Search Results (3,663)

Cross-interface architectures are increasingly central to large-scale ocean observation systems, where underwater sensor nodes transmit data to spatially distributed buoys that relay information to terrestrial networks. In these deployments, the inherent broadcast nature of underwater acoustic (UWA) propagation enables a single node’s signals to be captured by multiple buoys. However, substantial and dynamic propagation delays lead to inherent reception asynchrony and severe multi-user interference. Conventional detection relies on large hydrophone arrays on single platforms and assumes strict synchronization, hindering scalability and elevating costs. This study proposes a distributed asynchronous reception framework for buoy-assisted UWA networks. Under a cloud software-defined acoustic (C-SDA) architecture, spatially separated buoys are treated as a virtual distributed multiple-input multiple-output (MIMO) receiver. We introduce a minimum-delay-based equivalent reconstruction to regularize the asynchronous structure, followed by blind channel identification and pilot-assisted synchronization for robust multi-user detection. By leveraging long-delay broadcast propagation as a source of spatial diversity, the framework facilitates scalable and cost-effective multi-user access. The results demonstrate that the architecture provides a practical paradigm for the underwater Internet of Things and long-term ocean observation. Full article

Condiment sauces such as soy sauce, fish sauce, oyster sauce, and Worcestershire sauce play a vital role in culinary practices and cultural identity, particularly in the Philippines. These sauces are distinguished by their unique volatile organic compound profiles, which define their aroma and flavor. With the growing demand for these condiment products, there is an increasing need for accurate and efficient methods to classify them, ensuring product authenticity and strengthening quality control. However, conventional approaches such as sensory evaluation and laboratory-based chemical analysis are often expensive, time-consuming, and subjective. To address this limitation, we used an electronic nose (e-nose) system integrated with a Support Vector Machine (SVM) classifier for the classification of dark condiment sauces. The system consists of an array of MQ-series gas sensors connected to an Arduino Mega 2560 for analog-to-digital conversion, with Raspberry Pi 5 serving as the primary processing unit. Sensor data undergo preprocessing steps, including standardization and dimensionality reduction through principal component analysis, before being classified using SVM. A total of 120 samples, consisting of 40 readings per condiment type, were used for training and testing, while 60 additional samples—15 per class—were reserved for validation. The e-nose system achieved a 95% classification performance, as evaluated using a confusion matrix and overall accuracy metrics. These results demonstrate the potential of the e-nose combined with SVM as a reliable tool for condiment classification. The system offers practical applications in quality control and product authentication. Future work may extend its capabilities toward spoilage detection, the integration of different gas sensors, and the classification of a wider variety of condiment sauces. Full article

Electronic nose systems are advanced technological tools that enable the objective evaluation of odors through sensor arrays mimicking the human olfactory mechanism and sophisticated data processing algorithms. These systems facilitate rapid, reproducible, and standardized measurement of chemical components in applications such as food safety, environmental monitoring, medical diagnostics, and industrial quality control. In this study, measurements obtained from electronic nose sensors were compared with olfactometry panelist assessments using n-butanol as a reference substance in accordance with the TS EN 13725 standard. Furthermore, machine learning algorithms, including Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), were applied to model the sensor data and evaluate their predictive accuracy. The results demonstrated the reliability and applicability of the electronic nose system, achieving training mean absolute percentage error (MAPE) values of 6.53% for PLS, 10.89% for SVR, and 0.15% for GPR. This study presents an innovative approach that systematically assesses the performance of electronic nose technology using a standardized reference odor and highlights the effectiveness of the modeling approach. Full article

This work presents the development and experimental characterization of a compact ultraviolet (UV) telescope based on silicon photomultipliers (SiPMs) designed for the detection of faint atmospheric optical tracks. Such transient optical phenomena include meteors, transient luminous events (TLEs), space debris reentries, and other faint atmospheric emissions. Nuclearite-induced atmospheric emission is considered as a benchmark case for evaluating the expected signal levels of rare luminous track events. We detail the fabrication, assembly, and testing of the SiPM sensor array, comprising parallel Geiger-mode avalanche diodes with high fill factor and photon detection efficiency, alongside custom readout electronics using self-triggering ASICs, precision optical components, and a stable mechanical mount. This photon-counting telescope provides a compact and mechanically robust alternative to conventional PMT-based systems, with demonstrated capability for detecting low-light atmospheric tracks under controlled laboratory conditions. Full article

This paper presents a miniaturized and highly accurate saltwater concentration monitoring system based on Capacitive Micromachined Ultrasonic Transducer (CMUT) array technology. The system incorporates a highly integrated CMUT array with a compact footprint of 5 mm × 5 mm, capable of both transmitting and receiving ultrasonic signals, which significantly contributes to the system’s miniaturization and portability. To ensure accurate compensation for temperature-dependent variations in sound velocity, a TA610A temperature sensor is integrated for continuous real-time monitoring of the salt solution temperature. By acquiring ultrasonic echo signals, the system calculates the time-of-flight (TOF) of the acoustic waves. Based on the TOF and real-time temperature data, the sound velocity is determined, and the salt concentration is subsequently derived with temperature compensation applied to enhance measurement accuracy. Experimental results show a measurement precision of 0.1% and a maximum absolute error of 0.02%, confirming the system’s high accuracy and robustness. Combining stability, reliability, and a compact real-time sensing design, the proposed CMUT-based system holds significant promise for practical deployment in various industrial and environmental monitoring scenarios. Full article

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

Snow avalanches pose a serious hazard in snow-covered, mountainous areas. In order to protect inhabited areas and infrastructure such as roads and railway lines, avalanche protection measures need to be taken. In addition to permanent, technical protection measures, temporary, organizational measures, which are based on risk assessments by local avalanche warning commissions, are utilized. These avalanche risk assessments rely on regional avalanche bulletins, weather forecasts, local expertise, and information on current snowpack conditions. Our research seeks to enhance knowledge of current snowpack and avalanche conditions by providing in situ monitoring of potential avalanche slopes. Therefore, we developed a novel sensor box array, peakr, consisting of multiple sensor units deployed by hand or by drone at key avalanche slope locations throughout the winter season. The sensors continuously measure temperature, humidity, position, and snowpack movement. Data are transmitted via LoRaWAN and GSM, stored locally, and accessed through a web platform. Automated analysis using a decision tree and event-detection algorithm triggers immediate alerts to responsible personnel via SMS and email. This paper presents an overview of the peakr sensor array and web platform, focusing on data analysis and avalanche events from the Arlberg ski resort in winter 2023/2024, supported by webcam time-lapse validation. Full article

Surface plasmon resonance (SPR) sensors based on nanostructured metasurfaces offer enhanced sensitivity through engineered electromagnetic responses. In this study, we present an analytical and numerical investigation of the plasmonic behavior of gold nanopillar (Au-NP) and nanohole (Au-NH) arrays under both p- and s-polarized illumination, employing the Effective Medium Theory (EMT) in combination with the Transfer Matrix Method (TMM). The study combines Effective Medium Theory (EMT) and the Transfer Matrix Method (TMM) to describe the macroscopic optical response of multilayer plasmonic systems. For p-polarization, the nanostructure geometry strongly modulates the real and imaginary parts of the effective permittivity, with nanoholes supporting stronger SPR coupling and reduced optical losses compared to nanopillars. Under s-polarization, the effective permittivity remains largely invariant, primarily driven by the filling fraction. The analysis reveals that polarization-dependent behavior arises from boundary-condition-mediated coupling mechanisms governing surface plasmon excitation, aligning with classical plasmonic theory. Benchmarking against analytical dispersion relations and published experimental data for Au/BK7 systems shows close agreement within ±2°, confirming the physical consistency of the EMT–TMM framework. These results provide a systematic description of how polarization and filling fraction jointly modulate SPR coupling. The results offer a foundation for the rational design of plasmonic coatings and SPR-supporting metasurfaces by elucidating macroscopic coupling trends; however, no quantitative sensor performance metrics, such as refractive index sensitivity or figure of merit, are evaluated in this work. Full article

Steel tape armored multicore cables are critical components in the transmission and distribution of power in medium- and low-voltage networks. It is difficult to measure current in the individual conductors of multicore cables because they are enclosed within multilayer protective structures (e.g., armor). The magnetic field–current inversion method provides a noncontact alternative for measuring conductor currents, derived from externally measured magnetic fields. However, the nonlinear magnetization effects of the steel tape armor disrupt the linear relationship between the magnetic field and currents, making accurate measurements challenging. To address this issue, we propose a noncontact current measurement method that incorporates the nonlinear effects of the armor layer. This method involves pre-calibrating the coefficient matrices, determining the angle formed between the magnetic sensor array and the multicore cable, and applying nonlinear fitting. This achieves a current measurement accuracy less than 5% and 5° in relative error and phase error, respectively. The proposed method avoids computationally intensive inverse operations, thereby enabling the realization of lightweight, low-cost current measurement terminals for practical field applications. Full article

This study presents a low-cost, small-scale single-chamber microbial fuel cell (MFC) toxicity biosensor fabricated on a printed circuit board (PCB) and a 3D-printed chamber with a volume of 120 μL. The anode consists of a screen-printed carbon electrode on the PCB, while the air cathode is a carbon paper electrode. To address poor adhesion of microorganisms to the smooth anode surface, femtosecond laser processing was used to fabricate a micropore array with 40 μm pores on the electrode. This method can create micropores on the anode surface without damaging the screen-printed electrodes, the PCB substrate, or the pads. These micropores increase the anode’s surface area and hydrophilicity, allowing more microbial coatings to firmly adhere to its surface. In this study, the MFC utilised Rhizobium rosettiformans W3, extracted from activated sludge at a wastewater treatment plant, as the anode microorganism. Its aerobic nature simplifies the design of MFCs, enabling a single-chamber structure and miniaturisation. Using formaldehyde solution as a toxicity sample to test the biosensor’s performance, a 0.1% concentration significantly reduced the sensor’s output power. Full article

The increasing diffusion of plant-based milk alternatives poses new challenges at the intersection of food safety and consumer experience, particularly regarding allergen cross-contamination and beverage performance during preparation. Traditional quality control strategies are typically confined to upstream production stages and are unable to address individualized risks and sensory variability at the point of consumption. In this study, we propose an embedded volatilomic sensing approach that combines metal oxide semiconductor (MOX) sensor arrays with lightweight artificial intelligence algorithms to enable real-time, on-device decision-making. The volatilome of four commercially available plant-based milk beverages (oat, almond, soy, and coconut) was characterized using GC–MS/SPME as a reference method, while a MOX-based electronic nose provided rapid, non-destructive sensing of volatile fingerprints. Linear Discriminant Analysis demonstrated clear discrimination among beverage types based on their volatile signatures, supporting the use of MOX sensor arrays as functional descriptors of compositional identity and process-related variability. Beyond beverage classification, the proposed framework is designed to support future implementation of (i) screening for anomalous volatilomic patterns potentially compatible with accidental cow’s milk carryover in shared preparation settings and (ii) adaptive tuning of preparation parameters (e.g., foaming-related settings) in smart beverage systems. The results highlight the role of embedded volatilomic intelligence as a unifying layer between personalized risk-aware screening and sensory-oriented process control, paving the way for intelligent food-processing appliances capable of autonomous, real-time adaptation at the point of consumption. Full article

Steels remain among the most widely used structural and engineering materials in modern infrastructure, energy systems, and industrial facilities. Their long-term reliability depends critically on the early detection of corrosion damage and microstructural degradation. This review surveys recent advances in two complementary families of non-destructive evaluation (NDE) methods: magnetic techniques, including magnetic Barkhausen noise (MBN), magnetic flux leakage (MFL), eddy current testing (ECT), and magnetic hysteresis analysis; and electrochemical methods including electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), scanning vibrating electrode technique (SVET), and electrochemical noise (EN). Recent progress in sensor miniaturization, signal processing algorithms, and multi-technique integration is reviewed. Particular attention is given to the sensitivity of these methods to microstructural changes reported in the literature, including carbide dissolution, phase transformations, temper embrittlement, and sensitization in stainless steels, as well as to the conditions under which such sensitivity has been demonstrated. The potential synergy between magnetic and electrochemical monitoring is discussed as a possible pathway toward more robust, condition-based maintenance frameworks. Challenges related to field deployment, environmental interference, calibration, and data interpretation are identified, and future directions—including machine learning-assisted analysis and multi-physics sensor arrays—are outlined. Full article

The coprime array, proposed in recent years as a special type of sparse array, combines the advantages of sparse sensing with the unique properties of prime numbers, enabling a larger array aperture and higher degrees of freedom with the same number of physical sensors. In underwater array signal processing, the high-resolution potential of coprime arrays has attracted significant attention. However, in complex ocean environments, leveraging the advantages of coprime arrays to achieve high-resolution and robust target detection still faces challenges posed by sensor failures. Element failures can disrupt the physical structure of the coprime array, leading to significantly increased energy in grating lobes and side lobes of the beam pattern, thereby raising the probability of false target azimuth identification. To address this issue, this paper analyzes the virtual array set mapped from the physical coprime array and proposes a multiplepath matching pursuit method for underwater vector coprime array target azimuth detection based on random virtual array set construction and verification techniques. Cases of continuous and non-continuous virtual arrays are analyzed, and corresponding solutions are proposed. Through simulations and analyses of sea trial data, it is demonstrated that the proposed method can achieve high-resolution target azimuth detection as well as robust target detection in the presence of physical sensor failures. Full article

Flexible pressure sensors have been increasingly proposed for clinical monitoring applications. However, the available evidence on the technical characteristics and the biomedical applications of these technologies remains fragmented. To fill this gap, this scoping review aimed to map the available literature (i) to identify the existing flexible pressure sensor matrices proposed for biomedical applications, their technical characteristics, and usage contexts, and (ii) to determine the systems integrated into bed-based support surfaces for clinical monitoring functions. The scoping review was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. PubMed, Scopus, and Web of Science databases were systematically searched to identify studies published between 2015 and 2025 that describe flexible pressure sensor matrices for biomedical monitoring and care applications. A total of 5021 records were screened, and 45 studies were included. Existing flexible pressure sensor matrices were mainly based on resistive and capacitive principles. Systems integrated into clinical support surfaces were primarily used for pressure distribution and posture monitoring, and spanned from experimental prototypes to commercially available technologies. A lack of technical specifications and relevant heterogeneity was observed among the studies. Flexible pressure sensors demonstrated potential for clinical monitoring, but standardized technological reporting and clinical validation protocols are needed to develop technically robust and clinically oriented pressure sensing solutions. Full article

A new universal construction of intermediate-layer-free solid-contact ion-selective electrodes using a novel inner electrode, namely microelectrodes array composed of a large number of individual microelectrodes, was developed. This approach eliminates the need for a conventional solid-contact intermediate layer while maintaining excellent electrochemical performance. The studies were performed on two membrane model systems: potassium-ion-sensitive membranes based on valinomycin and nitrate-ion-sensitive membranes based on tridodecyldimethylammonium nitrate. In both cases, the membrane was applied directly onto the surface of the electrode substrate. The obtained results with such an ion-selective electrode based on a gold microelectrode array (GMA), a glassy carbon electrode (GCE), and a gold electrode (GE) were compared. It has been proven that, despite the lack of solid contact, whether in the form of an intermediate layer or as an addition directly to the membrane, ion-selective electrodes based on gold microelectrode arrays were characterized by very good analytical parameters. For those electrodes, a notable improvement in stability, reversibility, and repeatability of the electrode potential was observed and compared with electrodes using a glassy carbon disc electrode or a gold disc electrode as the electrode substrate. Thanks to the use of such an innovative electrode substrate, the final sensor preparation is shortened and simplified while maintaining good performance and stable readings. Full article