Search Results (29,582)

We propose a phase-aware complex-spectrogram autoencoder (AE) for preprocessing raw vibration signals of rotating electrical machines. The AE reconstructs normal components and separates fault components as residuals, guided by an input-phasor phase-orthogonality regularization that defines parallel/orthogonal residuals with respect to the local signal phase. We use a U-Net-based AE with a mask-bias head to refine local magnitude and phase. Decisions are based on residual features—magnitude/shape, frequency distribution, and projections onto the normal manifold. Using the AI Hub open dataset from field ventilation motors, we evaluate eight representative motor cases (2.2–5.5 kW: misalignment, unbalance, bearing fault, belt looseness). The preprocessing yielded clear residual patterns (low-frequency floor rise, resonance-band peaks, harmonic-neighbor spikes), and achieved an area under the receiver operating characteristic curve (ROC-AUC) = 0.998–1.000 across eight cases, with strong leave-one-file-out generalization and good calibration (expected calibration error (ECE) ≤ 0.023). The results indicate that learning to remove normal structure while enforcing phase consistency provides an unsupervised front-end that enhances fault evidence while preserving interpretability on field data. Full article

In this paper, a reduced-complexity cross-domain iterative detection for orthogonal time frequency space (OTFS) modulation is proposed that exploits channel properties in both time and delay–Doppler domains. Specifically, we first show that in the time-domain effective channel, the path delay only introduces interference among samples in adjacent time slots, while the Doppler becomes a phase term that does not affect the channel sparsity. This investigation indicates that the effects of delay and Doppler can be decoupled and treated separately. This “band-limited” matrix structure further motivates us to apply a reduced-size linear minimum mean square error (LMMSE) filter to eliminate the effect of delay in the time domain, while exploiting the cross-domain iteration for minimizing the effect of Doppler by noticing that the time and Doppler are a Fourier dual pair. Furthermore, we apply eigenvalue decomposition to the reduced-size LMMSE estimator, which makes the computational complexity independent of the number of cross-domain iterations, thus significantly reducing the computational complexity. The bias evolution and variance evolution are derived to evaluate the average MSE performance of the proposed scheme, which shows that the proposed estimators suffer from only negligible estimation bias in both time and DD domains. Particularly, the state (MSE) evolution is compared with bounds to verify the effectiveness of the proposed scheme. Simulation results demonstrate that the proposed scheme achieves almost the same error performance as the optimal detection, but only requires a reduced complexity. Full article

In 3.5 wt% NaCl solution used to simulate seawater, the individual (self-corrosion) and coupled (galvanic) corrosion behaviors of TA22 titanium alloy and 304 stainless steel were systematically investigated at 25 °C, 35 °C, 45 °C and 55 °C. Post-corrosion surfaces were characterized by scanning electron microscopy (SEM), three-dimensional profilometry and X-ray photoelectron spectroscopy (XPS). The results demonstrated that elevating temperature decreased the compactness and protective quality of the passive film on both alloys, as indicated by increasing donor densities and positive shifts in flat-band potentials. Distinct pitting corrosion occurred on 304 SS above 45 °C. Upon galvanic coupling, the passive film on TA22 was modified in both structure and composition, exhibiting a decreased TiO₂ content and increased lower valence oxides (Ti₂O₃, TiO). The galvanic effect intensified with temperature, leading to progressively aggravated corrosion of 304 SS, characterized by increased pit density, diameter, and depth compared to its self-corrosion state. Full article

We present the design of an optically transparent, flexible, and tunable microwave absorber covering the X and Ku frequency bands. The absorber is based on a metasurface composed of a periodic array of graphene spiral resonators (GSRs) attached to an ultrathin PET film placed over an ITO-backed dielectric spacer. An equivalent circuit model (ECM), described by closed-form equations, is proposed to optimize the structure for maximum absorption within the target frequency range. The optimized absorber achieves a peak absorbance of 99.7% for normally incident waves while maintaining over 90% absorption at various incident angles in the frequency range from 8.5 GHz to 17.4 GHz. In addition, a double-layer graphene spiral resonator (DGSR) metasurface is proposed to extend the absorber’s operational bandwidth, demonstrating a bandwidth enhancement of approximately 3 GHz and a relative bandwidth of 90% without compromising miniaturization or incident angle stability. Given their remarkable attributes, both GSR and DGSR configurations show great potential for applications in radar stealth technology and transparent electromagnetic compatibility. Full article

This paper proposed an adaptive bandwidth Phase-Locked Loop (PLL) that integrates integer-N and fractional-N switching for energy-efficient RF synthesis in IoT and mobile applications. The architecture exploits wide-bandwidth integer-N mode for rapid lock acquisition, then seamlessly transitions to narrow-bandwidth fractional-N mode for high-resolution synthesis and noise optimization. The architecture features a bandwidth-reconfigurable loop filter with intelligent switching control that monitors phase error dynamics. A novel adaptive digital noise filter mitigates $\Delta \Sigma$ quantization noise, replacing conventional synchronous delay lines. The multi-loop structure incorporates a high-resolution digital phase detector to enhance frequency accuracy and minimize jitter across both operating modes. With 180 nm CMOS technology, the PLL consumes 13.2 mW, while achieving $-119$ dBc/Hz in-band phase noise and 1 $p{s}_{rms}$ integrated jitter. With an operating frequency range at 2.9–3.2 GHz from a 1.8 V supply, the circuit achieves a worst case fractional spur of −62.7 dBc, which corresponds to a figure of merit (FOM) of −228.8 dB. Lock time improvements of 70% are demonstrated compared to single-mode implementations, making it suitable for high-precision, low-power wireless communication systems requiring agile frequency synthesis. Full article

Satellite remote sensing has emerged as a reliable and cost-effective approach for monitoring inland water quality, offering spatial and temporal advantages over traditional in situ methods. Lake Victoria, the largest tropical lake and a critical freshwater resource for East Africa, faces increasing eutrophication driven by nutrient inflows from agriculture, urbanization, and industrial activities. This study assessed the spatiotemporal dynamics of water quality along Uganda’s Lake Victoria coast by integrating field measurements (2014–2024) with Landsat 8/9 imagery. Chlorophyll-a, a proxy for algal blooms, and Secchi disk depth, an indicator of water clarity, were selected as key parameters. Cloud-free satellite images were processed using the Dark Object Subtraction method, and spectral reflectance values were correlated with field data. Linear regression models from single bands and band ratios showed strong performance, with adjusted R² values of up to 0.88. When tested on unseen data, the models achieved R² values above 0.70, confirming robust predictive ability. Results revealed high algal concentrations for nearshore and clearer offshore waters. These models provide an efficient framework for monitoring eutrophication, guiding restoration priorities, and supporting sustainable water management in Lake Victoria. Full article

The simulation of multidimensional wave propagation with variable material parameters is a computationally intensive task, with applications from seismology to electromagnetics. While quantum computers offer a promising path forward, their algorithms are often analyzed in the abstract oracle model, which can mask the high gate-level complexity of implementing those oracles. We present a framework for constructing a quantum algorithm for the multidimensional wave equation with a variable speed profile. The core of our method is a decomposition of the system Hamiltonian into sets of mutually commuting Pauli strings, paired with a dedicated diagonalization procedure that uses Clifford gates to minimize simulation cost. Within this framework, we derive explicit bounds on the number of quantum gates required for Trotter–Suzuki-based simulation. Our analysis reveals significant computational savings for structured block-model speed profiles compared to general cases. Numerical experiments in three dimensions confirm the practical viability and performance of our approach. Beyond providing a concrete, gate-level algorithm for an important class of wave problems, the techniques introduced here for Hamiltonian decomposition and diagonalization enrich the general toolbox of quantum simulation. Full article

The erosion mechanism of hydrocyclones under air column conditions is still unclear. In this paper, Computational Fluid Dynamics–Discrete Phase Model (CFD-DPM) technology is adopted to perform transient simulations of the three-phase flow (liquid–gas–solid) within a hydrocyclone. The Reynolds Stress Model (RSM) and Volume of Fluid (VOF) model are adopted to simulate the continuous phase flow field within the hydrocyclone, while the DPM coupled with the Oka erosion model is used to predict the particle flow and erosion mechanisms on each wall within the hydrocyclone. The particle sizes considered are 15 μm, 30 μm, 60 μm, 100 μm, 150 μm, and 200 μm, respectively, with a density of 2600 kg/m³. The particle velocity is consistent with the fluid velocity at 5 m/s, the total mass flow rate is 6 g/s, and the volume fraction is less than 10%. The results indicate that the cone section suffers the severest erosion, followed by the overflow pipe, column section, infeed section, and roof section. The erosion in the cone section reaches its maximum value near the underflow port, with an erosion rate approximately 6.8 times that of the upper cone section. The erosion distribution in the overflow pipe is uneven. The erosion of the column section exhibits a spiral banded distribution with a relatively large pitch. The erosion rate in the infeed section is approximately 1.47 times that of the roof section. Full article

High-resolution remote sensing (HRRS) imagery enables the extraction of cropland information with high levels of detail, especially when combined with the impressive performance of deep convolutional neural networks (DCNNs) in understanding these images. Comprehending the factors influencing DCNNs’ performance in HRRS cropland extraction is of considerable importance for practical agricultural monitoring applications. This study investigates the impact of classifier selection and different training data characteristics on the HRRS cropland classification outcomes. Specifically, Gaofen-1 composite images with 2 m spatial resolution are employed for HRRS cropland extraction, and two county-wide regions with distinct agricultural landscapes in Shandong Province, China, are selected as the study areas. The performance of two deep learning (DL) algorithms (UNet and DeepLabv3+) and a traditional classification algorithm, Object-Based Image Analysis with Random Forest (OBIA-RF), is compared. Additionally, the effects of different band combinations, crop growth stages, and class mislabeling on the classification accuracy are evaluated. The results demonstrated that the UNet and DeepLabv3+ models outperformed OBIA-RF in both simple and complex agricultural landscapes, and were insensitive to the changes in band combinations, indicating their ability to learn abstract features and contextual semantic information for HRRS cropland extraction. Moreover, compared with the DL models, OBIA-RF was more sensitive to changes in the temporal characteristics. The performance of all three models was unaffected when the mislabeling error ratio remained below 5%. Beyond this threshold, the performance of all models decreased, with UNet and DeepLabv3+ showing similar performance decline trends and OBIA-RF suffering a more drastic reduction. Furthermore, the DL models exhibited relatively low sensitivity to the patch size of sample blocks and data augmentation. These findings can facilitate the design of operational implementations for practical applications. Full article

Transformer reliability is crucial to grid security, with core loosening a common fault. This paper proposes a transformer core loosening fault diagnosis method based on a fusion feature extraction approach and Categorical Boosting (CatBoost) optimized by the Crested Porcupine Optimizer (CPO) algorithm. Firstly, the audio signal is decomposed into six Intrinsic Mode Functions (IMF) components through Variational Mode Decomposition (VMD). This paper utilizes Gaussian membership functions to quantify the energy proportion, central frequency, and kurtosis of IMF and constructs a fuzzy entropy discrimination function. Then, the IMF noise components are removed through an adaptive threshold. Subsequently, the denoised signal undergoes a wavelet packet transform instead of a short-time Fourier transform to optimize Mel-frequency cepstral coefficients (WPT-MFCC), combining time-domain statistical features and frequency-band energy distribution to form a 24-dimensional fusion feature. Finally, the CatBoost algorithm is employed to validate the effects of different feature schemes. The CPO is introduced to optimize its iteration number, learning rate, tree depth, and random strength parameters, thereby enhancing overall performance. The CPO-optimized CatBoost model had 99.0196% fault recognition accuracy in experimental testing, 15% better than the standard CatBoost. Accuracy exceeded 90% even under extreme 0 dB noise. This method makes fault diagnosis more accurate and reliable. Full article

Ordovician marine red beds (OMRBs) are widely developed along the margins of Gondwana and represent distinctive limestone facies. These red beds are known for their diverse sedimentary structures and have been described by scholars as the “fashionable facies” in geological history. However, their characteristics and classification remain controversial. Multiple hypotheses about their origin have also hindered a clear understanding of these strata. Therefore, this study focuses on the Xiangxi area (South China) and presents a detailed analysis of the sedimentary structures of marine red beds, building on previous research on OMRBs in South China. Based on genetic features, we divide the most debated “nodule-like” and “cracked” structures—previously identified by earlier researchers—into ten subtypes. Three key genetic end-members are identified among these subtypes: breccia, patch, and argillaceous band. Detailed studies using microslab analysis, scanning electron microscopy, geochemistry, and paleontology were carried out on these three end-members. The results confirm that the Ordovician marine red beds were mainly deposited in a shallow marine environment, with the red coloration primarily derived from continental sources. As the sea level rose, the color of the red beds lightened, and the dominant sedimentary structures shifted from breccia end-members to argillaceous band end-members. Additionally, this study identified a vertically penetrating argillaceous band controlled by syndepositional compressive stress, which may be linked to NW-directed compression from the Kwangsian Orogeny. Evidence from tectonic styles, biofacies migration, and chronostratigraphy supports this hypothesis. Full article

This article presents an innovative chaotic communication scheme that integrates the multiuser access technique known as Wideband Code Division Multiple Access (W-CDMA) with the chaos-based selective strategy Chaos-Based Selective Symbol (CSS) and the unconventional modulation Chaos Parameter Shift Keying (CPSK). The system is designed to operate in the 2.45 GHz band and provides a robust and efficient alternative to conventional schemes such as Quadrature Amplitude Modulation (QAM). The proposed CPSK modulation enables the encoding of information for multiple users by regulating the 36 parameters of a Reconfigurable Chaotic Oscillator (RCO), theoretically allowing the simultaneous transmission of up to $2^{24}$ independent users over the same channel. The CSS technique encodes each user’s information using a unique chaotic segment configuration generated by the RCO; this serves as a reference for binary symbol encoding. W-CDMA further supports the concurrent transmission of data from multiple users through orthogonal sequences, minimizing inter-user interference. The system was digitally implemented on the Artix-7 AC701 FPGA (XC7A200TFBG676-2) to evaluate logic-resource requirements, while RF validation was carried out using a ZedBoard FPGA equipped with an AD9361 transceiver. Experimental results demonstrate optimal performance in the 2.45 GHz band, confirming the effectiveness of the chaos-based W-CDMA approach as a multiuser access technique for high-spectral-density environments and its potential for use in 5G applications. Full article

Donor–π–acceptor (D–π–A) dyes have garnered significant attention due to their unique optical properties and potential applications in various fields, including optoelectronics, chemical sensing and bioimaging. This study presents the design, synthesis, and comprehensive photophysical investigation of a series of ionic dyes incorporating five- and six-membered heterocyclic rings as electron-donating and electron-withdrawing units, respectively. The influence of the dye structure, i.e., (a) the systematically varied heteroatom (NMe, S and O) in donor moiety, (b) benzannulation of the acceptor part and (c) position of the donor vs. acceptor, on the photophysical properties was evaluated by steady-state and time-resolved spectroscopy across solvents of varying polarity. To probe solvatochromic behavior, the Reichardt parameters and the Catalán four-parameter scale, including polarizability (SP), dipolarity (SdP), acidity (SA) and basicity (SB) parameters, were applied. Emission dynamics were further analyzed through time-resolved fluorescence spectroscopy employing multi-exponential decay models to accurately describe fluorescence lifetimes. Time-dependent density functional theory (TDDFT) calculations supported the experimental findings by elucidating electronic structures, charge-transfer character, and dipole moments in the ground and excited states. The experimental results show the introduction of O or S instead of NMe causes substantial hypsochromic shifts in the absorption and emission bands. Benzannulation enhances the photoinduced charge transfer and causes red-shifted absorption spectra to be obtained without deteriorating the emission properties. Hence, by introducing an appropriate modification, it is possible to design materials with tunable photophysical properties for practical applications, e.g., in opto-electronics or sensing. Full article

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring. Full article

Quercetin, a key flavonoid in Anoectochilus roxburghii (Wall.) Lindl., plays an important role in determining the pharmacological value of this medicinal herb. However, traditional methods for quercetin quantification are destructive and time-consuming, limiting their application in real-time quality monitoring. This study investigates the hyperspectral response characteristics of quercetin using near-infrared hyperspectral imaging and establishes a feature-based model to explore its detectability in A. roxburghii leaves. We scanned standard quercetin solutions of known concentration under the same imaging conditions as the leaves to produce a dilution series. Feature-selection methods used included the successive projections algorithm (SPA), Pearson correlation, and competitive adaptive reweighted sampling (CARS). A 1D convolutional neural network (1D-CNN) trained on SPA-selected wavelengths yielded the best prediction performance. These key wavelengths—particularly the 923 nm band—showed strong theoretical and statistical relevance to quercetin’s molecular absorption. When applied to plant leaf spectra, the standard-trained model produced continuous predicted quercetin values that effectively distinguished cultivars with varying flavonoid contents. PCA visualization and ROC-based classification confirmed spectral transferability and potential for functional evaluation. This study demonstrates a non-destructive, spatially resolved, and biochemically interpretable strategy for identifying bioactive markers in plant tissues, offering a methodological basis for future hyperspectral inversion studies and intelligent quality assessment in herbal medicine. Full article