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This paper describes a paper-based biochemical sensor that realizes redox cycling with close interelectrode distance. Two electrodes, the generator and collector electrodes, can detect steady-state oxidation and reduction currents when suitable potential is held at each electrode. The sensor has two gold plates on both sides of a piece of chromatography paper and defines the interelectrode distance by the thickness of the paper (180μm) without any micro-fabrication processes. Our proposed sensor geometry has successfully exhibited signatures of redox cycling. As a result, the concentration of ferrocyanide as reversible redox molecules was successfully quantified under the interference by ascorbic acid as a strong irreversible reducing agent. This was possible because the ascorbic acids are completely consumed by the irreversible reaction, while maintaining redox cycling of reversible ferrocyanide. This suggests that a sensor based on the redox cycling method will be suitable for detecting target molecules at low concentration. [ABSTRACT FROM AUTHOR]

Paper-based platforms can be a promising choice as portable sensors due to their low-cost and facile fabrication, ease of use, high sensitivity, specificity and flexibility. By combining the qualities of these 3D platforms with the optical properties of gold nanoparticles, it is possible to create efficient nanodevices with desired biosensing functionalities. In this work, we propose a new plasmonic paper-based dual localized surface plasmon resonance–surface-enhanced Raman scattering (LSPR-SERS) nanoplatform with improved detection abilities in terms of high sensitivity, uniformity and reproducibility. Specifically, colloidal gold nanorods (GNRs) with a well-controlled plasmonic response were firstly synthesized and validated as efficient dual LSPR-SERS nanosensors in solution using the p-aminothiophenol (p-ATP) analyte. GNRs were then efficiently immobilized onto the paper via the immersion approach, thus obtaining plasmonic nanoplatforms with a modulated LSPR response. The successful deposition of the nanoparticles onto the cellulose fibers was confirmed by LSPR measurements, which demonstrate the preserved plasmonic response after immobilization, as well as by dark-field microscopy and scanning electron microscopy investigations, which confirm their uniform distribution. Finally, a limit of detection for p-ATP as low as 10-12 M has been achieved by our developed SERS-based paper nanoplatform, proving that our optimized plasmonic paper-based biosensing design could be further considered as an excellent candidate for miniaturized biomedical applications. [ABSTRACT FROM AUTHOR]

Breath analysis is considered to be an effective method for point-of-care diagnosis due to its noninvasiveness, quickness and simplicity. Gas sensors for breath analysis require detection of low-concentration substances. In this paper, we propose that reduction of the background current improves the limit of detection of enzymatic biogas sensors utilizing chromatography paper. After clarifying the cause of the background current, we reduced the background current by improving the fabrication process of the sensors utilizing paper. Finally, we evaluated the limit of detection of the sensor with the sample vapor of ethanol gas. The experiment showed about a 50% reduction of the limit of detection compared to previously-reported sensor. This result presents the possibility of the sensor being applied in diagnosis, such as for diabetes, by further lowering the limit of detection. [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future. [ABSTRACT FROM AUTHOR]

With the application of robotics in security monitoring, medical care, image analysis, and other high-privacy fields, vision sensor data in robotic operating systems (ROS) faces the challenge of enhancing secure storage and transmission. Recently, it has been proposed that the distributed advantages of blockchain be taken advantage of to improve the security of data in ROS. Still, it has limitations such as high latency and large resource consumption. To address these issues, this paper introduces PrivShieldROS, an extended robotic operating system developed by InterPlanetary File System (IPFS), blockchain, and HybridABEnc to enhance the confidentiality and security of vision sensor data in ROS. The system takes advantage of the decentralized nature of IPFS to enhance data availability and robustness while combining HybridABEnc for fine-grained access control. In addition, it ensures the security and confidentiality of the data distribution mechanism by using blockchain technology to store data content identifiers (CID) persistently. Finally, the effectiveness of this system is verified by three experiments. Compared with the state-of-the-art blockchain-extended ROS, PrivShieldROS shows improvements in key metrics. This paper has been partly submitted to IROS 2024. [ABSTRACT FROM AUTHOR]

In this paper, a new sensor is proposed to efficiently gather crucial information on corrosion phenomena and their progression within steel components. Fabricated with plastic optical fibers (POF), the sensor can detect corrosion-induced physical changes in the appearance of monitoring points within the steel material. Additionally, the new sensor incorporates an innovative structure that efficiently utilizes bi-directional optical transmission in the POF, simplifying the installation procedure and reducing the total cost of the POF cables by as much as 50% when monitoring multiple points. Furthermore, an extremely compact dummy sensor with the length of 5 mm and a diameter of 2.2 mm for corrosion-depth detection was introduced, and its functionality was validated through experiments. This paper outlines the concept and fundamental structure of the proposed sensor; analyzes the results of various experiments; and discusses its effectiveness, prospects, and economic advantages. [ABSTRACT FROM AUTHOR]

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water. [ABSTRACT FROM AUTHOR]

The dependable functioning of switchgear is essential to maintain the stability of power supply systems. Partial discharge (PD) is a critical phenomenon affecting the insulation of switchgear, potentially leading to equipment failure and accidents. PDs are generally grouped into metal particle discharge, suspended discharge, and creeping discharge. Different types of PDs are closely related to the severity of a PD. Partial discharge pattern recognition (PDPR) plays a vital role in the early detection of insulation defects. In this regard, a Back Propagation Neural Network (BPNN) for PDPR in switchgear is proposed in this paper. To eliminate the sensitivity to initial values of BPNN parameters and to enhance the generalized ability of the proposed BPRN, an improved Mantis Search Algorithm (MSA) is proposed to optimize the BPNN. The improved MSA employs some boundary handling strategies and adaptive parameters to enhance the algorithm's efficiency in optimizing the network parameters of BPNN. Principal Component Analysis (PCA) is introduced to reduce the dimensionality of the feature space to achieve significant time saving in comparable recognition accuracy. The initially extracted 14 feature values are reduced to 7, reducing the BPNN parameter count from 183 with 14 features to 113 with 7 features. Finally, numerical results are presented and compared with Decision Tree (DT), k-Nearest Neighbor classifiers (KNN), and Support Vector Machine (SVM). The proposed method in this paper exhibits the highest recognition accuracy in metal particle discharge and suspended discharge. [ABSTRACT FROM AUTHOR]

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

In many areas of engineering, the design of a new system usually involves estimating performance-related parameters from early stages of the project to determine whether a given solution will be compliant with the defined requirements. This aspect is particularly relevant during the design of satellite payloads, where the target environment is not easily accessible in most cases. In the context of Earth observation sensors, this problem has been typically solved with the help of a set of complex pseudo-empirical models and/or expensive laboratory equipment. This paper describes a more practical approach: the illumination conditions measured by an in-orbit payload are recreated on ground with the help of a replica of the same payload so the performance of another Earth observation sensor in development can be evaluated. The proposed method is specially relevant in the context of small satellites, as the possibility of having extra units devoted to these tasks becomes greater as costs are reduced. The results obtained using this method in an actual space mission are presented in this paper, giving valuable information that will help in further stages of the project. [ABSTRACT FROM AUTHOR]

The miniaturization and low power consumption characteristics of RF MEMS (Radio Frequency Microelectromechanical System) switches provide new possibilities for the development of microsatellites and nanosatellites, which will play an increasingly important role in future space missions. This paper provides a comprehensive review of RF MEMS switches in satellite communication, detailing their working mechanisms, performance optimization strategies, and applications in reconfigurable antennas. It explores various driving mechanisms (electrostatic, piezoelectric, electromagnetic, thermoelectric) and contact mechanisms (capacitive, ohmic), highlighting their advantages, challenges, and advancements. The paper emphasizes strategies to enhance switch reliability and RF performance, including minimizing the impact of shocks, reducing driving voltage, improving contacts, and appropriate packaging. Finally, it discusses the enormous potential of RF MEMS switches in future satellite communications, addressing their technical advantages, challenges, and the necessity for further research to optimize design and manufacturing for broader applications and increased efficiency in space missions. The research findings of this review can serve as a reference for further design and improvement of RF MEMS switches, which are expected to play a more important role in future aerospace communication systems. [ABSTRACT FROM AUTHOR]

The electrical protection of power networks with fault contribution from inverter-based power sources imposes new application challenges that have to be dealt with by protection engineers. This paper describes the development of a study case model of an HVDC-MMC link for testing the protection behaviour of connected converter transformers. The paper summarises the implementation and validation of the converter control as well as enhancements to provide Fault Ride-Through capability and fast fault current injection as required by the German Technical Connection Rules for HVDC. The grid code standard requires positive- and negative-sequence reactive current injection in the case of grid faults. A Doubled Decoupled Synchronous Reference Frame Phase Locked Loop (DDSRF-PLL) for Vector Current Control (VCC) is implemented. Additionally, a Fault Detection and Fault Ride-Through Reference Generator with a Current Limitation strategy is introduced. Though these techniques are well described in the literature, the DDSRF is improved for current control stability. The relationship between the parameters of the PLL and the control, as well as the behaviour of the protection system, are demonstrated. Grid faults with large voltage dips pose a significant challenge to the stability of the control system. Nevertheless, it is shown that with the developed model, it is possible to make general statements about the protection behaviour in an inverter-based environment. [ABSTRACT FROM AUTHOR]

Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves' energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth. [ABSTRACT FROM AUTHOR]

Digital twins aim to optimize practices implemented in various sectors by bridging the gap between the physical and digital worlds. Focusing on open-field agriculture, livestock farming, and forestry and reviewing the current applications in these domains, this paper reveals the multifaceted roles of digital twins. Diverse key aspects are examined, including digital twin integration and maturity level, means of data acquisition, technological capabilities, and commonly used input and output features. Through the prism of four primary research questions, the state of the art of digital twins, the extent of their achieved integration, and an overview of the critical issues and potential advancements are provided in the landscape of the sectors under consideration. The paper concludes that in spite of the remarkable progress, there is a long way towards achieving full digital twin. Challenges still persist, while the key factor seems to be the integration of expert knowledge from different stakeholders. In light of the constraints identified in the review analysis, a new sector-specific definition for digital twins is also suggested to align with the distinctive characteristics of intricate biotic and abiotic systems. This research is anticipated to serve as a useful reference for stakeholders, enhancing awareness of the considerable benefits associated with digital twins and promoting a more systematic and comprehensive exploration of this transformative topic. [ABSTRACT FROM AUTHOR]

This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper's structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT. [ABSTRACT FROM AUTHOR]

The process of image fusion is the process of enriching an image and improving the image's quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today's deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes. [ABSTRACT FROM AUTHOR]

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed. [ABSTRACT FROM AUTHOR]

Underwater long-endurance platforms are crucial for continuous oceanic observation, allowing for sustained data collection from a multitude of sensors deployed across diverse underwater environments. They extend mission durations, reduce maintenance needs, and significantly improve the efficiency and cost-effectiveness of oceanographic research endeavors. This paper investigates the closed-loop depth control of actuation systems employed in underwater vehicles, focusing on the energy consumption of two different mechanisms: variable buoyancy and propeller actuated devices. Using a prototype previously developed by the authors, this paper presents a detailed model of the vehicle using both actuation solutions. The proposed model, although being a linear-based one, accounts for several nonlinearities that are present such as saturations, sensor quantization, and the actuator brake model. Also, it allows a simple estimation of the energy consumption of both actuation solutions. Based on the developed models, this study then explores the intricate interplay between energy consumption and control accuracy. To this end, several PID-based controllers are developed and tested in simulation. These controllers are used to evaluate the dynamic response and power requirements of variable buoyancy systems and propeller actuated devices under various operational conditions. Our findings contribute to the optimization of closed-loop depth control strategies, offering insights into the trade-offs between energy efficiency and system effectiveness in diverse underwater applications. [ABSTRACT FROM AUTHOR]

Channel modeling is a first step towards the successful projecting of any wireless communication system. Hence, in this paper, we analyze the performance at the output of a multi-branch selection combining (SC) diversity receiver in a wireless environment that has been distracted by fading and co-channel interference (CCI), whereby the fading is modelled by newer Beaulieu-Xie (BX) distribution, and the CCI is modelled by the κ-µ distribution. The BX distribution provides the ability to include in consideration any number of line-of-sight (LOS) useful signal components and non-LOS (NLOS) useful signal components. This distribution contains characteristics of some other fading models thanks to its flexible fading parameters, which also applies to the κ-µ distribution. We derived here the expressions for the probability density function (PDF) and cumulative distribution function (CDF) for the output signal-to-co-channel interference ratio (SIR). After that, other performances are obtained, namely: outage probability (Pout), channel capacity (CC), moment-generating function (MGF), average bit error probability (ABEP), level crossing rate (LCR), and average fade duration (AFD). Numerical results are presented in several graphs versus the SIR for different values of fading and CCI parameters, as well as the number of input branches in the SC receiver. Then, the impact of parameters on all performance is checked. From our numerical results, it is possible to directly obtain the performance for all derived and displayed quantities for cases of previously known distributions of fading and CCI by inserting the appropriate parameter values. In the second part of the paper, a workflow for automated network experimentation relying on the synergy of Large Language Models (LLMs) and model-driven engineering (MDE) is presented, while the previously derived expressions are used for evaluation. Due to the aforementioned, the biggest value of the obtained results is the applicability to the cases of a large number of other distributions for fading and CCI by replacing the corresponding parameters in the formulas for the respective performances. [ABSTRACT FROM AUTHOR]

In response to the numerous challenges faced by traditional human pose recognition methods in practical applications, such as dense targets, severe edge occlusion, limited application scenarios, complex backgrounds, and poor recognition accuracy when targets are occluded, this paper proposes a YOLO-Pose algorithm for human pose estimation. The specific improvements are divided into four parts. Firstly, in the Backbone section of the YOLO-Pose model, lightweight GhostNet modules are introduced to reduce the model's parameter count and computational requirements, making it suitable for deployment on unmanned aerial vehicles (UAVs). Secondly, the ACmix attention mechanism is integrated into the Neck section to improve detection speed during object judgment and localization. Furthermore, in the Head section, key points are optimized using coordinate attention mechanisms, significantly enhancing key point localization accuracy. Lastly, the paper improves the loss function and confidence function to enhance the model's robustness. Experimental results demonstrate that the improved model achieves a 95.58% improvement in mAP50 and a 69.54% improvement in mAP50-95 compared to the original model, with a reduction of 14.6 M parameters. The model achieves a detection speed of 19.9 ms per image, optimized by 30% and 39.5% compared to the original model. Comparisons with other algorithms such as Faster R-CNN, SSD, YOLOv4, and YOLOv7 demonstrate varying degrees of performance improvement. [ABSTRACT FROM AUTHOR]

The bottom platform is an important underwater sensor that can be used in communications, early warning, monitoring, and other fields. It may be affected by earthquakes, winds, waves, and other loads in the working environment, causing changes in posture and affecting its sensing function. Therefore, it is of practical engineering significance to analyze the force conditions and posture changes in the bottom platform. In order to solve the problem of postural stability of the underwater bottom platform, this paper establishes a fluid and structural simulation model of the underwater bottom platform. First, computational fluid dynamics (CFD) technology is used to solve the velocity distribution and forces in the watershed around the bottom platform under a 3 kn ocean current, where the finite element method (FEM) numerical calculation method is used to solve the initial equilibrium state of the bottom platform after it is buried. On this basis, this paper calculates the forces on the bottom platform and the posture of the bottom platform at different burial depths under the action of ocean currents. Additionally, the effects of different burial depths on the maximum displacement, deflection angle, and postural stability of the bottom platform are studied. The calculation results show that when the burial depth is greater than 0.6 m, and the deflection angle of the bottom platform under the action of the 3 kn sea current is less than 5°, the bottom platform can maintain a stable posture. This paper could be used to characterize the postural stability of underwater bottom platforms at different burial depths for the application of underwater sensors in ocean engineering. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) play a significant role in ocean-related research fields as tools for human exploration and the development of marine resources. However, the uncertainty of the underwater environment and the complexity of underwater motion pose significant challenges to the fault-tolerant control of AUV actuators. This paper presents a fault-tolerant control strategy for AUV actuators based onTakagi and Sugeno (T-S) fuzzy logic and pseudo-inverse quadratic programming under control constraints, aimed at addressing potential actuator faults. Firstly, considering the steady-state performance and dynamic performance of the control system, a T-S fuzzy controller is designed. Next, based on the redundant configuration of the actuators, the propulsion system is normalized, and the fault-tolerant control of AUV actuators is achieved using the pseudo-inverse method under thrust allocation. When control is constrained, a quadratic programming approach is used to compensate for the input control quantity. Finally, the effectiveness of the fuzzy control and fault-tolerant control allocation methods studied in this paper is validated through mathematical simulation. The experimental results indicate that in various fault scenarios, the pseudo-inverse combined with a nonlinear quadratic programming algorithm can compensate for the missing control inputs due to control constraints, ensuring the normal thrust of AUV actuators and achieving the expected fault-tolerant effect. [ABSTRACT FROM AUTHOR]

This paper focuses on mission planning and cooperative navigation algorithms for multi-drone systems aimed at LiDAR-based mapping. It aims at demonstrating how multi-UAV cooperation can be used to fulfill LiDAR data georeferencing accuracy requirements, as well as to improve data collection capabilities, e.g., increasing coverage per unit time and point cloud density. These goals are achieved by exploiting the CDGNSS/Vision paradigm and properly defining the formation geometry and the UAV trajectories. The paper provides analytical tools to estimate point density considering different types of scanning LIDAR and to define attitude/pointing requirements. These tools are then used to support centralized cooperation-aware mission planning aimed at complete coverage for different target geometries. The validity of the proposed framework is demonstrated through numerical simulations considering a formation of three vehicles tasked with a powerline inspection mission. The results show that cooperative navigation allows for the reduction of angular and positioning estimation uncertainties, which results in a georeferencing error reduction of an order of magnitude and equal to 16.7 cm in the considered case. [ABSTRACT FROM AUTHOR]

The presented paper introduces a novel path planning algorithm designed for generating low-cost trajectories that fulfill mission requirements expressed in Linear Temporal Logic (LTL). The proposed algorithm is particularly effective in environments where cost functions encompass the entire configuration space. A core contribution of this paper is the presentation of a refined approach to sampling-based path planning algorithms that aligns with the specified mission objectives. This enhancement is achieved through a multi-layered framework approach, enabling a simplified discrete abstraction without relying on mesh decomposition. This abstraction is especially beneficial in complex or high-dimensional environments where mesh decomposition is challenging. The discrete abstraction effectively guides the sampling process, influencing the selection of vertices for extension and target points for steering in each iteration. To further improve efficiency, the algorithm incorporates a deep learning-based extension, utilizing training data to accurately model the optimal trajectory distribution between two points. The effectiveness of the proposed method is demonstrated through simulated tests, which highlight its ability to identify low-cost trajectories that meet specific mission criteria. Comparative analyses also confirm the superiority of the proposed method compared to existing methods. [ABSTRACT FROM AUTHOR]

Collaborative crowdsensing is a team collaboration model that harnesses the intelligence of a large network of participants, primarily applied in areas such as intelligent computing, federated learning, and blockchain. Unlike traditional crowdsensing, user recruitment in collaborative crowdsensing not only considers the individual capabilities of users but also emphasizes their collaborative abilities. In this context, this paper takes a unique approach by modeling user interactions as a graph, transforming the recruitment challenge into a graph theory problem. The methodology employs an enhanced Prim algorithm to identify optimal team members by finding the maximum spanning tree within the user interaction graph. After the recruitment, the collaborative crowdsensing explored in this paper presents a challenge of unfair incentives due to users engaging in free-riding behavior. To address these challenges, the paper introduces the MR-SVIM mechanism. Initially, the process begins with a Gaussian mixture model predicting the quality of users' tasks, combined with historical reputation values to calculate their direct reputation. Subsequently, to assess users' significance within the team, aggregation functions and the improved PageRank algorithm are employed for local and global influence evaluation, respectively. Indirect reputation is determined based on users' importance and similarity with interacting peers. Considering the comprehensive reputation value derived from the combined assessment of direct and indirect reputations, and integrating the collaborative capabilities among users, we have formulated a feature function for contribution. This function is applied within an enhanced Shapley value method to assess the relative contributions of each user, achieving a more equitable distribution of earnings. Finally, experiments conducted on real datasets validate the fairness of this mechanism. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by small objects in remote sensing images, such as low resolution, complex backgrounds, and severe occlusions, this paper proposes a lightweight improved model based on YOLOv8n. During the detection of small objects, the feature fusion part of the YOLOv8n algorithm retrieves relatively fewer features of small objects from the backbone network compared to large objects, resulting in low detection accuracy for small objects. To address this issue, firstly, this paper adds a dedicated small object detection layer in the feature fusion network to better integrate the features of small objects into the feature fusion part of the model. Secondly, the SSFF module is introduced to facilitate multi-scale feature fusion, enabling the model to capture more gradient paths and further improve accuracy while reducing model parameters. Finally, the HPANet structure is proposed, replacing the Path Aggregation Network with HPANet. Compared to the original YOLOv8n algorithm, the recognition accuracy of mAP@0.5 on the VisDrone data set and the AI-TOD data set has increased by 14.3% and 17.9%, respectively, while the recognition accuracy of mAP@0.5:0.95 has increased by 17.1% and 19.8%, respectively. The proposed method reduces the parameter count by 33% and the model size by 31.7% compared to the original model. Experimental results demonstrate that the proposed method can quickly and accurately identify small objects in complex backgrounds. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz's ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves A C C r o b u s t scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model's adversarial robustness while maintaining a high accuracy rate. [ABSTRACT FROM AUTHOR]

The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model's generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model's discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely "Abdominal and Direct Fetal ECG Database" and "Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations", resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper's model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols. [ABSTRACT FROM AUTHOR]

The advancement of novel water treatment technologies requires the implementation of both accurate data measurement and recording processes. These procedures are essential for acquiring results and conducting thorough analyses to enhance operational efficiency. In addition, accurate sensor data facilitate precise control over chemical treatment dosages, ensuring optimal water quality and corrosion inhibition while minimizing chemical usage and associated costs. Under this framework, this paper describes the sensoring and monitoring solution for a hybrid system based on a cooling tower (CT) connected to mechanical vapor compression (MVC) equipment for desalination and brine concentration purposes. Sensors connected to the data commercial logger solution, Almemo 2890-9, are also discussed in detail such as temperature, relative humidity, pressure, flow rate, etc. The monitoring system allows remote control of the MVC based on a server, GateManager, and TightVNC. In this way, the proposed solution provides remote access to the hybrid system, being able to visualize gathered data in real time. A case study located in Cartagena (Spain) is used to assess the proposed solution. Collected data from temperature transmitters, pneumatic valves, level sensors, and power demand are included and discussed in the paper. These variables allow a subsequent forecasting process to estimate brine concentration values. Different sample times are included in this paper to minimize the collected data from the hybrid system within suitable operation conditions. This solution is suitable to be applied to other desalination processes and locations. [ABSTRACT FROM AUTHOR]

This paper studies the maximum reliability of multi-hop relay UAVs, in which UAVs provide wireless services for remote users as a coded cooperative relay without an end-to-end direct communication link. In this paper, the analytical expressions of the total power loss and total bit error rate are derived as reliability measures. First, based on the environmental statistical parameters, a LOS probability model is proposed. Then, the problem of minimizing the bit error rate of static and mobile UAVs is studied. The goal is to minimize the total bit error rate by jointly optimizing the height, elevation, power and path loss and introducing the maximum allowable path loss constraints, transmission power allocation constraints, and UAV height and elevation constraints. At the same time, the total path loss is minimized to achieve maximum ground communication coverage. However, the formulated joint optimization problem is nonconvex and generally difficult to solve. Therefore, we decomposed the problem into two subproblems and proposed an effective joint optimization iteration algorithm. Finally, the simulation results are given, and the analysis shows that the optimal height of different reliability measures is slightly different; thus, using the mobility of UAVs can improve the reliability of communication performance. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue titled "Machine Learning in Computer Vision and Image Sensing: Theory and Applications." The editorial highlights the diverse applications of machine learning (ML) models in various domains such as medical imaging, signal processing, remote sensing, and human activity detection. The special issue includes 11 papers that cover topics such as image segmentation, fluvial navigation, Alzheimer's disease classification, pneumothorax detection, lung cancer malignancy prediction, amniotic fluid volume detection, COVID-19 detection, and Parkinson's disease detection. The papers showcase the progress and potential of ML models in computer vision applications and provide valuable insights for future research. [Extracted from the article]

Track smoothness has become an important factor in the safe operation of high-speed trains. In order to ensure the safety of high-speed operations, studies on track smoothness detection methods are constantly improving. This paper presents a track irregularity identification method based on CNN-Bi-LSTM and predicts track irregularity through car body acceleration detection, which is easy to collect and can be obtained by passenger trains, so the model proposed in this paper provides an idea for the development of track irregularity identification method based on conventional vehicles. The first step is construction of the data set required for model training. The model input is the car body acceleration detection sequence, and the output is the irregularity sequence of the same length. The fluctuation trend of the irregularity data is extracted by the HP filtering (Hodrick Prescott Filter) algorithm as the prediction target. The second is a prediction model based on the CNN-Bi-LSTM network, extracting features from the car body acceleration data and realizing the point-by-point prediction of irregularities. Meanwhile, this paper proposes an exponential weighted mean square error with priority inner fitting (EIF-MSE) as the loss function, improving the accuracy of big value data prediction, and reducing the risk of false alarms. In conclusion, the model is verified based on the simulation data and the real data measured by the high-speed railway comprehensive inspection train. [ABSTRACT FROM AUTHOR]

This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices. [ABSTRACT FROM AUTHOR]

Electroosmotic experiments obtain the electroosmotic pressure coefficient of a rock sample by measuring the excitation voltage at both ends of the sample and the pressure difference caused by the excitation voltage. The electroosmotic pressure is very weak and buried in the background noise, which is the most difficult signal to measure in the dynamic-electric coupling experiment, so it is necessary to improve its signal-to-noise ratio. In this paper, for the low signal-to-noise ratio of electroosmotic pressure, the dual pressure sensor method is proposed, i.e., two pressure sensors of the same type are used to measure electroosmotic pressure. Two different data extraction methods, Fast Fourier Transform and Locked Amplification, are utilized to compare the dual pressure sensor method of this paper with the existing single pressure sensor method. The relationship between the electroosmotic pressure coefficient and the excitation frequency, mineralization, permeability, and porosity is analyzed and discussed. [ABSTRACT FROM AUTHOR]

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker's direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot's surroundings. [ABSTRACT FROM AUTHOR]

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal's effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS. [ABSTRACT FROM AUTHOR]

The article reviews issues associated with the operation of stationary and non-stationary electronic fire alarm systems (FASs). These systems are employed for the fire protection of selected buildings (stationary) or to monitor vast areas, e.g., forests, airports, logistics hubs, etc. (non-stationary). An FAS is operated under various environmental conditions, indoor and outdoor, favourable or unfavourable to the operation process. Therefore, an FAS has to exhibit a reliable structure in terms of power supply and operation. To this end, the paper discusses a representative FAS monitoring a facility and presents basic tactical and technical assumptions for a non-stationary system. The authors reviewed fire detection methods in terms of fire characteristic values (FCVs) impacting detector sensors. Another part of the article focuses on false alarm causes. Assumptions behind the use of unmanned aerial vehicles (UAVs) with visible-range cameras (e.g., Aviotec) and thermal imaging were presented for non-stationary FASs. The FAS operation process model was defined and a computer simulation related to its operation was conducted. Analysing the FAS operation process in the form of models and graphs, and the conducted computer simulation enabled conclusions to be drawn. They may be applied for the design, ongoing maintenance and operation of an FAS. As part of the paper, the authors conducted a reliability analysis of a selected FAS based on the original performance tests of an actual system in operation. They formulated basic technical and tactical requirements applicable to stationary and mobile FASs detecting the so-called vast fires. [ABSTRACT FROM AUTHOR]

Cracks provide the earliest and most immediate visual response to structural deterioration of asphalt pavements. Most of the current methods for crack detection are based on visible light sensors and convolutional neural networks. However, such an approach obviously limits the detection to daytime and good lighting conditions. Therefore, this paper proposes a crack detection technique cross-modal feature alignment of YOLOV5 based on visible and infrared images. The infrared spectrum characteristics of silicate concrete can be an important supplement. The adaptive illumination-aware weight generation module is introduced to compute illumination probability to guide the training of the fusion network. In order to alleviate the problem of weak alignment of the multi-scale feature map, the FA-BIFPN feature pyramid module is proposed. The parallel structure of a dual backbone network takes 40% less time to train than a single backbone network. As determined through validation on FLIR, LLVIP, and VEDAI bimodal datasets, the fused images have more stable performance compared to the visible images. In addition, the detector proposed in this paper surpasses the current advanced YOLOV5 unimodal detector and CFT cross-modal fusion module. In the publicly available bimodal road crack dataset, our method is able to detect cracks of 5 pixels with 98.3% accuracy under weak illumination. [ABSTRACT FROM AUTHOR]

Additive manufacturing of soft magnetic materials is a promising technology for creating topologically optimized electrical machines. High-performance electrical machines can be made from high-silicon-content FeSi alloys. Fe-6.5wt%Si material has exceptional magnetic properties; however, manufacturing this steel with the classical cold rolling methodology is not possible due to the brittleness of this material. Laser powder bed fusion technology (L-PBF) offers a solution to this problem. Finding the optimal printing parameters is a challenging task. Nevertheless, it is crucial to resolve the brittleness of the created materials so they can be used in commercial applications. The temperature dependence of magnetic hysteresis properties of Fe-6.5wt%Si materials is presented in this paper. The magnetic hysteresis properties were examined from 20 °C to 120 °C. The hysteresis measurements were made by a precision current generator–based hysteresis measurement tool, which uses fast Fourier transformation–based filtering techniques to increase the accuracy of the measurements. The details of the applied scalar hysteresis sensor and the measurement uncertainties were discussed first in the paper; then, three characteristic points of the static hysteresis curve of the ten L-PBF-manufactured identical toroidal cores were investigated and compared at different temperatures. These measurements show that, despite the volumetric ratio of the porosities being below 0.5%, the mean crack length in the samples is not significant for the examined samples. These small defects can cause a significant 5% decrement in some characteristic values of the examined hysteresis curve. [ABSTRACT FROM AUTHOR]

The BC501A sensor is a liquid scintillator frequently used in nuclear physics for detecting fast neutrons. This paper describes a hardware implementation of digital pulse shape analysis (DPSA) for real-time analysis. DPSA is an algorithm that extracts the physically relevant parameters from the detected BC501A signals. The hardware solution is implemented in a MicroTCA system that provides the physical, mechanical, electrical, and cooling support for an AMC board (NAMC-ZYNQ-FMC) with a Xilinx ZYNQ Ultrascale-MP SoC. The Xilinx FPGA programmable logic implements a JESD204B interface to high-speed ADCs. The physical and datalink JESD204B layers are implemented using hardware description language (HDL), while the Xilinx high-level synthesis language (HLS) is used for the transport and application layers. The DPSA algorithm is a JESD204B application layer that includes a FIR filter and a constant fraction discriminator (CFD) function, a baseline calculation function, a peak detection function, and an energy calculation function. This architecture achieves an analysis mean time of less than 100 µs per signal with an FPGA resource utilization of about 50% of its most used resources. This paper presents a high-performance DPSA embedded system that interfaces with a 1 GS/s ADC and performs accurate calculations with relatively low latency. [ABSTRACT FROM AUTHOR]

With the development of biometric identification technology, finger vein identification has received more and more widespread attention for its security, efficiency, and stability. However, because of the performance of the current standard finger vein image acquisition device and the complex internal organization of the finger, the acquired images are often heavily degraded and have lost their texture characteristics. This makes the topology of the finger veins inconspicuous or even difficult to distinguish, greatly affecting the identification accuracy. Therefore, this paper proposes a finger vein image recovery and enhancement algorithm using atmospheric scattering theory. Firstly, to normalize the local over-bright and over-dark regions of finger vein images within a certain threshold, the Gamma transform method is improved in this paper to correct and measure the gray value of a given image. Then, we reconstruct the image based on atmospheric scattering theory and design a pixel mutation filter to segment the venous and non-venous contact zones. Finally, the degraded finger vein images are recovered and enhanced by global image gray value normalization. Experiments on SDUMLA-HMT and ZJ-UVM datasets show that our proposed method effectively achieves the recovery and enhancement of degraded finger vein images. The image restoration and enhancement algorithm proposed in this paper performs well in finger vein recognition using traditional methods, machine learning, and deep learning. The recognition accuracy of the processed image is improved by more than 10% compared to the original image. [ABSTRACT FROM AUTHOR]

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user's decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution's benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system. [ABSTRACT FROM AUTHOR]

In tunnel boring projects, wear and tear in the tooling system can have significant consequences, such as decreased boring efficiency, heightened maintenance costs, and potential safety hazards. In this paper, a fault diagnosis method for TBM tooling systems based on SAV−SVDD failure location (SSFL) is proposed. The aim of this method is to detect faults caused by disk cutter wear during the boring process, which diminishes the boring efficiency and is challenging to detect during construction. This paper uses SolidWorks to create a complete three−dimensional model of the TBM hydraulic thrust system and tool system. Then, dynamic simulations are performed with Adams. This helps us understand how the load on the propulsion hydraulic cylinder changes as the TBM tunneling tool wears to different degrees during construction. The hydraulic propulsion system was modeled and simulated using AMESIM software. Utilizing the load on the hydraulic propulsion cylinder as an input signal, pressure signals from the two chambers of the hydraulic cylinder and the system's flow signal were acquired. This enabled an in−depth exploration of the correlation between these acquired signals and the extent of the tooling system failure. Following this analysis, a collection of normal sample data and sample data representing different degrees of disk cutter abrasions was amassed for further study. Next, an SSFL network model for locating the failure area of the cutter was established. Fault sample data were used as the input, and the accuracy of the fault diagnosis model was tested. The test results show that the performance of the SSFL network model is better than that of the SAE−SVM and SVDD network models. The SSFL model achieves 90% accuracy in determining the failure area of the cutter head. The model effectively identifies the failure regions, enabling timely tool replacement to avoid decreased boring efficiency under wear conditions. The experimental findings validate the feasibility of this approach. [ABSTRACT FROM AUTHOR]

Combining multiple devices for localization has important applications in the military field. This paper exploits the land-based short-wave platforms and satellites for fusion localization. The ionospheric reflection height error and satellite position errors have a great impact on the short-wave localization and satellite localization accuracy, respectively. In this paper, an iterative constrained weighted least squares (ICWLS) algorithm is proposed for these two kinds of errors. The algorithm converts the nonconvex equation constraints to linear constraints using the results of the previous iteration, thus ensuring convergence to the globally optimal solution. Simulation results show that the localization accuracy of the algorithm can reach the corresponding Constrained Cramér–Rao Lower Bound (CCRLB). Finally, the localization results of the two methods are fused using Kalman filtering. Simulations show that the fused localization accuracy is improved compared to the single-means localization. [ABSTRACT FROM AUTHOR]

Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based damage identification method using Variational Autoencoder neural networks. The approach involves initially constructing a Variational Autoencoder network model for bridge damage detection, optimizing parameters such as loss functions and learning rates for the model, and ultimately utilizing response data from limited sensors for model training analysis to determine the structural state. The contribution of this paper lies in the ability to identify structural damage without baseline data using response data from a small number of sensors, reducing sensor costs and enhancing practical applications in engineering. The effectiveness of the proposed method is demonstrated through numerical simulations and experimental structures. The results show that the method can identify the location of damage under different damage conditions, exhibits strong robustness in detecting multiple damages, and further enhances the accuracy of identifying bridge structures. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss the usability of vibrations as energy sources, for the implementation of energy self-sufficient wireless sensing platforms within the Industrial Internet of Things (IIoT) framework. In this context, this paper proposes to equip vibrating assets like machinery with piezoelectric sensors, used to set up energy self-sufficient sensing platforms for hard-to-reach positions. Preliminary measurements as well as extended laboratory tests are proposed to understand the behavior of commercial piezoelectric sensors when employed as energy harvesters. First, a general architecture for a vibration-powered LoRaWAN-based sensor node is proposed. Final tests are then performed to identify an ideal trade-off between sensor sampling rates and energy availability. The target is to ensure continuous operation of the device while guaranteeing a charging trend of the storage component connected to the system. In this context, an Ultra-Low-Power Energy-Harvesting Integrated Circuit plays a crucial role by ensuring the correct regulation of the output with very high efficiency. [ABSTRACT FROM AUTHOR]

This paper focuses on the characterization of radio propagation, and data communication in a marine environment. More specifically, we consider signal propagation when three different sub-gigahertz industrial, scientific, and medical (ISM) bands, i.e., 169 MHz, 434 MHz, and 868 MHz, are used. The main focus of the paper is to evaluate the path loss (PL), i.e., the power loss that a propagation radio wave would experience when communication occurs between a sail boat and a buoy. We describe the measurement results obtained performing three different radio power measurement campaigns, at the three different aforementioned ISM sub-gigahertz bands. We also want to correlate the radio propagation quality with the weather conditions present in the measurement areas. The obtained results show that higher distances are achieved by transmitting at lower frequencies, i.e., 169 MHz, and, on average, the propagation is directly dependent from the dew point index. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the Internet to IoT applications to provide smart IoT services and products. Deploying, managing, and maintaining IoT devices for the exclusive use of an individual IoT application is inefficient and involves significant costs and effort that often outweigh the benefits. On the other hand, enabling large numbers of IoT applications to share available third-party IoT devices, which are deployed and maintained independently by a variety of IoT device providers, reduces IoT application development costs, time, and effort. To achieve a positive cost/benefit ratio, there is a need to support the sharing of third-party IoT devices globally by providing effective IoT device discovery, use, and pay between IoT applications and third-party IoT devices. A solution for global IoT device sharing must be the following: (1) scalable to support a vast number of third-party IoT devices, (2) interoperable to deal with the heterogeneity of IoT devices and their data, and (3) IoT-owned, i.e., not owned by a specific individual or organization. This paper surveys existing techniques that support discovering, using, and paying for third-party IoT devices. To ensure that this survey is comprehensive, this paper presents our methodology, which is inspired by Systematic Literature Network Analysis (SLNA), combining the Systematic Literature Review (SLR) methodology with Citation Network Analysis (CNA). Finally, this paper outlines the research gaps and directions for novel research to realize global IoT device sharing. [ABSTRACT FROM AUTHOR]