Search Results (403)

In this work, the structural, optical, vibrational, and stability properties of a series of Al_xAs_x nanostructures are systematically investigated using density functional theory (DFT) and time-dependent density functional theory (TD-DFT). Starting from the fundamental cubic-like Al₄As₄ building block, progressively larger nanostructures were constructed through directional elongation and structural rearrangements, allowing for the exploration of one-dimensional chains, two-dimensional planar structures, and several exotic geometries. The calculated UV–visible absorption spectra reveal that structural dimensionality and topology strongly influence the electronic transitions of the nanostructures, with elongated and distorted configurations exhibiting broader absorption features and richer spectral distribution. Vibrational analysis shows that increasing structural complexity and reducing symmetry lead to a higher density of IR-active modes and more complex infrared spectra. The stability of the nanostructures is evaluated through binding energy calculations, which indicate a clear size-dependent stabilization trend, with the Al₂₄As₂₄-L1 configuration exhibiting the highest stability among the examined systems. In addition, the calculated HOMO-LUMO gaps reveal the semiconducting character of the clusters and demonstrate their sensitivity to geometric topology. The present results establish clear structure–property relationships between dimensional growth and the optical response of AlAs nanoparticles and provide theoretical reference data for future experimental investigations of III-V semiconductor nanostructures. Full article

Underwater object detection plays a vital role in marine exploration and resource exploitation. However, complex underwater environment leads to severe color deviation, blurring, and information loss of small targets, which greatly restrict detection performance. To address these problems, this paper integrates the Channel Attention and Spatial Attention Block (CASAB) attention mechanism into residual blocks based on generative adversarial networks to correct color distortion and improve the clarity of degraded underwater images. For underwater small object detection, MobileNetV2 is selected as the backbone network within the Faster R-CNN framework, and a multi-scale feature fusion strategy is adopted to reduce feature loss caused by repeated downsampling. In the detection head, coordinate attention and parallel dilated convolution are further integrated to suppress background noise and expand the receptive field of feature extraction. Experimental results on the Underwater Robot Professional Contest (URPC) dataset demonstrate that the proposed method yields gains of 10.06%, 9.43%, and 12.29% in three evaluation metrics: Underwater Image Quality Measure (UIQM), Underwater Colour Image Quality Evaluation (UCIQE) and Natural Image Quality Evaluator (NIQE), together with 7.81% in Mean Average Precision (mAP) and an 8.57% increase in Mean Recall (mRecall). These results demonstrate the effectiveness of all improvements. Full article

Underwater image super-resolution plays an important role in marine exploration, as it aims to recover clearer and higher-resolution images from degraded low-resolution observations. However, in underwater environments, this task is particularly challenging due to non-uniform spectral attenuation and particle scattering. Underwater, red light fades quickly, leading to color distortion and loss of details. To address this while keeping the model lightweight, we propose a dynamic color-space network for single-image super-resolution. Traditional methods use fixed color conversion and cannot handle non-uniform attenuation well. We instead use a learnable module to adaptively obtain luminance (Y) and chrominance (CbCr) representations. Building upon this representation, we employ a highly efficient dual-branch architecture where the Y-channel features are processed using light Mamba blocks to capture spatial dependencies with linear complexity, while the CbCr-channel features are restored by a lightweight convolutional neural network. Finally, a lightweight residual model utilizing depth-wise separable convolutions (DSC), deformable convolutions, and pixel attention mechanisms is introduced to refine the features and suppress artifacts. Experimental results demonstrate that while achieving competitive restoration quality, the proposed method drastically reduces computational complexity and parameter count compared to other large-scale models. This balance between visual quality and computational efficiency makes the proposed method well-suited for real-time deployment on resource-constrained autonomous underwater vehicles (AUVs). Full article

Target speaker extraction (TSE) aims to isolate speech spoken by a target speaker out of a mixture using speaker information in an enrollment utterance. Recently, several methods have been proposed that exploit the relationship between the enrollment utterance and the input mixture using cross-attention, without extracting speaker embeddings from the enrollment. Previous approaches applied the cross-attention to the encoded representations or to the real and imaginary parts of the compressed spectrograms separately, which may not have a physical meaning. In this paper, we propose a two-stage TSE method with a physically interpretable modified cross-attention block and a dual post-refinement structure. In the first stage, the attention weights to fuse the enrollment and mixture are derived from the cross-correlation between the complex spectra for the two signals in a form analogous to the phase-sensitive mask. The fused features along with the mixture features were subsequently fed into a speech extraction network to obtain a coarsely extracted target speech. The second stage consists of two parallel branches, where one branch refines the first-stage output using the enrollment in a similar way to the first stage, and the other utilizes the mixture to complement possibly attenuated target speech. In addition, the low-dimensional speaker embeddings extracted from the enrollment and the first-stage output are incorporated into the second stage to exploit the speaker discriminability. Experimental results show that the proposed method consistently outperformed existing TSE methods on the Libri2Mix dataset under both clean and noisy conditions, in terms of speech quality, speech intelligibility, and signal distortion measures. Full article

Voltage source converters (VSCs), together with their inner current and outer power/voltage control loops, are fundamental building blocks in the modern, converter-dominated power systems, particularly within high-voltage DC (HVDC) frameworks. Selecting effective control methods for VSCs is essential to ensure the stability, power quality, and dynamic performance of HVDC grids. This paper seeks to advance the current body of research by delivering an in-depth, consistent, unified framework and systematic examination of VSC control architectures within HVDC networks. It thoroughly explores various control strategies for VSCs interfacing with HVDC grids, such as grid-following and grid-forming strategies, with particular emphasis on both stationary ($\alpha \beta$) and synchronous (dq) reference frames. Moreover, the paper provides a comprehensive analysis of the theoretical underpinnings and decoupled control strategies, like the feedforward decoupling of the d- and q-axis currents in the dq frame and the inherently decoupled structure of the αβ frame. Additionally, advanced filtering techniques, including Moving Average Filter (MAF), Cascaded Delayed Signal Cancellation (DSC), and LCL filters, are analyzed. In addition, harmonic mitigation strategies, like parallel/multiple resonant (PR) terms in the αβ frame and cascaded notch filters in the dq frame, are presented. Furthermore, precise power control approaches and synchronization methods are discussed in detail. Also, this paper presents a detailed comparison of the performance characteristics of phase-locked loop (PLL) and frequency-locked loop (FLL) in response to grid frequency variations. Moreover, this paper proposes circuit representations and VSC models in both synchronous and stationary reference frames. The simulation results corroborate the theoretical insights discussed in the paper under various operational conditions, including initial responses, grid disturbances, three-phase-to-ground temporary fault scenarios, harmonic distortions, and load imbalances, in terms of overshoot, settling time, active- and reactive-power fluctuation reduction, voltage unbalance factor, total harmonic distortion, and post-fault convergence time, all evaluated in accordance with the limits defined in EN-50160. This comprehensive comparison of the presented control strategies facilitates researchers in identifying the most appropriate controller depending on their specific application requirements. Full article

With the prevalence of telemedicine and digital diagnosis, the security and integrity of medical images transmitted over open networks have become critical issues. To effectively defend against malicious tampering and ensure the reliability of diagnostic information, this study proposes a block-based image authentication and tamper detection framework (KPP-BA). This framework integrates key-dependent pixel permutation, hash-based message authentication code (HMAC)-SHA256 hash verification, and a parity-based 3-LSB minimal distortion embedding strategy. The core innovation lies in utilizing pseudo-random pixel permutation to disrupt spatial correlation within blocks, thereby effectively resisting collage and statistical analysis attacks. Furthermore, by combining the avalanche effect of HMAC-SHA256 with hybrid bit-plane feature extraction, the proposed method ensures extremely high sensitivity to subtle tampering. Experimental results on a dataset comprising 300 medical images demonstrate that the proposed method maintains superior visual quality while ensuring security, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 54.15 of 0.5 bit per pixel (bpp). Moreover, against various tampering attacks—including masking, copy–paste, circle masking, and collage—the method exhibits exceptional detection capabilities with an average detection accuracy of 99.99%. Compared with seven state-of-the-art methods, the proposed framework demonstrates significant advantages in both image fidelity and tamper localization precision, validating its feasibility and robustness for secure medical image transmission applications. Full article

Stereo vision is essential for passive three-dimensional perception in resource-constrained applications that require low power consumption, predictable latency, and explainable geometry. Although deep learning architectures dominate recent benchmarks, the classical block-matching pipeline remains a foundational approach. Optimizing this pipeline involves navigating complex trade-offs among matching robustness, map density, and computational efficiency. This study systematically surveys and physically validates the classical stereo framework. After revisiting geometric first principles, three matching costs (SAD, NCC, ZNCC) are benchmarked alongside Sobel preprocessing and structural refinements, with subsequent validation using a calibrated consumer webcam rig. Middlebury benchmarks (2001–2021) indicate that while SAD fails under complex radiometric distortion, NCC consistently achieves superior quantitative metrics, incurring only a 1.2-fold computational overhead. Extending the disparity search range improves foreground localization, while block size imposes a trade-off between resolving the aperture problem and preserving fine geometric detail. To bridge theoretical analysis and practical deployment, the pipeline is validated using a custom-calibrated consumer stereo rig. The optimized Sobel-NCC architecture is then evaluated for real-time edge deployment on constrained hardware (NVIDIA Jetson Nano) and narrow-baseline sensors (OAK-D SR) in the context of agricultural robotic manipulation. By prioritizing metric precision over dense prediction, the classical pipeline reconstructs target surfaces with approximately 1 cm depth accuracy at 21 frames per second. These results demonstrate that optimized local algorithms offer deterministic and reliable geometric foundations for real-time edge-computed robotics. Although neural networks are essential for dense reconstructions in ill-posed regions, the foundational principles established here remain indispensable for advanced stereo vision system deployment. Full article

Line confocal sensors provide non-contact, high-resolution, and high-efficiency measurement and can be integrated into optical measurement systems such as Photon for three-dimensional topography measurement of complex surfaces. However, installation-induced tilt-pose errors of the sensor can couple height information with lateral position, thereby reducing the accuracy of profile reconstruction. To address this issue, this paper proposes a calibration-block-based tilt-pose error identification and compensation method for line confocal sensors. Using the known geometric features of the calibration block, the proposed method establishes a mapping relationship between sensor tilt-pose errors and measured profile distortion. Sensitivity analysis is performed to identify the dominant error components, and the tilt-pose errors are estimated in a single identification process, enabling quantitative compensation of the measured point cloud. Experimental results show that, after calibration and compensation, the maximum Z-direction height difference in the overlapping profile region of the calibration block is reduced from 12.782 μm to 0.307 μm. The proposed method requires no complex external alignment devices and provides an effective approach for high-precision integrated applications of line confocal sensors. Full article

In the development of deep shale gas horizontal wells, precise geo-steering relies heavily on downhole sensor networks to acquire extensive formation and engineering parameters. Coiled tubing (CT) provides a promising acoustic waveguide for downhole sensing systems, but conventional acoustic sources rely on gravity-induced interfacial preload. Under highly deviated or horizontal well conditions, the loss of the axial gravity component may induce contact–nonlinearity instability, resulting in waveform distortion and spectral pollution. To address this limitation, a constant-stiffness preloading method based on elastic compliance control is proposed, together with a modal reconstruction strategy achieved by removing high-density tungsten blocks. A fluid–solid coupled dynamic model incorporating contact nonlinearity is established to reveal the dynamic separation mechanism of the acoustic source interface under varying gravity-vector conditions. Wave spring assemblies are then used to reconstruct the mechanical boundary and physically suppress time-domain clipping. Full-scale ground circulation experiments on a 1500 ft CT string show that the proposed method decouples acoustic-source performance from wellbore trajectory. Waveform asymmetry is reduced from 18.4% to 2.1%, and total harmonic distortion decreases from 12.5% to 1.8%. In addition, the first-order longitudinal natural frequency is shifted from 420 Hz to 2850 Hz, avoiding low-frequency pump noise and achieving a 12 dB SNR improvement. This physical-layer gain provides an optimized signal baseline for receiver-end demodulation algorithms. Ultimately, this study provides a robust physical-layer solution for acoustic telemetry in complex deep-earth environments, advancing the reliability of data interaction in downhole sensing systems. Full article

This study presents a field-validated, scenario-based two-layer dispatch framework for sustainable rural electrification, demonstrated at the Khlong Ruea hybrid microgrid (50 kW micro-hydro, 20 kWp PV, 48 kWh LiFePO₄ BESS, 48 kW diesel) in Chumphon Province, southern Thailand. The framework combines an offline mixed-integer linear program (MILP) with scenario-based uncertainty handling (k-medoid clustering, N = 8; CVaR penalty at α = 0.9) and an operator-assisted execution layer implementing source transitions via manual changeover switches. A Fluke 435 IEC 61000-4-30 Class-A field campaign with stationary block-bootstrap inference (B = 2000 resamples, 10 min blocks) documented substantial power quality improvements under BESS supply: the three-phase average THD-V reduced from 5.4% to 2.9% with 95% confidence intervals that do not overlap between the two supply modes; the THD-I dropped from 55.8% to 4.9% (Phase A; 91.2% reduction; three-phase average 64.0% → 7.8%); the voltage unbalance fell from 0.86% to 0.03%; and the displacement power factor improved from 0.92 to 0.95. IEEE Std 1459-2010 decomposition reveals that 93% of the non-fundamental apparent power under diesel supply is attributable to current-distortion volt-amperes (Dᵚ = 4737 VA vs. 283 VA under BESS). A composite power quality index confirms that diesel operation fails the IEEE 519-2022 current-distortion limits while BESS supply satisfies all EN 50160 and IEEE 519-2022 thresholds (PQI: 0.75 vs. 3.89). A 365-day closed-loop simulation confirmed an 18.4% reduction in annual operating cost and a 27.6% reduction in diesel runtime relative to a rule-based baseline, while maintaining LPSP at or below 0.53%. Techno-economic projection from field-verified HOMER inputs reduced the levelized cost of electricity from approximately 0.69 USD/kWh (diesel-only) to 0.36 USD/kWh for the proposed PV + BESS + Hydro + Diesel configuration, which retains diesel as a low-utilization backup at a near-100% renewable energy share. The same configuration delivered a 47.9% net present cost advantage over diesel-only operation and a 12.8 t (82%) annual CO₂ reduction. Manual source-transfer interruptions of 1–3 min are fully characterized, and a cost-estimated ATS + SCADA upgrade roadmap is defined. Full article

This study provides a rigorous analysis of the phase stability, mechanical behavior, and surface integrity of a non-equiatomic (FeCoNiCr)₈₅Ga₁₅ high-entropy alloy (HEA). By transitioning from the conventional equiatomic design to a gallium-doped 3d-transition metal matrix, we explore the interplay between lattice distortion and phase separation. Synthesized via vacuum arc melting, the as-cast alloy exhibits a non-homogeneous dendritic morphology consisting of a Cr-Fe-Co rich face-centered cubic (FCC) matrix and Ni-Ga rich body-centered cubic (BCC) interdendritic regions. While global thermodynamic criteria (δ = 3.65, ΔH_mix = −9.28 kJ/mol, and Ω = 2.23) favor single-phase solid solution stability, the Valence Electron Concentration (VEC = 7.46) precisely forecasts this dual-phase structure. Following low-temperature annealing at 250 °C for 24 h, high lattice strain energy drives a significant morphological transformation where the continuous interdendritic network resolves into discrete, phase-separated B2/BCC “islands”. Mechanical and tribological characterizations reveal that this low-temperature thermal activation triggers precipitate hardening; the macro-hardness increases from 146 ± 11 HB to 153 ± 7.5 HB and the micro-hardness rises from 186 ± 4 HV_0.5 to 206 ± 17.5 HV_0.5, yielding enhanced resistance to oxidation-delamination wear. However, electrochemical evaluation in a 3.5 wt.% NaCl solution highlights a fundamental trade-off: the formation of localized galvanic micro-cells between the phase-separated islands and the matrix causes the corrosion current density (i_corr) to increase from ≈10⁻⁹ A/cm² in the as-cast state to ≈10⁻⁶ A/cm² post-heat treatment, accompanied by a heightened susceptibility to localized pitting. These findings elucidate the primary role of electronic structure and minor p-block additions in regulating the lifecycle performance of transition metal HEAs under extreme conditions. Full article

Sybil attacks remain a primary challenge for Proof-of-Stake (PoS) blockchain systems, as low-cost identity creation can distort validator participation and limit consensus reliability. This study proposes a hybrid participation–governance framework that integrates Attribute-Based Access Control (ABAC) and Reputation-Based Access Control (RpBAC) with a trust-based PoS workflow to reduce the influence of suspicious identities during validator selection and block validation. The proposed framework also incorporates graylisting and dynamic reward–penalty updates to support adaptive participation control. The strategy was evaluated in a simulation environment informed by Ethereum-derived block metadata, using network sizes ranging from 100 to 1000 nodes and Sybil attack ratios of 30%, 40%, and 50%. Its performance was compared with PoS-only and PoS + ABAC baselines using both security and performance indicators. The results show that the full ABAC + RpBAC configuration achieved the strongest and most stable security performance across the evaluated settings while introducing additional overhead at larger network sizes. These findings suggest that combining policy-based eligibility control with behavior-based reputation control strengthens the resilience against Sybil in PoS-like blockchain environments. However, this improvement requires a measurable trade-off between security and performance. Full article

Single image super-resolution (SR) is an important part of image processing, which aims to improve the spatial resolution of images. This is a typical ill-posed inverse problem. The main difficulty is that a low-resolution image block usually corresponds to multiple high-resolution image blocks. The existing methods cannot provide enough correlation to determine the unique high-resolution image block, which leads to artifacts and image distortion in the reconstructed image. To address this problem, a method (EHNet) is proposed to achieve super-resolution by using a Hybrid-Channel Fusion Block (HCFB) and an Enhanced Dual-Convolution Block (EDCB). The EDCB effectively enhances the network’s ability to capture image details and textures by combining local and global feature processing. The HCFB strengthens the information interaction between channels by combining channel segmentation with large-kernel convolution, fully explores feature dependencies, and thus optimizes the feature extraction effect. Experimental results show that the super-resolution reconstructed image of EHNet achieves 32.59 dB PSNR and 0.9006 SSIM on the Set5 ×4 SR benchmark, outperforming several state-of-the-art SR methods. In addition, the model exhibits notable improvements in artifact suppression, and the reconstructed image’s subjective visual impact surpasses that of other current techniques. Full article

In this paper, we propose a block-distortion-free reversible data hiding method for encryption-then-compression (EtC) images that supports fully flexible access privileges without constraints on the restoration order. The proposed approach redesigns the pre-processing strategy of previous work to ensure a clear separation of processing roles between the image owner and the data hider. It also introduces a pixel-value modification process that divides the target range into two regions to mitigate the influence of negative–positive inversion during restoration. As a result, block distortion in marked images is eliminated while preserving role separation between the image owner and the data hider. The proposed method offers four key advantages: flexible access privileges, elimination of block distortion, explicit role separation, and competitive hiding capacity comparable to existing methods with flexible restoration capabilities. Experimental results demonstrate that the proposed method achieves a high marked-image quality and competitive hiding capacity while maintaining the compression performance of marked EtC images. Furthermore, security analysis confirms the robustness of the generated EtC images against a representative ciphertext-only attack. Full article

Reliable front-end pressure-pulse generation is critical to mud-pulse telemetry because waveform distortion introduced at the rotary valve propagates through the telemetry chain and reduces downstream recoverability. This paper targets accurate and computationally tractable control of an intelligent drill-bit rotary valve under actuator limits, parameter drift, and downhole-like disturbances. A control-oriented electromechanical–hydraulic grey-box model is established, and a real-time lexicographic model predictive control (MPC) framework with candidate pre-screening, move blocking, and online correction/compensation is developed and compared with proportional–integral–derivative (PID) control and conventional MPC. Under a sampling period of $T_s=20\mathrm{ms}$, the proposed controller reduces the step-tracking rise time from $2.18\mathrm{s}$ to $1.76\mathrm{s}$ and the steady-state pressure error from $0.1208\mathrm{MPa}$ to $0.0292\mathrm{MPa}$ relative to conventional MPC. In the pulse-output and mismatch–disturbance scenarios, it further maintains lower steady-state pressure error while reducing the cumulative input variation from $51.0$ to $11.5$ and from $121.5$ to $19.5$, respectively. The observed 99th-percentile and worst-case MATLAB workstation execution times remain below one sampling period, while supplementary mismatch–disturbance sensitivity maps indicate a favorable accuracy–timing compromise within the tested numerical envelope. These results support the proposed method as a simulation-validated candidate for low-complexity rotary-valve control and motivate subsequent bench/hardware-in-the-loop (HIL) validation rather than field-qualified deployment claims. Full article