Search Results (4,461)

This systematic review presents a critical analysis of multifunctional nanowire sensors, with explicit selection criteria for included studies: we focus on peer-reviewed research, prioritizing studies on semiconductor (ZnO, TiO₂, Si), metal (Ag, Au), and carbon-based (CNT) nanowires that report structural innovations, performance breakthroughs, or industrial scalability. We systematically analyze their structural characteristics, advanced fabrication techniques (hydrothermal synthesis, magnetron sputtering, PECVD), and application performance across biosensing, pressure sensing, and gas monitoring. Unlike existing reviews limited to single material classes or application scenarios, this work advances the field by integrating three novel perspectives: it delivers a cross-material comparison of nanowire structure–performance relationships, incorporates an analysis of fabrication strategy scalability for industrial translation, and synthesizes unresolved challenges and future directions. Nanowire sensors exhibit superior sensitivity, rapid response, and broad detection ranges compared to conventional sensors, with significant potential to advance healthcare, environmental monitoring, and flexible electronics. Full article

Accurate engine fault classification with limited labeled data is critical for the safety and reliability of rotating machinery. This task is challenging because operating regimes are time-varying, and key variables must satisfy physical constraints, under which traditional feature classifier pipelines degrade and deep networks tend to overfit. We propose PhysGMM-MoE, a physics-aware Gaussian Mixture Model (GMM)-Mixture-of-Experts (MoE) framework for small-sample engine fault classification. At the data level, PhysGMM-MoE fits class-conditional, regime-aware GMMs and performs physically constrained, distance-based quality control to selectively augment minority classes while preserving engine operating semantics. At the model level, a heterogeneous pool of lightweight statistical experts and a lightweight Transformer-based deep expert (ECFT-Transformer) capture complementary neighborhood cues and high order multi-sensor correlations, and an L2-regularized logistic regression meta-learner fuses expert outputs via stacking. We evaluate fault classification on the 3500-DEFault diesel-engine dataset using the adopted eight-class cylinder-fault labeling (H, F1–F7) built from in-cylinder pressure statistics and torsional-vibration harmonics; although severity levels exist in the dataset, this study focuses on classification rather than severity estimation. With 40 training samples per class, PhysGMM-MoE achieves a mean accuracy of 0.9875, exceeding SMOTE+XGBoost by 0.0086, and attains the best macro precision/recall/F1 of 0.9878/0.9826/0.9889, demonstrating strong performance under the adopted small-sample setting. Full article

A Hybrid-Frequency Sampling Tactile Sensing System Based on a Flexible Piezoresistive Sensor Array is presented for reliable and real-time tactile perception under dynamic loading conditions. While recent studies have developed multi-channel tactile arrays, most systems remain limited by time-dependent drift in channel responses, inconsistent dynamic behavior, or insufficient temporal resolution under simultaneous loading. In this work, a system-level design integrating a flexible piezoresistive sensor array with a real-time data acquisition module is developed, incorporating a hybrid-frequency sampling strategy to reduce system complexity while preserving reliable dynamic response in key sensing channels. Register-Transfer Level (RTL) simulation verified that the hardware scheduler rigorously executed the deterministic scanning logic, demonstrating a strict one-to-one correspondence with the physical hardware signals. The array consists of 34 piezoresistive sensing nodes embedded in an elastomeric substrate. Under the implemented hybrid-frequency sampling scheme, the system achieves an overall effective acquisition bandwidth of approximately 36.9 kHz, while maintaining a repeatability better than 4.9% and robust mechanical durability under cyclic bending deformation. Dynamic loading validation was performed using a self-developed pressure comparison platform for measuring the normal contact force applied on the tactile surface, serving as ground-truth data to verify that the voltages acquired by the proposed system accurately correspond to the actual applied force. Quantitative analysis shows a strong linear correlation (R² ≈ 0.98) between the e-skin outputs and the reference forces. The recorded responses exhibit clear intensity-dependent trends and good temporal correspondence among sensing nodes, successfully distinguishing tactile stimuli such as gentle tapping, moderate pressing, and firm contact. The system also captures dynamic tactile responses during finger stroking, showing characteristic multi-unit activation patterns under spatiotemporally varying contact conditions. Compared with previously reported tactile systems typically operating below 100 Hz, the proposed design achieves an approximately 10× enhancement in effective sampling capability while significantly reducing system complexity through hybrid-frequency sampling, thereby supporting reliable dynamic tactile sensing in multi-unit arrays. These results demonstrate that the proposed system provides a practical and scalable hardware platform for dynamic tactile sensing in robotics, human–machine interaction, and wearable tactile systems. Full article

The difficulties of continuously monitoring cardiac patients in general hospital wards are still present because of the manual charting system and the slow clinical reaction to worsening physiological state. This paper outlines an edge- and fog-based Internet of Things (IoT) healthcare system to acquire, process, and prioritize the vital signs of patients in real time to minimize the alert latency and increase the time of clinical interventions. Wearable 12-lead ECG sensors transmit physiological measurements, such as heart rate, blood pressure, and oxygen saturation, to an intelligent edge service, where preprocessing, triage by threshold, and machine learning ECG classification are performed, and selective synchronization of physiological data with a cloud backend and data delivery to the clinician are made possible by a mobile application. The proposed architecture combines a ribbon-like streaming scheme, Flask-based gateway services, and Firebase Firestore to coordinate scalable mob/cloud with the help of multi-client data dissemination. To encompass borderline clinical deterioration, which is often unnoticed by conventional threshold systems, physiological parameters are classified into normal, alarming, emergency, and a new state, average. The Pan–Tompkins++ peak detector algorithm and multiple edge-resident classifiers, such as random forest, XGBoost, decision tree, naive Bayes, K-nearest neighbor, and support vector machine, are used to analyze the ECG waveforms. Experimental analysis of PhysioNet datasets and tests in real wards prove that the ensemble models can reach the highest possible ECG classification precision of 91.96 percent and snapshot-driven mobile alerts can decrease routine patient evaluation time by several minutes, to an average of 15.23 ± 2.71 s. These results suggest that edge-centric IoT systems can be appropriate in latency-critical hospital settings and that fog-based coordination is useful in next-generation smart healthcare systems. Full article

In this study, an enzyme-free electrochemical sensor based on zinc oxide (ZnO) nanorods synthesized by the thermal decomposition of zinc acetate is presented. The suggested approach ensures simplicity, environmental friendliness, and scalability of the process without the use of an autoclave or high pressure. The morphology and structure of the samples are studied using SEM, TEM, XRD, Raman, FTIR, XPS, PL, and UV-Vis spectroscopy. It is found that heat treatment at 450 °C increases the degree of crystallinity, increases the size of crystallites, and reduces the concentration of surface defects, which leads to improved optical and electrochemical characteristics of the material. Beyond conventional sensitivity metrics, our study demonstrates that the selective detection of ascorbic acid (AA) and uric acid (UA) can be achieved by controlling the applied potential on a single ZnO electrode, an approach that leverages differences in redox energetics and surface interaction dynamics rather than complex surface functionalization. It is shown in this work that the synthesized ZnO samples subjected to heat treatment in air at 450 °C exhibit high sensitivity to ascorbic acid (9951.87 μA·mM⁻¹·cm⁻²; LoD = 1.11 μM) at a potential of 0.2 V and to uric acid (5762.48 μA·mM⁻¹·cm⁻²; LoD = 1.71 μM) in a phosphate buffer solution (pH 7) at a potential of 0.4 V with a linear range of 3 mM, offering a way to create simplified multicomponent electrochemical biosensors based on potential-controlled selectivity. Full article

Background/Objectives: Wearable technologies become increasingly important in surveillance of biometric parameters in pregnant women; however, early-stage usability data on wearable form factors specifically designed for pregnant women remain limited. This study evaluated the usability and acceptance of three non-functional wearable garment prototypes intended for future breathing exercise guidance and sleep-related applications. The prototypes incorporated sensor dummies that were technically capable of operation but intentionally deactivated for this usability pilot study. Methods: Eighteen pregnant women (second and third trimester) and twelve non-pregnant women tested three prototypes (Bra, Strap, Maternity Belt (hereafter Belt)) for 24 h. Usability was assessed using structured, participant-completed questionnaires addressing fit, material properties, comfort, and wear-related issues immediately after fitting (T0) and after 24 h of wear (T24). Analyses were descriptive and exploratory. Results: Among pregnant women, the Bra prototype showed consistently favorable usability ratings across multiple domains, particularly after extended wear, whereas the Belt demonstrated declining ratings related to fit and comfort over time. The Strap showed intermediate usability with specific strengths related to pressure and friction. In non-pregnant women, usability ratings were largely comparable between the Bra and Strap, with no clear preference pattern. No systematic differences were observed between pregnant and non-pregnant groups. Conclusions: This exploratory usability study suggests that garment form factor plays a critical role in acceptability during pregnancy. The Bra prototype demonstrated the most favorable usability profile among pregnant women, while the Belt revealed design limitations that warrant further modification. These findings provide formative guidance for the development of functional maternal wearables, with future studies integrating objective testing and validated measures to optimize performance and evaluate adherence in larger cohorts. Full article

Nowadays, thermal runaway accidents involving lithium batteries in new energy vehicles and energy storage power stations occur frequently, with battery deformation pressure as the core precursor signal. Traditional battery protection schemes suffer from limitations, including wired connections, limited real-time remote monitoring, and insufficient sensing accuracy, rendering them unable to meet the safety monitoring needs of large-scale battery modules. Therefore, a high-precision pressure-sensing battery protection system based on the Internet of Things has been developed. This paper selects a MEMS high-precision pressure sensor with an accuracy of ±0.1 kPa to design an IoT sensing node based on the STM32L431 and LoRa/Wi-Fi 6, integrating pressure sensing and wireless communication. It proposes a sliding-average filtering and wavelet denoising algorithm, as well as a temperature-compensation calibration model, to optimize sensing accuracy. Additionally, it constructs a hierarchical early warning model based on pressure thresholds. The experiment demonstrates that the sensor achieves a detection accuracy of 99.2%, a response delay of less than 50 ms, a transmission packet loss rate of less than 0.5%, an end-to-end delay of less than 200 ms, and an early warning accuracy rate of 99.2% under battery overcharge/overtemperature conditions. The innovation of this study lies in the first integration of high-precision pressure sensing and IoT communication for battery protection. A low-power IoT sensing node tailored for battery aging scenarios has been designed, validating the novel application value of IoT sensing in the safety monitoring of new energy equipment. This system fills a gap in IoT pressure-sensing technology for battery protection, enabling practical applications and serving as a reference for implementing integrated sensing and communication technology. Full article

This paper presents a high-temperature silicon resonant pressure microsensor capable of stable operation up to 175 °C and 175 MPa, addressing the critical need for reliable pressure monitoring in deep well drilling and petroleum exploration. To overcome the inherent trade-off between pressure range and sensitivity in diaphragm-based sensors, the sensor incorporates V-shaped micro-beam supports that convert radial compressive stress into supplementary axial tensile stress on the resonant beams. This innovative force-transmission structure enhances both pressure resistance and positive stress sensitivity, enabling range extension while maintaining adequate sensitivity. A key feature of this work is the implementation of a frequency-ratio measurement scheme utilizing a dedicated pressure-insensitive reference resonator. This approach effectively eliminates the dependence on the stability of the external crystal oscillator frequency, a significant source of error in high-temperature environments where stable clock sources are costly or unavailable. Experimental results demonstrate that the fabricated sensor achieves a pressure sensitivity of 723.56 ppm/MPa for Resonator I and −436.60 ppm/MPa for Resonator II. The frequency-ratio output scheme maintains a measurement accuracy better than 0.02% FS (within the 0–36 MPa verification range) even when using a low-stability oscillator at 125 °C, significantly outperforming conventional direct-frequency measurement methods. The sensor’s combination of an extended pressure range, high-temperature capability, and robust frequency-ratio output offers a promising solution for high-precision pressure sensing in extreme downhole conditions. Full article

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions. Full article

Accurate measurement of liquid flow velocity is crucial for pipeline management in the petroleum industry. Traditional flowmeters, such as ultrasonic, electromagnetic, and pressure-based devices, provide only point measurements and often require intrusive installation. Distributed Acoustic Sensing (DAS) offers a non-intrusive alternative by converting optical fibers into continuous acoustic sensors with meter-scale resolution. In this study, a High-Definition DAS system was applied in a laboratory flow loop to monitor single-phase liquid flow. The recorded signals were analyzed in both time and frequency domains. Results showed that the pump operating frequency dominated the spectral energy, accompanied by dispersive features. Power spectral density (PSD) increased linearly with flow rate, while Doppler-induced frequency shifts in the dominant component enabled velocity prediction. A regression model achieved a high coefficient of determination (R² = 0.9928), confirming the strong predictive capability. These findings highlight DAS as a reliable and scalable solution for non-intrusive liquid flow monitoring in pipelines. Full article

Continuous plantar-pressure monitoring is important for objective gait analysis and early detection of abnormal loading; however, many existing solutions remain laboratory-bound (force plates and instrumented walkways) or rely on costly in-shoe multilayer sensor arrays. Here, we developed and optimized a fully screen-printed pressure-sensing insole based on carbon–polymer nanocomposite layers, with an emphasis on manufacturability and process control to bridge the gap between proof-of-concept force-sensitive resistor (FSR)-based insoles and scalable printed-electronics manufacturing workflows. Composite pastes containing carbon fillers (graphene nanoplatelets, carbon black, and graphite) were formulated to improve sensor repeatability and sensitivity. Sensors were characterized under compression loads from 100 N to 1300 N, showing a sensitivity of 10.5 ± 2.8 Ω per 100 N and a sheet-to-sheet coefficient of variation of 22.1% in resistance response. The effects of paste composition, screen mesh density, electrode layout, and lamination on sensitivity and repeatability were systematically evaluated. In addition, correlation analysis of resistance values from integrated quality-control meanders proved useful for monitoring screen-printing process stability. The final insole integrates printed carbon sensing pads and contacts, a dielectric spacer, and an adhesive layer in a thin, flexible format suitable for integration with wearable electronics. In practical static-load tests, repeated manual placement of weights yielded coefficients of variation as low as 4% at 500 g and a detection limit of ~0.1 N, comparable to a very light finger touch. These results demonstrate that low-cost screen-printed electronics can provide robust pressure sensing for wearable plantar-pressure monitoring. Full article

Background: Cardiovascular disease (CVD) remains the leading cause of death and disability worldwide. Among its subtypes, coronary artery disease (CAD) is the most common and often develops silently, without noticeable symptoms. CAD-related murmurs typically fall below the human hearing threshold, limiting the effectiveness of traditional stethoscope-based auscultation. Currently, the gold standard for CAD diagnosis is coronary angiography, an invasive and expensive procedure usually reserved for symptomatic patients. This highlights the global need for a non-invasive, cost-effective pre-screening tool for asymptomatic CAD detection. Objectives: This study investigates the effectiveness of a wearable vest equipped with multiple digital stethoscopes to detect CAD. By applying signal processing and machine learning to multichannel phonocardiogram (PCG) data, we aim to evaluate the accuracy of CAD detection. We further assess the impact of incorporating patient metadata to enhance model performance. Methods: Data were collected from 40 CAD patients and 40 non-CAD individuals using a wearable vest with seven embedded PCG sensors. Subjects performed 10 s breath-hold recordings in a clinical setting. Linear-frequency cepstral coefficients were extracted from the PCG signals and classified using a support vector machine. Metadata, including body mass index, blood pressure, type 2 diabetes, and hypertension, were integrated to assess performance gains. Results: A combination of four channels achieved an accuracy of 80.44%, a 7% improvement over the best single-channel result. Incorporating metadata increased accuracy to 82.08%. Conclusions: The wearable vest demonstrated promising clinical potential, exceeding a 75% sensitivity-specificity average, and may support accessible, automated CAD screening in future validated settings. Full article

Achieving precise sliding perception is crucial for enhancing human–machine interactions. Despite the extensive investigation of tactile sensors for static pressure detection, they still face challenges in detecting dynamic information such as sliding direction, speed, pressure and position in interactive touch scenarios. Herein, we propose a self-powered tactile interface that realizes motion-to-electricity generation by electrostatically regulating the carrier concentration and transport in the semiconductive layer with a top gate in sliding movement. This tactile sliding interface can distinguish various dynamic mechanical information by generating voltage signals related to the sliding direction, speed, pressure, and touch position without external bias voltage. By combining machine-learning algorithms, electrical signals of six representative sliding-touch interactions were accurately classified with a recognition accuracy of 98.33%. Furthermore, by integrating sensors into the smartphone’s side button, customizable functions such as volume control, screen unlocking, and music switching were achieved. This work provides an innovative mechanism for sliding sensing in interactive electronic and intelligent control systems. Full article

Piezoresistive pressure sensing has broad application prospects in wearable fields such as human–machine interaction, physiological signal detection, and electronic skin. As a high-performance conductive filler, multi-walled carbon nanotubes (MWCNTs) have demonstrated extensive application potential across various domains. However, polymer composites filled with MWCNTs exhibit complex behavior during the printing process, which increases the difficulty of applying extrusion-based 3D printing technology. To this end, this study systematically investigated the extrusion 3D printing process of MWCNTs/polydimethylsiloxane (PDMS) composites. In this research, MWCNTs/PDMS composites with MWCNTs mass fractions of 1 wt%, 2 wt%, 3 wt%, and 4 wt% were prepared. The printability of the materials at each ratio was systematically explored, and rational printing process parameters were determined. On this basis, the influence of MWCNTs mass fraction on sensor performance was analyzed through tensile testing. Finally, three sets of experiments, including palm gesture recognition and gripping tests, elbow joint motion monitoring, and continuous pressure monitoring, successfully verified the feasibility of the fabricated sensors in human motion monitoring. The results demonstrate that the sensors made of this composite material via extrusion 3D printing possess excellent application potential in the field of flexible wearable electronics. Full article

During long-term operation of power transformers, oil temperature and pressure exhibit strong non-stationarity and multi-scale coupling, which makes early-stage breathing system faults difficult to detect accurately. To address this issue, this paper proposes an integrated diagnosis and early-warning method for transformer breathing systems. It combines a multi-parameter optical sensor with a deep-learning algorithm. The pressure–temperature optical sensing system based on Fabry–Pérot (F–P) interferometry and fiber Bragg grating (FBG) technology is developed to achieve high-precision synchronous measurement of pressure and temperature. To handle the non-stationary and multi-scale characteristics of the measured signals, a swarm-intelligence-optimized variational mode decomposition (VMD) method is employed to adaptively decompose time series temperature and pressure data. On this basis, a joint forecasting model integrating a temporal convolutional network (TCN) and an inverted Transformer (iTransformer) is constructed to capture both local temporal dynamics and long-term dependencies. Furthermore, based on the pressure equilibrium mechanism of transformer breathing systems, oil temperature and equivalent oil level are inferred, and abnormality criteria suitable for both multi-point and single-point monitoring are established. Experimental and field tests on a 220 kV transformer demonstrate that the proposed method outperforms conventional models in prediction accuracy. Full article