bluuebunny/crossref_metadata_2025 huggingface dataset:
Hyperspectral Image Classification Based on Adaptive Global–Local Feature Fusion
Authors: Yang Chunlan , Kong Yi , Wang Xuesong , Cheng Yuhu
Published: May 2024

Labeled hyperspectral image (HSI) information is commonly difficult to acquire, so the lack of valid labeled data becomes a major puzzle for HSI classification. Semi-supervised methods can efficiently exploit unlabeled and labeled data for classification, which is highly valuable. Graph-based semi-supervised methods only focus on HSI local or global data and cannot fully utilize spatial–spectral information; this significantly limits the performance of classification models. To solve this problem, we propose an adaptive global–local feature fusion (AGLFF) method. First, the global high-order and local graphs are adaptively fused, and their weight parameters are automatically learned in an adaptive manner to extract the consistency features. The class probability structure is then used to express the relationship between the fused feature and the categories and to calculate their corresponding pseudo-labels. Finally, the fused features are imported into the broad learning system as weights, and the broad expansion of the fused features is performed with the weighted broad network to calculate the model output weights. Experimental results from three datasets demonstrate that AGLFF outperforms other methods.
Extraction of Factors Strongly Correlated with Lightning Activity Based on Remote Sensing Information
Authors: Zhang Haochen , Deng Yeqiang , Wang Yu , Lan Lei , Wen Xishan , Fang Chaoying , Xu Jun
Published: May 2024

Thunderstorms are a common natural phenomenon posing significant hazards to power systems, structures, and humans. With technological advancements, protection against lightning is gradually shifting from passive to active measures, which require the prediction of thunderstorm occurrences. Current research on lightning warning relies on various data sources, such as satellite data and atmospheric electric field data. However, these studies have placed greater emphasis on the process of warning implementation, overlooking the correlation between parameters used for lightning warning and lightning phenomena. This study relied on the ERA5 dataset and lightning location dataset from 117.5°E to 119.5°E longitude and 24.5°N to 25.5°N latitude during 2020–2021, utilizing Kriging interpolation to standardize the spatiotemporal precision of different parameters. After that, we conducted preliminary screening of the involved parameters based on the chi-squared test and utilized the Apriori algorithm to identify parameter intervals that were strongly associated with the occurrence of lightning. Subsequently, we extracted strong association rules oriented towards the occurrence of lightning and analyzed those rules with respect to lightning current amplitude, types, and ERA5 parameters. We found that thunderstorm phenomena are more likely to occur under specific ranges of temperature, humidity, and wind speed conditions, and we determined their parameter ranges. After that, we divided the target area into regions with different levels of lightning probability based on the strong association rules. By comparing the actual areas where lightning phenomena occurred with the areas at high risk of lightning based on ERA5 parameters, we validated the credibility of the obtained strong association rules.
Scaling Properties of Magnetic Field Fluctuations in the High-Latitude Ionosphere
Authors: Mestici Simone , Giannattasio Fabio , De Michelis Paola , Berrilli Francesco , Consolini Giuseppe
Published: May 2024

Space plasma turbulence plays a relevant role in several plasma environments, such as solar wind and the Earth’s magnetosphere–ionosphere system, and is essential for describing their complex coupling. This interaction gives rise to various phenomena, including ionospheric irregularities and the amplification of magnetospheric and ionospheric currents. The structure and dynamics of these currents have relevant implications, for example, in studying ionospheric heating and the nature of electric and magnetic field fluctuations in the auroral and polar environments. In this study, we investigate the nature of small-scale fluctuations characterizing the ionospheric magnetic field in response to different geomagnetic conditions. We use high-resolution (50 Hz) magnetic data from the ESA’s Swarm mission, collected during a series of high-latitude crossings, to probe the scaling features of magnetic field fluctuations in auroral and polar cap regions at spatial scales still poorly explored. Our findings reveal that magnetic field fluctuations in field-aligned currents (FACs) and polar cap regions across both hemispheres are characterized by different scaling properties, suggesting a distinct driver of turbulence. Furthermore, we find that geomagnetic activity significantly influences the nature of energy dissipation in FAC regions, leading to more localized filamentary structures toward smaller scales.
Monitoring of Curing Process of Epoxy Resin by Long-Period Fiber Gratings
Authors: Ivanov Oleg V. , Bhavsar Kaushal , Morgan-Clague Oliver , Gilbert James M.
Published: May 2024

The curing of epoxy resin is a complex thermo-chemical process that is difficult to monitor using existing sensing systems. We monitored the curing process of an epoxy resin by using long-period fiber gratings. The refractive index of the epoxy resin increases during the curing process and can be measured to determine the degree of curing. We employed long-period fiber gratings that are sensitive to the refractive index of an external medium for the measurement of refractive index changes in the resin. We observed that the resonances of long-period fiber gratings increased their depth with the increased refractive index of the resin, which was well described by our simulation taking the coupling to radiation modes into account. We demonstrated that the degree of cure can be estimated from the depth of the grating resonances using a phenomenological model. At the same time, long-period fiber gratings are sensitive to temperature variations and internal strains that are induced during curing. These factors may affect the measurements of curing degree and should also be addressed.
Variable Temporal Length Training for Action Recognition CNNs
Authors: Li Tan-Kun , Chan Kwok-Leung , Tjahjadi Tardi
Published: May 2024

Most current deep learning models are suboptimal in terms of the flexibility of their input shape. Usually, computer vision models only work on one fixed shape used during training, otherwise their performance degrades significantly. For video-related tasks, the length of each video (i.e., number of video frames) can vary widely; therefore, sampling of video frames is employed to ensure that every video has the same temporal length. This training method brings about drawbacks in both the training and testing phases. For instance, a universal temporal length can damage the features in longer videos, preventing the model from flexibly adapting to variable lengths for the purposes of on-demand inference. To address this, we propose a simple yet effective training paradigm for 3D convolutional neural networks (3D-CNN) which enables them to process videos with inputs having variable temporal length, i.e., variable length training (VLT). Compared with the standard video training paradigm, our method introduces three extra operations during training: sampling twice, temporal packing, and subvideo-independent 3D convolution. These operations are efficient and can be integrated into any 3D-CNN. In addition, we introduce a consistency loss to regularize the representation space. After training, the model can successfully process video with varying temporal length without any modification in the inference phase. Our experiments on various popular action recognition datasets demonstrate the superior performance of the proposed method compared to conventional training paradigm and other state-of-the-art training paradigms.
Innovative Non-Invasive and Non-Intrusive Precision Thermometry in Stainless-Steel Tanks Using Ultrasound Transducers
Authors: Bouzid Ahmed , Chidami Saad , Lailler Tristan Quentin , García Adrián Carrillo , Ould-Bachir Tarek , Chaouki Jamal
Published: May 2024

Measuring temperature inside chemical reactors is crucial to ensuring process control and safety. However, conventional methods face a number of limitations, such as the invasiveness and the restricted dynamic range. This paper presents a novel approach using ultrasound transducers to enable accurate temperature measurements. Our experiments, conducted within a temperature range of 28.8 to 83.8 °C, reveal a minimal temperature accuracy of 98.6% within the critical zone spanning between 70.5 and 75 °C, and an accuracy of over 99% outside this critical zone. The experiments focused on a homogeneous environment of distilled water within a stainless-steel tank. This approach will be extended in a future research in order to diversify the experimental media and non-uniform environments, while promising broader applications in chemical process monitoring and control.
Precise GDP Spatialization and Analysis in Built-Up Area by Combining the NPP-VIIRS-like Dataset and Sentinel-2 Images
Authors: Chen Zijun , Wang Wanning , Zong Haolin , Yu Xinyang
Published: May 2024

Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization and analysis of GDP23 in a built-up area by combining multi-source remote sensing images. In this study, the NPP-VIIRS-like dataset and Sentinel-2 multi-spectral remote sensing images in six years were combined to precisely spatialize and analyze the variation patterns of the GDP23 in the built-up area of Zibo city, China. Sentinel-2 images and the random forest (RF) classification method based on PIE-Engine cloud platform were employed to extract built-up areas, in which the NPP-VIIRS-like dataset and comprehensive nighttime light index were used to indicate the nighttime light magnitudes to construct models to spatialize GDP23 and analyze their change patterns during the study period. The results found that (1) the RF classification method can accurately extract the built-up area with an overall accuracy higher than 0.90; the change patterns of built-up areas varied among districts and counties, with Yiyuan county being the only administrative region with an annual expansion rate of more than 1%. (2) The comprehensive nighttime light index is a viable indicator of GDP23 in the built-up area; the fitted model exhibited an R2 value of 0.82, and the overall relative errors of simulated GDP23 and statistical GDP23 were below 1%. (3) The year 2018 marked a significant turning point in the trajectory of GDP23 development in the study area; in 2018, Zhoucun district had the largest decrease in GDP23 at −52.36%. (4) GDP23 gradation results found that Zhangdian district exhibited the highest proportion of high GDP23 (>9%), while the proportions of low GDP23 regions in the remaining seven districts and counties all exceeded 60%. The innovation of this study is that the GDP23 in built-up areas were first precisely spatialized and analyzed using the NPP-VIIRS-like dataset and Sentinel-2 images. The findings of this study can serve as references for formulating improved city planning strategies and sustainable development policies.
CyberSentinel: A Transparent Defense Framework for Malware Detection in High-Stakes Operational Environments
Authors: Basak Mainak , Han Myung-Mook
Published: May 2024

Malware classification is a crucial step in defending against potential malware attacks. Despite the significance of a robust malware classifier, existing approaches reveal notable limitations in achieving high performance in malware classification. This study focuses on image-based malware detection, where malware binaries are transformed into visual representations to leverage image classification techniques. We propose a two-branch deep network designed to capture salient features from these malware images. The proposed network integrates faster asymmetric spatial attention to refine the extracted features of its backbone. Additionally, it incorporates an auxiliary feature branch to learn missing information about malware images. The feasibility of the proposed method has been thoroughly examined and compared with state-of-the-art deep learning-based classification methods. The experimental results demonstrate that the proposed method can surpass its counterparts across various evaluation metrics.
Multi-View Metal Parts Pose Estimation Based on a Single Camera
Authors: Chen Chen , Jiang Xin
Published: May 2024

Pose estimation of metal parts plays a vital role in industrial grasping areas. It is challenging to obtain complete point clouds of metal parts because of their reflective properties. This study introduces an approach for recovering the 6D pose of CAD-known metal parts from images captured by a single RGB camera. The proposed strategy only requires RGB images without depth information. The core idea of the proposed method is to use multiple views to estimate the metal parts’ pose. First, the pose of metal parts is estimated in the first view. Second, ray casting is employed to simulate additional views with the corresponding status of the metal parts, enabling the calculation of the camera’s next best viewpoint. The camera, mounted on a robotic arm, is then moved to this calculated position. Third, this study integrates the known camera transformations with the poses estimated from different viewpoints to refine the final scene. The results of this work demonstrate that the proposed method effectively estimates the pose of shiny metal parts.
A Video Mosaicing-Based Sensing Method for Chicken Behavior Recognition on Edge Computing Devices
Authors: Teterja Dmitrij , Garcia-Rodriguez Jose , Azorin-Lopez Jorge , Sebastian-Gonzalez Esther , Nedić Daliborka , Leković Dalibor , Knežević Petar , Drajić Dejan , Vukobratović Dejan
Published: May 2024

Chicken behavior recognition is crucial for a number of reasons, including promoting animal welfare, ensuring the early detection of health issues, optimizing farm management practices, and contributing to more sustainable and ethical poultry farming. In this paper, we introduce a technique for recognizing chicken behavior on edge computing devices based on video sensing mosaicing. Our method combines video sensing mosaicing with deep learning to accurately identify specific chicken behaviors from videos. It attains remarkable accuracy, achieving 79.61% with MobileNetV2 for chickens demonstrating three types of behavior. These findings underscore the efficacy and promise of our approach in chicken behavior recognition on edge computing devices, making it adaptable for diverse applications. The ongoing exploration and identification of various behavioral patterns will contribute to a more comprehensive understanding of chicken behavior, enhancing the scope and accuracy of behavior analysis within diverse contexts.
Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication
Authors: Ušinskis Vygantas , Makulavičius Mantas , Petkevičius Sigitas , Dzedzickis Andrius , Bučinskas Vytautas
Published: May 2024

Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the main challenges is communication between different traffic objects. For instance, a mobile robot system can work as a standalone autonomous system reacting to a static environment and avoiding obstacles to reach a target. Nevertheless, more intensive communication and decision making is needed when additional dynamic objects and other autonomous systems are present in the same working environment. Traffic is a complicated environment consisting of vehicles, pedestrians, and various infrastructure elements. To apply autonomous systems in this kind of environment it is important to integrate object localization and to guarantee functional and trustworthy communication between each element. To achieve this, various sensors, communication standards, and equipment are integrated via the application of sensor fusion and AI machine learning methods. In this work review of vehicular communication systems is presented. The main focus is the researched sensors, communication standards, devices, machine learning methods, and vehicular-related data to find existing gaps for future vehicular communication system development. In the end, discussion and conclusions are presented.
Deploying Wireless Sensor Networks in Multi-Story Buildings toward Internet of Things-Based Intelligent Environments: An Empirical Study
Authors: Sarkar Nurul I. , Gul Sonia
Published: May 2024

With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research’s findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments.
Can We Target Close Therapeutic Goals in the Gait Re-Education Algorithm for Stroke Patients at the Beginning of the Rehabilitation Process?
Authors: Wareńczak-Pawlicka Agnieszka , Lisiński Przemysław
Published: May 2024

(1) Background: The study aimed to determine the most important activities of the knee joints related to gait re-education in patients in the subacute period after a stroke. We focused on the tests that a physiotherapist could perform in daily clinical practice. (2) Methods: Twenty-nine stroke patients (SG) and 29 healthy volunteers (CG) were included in the study. The patients underwent the 5-meter walk test (5mWT) and the Timed Up and Go test (TUG). Tests such as step up, step down, squat, step forward, and joint position sense test (JPS) were also performed, and the subjects were assessed using wireless motion sensors. (3) Results: We observed significant differences in the time needed to complete the 5mWT and TUG tests between groups. The results obtained in the JPS show a significant difference between the paretic and the non-paretic limbs compared to the CG group. A significantly smaller range of knee joint flexion (ROM) was observed in the paretic limb compared to the non-paretic and control limbs in the step down test and between the paretic and non-paretic limbs in the step forward test. (4) Conclusions: The described functional tests are useful in assessing a stroke patient’s motor skills and can be performed in daily clinical practice.
Deep Learning-Based Nystagmus Detection for BPPV Diagnosis
Authors: Mun Sae Byeol , Kim Young Jae , Lee Ju Hyoung , Han Gyu Cheol , Cho Sung Ho , Jin Seok , Kim Kwang Gi
Published: May 2024

In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystagmus detection models. Among the four deep learning architectures used in this study, the CNN1D model proposed as a nystagmus detection model demonstrated the best performance, exhibiting a sensitivity of 94.06 ± 0.78%, specificity of 86.39 ± 1.31%, precision of 91.34 ± 0.84%, accuracy of 91.02 ± 0.66%, and an F1-score of 92.68 ± 0.55%. These results indicate the high accuracy and generalizability of the proposed nystagmus diagnosis algorithm. In conclusion, this study validates the practicality of deep learning in diagnosing BPPV and offers avenues for numerous potential applications of deep learning in the medical diagnostic sector. The findings of this research underscore its importance in enhancing diagnostic accuracy and efficiency in healthcare.
Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping
Authors: Zhan Yinglun , Zhou Yuzhen , Bai Geng , Ge Yufeng
Published: May 2024

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation—identifying crops from the background—crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40% higher Intersection-over-Union (IoU) than the threshold method and 11% over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code.
Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework
Authors: Siami Mohammad , Barszcz Tomasz , Zimroz Radoslaw
Published: May 2024

In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes.
Electrocardiography Classification with Leaky Integrate-and-Fire Neurons in an Artificial Neural Network-Inspired Spiking Neural Network Framework
Authors: Rana Amrita , Kim Kyung Ki
Published: May 2024

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomalies. This traditional method, while effective, demands significant expertise and is susceptible to inaccuracies due to its manual nature. In the realm of computational analysis, Artificial Neural Networks (ANNs) have gained prominence across various domains, which can be attributed to their superior analytical capabilities. Conversely, Spiking Neural Networks (SNNs), which mimic the neural activity of the brain more closely through impulse-based processing, have not seen widespread adoption. The challenge lies primarily in the complexity of their training methodologies. Despite this, SNNs offer a promising avenue for energy-efficient computational models capable of displaying a high-level performance. This paper introduces an innovative approach employing SNNs augmented with an attention mechanism to enhance feature recognition in ECG signals. By leveraging the inherent efficiency of SNNs, coupled with the precision of attention modules, this model aims to refine the analysis of cardiac signals. The novel aspect of our methodology involves adapting the learned parameters from ANNs to SNNs using leaky integrate-and-fire (LIF) neurons. This transfer learning strategy not only capitalizes on the strengths of both neural network models but also addresses the training challenges associated with SNNs. The proposed method is evaluated through extensive experiments on two publicly available benchmark ECG datasets. The results show that our model achieves an overall accuracy of 93.8% on the MIT-BIH Arrhythmia dataset and 85.8% on the 2017 PhysioNet Challenge dataset. This advancement underscores the potential of SNNs in the field of medical diagnostics, offering a path towards more accurate, efficient, and less resource-intensive analyses of heart diseases.
LidPose: Real-Time 3D Human Pose Estimation in Sparse Lidar Point Clouds with Non-Repetitive Circular Scanning Pattern
Authors: Kovács Lóránt , Bódis Balázs M. , Benedek Csaba
Published: May 2024

In this paper, we propose a novel, vision-transformer-based end-to-end pose estimation method, LidPose, for real-time human skeleton estimation in non-repetitive circular scanning (NRCS) lidar point clouds. Building on the ViTPose architecture, we introduce novel adaptations to address the unique properties of NRCS lidars, namely, the sparsity and unusual rosetta-like scanning pattern. The proposed method addresses a common issue of NRCS lidar-based perception, namely, the sparsity of the measurement, which needs balancing between the spatial and temporal resolution of the recorded data for efficient analysis of various phenomena. LidPose utilizes foreground and background segmentation techniques for the NRCS lidar sensor to select a region of interest (RoI), making LidPose a complete end-to-end approach to moving pedestrian detection and skeleton fitting from raw NRCS lidar measurement sequences captured by a static sensor for surveillance scenarios. To evaluate the method, we have created a novel, real-world, multi-modal dataset, containing camera images and lidar point clouds from a Livox Avia sensor, with annotated 2D and 3D human skeleton ground truth.
Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks
Authors: Colantonio Lorenzo , Equeter Lucas , Dehombreux Pierre , Ducobu François
Published: May 2024

The degradation of the cutting tool and its optimal replacement is a major problem in machining given the variability in this degradation even under constant cutting conditions. Therefore, monitoring the degradation of cutting tools is an important part of the process in order to replace the tool at the optimal time and thus reduce operating costs. In this paper, a cutting tool degradation monitoring technique is proposed using bootstrap-based artificial neural networks. Different indicators from the turning operation are used as input to the approach: the RMS value of the cutting force and torque, the machining duration, and the total machined length. They are used by the approach to estimate the size of the flank wear (VB). Different neural networks are tested but the best results are achieved with an architecture containing two hidden layers: the first one containing six neurons with a Tanh activation function and the second one containing six neurons with an ReLu activation function. The novelty of the approach makes it possible, by using the bootstrap approach, to determine a confidence interval around the prediction. The results show that the networks are able to accurately track the degradation and detect the end of life of the cutting tools in a timely manner, but also that the confidence interval allows an estimate of the possible variation of the prediction to be made, thus helping in the decision for optimal tool replacement policies.
Design and Evaluation of a Low-Power Wide-Area Network (LPWAN)-Based Emergency Response System for Individuals with Special Needs in Smart Buildings
Authors: Safi Habibullah , Jehangiri Ali Imran , Ahmad Zulfiqar , Ala’anzy Mohammed Alaa , Alramli Omar Imhemed , Algarni Abdulmohsen
Published: May 2024

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.
Study on TPD Phasemeter to Suppress Low-Frequency Amplitude Fluctuation and Improve Fast-Acquiring Range for GW Detection
Authors: Ming Min , Zhang Jingyi , Duan Huizong , Li Zhu , Huang Xiangqing , Tu Liangcheng , Yeh Hsien-Chi
Published: May 2024

A phasemeter as a readout system for the inter-satellite laser interferometer in a space-borne gravitational wave detector requires not only high accuracy but also insensitivity to amplitude fluctuations and a large fast-acquiring range. The traditional sinusoidal characteristic phase detector (SPD) phasemeter has the advantages of a simple structure and easy realization. However, the output of an SPD is coupled to the amplitude of the input signal and has only a limited phase-detection range due to the boundedness of the sinusoidal function. This leads to the performance deterioration of amplitude noise suppression, fast-acquiring range, and loop stability. To overcome the above shortcomings, we propose a phasemeter based on a tangent phase detector (TPD). The characteristics of the SPD and TPD phasemeters are theoretically analyzed, and a fixed-point simulation is further carried out for verification. The simulation results show that the TPD phasemeter tracks the phase information well and, at the same time, suppresses the amplitude fluctuation to the noise floor of 1 μrad/Hz1/2, which meets the requirements of GW detection. In addition, the maximum lockable step frequency of the TPD phasemeter is almost three times larger than the SPD phasemeter, indicating a greater fast-acquiring range.
Mathematical-Physics Analyses of the Nozzle Shaping at the Aperture Gas Outlet into Free Space under ESEM Pressure Conditions
Authors: Šabacká Pavla , Maxa Jiří , Švecová Jana , Talár Jaroslav , Binar Tomáš , Bayer Robert , Bača Petr , Dostalová Petra , Švarc Jiří
Published: May 2024

The paper presents a methodology that combines experimental measurements and mathematical-physics analyses to investigate the flow behavior in a nozzle-equipped aperture associated with the solution of its impact on electron beam dispersion in an environmental scanning electron microscope (ESEM). The shape of the nozzle significantly influences the character of the supersonic flow beyond the aperture, especially the shape and type of shock waves, which are highly dense compared to the surrounding gas. These significantly affect the electron scattering, which influences the resulting image. This paper analyzes the effect of aperture and nozzle shaping under specific low-pressure conditions and its impact on the electron dispersion of the primary electron beam.
Piezoelectric Transducer-Based Structural Health Monitoring
Authors: Yu Lingyu , Shen Yanfeng , Giurgiutiu Victor
Published: May 2024

Piezoelectric effects were first discovered more than a hundred years ago and, since then, have been widely used across various fields [...]
Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths
Authors: Ahmed Serien , Ahmed Majid , Bensmida Souheil , Hammi Oualid
Published: May 2024

The feasibility of implementing digital predistortion for next-generation wireless communication is faced with a dilemma due to the ever-increasing demand for faster data rates. This causes the utilized bandwidth to increase significantly, as seen in the 5G NR standard in which bandwidths as high as 400 MHz are utilized. Hence, the development of new predistortion techniques in which the forward and feedback paths operate at lower sampling rates is of utmost importance to realize efficient and practical predistortion solutions. In this work, a novel predistortion technique is presented by which the predistortion is divided between the digital and analog domains. The predistorter is composed of a memoryless AM/AM gain function that is implementable in the analog domain, and a nonlinear model with memory effects in the digital domain to relax the sampling rate requirements on both the forward and feedback paths. Experimental validation was carried out with a 20 MHz and a 40 MHz 5G signal, and the results indicate minimal linearization degradation with a sampling rate reduction of 50% and 30%, respectively. This sampling rate reduction is concurrently applied in the digital-to-analog converter of the forward path and the analog-to-digital converter of the feedback path.
Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions
Authors: Moroto Yuya , Maeda Keisuke , Togo Ren , Ogawa Takahiro , Haseyama Miki
Published: May 2024

This paper proposes a multimodal Transformer model that uses time-series data to detect and predict winter road surface conditions. For detecting or predicting road surface conditions, the previous approach focuses on the cooperative use of multiple modalities as inputs, e.g., images captured by fixed-point cameras (road surface images) and auxiliary data related to road surface conditions under simple modality integration. Although such an approach achieves performance improvement compared to the method using only images or auxiliary data, there is a demand for further consideration of the way to integrate heterogeneous modalities. The proposed method realizes a more effective modality integration using a cross-attention mechanism and time-series processing. Concretely, when integrating multiple modalities, feature compensation through mutual complementation between modalities is realized through a feature integration technique based on a cross-attention mechanism, and the representational ability of the integrated features is enhanced. In addition, by introducing time-series processing for the input data across several timesteps, it is possible to consider the temporal changes in the road surface conditions. Experiments are conducted for both detection and prediction tasks using data corresponding to the current winter condition and data corresponding to a few hours after the current winter condition, respectively. The experimental results verify the effectiveness of the proposed method for both tasks. In addition to the construction of the classification model for winter road surface conditions, we first attempt to visualize the classification results, especially the prediction results, through the image style transfer model as supplemental extended experiments on image generation at the end of the paper.
Design and Manufacture of Multifunctional 3-D Smart Skins with Embedded Sensor Networks for Robotic Applications
Authors: Ransom Elliot , Chen Xiyuan , Mangram William , Nasrollahi Amir , Topac Tanay , Chang Fu-Kuo
Published: May 2024

An investigation was performed to develop a process to design and manufacture a 3-D smart skin with an embedded network of distributed sensors for non-developable (or doubly curved) surfaces. A smart skin is the sensing component of a smart structure, allowing such structures to gather data from their surrounding environments to make control and maintenance decisions. Such smart skins are desired across a wide variety of domains, particularly for those devices where their surfaces require high sensitivity to external loads or environmental changes such as human-assisting robots, medical devices, wearable health components, etc. However, the fabrication and deployment of a network of distributed sensors on non-developable surfaces faces steep challenges. These challenges include the conformal coverage of a target object without causing prohibitive stresses in the sensor interconnects and ensuring positional accuracy in the skin sensor deployment positions, as well as packaging challenges resulting from the thin, flexible form factor of the skin. In this study, novel and streamlined processes for making such 3-D smart skins were developed from the initial sensor network design to the final integrated skin assembly. Specifically, the process involved the design of the network itself (for which a physical simulation-based optimization was developed), the deployment of the network to a targeted 3D surface (for which a specialized tool was designed and implemented), and the assembly of the final skin (for which a novel process based on dip coating was developed and implemented.)
The Effect of Training on Localizing HoloLens-Generated 3D Sound Sources
Authors: Ryu Wonyeol , Lee Sukhan , Park Eunil
Published: May 2024

Sound localization is a crucial aspect of human auditory perception. VR (virtual reality) technologies provide immersive audio platforms that allow human listeners to experience natural sounds based on their ability to localize sound. However, the simulations of sound generated by these platforms, which are based on the general head-related transfer function (HRTF), often lack accuracy in terms of individual sound perception and localization due to significant individual differences in this function. In this study, we aimed to investigate the disparities between the perceived locations of sound sources by users and the locations generated by the platform. Our goal was to determine if it is possible to train users to adapt to the platform-generated sound sources. We utilized the Microsoft HoloLens 2 virtual platform and collected data from 12 subjects based on six separate training sessions arranged in 2 weeks. We employed three modes of training to assess their effects on sound localization, in particular for studying the impacts of multimodal error, visual, and sound guidance in combination with kinesthetic/postural guidance, on the effectiveness of the training. We analyzed the collected data in terms of the training effect between pre- and post-sessions as well as the retention effect between two separate sessions based on subject-wise paired statistics. Our findings indicate that, as far as the training effect between pre- and post-sessions is concerned, the effect is proven to be statistically significant, in particular in the case wherein kinesthetic/postural guidance is mixed with visual and sound guidance. Conversely, visual error guidance alone was found to be largely ineffective. On the other hand, as far as the retention effect between two separate sessions is concerned, we could not find any meaningful statistical implication on the effect for all three error guidance modes out of the 2-week session of training. These findings can contribute to the improvement of VR technologies by ensuring they are designed to optimize human sound localization abilities.
AK-MADDPG-Based Antijamming Strategy Design Method for Frequency Agile Radar
Authors: Zhu Zhidong , Deng Xiaoying , Dong Jian , Feng Cheng , Fu Xiongjun
Published: May 2024

Frequency agility refers to the rapid variation of the carrier frequency of adjacent pulses, which is an effective radar active antijamming method against frequency spot jamming. Variation patterns of traditional pseudo-random frequency hopping methods are susceptible to analysis and decryption, rendering them ineffective against increasingly sophisticated jamming strategies. Although existing reinforcement learning-based methods can adaptively optimize frequency hopping strategies, they are limited in adapting to the diversity and dynamics of jamming strategies, resulting in poor performance in the face of complex unknown jamming strategies. This paper proposes an AK-MADDPG (Adaptive K-th order history-based Multi-Agent Deep Deterministic Policy Gradient) method for designing frequency hopping strategies in frequency agile radar. Signal pulses within a coherent processing interval are treated as agents, learning to optimize their hopping strategies in the case of unknown jamming strategies. Agents dynamically adjust their carrier frequencies to evade jamming and collaborate with others to enhance antijamming efficacy. This approach exploits cooperative relationships among the pulses, providing additional information for optimized frequency hopping strategies. In addition, an adaptive K-th order history method has been introduced into the algorithm to capture long-term dependencies in sequential data. Simulation results demonstrate the superior performance of the proposed method.
A Reliability Assessment Method for Complex Systems Based on Non-Homogeneous Markov Processes
Authors: Pan Xiaolei , Chen Hongxiao , Shen Ao , Zhao Dongdong , Su Xiaoyan
Published: May 2024

The Markov method is a common reliability assessment method. It is often used to describe the dynamic characteristics of a system, such as its repairability, fault sequence and multiple degradation states. However, the “curse of dimensionality”, which refers to the exponential growth of the system state space with the increase in system complexity, presents a challenge to reliability assessments for complex systems based on the Markov method. In response to this challenge, a novel reliability assessment method for complex systems based on non-homogeneous Markov processes is proposed. This method entails the decomposition of a complex system into multilevel subsystems, each with a relatively small state space, in accordance with the system function. The homogeneous Markov model or the non-homogeneous Markov model is established for each subsystem/system from bottom to top. In order to utilize the outcomes of the lower-level subsystem models as inputs to the upper-level subsystem model, an algorithm is proposed for converting the unavailability curve of a subsystem into its corresponding 2×2 dynamic state transition probability matrix (STPM). The STPM is then employed as an input to the upper-level system’s non-homogeneous Markov model. A case study is presented using the reliability assessment of the Reactor Protection System (RPS) based on the proposed method, which is then compared with the models based on the other two contrast methods. This comparison verifies the effectiveness and accuracy of the proposed method.
Non-Destructive Testing of Carbon Fiber-Reinforced Plastics (CFRPs) Using a Resonant Eddy Current Sensor
Authors: Ma Ming , Liu Shiyu , Zhang Ronghua , Zhang Qiong , Wu Yi , Chen Bailiang
Published: May 2024

Eddy current testing (ECT) is commonly used for the detection of defects inside metallic materials. In order to achieve the effective testing of CFRP materials, increasing the operating frequency or improving the coil structure is a common method used by researchers. Higher or wider operating frequencies make the design of the ADC’s conditioning circuit complex and difficult to miniaturize. In this paper, an LC resonator based on inductance-to-digital converters (LDCs) is designed to easily detect the resonant frequency response to the state of the material under test. The reasonableness of the coil design is proven by simulation. The high signal-to-noise ratio (SNR) and detection sensitivity of the LC resonator are demonstrated through comparison experiments involving multiple probes. The anti-interference capability of the LC resonator in CFRP defect detection is demonstrated through various interference experiments.
Sea Surface Height Measurements Based on Multi-Antenna GNSS Buoys
Authors: Xue Xiaoming , Yang Jichao , Zhao Qing , Wang Shengli , Zhao Ranshuo , Shao Hulin
Published: May 2024

Sea level monitoring is an essential foundational project for studying global climate change and the rise in sea levels. Satellite radar altimeters, which can sometimes provide inaccurate sea surface height data near the coast, are affected by both the instrument itself and geophysical factors. Buoys equipped with GNSS receivers offer a relatively flexible deployment at sea, allowing for long-term, high-precision measurements of sea surface heights. When operating at sea, GNSS buoys undergo complex movements with multiple degrees of freedom. Attitude measurements are a crucial source of information for understanding the motion state of the buoy at sea, which is related to the buoy’s stability and reliability during its development. In this study, we designed and deployed a four-antenna GNSS buoy with both position and attitude measurement capabilities near Jimiya Wharf in Qingdao, China, to conduct offshore sea surface monitoring activities. The GNSS data were processed using the Precise Point Positioning (PPK) method to obtain a time series of sea surface heights, and the accuracy was evaluated using synchronous observation data from a small sea surface height radar. The difference between the GNSS buoy and the full-time radar was calculated, resulting in a root-mean-square error (RMSE) of 1.15 cm. Concurrently, the attitude of the GNSS buoy was calculated using multi-antenna technology, and the vertical elevation of the GNSS buoy antenna was corrected using the obtained attitude data. The RMSE between the corrected GNSS buoy data and the high ground radar was 1.12 cm, indicating that the four-antenna GNSS buoy can not only acquire high-precision coastal sea level data but also achieve synchronous measurement of the buoy’s attitude. Furthermore, the data accuracy was also improved after the sea level attitude correction.