Search Results (302)

Background: Acne vulgaris affects up to 80% of individuals aged 11–30 years and frequently results in permanent scarring with significant psychosocial impact. This prospective single-arm case series evaluated the safety and high-frequency ultrasound-assessed morphological changes in a combined protocol integrating subcision, PEGDE-crosslinked hyaluronic acid supplemented with calcium hydroxyapatite (CaHA), and fractional 1470 nm diode laser therapy in patients with facial atrophic acne scars. Methods: Twenty patients (aged 18–42 years, Fitzpatrick phototypes I–II) with moderate-to-severe atrophic acne scars underwent subcision of fibrotic adhesions using a 22G cannula combined with a single subcutaneous injection of 2 mL PEGDE-crosslinked hyaluronic acid with CaHA microparticles on day 0, followed by two sessions of fractional 1470 nm diode laser therapy on days 7 and 28. Scar depth and diameter were assessed using high-frequency ultrasound (48 MHz) at baseline and on days 28, 49, 77, and 139. Results: All participants completed the protocol without serious adverse events. High-frequency ultrasound demonstrated progressive reductions in mean scar depth (from 0.35 to 0.05 mm; −86%) and scar diameter (from 4.27 to 1.06 mm; −75%) by day 139, with reductions continuing beyond the active treatment phase. In linear mixed-effects models accounting for within-patient clustering of the two lesions assessed per participant, the reductions in both depth and diameter were statistically significant at every follow-up timepoint relative to baseline (all p < 0.001). These ultrasound findings were not corroborated by a control group, blinded assessment, validated clinical grading, or patient-reported outcomes. Conclusions: In this single-arm case series, the combined subcision, PEGDE-crosslinked HA–CaHA filler, and fractional 1470 nm diode laser protocol was well tolerated and associated with progressive, sustained reductions in high-frequency ultrasound-measured scar depth and diameter. As an uncontrolled, unblinded study without validated clinical grading or patient-reported outcomes, these findings are preliminary and require confirmation in larger, controlled trials. Full article

The integration of photovoltaic (PV) and wind turbine (WT) systems into modern power grids demands not only accurate component-level models but also a holistic understanding of their coordinated operation. This review bridges the gap between low-level device physics and high-level system coordination, offering a dual perspective often overlooked in existing surveys that treat generation and management separately. We systematically analyze PV models, from single-diode equivalent circuits to data-driven approaches, and WT models, ranging from aerodynamic and mechanical representations to simplified electrical equivalents suitable for stability studies. Critically, we then shift focus to the system level by examining energy management systems (EMS) that enable hybrid PV–WT coordination. Unlike prior reviews that emphasize either component accuracy or dispatch strategies alone, this paper highlights the emerging synergy between hybrid PV–WT modeling and EMS architectures. By identifying mismatches between model fidelity and EMS requirements, this review maps a pathway towards more integrated hybrid renewable systems. The discussion synthesizes key trade-offs in scalability, uncertainty handling, and real-time feasibility, underscoring that true potential is unlocked only through intelligent integration of component models and control architectures. Full article

Photovoltaic (PV) module faults, such as hotspots, diode short circuits, occlusions, and shadows, degrade power generation efficiency and safety. Existing manual inspection and single-modality methods show limited robustness under complex conditions, especially with illumination variations and weak thermal responses, while most deep learning approaches fail to exploit the complementarity of visible and infrared modalities. To address this issue, a dual-modality visible–infrared fusion framework based on YOLO11 is proposed, integrating a multi-scale pyramid pooling and dilated convolution module (MSPPD), a gradient-aware fusion module (GAFusion), and a dynamic convolution and element-wise scaling detection head (Detect-DEhead). GAFusion enhances cross-modal structural consistency and reduces feature misalignment and information loss during fusion by introducing gradient-aware feature interaction. Shape-IoU loss is employed to improve localization accuracy. The proposed method improves mean average precision (mAP)@0.5 from 86.7% to 88.1%, while reducing parameters, computational cost, and model size from 4.3 M to 3.7 M, 11.42 GFLOPs to 9.37 GFLOPs, and 9.1 MB to 7.9 MB, respectively. With Shape-IoU, mAP@0.5 reaches 88.4%, and recall increases from 78.5% to 84.9%. Experiments on the FLIR Thermal dataset achieve gains of 2.2%, 1.6%, and 2.7% in precision, recall, and mAP@0.5. The method achieves an effective trade-off between accuracy and efficiency for intelligent PV module inspection. Full article

High-power laser diodes are extensively utilized in advanced optoelectronic systems. These devices typically operate under high-current injection conditions, under which intrinsic parasitic parameters become non-negligible and exert a substantial influence on their electro-optical response characteristics. Furthermore, when multiple single emitters are monolithically integrated into a linear array along the epitaxial-layer direction on a single substrate, additional parasitic elements are inevitably introduced. These parameters are critical for characterizing the output performance of high-power laser diodes. This paper presents the implementation of an equivalent circuit model for large-signal laser-diode operation within the Advanced Design System (ADS) computer-aided environment. The proposed model enables accurate simulation of the device’s operating-voltage waveform and optical-output-power response under both DC steady-state and pulsed-transient driving conditions, thereby achieving a coupled representation of electrical behavior and optical emission. Sensitivity analysis of various parasitic elements is performed to systematically evaluate their influence on output characteristics and device reliability. The results provide theoretical guidance for structural optimization and packaging design, offering new insights into future modeling and reliability assessment of high-power laser diodes. Full article

Real-time tracking of high-speed targets in autonomous systems requires detection and decision-making pipelines that can operate within sub-millisecond time budgets. Single Photon Avalanche Diode (SPAD) sensors are well suited for this task, offering 10 kHz Time-of-Flight (ToF) measurements with picosecond timing precision. However, processing such high-frequency time-series data with conventional deep learning models introduces computational bottlenecks that are difficult to handle on resource-constrained embedded hardware. This paper presents an ultra-lightweight, dual-head architecture built on the MiniRocket transformation algorithm, where a single shared feature extractor simultaneously feeds two independent decision pathways: one for multi-class target classification and one for 3-parameter kinematic regression covering velocity, pitch, and yaw. As a single-pixel sensor, the device provides only 1D range information; lateral 3D spatial localization is outside the scope of this work. To the best of the authors’ knowledge, this is the first application of MiniRocket to continuous kinematic estimation from high-frequency sensor data. Since collecting labeled physical flight data at these speeds is largely infeasible, a physics-based ray-casting simulation was developed to generate a 55,440-sample dataset across four 3D CAD target models under varying speed (100–450 m/s), orientation, and noise conditions. The proposed architecture achieves 98.6% classification accuracy and a velocity Mean Absolute Error (MAE) of 0.26 m/s, with orientation estimation yielding a pitch MAE of 3.47° and a yaw MAE of 2.46°—values consistent across all five cross-validation folds, indicating that the orientation performance floor is governed by the sensor’s physical angular resolution rather than by model capacity. With approximately 27,000 trainable parameters, the system completes full dual-task inference in 0.56 ms on a 16-core CPU (1785 Frames Per Second-FPS), satisfying the 1 ms real-time constraint of a 10 kHz sensor without GPU acceleration. It should be noted that the single-pixel SPAD architecture provides only 1D range-along-beam information; full 3D spatial localization is physically not extractable from a single sensor and is not addressed in this study. Full article

The performance of single-photon avalanche diodes (SPADs) is highly dependent on the operating temperature, while traditional SPAD models neglect the self-heating effect induced by avalanche current during long-term device operation, leading to insufficient prediction accuracy. This paper proposes an electro-thermal coupled SPAD simulation model that self-consistently integrates the transient thermal effects of the avalanche process with temperature-dependent electrical parameters, including junction capacitance, breakdown voltage, impact ionization coefficients, and Shockley–Read–Hall (SRH) recombination rates. The complete electro-thermal coupled model is constructed based on Sentaurus-TCAD thermal simulation and Virtuoso circuit simulation and implemented via the Verilog-A language. Simulation results demonstrate that after the device operates for 100 μs under repeated avalanche-quenching processes, the self-heating effect causes a 0.34 V shift in breakdown voltage, increases the device dead time by 3.34 ps, and simultaneously reduces the photon detection probability and elevates the dark count rate. This study conducts a systematic investigation into the performance degradation mechanism of SPAD devices induced by the self-heating effect, laying a theoretical foundation at the device self-heating level for subsequent research on the electrothermal interaction between quenching circuits and device bodies. Full article

Single-event effects (SEEs) present a significant challenge to the radiation reliability of integrated circuits. Conventional SEE analysis methods for single-photon avalanche diode (SPAD) devices primarily rely on Sentaurus Technology Computer-Aided Design (TCAD) numerical simulation, which is computationally intensive and time-consuming. In this study, we propose a generalized deep learning (DL) model, using a silicon-based SPAD device with a double-junction double-buried-layer (DJDB) structure fabricated in 180 nm CMOS process as the research subject. By incorporating key parameters that influence SEEs as model inputs, the proposed approach enables rapid prediction of critical parameter metrics, including transient current peaks and dark count rates. Experimental results show that the DL model achieves a prediction accuracy of 97.32% for transient current peaks and 99.87% for dark count rates, demonstrating extremely high prediction precision. To further validate the generalization capability of the proposed network, the model is applied to predict the detection performance of the DJDB-SPAD device. The prediction accuracies for four key performance parameters all exceed 97.5%, further confirming the accuracy and robustness of the developed model. Meanwhile, compared with the conventional Sentaurus TCAD simulation method, the proposed method achieves a 336-fold improvement in computational efficiency. Overall, this method realizes the dual advantages of high precision and high efficiency, which provides an efficient and accurate technical solution for the rapid characteristic analysis and reliability evaluation of SPAD devices under single-event effects (SEEs). Full article

This study presents a TCAD model of a SiGe HBT designed for high-speed data transfer, with a cutoff frequency of 246.5 GHz and a β-value up to 416.7. Comprehensive single-event transient (SET) irradiation simulations were performed by injecting charges at different junctions with various angles. The influence of SET on data transfer was further evaluated at circuit level by loading the SET model from TCAD simulation into a high-speed laser diode driver circuit. Hence, this work employed a collector dummy structure in the designed HBT to build radiation-hardened devices. Simulation results indicate significant mitigation of the single-event transient current, which could be reduced to 10%, compared with non-hardened devices. Full article

Active non-line-of-sight (NLOS) human detection aims to infer the presence of hidden individuals by analyzing indirectly reflected photons between a relay surface and occluded targets. In this study, a single-photon avalanche diode (SPAD) and time-correlated single-photon counting (TCSPC)-based acquisition system were used to measure time–photon waveforms in controlled NLOS environments designed to represent post-disaster rubble scenarios. Although the effective temporal resolution of the system is limited by the detector timing jitter and laser pulse width, the recorded transient signals retain distinguishable intensity and temporal delay patterns associated with the primary and secondary reflections. To construct a representative dataset, measurements were collected under varying subject poses, orientations, and surrounding object configurations. The recorded signals were processed using a unified preprocessing pipeline that included normalization, histogram shaping, and signal windowing. Three machine learning models, namely, Convolutional Neural Network, Gated Recurrent Unit, and Random Forest, were trained and evaluated for human presence classification. All models achieved full sensitivity in detecting human presence; however, notable differences emerged in the classification of human-absent scenarios. Among the tested approaches, random forest achieved the highest overall accuracy and specificity, demonstrating stronger robustness to statistical variations in time–photon histograms under limited photon conditions. These results suggest that tree-based classifiers capture amplitude distribution patterns and temporal dispersion characteristics more effectively than deep neural architectures under the present acquisition constraints. Overall, the findings indicate that low-cost SPAD-based NLOS sensing systems can provide reliable human detection in indirect-observation scenarios. Full article

The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications. Full article

Accurate time-domain photovoltaic (PV) models are needed to evaluate performance under outdoor variability beyond STC datasheet conditions. This paper presents a traceable modeling workflow based on the standard single-diode formulation, implemented in MATLAB/Simulink (R2023a) as a modular white-box architecture that explicitly resolves photocurrent generation and loss mechanisms (diode recombination, shunt leakage, and series resistance effects) with temperature-consistent propagation through $V_T\left(T\right)$ and saturation-current terms. The method couples optical boundary conditions to the electrical model by embedding plane-of-array (POA) excitation via the incidence angle $\theta \left(t\right)$ and roof albedo directly into the photocurrent source term, preserving the causal chain from mounting geometry to electrical response. Calibration is separated from prediction by initializing key parameters using the standard Simulink PV block and then freezing them for time-domain evaluation. The workflow is validated on a 395 W rooftop prototype using 1 min resolved POA irradiance (ISO 9060:2018 Class A radiometric chain) and module temperature (IEC 60751 Class A Pt100), synchronized with electrical measurements. Over a multi-week campaign, the model exhibits high fidelity, with a worst-case relative current error of ~1.1% and a consistently low bias and dispersion, quantified by ME, MAE, RMSE, ${\sigma }_e$, and thresholded MAPE. Full article

The recent advent of charging infrastructure on an Electric Vehicles (EVs) poses a severe problem with effect on the power grid in terms of harmonic distortion, mostly caused by the nonlinear loads on the electric power produced by charging stations, diode bridge rectifiers, and switching converters. These harmonics continuously negatively influence power quality by increasing system and grid current, voltage total harmonic distortion (THD), power factor, and voltage regulation, and lowering the overall efficiency of the system at high rates that exceed IEEE 519 harmonic standards. This paper develops a thorough design and critical analysis of four topologies of harmonic passive filter, including single-tuned filter (STF), double-tuned filter (DTF), high-pass filter (HPF), and C-type high-pass filter (CHPF), to alleviate harmonics and enhance power quality on grid-tied charging stations of electric vehicles. A generalized structure is modeled and simulated in MATLAB/Simulink R2021a at a charging load of an EV charging load for all the filters under the same conditions and evaluated based on the current THD (ITHD), voltage THD (VTHD), input power factor (PF), voltage regulation (VR), and efficiency (η). The findings show that STF has an ITHD of 8.3%, VTHD of 4.6%, PF of 0.92, VR of 6.2%, and efficiency of 91.3%; DTF has an ITHD of 6.1%, VTHD of 3.9%, PF of 0.95, VR of 5.4%, and 93.5%; HPF has an ITHD of 5.6%, VTHD of 3.5%, 0.96 PF, 5.0% of VR, and 94.2% efficiency. The effectiveness of the proposed CHPH is superior to all other traditional approaches and has the lowest ITHD and VTHD, 3.7% and 2.1%, respectively, the highest PF of 0.987, a better VR of 3.8%, and a higher efficiency of 96.2%. The proposed CHPF shows the high-performance characteristics as reflected in the harmonic reduction, improved voltage stability, power factor, and efficiency. The suggested CHPF complies with IEEE 519 standards and provides better grid compatibility with modern EV charging applications. Full article

Thermally grown amorphous SiO₂ (a-SiO₂) on Si is widely used in microfluidic and biointerface devices, where surface charge governs capillary flows. We used amplitude-modulation Kelvin probe force microscopy (AM-KPFM) in air to test whether low-power visible light modulates a-SiO₂ surface potential and to derive mathematical charging-discharging models. Single-point contact potential difference (CPD) was recorded on ~0.6 µm p-type a-SiO₂ on p-type monocrystalline Si during repeated illumination cycles with continuous-wave diode lasers at 405, 505, and 685 nm delivered by optical fiber. The 405 and 505 nm wavelengths produced reproducible negative CPD shifts with steady-state values of ~−28 mV and ~−16 mV, while 685 nm stayed within noise (±2.5 mV). The 405 nm response followed bi-exponential kinetics with fast (tens of seconds) and slow (hundreds of seconds) components dominated by the slow process; after switch-off, CPD relaxed only from ~−28 to ~−23 mV over ~10³ s, indicating retention for ≥10³–10⁴ s. The 505 nm charging trace fit a single slower xponential, whereas discharging could not be fit robustly. These results demonstrate wavelength-dependent optical tuning of a-SiO₂ surface potential and provide compact kinetic descriptors for comparing charging, discharging, and retention. Full article

This study examines the impact of photovoltaic (PV) modeling fidelity utilizing single-diode (SDM), double-diode (DDM), and triple-diode (TDM) representations on the precision of hydrogen production (H₂P) estimates when integrated with various electrolyzer technologies, specifically proton exchange membrane (PEM), alkaline (AEL), and solid oxide electrolysis cells (SOECs). Precise evaluation of solar-powered green hydrogen (H₂) systems necessitated a dependable estimate of PV power under authentic working circumstances. Hourly site-specific irradiance and ambient temperature (T_a) data for Riyadh, Saudi Arabia, were used to calculate PV power outputs, which were then sent to physically based electrolyzer models regulated by electrochemical voltage relationships and Faraday’s law. The findings indicate that while all PV models display the same seasonal patterns, SDM somewhat overestimates yearly PV energy in comparison to DDM and TDM, with relative errors around 0.03%. These discrepancies somewhat affect H₂ yield estimations but do not change the relative ranking of electrolyzer technology. Among the assessed options, SOEC consistently produced the highest H₂ output, generating approximately 21.8% more H₂ than PEM and 9.1% more than AEL, with annual yields of 62.46–62.47 g for PEM, 69.70–69.71 g for AEL, and 76.04–76.05 g for SOEC across the SDM, DDM, and TDM frameworks under equivalent solar power inputs. The findings indicate that the selection of electrolyzer technology significantly impacts H₂P more than the choice of a PV model, while high-fidelity PV modeling is crucial for a physically realistic and precise system-level assessment of integrated PV-H₂ energy systems. Full article

Visible Light Positioning (VLP) is widely regarded as a promising technology for high-precision indoor localization due to its immunity to radio-frequency interference and compatibility with existing Light-Emitting Diode (LED) lighting infrastructure. Despite recent progress, current VLP systems remain fundamentally limited by nonlinear received signal strength (RSS) characteristics, unknown transmitter orientations, and dynamic indoor disturbances. Existing solutions typically address these challenges in isolation, resulting in limited robustness and scalability. This paper proposes SCENE-VLP (Sequential Deep Learning with Feature Compression and Optimal State Estimation), a structured positioning framework that integrates feature compression, temporal sequence modeling, and probabilistic state refinement within a unified estimation pipeline. Specifically, SCENE-VLP combines Principal Component Analysis (PCA) and Denoising Autoencoders (DAE) for linear and nonlinear observation conditioning, Gated Recurrent Units (GRU) for modeling temporal dependencies in RSS sequences, and Kalman-based filtering (KF/EKF) for recursive state-space refinement. The framework is formulated as a hierarchical approximation of the nonlinear observation model, linking data-driven measurement learning with Bayesian state estimation. A systematic ablation study across multiple scenarios, including same-dataset evaluation and cross-dataset generalization, demonstrates that each component provides complementary benefits. Feature compression reduces redundancy while preserving dominant signal structure; GRU significantly improves robustness over static regression; and recursive filtering consistently reduces positioning error compared to unfiltered predictions. While both KF and EKF improve performance, EKF provides incremental refinement under mild nonlinearities. Extensive simulations conducted on an indoor dataset collected from a realistic deployment with eight ceiling-mounted LEDs and a single photodetector (PD) show that SCENE-VLP achieves sub-decimeter localization accuracy, with P50 and P95 errors of 1.84 cm and 6.52 cm, respectively. Cross-scenario evaluation further confirms stable generalization and statistically consistent improvements. These results demonstrate that the structured integration of observation conditioning, temporal modeling, and Bayesian refinement yields measurable gains beyond partial pipeline configurations, establishing SCENE-VLP as a robust and scalable solution for next-generation indoor visible light positioning systems. Full article