Search Results (246)

Recent advances in plant electrophysiology and machine learning suggest that bioelectric signals in plants may encode environmentally relevant information beyond physiological processes. In this study, we present a novel framework to analyse waveforms from real-time bioelectrical potentials recorded in vascular plants. Using a multi-channel electrophysiological monitoring system, we acquired continuous data from Vitis vinifera samples in a vineyard plantation under natural conditions. Plants were in different health conditions: healthy; under the infection of Flavescence dorée; plants in recovery from the same disease; and dead stumps. These signals were used as input features for an ensemble of complex machine learning models, including recurrent neural networks, trained to infer short-term meteorological parameters such as temperature and humidity. The models demonstrated predictive capabilities, with accuracy comparable to sensor-based benchmarks between one and two degree Celsius for temperature, particularly in forecasting rapid weather transitions. Feature importance analysis revealed plant-specific electrophysiological patterns that correlated with ambient conditions, suggesting the existence of biological pre-processing mechanisms sensitive to microclimatic fluctuations. This bioinspired approach opens new directions for developing plant-integrated environmental intelligence systems, offering passive and biologically rooted strategies for ultra-local forecasting—especially valuable in remote, sensor-sparse, or climate-sensitive regions. Our findings contribute to the emerging field of plant-based sensing and biomimetic environmental monitoring, expanding the role of flora to biosensors, useful in Earth system observation tasks. Full article

This paper presents a fully passive and wireless glucose concentration sensor that integrates a capacitively coupled complementary split-ring resonator (CSRR) with an H-slot UHF RFID antenna. The CSRR serves as the primary sensing element, where changes in glucose concentration alter the effective permittivity of the surrounding solution, thereby modifying the resonator capacitance and shifting its resonance behavior. Through near-field capacitive coupling, these dielectric variations affect the antenna input impedance and backscatter response, enabling wireless sensing by modulating the maximum read range. The proposed sensor operates within the 902–928 MHz UHF RFID band and is interrogated using commercial RFID readers, eliminating the need for specialized laboratory equipment such as vector network analyzers. Full-wave electromagnetic simulations and experimental measurements validate the sensor performance, demonstrating a variation in the read range from 6.23 m to 4.67 m as glucose concentration increases from 50 to 200 mg/dL. Moreover, the sensor exhibits excellent linearity, with a high coefficient of determination ($R^2=0.986$) based on the curve-fitted data. These results underscore the feasibility of the proposed sensor as a low-cost and fully portable platform for concentration monitoring, with potential applications in liquid characterization and chemical sensing. Full article

Industrial Wireless Sensor Networks (IWSNs) play a critical role in Industry 4.0 environments, enabling real-time monitoring and control of industrial processes. However, existing lightweight authentication protocols for IWSNs remain vulnerable to sophisticated security attacks because of inadequate initial authentication phases. This study presents a security analysis of Gope et al.’s PUF-based authentication protocol for IWSNs and identifies critical vulnerabilities that enable man-in-the-middle (MITM) and denial-of-service (DoS) attacks. We demonstrate that Gope et al.’s protocol is susceptible to MITM attacks during both authentication and Secure Periodical Data Collection (SPDC), allowing adversaries to derive session keys and compromise communication confidentiality. Our analysis reveals that the sensor registration phase of the protocol lacks proper authentication mechanisms, enabling attackers to perform unauthorized PUF queries and subsequently mount successful attacks. To address these vulnerabilities, we propose an enhanced authentication scheme that introduces a sensor-initiated registration process. In our improved protocol, sensor nodes generate and control PUF challenges rather than passively responding to gateway requests. This modification prevents unauthorized PUF queries while preserving the lightweight characteristics essential for resource-constrained IWSN deployments. Security analysis demonstrates that our enhanced scheme effectively mitigates the identified MITM and DoS attacks without introducing significant computational or communication overhead. The proposed modifications maintain compatibility with the existing IWSN infrastructure while strengthening the overall security posture. Comparative analysis shows that our solution addresses the security weaknesses of the original protocol while preserving its practical advantages for industrial use. The enhanced protocol provides a practical and secure solution for real-time data access in IWSNs, making it suitable for deployment in mission-critical industrial environments where both security and efficiency are paramount. Full article

This study presents a wireless, non-invasive sensing system for monitoring the dielectric permittivity of materials, with a particular focus on applications in cultural heritage conservation. The system integrates a passive split-ring resonator tag, electromagnetically coupled to a compact antipodal Vivaldi antenna, operating in the reactive near-field region. Both numerical simulations and experimental measurements demonstrate that shifts in the antenna’s reflection coefficient resonance frequency correlate with variations in the dielectric permittivity of the material under test. A calibration curve was established using reference materials—including low-density polyvinylchloride, polytetrafluoroethylene, polymethyl methacrylate, and polycarbonate—and validated through precise permittivity measurements. The system was subsequently applied to wood samples (fir, poplar, beech, and oak) at different humidity levels, revealing a sigmoidal relationship between moisture content and permittivity. The behavior was also confirmed using a portable and low-cost setup, consisting of a point-like coaxial sensor that could be easily moved and positioned as needed, enabling localized measurements on specific areas of interest of the sample, together with a miniaturized Vector Network Analyzer. These results underscore the potential of this portable, contactless, and scalable sensing platform for real-world monitoring of cultural heritage materials, enabling minimally invasive assessment of their structural and historical integrity. Moreover, by enabling the estimation of moisture content through dielectric permittivity, the system provides an effective method for early detection of water-induced deterioration in wood-based heritage items. This capability is particularly valuable for preventive conservation, as excessive moisture—often indicated by permittivity values above critical thresholds—can trigger biological or structural degradation. Full article

The use of remote sensing technologies for mapping forest fires has experienced significant growth in recent decades, driven by advancements in remote sensors, processing platforms, and artificial intelligence algorithms. This study presents a review of 192 scientific articles published between 1990 and 2024, selected using PRISMA criteria from the Scopus database. Trends in the use of active and passive sensors, spectral indices, software, and processing platforms as well as machine learning and deep learning approaches are analyzed. Bibliometric analysis reveals a concentration of publications in Northern Hemisphere countries such as the United States, Spain, and China as well as in Brazil in the Southern Hemisphere, with sustained growth since 2015. Additionally, the publishers, journals, and authors with the highest scientific output are identified. The normalized burn ratio (NBR) and the normalized difference vegetation index (NDVI) were the most frequently used indices in fire mapping, while random forest (RF) and convolutional neural networks (CNN) were prominent among the applied algorithms. Finally, the main technological and methodological limitations as well as emerging opportunities to enhance fire detection, monitoring, and prediction in various regions are discussed. This review provides a foundation for future research in remote sensing applied to fire management. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

The signal-processing architecture of passive surface acoustic wave (SAW) sensors presents significant implementation challenges due to its radar-like operational principle and the inherent complexity of discrete component-based hardware design. While System-in-Package (SiP) has demonstrated remarkable success in miniaturizing electronic systems for smartphones, automotive electronics, and IoT applications, its potential for revolutionizing SAW sensor interrogator design remains underexplored. This paper presents a novel architecture that synergistically combines time-domain ADC design with SiP-based miniaturization to achieve unprecedented simplification of SAW sensor readout systems. The proposed time-domain ADC incorporates an innovative delay chain calibration methodology that integrates physical unclonable function (PUF) principles during time-to-digital converter (TDC) characterization, enabling the simultaneous generation of unique system IDs. The experimental results demonstrate that the integrated security mechanism provides variable-length bit entropy for device authentication, and has a reliability of 97.56 and uniqueness of 49.43, with 53.28 uniformity, effectively addressing vulnerability concerns in distributed sensor networks. The proposed SiP is especially suitable for space-constrained IoT applications requiring robust physical-layer security. This work advances the state-of-the-art wireless sensor interfaces by demonstrating how time-domain signal processing and advanced packaging technologies can be co-optimized to address performance and security challenges in next-generation sensor systems. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

This article reports on the interrogation of fiber Bragg grating (FBG)-based sensors that have been multiplexed in a ladder topology. In each line of this topology, FBGs with different wavelengths are connected. In addition, delay fibers have been inserted between each line to enable reflections from different lines to be distinguished. Seven FBGs are interrogated simultaneously by applying time- and wavelength-division multiplexing techniques. To improve the signal-to-noise ratio of the weak reflected signals, the heterodyne detection technique is applied. Through the simulation of three different failure cases, we evaluate the fault detection capability of our method. Full article

The growing demand for renewable energy has underscored the importance of wind power, with wind turbines playing a pivotal role in sustainable electricity generation. However, wind turbine blades are exposed to various challenges, particularly flow-induced vibrations (FIVs), including vortex-induced vibrations, flutter, and galloping, which significantly impact the performance, efficiency, reliability, and lifespan of turbines. This review presents an in-depth analysis of wind turbine blade technology, covering the fundamental principles of operation, aerodynamic characteristics, material selection, and failure mechanisms. It examines the effects of these vibrations on blade integrity and turbine performance, highlighting the need for effective vibration suppression techniques. The paper also discusses current advancements in maintenance strategies, including active and passive vibration control methods, sensor networks, and drone-based inspections, aimed at improving turbine reliability and reducing operational costs. Furthermore, emerging technologies, such as artificial intelligence (AI)-driven prognostic assessments and novel materials for vibration damping, are explored as potential solutions to enhance turbine performance. The review emphasizes the importance of continued research in addressing the challenges posed by FIVs, particularly for offshore turbines operating in harsh environments. Full article

Entangled multiphoton is an ideal resource for quantum information technology. Here, narrow-bandwidth hyper-entangled quadphoton is theoretically demonstrated by quantizing degenerate Zeeman sub states through spontaneous eight-wave mixing (EWM) in a hot ⁸⁵Rb. Polarization-based energy-time entanglement (output) under multiple polarized dressings is presented in detail with uncorrelated photons and Raman scattering suppressed. High-dimensional entanglement is contrived by passive non-Hermitian characteristic, and EWM-based quadphoton is genuine quadphoton with quadripartite entanglement. High quadphoton production rate is achieved from co-action of four strong input fields, and electromagnetically induced transparency (EIT) slow light effect. Atomic passive non-Hermitian characteristic provides the system with acute coherent tunability around exceptional points (EPs). The results unveil multiple coherent channels (~8) inducing oscillations with multiple periods (~19) in quantum correlations, and high-dimensional (~8) four-body entangled quantum network (capacity ~65536). Coexistent hyper and high-dimensional entanglements facilitate high quantum information capacity. The system can be converted among three working states under regulating passive non-Hermitian characteristic via triple polarized dressing. The research provides a promising approach for applying hyper-entangled multiphoton to tunable quantum networks with high information capacity, whose multi-partite entanglement and multiple-degree-of-freedom properties help optimize the accuracy of quantum sensors. Full article

High-voltage direct-current (HVdc) transmission lines are gaining more attention as an integral part of modern power system networks. Monitoring the dc current is important for metering and the development of dynamic line rating control schemes. However, this has been a challenging task, and there is a need for wireless sensing methods with high accuracy and a dynamic range. Conventional methods require direct contact with the high-voltage conductors and utilize bulky and complex equipment. In this paper, an ultra-high-frequency (UHF) radio frequency identification (RFID)-based sensor is introduced for the monitoring of the dc current of an HVdc transmission line. The sensor is composed of a passive RFID tag with a custom-designed antenna, integrated with a Hall effect magnetic field device and an RF power harvesting unit. The dc current is measured by monitoring the dc magnetic field around the conductor using the Hall effect device. The internal memory of the RFID tag is encoded with the magnetic field data. The entire RFID sensor can be wirelessly powered and interrogated using a conventional RFID reader. The advantage of this approach is that the sensor does not require batteries and does not need additional maintenance during its lifetime. This is an important feature in a high-voltage environment where any maintenance requires either an outage or special equipment. In this paper, the detailed design of the RFID sensor is presented, including the antenna design and measurements for both the RFID tag and the RF harvesting section, the microcontroller interfacing design and testing, the magnetic field sensor calibration, and the RF power harvesting section. The UHF RFID-based magnetic field sensor was fabricated and tested using a laboratory experimental setup. In the experiment, a 40 mm-diameter-aluminum conductor, typically used in 500 kV HVdc transmission lines carrying a dc current of up to 1200 A, was used to conduct dc current tests for the fabricated sensor. The sensor was placed near the conductor such that the Hall effect device was close to the surface of the conductor, and readings were acquired by the RFID reader. The sensitivity of the entire RFID sensor was 30 mV/mT, with linear behavior over a magnetic flux density range from 0 mT to $4.5$ mT. Full article

Recently, a new concept has emerged for the development of Industrial Internet of Things (IIoT) applications, the Industrializable Industrial Internet of Things (I3oT). As a criterion for the design of industrial applications, the I3oT imposes the exclusive use of pre-installed elements in the company such as PLCs, sensors, IT/OT networks, etc., trying to minimize the impact on the factories and guaranteeing a cheap and assumable scalability for companies, something that cannot be implemented with the vast majority of IIoT applications available in the market. In our previous work, we have used I3oT applications for predictive maintenance on different components: cylinders, presses, welding clamps and also energy-saving tools, detection of bottlenecks and sub-bottlenecks, etc., all of them generalized for the entire factory. However, the main drawback comes from the flow of data through the IT/OT network. This article presents the Cross-PLC, a tool to allow massive data extraction using the company’s IT/OT network by communicating with any type of PLC or brand existing in the market. The Cross-PLC performs passive listening, and through different communication criteria, the Cross-PLC becomes a virtual PLC containing all the parameters necessary for the I3oT applications developed. This article presents the design of this tool, its implementation and use at Ford Factory in Almussafes (Valencia). Full article

This review paper aims to present a comprehensive overview of the evaluation, simulation, and control of heating, ventilation, and air conditioning (HVAC) systems in nuclear power plants (NPPs), specifically highlighting their importance in maintaining operational safety, thermal performance, and energy efficiency. The study’s authors summarize recent developments in HVAC technologies, such as passive cooling systems, data-driven energy management frameworks, and intelligent control strategies, to cope with the specific challenges of NPPs. Various passive cooling systems, including heat pipes, thermosyphons, and loop heat pipes, have proven themselves by their ability to remove residual heat from spent fuel pools and reactors power plants with high efficiency. Through experimental studies, they have shown their ability to eliminate operational vulnerability to accidents or guarantee any desired long-term cooling. Intelligent sensor networks allow a more data-driven approach to HVAC control, enabling online energy management frameworks and advanced intelligent control systems. These exhibit considerable promise for optimizing HVAC performance, decreasing energy consumption, and improving operational flexibility in multi-zone systems. Such capabilities are ideal for addressing the dynamic and safety-critical nature of NPPs. They are first enabled by the use of these technologies for real-time monitoring, predictive maintenance, and adaptive control. When applied with advanced HVAC control systems, passive cooling techniques provide an exciting route to improve safety and energy efficiency. An overview of the key findings is that robust thermal management solutions combined with intelligent control and intelligent adaptation are essential when addressing the rapidly evolving demands of nuclear energy systems. This work highlights the priorities in the next generation of nuclear power plants, which should actively pursue seamless integration of out-of-system technologies into existing NPP infrastructures, enabling scalable, cost-effective, and resilient solutions. Full article