-Sidelobe-to-Noise Ratio).
Study on Land Subsidence Simulation Based on a Back-Propagation Neural Network Combined with the Sparrow Search Algorithm
Authors: Zhu Xueqi , Zhu Wantian , Guo Lin , Ke Yinghai , Li Xiaojuan , Zhu Lin , Sun Ying , Liu Yaxuan , Chen Beibei , Tian Jinyan , Gong Huili
Published: June 2023

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing’s subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing’s Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention.
3D Point Cloud Object Detection Algorithm Based on Temporal Information Fusion and Uncertainty Estimation
Authors: Xie Guangda , Li Yang , Wang Yanping , Li Ziyi , Qu Hongquan
Published: June 2023

In autonomous driving, LiDAR (light detection and ranging) data are acquired over time. Most existing 3D object detection algorithms propose the object bounding box by processing each frame of data independently, which ignores the temporal sequence information. However, the temporal sequence information is usually helpful to detect the object with missing shape information due to long distance or occlusion. To address this problem, we propose a temporal sequence information fusion 3D point cloud object detection algorithm based on the Ada-GRU (adaptive gated recurrent unit). In this method, the feature of each frame for the LiDAR point cloud is extracted through the backbone network and is fed to the Ada-GRU together with the hidden features of the previous frames. Compared to the traditional GRU, the Ada-GRU can adjust the gating mechanism adaptively during the training process by introducing the adaptive activation function. The Ada-GRU outputs the temporal sequence fusion features to predict the 3D object in the current frame and transmits the hidden features of the current frame to the next frame. At the same time, the label uncertainty of the distant and occluded objects affects the training effect of the model. For this problem, this paper proposes a probability distribution model of 3D bounding box coordinates based on the Gaussian distribution function and designs the corresponding bounding box loss function to enable the model to learn and estimate the uncertainty of the positioning of the bounding box coordinates, so as to remove the bounding box with large positioning uncertainty in the post-processing stage to reduce the false positive rate. Finally, the experiments show that the methods proposed in this paper improve the accuracy of the object detection without significantly increasing the complexity of the algorithm.
eWaSR—An Embedded-Compute-Ready Maritime Obstacle Detection Network
Authors: Teršek Matija , Žust Lojze , Kristan Matej
Published: June 2023

Maritime obstacle detection is critical for safe navigation of autonomous surface vehicles (ASVs). While the accuracy of image-based detection methods has advanced substantially, their computational and memory requirements prohibit deployment on embedded devices. In this paper, we analyze the current best-performing maritime obstacle detection network, WaSR. Based on the analysis, we then propose replacements for the most computationally intensive stages and propose its embedded-compute-ready variant, eWaSR. In particular, the new design follows the most recent advancements of transformer-based lightweight networks. eWaSR achieves comparable detection results to state-of-the-art WaSR with only a 0.52% F1 score performance drop and outperforms other state-of-the-art embedded-ready architectures by over 9.74% in F1 score. On a standard GPU, eWaSR runs 10× faster than the original WaSR (115 FPS vs. 11 FPS). Tests on a real embedded sensor OAK-D show that, while WaSR cannot run due to memory restrictions, eWaSR runs comfortably at 5.5 FPS. This makes eWaSR the first practical embedded-compute-ready maritime obstacle detection network. The source code and trained eWaSR models are publicly available.
Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots
Authors: Gongora Andres , Monroy Javier , Rahbar Faezeh , Ercolani Chiara , Gonzalez-Jimenez Javier , Martinoli Alcherio
Published: June 2023

The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot’s control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel.
Non-Contact Assessment of Swallowing Dysfunction Using Smartphone Captured Skin Displacements
Authors: Chesney Nikyta , Khwaounjoo Prashanna , Huckabee Maggie-Lee , Cakmak Yusuf Ozgur
Published: June 2023

Early and accurate dysphagia diagnosis is essential for reducing the risk of associated co-morbidities and mortalities. Barriers to current evaluation methods may alter the effectiveness of identifying at-risk patients. This preliminary study evaluates the feasibility of using iPhone X-captured videos of swallowing as a non-contact dysphagia screening tool. Video recordings of the anterior and lateral necks were captured simultaneously with videofluoroscopy in dysphagic patients. Videos were analyzed using an image registration algorithm (phase-based Savitzky–Golay gradient correlation (P-SG-GC)) to determine skin displacements over hyolaryngeal regions. Biomechanical swallowing parameters of hyolaryngeal displacement and velocity were also measured. Swallowing safety and efficiency were assessed by the Penetration Aspiration Scale (PAS), Residue Severity Ratings (RSR), and the Normalized Residue Ratio Scale (NRRS). Anterior hyoid excursion and horizontal skin displacements were strongly correlated with swallows of a 20 mL bolus (rs = 0.67). Skin displacements of the neck were moderately to very strongly correlated with scores on the PAS (rs = 0.80), NRRS (rs = 0.41–0.62), and RSR (rs = 0.33). This is the first study to utilize smartphone technology and image registration methods to produce skin displacements indicating post-swallow residual and penetration-aspiration. Enhancing screening methods provides a greater chance of detecting dysphagia, reducing the risk of negative health impacts.
Uncertainty Prediction for Monocular 3D Object Detection
Authors: Mun Junghwan , Choi Hyukdoo
Published: June 2023

For object detection, capturing the scale of uncertainty is as important as accurate localization. Without understanding uncertainties, self-driving vehicles cannot plan a safe path. Many studies have focused on improving object detection, but relatively little attention has been paid to uncertainty estimation. We present an uncertainty model to predict the standard deviation of bounding box parameters for a monocular 3D object detection model. The uncertainty model is a small, multi-layer perceptron (MLP) that is trained to predict uncertainty for each detected object. In addition, we observe that occlusion information helps predict uncertainty accurately. A new monocular detection model is designed to classify occlusion levels as well as to detect objects. An input vector to the uncertainty model contains bounding box parameters, class probabilities, and occlusion probabilities. To validate predicted uncertainties, actual uncertainties are estimated at the specific predicted uncertainties. The accuracy of the predicted values is evaluated using these estimated actual values. We find that the mean uncertainty error is reduced by 7.1% using the occlusion information. The uncertainty model directly estimates total uncertainty at the absolute scale, which is critical to self-driving systems. Our approach is validated through the KITTI object detection benchmark.
Integration of Virtual Reality in the Control System of an Innovative Medical Robot for Single-Incision Laparoscopic Surgery
Authors: Covaciu Florin , Crisan Nicolae , Vaida Calin , Andras Iulia , Pusca Alexandru , Gherman Bogdan , Radu Corina , Tucan Paul , Al Hajjar Nadim , Pisla Doina
Published: June 2023

In recent years, there has been an expansion in the development of simulators that use virtual reality (VR) as a learning tool. In surgery where robots are used, VR serves as a revolutionary technology to help medical doctors train in using these robotic systems and accumulate knowledge without risk. This article presents a study in which VR is used to create a simulator designed for robotically assisted single-uniport surgery. The control of the surgical robotic system is achieved using voice commands for laparoscopic camera positioning and via a user interface developed using the Visual Studio program that connects a wristband equipped with sensors attached to the user’s hand for the manipulation of the active instruments. The software consists of the user interface and the VR application via the TCP/IP communication protocol. To study the evolution of the performance of this virtual system, 15 people were involved in the experimental evaluation of the VR simulator built for the robotic surgical system, having to complete a medically relevant task. The experimental data validated the initial solution, which will be further developed.
Meniscus-Corrected Method for Broadband Liquid Permittivity Measurements with an Uncalibrated Vector Network Analyzer
Authors: Kalisiak Michał , Lewandowski Arkadiusz , Wiatr Wojciech
Published: June 2023

We present a novel broadband permittivity characterization method for liquids measured in a semi-open vertically oriented test cell with an uncalibrated vector network analyzer. For this goal, we utilize three scattering matrices measured at different levels of liquid in the cell. With mathematical operations, we remove the effects of systematic measurement errors caused by both the vector network analyzer and a meniscus shaping the top of the liquid samples in such a type of test cell. To the best authors’ knowledge, this is the first of such a calibration-independent method dealing with meniscus. We verify its validity by comparing our results with the data available in the literature and with the outcomes of our previously published calibration-dependent meniscus removal method (MR) for propan-2-ol (IPA), a 50% aqueous solution of IPA and distilled water. The new method yields results comparable with the MR method, at least for IPA and the IPA solution, revealing, however some problems when testing high-loss water samples. Nevertheless, it allows one to cut down on expenditures in the system calibration engaging skilled labor and expensive standards.
Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil
Authors: Gul Zeki , Bora Sebnem
Published: June 2023

Due to the integration of artificial intelligence with sensors and devices utilized by Internet of Things technology, the interest in automation systems has increased. One of the common features of both agriculture and artificial intelligence is recommendation systems that increase yield by identifying nutrient deficiencies in plants, consuming resources correctly, reducing damage to the environment and preventing economic losses. The biggest shortcomings in these studies are the scarcity of data and the lack of diversity. This experiment aimed to identify nutrient deficiencies in basil plants cultivated in a hydroponic system. Basil plants were grown by applying a complete nutrient solution as control and non-added nitrogen (N), phosphorous (P) and potassium (K). Then, photos were taken to determine N, P and K deficiencies in basil and control plants. After a new dataset was created for the basil plant, pretrained convolutional neural network (CNN) models were used for the classification problem. DenseNet201, ResNet101V2, MobileNet and VGG16 pretrained models were used to classify N, P and K deficiencies; then, accuracy values were examined. Additionally, heat maps of images that were obtained using the Grad-CAM were analyzed in the study. The highest accuracy was achieved with the VGG16 model, and it was observed in the heat map that VGG16 focuses on the symptoms.
Multi-Sensor Data Fusion Solutions for Blind and Visually Impaired: Research and Commercial Navigation Applications for Indoor and Outdoor Spaces
Authors: Theodorou Paraskevi , Tsiligkos Kleomenis , Meliones Apostolos
Published: June 2023

Several assistive technology solutions, targeting the group of Blind and Visually Impaired (BVI), have been proposed in the literature utilizing multi-sensor data fusion techniques. Furthermore, several commercial systems are currently being used in real-life scenarios by BVI individuals. However, given the rate by which new publications are made, the available review studies become quickly outdated. Moreover, there is no comparative study regarding the multi-sensor data fusion techniques between those found in the research literature and those being used in the commercial applications that many BVI individuals trust to complete their everyday activities. The objective of this study is to classify the available multi-sensor data fusion solutions found in the research literature and the commercial applications, conduct a comparative study between the most popular commercial applications (Blindsquare, Lazarillo, Ariadne GPS, Nav by ViaOpta, Seeing Assistant Move) regarding the supported features as well as compare the two most popular ones (Blindsquare and Lazarillo) with the BlindRouteVision application, developed by the authors, from the standpoint of Usability and User Experience (UX) through field testing. The literature review of sensor-fusion solutions highlights the trends of utilizing computer vision and deep learning techniques, the comparison of the commercial applications reveals their features, strengths, and weaknesses while Usability and UX demonstrate that BVI individuals are willing to sacrifice a wealth of features for more reliable navigation.
Multi-Modality Adaptive Feature Fusion Graph Convolutional Network for Skeleton-Based Action Recognition
Authors: Zhang Haiping , Zhang Xinhao , Yu Dongjin , Guan Liming , Wang Dongjing , Zhou Fuxing , Zhang Wanjun
Published: June 2023

Graph convolutional networks are widely used in skeleton-based action recognition because of their good fitting ability to non-Euclidean data. While conventional multi-scale temporal convolution uses several fixed-size convolution kernels or dilation rates at each layer of the network, we argue that different layers and datasets require different receptive fields. We use multi-scale adaptive convolution kernels and dilation rates to optimize traditional multi-scale temporal convolution with a simple and effective self attention mechanism, allowing different network layers to adaptively select convolution kernels of different sizes and dilation rates instead of being fixed and unchanged. Besides, the effective receptive field of the simple residual connection is not large, and there is a great deal of redundancy in the deep residual network, which will lead to the loss of context when aggregating spatio-temporal information. This article introduces a feature fusion mechanism that replaces the residual connection between initial features and temporal module outputs, effectively solving the problems of context aggregation and initial feature fusion. We propose a multi-modality adaptive feature fusion framework (MMAFF) to simultaneously increase the receptive field in both spatial and temporal dimensions. Concretely, we input the features extracted by the spatial module into the adaptive temporal fusion module to simultaneously extract multi-scale skeleton features in both spatial and temporal parts. In addition, based on the current multi-stream approach, we use the limb stream to uniformly process correlated data from multiple modalities. Extensive experiments show that our model obtains competitive results with state-of-the-art methods on the NTU-RGB+D 60 and NTU-RGB+D 120 datasets.
Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study
Authors: Wei Chihang , Song Zhihuan
Published: June 2023

Subsurface inclusions are one of the most common defects that affect the inner quality of continuous casting slabs. This increases the defects in the final products and increases the complexity of the hot charge rolling process and may even cause breakout accidents. The defects are, however, hard to detect online by traditional mechanism-model-based and physics-based methods. In the present paper, a comparative study is carried out based on data-driven methods, which are only sporadically discussed in the literature. As a further contribution, a scatter-regularized kernel discriminative least squares (SR-KDLS) model and a stacked defect-related autoencoder back propagation neural network (SDAE-BPNN) model are developed to improve the forecasting performance. The scatter-regularized kernel discriminative least squares is designed as a coherent framework to directly provide forecasting information instead of low-dimensional embeddings. The stacked defect-related autoencoder back propagation neural network extracts deep defect-related features layer by layer for a higher feasibility and accuracy. The feasibility and efficiency of the data-driven methods are demonstrated through case studies based on a real-life continuous casting process, where the imbalance degree drastically vary in different categories, showing that the defects are timely (within 0.01 ms) and accurately forecasted. Moreover, experiments illustrate the merits of the developed scatter-regularized kernel discriminative least squares and stacked defect-related autoencoder back propagation neural network methods regarding the computational burden; the F1 scores of the developed methods are clearly higher than common methods.
EMD-Based Energy Spectrum Entropy Distribution Signal Detection Methods for Marine Mammal Vocalizations
Authors: Wen Chai-Sheng , Lin Chin-Feng , Chang Shun-Hsyung
Published: June 2023

To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information theory to detect marine mammal vocalizations. The proposed detection algorithm has five main steps: sampling, energy characteristics analysis, marginal frequency distribution, feature extraction, and detection, which involve four signal feature extraction and analysis algorithms: energy ratio distribution (ERD), energy spectrum distribution (ESD), energy spectrum entropy distribution (ESED), and concentrated energy spectrum entropy distribution (CESED). In an experiment on 500 sampled signals (blue whale vocalizations), in the competent intrinsic mode function (IMF2) signal feature extraction function distribution of ERD, ESD, ESED, and CESED, the areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were 0.4621, 0.6162, 0.3894, and 0.8979, respectively; the Accuracy scores were 49.90%, 60.40%, 47.50%, and 80.84%, respectively; the Precision scores were 31.19%, 44.89%, 29.44%, and 68.20%, respectively; the Recall scores were 42.83%, 57.71%, 36.00%, and 84.57%, respectively; and the F1 scores were 37.41%, 50.50%, 32.39%, and 75.51%, respectively, based on the threshold of the optimal estimated results. It is clear that the CESED detector outperforms the other three detectors in signal detection and achieves efficient sound detection of marine mammals.
Improving Accuracy of Real-Time Positioning and Path Tracking by Using an Error Compensation Algorithm against Walking Modes
Authors: Gong Jiale , Li Ziyang , Chen Mingzhu , Wang Hong , Hu Dongmo
Published: June 2023

Wide-range application scenarios, such as industrial, medical, rescue, etc., are in various demand for human spatial positioning technology. However, the existing MEMS-based sensor positioning methods have many problems, such as large accuracy errors, poor real-time performance and a single scene. We focused on improving the accuracy of IMU-based both feet localization and path tracing, and analyzed three traditional methods. In this paper, a planar spatial human positioning method based on high-resolution pressure insoles and IMU sensors was improved, and a real-time position compensation method for walking modes was proposed. To validate the improved method, we added two high-resolution pressure insoles to our self-developed motion capture system with a wireless sensor network (WSN) system consisting of 12 IMUs. By multi-sensor data fusion, we implemented dynamic recognition and automatic matching of compensation values for five walking modes, with real-time spatial-position calculation of the touchdown foot, enhancing the 3D accuracy of its practical positioning. Finally, we compared the proposed algorithm with three old methods by statistical analysis of multiple sets of experimental data. The experimental results show that this method has higher positioning accuracy in real-time indoor positioning and path-tracking tasks. The methodology can have more extensive and effective applications in the future.
A Smart Card-Based Two-Factor Mutual Authentication Scheme for Efficient Deployment of an IoT-Based Telecare Medical Information System
Authors: Khan Muhammad Asghar , Alhakami Hosam , Alhakami Wajdi , Shvetsov Alexey V. , Ullah Insaf
Published: June 2023

The integration of the Internet of Things (IoT) and the telecare medical information system (TMIS) enables patients to receive timely and convenient healthcare services regardless of their location or time zone. Since the Internet serves as the key hub for connection and data sharing, its open nature presents security and privacy concerns and should be considered when integrating this technology into the current global healthcare system. Cybercriminals target the TMIS because it holds a lot of sensitive patient data, including medical records, personal information, and financial information. As a result, when developing a trustworthy TMIS, strict security procedures are required to deal with these concerns. Several researchers have proposed smart card-based mutual authentication methods to prevent such security attacks, indicating that this will be the preferred method for TMIS security with the IoT. In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. The results of a security analysis indicate that the newly contributed scheme is resistant to a wide variety of cryptographic attacks. A comparison of computation and communication costs demonstrates that the proposed scheme is more cost-effective than existing schemes.
Low-Loss Pogo Pin Probe Card with a Coupling Isolation Structure up to 50 GHz
Authors: Lee K. M. , Ahn S. , Park E. , Kim M.
Published: June 2023

A design for a millimeter wave RF probe card that removes resonance is proposed. The designed probe card optimizes the position of the ground surface and the signal pogo pins to resolve the resonance and signal loss issues that occur when connecting a dielectric socket and a PCB. At millimeter wave frequencies, the height of the dielectric socket and pogo pin matches the length of half a wavelength, allowing the socket to act as a resonator. When the leakage signal from the PCB line is coupled to the 2.9 mm high socket with pogo pins, resonance at a frequency of 28 GHz is generated. The probe card uses the ground plane as a shielding structure to minimize this resonance and radiation loss. The importance of the signal pin location is verified via measurements in order to address the discontinuity caused by field polarity switching. A probe card fabricated using the proposed technique exhibits an insertion loss performance of −8 dB up to 50 GHz and eliminates resonance. A signal with an insertion loss of −3.1 dB can be transmitted to a system-on-chip in a practical chip test.
Comparison of the Usability of Apple M2 and M1 Processors for Various Machine Learning Tasks
Authors: Kasperek David , Antonowicz Pawel , Baranowski Marek , Sokolowska Marta , Podpora Michal
Published: June 2023

Thispaper compares the usability of various Apple MacBook Pro laptops were tested for basic machine learning research applications, including text-based, vision-based, and tabular data. Four tests/benchmarks were conducted using four different MacBook Pro models—M1, M1 Pro, M2, and M2 Pro. A script written in Swift was used to train and evaluate four machine learning models using the Create ML framework, and the process was repeated three times. The script also measured performance metrics, including time results. The results were presented in tables, allowing for a comparison of the performance of each device and the impact of their hardware architectures.
Digital Twin-Driven Tool Condition Monitoring for the Milling Process
Authors: Natarajan Sriraamshanjiev , Thangamuthu Mohanraj , Gnanasekaran Sakthivel , Rakkiyannan Jegadeeshwaran
Published: June 2023

Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.
Surface Coal Mine Soils: Evidence for Chronosequence Development
Authors: Smart Kyle E. , Singer David M.
Published: June 2023

Anthropogenic changes to soil properties and development can dominate soil systems, particularly in coal mining-impacted landscapes of the Appalachian region of the United States. Historical mining operations deposited spoils which are developing into mine soils in chronosequences, allowing for a correlation between emplacement age and rates of change in soil properties. The study site was in the Huff Run Watershed (Mineral City, OH, USA) with a series of eleven spoil piles that were deposited over a 30-year time period. Surface soils were analyzed for bulk density, loss on ignition (LOI) as a proxy for organic matter, particle size, and bulk mineralogical (by X-ray diffraction) and elemental (by X-ray fluorescence) compositions. The following linear trends were observed across the transect from older to younger mine soils: bulk density increased from 1.0 cm−3 to 1.5 g cm−3; LOI decreased from ~20% to 5%; the content of sand-sized particles and quartz decreased from ~50% to 30% and 50% to 25%, respectively, with a corresponding increase in the contribution of clay mineral from ~25% to 60%; and Fe and other trace metals (Cu, Ni, Pb, Sb, Sn, and Te) decreased in concentration, while Al, Mg, and K increased in concentration. These trends are likely the result of: (1) organic matter accumulation as vegetation becomes more abundant over time; (2) transport of clays out of more recently emplaced waste; and (3) oxidative dissolution of primary sulfides releasing Fe and other trace metals followed by re-precipitation of secondary Fe-phases and trace metal sequestration. The findings presented here provide insight into the future behavior of these materials and can potentially be used to assess the inferred age of previously unexamined mine soils across a wider geographic area. These results can also inform decisions related to reclamation activities and ecosystem restoration.
Long Term of Soil Carbon Stock in No-Till System Affected by a Rolling Landscape in Southern Brazil
Authors: Thomaz Edivaldo L. , Kurasz Julliane P.
Published: June 2023

In the 1960s, a conservationist agricultural practice known as a “no-tillage system” was adopted. Several benefits such as soil erosion reduction and soil carbon sequestration, among others, could be ascribed to no-till systems. Therefore, it is important to evaluate the long-term sustainability of this agricultural system in different environments. This study has the objective to evaluate the soil organic carbon dynamic in a no-till system (40-year) and on a rolling landscape in Southern Brazil. A systematic grid with four transversal–longitudinal transects was used for soil sampling. Soil samples from 0–20, 20–40, and 40–60 cm depths were collected (16 trenches × 3 depths × 1 sample per soil layer = 48), and a forest nearby was used as control (4 trenches × 3 depths × 1 sample = 12). The soil at the forest site showed 20% more carbon stock than no-till at the 0–20 cm soil depth. However, the entire no-till soil profile (0–60 cm) showed similar soil carbon as forest soil. The soil carbon stock (0–20 cm) in no-till was depleted at a rate of 0.06 kg C m−2 year−1, summing up to a carbon loss of 2.43 kg C m−2. In addition, the non-uniform hillslope affected the soil carbon redistribution through the landscape, since the convex hillslope was more depleted in carbon by 37% (15.87 kg C m−2) when compared to the concave sector (25.27 kg C m−2). On average, the soil carbon loss in the subtropical agroecosystem was much lower than those reported in literature, as well as our initial expectations. In addition, the no-till system was capable of preserving soil carbon in the deepest soil layers. However, presently, the no-till system is losing more carbon in the topsoil at a rate greater than the soil carbon input.