Search Results (165)

While spatially transmissive or reflective metasurfaces have achieved unprecedented wavefront control in free space, the paradigm shift toward on-chip waveguide-integrated architectures presents novel challenges for constructing compact and scalable photonic systems. Existing on-chip holographic schemes are typically constrained by the complexity of meta-atom structures, limited multiplexing capacity, and strict dependence on specific polarization states. This report comprehensively elucidates a novel on-chip metasurface architecture that relies exclusively on a unified detour phase modulation mechanism to achieve high-capacity, multi-channel holographic multiplexing. By deeply integrating a phase-displacement-joint displacement algorithmic framework with the simulated annealing global optimization algorithm, this design highly circumvents the necessity for complex anisotropic meta-atom geometries and the physical superposition of multiple phase mechanisms. Within an ultra-compact physical footprint of 55.55 × 55.55 μm², the architecture successfully achieves customized holographic reconstruction at specific far-field planes. When discrete TE modes in the visible spectrum are injected from orthogonal lateral directions, distinctly different target holograms are reconstructed in the far field without crosstalk. This mechanism establishes a robust four-wavelength, four-channel independent coding framework. The findings not only elucidate a simplified and highly scalable methodology for ultra-high-density on-chip displays but also provide profound theoretical guidance and technical support for cutting-edge applications such as augmented reality, secure optical communications, and high-density optical data storage. Full article

Tooth agenesis is a common developmental anomaly of the human dentition, ranging from hypodontia to oligodontia, yet its marked phenotypic variability remains insufficiently explained. This review synthesizes developmental and molecular evidence on epithelial–mesenchymal interactions during early odontogenesis and proposes a signaling-threshold framework for human tooth agenesis. We focus on the coordinated roles of Wnt/β-catenin, bone morphogenetic protein (BMP), fibroblast growth factor (FGF), and Sonic hedgehog (SHH) pathways and on recurrent disease-associated genes, including MSX1, PAX9, WNT10A, and AXIN2, as quantitative modulators of pathway activity rather than binary determinants of tooth identity. Within this framework, successful tooth initiation may depend on whether integrated signaling output exceeds a field-specific activation threshold within spatially graded developmental regions of the dental arch. Differences in signaling amplitude, duration, and transcriptional responsiveness may therefore account for distal tooth susceptibility, variable penetrance, arch asymmetry, and the broad clinical spectrum from mild hypodontia to severe oligodontia. By integrating molecular genetics with developmental field theory, this model provides a testable systems-level explanation for selective tooth absence and highlights priority directions for future functional and genotype–phenotype studies. Full article

Accurate prediction of the Remaining Useful Life (RUL) of special gas cylinders is critical for industrial safety management. However, the nonlinear, strongly coupled degradation behaviors of these cylinders, combined with non-stationary and high-noise monitoring data, limit the performance of single deep learning models. Traditional hyperparameter tuning and signal processing methods often fail to meet the required prediction accuracy. To address these challenges, this study proposes a hybrid SSA–PSO–ResNet–LSTM–Attention framework for RUL prediction of special gas cylinders. The framework first applies Singular Spectrum Analysis (SSA) to decompose and reconstruct the 12-dimensional multi-source sensor signals, effectively suppressing noise while extracting core degradation trends. Subsequently, a ResNet–LSTM–Attention collaborative model is constructed, where ResNet ensures stable spatial feature propagation, LSTM captures long- and short-term temporal dependencies, and a multi-head attention mechanism emphasizes critical time steps associated with abrupt degradation. Furthermore, a Particle Swarm Optimization (PSO) algorithm is employed to globally optimize key hyperparameters, including the number of convolutional kernels, LSTM hidden units, and learning rate, mitigating the subjectivity of manual tuning. Experimental validation is conducted on 1000 real monitoring samples from 100 composite material gas cylinders, with a cylinder ID-based 7:1:2 train–validation–test split and stratified sampling covering four operating conditions. PSO optimizes hyperparameters using the validation set RMSE as the fitness function, and the test set is exclusively used for final performance evaluation. All results are reported as the mean ± standard deviation from grouped 5-fold cross-validation on the cylinder-wise partition. The proposed model achieves a test RMSE of 71.55, MAE of 50.63, and R² of 0.9584, representing a 34.2% and 30.2% reduction in RMSE and MAE, respectively, compared with the second-best CNN-LSTM model, and significantly outperforming SVR, MLP, and other benchmark models. Ablation studies confirm the positive synergistic effect of each component, with the removal of either the attention mechanism or the ResNet module causing substantial performance degradation. By employing physically calibrated RUL labels and a balanced multi-condition dataset, the proposed framework achieves high predictive accuracy and good potential for industrial application, providing an effective solution for RUL prediction of special gas cylinders and similar high-pressure vessels, with potential applications in intelligent maintenance of complex industrial equipment. Full article

High-Frequency (HF) radar remote sensing offers a unique capability to detect mesoscale air-sea interactions under strong monsoon conditions. This study leveraged HF radar-derived surface currents, buoy observations, and reanalysis data to systematically investigate the driving mechanism of the sea–land breeze (SLB) on surface currents in the Taiwan Strait during the strong winter monsoon. To address the challenge of extracting weak signals from a dominant background flow, we employed the Separation of the Regional Wind Field (SRWF) method and the complex demodulation spectrum shifting technique. The results demonstrate that HF radar observations confirm the presence of regular SLB activity even under the strong monsoon, with its intensity modulated by the land–sea temperature difference influenced by cloud cover. Spatial correlation analysis reveals that the SLB significantly drives diurnal variations in the surface current, with its impact extending up to 110 km offshore and a maximum amplitude of approximately 2.2 cm/s. Additionally, the analysis reveals that the duration of SLB events critically influences the current response: events lasting 7 days produce a stronger and more spatially coherent correlation with the diurnal currents than shorter 5-day events. Furthermore, harmonic analysis indicates that the SLB’s energy primarily affects the non-tidal residual current, with no significant impact on the principal diurnal tidal constituents ($O_1$, $K_1$). This work not only quantifies the SLB-current coupling during sustained SLB events in a strong monsoon regime but, more importantly, demonstrates the capability of HF radar remote sensing for resolving weak signals in complex, high-energy environments, providing a robust methodological framework and valuable insights for regional marine environmental forecasting. Full article

Forest, shrubland, and grassland exhibit highly overlapping characteristics, and single-modal remote sensing data cannot simultaneously capture both spectral and structural information. Moreover, multimodal fusion learning of optical and SAR data faces challenges such as the lack of high-quality samples and difficulties in effective cross-modal feature fusion. Therefore, a high-resolution multimodal remote sensing feature dataset (GF23FSG) is constructed for the fine classification of forest, shrubland, and grassland, and a Cross-modal Adaptive Structure Fusion Network (CASFNet) is proposed. In response to the feature heterogeneity of optical and SAR, a cross-modal adaptive fusion module based on spatial alignment and a dynamic weight allocation strategy is proposed, which effectively enhances the learning of spectral–spectrum heterogeneous features. In addition, a multi-level auxiliary supervision mechanism is introduced to strengthen feature representation learning. Gradient constraints are further imposed on deep-level features to improve the model’s ability to capture and learn deep cross-modal representations, thereby effectively mitigating representation degradation during the feature fusion process. Experiments on the self-constructed GF23FSG dataset and the publicly available SEN12MS dataset achieve OA of 77.38% and 71.84%, respectively, demonstrating superior classification performance compared with SOTA methods. In addition, comparative analysis with public land cover products and field samples further confirm the reliability and generalization performance of the proposed dataset and model for the fine classification of forest, shrubland, and grassland. This study provides a new solution for the fine classification of forest, shrubland, and grassland from multimodal remote sensing images from the perspectives of dataset construction and methodological design. Full article

Visible light communication (VLC) employs light-emitting diodes (LEDs) for simultaneous illumination and wireless data transmission, offering advantages such as unlicensed spectrum, immunity to electromagnetic interference, and intrinsic security. Conventional PAM-VLC transmitters generally rely on a single high-power LED driven by analog front-end components, such as digital-to-analog converters and power amplifiers, which increase hardware complexity, power consumption, and thermal burden. To address these limitations, this paper proposes an energy-efficient spatial-combining VLC transmitter in which multiple LEDs are directly driven by FPGA GPIO ports, without using DACs or power amplifiers. Multilevel PAM is digitally realized by controlling the number of activated LEDs, and the emitted optical signals are spatially combined through an optical lens. Experimental results demonstrate reliable 1 m free-space transmission. At a bit-error rate (BER) of 3.8 × 10⁻³, the proposed scheme achieves SNR gains of 0.75 dB for PAM-4 and 0.8 dB for PAM-8 over the conventional pulse amplitude modulation (PAM)-VLC architecture. Moreover, the proposed transmitter reduces power consumption by 38.7%. These results confirm that digitally driven multi-LED spatial combining is a promising solution for low-cost and energy-efficient VLC systems. Full article

Aero-engine defects often exhibit micro-scale and high-frequency characteristics under complex metallic textures, which makes precise segmentation difficult. Most existing pixel-level methods rely on spatial-domain modeling and lack frequency-domain decoupling. As a result, high-frequency details are easily hidden by low-frequency background information. In addition, repeated downsampling weakens the representation of fine-grained structures, leading to inaccurate boundary localization and limited robustness. To address these issues, a spectrum-driven hierarchical learning network is proposed for aero-engine defect segmentation. First, a dual-band spectral module is constructed using the discrete cosine transform to separate high-frequency and low-frequency components, providing stable and physically meaningful frequency-domain priors for the network. Second, a detail-guided module is designed where high-frequency features adaptively guide skip connections, compensating information loss during encoding and improving boundary recovery. Furthermore, a low-frequency-driven region-aware modeling module is developed. The internal defect regions, boundary areas, and background regions are modeled hierarchically. A dynamic hyper-kernel generation mechanism performs region-sensitive convolutional modeling, improving adaptation to complex structural variations. Extensive experiments on the Turbo19 and NEU-Seg datasets demonstrate that the proposed method produces accurate defect boundaries and achieves mIoU scores of 89.82% and 91.44%, improving over the second-best method by 5.22% and 4.42%, respectively. Full article

Recent years have witnessed significant progress in deep learning-based shadow removal. However, most prior methods operate primarily in the spatial domain or rely on coarse frequency cues, while the informative role of amplitude components in the frequency domain remains largely unexplored. The amplitude spectrum encodes spectral energy that reflects global illumination and fine texture that strongly influence shadow appearance. Motivated by this observation, we propose AmpFormer, a U-shaped transformer architecture that explicitly models amplitude information for robust shadow correction. Central to AmpFormer is a lightweight SFR module inserted at each encoder–decoder stage: SFR extracts multi-scale amplitude cues from compact spectral representations, learns per-channel adaptive gains and subtle phase adjustments, and injects the recalibrated frequency features into the spatial stream. To further encourage amplitude-aware restoration, we introduce an amplitude loss that explicitly regularizes spectral energy with emphasis on global illumination consistency. Extensive experiments on standard benchmarks demonstrate that AmpFormer achieves state-of-the-art restoration quality while offering a favorable computational-efficiency-accuracy trade-off, validating the practical benefit of amplitude-aware frequency modeling for shadow removal. Full article

Environmental loading is a major driver of nonlinear GNSS vertical displacements, yet its spatiotemporal heterogeneity remains insufficiently understood in regions with complex topography. In this study, we investigate the environmental loading effects on GNSS vertical motions across Southwest China using observations from a network of 66 stations. Singular Spectrum Analysis (SSA) and Empirical Orthogonal Function (EOF) analysis were applied to extract annual signals, while component-wise RMS reduction quantified hydrological and atmospheric loading contributions. Spatial statistical analysis, cross-wavelet transform, and k-means clustering examined correlation patterns and phase hysteresis between GNSS observations and modeled loads. Results show that hydrological loading dominates seasonal vertical oscillations, but crustal responses exhibit pronounced spatial heterogeneity controlled by regional topography and hydro-climatic gradients. EOF analysis reveals a dipole pattern induced by the Hengduan Mountains’moisture-blocking effect. Atmospheric loading anomalously dominates the eastern Sichuan Basin, whereas Yunnan displays strong amplitudes with high heterogeneity due to karst hydrogeology. Phase analysis identifies three distinct regimes: a rapid elastic response on the Tibetan Plateau, (with the lag of ~20 ± 5 days, correlation coefficient R ≈ 0.65), intermediate delays in Yunnan (~60 ± 5 days, R ≈ 0.58), and pronounced hysteresis in the Sichuan Basin (~105 ± 5 days, R ≈ 0.38) linked to slow groundwater diffusion and poroelastic processes. These findings highlight the critical role of local hydrogeological dynamics in modulating GNSS vertical deformation and provide new insights for improving environmental loading corrections in complex mountainous regions. Full article

Automatic modulation recognition (AMR) is increasingly relevant to communication-sensing front ends in robotic and human–robot collaborative systems, where reliable spectrum awareness and adaptive wireless reception are desired. However, existing methods often degrade sharply at low signal-to-noise ratios (SNRs), and large language models (LLMs) are not natively compatible with continuous I/Q signals due to the inherent modality gap. We propose BioLAMR, a GPT-2 adaptation framework for AMR inspired by the auditory system’s parallel time–frequency processing and cortical hierarchy. The framework combines bio-inspired dual-domain feature extraction with parameter-efficient LLM adaptation. BioLAMR includes three components. First, a lightweight dual-domain fusion (LDDF) module extracts complementary time- and frequency-domain features and fuses them through channel and spatial attention. Second, a convolutional embedding module converts continuous I/Q signals into GPT-2-compatible sequences without discrete tokenization. Third, a hierarchical fine-tuning strategy updates only 8.9% of parameters to preserve pretrained knowledge while adapting to modulation recognition. Experiments on the RadioML2016.10a and RadioML2016.10b benchmarks show that BioLAMR achieves overall accuracies of 64.99% and 67.43%, outperforming the strongest competing method by 2.60 and 2.47 percentage points, respectively. Under low-SNR conditions, it reaches 36.78% and 38.14%, the best results among the compared methods. Ablation studies verify the contribution of each component. These results demonstrate that combining dual-domain signal modeling with parameter-efficient GPT-2 adaptation is an effective route to robust AMR in challenging wireless environments. Full article

Optogenetic modulation employs light-sensitive proteins known as opsins to regulate cellular activity. A unique therapeutic application of this technique involves modulating pain perception by selectively targeting neural pathways within the spinal cord. Multi-Characteristic Opsin (MCO) represents an innovative optogenetic actuator capable of activation across a broad spectrum of light wavelengths, exhibiting a slow depolarizing phase that resembles natural photoreceptors. This study examines the current advancements in spinal optogenetic modulation utilizing MCO for pain management. Due to its high sensitivity, MCO facilitates minimally invasive, remotely controlled optogenetic modulation of spinal neurons. This approach enables the regulation of extensive spatial regions, provided the MCO channel receives sufficient light intensity to surpass the activation threshold. Nano-enhanced optical delivery (NOD) successfully transfected spinal neurons with the GAD67-MCO2-mCherry construct, as confirmed by membrane-localized mCherry fluorescence with DAPI-labeled nuclei. Using this platform, 5 Hz spinal optogenetic stimulation produced a significant reduction in formalin-evoked pain behaviors, demonstrating frequency-specific modulation of spinal pain circuits. Neither 2 Hz nor 10 Hz stimulation yielded comparable analgesic effects, underscoring the importance of precise stimulation parameters. The therapeutic impact also depended on transfection efficiency: reducing the fGNR–plasmid concentration diminished MCO expression and weakened the analgesic response. Together, these results show that effective spinal optogenetic pain modulation requires both optimal stimulation frequency and robust gene delivery. Full article

The inversion of wind and wave parameters in high-frequency ocean remote sensing radar relies heavily on the sea echo Doppler power spectrum. However, the accuracy of parameter inversion is often compromised by radio frequency interference (RFI), which distorts the Doppler spectral power distribution. Existing RFI suppression algorithms primarily focus on enhancing the signal-to-interference-plus-noise ratio post-mitigation, while insufficient attention has been paid to the spectral power fluctuations induced by these suppression processes. To address this issue, this study proposes a narrowband RFI suppression scheme that combines inter-pulse phase agility (IPA) with orthogonal projection (OP). An optimized aperiodic sequence is used to modulate the inter-pulse phases of the transmitted waveform, thus uniformly dispersing the sea echo power across the entire Doppler spectrum. Spatial OP is then applied to suppress RFI stripes on the range-Doppler spectrum, a process in which only the sea echo samples masked by the RFI stripes are affected. Finally, phase compensation restores the sea echo coherence and disperses residual RFI power uniformly into the Doppler domain, minimizing its localized impact. Simulations and semi-synthetic tests involving real-world interference verify that the proposed scheme effectively suppresses RFI while alleviating spectral distortion in the sea-echo Doppler spectrum. Full article

Spectrum scarcity has become a critical issue due to the rapid deployment of fifth-generation (5G) networks and the explosive growth of future wireless data traffic. Spectrally Efficient Frequency Division Multiplexing (SEFDM) is a promising technique to enhance spectral efficiency by compressing subcarrier spacing and allowing spectral overlap; however, it suffers from severe inter-carrier interference (ICI) caused by the loss of orthogonality. In particular, under Rayleigh fading channels, the combined effects of ICI and multipath fading lead to significant degradation in bit error rate (BER) performance. Conventional SEFDM systems employing a cyclic prefix (CP) encounter an unavoidable error floor due to residual interference stemming from non-orthogonality. On the other hand, while zero-padding (ZP)-based SEFDM offers superior multipath tolerance, further enhancement in communication quality is still desired. This paper proposes a novel receiver architecture utilizing sector antennas to spatially separate multipath components based on the angle of arrival (AoA). Furthermore, we investigate and compare sector selection algorithms specifically tailored for SEFDM systems. Simulation results demonstrate that the proposed method, employing a sector selection scheme based on the maximum channel response power, effectively suppresses inter-symbol interference (ISI) and improves BER performance for both CP-SEFDM and ZP-SEFDM. Furthermore, our quantitative evaluations confirm that the proposed architecture successfully achieves the theoretical maximum spectral efficiency even in higher-order modulation schemes (16QAM), while maintaining a low computational complexity compared to conventional spatial diversity techniques. Full article

Citrus trees exhibit severe alternate bearing, resulting in significant annual yield fluctuations and posing substantial challenges to orchard management planning. Accurate citrus bud counting provides an effective solution by supplying essential data for tree-level and orchard-level yield prediction. However, citrus buds are extremely small (5–10 mm in diameter) and are frequently occluded by leaves during the flowering stage, which makes precise detection highly challenging in complex orchard environments. To address these challenges, this paper proposes a Contextual and Frequency-Aware Detector (CFADet) for robust citrus bud detection. Specifically, an Enhanced Feature Fusion (EFF) module is introduced in the neck to refine multi-scale feature aggregation and strengthen information flow for small targets. A Contextual Boundary Enhancement Module (CBEM) is designed to capture surrounding contextual cues and enhance boundary representation through dimensional interaction and max-pooling operations. To suppress background interference, a Frequency-Aware Module (FAM) is developed to adaptively recalibrate frequency components in the amplitude spectrum, thereby enhancing target features while reducing background noise. In addition, Spatial-to-Depth Convolution (SPDConv) is employed to reconstruct the backbone to preserve fine-grained bud features while reducing model parameters. Experimental results show that CFADet achieves 81.1% precision, 80.9% recall, 81.0% F1-score, and 87.8% mAP, with stable real-time performance on mobile devices in practical orchard scenarios. This study presents a preliminary investigation into robust citrus bud detection in real-world orchard environments and provides a promising technical foundation for intelligent orchard monitoring and early yield estimation, while further validation on larger and more diverse datasets is still required. Full article

The regulation of chloride levels is a crucial part of controlling inhibitory signals, but does not occur uniformly throughout the body. Recent data suggest that chloride is regulated within localized “microdomains” which are defined by the interaction of KCC2 and NKCC1, structural restraints on cells due to their internal structure, the metabolic condition of the cell, and the external environment modified by astrocytes. The gradients of chloride concentrations within these compartment-specific microdomains define the local chloride reversal potential, and thereby determine the directionality (i.e., whether excitatory or inhibitory), magnitude, and timing of GABAergic inhibition. The disruption of this organized chloride gradient within microdomains impairs the stability of inhibitory activity at multiple levels of integration, including dendritic input, spike timing, interneuron synchronization, and network oscillation. Disturbances in inhibitory stability have been found in a variety of diseases, including epilepsy, neonatal seizure, neuropathic pain, and schizophrenia-spectrum disorders. This supports the hypothesis that disturbances in chloride homeostasis lead to a loss of stability in cortical circuits. This review will provide a synthesis of the molecular, spatial, and circuit level principles involved in the regulation of chloride and discuss how failures of these mechanisms produce clinically relevant disturbances in inhibitory signal processing. In addition, we will be discussing new therapeutic strategies for the restoration of chloride homeostasis, including KCC2 repair, selective modulation of NKCC1, targeting astrocytes, and microenvironmental engineering. Overall, the studies reviewed here provide a unified model for understanding the pathophysiology of inhibitory dysfunction, and demonstrate that the regulation of chloride microdomains provides a novel and promising area of research for translational intervention. Full article