Search

With the rapid development of digital technologies such as intelligent control and information communication, digitization has gradually become an inevitable trend to promote technology innovation and industrial upgrading for the power grid. As the basis of digital technology, digital sensing technology will gradually penetrate all aspects of the power system for comprehensive perception and high operational reliability. First, this article introduces the application status and technical requirements of power intelligent sensor technology in various aspects of the power system, including the generation, transmission, transformation, distribution, and consumption of electricity. Next, domestic and foreign scholars' latest research achievements in power sensing technology are described from three major aspects, that is, optical fiber sensing technology, MEMS sensing technology, and sensor self‐powered technology. Finally, future challenges for the electric power intelligent sensors are discussed and an outlook of future widespread application in the clean, safe, and efficient digital power grid is provided. [ABSTRACT FROM AUTHOR]

Flexible ultraviolet (UV) light detection technology has important applications in wearable devices, smart sensors, and other fields and attracts much attention in recent years. However, for most semiconductor‐based UV detectors, the elastic modulus between rigid semiconductors and flexible substrates is mismatched, which makes it difficult to fabricate UV detectors that meet the needs of wearable devices. Herein, a fully flexible, large‐scale, skin‐friendly UV photodetector component centered on photo‐responsive worm‐like polymer nanoparticles (NPs) is developed, and the resulting device can quantitatively detect UV illumination. Skin‐friendly poly(vinyl alcohol) (PVA), amphiphilic azobenzene‐containing polymer NPs (AzNPs), and water‐soluble ionic liquids (IL) are formed into (AzNPs‐IL)/PVA fabrics by electrospinning. There are interactions such as hydrogen bonding among PVA, AzNPs, and IL, which make the material system stable. The UV detector made of the fabric realizes UV sensing through the illuminance–mechanical stress–electrical signal conversion mechanism. It is capable of achieving a response time of 9 s, a detection range of 10–150 mW cm−2, and stability for 1000 cycle tests upon 365 nm UV irradiation. Moreover, it has good skin affinity, and the water contact angle of the fabric is only 23.57°, which holds great promise for wearable smart devices. [ABSTRACT FROM AUTHOR]

The on‐chip extraction of dynamic information from a scene can be addressed with either frame‐based CMOS vision, also called smart image sensors, or dynamic vision sensors, also known as event cameras. When implemented with a pinned photodiode (PPD) as a 4‐transistor active pixel sensor (4T‐APS), the former brings about the benefits of low temporal noise and dark current but without high dynamic range (HDR). The latter comes with the benefits of HDR and a fast event detection rate with low power consumption. The drawback is the background activity noise, which leads to additional hardware or algorithms to keep it low. This paperanalyses the mismatch and noise of a global shutter 4T‐APS implementation with local HDR through an overflow capacitor and correlated double sampling (CDS) to provide low noise events through frame differencing. The aim is to narrow the gap with dynamic vision sensors in terms of event rate and dynamic range. We show that our solution would be competitive with event cameras in scenarios with slow moving objects and a relatively wide dynamic range (85 dB). [ABSTRACT FROM AUTHOR]

Sensor clouds are formed by IP-enabled wireless sensors and Internet of Things devices that are used for sensing and actuation in commercial and industrial applications. Data collected by the sensors are consolidated by distributed cloud data consolidation (DCS) servers to be utilized as raw sensory information by applications running data analytics and actuation functions. Alternatively, DC servers may feed sensor data to the cloud-hosted Big Data Analytics (BDS) servers. Sensor clouds and their respective DCS servers, as well as BDS servers, may form different security realms. These security realms' ownership structures are complicated and differ from standard database servers, necessitating a dependable authentication technique to provide trusted access to DC and BDS servers. This paper proposes a new multiparty authentication framework to authenticate applications requesting access to the DCS and BDS servers without direct human or application access to the sensors and actuators. Only DC servers are permitted to communicate with sensors/actuators, and only applications certified by a Session Authority Cloud are granted access to DCS/BDS servers via an authentication protocol that includes many information and key exchanges. This solution may assure the reliable deployment of sensor clouds in different critical application domains (i.e., industry, commercial, national security, and defense, etc.) while reducing the potential of direct espionage of sensed/actuated systems. Linear Temporal Logic is used to explicitly analyze and establish the correctness of the presented framework. OPNET modeling and simulations are used to illustrate the protocol's design and operations. The results demonstrate that multiparty authentication is conceivable for Sensor cloud computing systems. [ABSTRACT FROM AUTHOR]

Interstory drift response is one of most important quantities to quickly assess performance and damages in buildings. Nevertheless, direct measurement of interstory drift is difficult and expensive, because a stationary reference is required to attach measurement devices. With the goal of accurate and fast reference-free estimation, this paper proposes a new strategy to determine dynamic interstory drifts using accelerations. In particular, a Tikhonov regularization is adopted in a generalized minimization problem to achieve an efficient and stable finite impulse response (FIR) filter. Furthermore, due to independent clocks in wireless sensors, accurate time synchronization of the records is critical; consequently, a strategy for accurate synchronization is also presented. Finally, the proposed strategy has been deployed on edge devices for onboard real-time interstory drift estimation. The proposed method for dynamic interstory drift estimation is validated, first, by numerical simulation using earthquake records as base excitation of linear and nonlinear buildings, as well as through laboratory shake table experiments. Both numerical and lab test results show good agreement of dynamic interstory drifts between the measured value and estimated results, demonstrating the efficacy of the proposed method to estimate the dynamic displacements of seismically excited structures. [ABSTRACT FROM AUTHOR]

The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage's foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. [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]

Traditional battery management systems (BMS) encounter significant challenges, including low precision in predicting battery states and complexities in managing batteries, primarily due to the scarcity of collected signals. The advancement towards a "smart battery", equipped with diverse sensor types, promises to mitigate these issues. This review highlights the latest developments in smart sensing technologies for batteries, encompassing electrical, thermal, mechanical, acoustic, and gas sensors. Specifically, we address how these different signals are perceived and how these varied signals could enhance our comprehension of battery aging, failure, and thermal runaway mechanisms, contributing to the creation of BMS that are safer and more reliable. Moreover, we analyze the limitations and challenges faced by different sensor applications and discuss the advantages and disadvantages of each sensing technology. Conclusively, we present a perspective on overcoming future hurdles in smart battery development, focusing on appropriate sensor design, optimized integration processes, efficient signal transmission, and advanced management systems. [ABSTRACT FROM AUTHOR]

Low-power and smart sensing systems for iron detection are necessary for in situ monitoring of water quality. Here, a potentiometric Fe2+-selective electrode (ISE) was fabricated based on cyanomethyl N-methyl-N-phenyl dithiocarbamate for the first time as an ionophore. Under optimal conditions, the ISE showed a Nernstian slope of 29.76 ± 0.6 mV per decade for Fe2+ ions over a wide concentration range from 1.0 × 10−1 to 1.0 × 10−5 M with a lower detection limit (LOD) of 1.0 × 10−6 M. The ISE interference of various cations on the potentiometric response was also investigated. The ISE had a response time less than 3 s and the lifetime was two months. Also, an automated, long-range (LoRa), wireless enabled sampling microfluidic device powered with a solar panel as an autonomous power source was developed for a continuous sampling and sensing process. The sensing platform was employed in the determination of Fe2+ in acid mine drainage and spiked water samples with an average recovery of 100.7%. This simple, inexpensive (below $350), portable sensing platform will allow for rapid real-time monitoring of ground-, drinking-, and industrial waters contaminated with iron. [ABSTRACT FROM AUTHOR]

This paper presents a semi‐passive smart contact lens platform for sensor and actuator applications. It focuses on design improvements made prior to human clinical trials involving basic considerations on comfort and safety, which are verified through animal studies. Observations of tissue temperature variation and post‐trial surgery examinations confirm that the 50 μW on‐lens wireless power delivery reduces the impact on tissue of previously reported SCL. Migration of wirelessly powered radio frequency identification (RFID)‐based passive SCL to semi‐passive systems is enabled with the proposed on‐lens power storage element. Furthermore, a switched capacitor converter extends the active time of the storage element and reduces electromagnetic influence. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Packaging solutions have recently evolved to become smart and intelligent thanks to technologies such as RFID tracking and communication systems, but the integration of sensing functionality in these systems is still under active development. In this paper, chipless RFID humidity sensors suitable for smart packaging are proposed together with a novel strategy to tune their performances and their operating range. The sensors are flexible, fast, low-cost and easy to fabricate and can be read wirelessly. The sensitivity and the humidity range where they can be used are adjustable by changing one of the sensor's structural parameters. Moreover, these sensors are proposed as double parameter sensors, using both the frequency shift and the intensity variation of the resonance peak for the measure of the relative humidity. The results show that the sensitivity can vary remarkably among the sensors proposed, together with the operative range. The sensor suitability in two specific smart packaging applications is discussed. In the first case, a threshold sensor in the low-humidity range for package integrity verification is analyzed, and in the second case, a more complex measurement of humidity in non-hermetic packages is investigated. The discussion shows that the sensor configuration can easily be adapted to the different application needs. [ABSTRACT FROM AUTHOR]

Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction. These models consume significant hardware resources and are not applicable to real-time applications. In this paper, RadarTCN, a novel lightweight network, is proposed that achieves high-accuracy online target classification using single-view radar image sequences only. In RadarTCN, 2D convolution and 3D-TCN are employed to extract spatiotemporal features sequentially. To reduce data dimensionality and computational complexity, a multi-layer max pooling down-sampling method is designed in a 2D convolution module. Meanwhile, the 3D-TCN module is improved through residual pruning and causal convolution is introduced for leveraging the performance of online target classification. The experimental results demonstrate that RadarTCN can achieve high-precision online target recognition for both range-angle and range-Doppler map sequences. Compared to the reference models on the CARRADA dataset, RadarTCN exhibits better classification performance, with fewer parameters and lower computational complexity. [ABSTRACT FROM AUTHOR]

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC's full potential. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to measure deformations in geomaterials using the recently developed 'Smart Pebble' sensors. Smart Pebbles were included in triaxial test specimens of unbound aggregates stabilized with geogrids. The sensors are equipped with an aggregate particle/position tracking algorithm that can manage uncertainty arising due to signal noise and random walk effects. Two Smart Pebbles were placed in each test specimen, one at specimen's mid-height, where a geogrid was installed in the mechanically stabilized specimen, and one towards the top of the specimen. Even with simple raw data processing, the trends on linear vertical acceleration indicated the ability of Smart Pebbles to assess the geomaterial configuration and applied stress states. Employing a Kalman filter-based algorithm, the Smart Pebble position coordinates were tracked during testing. The specimen's resilient deformations were simultaneously recorded. bender element shear wave transducer pairs were also installed on the specimens to further validate the Smart Pebble small-strain responses. The results indicate a close agreement between the BE sensors and Smart Pebbles estimates towards local stiffness enhancement quantification in the geogrid specimen. The study findings confirm the viability of using the Smart Pebbles in describing the resilient behavior of an aggregate material under repeated loading. [ABSTRACT FROM AUTHOR]

Solution blow spinning is a technology that uses high-pressure gas to prepare spinning solution into nanofibers. It has the advantages of high production efficiency and easy operation, and the prepared nanofibers have a large specific surface area, high porosity, and flexible surface functionalization. Therefore, solution blow spinning has received more and more attention in the food field. This paper first introduces the principle of solution blow spinning, the influence of spinning liquid properties, process parameters, and environmental factors on the spinning process. Secondly, it introduces the materials and ways of applying solution blow-spun nanofibers in the food field, such as bacteriostatic food packaging, slow-release food packaging, and intelligent sensors. Finally, the application of solution blow spinning technology in food is summarized and prospected. This paper provides a reference for further research and application of solution blow spinning in the food field at a later stage. [ABSTRACT FROM AUTHOR]

Smart and connected physical products enable numerous novel value creations based on embedded sensor data. However, during the product design process, it appears that these value-creation opportunities are not sufficiently explored. To address this gap, a general and accessible ideation tool is proposed to structure a systematic exploration. The tool has been developed from the data sheets of a large number of various embedded sensors. A non-exhaustive list of 14 main sensor capabilities was thus structured to form the core of the tool. During the creative phases of product design, these capabilities are combined with a convenient set of inputs to support the generation of value-creation concepts. A focus group to evaluate the tool was carried out with a medium-sized manufacturer of connected shared city bikes. Expert product designers generated concepts with and without the tool. The results of the experiment suggest that the tool enables the generation of concepts that are more novel and of better quality in comparison to traditional ideation approaches. In addition, participants reported a high level of appreciation for the tool. They also noted a high level of creative exploration and collaboration facilitated by the tool. [ABSTRACT FROM AUTHOR]

Purpose: At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications. Design/methodology/approach: Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications. Findings: This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development. Originality/value: To the best of the authors' knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear. [ABSTRACT FROM AUTHOR]

The need for speeding up data analytics increases inevitably due to the need for extracting valuable information from social media, data generated by smart devices with sensors, patterns of people's communications over the web, items viewed and bought by global-scale customers, cloud applications, etc., all of which take part in the "Big Data." Such kind of interaction data is very well represented as sparse graphs to enable the graph analytics, which requires efficient underlying kernels. The breadth-first search (BFS)-based traversal is a commonly used kernel in graph algorithms such as the betweenness centrality algorithm for centrality analysis. In this work, we focus on parallel BFS operations and propose hypergraph-based combinatorial models that aim at reducing cache misses and hence exploiting data locality during the parallel BFS operations. Our models are based on finding new vertex visit orders so that locality in accessing the data associated with vertices is exploited. Experiments on graphs arising in a wide range of applications show that our proposed models achieve on average 9% performance improvement in the CPU-based Ligra data analytics framework. [ABSTRACT FROM AUTHOR]

Polymers are one of the most popular materials for next-generation flexible sensing device fabrication due to their tunable mechanical and electrical properties. A series of prior research studies in the field of smart flexible and wearable sensing illustrates the potential of various polymer and composite materials to be applied in sensor development. In this direction, mechanical compliance plays a vital role as it ensures the stability and reliability of the fabricated sensor. Therefore, engineering mechanical compliance for the development of smart flexible solutions has emerged as a significant area of research. Furthermore, the usage of flexible sensing devices is rapidly increasing in the field of healthcare devices and robotic automation. This feature article summarizes the relevant contributions of the authors in the field of engineered polymers and composites for flexible sensor development with a focus on healthcare and physical sensing applications. We discuss the polymer and composite materials, their characteristics, fabrication technologies, finite element method analysis, and examples of flexible physical sensors, i.e. pressure, strain, and temperature sensors, for various wearable healthcare applications and robotic automation. Finally, we discuss examples of multi-sensory systems having flexible sensors. [ABSTRACT FROM AUTHOR]

Automated high-throughput liquid handling operations in biolabs necessitate miniaturised and automatised equipment for effective space utilisation and system integration. This paper presents a thermal segment microwell plate control unit designed for enhanced microwell-based experimentation in liquid handling setups. The development of this device stems from the need to move towards geometry standardization and system integration of automated lab equipment. It incorporates features based on Smart Sensor and Sensor 4.0 concepts. An enzymatic activity assay is implemented with the developed device on a liquid handling station, allowing fast characterisation via a high-throughput approach. The device outperforms other comparable devices in certain metrics based on automated liquid handling requirements and addresses the needs of future biolabs in automation, especially in highthroughput screening. [ABSTRACT FROM AUTHOR]

This article examines resource scheduling optimization for cyber‐physical systems that include a smart sensor, a remote estimator, and an attacker. The attacker's objective is to use denial‐of‐service (DoS) attack to jam the wireless channel, causing packet loss and reducing system performance. We consider a more realistic scenario where other interfering sources exist and conduct research based on the signal‐to‐interference‐plus‐noise ratio (SINR) model. Through this model, we investigate two cases: constant high or low interference power and time‐varying interference power. Different levels of attack power lead to varying packet loss rates, and the same attack power may result in different packet loss rates in different interference environments. Therefore, the attacker needs to consider the impact of other interferences to find the optimal attack strategy that increases the estimation error of the information‐physical system while reducing energy consumption. We describe this optimization problem in a Markov decision process (MDP) framework and demonstrate the existence of an optimal deterministic Markovian strategy. We also establish the monotonicity of the optimal strategy in two different cases and investigate the extreme case. Simulation results support our theoretical results. [ABSTRACT FROM AUTHOR]

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies. [ABSTRACT FROM AUTHOR]

This paper presents a suitably general model for resistive displacement sensors where the model parameters depend on the current sensor conditions, thereby capturing wearout and failure, and proposes a novel fault detection method that can be seamlessly applied during sensor operation, providing self-diagnostic capabilities. On the basis of the estimation of model parameters, an innovative self-compensation method is derived to increase the accuracy of sensors subject to progressive wearout. The proposed model and methods have been validated by both numerical simulations and experimental tests on two real resistive displacement sensors, placed in undamaged and faulty conditions, respectively. The fault detection method has shown an accuracy of 97.2%. The position estimation error is < ±0.2% of the full-scale span for the undamaged sensor, while the self-compensation method successfully reduces the position estimation error from ±15% to approximately ±2% of the full-scale span for the faulty sensor. [ABSTRACT FROM AUTHOR]

This paper describes the use of wireless smart sensors for examining the underlying mechanism for the wind-induced vibration of high-mast illumination pole (HMIP) structures. HMIPs are tall, slender structures with low inherent damping. Video recordings of multiple HMIPs showed considerable vibrations of these HMIPs under wind loading in the state of Kansas. The HMIPs experienced cyclic large-amplitude displacements at the top, which can produce high-stress demand and lead to fatigue cracking at the bottom of the pole. In this study, the natural frequencies of the HMIP were assessed using pluck tests and finite element modeling, and the recorded vibration frequencies were obtained through computer vision-based video analysis. Meanwhile, a 30.48 m tall HMIP with three LED luminaires made of galvanized steel located in Wakeeney, Kansas, was selected for long-term vibration monitoring using wireless smart sensors to investigate the underlying mechanism for the excessive wind-induced vibrations. Data analysis with the long-term monitoring data indicates that while vortex-induced vibration occurs frequently at relatively low amplitude, buffeting-induced vibration was the leading cause of the excessive vibrations of the monitored HMIP. The findings provide crucial information to guide the design of vibration mitigation strategies for these HMIP structures. [ABSTRACT FROM AUTHOR]

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests. [ABSTRACT FROM AUTHOR]

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices' hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems' development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity's complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs. [ABSTRACT FROM AUTHOR]

Assessing the effectiveness of physical education instruction, students' learning, and the feedback received from the teaching process are all vital components of the physical education teaching process in colleges and universities. Improving the quality of physical education instruction in these settings is essential. With its ability to drive the digital revolution of physical education in schools, intelligent technology is bringing about significant changes in the field of education and drawing attention from people from all walks of life. To assess intelligent technology's impact on physical education instruction in a scientific manner, this study utilizes the latest intelligent analysis and sensing data mining to design an intelligent physical education measurement and evaluation model, which utilizes GPS positioning, built-in maps, and gravity sensing to provide real-time feedback on the trajectory, distance, and time of the movement, and then calculates the real-time and average speed of the movement, as different students' body postures to achieve the the same effect when the required speed is not the same, this paper randomly selected students with different BMI index for empirical analysis. The experimental results show that the principal components of the factor analysis extracted four common factors with a cumulative contribution rate of 69.5%, and the test-retest reliability of the four dimensions is 0.665–0.862. [ABSTRACT FROM AUTHOR]

Relative localization (RL) and circumnavigation is a highly challenging problem that is crucial for the safe flight of multi-UAVs (multiple unmanned aerial vehicles). Most methods depend on some external infrastructure for positioning. However, in some complex environments such as forests, it is difficult to set up such infrastructures. In this paper, an approach to infrastructure-free RL estimations of multi-UAVs is investigated for circumnavigating a slowly drifting UGV0 (unmanned ground vehicle 0), where UGV0 serves as the RL and circumnavigation target. Firstly, a discrete-time direct RL estimator is proposed to ascertain the coordinates of each UAV relative to the UGV0 based on intelligent sensing. Secondly, an RL fusion estimation method is proposed to obtain the final estimate of UGV0. Thirdly, an integrated estimation control scheme is also proposed for the application of the RL fusion estimation method to circumnavigation. The convergence and the performance are analyzed. The simulation results validate the effectiveness of the proposed algorithm for RL fusion estimations and of the integrated scheme. [ABSTRACT FROM AUTHOR]

In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data. The data are obtained with a GBSAR operating at 24 GHz with both vertical (VV) and horizontal (HH) polarization, resulting in two matrices (VV and HH) per observed scene. We present several approaches demonstrating the integration of such data into classification models based on a modified ResNet18 architecture. We also introduce a novel Siamese architecture tailored to accommodate the dual input radar data. The results indicate that a simple concatenation method is the most promising approach and underscore the importance of considering antenna polarization and merging strategies in deep learning applications based on radar data. [ABSTRACT FROM AUTHOR]

In recent years, attention to the realization of a distributed fiber-optic microphone for the detection and recognition of the human voice has increased, whereby the most popular schemes are based on φ-OTDR. Many issues related to the selection of optimal system parameters and the recognition of registered signals, however, are still unresolved. In this research, we conducted theoretical studies of these issues based on the φ-OTDR mathematical model and verified them with experiments. We designed an algorithm for fiber sensor signal processing, applied a testing kit, and designed a method for the quantitative evaluation of our obtained results. We also proposed a new setup model for lab tests of φ-OTDR single coordinate sensors, which allows for the quick variation of their parameters. As a result, it was possible to define requirements for the best quality of speech recognition; estimation using the percentage of recognized words yielded a value of 96.3%, and estimation with Levenshtein distance provided a value of 15. [ABSTRACT FROM AUTHOR]

In recent years, soft robotic sensors have rapidly advanced to endow robots with the ability to interact with the external environment. Here, we propose a polymer optical fiber (POF) sensor with sensitive and stable detection performance for strain, bending, twisting, and pressing. Thus, we can map the real-time output light intensity of POF sensors to the spatial morphology of the elastomer. By leveraging the intrinsic correlations of neighboring sensors and machine learning algorithms, we realize the spatially resolved detection of the pressing and multi-dimensional deformation of elastomers. Specifically, the developed intelligent sensing system can effectively recognize the two-dimensional indentation position with a prediction accuracy as large as ~99.17%. The average prediction accuracy of combined strain and twist is ~98.4% using the random forest algorithm. In addition, we demonstrate an integrated intelligent glove for the recognition of hand gestures with a high recognition accuracy of 99.38%. Our work holds promise for applications in soft robots for interactive tasks in complex environments, providing robots with multidimensional proprioceptive perception. And it also can be applied in smart wearable sensing, human prosthetics, and human–machine interaction interfaces. [ABSTRACT FROM AUTHOR]

The technology landscape has been dynamically reshaped by the rapid growth of the Internet of Things, introducing an era where everyday objects, equipped with smart sensors and connectivity, seamlessly interact to create intelligent ecosystems. IoT devices are highly heterogeneous in terms of software and hardware, and many of them are severely constrained. This heterogeneity and potentially constrained nature creates new challenges in terms of security, privacy, and data management. This work proposes a Monitoring-as-a-Service platform for both monitoring and management purposes, offering a comprehensive solution for collecting, storing, and processing monitoring data from heterogeneous IoT networks for the support of diverse IoT-based applications. To ensure a flexible and scalable solution, we leverage the FIWARE open-source framework, also incorporating blockchain and smart contract technologies to establish a robust integrity verification mechanism for aggregated monitoring and management data. Additionally, we apply automated workflows to filter and label the collected data systematically. Moreover, we provide thorough evaluation results in terms of CPU and RAM utilization and average service latency. [ABSTRACT FROM AUTHOR]

Smart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings. [ABSTRACT FROM AUTHOR]

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network's overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes. [ABSTRACT FROM AUTHOR]

Rockfalls can often pose a significant risk to the public if protective designs do not properly account for block movement downslope and onto infrastructure facilities. Assessing these hazards is challenging, especially as current empirical and computational methods for predicting trajectories of falling blocks generally do not include the contribution associated with rotational behavior. Research undertaken at the University of New Hampshire, USA, over the last decade has led to the development of Smart Rock (SR) sensors inserted in natural rocks to evaluate these events from the perspective of the falling rock. The latest SRs consist of 3 D printed capsules 58.0 mm in length and 25.4 mm in diameter, equipped with a ±400 g and a ±16 g 3-axis accelerometer, a ± 4000 dps high-rate gyroscope, and an altimeter. Approximately 80 field experiments conducted in New Hampshire and Vermont provided SR data on rockfall at ten different sites. The Smart Rock data allowed more in-depth evaluations of accelerations, rotation rates, and modes of motion with precise time intervals, which cannot be easily captured in video recording systems or other instrumentation techniques. Such measurements are essential to improve our understanding and modeling of rockfall events for more economical and safer design of protective systems. [ABSTRACT FROM AUTHOR]

Development of smart nanocomposites by reinforcing carbon nanomaterials into polymer composite materials has gained significant attention due to their extensive potential applications, especially in the field of monitoring of mechanical, aerospace and infrastructure systems. These smart nanocomposites exhibit excellent electro-mechanical sensing capability. Carbon nanotubes (CNTs) with outstanding electrical, mechanical, and thermal properties can be reinforced into polymer composite materials by appropriate dispersion to produce polymer/CNTs-based smart nanocomposites with excellent sensitivity. Understanding the processing, characterization and properties of the nanocomposites will help in yielding a deeper insight into the effect of incorporation of carbon nanotubes (CNTs) into the matrix of the polymer composites. This study provides a comprehensive overview (primarily in the last 2 decades) of the major influencing factors on the underlying mechanisms involved in the fabrication and performance of nanocomposites which can cause a paradigm shift in instrumentation and sensing for structural health monitoring (SHM). Therefore, this review article presents the recent advancement in the development of CNTs-reinforced polymer nanocomposites-based sensors encompassing the mechanism for electrical conductivity/piezo properties, fabrication, applications for various types of sensing, and the futuristic application for smart sensing through appropriate tailoring of nanocomposites using CNTs. [ABSTRACT FROM AUTHOR]

The increased interest in sensing tattoos reflects a shift in wearable technology, emphasizing their flexible, skin-adherent nature. These devices, driven by advancements in nanotechnology and materials science, offer highly sensitive and customizable sensors. The growing body of research in this area indicates a rising curiosity in their design and applications, with potential uses ranging from vital sign monitoring to biomarker detection. Sensing tattoos present a promising avenue in wearable healthcare technology, attracting attention from researchers, clinicians, and technology enthusiasts. The objective of this study is to analyze the development, application, and integration of the sensing tattoos in the health domain. A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 37 studies. Sensing tattoos hold transformative potential in health monitoring and physiological sensing, driven by their focus on affordability, user-friendly design, and versatile sensorization solutions. Despite their promise, ongoing refinement is essential, addressing limitations in adhesion, signal quality, biocompatibility, and regulatory complexities. Identified opportunities, including non-invasive health monitoring, multiplexed detection, and cost-effective fabrication methods, open avenues for personalized healthcare applications. However, bridging gaps in medical device standards, cybersecurity, and regulatory compliance is imperative for seamless integration. A key theme calls for a holistic, user-centric approach, emphasizing interdisciplinary collaboration. Balancing innovation with practicality, prioritizing ethics, and fostering collaboration are crucial for the evolution of these technologies. The dynamic state of the field is evident, with active exploration of new frontiers. This overview also provides a roadmap, urging scholars, industry players, and regulators to collectively contribute to the responsible integration of sensing tattoos into daily life. [ABSTRACT FROM AUTHOR]

Sensor data that is often collected in the Internet of Things (IoT) or any smart city environment should be protected against security and privacy concerns. Often, sensor data that is shared by devices in smart cities contains sensitive or private information that can often be shared over different networks and by different smart applications. In the last decade, the area of Privacy-Preserving Data Mining (PPDM) has received a lot of attention as the amount of data received and collected daily is huge. Unfortunately, PPDM mostly applies to binary data. To improve the usefulness of PPDM, we present a more usable version for smart cities called Privacy-Preserving Utility Mining (PPUM), in the form of a Maximal Sensitive Utility-Maximal Sensitive ConflIct (MSU-MSI) algorithm. MSU-MSI finds any conflicting items that may contain sensitive itemsets with high-utility and sanitizes them, stripping them of sensitive and private information while maintaining utility. Any transactions encountered that contain sensitive itemsets are first fed through sanitization processes. This is followed by calculating the total number of items that conflict, and then removing them so sanitization processes can operate more efficiently so as to not redo known sanitization processes. We conduct an in-depth experimental analysis, where our detailed methodology is compared directly with state-of-the-art frameworks such as MSU-MIU, MSU-MAU, HHUIF and MSCIF. Our proposed MSU-MSI shows a higher performance in missing cost, in particular when dealing with highly dense or highly sparse datasets. Moreover, our novel framework is shown to achieve an excellent performance with regards to similarity in database structure and database utility. [ABSTRACT FROM AUTHOR]

In our daily life, flexible flow sensors endow us with a 'sixth sense' capability, i.e. 'touch' the fluids, improving living quality. Although there are kinds of flexible flow sensors developed to implement this capability, they still have insufficient sensitivity and limited intelligent applications in daily life. Biomimetic engineering provides us with a powerful and effective approach to develop highly sensitive and intelligent flow sensing systems served in our life, comparable to that in creatures. Here, in this review, we present a comprehensive review of recent studies on the flexible flow sensors for human intelligent life. Firstly, we briefly introduce the excellent flow sensing systems selected by nature, and typical design strategies of artificial flexible flow sensors. Furthermore, we collect and exhibit kinds of flexible flow sensors and their applications in intelligent and digital life. Finally, we discuss the challenges and future perspectives of the flexible flow sensor for the metaverse applications. [ABSTRACT FROM AUTHOR]

The development of all‐in‐one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real‐time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet‐of‐Things (IoT) has led to the increasing importance of in‐sensor computing technology, which places computational power at the edge of the data‐flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two‐dimensional (2D) α‐In2Se3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate‐tunable linearity of excitatory and inhibitory functions in photon‐induced short‐term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in‐sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre‐programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature‐enhanced scenarios. The findings demonstrate the potential of ferroelectric α‐In2Se3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel. [ABSTRACT FROM AUTHOR]

Cultural heritage faces recurring degradation processes and natural aging phenomena, demanding the envisioning of innovative preservation solutions inspired by cutting‐edge scientific research. Over extended time frames, current preservation strategies often prove inadequate in preserving the different constituent materials of cultural assets, which are thus threatened by their inherent fragility and by the complex interactions with the surrounding environment. The distinctive properties of graphene and graphene‐related materials (GRMs) now offer unexplored opportunities in the field of cultural heritage, addressing various forms of deterioration phenomena. This work critically analyzes early‐stage literature on the use of graphene and GRMs. Strengths, weaknesses, and limitations in anti‐corrosion, anti‐fading, and consolidation properties of graphene and GRMs are thoroughly investigated, along with their possible applications in smart sensors to monitor the state of health of endangered artifacts. The aim is to elucidate how specific characteristics of graphene and GRMs can be applied to the conservation, diagnostics, and monitoring of artistic and archaeological assets. Future perspectives in the design of stable, long‐lasting, and compatible graphene‐based solutions for cultural heritage protection are highlighted, providing a detailed discussion on potentials and pitfalls. [ABSTRACT FROM AUTHOR]