Search Results (509)

Amplitude-modulation magnetic particle imaging (AM-MPI) has strong potential for real-time navigation of magnetic nanoparticles because it provides tracer-specific spatial feedback using a narrowband acquisition scheme with relatively low excitation power and simplified signal detection. However, improving spatial resolution by increasing the selection-field gradient becomes increasingly difficult as scanner dimensions increase. This study investigates whether field orientation can be used to improve and control the spatial resolution of three-dimensional AM-MPI with field-free-point (FFP) and field-free-line (FFL) encoding. Four scan-receive configurations were analyzed using a matrix point-spread-function (PSF) model, followed by two-point phantom simulations at equal physical and FWHM-normalized source separations. With a common y-directed scan, FFP-y produced a single-peaked collinear h_yy response with FWHM values of 1.29 mm along y and 5.89 mm along x and z. FFL-y retained the same y-direction FWHM while reducing the z-direction FWHM to 2.94 mm. FFP-x selected the transverse h_xy component, producing a central null and a multi-lobe response, whereas FFL-x was a null channel for the adopted field geometry. At equal physical spacing, the first tested separation satisfying the adopted Rayleigh-type criterion (M ≥ 0.26) was 3 mm along y for both FFP-y and FFL-y, 5 mm along z for FFL-y, and 7 mm along x and z for FFP-y. FFL-y also provided better z-direction separability than FFP-y. After normalization by the corresponding directional FWHM, the single-peaked responses showed similar two-point separability, indicating that the differences observed at equal physical spacing were mainly associated with directional PSF width. These results show that field orientation affects both the topology and directional resolution of the AM-MPI response and can be considered together with selection-field design when optimizing AM-MPI systems for nanoparticle navigation. Full article

Lightning discharges over the Tibetan Plateau are monitored by ground-based networks that locate radiation sources from their low-frequency radio emissions. This study uses the Qinghai Datong network in the northeastern Tibetan Plateau, which records the 50 kHz to 2.5 MHz band over a small area to locate lightning in three dimensions. In such networks, the accuracy of three-dimensional location depends critically on the consistency of the signals recorded across stations, which is progressively degraded by aging analog front-ends and by complex electromagnetic noise. To address this, we propose a phase-preserving denoising scheme, termed WZ, that suppresses both broadband and narrowband noise while keeping the relative timing between stations essentially unchanged, so that the arrival times used for location are preserved. The improvement is illustrated with both simulations and real data. In Monte Carlo simulations, WZ improves the signal-to-noise ratio by 10 dB and reduces the time-of-arrival error to 0.3 μs. Applied to two intracloud flashes of contrasting morphology, WZ recovers substantially more radiation sources and more continuous discharge channels than conventional filtering, at no cost to fit quality, allowing, for example, the downward development of the channel to be tracked quantitatively. The method requires no change to the existing hardware and can be applied to archived data, making it a practical way to improve both current and historical records from long-running low-frequency lightning networks. Full article

Multiphase swirling flows in confined spaces induce highly destructive, nonlinear fluid–structure interaction (FSI) vibrations. Understanding the underlying physical mechanisms is critical for ensuring the safety of industrial operations. This study proposes a mesoscopic multiscale framework coupling the Multi-Relaxation Time Lattice Boltzmann Method with Large Eddy Simulation (MRT-LBM-LES) and the Flügge thin-walled cylindrical shell equations to analyze two-way FSI responses. Variational Mode Decomposition (VMD) and the Hilbert–Huang Transform (HHT) are employed to decouple non-stationary broadband excitation signals. The macroscopic topological evolution of the swirling air core—from initial depression to critical breakthrough—is accurately captured. Dynamic mapping reveals a strict time-domain phase-locking mechanism between macroscopic flow instability and microscopic high-frequency structural excitation caused by cavitation bubble collapse. Furthermore, a dimensionless cross-scale energy cascade index is defined to quantify energy transfer. Results indicate that while higher discharge flow rates delay the critical breakthrough, they trigger a delayed, high-amplitude step mutation in the energy cascade, amplifying the global cumulative excitation energy by nearly 75%. Notably, the dominant high-frequency excitation consistently converges within a narrow band of 760 Hz to 790 Hz, independent of flow rate variations. These findings provide a theoretical foundation for unsteady excitation source localization and targeted vibration reduction in complex industrial pipeline networks. Full article

Line-spectrum features in ship-radiated noise are essential for the analysis and recognition of passive sonar targets. Robust automatic extraction from low-frequency analysis and recording (LOFAR) spectrograms remains challenging in underwater acoustic environments owing to strong background fluctuations and interference. Supervised learning-based methods are further constrained by the limited availability of manually annotated data. This study proposes an automatic LOFAR line-spectrum extraction method that combines hybrid dataset construction with a continuity-aware U-Net (CAU-Net). Simulated and measured samples are integrated into a hybrid training dataset. A pseudo-label generation strategy combining two-pass split-window (TPSW) responses with inter-frame continuity constraints incorporates unlabeled measured samples into training. In addition, a temporal continuity modeling module combines multi-range inter-frame context with local frequency information, improving the extraction of weak components with pronounced energy variations. On an independent test set with known line-spectrum references, CAU-Net achieved an F1 score of $0.9635\pm 0.0012$ and a line-location accuracy (LLA) of $0.9756\pm 0.0023$ over five random seeds. It also maintained the highest F1 and LLA across the tested signal-to-noise ratio (SNR) range. Qualitative results on complete ShipsEar recordings illustrate that CAU-Net provides visually clearer weak narrowband responses while suppressing scattered background and transient-interference responses. Full article

Rolling bearings, as a key component of rotating machinery, require precise fault diagnosis to ensure the safe and reliable operation of industrial systems. Nevertheless, the performance of traditional narrowband filtering demodulation (NFD) methods is constrained by inherent limitations of spectral segmentation frameworks and insufficient discriminative capability of feature indicators (FIs). To address these limitations, this paper proposes a new NFD method based on an adaptive multi-level spectra segmentation strategy. Firstly, using power spectral density (PSD) as the analysis basis, an iterative framework is constructed to obtain multi-level spectral trend lines (STLs), which achieves multi-perspective characterization of spectral features. Secondly, the local minimum points of the STLs are used as the segmentation boundaries to extract the demodulation frequency band. Subsequently, a robust blind feature indicator, synergistic characterization criterion (SCC), is proposed, which can simultaneously fully evaluate periodicity and impulsiveness, guiding the selection of the optimal demodulation frequency band (ODFB). Finally, based on the enhanced demodulation spectrum, power exponent transformation is introduced to construct a generalized spectral family, and the adaptive determination of the optimal transformation parameter is guided by frequency-domain signal-to-noise ratio (FDSNR), thereby obtaining the generalized enhanced demodulation spectrum (GEDS). Validation experiments on laboratory and public datasets demonstrate that the proposed method outperforms Fast Kurtogram, Autogram, and CFFsgram, with average improvements of 63.86% and 89.06% in mean-peak ratio (MPR) and fault feature coefficient (FFC), respectively, and provides a new perspective for NFD and expands its application potential in bearing fault diagnosis and condition monitoring. Full article

The evolution of Advanced Metering Infrastructure (AMI) requires reliable, energy-efficient, and scalable communication technologies for connecting large numbers of smart meters and gateways with utility backend systems. Among 3GPP Cellular Internet of Things (CIoT) technologies, Narrowband IoT (NB-IoT) and LTE-M are promising candidates due to their extended coverage, low cost, and power efficiency. However, selecting between them remains challenging because performance depends on deployment environments, spatial distribution, and radio signal conditions. This study addresses the CIoT network selection problem in AMI networks by applying machine learning to predict the appropriate communication technology from smart meter location and Reference Signal Received Power (RSRP). Three supervised learning algorithms, namely Decision Tree, Support Vector Machine, and XGBoost, were evaluated using field measurement datasets from two AMI deployment areas. A spatial holdout strategy was applied to assess performance in unseen geographical regions. Decision Tree achieved the best performance in Area 1, with an accuracy of 0.7143 and an F1-score of 0.6154. In Area 2, XGBoost achieved the highest performance, with an accuracy of 0.9732 and an F1-score of 0.9388. The results demonstrate the feasibility of ML-based CIoT selection under spatially heterogeneous and imbalanced deployment conditions. Full article

Vegetation indices (VIs) are widely used as efficient proxies for canopy structure and pigment dynamics, whereas solar-induced chlorophyll fluorescence (SIF) provides a direct indicator of photosynthetic activity. Accurate monitoring of these variables is essential for ecosystem assessment, agricultural management, and satellite product validation. However, long-term field spectroscopy is often influenced by instrument aging and environmental variability, altering radiometric calibration coefficients and introducing uncertainty. In this study, we systematically evaluated the impact of interannual calibration coefficients derived in 2023 and 2024 on six commonly used VIs and SIF. These variables were retrieved from ground-based hyperspectral observations acquired continuously using FLAME-T and QEPRO+ spectrometers over a full rice-growing season. Furthermore, MODIS-like broadband indices were simulated through spectral convolution with sensor response functions. Results revealed clear interannual differences in calibration coefficients, particularly in the blue (450–500 nm) and red-edge (700–800 nm) regions. PRI exhibited the highest sensitivity to calibration changes. SIF also showed pronounced sensitivity due to its intrinsically weak signal and strong channel dependence. Conversely, NDVI and ARVI were highly robust, with the calibration-induced bias of NDVI remaining near zero, benefiting from its normalized formulation and strong near-infrared reflectance. Broadband simulation modified the propagation of uncertainty in an index-dependent manner. Compared with narrowband counterparts, broadband EVI, SAVI, and PRI showed reduced sensitivity, whereas NDVI exhibited a slight sensitivity increase. These findings demonstrate a hierarchy of calibration sensitivity governed by signal strength, spectral band selection, and algorithm design. They emphasize the necessity of frequency-dependent calibration strategies for reliable products in satellite validation studies. Full article

This article proposes, to the best of our knowledge, the first dual-port compact dual-polarized ultrawideband wearable textile antenna covering lower UHF bands for off-body communications in Internet-of-Things-based wireless body area network (IoT-based WBAN) scenarios. The antenna covers key bands for diverse services, including the 470–510 MHz LoRa WAN, 700 MHz offline emergency communication, 900 MHz NB-IoT, and 1–1.2 GHz satellite internet bands. The antenna adopts a square-ring loaded wide slot structure and a multi-mode resonant feeding structure to achieve ultrawideband operation. Moreover, it utilizes oppositely placed advanced microstrip feeding networks to excite the horizontal and vertical polarization modes, respectively, and four narrow slots around the wide slot to extend the current path, thus enabling a compact size of 0.30 × 0.28 × 0.0035 λ_l³ (where λ_l is the largest operating wavelength). Measured −10 dB impedance bandwidths are 119.1% (0.35–1.38 GHz) for Port 1 and 115.9% (0.39–1.37 GHz) for Port 2 on the human body, with more than 19 dB port isolation over the operating band. The measured average gains are about 4.21 dBi for Port 1 and 3.54 dBi for Port 2 on the human body, respectively. Specific absorption rate analysis confirms compliance with the IEEE C95.1 limit at 0.5 W input power. Wireless transmission experiments at IoT bands further validate reliable off-body links with excellent signal-to-noise ratios for both polarizations. The antenna shall be very attractive for off-body communications in IoT-based WBAN scenarios. Full article

This work targets four core bottlenecks restricting electromagnetic susceptibility (EMS) fault diagnosis of digital radar boards, including cold-start modeling caused by scarce fault samples, low-efficiency scanning prone to omitting narrow sensitive frequencies, insufficient feature representation of electromagnetic signals, and severe overfitting induced by imbalanced data. An integrated EMS dataset construction framework is accordingly proposed for radar timing control boards based on closed-loop electromagnetic interference (EMI)injection and multidimensional key signal point (KSP) monitoring. The framework adopts an adaptive coarse-to-fine scanning scheme to rapidly lock sensitive bands and accurately resolve fault boundaries. It further incorporates the hybrid synthetic minority oversampling technique and random undersampling (SMOTE-RU) strategy and time–frequency feature filtering to balance sample distribution and remove redundant information, yielding standardized high-quality EMS feature datasets. We validate dataset quality on a suite of classic machine learning and deep learning models. Test results show that all baseline networks attain classification accuracy above 85.1%, while the convolutional block attention module (CBAM) attention-augmented model delivers the highest accuracy of 95.0%. Cross-board tests on dissimilar radar boards show that fine-tuning with 10% target samples drastically raises cross-domain accuracy, and moderate interference yields the best generalization above 91.2%. Incurring minimal retraining costs, this lightweight scheme achieves strong cross-hardware generalization to build EMS databases for radar timing boards and implement multiclass fault diagnosis, effectively solving the cold-start problem for new hardware with insufficient historical fault data. Full article

This paper investigates the influence of weak but structured high-frequency disturbances on the linear system identification of lateral vehicle dynamics using experimental measurement data. The analyzed dataset originates from previously conducted driver-in-the-loop experiments involving free-driving and slalom maneuvers. Frequency-domain analysis confirms that the dominant vehicle dynamics are concentrated below approximately 2–3 Hz, while a weak but persistent narrow-band disturbance around 10 Hz is consistently present in the steering signal. To investigate the influence of this disturbance on identification, different disturbance-handling strategies are compared, including notch filtering, low-pass filtering, ARX, IV-ARX, and ARMAX model structures. The comparison considers prediction performance, model complexity, identified dynamics, and robustness under different measurement conditions. The results show that increasing the deterministic model order is generally less effective than either targeted preprocessing or explicit noise modeling. When the disturbance is spectrally well separated from the relevant vehicle dynamics, notch filtering combined with a low-order ARX model provides the most effective solution. If preprocessing is not possible, ARMAX models achieve comparable performance by representing part of the disturbance through the noise model. IV-ARX models are used as a benchmark to verify that the main conclusions remain valid under possible closed-loop bias. Based on these findings, a practical engineering workflow is proposed for selecting appropriate disturbance-handling strategies according to the spectral characteristics of the measured signals. The proposed methodology provides guidance for robust control-oriented identification of lateral vehicle dynamics using realistic measurement data. Full article

Global navigation satellite systems (GNSSs) are essential for navigation in aviation, transportation, and autonomous systems, yet they remain vulnerable to intentional interference such as jamming and spoofing. Unlike prior studies that primarily focus on positioning error, this work emphasizes acquisition-phase behavior, analyzing the impact of interference on time-to-first-fix (TTFF) and post-attack reacquisition time. A controlled and repeatable laboratory testbed based on software-defined radio (SDR) was developed to emulate Global Positioning System (GPS) L1 and Galileo E1 signals under multiple interference scenarios, including narrowband jamming, static spoofing, and dynamic spoofing. Five commercial GNSS receivers were evaluated under identical conditions. The results show that jamming causes an immediate loss of positioning capability, reducing the empirical navigation-fix probability to near zero and significantly increasing reacquisition time, with recovery-phase empirical fix probabilities ranging from 0.062 to 0.991 depending on receiver class. In contrast, spoofing maintains high attack-phase empirical navigation-fix probabilities ranging from 0.730 to 0.907 while introducing persistent and undetected errors. Static position spoofing was found to produce position offsets that persisted into the recovery phase, delaying the return to the authentic navigation solution. For most receivers, however, correct positioning was restored within the observation window. Multi-constellation spoofing further increases attack effectiveness, raising fix continuity by more than 0.15 compared to single-constellation cases. Multi-band receivers demonstrate increased resilience by delaying spoof acceptance by more than 4 min in extended scenarios, rather than preventing it entirely. The proposed methodology enables reproducible evaluation of GNSS receiver robustness and demonstrates that navigation-fix continuity alone is not a reliable indicator of navigation integrity during spoofing attacks. Overall, the results demonstrate that navigation-fix continuity alone cannot be regarded as a reliable indicator of navigation integrity and highlight the importance of complementary integrity-monitoring mechanisms for GNSS-dependent systems. The reported observations were obtained under controlled laboratory conditions and should be interpreted within the context of the adopted experimental methodology rather than as a direct representation of operational performance in real-world environments. Full article

The Crab pulsar produces extremely intense, short-duration radio bursts known as giant pulses (GPs). We introduce a cumulative-energy diagnostic to quantify the apparent spectral extent of individual Crab giant pulses across the ultra-wideband low-frequency (UWL) receiver band, aiming to build a reproducible method for describing the frequency-domain concentration of emission and to characterize the observed spectral diversity of Crab GPs. Using UWL receiver on the Murriyang (Parkes) radio telescope, we present a systematic study of GPs from the Crab pulsar (PSR J0534+2200). We introduce an empirical classification scheme based on the cumulative distribution function of the pulse energy as a function of observing frequency. We use this diagnostic to separate events with apparent spectral concentration from events with broader spectral coverage. Under this empirical classification scheme, most detected events show apparent spectral concentration within a limited frequency range. Events classified as apparently spectrally concentrated contain most of their measured relative energy within limited frequency ranges, whereas broadband events show more extended spectral coverage. We emphasize that this classification describes the observed spectral extent and should not by itself be interpreted as proof of intrinsically narrow-band emission. Spectral fitting shows that most apparently spectrally concentrated (ASC) GPs have negative spectral indices, while a few events exhibit positive slopes, indicating substantial spectral diversity within the sample. The 3$\sigma$ widths of ASC main pulse GPs appear to cluster around two characteristic ranges, although this feature should be interpreted with caution given the finite time resolution of the data. The energy distribution of ASC main pulse GPs is broadly consistent with a log-normal functional form at low-to-intermediate energies and resembles a power-law-like tail at the high-energy end. The waiting-time distribution can be described by a Weibull function, while a sliding-window comparison with Monte Carlo realizations of a Poisson distribution shows no statistically significant deviation from temporal independence over the present 18.9-min observing span. The CDF-based classification method developed here is transferable to other wideband receiver data, provided that careful consideration is given to the instrumental bandpass, frequency-dependent sensitivity, RFI masking, and signal-to-noise thresholds. These results provide observational constraints on the phenomenology of Crab GPs and may be useful for future studies of pulsar coherent emission and related radio transients. Full article

To address the problems in high-speed railway communication, such as large signal penetration loss through carriages, difficulty in long-distance strip coverage, and limited coverage range of traditional base station antennas, this paper designs a cylindrical Luneburg lens antenna operating at the 1800/FA frequency bands. A dual-polarized feed antenna with a dipole structure is designed, loaded with X-shaped metal strips for out-of-band suppression, and integrated with a four-layer dielectric stratified cylindrical Luneburg lens, which uses its graded permittivity distribution to achieve beam focusing, enhance gain, narrow the horizontal beamwidth, and maintain a wide vertical beamwidth. Simulation results show that the lens can stably improve the gain by about 5 dBi; measured results indicate that the antenna has port isolation higher than 35 dB, good impedance matching, and measured gain of 12.4–13.3 dBi within the 1.7–2.1 GHz band, which is highly consistent with the simulation. This antenna can effectively adapt to the long-distance strip coverage scenario along high-speed railways, reduce the base station deployment density, and provide an engineering solution for the optimization of high-speed railway communication coverage. Full article

Narrowband Internet of Things (NB-IoT) is a 3GPP-standardised low-power wide-area network (LPWAN) technology designed for massive machine-type communications in challenging propagation environments. Despite its growing deployment, empirical channel data for Thailand’s diverse terrain—urban dense, urban outdoor, suburban, rural, and forest/mountain—remains limited in the open literature. This paper presents a composite channel characterisation study encompassing sixteen measurement sites across five environment classes in central and western Thailand. A composite channel model combining log-distance path loss, log-normal shadowing, and Nakagami-m fast fading is applied across all sites, yielding 8000 reference signal received power (RSRP) samples. Path loss exponents range from n = 2.2 (rural) to n = 4.0 (forest/mountain), back-calculated Nakagami-m parameters from m = 0.44 to m = 3.51, and shadowing standard deviations from σ_sh = 4.16 to 8.38 dB; ECL distributions are derived for all five environment classes. The back-calculated Nakagami-m parameters reveal a coherence gradient from sub-Rayleigh forest terrain (m < 1) through urban Rayleigh (m = 1.00) to near-Rician rural conditions (m > 2)—a fading hierarchy not previously reported for NB-IoT in Thailand. Results confirm that the composite channel model accurately characterises RSRP distributions and provides actionable network planning parameters for NB-IoT deployment in varied Thai terrain. Full article

Detecting weak radar targets in complex sea conditions is inherently challenging due to non-stationary sea clutter and sea spikes. Furthermore, traditional dictionary learning algorithms for clutter suppression suffer from high computational complexity. To address these issues, this paper proposes an efficient sea clutter suppression method cascading Block Coordinate Descent (BCD)-accelerated dictionary learning with Tunable Q-factor Wavelet Transform (TQWT) denoising. During dictionary learning, a BCD strategy replaces global Singular Value Decomposition (SVD) with analytical optimization. Combined with an adaptive soft-thresholding operator, this enables low-complexity joint optimization of dictionary atoms and sparse coefficients, drastically reducing training time. Subsequently, a batch-adaptive Orthogonal Matching Pursuit (OMP) algorithm featuring Gram matrix precomputation and a dual-stop mechanism achieves efficient reconstruction and preliminary cancellation of clutter components. Finally, TQWT is applied to filter out residual non-stationary clutter and noise by leveraging its narrowband feature representation and shift invariance. Experiments on measured radar data from the IPIX database and datasets published by the Journal of Radars demonstrate that the proposed method significantly outperforms traditional K-SVD-based algorithms. Specifically, it improves the average signal-to-clutter-plus-noise ratio (SCNR) by 17.48 dB and requires a total execution time of only 7.99 s, achieving a highly favorable trade-off between suppression performance and computational efficiency. Full article