bluuebunny/crossref_metadata_2025 huggingface dataset:
A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell
Authors: Radaš Ivan , Matić Luka , Šunde Viktor , Ban Željko
Published: February 2024

This paper proposes a new method for assessing the state of charge (SoC) and identifying the types of different lithium-ion cells used in the battery systems of light electric vehicles. A particular challenge in the development of this method was the SoC estimation time, as the method is intended for implementation in the control system of a bicycle charging station, where the state of charge must be determined immediately after the bicycle is plugged in in order to start the charging process as quickly as possible according to the appropriate charging algorithm. The method is based on the identification of the transfer function, i.e., the dynamic response of the battery voltage to the current pulse. In the learning phase of this method, a database of reference transfer functions and corresponding SoCs for a specific type of battery cell is created. The transfer functions are described by coefficients determined through the optimization procedure. The algorithm for estimating the unknown battery cell SoCs is based on the comparison of the measured voltage response with the responses of the reference transfer functions from the database created during the learning process to the same current signal. The comparison is made by calculating the integral of the square error (ISE) between the response of the specific reference transfer function and the measured voltage response of the battery cell. Each transfer function corresponds to a specific SoC and cell type. The specific SoC of the unknown battery is determined by quadratic interpolation of the SoC near the reference point with the smallest ISE for each battery type. The cell type detection algorithm is based on the fact that the integral squared error criterion near the actual SoC for the actual cell type changes less than the squared error criterion for any other battery cell type with the same SoC. An algorithm for estimating the SoC and cell type is described and tested on several different cell types. The relative error between the estimated SoC and the actual SoC was used as a measure of the accuracy of the algorithm, where the actual SoC was calculated using the Coulomb counting method.
Bioenergy and Biopesticides Production in Serbia—Could Invasive Alien Species Contribute to Sustainability?
Authors: Pušić Magdalena , Ljubojević Mirjana , Prvulović Dejan , Kolarov Radenka , Tomić Milan , Simikić Mirko , Vejnović Srđan , Narandžić Tijana
Published: February 2024

The critical role of energy in contemporary life and the environmental challenges associated with its production imply the need for research and exploration of its novel resources. The present review paper emphasizes the continuous exploitation of non-renewable energy sources, suggesting the transition toward renewable energy sources, termed ‘green energy’, as a crucial step for sustainable development. The research methodology involves a comprehensive review of articles, statistical data analysis, and examination of databases. The main focus is biomass, a valuable resource for bioenergy and biopesticide production, highlighting not only its traditional diverse sources, such as agricultural waste and industrial residues, but also non-edible invasive alien plant species. This study explores the utilization of invasive alien species in circular economy practices, considering their role in bioenergy and biopesticide production. The potential conflict between bioproduct acquisition and food sector competition is discussed, along with the need for a shift in approaching non-edible biomass sources. The paper emphasizes the untapped potential of under-explored biomass resources and the necessity for policy alignment and public awareness. Species with a significant potential for these sustainable strategies include Acer negundo L., Ailanthus altisima (Mill.) Swingle., Amorpha fruticosa L., Elaengus angustifolia L., Falopia japonica (Houtt.) Ronse Decr., Hibiscus syriacus L., Koelreuteria paniculata Laxm., Paulownia tomentosa Siebold and Zucc., Partenocissus quenquefolia (L.) Planch., Rhus typhina L., Robinia pseudoacacia L. and Thuja orientalis L. In conclusion, the paper highlights the intertwined relationship between energy, environmental sustainability, and circular economy principles, providing insights into Serbia’s efforts and potential in adopting nature-based solutions for bioenergy and biopesticides acquisition.
Online Partition-Cooling System of Hot-Rolled Electrical Steel for Thermal Roll Profile and Its Industrial Application
Authors: Wang Qiuna , Sun Jiquan , Yang Jiaxuan , Wang Haishen , Dong Lijie , Jiao Yanlong , Li Jieming , Zhi Zhenyang , Yang Lipo
Published: February 2024

The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. However, it is always overlooked due to its dynamic uncertainty. To solve this problem, it is necessary to achieve online cooling-status control for the local thermal expansion of rolls. Based on the existing structure of a mill, a pair of special partition-cooling beams with an intelligent cooling system was designed. For high efficiency and practicality, a new online predictive model was established for the dynamic temperature field of the hot-rolling process. An equivalent treatment was applied to the boundary condition corresponding to the practical cooling water flow. In addition, by establishing the corresponding target distribution curve for the partitioned water flow cooling, online water-flow-partitioning control of the thermal roll profile was achieved. In the practical application process, a large number of onsite results exhibited that the predicted error was within 5% compared to the experimental results. The temperature difference between the upper and lower rolls was within 5 °C, and the temperature difference on both sides of the rolls was controlled within 0.7 °C. The hit rate of convexity (C40) increased by 33%. It was demonstrated that the partition-cooling processes of hot rolling are effective for the local shape and special convexity. They are able to serve as a better control method in the hot-rolling process.
Research on Fluid–Solid Coupling Mechanism around Openhole Wellbore under Transient Seepage Conditions
Authors: Liu Erhu , Zhou Desheng , Su Xu , Wang Haiyang , Liu Xiong , Xu Jinze
Published: February 2024

Hydraulic fracturing is one of the most important enhanced oil recovery technologies currently used to develop unconventional oil and gas reservoirs. During hydraulic fracture initiation, fluid seeps into the reservoir rocks surrounding the wellbore, inducing rock deformation and changes in the stress field. Analyzing the fluid–solid coupling mechanism around the wellbore is crucial to the construction design of fracturing technologies such as pulse fracturing and supercritical carbon dioxide fracturing. In this study, a new transient fluid–solid coupling model, capable of simulating the pore pressure field and effective stress field around the wellbore, was established based on the Biot consolidation theory combined with the finite difference method. The numerical results are in excellent agreement with the analytical solutions, indicating the reliability of the model and the stability of the computational approach. Using this model, the influence of seepage parameters and reservoir properties on the fluid–solid coupling around the open-hole wellbore was investigated. The simulation results demonstrate that, during wellbore pressurization, significant changes occur in the pore pressure field and effective stress field near the wellbore. The fluid–solid coupling effect around the wellbore returns to its initial state when the distance exceeds four times the radius away from the wellbore. As the fluid viscosity and wellbore pressurization rate decrease, the pore pressure field and effective circumferential stress (ECS) field around the wellbore become stronger. Adjusting the fluid viscosity and wellbore pressurization rate can control the effect of seepage forces on the rock skeleton during wellbore fluid injection. For the same injection conditions, rocks with q higher Young’s modulus and Poisson’s ratio exhibit stronger pore pressure fields and ECS fields near the wellbore. This model furnishes a dependable numerical framework for examining the fluid–solid coupling mechanism surrounding the open-hole wellbore in the initiation phase of hydraulic fractures.
The Sardinian Mammoth’s Evolutionary History: Lights and Shadows
Authors: Palombo Maria Rita , Zedda Marco , Zoboli Daniel
Published: February 2024

The dwarf Sardinian mammoth, Mammuthus lamarmorai, is a well-known species frequently cited in the literature; however, the fossil record of the Pleistocene Sardinian mammoths mainly consists of isolated remains (an incomplete skeleton from Guardia Pisano Hill, isolated teeth and a largely incomplete tibia from different localities, and some footprints from Funtana Morimenta), which have been found in sites presumably ranging in age from the late Middle to the Late Pleistocene. All of the remains have been ascribed to a single species of an endemic mammoth, Mammuthus lamarmorai, which is moderately reduced in size. The paucity of remains increases uncertainties about the chronological range of Sardinian mammoth remains, some of which are currently missing, while others lack sound information about their exact provenance or were removed and collected without contextual information. As a result, the different sizes of molariform teeth from different localities, the lack of chewing tooth remains at Guardia Pisano Hill, and the doubtful chronology of some remains hamper any attempt to infer whether one or more species that originated from an anagenetic or radiative evolutionary process or through multiple arrivals from the mainland inhabited the island. Therefore, the continental ancestor or ancestors of Sardinian mammoth populations and the time and number of dispersals of the ancestral taxon or taxa have long been debated, and the persistence through time of mammoth endemic populations still remains an unsolved matter. This research summarizes and critically reviews our knowledge about the Sardinian endemic mammoth, provides new evidence about the Sardinian mammoth’s ancestor and the possible time of its dispersal from the mainland to the island, gives new information about the Sardinian mammoth’s histology and physical characteristics, and highlights some focal, unsolved questions (e.g., morphological and dimensional differences in dentition, number of Sardinian mammoth species, population dynamics, decline, and disappearance). Further research and increasing data, which will enable taphonomic, spectrometric, and dating studies, will provide better results to solve the remaining questions.
Back to Exile: Current Jewish Critiques of the Jewish State
Authors: Lapidot Elad
Published: February 2024

This article reviews recent books by Jewish thinkers that critique the idea of a Jewish state from the perspective of Jewish exile. It outlines two main approaches. The first, secular approach, rejects the Jewish state in favor of a secular state, seeing Judaism itself as the problem, whether arising from biblical violence or collective identity. The second, post-secular approach, rejects the Jewish state as secular, and finds resources in Jewish tradition for an alternative political vision centered on exile, understood as resistance to sovereignty and violence. This article argues that Jewish opposition to the Jewish state aims to limit sovereignty, integrate Jews into the Middle East space, and recover an exilic Jewish tradition of social ethics and pluralism. The idea of exile thus provides resources for envisioning decolonization and coexistence in Israel–Palestine.
Technogenic Reservoirs Resources of Mine Methane When Implementing the Circular Waste Management Concept
Authors: Brigida Vladimir , Golik Vladimir Ivanovich , Voitovich Elena V. , Kukartsev Vladislav V. , Gozbenko Valeriy E. , Konyukhov Vladimir Yu. , Oparina Tatiana A.
Published: February 2024

From a commercial viewpoint, mine methane is the most promising object in the field of reducing emissions of climate-active gases due to circular waste management. Therefore, the task of this research is to estimate the technogenic reservoirs resources of mine methane when implementing the circular waste management concept. The novelty of the authors’ approach lies in reconstructing the response space for the dynamics of methane release from the front and cross projections: CH4 = ƒ(S; t) and CH4 = ƒ(S; L), respectively. The research established a polynomial dependence of nonlinear changes in methane concentrations in the mixture extracted by type 4 wells when a massif is undermined as a result of mining in a full-retreat panel. And the distance from the face to the start of mining the panel is reduced by 220 m. For this reason, the emission of mine methane, in case of degasification network disruption in 15 days, can amount to more than 660 thousand m3 only for wells of type no. 4.
Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models
Authors: Pan Weihao , Li Hualong , Zhou Xiaobo , Jiao Jun , Zhu Cheng , Zhang Qiang
Published: February 2024

In order to solve the problem of low recognition accuracy of traditional pig sound recognition methods, deep neural network (DNN) and Hidden Markov Model (HMM) theory were used as the basis of pig sound signal recognition in this study. In this study, the sounds made by 10 landrace pigs during eating, estrus, howling, humming and panting were collected and preprocessed by Kalman filtering and an improved endpoint detection algorithm based on empirical mode decomposition-Teiger energy operator (EMD-TEO) cepstral distance. The extracted 39-dimensional mel-frequency cepstral coefficients (MFCCs) were then used as a dataset for network learning and recognition to build a DNN- and HMM-based sound recognition model for pig states. The results show that in the pig sound dataset, the recognition accuracy of DNN-HMM reaches 83%, which is 22% and 17% higher than that of the baseline models HMM and GMM-HMM, and possesses a better recognition effect. In a sub-dataset of the publicly available dataset AudioSet, DNN-HMM achieves a recognition accuracy of 79%, which is 8% and 4% higher than the classical models SVM and ResNet18, respectively, with better robustness.
Telehealth-Enabled In-Home Elbow Rehabilitation for Brachial Plexus Injuries Using Deep-Reinforcement-Learning-Assisted Telepresence Robots
Authors: Khan Muhammad Nasir , Altalbe Ali , Naseer Fawad , Awais Qasim
Published: February 2024

Due to damage to the network of nerves that regulate the muscles and feeling in the shoulder, arm, and forearm, brachial plexus injuries (BPIs) are known to significantly reduce the function and quality of life of affected persons. According to the World Health Organization (WHO), a considerable share of global disability-adjusted life years (DALYs) is attributable to upper limb injuries, including BPIs. Telehealth can improve access concerns for patients with BPIs, particularly in lower-middle-income nations. This study used deep reinforcement learning (DRL)-assisted telepresence robots, specifically the deep deterministic policy gradient (DDPG) algorithm, to provide in-home elbow rehabilitation with elbow flexion exercises for BPI patients. The telepresence robots were used for a six-month deployment period, and DDPG drove the DRL architecture to maximize patient-centric exercises with its robotic arm. Compared to conventional rehabilitation techniques, patients demonstrated an average increase of 4.7% in force exertion and a 5.2% improvement in range of motion (ROM) with the assistance of the telepresence robot arm. According to the findings of this study, telepresence robots are a valuable and practical method for BPI patients’ at-home rehabilitation. This technology paves the way for further research and development in telerehabilitation and can be crucial in addressing broader physical rehabilitation challenges.
Bi-Directional Long Short-Term Memory-Based Gait Phase Recognition Method Robust to Directional Variations in Subject’s Gait Progression Using Wearable Inertial Sensor
Authors: Jeon Haneul , Lee Donghun
Published: February 2024

Inertial Measurement Unit (IMU) sensor-based gait phase recognition is widely used in medical and biomechanics fields requiring gait data analysis. However, there are several limitations due to the low reproducibility of IMU sensor attachment and the sensor outputs relative to a fixed reference frame. The prediction algorithm may malfunction when the user changes their walking direction. In this paper, we propose a gait phase recognition method robust to user body movements based on a floating body-fixed frame (FBF) and bi-directional long short-term memory (bi-LSTM). Data from four IMU sensors attached to the shanks and feet on both legs of three subjects, collected via the FBF method, are processed through preprocessing and the sliding window label overlapping method before inputting into the bi-LSTM for training. To improve the model’s recognition accuracy, we selected parameters that influence both training and test accuracy. We conducted a sensitivity analysis using a level average analysis of the Taguchi method to identify the optimal combination of parameters. The model, trained with optimal parameters, was validated on a new subject, achieving a high test accuracy of 86.43%.
Low Velocity Impact Monitoring of Composite Tubes Based on FBG Sensors
Authors: Huan Shengsheng , Lu Linjiao , Shen Tao , Du Jianke
Published: February 2024

Carbon fiber reinforced composites (CFRP) are susceptible to hidden damage from low velocity external impacts during their service life. To ensure the proper monitoring of the state of the composites, it is crucial to predict the location of an impact event. In this paper, fiber Bragg grating (FBG) sensors are affixed to the surface of a carbon fiber composite tube, and an optical sensing interrogator is used to capture the central wavelength shift of the FBG sensors due to low-velocity impacts. A discrete wavelet transform is used for noise reduction in the response signals. Then, the differences in the captured response signals of the FBG sensors at different locations of the impact were analyzed. Moreover, two methods were implemented to predict the location of low-velocity impacts, according to the differences in the captured response signals. The BP neural network-based method utilized three data sets to train the neural network, resulting in an average localization error of 20.68 mm. In contrast, the method based on error outliers selected a specific data set as the reference dataset, achieving an average localization error of 13.98 mm. The comparison of the predicted results shows that the latter approach has a higher predictive accuracy and does not require a significant amount of data.
Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network
Authors: Guo Lie , Ge Pingshu , Shi Zhenzhou
Published: February 2024

Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based on the Channel Attention mechanism (CA-HDC) is developed to extract features, which improves the lane feature extraction in complicated environments and solves the problem of poor robustness of the traditional PINet. A lane information fusion module and a trajectory adjustment module based on the foresight information are developed. A socially acceptable trajectory with Generative Adversarial Networks (S-GAN) is developed to reduce the error of the trajectory prediction algorithm. The lane detection accuracy in special scenarios such as crowded, shadow, arrow, crossroad, and night are improved on the CULane dataset. The average F1-measure of the proposed lane detection has been increased by 4.1% compared to the original PINet. The trajectory prediction test based on D2-City indicates that the average displacement error of the proposed trajectory prediction algorithm is reduced by 4.27%, and the final displacement error is reduced by 7.53%. The proposed algorithm can achieve good results in lane detection and multi-object trajectory prediction tasks.
Energy-Efficient BWP Configuration for Multi-Slice Users
Authors: Saad Joe , Khawam Kinda , Yassin Mohamad , Costanzo Salvatore
Published: February 2024

Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users’ battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users’ energy efficiency and quality of service.
Stretchable Nanofiber-Based Felt as a String Electrode for Potential Use in Wearable Glucose Biosensors
Authors: Seufert Bianca , Thomas Sylvia , Takshi Arash
Published: February 2024

Nanofiber technology is leading the revolution of wearable technology and provides a unique capability to fabricate smart textiles. With the novel fabrication technique of electrospinning, nanofibers can be fabricated and then manufactured into a durable conductive string for the application of smart textiles. This paper presents an electrospun nanofiber mesh-based (NF-Felt) string electrode with a conducting polymer coating for an electrochemical enzymatic glucose sensor. The surface area of a nanofiber matrix is a key physical property for enhanced glucose oxidase (GOx) enzyme binding for the development of an electrochemical biosensor. A morphological characterization of the NF-Felt string electrode was performed using scanning electron microscopy (SEM) and compared with a commercially available cotton–polyester (Cot-Pol) string coated with the same conducting polymer. The results from stress–strain testing demonstrated high stretchability of the NF-Felt string. Also, the electrochemical characterization results showed that the NF-Felt string electrode was able to detect a glucose concentration in the range between 0.0 mM and 30.0 mM with a sensitivity of 37.4 μA/mM·g and a detection limit of 3.31 mM. Overall, with better electrochemical performance and incredible flexibility, the NF-Felt-based string electrode is potentially more suitable for designing wearable biosensors for the detection of glucose in sweat.
Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor
Authors: Wang Qiang , Cheng Tao , Lu Yijun , Liu Haichuan , Zhang Runhua , Huang Jiandong
Published: February 2024

This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of “Ease of Installation” as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model’s performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.
A Light Multi-View Stereo Method with Patch-Uncertainty Awareness
Authors: Liu Zhen , Wu Guangzheng , Xie Tao , Li Shilong , Wu Chao , Zhang Zhiming , Zhou Jiali
Published: February 2024

Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a fixed range for all the pixels during inverse depth sampling can adversely affect depth estimation. To address these challenges, we present a novel learning-based multi-view stereo method incorporating attention mechanisms and an adaptive depth sampling strategy. Firstly, we propose a lightweight, coarse-feature-enhanced feature pyramid network in the feature extraction stage, augmented by a coarse-feature-enhanced module. This module integrates features with channel and spatial attention, enriching the contextual features that are crucial for the initial depth estimation. Secondly, we introduce a novel patch-uncertainty-based depth sampling strategy for depth refinement, dynamically configuring depth sampling ranges within the GRU-based optimization process. Furthermore, we incorporate an edge detection operator to extract edge features from the reference image’s feature map. These edge features are additionally integrated into the iterative cost volume construction, enhancing the reconstruction accuracy. Lastly, our method is rigorously evaluated on the DTU and Tanks and Temples benchmark datasets, revealing its low GPU memory consumption and competitive reconstruction quality compared to other learning-based MVS methods.
Ambient Monitoring Portable Sensor Node for Robot-Based Applications
Authors: Al-Okby Mohammed Faeik Ruzaij , Junginger Steffen , Roddelkopf Thomas , Huang Jiahao , Thurow Kerstin
Published: February 2024

The leakage of gases and chemical vapors is a common accident in laboratory processes that requires a rapid response to avoid harmful effects if humans and instruments are exposed to this leakage. In this paper, the performance of a portable sensor node designed for integration with mobile and stationary robots used to transport chemical samples in automated laboratories was tested and evaluated. The sensor node has four main layers for executing several functions, such as power management, control and data preprocessing, sensing gases and environmental parameters, and communication and data transmission. The responses of three metal oxide semiconductor sensors, BME680, ENS160, and SGP41, integrated into the sensing layer have been recorded for various volumes of selected chemicals and volatile organic compounds, including ammonia, pentane, tetrahydrofuran, butanol, phenol, xylene, benzene, ethanol, methanol, acetone, toluene, and isopropanol. For mobile applications, the sensor node was attached to a sample holder on a mobile robot (ASTI ProBOT L). In addition, the sensor nodes were positioned close to automation systems, including stationary robots. The experimental results revealed that the tested sensors have a different response to the tested volumes and can be used efficiently for hazardous gas leakage detection and monitoring.
The Application of High-Resolution, Embedded Fibre Optic (FO) Sensing for Large-Diameter Composite Steel/Plastic Pipeline Performance under Dynamic Transport Loads
Authors: Cassidy Nigel J. , O’Regan Paul , Luo Sha , Chapman David N. , Jefferson Ian
Published: February 2024

Distributed optical fibre sensing (DOFS)-based strain measurement systems are now routinely deployed across infrastructure health monitoring applications. However, there are still practical performance and measurement issues associated with the fibre’s attachment method, particularly with thermoplastic pipeline materials (e.g., high-density polyethylene, HDPE) and adhesive affixment methods. In this paper, we introduce a new optical fibre installation method that utilises a hot-weld encapsulation approach that fully embeds the fibre onto the pipeline’s plastic surface. We describe the development, application and benefits of the new embedment approach (as compared to adhesive methods) and illustrate its practical performance via a full-scale, real-world, dynamic loading trial undertaken on a 1.8 m diameter, 6.4 m long stormwater pipeline structure constructed from composite spiral-wound, steel-reinforced, HDPE pipe. The optical frequency domain reflectometry (OFDR)-based strain results show how the new method improves strain transference and dynamic measurement performance and how the data can be easily interpreted, in a practical context, without the need for complex strain transfer functions. Through the different performance tests, based on UK rail-road network transport loading conditions, we also show how centimetre- to metre-scale strain variations can be clearly resolved at the frequencies and levels consistent with transport- and construction-based, buried infrastructure loading scenarios.
Secure Aggregation Protocol Based on DC-Nets and Secret Sharing for Decentralized Federated Learning
Authors: Pereira Diogo , Reis Paulo Ricardo , Borges Fábio
Published: February 2024

In the era of big data, millions and millions of data are generated every second by different types of devices. Training machine-learning models with these data has become increasingly common. However, the data used for training are often sensitive and may contain information such as medical, banking, or consumer records, for example. These data can cause problems in people’s lives if they are leaked and also incur sanctions for companies that leak personal information for any reason. In this context, Federated Learning emerges as a solution to the privacy of personal data. However, even when only the gradients of the local models are shared with the central server, some attacks can reconstruct user data, allowing a malicious server to violate the FL principle, which is to ensure the privacy of local data. We propose a secure aggregation protocol for Decentralized Federated Learning, which does not require a central server to orchestrate the aggregation process. To achieve this, we combined a Multi-Secret-Sharing scheme with a Dining Cryptographers Network. We validate the proposed protocol in simulations using the MNIST handwritten digits dataset. This protocol achieves results comparable to Federated Learning with the FedAvg protocol while adding a layer of privacy to the models. Furthermore, it obtains a timing performance that does not significantly affect the total training time, unlike protocols that use Homomorphic Encryption.
Security and Reliability Analysis of the Power Splitting-Based Relaying in Wireless Sensors Network
Authors: Tran Minh , Tu Lam-Thanh , Minh Bui Vu , Nguyen Quang-Sang , Rejfek Lubos , Lee Byung Moo
Published: February 2024

This paper studies the security and reliability of the power splitting (PS)-based relaying in the Internet of Things (IoT) networks with the help of a jammer. Based on the considered system model, we derive outage probability (OP) and intercept probability (IP) under two distinguished schemes, namely, the static PS relaying (SPSR) scheme and the dynamic PS relaying (DPSR) scheme. More precisely, the PS ratio of the former is a constant number, while the latter is optimally adjusted in order to minimize the OP and counts only on the channel gain of the second hop. Numerical results are provided to not only verify the accuracy of the proposed mathematical framework but also identify the trends of both OP and IP with respect to several important parameters. Our findings unveil that the OP and IP have contradictory behavior with respect to the transmit power and number of sources. Moreover, the performance of the DPSR scheme is superior to that of the SPSR scheme.
Crescent Antennas as Sensors: Case of Sensing Brain Pathology
Authors: Anwar Usman , Arslan Tughrul , Lomax Peter
Published: February 2024

Microstrip crescent antennas offer compactness, conformability, low profile, high sensitivity, multi-band operability, cost-effectiveness and ease of fabrication in contrast to bulky, rigid horn, helical and Vivaldi antennas. This work presents crescent sensors for monitoring brain pathology associated with stroke and atrophy. Single- and multi-element crescent sensors are designed and validated by software simulations. The fabricated sensors are integrated with glasses and experimentally evaluated using a realistic brain phantom. The performance of the sensors is compared in terms of peak gain, directivity, radiation performance, flexibility and detection capability. The crescent sensors can detect the pathologies through the monitoring of backscattered electromagnetic signals that are triggered by dielectric variations in the affected tissues. The proposed sensors can effectively detect stroke and brain atrophy targets with a volume of 25 mm3 and 56 mm3, respectively. The safety of the sensors is examined through the evaluation of Specific Absorption Rate (peak SAR < 1.25 W/Kg, 100 mW), temperature increase within brain tissues (max: 0.155 °C, min: 0.115 °C) and electric field analysis. The results suggest that the crescent sensors can provide a flexible, portable and non-invasive solution to monitor degenerative brain pathology.
Engineering Features from Raw Sensor Data to Analyse Player Movements during Competition
Authors: Antonini Valerio , Mileo Alessandra , Roantree Mark
Published: February 2024

Research in field sports often involves analysis of running performance profiles of players during competitive games with individual, per-position, and time-related descriptive statistics. Data are acquired through wearable technologies, which generally capture simple data points, which in the case of many team-based sports are times, latitudes, and longitudes. While the data capture is simple and in relatively high volumes, the raw data are unsuited to any form of analysis or machine learning functions. The main goal of this research is to develop a multistep feature engineering framework that delivers the transformation of sequential data into feature sets more suited to machine learning applications.
Sensing of Continuum Robots: A Review
Authors: Sincak Peter , Prada Erik , Miková Ľubica , Mykhailyshyn Roman , Varga Martin , Merva Tomas , Virgala Ivan
Published: February 2024

The field of continuum robotics is rapidly developing. The development of new kinematic structures, locomotion principles and control strategies is driving the development of new types of sensors and sensing methodologies. The sensing in continuum robots can be divided into shape perception and environment perception. The environment perception is focusing on sensing the interactions between the robot and environment. These sensors are often embedded on an outer layer of the robots, so the interactions can be detected. The shape perception is sensing the robot’s shape using various principles. There are three main groups of sensors that use the properties of electricity, magnetism and optics to measure the shape of the continuum robots. The sensors based on measuring the properties of electricity are often based on measuring the electrical resistance or capacitance of the flexible sensor. Sensors based on magnetism use properties of permanent magnets or coils that are attached to the robot. Their magnetic field, flux or other properties are then tracked, and shape reconstruction can be performed. The last group of sensors is mostly based on leveraging the properties of traveling light through optical fibers. There are multiple objectives of this work. Objective number one is to clearly categorize the sensors and make a clear distinction between them. Objective number two is to determine the trend and progress of the sensors used in continuum robotics. And finally, the third objective is to define the challenges that the researchers are currently facing. The challenges of sensing the shape or the interaction with the environment of continuum robots are currently in the miniaturization of existing sensors and the development of novel sensing methods.
A Novel Method for Monocular Depth Estimation Using an Hourglass Neck Module
Authors: Oh Seung-Jin , Lee Seung-Ho
Published: February 2024

In this paper, we propose a novel method for monocular depth estimation using the hourglass neck module. The proposed method has the following originality. First, feature maps are extracted from Swin Transformer V2 using a masked image modeling (MIM) pretrained model. Since Swin Transformer V2 has a different patch size for each attention stage, it is easier to extract local and global features from images input by the vision transformer (ViT)-based encoder. Second, to maintain the polymorphism and local inductive bias of the feature map extracted from Swin Transformer V2, a feature map is input into the hourglass neck module. Third, deformable attention can be used at the waist of the hourglass neck module to reduce the computation cost and highlight the locality of the feature map. Finally, the feature map traverses the neck and proceeds through a decoder, comprised of a deconvolution layer and an upsampling layer, to generate a depth image. To evaluate the objective reliability of the proposed method in this paper, we used the NYU Depth V2 dataset to compare and evaluate the methods published in other papers. As a result of the experiment, the RMSE value of the novel method for monocular depth estimation using the hourglass neck module proposed in this paper was 0.274, which was lower than those published in other papers. The lower the RMSE value, the better the depth estimation method; therefore, its efficiency compared to other techniques has been proven.
Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors
Authors: Li Yongjun , Zhu Yuefei , Fei Jinlong , Wu Wei
Published: February 2024

Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our proposed metric not only incorporates geographical information but also integrates semantic, temporal, and query data, serving as a powerful tool to foster semantic diversity, ensure high servifice similarity, and promote spatial dispersion. We extensively evaluate our technique by constructing a comprehensive metric for Dongcheng District, Beijing, using road network data obtained through the OSMNX package and semantic and temporal information acquired through Gaode Map. This holistic approach proves highly effective in mitigating adversarial attacks based on background knowledge. Compared with existing methods, our proposed protection mechanism showcases a minimum 50% reduction in service quality and an increase of at least 0.3 times in adversarial attack error using a real-world dataset from Geolife. The simulation results underscore the efficacy of our protection mechanism in significantly enhancing user privacy compared to existing methodologies in the LBS location privacy-protection framework. This adjustment more fully reflects the authors’ preference while maintaining clarity about the role of Geo-I as a protection mechanism within the broader framework of LBS location privacy protection.
UWB Antenna with Enhanced Directivity for Applications in Microwave Medical Imaging
Authors: Awan Dawar , Bashir Shahid , Khan Shahid , Al-Bawri Samir Salem , Dalarsson Mariana
Published: February 2024

Microwave medical imaging (MMI) is experiencing a surge in research interest, with antenna performance emerging as a key area for improvement. This work addresses this need by enhancing the directivity of a compact UWB antenna using a Yagi-Uda-inspired reflector antenna. The proposed reflector-loaded antenna (RLA) exhibited significant gain and directivity improvements compared to a non-directional reference antenna. When analyzed for MMI applications, the RLA showed a maximum increase of 4 dBi in the realized gain and of 14.26 dB in the transmitted field strength within a human breast model. Moreover, it preserved the shape of time-domain input signals with a high correlation factor of 94.86%. To further validate our approach, another non-directional antenna with proven head imaging capabilities was modified with a reflector, achieving similar directivity enhancements. The combined results demonstrate the feasibility of RLAs for improved performance in MMI systems.
Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement
Authors: Zhang Hongchao , Song Bao , Xu Junming , Li Hu , Li Shuhui
Published: February 2024

Because of its uneven and large slope, unstructured pavement presents a great challenge to obtaining the adhesion coefficient of pavement. An estimation method of the peak adhesion coefficient of unstructured pavement on the basis of the extended Kalman filter is proposed in this paper. The identification accuracy of road adhesion coefficients under unstructured pavement is improved by introducing the equivalent suspension model to optimize the calculation of vertical wheel load and modifying vehicle acceleration combined with vehicle posture data. Finally, the multi-condition simulation experiments with Carsim are conducted, the estimation accuracy of the adhesion coefficient is at least improved by 3.6%, and then the precision and effectiveness of the designed algorithm in the article are verified.
Qhali: A Humanoid Robot for Assisting in Mental Health Treatment
Authors: Pérez-Zuñiga Gustavo , Arce Diego , Gibaja Sareli , Alvites Marcelo , Cano Consuelo , Bustamante Marlene , Horna Ingrid , Paredes Renato , Cuellar Francisco
Published: February 2024

In recent years, social assistive robots have gained significant acceptance in healthcare settings, particularly for tasks such as patient care and monitoring. This paper offers a comprehensive overview of the expressive humanoid robot, Qhali, with a focus on its industrial design, essential components, and validation in a controlled environment. The industrial design phase encompasses research, ideation, design, manufacturing, and implementation. Subsequently, the mechatronic system is detailed, covering sensing, actuation, control, energy, and software interface. Qhali’s capabilities include autonomous execution of routines for mental health promotion and psychological testing. The software platform enables therapist-directed interventions, allowing the robot to convey emotional gestures through joint and head movements and simulate various facial expressions for more engaging interactions. Finally, with the robot fully operational, an initial behavioral experiment was conducted to validate Qhali’s capability to deliver telepsychological interventions. The findings from this preliminary study indicate that participants reported enhancements in their emotional well-being, along with positive outcomes in their perception of the psychological intervention conducted with the humanoid robot.
LRPL-VIO: A Lightweight and Robust Visual–Inertial Odometry with Point and Line Features
Authors: Zheng Feixiang , Zhou Lu , Lin Wanbiao , Liu Jingyang , Sun Lei
Published: February 2024

Visual-inertial odometry (VIO) algorithms, fusing various features such as points and lines, are able to improve their performance in challenging scenes while the running time severely increases. In this paper, we propose a novel lightweight point–line visual–inertial odometry algorithm to solve this problem, called LRPL-VIO. Firstly, a fast line matching method is proposed based on the assumption that the photometric values of endpoints and midpoints are invariant between consecutive frames, which greatly reduces the time consumption of the front end. Then, an efficient filter-based state estimation framework is designed to finish information fusion (point, line, and inertial). Fresh measurements of line features with good tracking quality are selected for state estimation using a unique feature selection scheme, which improves the efficiency of the proposed algorithm. Finally, validation experiments are conducted on public datasets and in real-world tests to evaluate the performance of LRPL-VIO and the results show that we outperform other state-of-the-art algorithms especially in terms of speed and robustness.