bluuebunny/crossref_metadata_2025 huggingface dataset:
Three-Dimensional Path Planning Optimization for Length Reduction of Optimal Path Applied to Robotic Systems
Authors: Chouridis Ilias , Mansour Gabriel , Tsagaris Apostolos
Published: December 2024

Path planning is an intertemporal problem in the robotics industry. Over the years, several algorithms have been proposed to solve it, but weaknesses are constantly identified by researchers, especially in creating an optimal path in a three-dimensional (3D) environment with obstacles. In this paper, a method to reduce the lengths of optimal 3D paths and correct errors in path planning algorithms is proposed. Optimization is achieved by combining the information of a generated two-dimensional (2D) path with the input 3D path. The 2D path is created by a proposed improved artificial fish swarm algorithm (AFSA) that contains several improvements, such as replacing the random behavior of the fish with a proposed one incorporating the model of the 24 possible movement points and utilizing an introduced model to assist the agent’s navigation called obstacles heatmap. Moreover, a simplified ray casting algorithm is integrated with the improved AFSA to further reduce the length of the final path. The improved algorithm effectually managed to find the optimal path in complex environments and significantly reduce the length of the formed path compared with other state-of-the-art methods. The path was implemented in real-world scenarios of drone and industrial robotic arm applications.
Backstepping-Based Nonsingular Terminal Sliding Mode Control for Finite-Time Trajectory Tracking of a Skid-Steer Mobile Robot
Authors: Teji Mulugeta Debebe , Zou Ting , Zeleke Dinku Seyoum
Published: December 2024

Skid-steer mobile robots (SSMRs) are ubiquitous in indoor and outdoor applications. Their accurate trajectory tracking control is quite challenging due to the uncertainties arising from the complex behavior of frictional force, external disturbances, and fluctuations in the instantaneous center of rotation (ICR) during turning maneuvers. These uncertainties directly disturb velocities, hindering the robot from tracking the velocity command. This paper proposes a nonsingular terminal sliding mode control (NTSMC) based on backstepping for a four-wheel SSMR to cope with the aforementioned challenges. The strategy seeks to mitigate the impacts of external disturbances and model uncertainties by developing an adaptive law to estimate the integrated lumped outcome. The finite time stability of the closed-loop system is proven using Lyapunov’s theory. The designed NTSMC input is continuous and avoids noticeable chattering. It was noted in the simulation analysis that the proposed control strategy is strongly robust against disturbance and modeling uncertainties, demonstrating effective trajectory tracking performance in the presence of disturbance and modeling uncertainties.
A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery
Authors: Hu Jianming , Zhi Xiyang , Zhang Bingxian , Shi Tianjun , Cui Qi , Sun Xiaogang
Published: December 2024

The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website.
Phase Spectrum Smoothing Demodulation: A New Frontier in eLoran Signal Processing for Enhanced Performance
Authors: Liu Shiyao , Yan Baorong , Hua Yu , Kou Wudian , Zhang Shougang , Xu Lu , Lu Jun
Published: December 2024

In the field of modern navigation and positioning, the ground-based eLoran system, serves as a vital backup to the global navigation satellite system (GNSS), which is crucial for numerous key applications. Signal demodulation, integral to eLoran’s precision timing and information transmission, significantly affects system performance. Aiming at the pulse position modulation (PPM) characteristics of eLoran signals, this paper introduces an innovative phase spectrum smoothing demodulation (PSSD) algorithm, crafted to improve demodulation performance under complex noisy and interference-laden conditions. Following a systematic review of existing demodulation techniques in eLoran, this paper details the theoretical foundation, key steps, and significant impact of parameter selection for the PSSD algorithm. Then, the unique advantages in dealing with noise, continuous wave, and skywave interference are analyzed and verified. Through extensive experimental validation under various SNR and interference conditions, the PSSD algorithm shows significant superiority in demodulation performance compared with the traditional envelope phase detection (EPD) algorithm. The effectiveness of the PSSD algorithm in interference mitigation and its stable performance across diverse conditions confirm its potential to meet the high-precision timing requirements of eLoran systems, contributing to the advancement of modern communication systems.
New Evidence of Holocene Faulting Activity and Strike-Slip Rate of the Eastern Segment of the Sunan–Qilian Fault from UAV-Based Photogrammetry and Radiocarbon Dating, NE Tibetan Plateau
Authors: Niu Pengfei , Han Zhujun , Guo Peng , Ma Siyuan , Ma Haowen
Published: December 2024

The eastern segment of the Sunan-Qilian Fault (ES-SQF) is located within the seismic gap between the 1927 M8.0 Gulang earthquake and the 1932 M7.6 Changma earthquake in China. It also aligns with the extension direction of the largest surface rupture zone associated with the 2022 Mw6.7 Menyuan earthquake. Understanding the activity parameters of this fault is essential for interpreting strain distribution patterns in the central–western segment of the Qilian–Haiyuan fault zone, located along the northeastern margin of the Tibetan Plateau, and for evaluating the seismic hazards in the region. High-resolution Google Earth satellite imagery and UAV (Unmanned Aerial Vehicle)-based photogrammetry provide favorable conditions for detailed mapping and the study of typical landforms along the ES-SQF. Combined with field geological surveys, the ES-SQF is identified as a continuous, singular-fault structure extending approximately 68 km in length. The fault trends in the WNW direction and along its trace, distinctive features, such as ridges, gullies, and terraces, show clear evidence of synchronous left lateral displacement. This study investigates the Qingsha River and the Dongzhong River. High-resolution digital elevation models (DEMs) derived from UAV imagery were used to conduct a detailed mapping of faulted landforms. An analysis of stripping trench profiles and radiocarbon dating of collected samples indicates that the most recent surface-rupturing seismic event in the area occurred between 3500 and 2328 y BP, pointing to the existence of an active fault from the Holocene epoch. Using the LaDiCaoz program to restore and measure displaced terraces at the study site, combined with geomorphological sample collection and testing, we estimated the fault’s slip rate since the Holocene to be approximately 2.0 ± 0.3 mm/y. Therefore, the ES-SQF plays a critical role in strain distribution across the central–western segment of the Qilian–Haiyuan fault zone. Together with the Tuolaishan fault, it accommodates and dissipates the left lateral shear deformation in this region. Based on the slip rate and the elapsed time since the last event, it is estimated that a seismic moment equivalent to Mw 7.5 has been accumulated on the ES-SQF. Additionally, with the significant Coulomb stress loading on the ES-SQF caused by the 2016 Mw 5.9 and 2022 Mw 6.7 Menyuan earthquakes, there is a potential for large earthquakes to occur in the future. Our results also indicate that high-resolution remote sensing imagery can facilitate detailed studies of active tectonics.
Mapping Windthrow Severity as Change in Canopy Cover in a Temperate Eucalypt Forest
Authors: Hinko-Najera Nina , Bentley Paul D. , Hislop Samuel , Bennett Alison C. , Burton Jamie E. , Fairman Thomas A. , Jellinek Sacha , Najera Umana Julio C. , Bennett Lauren T.
Published: December 2024

Storm events are significant disturbance agents that can cause considerable forest damage through windthrow. Assessment and mapping of the extent and severity of windthrow is critical to provide reliable information to forest managers to prioritize post-storm hazard reduction (including public safety and fire risk) and to guide restoration activities. Detailed on-ground assessments after windthrow are often impossible due to lack of access and safety concerns. In 2021, severe windstorms caused unprecedented and extensive windthrow in a temperate eucalypt forest in south-eastern Australia. The purpose of this study is to quantify the severity and extent of the damaged forest area as the change in percentage canopy cover using remotely sensed data. We assessed percentage canopy cover from high-resolution aerial images of 455 randomly selected plots in disturbed and undisturbed areas to train a model and machine learning framework to predict landscape scale canopy cover from Sentinel-2 images. A random forest model using all single bands and percentiles best predicted the canopy cover (R2 = 0.69). Sentinel-2 images were then used to predict canopy cover pre- and post-windthrow to assess and map the severity of windthrow as the change in percentage canopy cover. Of the total 63,471 ha of forest area assessed, 63% (39,987 ha) was impacted by windthrow, with 46% at low severity (<30% canopy cover loss), 11% at moderate (30–50% canopy cover loss) and 6% at high severity (>50% canopy cover loss). Our study provides the first quantitative mapping of windthrow severity mapping for a temperate eucalypt forest in Australia that demonstrates an effective remote assessment methodology and provides critical information to support post-windthrow management decisions.
Hole-Free Symmetric Complementary Sparse Array Design for High-Precision DOA Estimation
Authors: Ma He , Liu Libao , Gan Zhihong , Gao Yang , Mao Xingpeng
Published: December 2024

Direction of arrival (DOA) estimation plays a critical role in remote sensing, where it aids in identifying and tracking multiple targets across complex environments, from atmospheric monitoring to resource mapping. Leveraging difference covariance array (DCA) for DOA estimation has become prevalent, particularly with sparse arrays capable of resolving more targets than the number of sensors. This paper proposes a new hole-free sparse array configuration for remote sensing applications to achieve improved DOA estimation performance using DCA. By symmetrically placing a minimum redundancy array (MRA) and its complementary MRA on both sides of a sparse uniform linear array (ULA), this configuration maximizes degrees of freedom (DOFs) and minimizes mutual coupling effects. Expressions for calculating sensor positions and optimal element allocation methods to maximize DOFs are derived. Simulation experiments in various scenarios have shown the advantages of the proposed array in DOA estimation, including a strong ability to estimate multi-targets, high angular resolution, low estimation error, and strong robustness to mutual coupling.
Gaussian–Student’s t Mixture Distribution-Based Robust Kalman Filter for Global Navigation Satellite System/Inertial Navigation System/Odometer Data Fusion
Authors: Wu Jiaji , Jiang Jinguang , Tang Yanan , Liu Jianghua
Published: December 2024

Multi-source heterogeneous information fusion based on the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/odometer is an important technical means to solve the problem of navigation and positioning in complex environments. The measurement noise of the GNSS/INS/odometer integrated navigation system is complex and non-stationary; it approximates a Gaussian distribution in an open-sky environment, and it has heavy-tailed properties in the GNSS challenging environment. This work models the measurement noise and one-step prediction as the Gaussian and Student’s t mixture distribution to adjust to different scenarios. The mixture distribution is formulated as the hierarchical Gaussian form by introducing Bernoulli random variables, and the corresponding hierarchical Gaussian state-space model is constructed. Then, the mixing probability of Gaussian and Student’s t distributions could adjust adaptively according to the real-time kinematic solution state. Based on the novel distribution, a robust variational Bayesian Kalman filter is proposed. Finally, two vehicle test cases conducted in GNSS-friendly and challenging environments demonstrate that the proposed robust Kalman filter with the Gaussian–Student’s t mixture distribution can better model heavy-tailed non-Gaussian noise. In challenging environments, the proposed algorithm has position root mean square (RMS) errors of 0.80 m, 0.62 m, and 0.65 m in the north, east, and down directions, respectively. With the assistance of inertial sensors, the positioning gap caused by GNSS outages has been compensated. During seven periods of 60 s simulated GNSS data outages, the RMS position errors in the north, east, and down directions were 0.75 m, 0.30 m, and 0.20 m, respectively.
An Environment Recognition Algorithm for Staircase Climbing Robots
Authors: Liu Yanjie , Wei Yanlong , Wang Chao , Wu Heng
Published: December 2024

For deformed wheel-based staircase-climbing robots, the accuracy of staircase step geometry perception and scene mapping are critical factors in determining whether the robot can successfully ascend the stairs and continue its task. Currently, while there are LiDAR-based algorithms that focus either on step geometry detection or scene mapping, few comprehensive algorithms exist that address both step geometry perception and scene mapping for staircases. Moreover, significant errors in step geometry estimation and low mapping accuracy can hinder the ability of deformed wheel-based mobile robots to climb stairs, negatively impacting the efficiency and success rate of task execution. To solve the above problems, we propose an effective LiDAR-Inertial-based point cloud detection method for staircases. Firstly, we preprocess the staircase point cloud, mainly using the Statistical Outlier Removal algorithm to effectively remove the outliers in the staircase scene and combine the vertical angular resolution and spatial geometric relationship of LiDAR to realize the ground segmentation in the staircase scene. Then, we perform post-processing based on the point cloud map obtained from LiDAR SLAM, extract the staircase point cloud and project and fit the staircase point cloud by Ceres optimizer, and solve the dimensional information such as depth and height of the staircase by combining with the mean filtering method. Finally, we fully validate the effectiveness of the method proposed in this paper by conducting multiple sets of SLAM and size detection experiments in real different staircase scenarios.
Optically Controlled Drug Delivery Through Microscale Brain–Machine Interfaces Using Integrated Upconverting Nanoparticles
Authors: Víg Levente , Zátonyi Anita , Csernyus Bence , Horváth Ágoston C. , Bojtár Márton , Kele Péter , Madarász Miklós , Rózsa Balázs , Fürjes Péter , Hermann Petra , Hakkel Orsolya , Péter László , Fekete Zoltán
Published: December 2024

The aim of this work is to incorporate lanthanide-cored upconversion nanoparticles (UCNP) into the surface of microengineered biomedical implants to create a spatially controlled and optically releasable model drug delivery device in an integrated fashion. Our approach enables silicone-based microelectrocorticography (ECoG) implants holding platinum/iridium recording sites to serve as a stable host of UCNPs. Nanoparticles excitable in the near-infrared (lower energy) regime and emitting visible (higher energy) light are utilized in a study. With the upconverted higher energy photons, we demonstrate the induction of photochemical (cleaving) reactions that enable the local release of specific dyes as a model system near the implant. The modified ECoG electrodes can be implanted in brain tissue to act as an uncaging system that releases small amounts of substance while simultaneously measuring the evoked neural response upon light activation. In this paper, several technological challenges like the surface modification of UCNPs, the immobilization of particles on the implantable platform, and measuring the stability of integrated UCNPs in in vitro and in vivo conditions are addressed in detail. Besides the chemical, mechanical, and optical characterization of the ready-to-use devices, the effect of nanoparticles on the original electrophysiological function is also evaluated. The results confirm that silicone-based brain–machine interfaces can be efficiently complemented with UCNPs to facilitate local model drug release.
Smart Concrete Using Optical Sensors Based on Bragg Gratings Embedded in a Cementitious Mixture: Cure Monitoring and Beam Test
Authors: Souza Edson , Pinheiro Pâmela , Coutinho Felipe , Dias João , Pilar Ronaldo , Pontes Maria José , Leal-Junior Arnaldo
Published: December 2024

Smart concrete is a structural element that can combine both sensing and structural capabilities. In addition, smart concrete can monitor the curing of concrete, positively impacting design and construction approaches. In concrete, if the curing process is not well developed, the structural element may develop cracks in this early stage due to shrinkage, decreasing structural mechanical strength. In this paper, a system of measurement using fiber Bragg grating (FBG) sensors for monitoring the curing of concrete was developed to evaluate autogenous shrinkage strain, temperature, and relative humidity (RH) in a single system. Furthermore, K-type thermocouples were used as reference temperature sensors. The results presented maximum autogenous shrinkage strains of 213.64 με, 125.44 με, and 173.33 με for FBG4, FBG5, and FBG6, respectively. Regarding humidity, the measured maximum relative humidity was 98.20 %RH, which was reached before 10 h. In this case, the recorded maximum temperature was 63.65 °C and 61.85 °C by FBG2 and the thermocouple, respectively. Subsequently, the concrete specimen with the FBG strain sensor embedded underwent a bend test simulating beam behavior. The measurement system can transform a simple structure like a beam into a smart concrete structure, in which the FBG sensors’ signal was maintained by the entire applied load cycles and compared with FBG strain sensors superficially positioned. In this test, the maximum strain measurements were 85.65 με, 123.71 με, and 56.38 με on FBG7, FBG8, and FBG3, respectively, with FBG3 also monitoring autogenous shrinkage strain. Therefore, the results confirm that the proposed system of measurement can monitor the cited parameters throughout the entire process of curing concrete.
Enhanced FFT–Root–MUSIC Algorithm Based on Signal Reconstruction via CEEMD–SVD for Joint Range and Velocity Estimation for FMCW Radar
Authors: Cao Jiaxin , Yi Huiyue , Zhang Wuxiong , Xu Hui
Published: December 2024

Frequency-modulated continuous-wave (FMCW) radar is used to extract range and velocity information from the beat signal. However, the traditional joint range–velocity estimation algorithms often experience significant performances degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a novel approach utilizing the complementary ensemble empirical mode decomposition (CEEMD) combined with singular value decomposition (SVD) to reconstruct the beat signal prior to applying the FFT-Root-MUSIC algorithm for joint range and velocity estimation. This results in a novel joint range–velocity estimation algorithm termed as the CEEMD-SVD-FFT-Root-MUSIC (CEEMD-SVD-FRM) algorithm. First, the beat signal contaminated with additive white Gaussian noise is decomposed using CEEMD, and an appropriate autocorrelation coefficient threshold is determined to select the highly correlated intrinsic mode functions (IMFs). Then, the SVD is applied to the selected highly correlated IMFs for denoising the beat signal. Subsequently, the denoised IMFs and signal residuals are combined to reconstruct the beat signal. Finally, the FFT-Root-MUSIC algorithm is applied to the reconstructed beat signal to estimate both the range and Doppler frequencies, which are then used to calculate the range and velocity estimates of the targets. The proposed CEEMD-SVD-FRM algorithm is validated though simulations and experiments, demonstrating significant improvement in the robustness and accuracy of range and velocity estimates for the FMCW radar due to the effective denoising of the reconstructed beat signal. Moreover, it substantially outperforms the traditional methods in low SNR environments.
A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models
Authors: Donas Athanasios , Kordatos Ioannis , Alexandridis Alex , Galanis George , Famelis Ioannis Th.
Published: December 2024

The aim of this study is to introduce and evaluate a dual filter that combines Radial Basis Function neural networks and Kalman filters to enhance the accuracy of numerical wave prediction models. Unlike the existing methods, which focus solely on systematic errors, the proposed framework concurrently targets both systematic and non-systematic parts of forecast errors, significantly reducing the bias and variability in significant wave height predictions. The produced filter is self-adaptive, identifying optimal Radial Basis Function network configurations through an automated process involving various network parameters tuning. The produced computational system is assessed using a time-window procedure applied across divergent time periods and regions in the Aegean Sea and the Pacific Ocean. The results reveal a consistent performance, outperforming classic Kalman filters with an average reduction of 53% in bias and 28% in RMSE, underlining the dual filter’s potential as a robust post-processing tool for environmental simulations.
Use of Laccase Enzymes as Bio-Receptors for the Organic Dye Methylene Blue in a Surface Plasmon Resonance Biosensor
Authors: Sánchez-Álvarez Araceli , Quintanilla-Villanueva Gabriela Elizabeth , Rodríguez-Quiroz Osvaldo , Rodríguez-Delgado Melissa Marlene , Villarreal-Chiu Juan Francisco , Sicardi-Segade Analía , Luna-Moreno Donato
Published: December 2024

Methylene blue is a cationic organic dye commonly found in wastewater, groundwater, and surface water due to industrial discharge into the environment. This emerging pollutant is notably persistent and can pose risks to both human health and the environment. In this study, we developed a Surface Plasmon Resonance Biosensor employing a BK7 prism coated with 3 nm chromium and 50 nm of gold in the Kretschmann configuration, specifically for the detection of methylene blue. For the first time, laccases immobilized on a gold surface were utilized as bio-receptors for this organic dye. The enzyme was immobilized using carbodiimide bonds with EDC/NHS crosslinkers, allowing for the analysis of samples with minimal preparation. The method demonstrated validation with a limit of detection (LOD) of 4.61 mg L−1 and a limit of quantification (LOQ) of 15.37 mg L−1, a working range of 0–100 mg L−1, and an R2 value of 0.9614 during real-time analysis. A rainwater sample spiked with methylene blue yielded a recovery rate of 122.46 ± 4.41%. The biosensor maintained a stable signal over 17 cycles and remained effective for 30 days at room temperature.
Development of Inductive Current Transformer 400/5/1 of Class 0.2S for Frequency Range 50 Hz–5 kHz
Authors: Kaczmarek Michal , Pacholczyk Blazej
Published: December 2024

This paper is devoted to the development of a window-type inductive current transformer (iCT) with a rated primary current equal to 400 A and two secondary windings with rated currents of 5 A and 1 A. Its novelty concerns the presentation of this process in the case of an iCT with a 0.2S accuracy class ensured not only for a sinusoidal current of a frequency of 50 Hz but also for the transformation of distorted current in the harmonic frequency range from 50 Hz to 5 kHz. The maximum permissible values of the current error and phase displacement are equal to ±0.2% and ±0.2°, respectively, and are the same as the limiting values for a 50 Hz sinusoidal current of a rated RMS value. In the design process, three materials were considered for the construction of the developed iCT 400/5/1: electrical steel, permalloy and nanocrystalline (Nano). Their wideband properties were analyzed and the choice of the Nano magnetic core was justified with the already-available data in the literature on the basis of elaborated design assumptions. In order to specify its particular type, four magnetic cores with different initial magnetic permeabilities were tested. It was demonstrated that, as far as more favorable magnetic properties also involved increased active power losses in the magnetic core, it was not an appropriate choice for the construction of the iCT, especially for the transformation of the current with a main frequency equal to 50 Hz or 60 Hz.
An Elastic Filtering Algorithm with Visual Perception for Vehicle GNSS Navigation and Positioning
Authors: Ma Wenzhuo , Yue Zhe , Lian Zengzeng , Li Kezhao , Sun Chenchen , Zhang Mengshuo
Published: December 2024

Amidst the backdrop of the profound synergy between navigation and visual perception, there is an urgent demand for accurate real-time vehicle positioning in urban environments. However, the existing global navigation satellite system (GNSS) algorithms based on Kalman filters fall short of precision. In response, we introduce an elastic filtering algorithm with visual perception for vehicle GNSS navigation and positioning. Firstly, the visual perception system captures real-time environmental data around the vehicle. It utilizes the interframe differential optical flow method and vehicle state switching characteristics to assess the current driving status. Secondly, we design an elastic filtering model specifically for various vehicle states. This model enhances the precision of Kalman filter-based GNSS navigation. In urban driving, vehicles often experience frequent stationary parking. To address this, we incorporate a zero-speed constraint to further refine vehicle location data when the vehicle is stationary. This constraint matches the data with the appropriate elastic filtering model. Ultimately, we conduct simulation and real-world vehicle navigation experiments to confirm the validity and rationality of our proposed algorithm. Compared with the conventional algorithm and the existing interactive multi-model algorithm, the proposed algorithm significantly improves the navigation and positioning accuracy of vehicle GNSS in urban environments. Compared to the commonly used constant acceleration (CA) and Constant Velocity (CV) models, there has been a significant improvement in positioning accuracy. Furthermore, when benchmarked against the more advanced interactive multi-model (IMM) model, the method proposed in this paper has enhanced the positioning accuracy enhancements in three dimensions: 21.8%, 20.9%, and 31.3%, respectively.
A Second-Order True-VCO ADC Employing a Digital Pseudo-DCO Suitable for Sensor Arrays
Authors: Loi Dante , Medina Victor , Corporales Luis Hernandez
Published: December 2024

This paper explores the implementation of a VCO-based ADC, achieving an ENOB of 12 bits with 1 MHz of a sampling rate in the audio bandwidth. The solution exploits the scalability and PVT invariance of a novel digital-to-frequency converter to reduce the size and consumed power. The architecture has been validated in a 130 nm CMOS technology node displaying a power consumption of 105.57 μW and a silicon footprint of 0.034 mm2 in a pseudo-differential configuration. Performance can be dynamically adjusted to trade off power consumption by resolution without changing the sampling rate. In addition, the proposed architecture benefits from multiple instantiations in the same SoC, making it particularly suitable for sensor array applications, such as biomedical sensors and spatial audio arrays.
Revolutionizing Free-Space Optics: A Survey of Enabling Technologies, Challenges, Trends, and Prospects of Beyond 5G Free-Space Optical (FSO) Communication Systems
Authors: Alimi Isiaka A. , Monteiro Paulo P.
Published: December 2024

As the demand for high-speed, low-latency communication continues to grow, free-space optical (FSO) communication has gained prominence as a promising solution for supporting the next generation of wireless networks, especially in the context of the 5G and beyond era. It offers high-speed, low-latency data transmission over long distances without the need for a physical infrastructure. However, the deployment of FSO systems faces significant challenges, such as atmospheric turbulence, weather-induced signal degradation, and alignment issues, all of which can impair performance. This paper offers a comprehensive survey of the enabling technologies, challenges, trends, and future prospects for FSO communication in next-generation networks, while also providing insights into the current mitigation strategies. The survey explores the critical enabling technologies such as adaptive optics, modulation schemes, and error correction codes that are revolutionizing FSO communication and addressing the unique challenges of FSO links. Also, the integration of FSO with radio frequency, millimeter-wave, and Terahertz technologies is explored, emphasizing hybrid solutions that enhance reliability and coverage. Additionally, the paper highlights emerging trends, such as the integration of FSO with artificial intelligence-driven optimization techniques and the growing role of machine learning in enhancing FSO system performance for dynamic environments. By analyzing the current trends and identifying key challenges, this paper emphasizes the prospects of FSO communication in the evolving landscape of 5G and future networks. In this regard, it assesses the potential of FSO to meet the demands for high-speed, low-latency communication and offers insights into its scalability, reliability, and deployment strategies for 5G and beyond. The paper concludes by identifying the open challenges and future research directions critical to realizing the full potential of FSO in next-generation communication systems.
Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence
Authors: Cai Zhengying , Du Jingshu , Huang Tianhao , Lu Zhuimeng , Liu Zeya , Gong Guoqiang
Published: December 2024

With the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing the issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes a novel edge computing method based on vehicle edge intelligence to solve the energy-efficient collision-free machine/AGV scheduling problem. First, a vehicle edge intelligence architecture was built, and the corresponding state transition diagrams for collision-free scheduling were developed. Second, the energy-efficient collision-free machine/AGV scheduling problem was modeled as a multi-objective function with electric capacity constraints, where production efficiency, collision prevention, and energy conservation were comprehensively considered. Third, an artificial plant community algorithm was explored based on the edge intelligence of AGVs. The proposed method utilizes a heuristic search and the swarm intelligence of multiple AGVs to realize energy-efficient collision-free scheduling and is suitable for deploying on embedded platforms for edge computing. Finally, a benchmark dataset was developed, and some benchmark experiments were conducted, where the results revealed that the proposed heuristic method could effectively instruct multiple automatic guided vehicles to avoid collisions with high energy efficiency.
Improving the Performance of Electrotactile Brain–Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials
Authors: Novičić Marija , Djordjević Olivera , Miler-Jerković Vera , Konstantinović Ljubica , Savić Andrej M.
Published: December 2024

Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users’ selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory–motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value.
Sudden Fall Detection of Human Body Using Transformer Model
Authors: Kibet Duncan , So Min Seop , Kang Hahyeon , Han Yongsu , Shin Jong-Ho
Published: December 2024

In human activity recognition, accurate and timely fall detection is essential in healthcare, particularly for monitoring the elderly, where quick responses can prevent severe consequences. This study presents a new fall detection model built on a transformer architecture, which focuses on the movement speeds of key body points tracked using the MediaPipe library. By continuously monitoring these key points in video data, the model calculates real-time speed changes that signal potential falls. The transformer’s attention mechanism enables it to catch even slight shifts in movement, achieving an accuracy of 97.6% while significantly reducing false alarms compared to traditional methods. This approach has practical applications in settings like elderly care facilities and home monitoring systems, where reliable fall detection can support faster intervention. By homing in on the dynamics of movement, this model improves both accuracy and reliability, making it suitable for various real-world situations. Overall, it offers a promising solution for enhancing safety and care for vulnerable populations in diverse environments.
Sensitivity Analysis of an Optical Interferometric Surface Stress Ethanol Gas Sensor with a Freestanding Nanosheet
Authors: Sogame Ryusei , Choi Yong-Joon , Noda Toshihiko , Sawada Kazuaki , Takahashi Kazuhiro
Published: December 2024

Ethanol (EtOH) gas detection has garnered considerable attention owing to its wide range of applications in industries such as food, pharmaceuticals, medical diagnostics, and fuel management. The development of highly sensitive EtOH-gas sensors has become a focus of research. This study proposes an optical interferometric surface stress sensor for detecting EtOH gas. The sensor incorporates a 100 nm-thick freestanding membrane of Parylene C and gas-sensitive polymethylmethacrylate (PMMA) fabricated within a microcavity on a Si substrate. The results showed that reducing the thickness of the freestanding Parylene C membrane is essential for achieving higher sensitivity. Previously, a 100-nm-thick membrane transfer onto microcavities was achieved using a surfactant-assisted release technique. However, polymerization inhibition caused by the surfactant presented challenges in forming ultrathin membranes of several tens of nanometers. In this study, we employed a surfactant-free release technique using a hydrophilic natural oxide layer to successfully form a 14-nm-thick freestanding Parylene C membrane. In contrast, the optimum thickness of the gas-adsorbed PMMA membrane was approximately 295 nm. Moreover, we demonstrated that this thinner membrane improved EtOH gas detection sensitivity by a factor of eight compared with our previously reported sensor. Thus, this study advances the field of nanoscale materials and sensor technology.
Measuring Precipitation via Microwave Bands with a High-Accuracy Setup
Authors: Sakkas Alexandros , Christofilakis Vasilis , Lolis Christos J. , Chronopoulos Spyridon K. , Peppas Kostas P.
Published: December 2024

The urgent need for timely and accurate precipitation estimations in the face of ongoing climate change and the increasing frequency and/or intensity of extreme weather events underscores the necessity for innovative approaches. Recently, several studies have focused on estimating the precipitation rate through induced attenuation of radio frequency (RF) signals, which are abundant in modern communication systems. Most research has concentrated on frequencies exceeding 10 GHz, as attenuation at lower frequencies is minimal, posing measurement challenges. This study aims to confront this limitation by introducing a high-precision experimental setup capable of detecting this subtle attenuation at frequencies under 10 GHz. The setup includes a transmitter and receiver optimized for operation at 2.07, 4.63, and 6.22 GHz, where minimal worldwide research exists. A power resolution below 10−5 dB in preliminary measurements demonstrated its effectiveness in quantifying signal attenuation due to precipitation across the specified frequencies. Moreover, a strong power law relationship was observed between signal attenuation and precipitation rate for all three frequencies, while, as expected, the higher the frequency, the more pronounced the signal attenuation was.
The Effects of Temporary Portable Rumble Strips on Vehicle Speeds in Road Work Zones
Authors: Al-Bayati Ahmed Jalil , Ali Mason , Alhomaidat Fadi , Bandara Nishantha , Chen Yuting
Published: December 2024

The safety of construction and maintenance work zones has been highlighted as a crucial aspect of construction management that requires special attention due to the increasing number of fatal and non-fatal injuries in recent years. Temporary traffic control (TTC) is required by the Occupational Safety and Health Administration (OSHA) to improve overall safety performance during road construction and maintenance projects. The fact that speeding and distracted drivers may overlook TTC warning signs and directions has been reported as one of the leading causes of work zone incidents. This study aimed to examine both the impact of temporary portable rumble strips (TPRSs) on traffic speeds and the response of different vehicle types in road work zones, including trucks and cars. Accordingly, field experiments were conducted in collaboration with the Road Commission for Oakland County (RCOC) in Michigan. The findings indicate that TPRSs have a statistically significant impact on the driving speed of light vehicle drivers but not on medium and heavy vehicles, such as trucks. This study contributes to the existing literature by quantifying the safety benefits of TPRS use, providing valuable data for policymakers and construction professionals. By demonstrating the effectiveness of TPRSs in reducing the speed of light vehicles, this research supports the implementation of these systems as a practical measure for enhancing safety within road construction work zones. Additionally, this study highlights the need for tailored approaches to address the limited impact on larger vehicles, underscoring the importance of developing complementary strategies to ensure comprehensive safety improvements across all vehicle types.
Chan Seeds (Hyptis suaveolens L.) Storage Proteins: Isolation, Characterization, and Cytotoxic Effect on Three Cancer Cell Lines
Authors: Toribio-Nava Elena , Estrada-Girón Yokiushirdhilgilmara , Velázquez-Juárez Gilberto , López-Mercado Janneth , Delgado-Saucedo Jorge I. , Castro-García Paola Beatriz , Gallegos-Arreola Martha Patricia , Garibaldi-Ríos Asbiel Felipe , Rodríguez-Arevalo Fabiola , Puebla-Pérez Ana María
Published: December 2024

Since pre-Hispanic times, chan seeds (Hyptis suaveolens L.) have been used as food and in traditional medicine. However, there is still a lack of knowledge about the antioxidant capacity and cytotoxic effect of their storage proteins on cancer cells. Thus, these were investigated in this study. The total protein content of the seeds was 19.5% (dry base), and its protein fractions were confirmed via Tris-Tricine-SDS-PAGE electrophoresis as 43.1% glutelins, 30.9% albumins, 23% globulins, and 2.9% prolamins. The antioxidant capacity determined by ABTS showed the highest percentage of inhibition for the prolamins fraction of 23.6% (half-maximal inhibitory concentration [IC50]: 1.38 µg protein/mL) and the lowest percentage for the glutelins fraction of 6.3% (IC50: 4.51 µg protein/mL). The cytotoxic activity against the murine lymphoma L5178Y, human cervical (Hela), and colorectal (Caco-2) cell lines revealed that prolamin exerted superior inhibition on the Hela and Caco-2 cancer cells, with IC50 values of 0.49 and 0.44 mg protein/mL, respectively. This study underscores the potential of chan seed proteins as natural antioxidants and anticancer agents.
Toward the Human Scale in Smart Cities: Exploring the Role of Active Mobility in Ecosystemic Urbanism
Authors: Correa Froylán , Bartorila Miguel , Ribeiro-Palacios Mónica , Pérez-Soto Gerardo I. , Rodríguez-Reséndiz Juvenal
Published: December 2024

Active Mobility (AM) currently presents an opportunity to change the paradigm of the competitive and dispersed city created by motorized mobility, revaluing the role of walking and cycling in generating more sustainable urban ecosystems. This article addresses the challenges and opportunities for AM to contribute to the regeneration of urban systems and the capacity for anticipation. This article analyzes AM using the Ecosystemic Urbanism (EU) as an analysis framework within its four axes: social cohesion, complexity, efficiency, and compactness and functionality. Through this analysis, the points of incidence of AM were identified within each of these axes. The study highlights the potential of AM to act as a transformative driver in urban development, integrating an ecological framework where urban systems are interconnected and mutually reinforced. This perspective reveals walking and cycling as a catalyst for reshaping urban interactions. In light of this, future cities must adopt a human urban scale through compactness that fosters complexity and diverse and engaging urban interactions. In addition, the enjoyability achieved through AM brings significant ecosystem benefits by promoting awareness of others, nature, and the interconnectedness between the individual and the city. This represents a new paradigm shift in which the automobile does not play the central role, allowing more sustainable ways of living together.
Digital Transformation for Era Society 5.0 and Resilience: Urgent Issues from Indonesia
Authors: Rohayati Yeti , Abdillah Abdillah
Published: December 2024

This research aims to analyze the development of digital transformation in Society 5.0 in Indonesia to identify challenges and opportunities in its implementation. This research method uses a qualitative exploratory approach with a case study on digital developments in Society 5.0 in Indonesia. The analysis in this study uses interactive techniques and is assisted via Nvivo 12 Plus. The results of this research show that the era of Society 5.0 has forced Indonesia to carry out digital transformation in various public sectors, such as governance and administration, economics, and socio-culture. In line with the demands of the Society 5.0 era, digital transformation in Indonesia seeks to realize super smart Society 5.0 in 2024 to develop capabilities to solve social problems that occur as a result of the emergence of the Industrial Revolution 4.0 to 5.0. This is intended to create ideal conditions in various public sectors, such as government, economics, and social culture. However, in line with these efforts, Indonesia faces challenges in digital transformation, such as digital transformation efforts, which to date have not been optimal including in human resources. In various regions in Indonesia, there are still problems with inadequate infrastructure for encouraging digital transformation in Indonesia. The contribution of this research is a reference in the development of the concept of digital transformation and Society 5.0. Apart from that, this research is also an evaluation material for the Indonesian government regarding the digital transformation that has been attempted.
Roma in the COVID-19 Crisis: Transformative Elements That Emerge from the Research
Authors: Valero Diana , Elboj Carmen , De Botton Lena
Published: December 2024

Research on the impact of the COVID-19 pandemic on the Roma community consistently demonstrates that it has exacerbated pre-existing social exclusion and marginalization. However, few studies have specifically addressed the transformative actions or strategies that helped to mitigate these adverse effects. This study aimed to identify and analyze transformative elements through a systematic review, following PRISMA guidelines. The search was conducted across Web of Science, SCOPUS, and Google Scholar, yielding 995 records, of which 46 were included in the final phase and 25 in our synthesis. Our analysis focused on community-led initiatives that addressed inequalities, particularly when Roma community members were involved in the design of solutions or took on mediation roles. The results underscore the critical importance of inclusive, community-driven actions in overcoming structural barriers and promoting long-term resilience within Roma communities during the pandemic.