Search Results (6,414)

This study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops—including maize, wheat, and rice—and designed to overcome the inefficiency and subjectivity of manual measurements and the high costs of laser-based phenotyping. A panoramic video of the seed is captured and processed through frame sampling to extract multi-view images. Structure-from-Motion (SFM) is employed for sparse reconstruction and camera pose estimation, while 3D Gaussian Splatting (3DGS) is utilized for high-fidelity dense reconstruction, generating detailed point cloud models. The subsequent point cloud preprocessing, filtering, and segmentation enable the extraction of key phenotypic parameters, including length, width, height, surface area, and volume. The experimental evaluations demonstrated a high measurement accuracy, with coefficients of determination (R²) for length, width, and height reaching 0.9361, 0.8889, and 0.946, respectively. Moreover, the reconstructed models exhibit superior image quality, with peak signal-to-noise ratio (PSNR) values consistently ranging from 35 to 37 dB, underscoring the robustness of 3DGS in preserving fine structural details. Compared to conventional multi-view stereo (MVS) techniques, the proposed method can achieve significantly improved reconstruction accuracy and visual fidelity. The key outcomes of this study confirm that the 3DGS-based pipeline provides a highly accurate, efficient, and scalable solution for digital phenotyping, establishing a robust foundation for its application across diverse crop species. Full article

This paper presents a robust control strategy for three-phase inverters that combines Sliding Mode Control with a Washout Filter (SMC-w) to achieve low harmonic distortion and high dynamic stability. The proposed approach addresses the critical challenge of maintaining the stability of a high-quality output signal while ensuring robustness against disturbances and adaptability under variable, unbalanced, and nonlinear loads. The proposed hybrid controller integrates the fast response and disturbance rejection capability of SMC with the filtering properties of the washout stage, effectively mitigating low-frequency chattering and steady-state offsets. A detailed stability analysis is provided to ensure the closed-loop convergence of the SMC–w. Simulation results obtained in MATLAB–Simulink demonstrate significant improvements in transient response, total harmonic distortion, and robustness under unbalanced and nonlinear load conditions compared to conventional control methods. The inverter demonstrated rapid tracking of the reference signals with a minimal error margin of 3%, effective frequency regulation with a low steady-state error, and resilience to input disturbances and load variations. For instance, under a load variation from 20 Ω to 5 Ω, the system maintained the output voltage accuracy within a 3% error threshold. In addition, the input perturbations and frequency shifts in the reference signals were effectively rejected, confirming the robustness of the control strategy. Furthermore, the integration of the SMC proved to be highly effective in reducing harmonic distortion and delivering a stable and high-quality sinusoidal output. The integration of the washout filter minimized the chattering phenomenon typically associated with the SMC, further enhancing the smooth response and reliability of the system. This study highlights the potential of SMC–w to optimize power quality and operational stability. This study offers significant insights into the development of advanced inverter systems that can operate in dynamic and challenging environments. Full article

In-band full-duplex (IBFD) communication systems offer a promising means of improving spectral efficiency by enabling simultaneous transmission and reception on the same frequency channel. Despite this advantage, self-interference (SI) remains a major challenge to their practical deployment. Among the different SI cancellation (SIC) techniques, this paper focuses on digital SIC methodologies tailored for multiple-input multiple-output (MIMO) wireless transceivers operating under digital beamforming architectures. Two distinct digital SIC approaches are evaluated, employing a generalized memory polynomial (GMP) model augmented with Itô–Hermite polynomial basis functions and a phase-normalized neural network (PNN) to effectively model the nonlinearities and memory effects introduced by transmitter and receiver hardware impairments. The robustness of the SIC is further evaluated under both single off-line training and closed-loop real-time adaptation, employing estimation techniques such as least squares (LS), least mean squares (LMS), and fast Kalman (FK) for model coefficient estimation. The performance of the proposed digital SIC techniques is evaluated through detailed simulations that incorporate realistic power amplifier (PA) characteristics, channel conditions, and high-order modulation schemes. Metrics such as error vector magnitude (EVM) and total bit error rate (BER) are used to assess the quality of the received signal after SIC under different signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) conditions. The results show that, for time-variant scenarios, a low-complexity adaptive SIC can be realized using a GMP model with FK parameter estimation. However, in time-invariant scenarios, an open-loop SIC approach based on PNN offers superior performance and maintains robustness across various modulation schemes. Full article

This study introduces a hybrid Brain–Computer (BCI) system with a robust and secure activation mechanism between sessions, aiming to minimize the negative effects of visual stimulus-based BCI systems on user eye health. The system is based on the integration of Electroencephalography (EEG) signals and Electrooculography (EOG) artefacts, and includes an LED stimulus operating at a frequency of 7 Hz for safe activation and objects moving in different directions. While the LED functions as an activation switch that reduces visual fatigue caused by traditional visual stimuli, moving objects provide command generation depending on the user’s intention. In order to evaluate the stability of the system against physiological and psychological conditions, data were collected from 15 participants in two different sessions. The Correlation Alignment (CORAL) method was applied to the data to reduce the variance between sessions and to increase stability. A Bootstrap Aggregating algorithm was used in the classification processes, and with the CORAL method, the system accuracy rate was increased from 81.54% to 94.29%. Compared to similar BCI approaches, the proposed system offers a safe activation mechanism that effectively adapts to users’ changing cognitive states throughout the day by reducing visual fatigue, despite using a low number of EEG channels, and demonstrates its practicality and effectiveness by performing on par or superior to other systems in terms of high accuracy and robust stability. Full article

Ballistocardiography (BCG) is a noninvasive modality for detecting cardiac activity. This study developed a robust atrial fibrillation (AF) detection algorithm using multiple BCG sensors at different locations and evaluated the improvement in accuracy by combining data from multiple sensors. We recorded the BCG using a piezoelectric rubber sheet sensor and an electrocardiogram in 84 participants (29 with AF and 55 without AF) in the supine position. Four BCGs (BCG1–4) were obtained using sensors placed from the head to the lumbar region (0, 25, 45, and 65 cm from the head). The BCG signals were divided into 32 s blocks and analyzed. After applying fast Fourier transform, we input the power spectrum, focusing on frequencies below 10 Hz, into machine learning (ML) classifiers to distinguish between AF and non-AF with parameter tuning. The AdaBoost classifier for BCG2 exhibited the highest accuracy (0.88) among the ML models for each sensor. When we applied the classifier to other BCGs, it achieved accuracies of 0.92, 0.73, and 0.78 for BCG1, 3, and 4, respectively. The combined model using multiple sensors exhibited an accuracy of 0.92. The optimized model for BCG2 was robust against shifts in the sensor toward the head and lumbar directions. A combined assessment using multiple sensors improved performance. Full article

This paper addresses the cooperative formation control problem for a heterogeneous unmanned system composed of Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) under coexisting constraints of actuator faults, time-varying communication topology, and communication delay. First, a unified dynamic model is established under the Euler–Lagrange framework. Building on this, a novel distributed adaptive fault-tolerant control (DAFTC) framework is proposed. This framework integrates a Dynamic Event-Triggered Mechanism (DETM) to address communication bandwidth limitations, alongside an adaptive fault-tolerant strategy to enhance system robustness. The novelty lies in the cohesive integration of DETM for communication efficiency and adaptive laws for online fault compensation (both loss of effectiveness and bias), while rigorously handling communication delays via Lyapunov–Krasovskii analysis. It is proven via Lyapunov stability analysis that the proposed control protocol ensures all signals in the closed-loop system remain semi-globally uniformly ultimately bounded, with the formation tracking error converging to an adjustable compact set. Simulation results demonstrate the framework’s effectiveness. Compared to periodic communication (0.1 s interval), the proposed DETM reduces the communication load by over 99.6%. Even when subjected to a 25% effectiveness fault and a 5 Nm bias fault, the root-mean-square (RMS) tracking error is maintained below 0.15 m, validating the system’s high performance and robustness. Full article

This study introduces a sensorless fault diagnosis method for efficiently detecting bearing faults in induction motors. The proposed method eliminates the need for torque sensors, frequency sensors, thermal cameras, or real-time Fast Fourier Transform (FFT) tools. Induction motors are commonly utilized in a variety of industrial applications, including fans, pumps, and home appliances, due to their straightforward construction, affordability, and robust reliability. Traditional bearing fault diagnosis methods often rely on additional hardware such as vibration or thermal sensors. Additionally, approaches employing Artificial Intelligence (AI) and real-time FFT processing require advanced and expensive hardware capabilities. However, many V/f control systems are primarily intended for cost-effective and simple implementations, making resource-intensive approaches undesirable. Therefore, such methods present limitations for these use cases. To address these challenges, this paper presents a sensorless detection technique that estimates torque via a flux observer, removing the dependence on external sensors. The estimated torque is processed using an offline FFT to identify amplitude changes within bearing fault frequency bands. Here, the FFT-based frequency analysis is performed offline and is used to design a targeted band-pass filter (BPF). The torque signal, after passing through the BPF, undergoes a straightforward threshold-based logic to assess the existence of faults. Compared to AI- or data-driven approaches, the proposed method provides a lightweight, interpretable, and sensorless solution without the need for additional training or high-end processors. Despite its straightforward approach, the technique achieves effective detection of bearing faults across various components and speeds, making it ideal for embedded and economically constrained motor applications. Full article

The increasing prevalence of lithium-ion batteries in energy storage and electric transportation has led to a rise in overcharge-induced thermal runaway (TR) incidents. Particularly, the TR of Lithium Iron Phosphate (LFP) batteries demonstrates distinct evolutionary stages and multimodal hazard signals. This study investigated the TR process of LFP batteries under various charging rates through five sets of gradient C-rate experiments, collecting multimodal data (temperature, voltage, gas, sound, and deformation). Drawing on the collected data, this study proposes a three-stage evolution model that systematically identifies key characteristic signals and tracks their progression pattern through each stage of TR. Subsequently, fusion-based models (for both single- and multi-rate scenarios) and a time-series-based LSTM model were developed to evaluate their classification accuracy and feature importance in the classification of TR stages. Results indicate that the fusion-based models offer greater generalization, while the LSTM model excels at modeling time-dependent dynamics. These models demonstrate complementary strengths, providing a comprehensive toolkit for risk assessment. Furthermore, for the severe TR stage, this study proposes an innovative three-dimensional dynamic emergency decision matrix comprising a toxicity index (TI), flammability index (FI), and visibility (V) to provide quantitative guidance for rescue operations in the post-accident phase. Ultimately, this study establishes a comprehensive, closed-loop framework for LFP battery safety, extending from multimodal signal acquisition and intelligent early warning to quantified emergency response. This framework provides both a robust theoretical basis and practical tools for managing TR risk throughout the entire battery lifecycle. Full article

MicroRNAs (miRNAs) are key post-transcriptional regulators of plant development and stress responses, with many being conserved across diverse plant lineages. In this study, we investigated the expression profiles of miRNAs and their corresponding target genes in Arachis stenosperma, a wild peanut relative that exhibits robust resistance to root-knot nematodes (RKN). Small RNA sequencing of nematode-infected roots identified 107 miRNA loci, of which 93 corresponded to conserved miRNA families and 14 represented novel candidates, designated as miRNOVO. Among these, 18 miRNAs belonging to 11 conserved families were identified as differentially expressed (DEMs). Notably, miR399 and miR319 showed the highest upregulation (logFC = 4.25 and 4.20), while miR393 and miR477 were the most downregulated (logFC = −0.83 and −0.79). Integrated analysis of miRNA and transcriptome data revealed several regulatory interactions involving key defense-related genes. These included NLR genes targeted by miR393 and miR477, hormone signaling components such as the auxin response factor ARF8 targeted by miR167, and the growth regulator GRF2 targeted by miR396. Additionally, miR408 was predicted to target laccase3, a gene involved in the oxidation of phenolic compounds, lignin biosynthesis, copper homeostasis and defense responses. Remarkably, four immune receptor genes belonging to the nucleotide-binding site leucine-rich repeat (NLR) family displayed inverse expression patterns relative to their regulatory miRNAs, suggesting miRNA-mediated post-transcriptional control during the early stages of nematode infection. These findings reveal both conserved and species-specific miRNA–mRNA modules associated with nematode resistance in A. stenosperma, highlighting promising targets for developing RKN-tolerant peanut cultivars through miRNA-based strategies. Full article

Conventional signal-to-image conversion methods often overlook the physical correspondence of vibration signals, limiting diagnostic interpretability. To address this, we propose a physics-guided image construction strategy that incorporates dimensionless indicators to adaptively weight grayscale regions, enhancing the physical consistency and the discriminability among different fault types. Furthermore, a novel Cheap Channel Obfuscation module is introduced to suppress noise, decouple feature channels, and preserve the critical information within lightweight models. Integrated with ShuffleNetV2, our method achieves high diagnostic accuracy. Experimental validation for CWRU and SEU bearing datasets yields accuracies of 100% and 99.91%, respectively, demonstrating superior performance with minimal parameters. This approach offers a technically robust and computationally efficient fault diagnosis solution, with promising potential for deployment in resource-limited industrial environments. Full article

Understanding how the human brain processes and interprets multimedia content represents a frontier challenge in neuroscience and artificial intelligence. This study introduces a novel approach to decode semantic information from electroencephalogram (EEG) signals recorded during visual stimulus perception. We present DCT-ViT, a spatial–temporal transformer architecture that pioneers automated semantic recognition from brain activity patterns, advancing beyond conventional brain state classification to interpret higher level cognitive understanding. Our methodology addresses three fundamental innovations: First, we develop a topology-preserving 2D electrode mapping that, combined with temporal indexing, generates 3D spatial–temporal representations capturing both anatomical relationships and dynamic neural correlations. Second, we integrate discrete cosine transform (DCT) embeddings with standard patch and positional embeddings in the transformer architecture, enabling frequency-domain analysis that quantifies activation variability across spectral bands and enhances attention mechanisms. Third, we introduce the Semantics-EEG dataset comprising ten semantic categories extracted from visual stimuli, providing a benchmark for brain-perceived semantic recognition research. The proposed DCT-ViT model achieves 72.28% recognition accuracy on Semantics-EEG, substantially outperforming LSTM-based and attention-augmented recurrent baselines. Ablation studies demonstrate that DCT embeddings contribute meaningfully to model performance, validating their effectiveness in capturing frequency-specific neural signatures. Interpretability analyses reveal neurobiologically plausible attention patterns, with visual semantics activating occipital–parietal regions and abstract concepts engaging frontal–temporal networks, consistent with established cognitive neuroscience models. To address systematic misclassification between perceptually similar categories, we develop a hierarchical classification framework with boundary refinement mechanisms. This approach substantially reduces confusion between overlapping semantic categories, elevating overall accuracy to 76.15%. Robustness evaluations demonstrate superior noise resilience, effective cross-subject generalization, and few-shot transfer capabilities to novel categories. This work establishes the technical foundation for brain–computer interfaces capable of decoding semantic understanding, with implications for assistive technologies, cognitive assessment, and human–AI interaction. Both the Semantics-EEG dataset and DCT-ViT implementation are publicly released to facilitate reproducibility and advance research in neural semantic decoding. Full article

Eleven novel asymmetric pyrimidine derivatives were synthesized. The pyrimidine core was functionalized with a coumarin chromophore and a pro-mesogenic fragment bearing either chiral or linear alkyl chains of variable length and substitution patterns. The thermal properties were investigated using polarized optical microscopy, differential scanning calorimetry, and small-angle X-ray scattering, revealing that only selected derivatives exhibited liquid crystalline phases with ordered columnar or smectic organizations. Linear and nonlinear optical properties were characterized by UV–Vis absorption, fluorescence spectroscopy, two-photon absorption, and second-harmonic generation. Optical responses were found to be highly sensitive to the substitution pattern: derivatives functionalized at the 4 and 3,4,5 positions exhibited enhanced 2PA cross-sections and pronounced SHG signals, whereas variations in alkyl chain length exerted only a minor influence. Notably, compounds forming highly ordered non-centrosymmetric mesophases produced robust SHG-active thin films. Importantly, strong SHG responses were obtained without the need for a chiral center, as the inherent asymmetry of the linear alkyl chain derivatives was sufficient to drive self-organization into non-centrosymmetric materials. These results demonstrate that asymmetric pyrimidine-based architectures combining π-conjugation and controlled supramolecular organization are promising candidates for nonlinear optical applications such as photonic devices, multiphoton imaging, and optical data storage. Full article

Inverse Synthetic Aperture Radar (ISAR) plays a vital role in the high-resolution imaging of marine targets, particularly under non-cooperative scenarios. However, resolution degradation due to limited observation angles and marine clutter such as wave-induced disturbances remains a major challenge. In this work, we propose a novel deep learning-based framework to enhance ISAR resolution in the presence of marine clutter and additive Gaussian noise, which performs direct restoration in the ISAR image domain after an IFFT2 back projection. Under small aspect sweeps with coarse range alignment, the network implicitly compensates for residual defocus and cross-range blur, while suppressing clutter and noise, to recover high-resolution complex ISAR images. Our approach leverages a residual neural network trained to learn a non-linear mapping between low-resolution and high-resolution ISAR images. The network is designed to preserve both magnitude and phase components, thereby maintaining the physical integrity of radar returns. Extensive simulations on synthetic marine vessel data demonstrate significant improvements in cross-range, outperforming conventional sparsity-driven methods. The proposed method also exhibits robust performance under conditions of low signal-to-noise ratio (SNR) and signal-to-wave ratio (SWR), effectively recovering weak scatterers and suppressing false artifacts. This work establishes a promising direction for data-driven ISAR image enhancement in noisy and cluttered maritime environments with minimal pre-processing. Full article

Synchronized oscillatory fluctuations in intracellular calcium concentration across extended neuronal networks represent a functional indicator of connectivity and signal coordination. In this study, a model of human immature neurons (differentiated from LUHMES precursors) has been used to establish a robust protocol for generating reproducible intracellular Ca²⁺ oscillations in both two-dimensional monolayers and three-dimensional spheroids. Oscillatory activity was induced by defined ionic conditions in combination with potassium channel blockade. It was characterized by stable frequencies of approximately 0.2 Hz and high synchronization indices across millimeter-scale cultures. These properties were consistently reproduced in independent experiments and across laboratories. Single-cell imaging confirmed that oscillations were coordinated throughout large cell populations. Pharmacological interventions demonstrated that neither excitatory nor inhibitory chemical synaptic transmission influenced oscillatory dynamics. Gap junction blockers completely disrupted synchronization, while leaving individual cell activity unaffected. Functional dye-transfer assays provided additional evidence for electrical coupling. This was further supported by connexin-43 expression profiles and immunostaining. Collectively, these findings indicate that synchronized Ca²⁺ oscillations in LUHMES cultures are mediated by gap junctional communication rather than by conventional synaptic mechanisms. This system offers a practical platform for studying fundamental principles of network coordination and for evaluating pharmacological or toxicological modulators of intercellular coupling. Moreover, it may provide a relevant human-based model to explore aspects of neuronal maturation and to assess compounds with potential neurodevelopmental toxicity. Full article

The precise reconstruction of target scattering centers (TSCs) using sensors plays a crucial role in feature extraction and identification of non-cooperative targets. Radar sensor networks (RSNs) are well suited for this task, as they are capable of illuminating targets from multiple aspect angles and rapidly capturing reflected signals. However, the complex geometry and diverse material composition of real-world targets result in significant variations in the radar cross-section (RCS) observed at different angles. Although these RCS responses are interrelated, they exhibit considerable angular diversity. Furthermore, achieving precise spatiotemporal registration and fully coherent processing is infeasible for RSNs composed of small mobile sensor platforms, such as drone swarms. Therefore, an intelligent algorithm is required to extract and accumulate correlated and meaningful information from the target echoes received by the RSN. In this work, a novel collaborative TSC reconstruction framework for RSNs is proposed. The framework performs similarity evaluation on wide-angle high-resolution range profiles (HRRPs) to achieve adaptive angular segmentation of TSC models. It combines the expectation–maximization (EM) algorithm with an enhanced Arctic puffin optimization (EAPO) algorithm to effectively integrate echo information from the RSN in a non-coherent manner, thereby enabling accurate TSC estimation. The proposed method outperforms existing mainstream approaches in terms of spatiotemporal registration requirements, estimation accuracy, and stability. Comparative experiments on measured datasets demonstrate the robustness of the framework and its adaptability to complex target scattering characteristics, confirming its practical value. Full article