Search Results (3,439)

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique acoustic channel characteristics and the inadequacy of traditional delay-based performance metrics that fail to capture the spatio-temporal value of information in dynamic underwater scenarios. This paper presents a comprehensive underwater ISAC framework centered on a novel Spatio-Temporal Information-Theoretic Freshness metric that fundamentally transforms resource allocation from delay minimization to value maximization. Unlike conventional approaches that treat all data equally, our spatio-temporal framework enables intelligent prioritization by recognizing that obstacle detection data directly ahead of an autonomous underwater vehicle (AUV) require immediate processing. Our framework addresses key underwater ISAC challenges through spatio-temporal-guided power allocation, adaptive beamforming, waveform optimization, and cooperative sensing strategies. Multi-agent reinforcement learning algorithms enable coordinated resource allocation and mission-critical information prioritization across heterogeneous networks comprising surface buoys, AUVs, and static sensors. Extensive simulations in realistic Munk profile acoustic environments demonstrate significant performance improvements. The spatio-temporal framework successfully filters spatially irrelevant data, resulting in substantial energy savings for battery-constrained underwater nodes. Full article

In automated container terminals (ACTs), rolling bearings of equipment serve as crucial power transmission components, and their performance directly determines the operational efficiency, reliability, and service life of the entire equipment. Rolling bearing fault detection and diagnosis are key means to improve production efficiency, reduce the safety risks, and achieve sustainable development of equipment in ACTs. However, existing rolling-bearing diagnosis models are vulnerable to environmental noise and interference, depressing accuracy and raising misclassification, and they seldom achieve both noise robustness and a lightweight design; robustness usually increases complexity, while compact networks degrade under low signal-to-noise ratios. Therefore, this paper proposes a noise-robust, lightweight, and interpretable deep learning framework for fault detection and diagnosis of rolling bearings in automated container terminal (ACT) equipment. The framework comprises four coordinated components, including Time-Domain Filter, Frequency-Domain Filter, Physical-Feature Extraction module, and Classification module, whose joint optimization yields complementary time–frequency representations and physics-aligned features, and fuses into robust diagnostic decisions under noisy and non-stationary environments. The first component highlights impulsive transients, the second component emphasizes harmonic and sideband modulation, the third module introduces two differentiable and rolling bearing-signal-informed objectives to align learning with characteristic bearing signatures by weighted-average kurtosis and an Lp/Lq-based envelope-spectral concentration index, and the last module integrates multi-layer convolutional neural networks (CNN) and Deep Kolmogorov–Arnold Networks (DeepKAN). Finally, two public datasets are employed to estimate the model’s performance, and results indicate that the proposed method outperforms others. Full article

This article describes systems generating high-frequency rotating magnetic fields for magnetic hyperthermia treatments. It covers two- and three-phase device systems powered by rectangular signals. A passive bandpass filter tuned to a specific frequency (100 kHz) is placed between the magnetic circuits and the DC power source powering the device. The paper compares the electrical parameters of both solutions, including the supply voltage, magnetic field strength amplitude H, and magnetizing current I_L as a function of the supply voltage (U_dc). At a fixed supply voltage U_dc, the magnetizing current I_L and the rotating magnetic field strength amplitude H are approximately twice as large for the three-phase system as for the two-phase system. The relationships between the magnetizing currents I_L and the magnetic field strength amplitude H as a function of the supply voltage U_dc are linear. Full article

This paper proposes a second-order low-pass Butterworth multiple-feedback (MFB) filter with a reconfigurable bandwidth and gain, implemented in a 28 nm CMOS. The filter supports independent tuning of the bandwidth from 10 MHz to 100 MHz and the gain from 0 dB to 19 dB, effectively addressing the challenge of a tightly coupled gain and quality factor in traditional MFB designs. Notably, compared to the widely adopted Tow–Thomas structure, the proposed filter achieves second-order filtering and the same degree of flexibility using only a single operational amplifier (OPA), significantly reducing both the power consumption and area. Additionally, an RC tuning circuit is employed to reduce fluctuations in the RC time constant under process, voltage, and temperature (PVT) variations. To meet the requirements for high linearity and low power consumption in broadband applications, a three-stage push–pull OPA with current re-use feedforward and an RC Miller compensation technique is proposed. With the current re-use feedforward, the OPA’s loop gain at 100 MHz is significantly enhanced from 22.34 dB to 28.75 dB, achieving a 2.14 GHz unity-gain bandwidth. Using this OPA, the filter achieves a 48.2 dBm in-band (IB) OIP3, a 53.4 dBm out-of-band (OOB) OIP3, and a figure of merit (FoM) of 185.5 dBJ⁻¹ at a100 MHz bandwidth while consuming only 3.6 mW from a 1.8 V supply. Full article

Driven by the rapid advancement of smart grid frameworks, the volume of visual data collected from power system diagnostic equipment has surged exponentially. A substantial portion of these images (30–40%) are redundant or highly similar, primarily due to periodic monitoring and repeated acquisitions from multiple angles. Traditional redundancy removal methods based on manual screening or single-feature matching are often inefficient and lack adaptability. In this paper, we propose a two-stage redundancy removal paradigm for power inspection imagery, which integrates abstract semantic priors with fine-grained perceptual details. The first stage combines an improved discrete cosine transform hash (DCT Hash) with the multi-scale structural similarity index (MS-SSIM) to efficiently filter redundant candidates. In the second stage, a Vision Transformer network enhanced with a hierarchical sparse attention mechanism precisely determines redundancy via cosine similarity between feature vectors. Experimental results demonstrate that the proposed method achieves an algorithm sensitivity of 0.9243, surpassing ResNet and VGG by 5.86 and 8.10 percentage points, respectively, highlighting its robustness and effectiveness in large-scale power grid redundancy detection. These results underscore the paradigm’s capability to balance efficiency and precision in complex visual inspection scenarios. Full article

Speaker recognition has become a critical component of human–robot interaction (HRI), enabling personalized services based on user identity, as the demand for home service robots increases. In contrast to conventional speech recognition tasks, recognition in home service robot environments is affected by varying speaker–robot distances and background noises, which can significantly reduce accuracy. Traditional approaches rely on hand-crafted features, which may lose essential speaker-specific information during extraction like mel-frequency cepstral coefficients (MFCCs). To address this, we propose a novel speaker recognition technique for intelligent robots that combines SincNet-based raw waveform processing with an adaptive neuro-fuzzy inference system (ANFIS). SincNet extracts relevant frequency features by learning low- and high-cutoff frequencies in its convolutional filters, reducing parameter complexity while retaining discriminative power. To improve interpretability and handle non-linearity, ANFIS is used as the classifier, leveraging fuzzy rules generated by fuzzy c-means (FCM) clustering. The model is evaluated on a custom dataset collected in a realistic home environment with background noise, including TV sounds and mechanical noise from robot motion. Our results show that the proposed model outperforms existing CNN, CNN-ANFIS, and SincNet models in terms of accuracy. This approach offers robust performance and enhanced model transparency, making it well-suited for intelligent home robot systems. Full article

Optimizing the accurate estimation algorithms for the State of Charge (SOC) and State of Health (SOH) of power batteries is crucial for improving the performance of electric vehicles. This paper takes lithium-ion batteries as the research object. The Singular Value Decomposition-Unscented Kalman Filter (SVDUKF) at a micro-time scale is used to estimate the battery’s State of Charge, and the traditional Extended Kalman Filter (EKF) at a macro-time scale is used to estimate impedance parameters and capacity. The two filters operate alternately, with the output of one serving as the input for the other, thereby establishing a joint estimation method for SOC and SOH based on the SVDUKF-EKF under a multi-time scale. The joint estimation method is verified under the Dynamic Stress Test (DST) condition and Federal Urban Driving Schedule (FUDS) condition. The results show that the SOH estimation error is within 2% under the DST condition and within 1% under the FUDS condition. The method exhibits high estimation accuracy and stability under both conditions. Full article

Conventional grid-forming (GFM) inverter control strategies often rely on fixed parameters and overlook the dynamic variation in energy stored in the DC link capacitor. This limitation can degrade transient performance and stability, particularly under power fluctuations and grid disturbances in renewable energy systems. To address this issue, this paper proposes an adaptive GFM control method that integrates real-time estimation of the DC link capacitor energy into the control loop. A Kalman filter-based observer is designed to estimate the capacitor energy state accurately and robustly using only local voltage and current measurements. The estimated energy deviation is then used to dynamically adjust key control parameters, including the virtual inertia and droop coefficients in the virtual synchronous generator (VSG) framework. These adaptive adjustments enhance the inverter’s damping and inertial behavior according to the internal energy buffer, improving performance under variable operating conditions. Simulation results in MATLAB/Simulink R2023b demonstrate that the proposed method significantly reduces power and voltage overshoots, shortens settling time, and improves DC link voltage regulation compared to conventional fixed-parameter control. Full article

Clean water security demands autonomous systems that sense, reason, and act at scale. We introduce AquaSurveil, a unified multi-agent platform coupling mobile robots, fixed IoT nodes, and privacy-preserving machine learning for continent-scale water health surveillance. The architecture blends Gaussian-process mapping with distributed particle filtering, multi-agent deep-reinforcement Voronoi coverage, GAN/LSTM anomaly detection, and sheaf-theoretic data fusion; components are tuned by Bayesian optimization and governed by Age-of-Information-aware power control. Evaluated on a 2.82-million-record dataset (1940–2023; five countries), AquaSurveil achieves up to 96% spatial-coverage efficiency, an ROC-AUC of 0.96 for anomaly detection, ≈95% state-estimation accuracy, and reduced energy consumption versus randomized patrols. These results demonstrate scalable, robust, and energy-aware water quality surveillance that unifies robotics, the IoT, and modern AI. Full article

This study is the first to directly compare natural dynamic penumbra shadows with experimentally replicated constant-intensity shadows on photovoltaic modules, providing new insights into the limitations of conventional shadow approximations found in the existing body of knowledge. Neutral density filters were deemed the most appropriate method for replicating a constant-intensity shadow, as they reduce visible light relatively uniformly across the primary silicon wavelength range. Preliminary experiments established the intensity values for each neutral density filter chosen to be able to match with the 29 dynamic penumbra shadows being replicated by both the size of shadow and the averaged intensity. The results revealed that while constant-intensity shadows and dynamic penumbra shadows produced similar overall power loss magnitudes, the constant-intensity shadows consistently led to higher losses, averaging 9.65% more, despite having the same average intensity and shadow size. Regression modelling showed similar curvature trends for both shading types (Adjusted R² = 0.895 for constant-intensity shadows and Adjusted R² = 0.743 for dynamic-intensity shadows), but statistical analyses, including the Mann–Whitney U-test (p = 0.00229), confirmed a significant difference between the power loss output for the two penumbra shadow conditions. Consequently, the null hypothesis was rejected, confirming that the simplified constant-intensity shadows represented in the literature cannot accurately replicate the behaviour of dynamic-intensity penumbra on photovoltaic modules. Full article

This paper presents an analysis of the dynamic properties of the Rogowski coil, primarily by determining the dynamic errors for several selected test signals and the upper bound of the dynamic error for two quality criteria: the integral-square error and the absolute error. A procedure for filtering and reproducing these signals is also presented. The foundation of the presented research is an equivalent circuit model of the Rogowski coil, developed primarily for applications in electrical power and electromechanical systems. Two novel aspects of this work are the determination of dynamic errors for the Rogowski coil and a graphical and quantitative comparison of their values. The research results presented in this paper may serve as a foundation for enhancing the accuracy and dynamic reliability of both the Rogowski coil and other devices (e.g., transformers and current transformers) used in the power industry and mechanical engineering, particularly in the condition monitoring of a broad range of power equipment and in the experimental analysis of electromechanical systems operating under variable load conditions. The findings also highlight the importance of accurate current measurement in modern energy systems, where transient and high-frequency components increasingly affect performance and reliability. Consequently, the presented methodology provides a useful framework for guiding sensor selection and signal processing strategies in advanced monitoring and control applications. Full article

This paper presents the design and analysis of a forklift hydraulic system utilizing an open-center configuration equipped with unloading (safety-overflow) valves and an emergency lowering mechanism. The hydraulic system includes an external gear pump, double-acting power cylinders, hydraulic distributors, and control valves. A comprehensive approach is undertaken to select system components based on catalog data and to model the flow rate, required torque, and power characteristics of the pump, along with load handling performance as a function of cylinder dimensions and hydraulic pressure. System behavior under various operating conditions is simulated using Automation Studio, enabling performance optimization and fault response assessment. The inclusion of unloading valves and an emergency button enhances system safety by enabling controlled pressure relief and emergency actuation. The impact of thermal effects, filter efficiency, and reservoir design on hydraulic fluid integrity is also addressed. This study aims to improve reliability, efficiency, and safety in hydraulic forklift systems while supporting informed design decisions using simulation-driven methodologies. Full article

Electroencephalography (EEG) is a non-invasive biosensing platform with a spatial-frequency content that is of significant relevance for a multitude of aspects in the neurosciences, ranging from optimal spatial sampling of the EEG to the design of spatial filters and source reconstruction. In the past, simplified spherical head models had to be used for this analysis. We propose a method for spatial frequency analysis in EEG for realistically shaped volume conductors, and we exemplify our method with a five-compartment Boundary Element Method (BEM) model of the head. We employ the recently developed technique for spatial harmonic analysis (Sphara), which allows for spatial Fourier analysis on arbitrarily shaped surfaces in space. We first validate and compare Sphara with the established method for spatial Fourier analysis on spherical surfaces, discrete spherical harmonics, using a spherical volume conductor. We provide uncertainty limits for Sphara. We derive relationships between the signal-to-noise ratio (SNR) and the required spatial sampling of the EEG. Our results demonstrate that conventional 10–20 sampling might misestimate EEG power by up to 50%, and even 64 electrodes might misestimate EEG power by up to 15%. Our results also provide insights into the targeting problem of transcranial electric stimulation. Full article

In this work, a controller hardware-in-the-loop (CHIL) simulation of a grid-connected three-phase inverter equipped with an LCL filter is implemented using a real-time digital simulator (RTDS) as the plant and a digital signal processor (DSP) as the control hardware. This work identifies and discusses the critical aspects of the CHIL implementation process, emphasizing the relevance of the control delays that arise from sampling, computation, and pulse width modulation (PWM), which also adversely affect system stability, accuracy, and performance. Time and frequency domains are used to validate the modeling of the system, either to represent large-signal or small-signal models. This work shows multiple representations of the system under study: the fundamental frequency model, the switched model, and the switched model controlled by the DSP, are used to validate the nonlinear model, whereas the impedance-based modeling is followed to validate the linear representation. The results demonstrate a strong correlation among the models, confirming that the delay effects are accurately captured in the different simulation approaches. This comparison provides valuable insights into configuration practices that improve the fidelity of CHIL-based validation and supports impedance-based stability analysis in power electronic systems. The findings are particularly relevant for wideband modeling and real-time studies in electromagnetic transient analysis. Full article

Accurate and non-invasive assessment of fat and muscle composition is crucial for biomedical monitoring to track health conditions in humans and pets, as well as for classifying meats in the meat industry. This study introduces a cost-effective, multifunctional ultra-wideband microwave system operating from 2.4 to 4.4 GHz, designed for rapid and non-destructive quantification of fat thickness, muscle thickness, and fat-to-muscle ratio in diverse ex vivo samples, including pork, beef, and oil–water mixtures. The compact handheld device integrates essential RF components such as a frequency synthesizer, directional coupler, logarithmic power detector, and a dual-polarized Vivaldi antenna. Bluetooth telemetry enables seamless real-time data transmission to mobile- or PC-based platforms, with each measurement completed in a few seconds. To enhance signal quality, a two-stage denoising pipeline combining low-pass filtering and Savitzky–Golay smoothing was applied, effectively suppressing noise while preserving key spectral features. Using a random forest regression model trained on resonance frequency and signal-loss features, the system demonstrates high predictive performance even under limited sample conditions. Correlation coefficients for fat thickness, muscle thickness, and fat-to-muscle ratio consistently exceeded 0.90 across all sample types, while mean absolute errors remained below 3.5 mm. The highest prediction accuracy was achieved in homogeneous oil–water samples, whereas biologically complex tissues like pork and beef introduced greater variability, particularly in muscle-related measurements. The proposed microwave system is highlighted as a highly portable and time-efficient solution, with measurements completed within seconds. Its low cost, ability to analyze multiple tissue types using a single device, and non-invasive nature without the need for sample pre-treatment or anesthesia make it well suited for applications in agri-food quality control, point-of-care diagnostics, and broader biomedical fields. Full article