Search Results (1,489)

Sensors and anti-interference technology have a complementary relationship. The anti-interference capability directly affects the measurement accuracy, reliability, and stability of sensors. In complex electromagnetic or natural environments, sensors are inevitably influenced by various interference sources. Effective anti-interference technology is the key to ensuring the normal operation of sensors, and suppressing interference signals is one of the key links to improving communication quality. This paper proposes a CNN-LSTM-based interference signal suppression algorithm, aiming to enhance the anti-interference capability of wireless communication systems through deep learning technology. The algorithm utilizes CNN to extract the spatial features of the signal and LSTM to capture the temporal dynamic characteristics of the signal, outputting a predicted signal to effectively suppress interference signals. The performance of the experimental simulation algorithm under different interference scenarios was evaluated and compared with three models: LSTM, BO-LSTM, and CNN-GRU. The results demonstrated that this algorithm had a small error and a high degree of regression fitting. Finally, the effectiveness of the algorithm was verified by using the signal propagation model based on ITU-R P.1546 and the publicly available noise datasets collected from the actual environment. The research shows that this algorithm can significantly suppress the influence of interference signals and environmental noise on useful signals, providing a basis for promoting the evolution of sensors towards higher reliability and robustness. Full article

Surface-enhanced Raman scattering (SERS) technology, leveraging its single-molecule-level detection sensitivity, molecular fingerprint recognition capability, and capacity for rapid, non-destructive analysis, has emerged as a pivotal analytical tool in food science, life sciences, and environmental monitoring. This review systematically summarizes recent advancements in SERS technology, encompassing its enhancement mechanisms (synergistic effects of electromagnetic and chemical enhancement), innovations in high-performance substrates (noble metal nanostructures, non-noble metal substrates based on semiconductors/graphene, and hybrid systems incorporating noble metals with functional materials), and its interdisciplinary applications. In the realm of food safety, SERS has enabled the ultratrace detection of pesticide residues, mycotoxins, and heavy metals, with flexible substrates and intelligent algorithms significantly enhancing on-site detection capabilities. Within biomedicine, the technique has been successfully applied to the rapid identification of pathogenic microorganisms, screening of tumor biomarkers, and viral diagnostics. For environmental monitoring, SERS platforms offer sensitive detection of heavy metals, microplastics, and organic pollutants. Despite challenges such as matrix interference and insufficient substrate reproducibility, future research directions aimed at developing multifunctional composite materials, integrating artificial intelligence algorithms, constructing portable devices, and exploring plasmon-catalysis synergy are poised to advance the practical implementation of SERS technology in precision diagnostics, intelligent regulation, and real-time monitoring. Full article

Despite the rapid advancement of smart-grid technologies, automated pointer meter reading in power substations remains a persistent challenge due to complex electromagnetic interference and dynamic field conditions. Traditional computer vision methods, typically designed for ideal imaging environments, exhibit limited robustness against real-world perturbations such as illumination fluctuations, partial occlusions, and motion artifacts. To address this gap, we propose PriKMet (Prior-Guided Pointer Meter Reader), a novel meter reading algorithm that integrates deep learning with domain-specific priors through three key contributions: (1) a unified hierarchical framework for joint meter detection and keypoint localization, (2) an intelligent meter reading method that fuses the predefined inspection route information with perception results, and (3) an adaptive offset correction mechanism for UAV-based inspections. Extensive experiments on a comprehensive dataset of 3237 substation meter images demonstrate the superior performance of PriKMet, achieving state-of-the-art meter detection results of 99.4% AP50 and 85.5% for meter reading accuracy. The real-time processing capability of the method offers a practical solution for modernizing power infrastructure monitoring. This approach effectively reduces reliance on manual inspections in complex operational environments while enhancing the intelligence of power maintenance operations. Full article

With the increasing complexity and integration density of System-in-Package (SiP) technologies, the demand for selective electromagnetic interference (EMI) shielding is growing. Conventional sputtering processes, while effective for conformal EMI shielding, lack selectivity and often require additional masking or post-processing steps. In this study, we propose a novel, laser-based approach for the selective removal of EMI shielding layers without physical masking. Numerical simulations were conducted to investigate the thermal and mechanical behavior of multilayer EMI shielding structures under two irradiation modes: full-area and laser scanning. The results showed that the laser scanning method induced higher interfacial shear stress, reaching up to 38.6 MPa, compared to full-area irradiation (12.5 MPa), effectively promoting delamination while maintaining the integrity of the underlying epoxy mold compound (EMC). Experimental validation using a nanosecond pulsed fiber laser confirmed that complete removal of the EMI shielding layer could be achieved at optimized laser powers (~6 W) without damaging the EMC, whereas excessive power (8 W) caused material degradation. The laser scanning speed was 50 mm/s, and the total laser irradiation time of the package was 0.14 s, which was very fast. This study demonstrates the feasibility of a non-contact, damage-free, and selective EMI shielding removal technique, offering a promising solution for next-generation semiconductor packaging. Full article

Partial discharge (PD) is not only a critical indicator but also a major accelerating factor of insulation degradation in power transformers. Accurate identification of PD types is essential for diagnosing insulation defects and locating faults in transformers. Traditional methods based on phase-resolved partial discharge (PRPD) patterns typically rely on expert interpretation and manual feature extraction, which are increasingly being supplanted by Convolutional Neural Networks (CNNs) due to their ability to automatically extract features and deliver high classification accuracy. However, the inherent subtlety and diversity of characteristic differences among PRPD patterns, coupled with substantial noise resulting from complex electromagnetic interference, present significant hurdles to achieving accurate identification. This paper proposes a transformer partial discharge identification method based on Deep Residual Shrinkage Network (DRSN) to address these challenges. The method integrates dual-path feature extraction to capture both local and global features, incorporates a channel-domain adaptive soft-thresholding mechanism to effectively suppress noise interference, and utilizes the Focal Loss function to enhance the model’s attention to hard-to-classify samples. To validate the proposed method, given the scarcity of diverse real-world transformer PD data, an experimental platform was utilized to generate and collect PD data by artificially simulating various discharge defect models, including tip discharge, surface discharge, air-gap discharge and floating discharge. Data diversity was then enhanced through sample augmentation and noise simulation, to minimize the gap between experimental data and real-world on-site data. Experimental results demonstrate that the proposed method achieves superior partial discharge recognition accuracy and strong noise robustness on the experimental dataset. For future work, it is essential to collect more real transformer PD data to further validate and strengthen the model’s generalization capability, thereby ensuring its robust performance and applicability in practical scenarios. Full article

An optical fiber force sensor based on the Vernier effect in cascaded Fabry–Perot interferometers (FPIs) formed by a barium tantalate microsphere and a section of polymethyl methacrylate (PMMA) optical fiber is proposed and investigated. Optical fiber sensors offer numerous advantages over their electronic counterparts, including immunity to electromagnetic interference and suitability for harsh environments. Despite these benefits, current optical fiber force sensors often face limitations in sensitivity, reliability, and fabrication costs. The proposed sensor has the potential to address these issues. Simulations and experimental results demonstrate that the sensor achieves a sensitivity of 9279.66 nm/N in a range of up to 3 mN. The sensor also exhibits excellent repeatability, making it a promising candidate for high-performance force monitoring in various challenging environments. Full article

In multi-user wireless communication scenarios, signal degradation caused by channel fading and co-channel interference restricts system capacity, while traditional enhancement schemes face challenges of high coordination complexity and hardware integration. This paper proposes an electromagnetic focusing method using a single-layer transmissive passive metasurface. A high-efficiency metasurface array is fabricated based on PCB technology, which utilizes subwavelength units for wide-range phase modulation to construct a multi-user energy convergence model in the WiFi band. By optimizing phase gradients through the geometric phase principle, the metasurface achieves collaborative wavefront manipulation for multiple target regions with high transmission efficiency, reducing system complexity compared to traditional multi-layer structures. Measurements in a microwave anechoic chamber and tests in an office environment demonstrate that the metasurface can simultaneously create signal enhancement zones for multiple users, featuring stable focusing capability and environmental adaptability. This lightweight design facilitates deployment in dense networks, providing an effective solution for signal optimization in indoor distributed systems and IoT communications. Full article

To address the small-sample training bottleneck and inadequate convergence efficiency of Deep Reinforcement Learning (DRL)-based communication anti-jamming methods in complex electromagnetic environments, this paper proposes a Generative Adversarial Network-enhanced Deep Q-Network (GA-DQN) anti-jamming method. The method constructs a Generative Adversarial Network (GAN) to learn the time–frequency distribution characteristics of short-period jamming and to generate high-fidelity mixed samples. Furthermore, it screens qualified samples using the Pearson correlation coefficient to form a sample set, which is input into the DQN network model for pre-training to expand the experience replay buffer, effectively improving the convergence speed and decision accuracy of DQN. Our simulation results show that under periodic jamming, compared with the DQN algorithm, this algorithm significantly reduces the number of interference occurrences in the early communication stage and improves the convergence speed, to a certain extent. Under dynamic jamming and intelligent jamming, the algorithm significantly outperforms the DQN, Proximal Policy Optimization (PPO), and Q-learning (QL) algorithms. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

Global Navigation Satellite System (GNSS) receivers may produce measurement outliers in real-world applications owing to various circumstances, including poor signal quality, multipath effects, data loss, satellite signal loss, or electromagnetic interference. This can lead to a noise distribution that is non-Gaussian heavy-tailed, affecting the effectiveness of satellite navigation filters. This paper presents a robust Extended Kalman Filter (EKF) based on the Maximum Correntropy Criterion with a Student’s t kernel (STMCCEKF) for GPS navigation under non-Gaussian noise. Unlike traditional EKF and Gaussian-kernel MCCEKF, the proposed method enhances robustness by leveraging the heavy-tailed Student’s t kernel, which effectively suppresses outliers and dynamic observation noise. A fixed-point iterative algorithm is used for state update, and a new posterior error covariance expression is derived. The simulation results demonstrate that STMCCEKF outperforms conventional filters in positioning accuracy and robustness, particularly in environments with impulsive noise and multipath interference. The Student’s t-distribution kernel efficiently mitigates heavy-tailed non-Gaussian noise, while it adaptively adjusts process and measurement noise covariances, leading to improved estimation performance. A detailed explanation of several key concepts along with practical examples are discussed to aid in understanding and applying the Global Positioning System (GPS) navigation filter. By integrating cutting-edge reinforcement learning with robust statistical approaches, this work advances adaptive signal processing and estimation, offering a significant contribution to the field. Full article

This paper presents a comparative analysis of electric field (E-field) mitigation in inductive power transfer (IPT) systems. It focuses on how distributed capacitor placement interacts with coil topology to influence E-field emissions. The study compares traditional sequential-winding coils and the alternating voltage phase coil (AVPC), which employs a sequential inversion winding (SIW) structure to enforce a 180° phase voltage opposition between adjacent turns. While capacitor segmentation is a known method for E-field reduction, this work is the first to systematically evaluate its effects across both conventional and phase-inverted coils. The findings reveal that capacitor placement serves as a topology-dependent design parameter. Finite Element Method (FEM) simulations and experimental validation show that while capacitor placement has a moderate influence on traditional coils due to in-phase voltage relationships, AVPC coils are highly sensitive to segmentation patterns. When capacitors align with the SIW phase structure, destructive interference significantly reduces E-field emissions. Improper capacitor placement disrupts phase cancellation and negates this benefit. This study resolves a critical design gap by establishing that distributed compensation acts as a tuning mechanism in conventional coils but becomes a primary constraint in phase-inverted topologies. The results demonstrate that precise capacitor placement aligned with the coil topology significantly enhances E-field mitigation up to 60% in AVPC coils, greatly outperforming traditional coil configurations and providing actionable guidance for high-power wireless charging applications. Full article

pH is a critical parameter requiring precise monitoring across scientific, industrial, and biological domains. Fluorescent pH probes offer a powerful alternative to traditional methods (e.g., electrodes, indicators), overcoming limitations in miniaturization, long-term stability, and electromagnetic interference. By utilizing photophysical mechanisms—including intramolecular charge transfer (ICT), photoinduced electron transfer (PET), and fluorescence resonance energy transfer (FRET)—these probes enable high-sensitivity, reusable, and biocompatible sensing. This review systematically details recent advances, categorizing probes by operational pH range: strongly acidic (0–3), weakly acidic (3–7), strongly alkaline (>12), weakly alkaline (7–11), near-neutral (6–8), and wide-dynamic range. Innovations such as ratiometric detection, organelle-specific targeting (lysosomes, mitochondria), smartphone colorimetry, and dual-analyte response (e.g., pH + Al³⁺/CN⁻) are highlighted. Applications span real-time cellular imaging (HeLa cells, zebrafish, mice), food quality assessment, environmental monitoring, and industrial diagnostics (e.g., concrete pH). Persistent challenges include extreme-pH sensing (notably alkalinity), photobleaching, dye leakage, and environmental resilience. Future research should prioritize broadening functional pH ranges, enhancing probe stability, and developing wide-range sensing strategies to advance deployment in commercial and industrial online monitoring platforms. Full article

Satellite communications are pivotal to global Internet access, connectivity, and the advancement of information warfare. Despite these importance, the open nature of satellite channels makes them vulnerable to eavesdropping, making the enhancement of interception resistance in satellite communications a critical issue in both academic and industrial circles. Within the realm of satellite communications, polarization modulation and quadrature techniques are essential for information transmission and interference suppression. To boost electromagnetic countermeasures in complex battlefield scenarios, this paper integrates multi-parameter weighted-type fractional Fourier transform (MP-WFRFT) with directional modulation (DM) algorithms, building upon polarization techniques. Initially, the operational mechanisms of the polarization-amplitude-phase modulation (PAPM), MP-WFRFT, and DM algorithms are elucidated. Secondly, it introduces a novel variable polarization-amplitude-phase modulation (VPAPM) scheme that integrates variable polarization with amplitude-phase modulation. Subsequently, leveraging the VPAPM modulation scheme, an exploration of the anti-interception capabilities of MP-WFRFT through parameter adjustment is presented. Rooted in an in-depth analysis of simulation data, the anti-scanning capabilities of MP-WFRFT are assessed in terms of scale vectors in the horizontal and vertical direction. Finally, exploiting the potential of the robust anti-scanning capabilities of MP-WFRFT and the directional property of antenna arrays in DM, the paper proposes a secure transmission technique employing directional variable polarization modulation with MP-WFRFT. The performance simulation analysis demonstrates that the integration of MP-WFRFT and DM significantly outperforms individual secure transmission methods, improving anti-interception performance by at least an order of magnitude at signal-to-noise ratios above 10 dB. Consequently, this approach exhibits considerable potential and engineering significance for its application within satellite communication systems. Full article

Amidst the escalating demand for high-precision structural health monitoring in large-scale engineering applications, carbon fiber-reinforced polymer fiber Bragg grating (CFRP-FBG) sensors have emerged as a pivotal technology for fatigue life evaluation, owing to their exceptional sensitivity and intrinsic immunity to electromagnetic interference. A time-series predictive architecture based on long short-term memory (LSTM) networks is developed in this work to facilitate intelligent fatigue life assessment of structures subjected to complex cyclic loading by capturing and modeling critical spectral characteristics of CFRP-FBG sensors, specifically the side-mode suppression ratio and main-lobe peak-to-valley ratio. To enhance model robustness and generalization, Principal Component Analysis (PCA) was employed to isolate the most salient spectral features, followed by data preprocessing via normalization and model optimization through the integration of the Adam optimizer and Dropout regularization strategy. Relative to conventional Backpropagation (BP) neural networks, the LSTM model demonstrated a substantial improvement in predicting the side-mode suppression ratio, achieving a 61.62% reduction in mean squared error (MSE) and a 34.99% decrease in root mean squared error (RMSE), thereby markedly enhancing robustness to outliers and ensuring greater overall prediction stability. In predicting the peak-to-valley ratio, the model attained a notable 24.9% decrease in mean absolute error (MAE) and a 21.2% reduction in root mean squared error (RMSE), thereby substantially curtailing localized inaccuracies. The forecasted confidence intervals were correspondingly narrower and exhibited diminished fluctuation, highlighting the LSTM architecture’s enhanced proficiency in capturing nonlinear dynamics and modeling temporal dependencies. The proposed method manifests considerable practical engineering relevance and delivers resilient intelligent assistance for the seamless implementation of CFRP-FBG sensor technology in structural health monitoring and fatigue life prognostics. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article