Search Results (699)

A parallel implementation of wavelet-based autofocus (WBA) was presented to accelerate recursive operations and reduce computational costs. WBA evaluates digital focus indices (DFIs) using first- or second-order moments of the wavelet coefficients in high-frequency subbands. WBA is generally accurate and reliable; however, its computational cost is high owing to biorthogonal decomposition. Thus, this study parallelized the Daubechies-6 wavelet and norms of the high-frequency subbands for the DFI. The kernels of the DFI computation were constructed using open sources for driving multicore processors (MCPs) and general processing units (GPUs). The standard C++, OpenCV, OpenMP, OpenCL, and CUDA open-source platforms were selected to construct the DFI kernels, considering hardware compatibility. The experiment was conducted using the MCP, peripheral GPUs, and CPU-resident GPUs on desktops for advanced users and compact devices for industrial applications. The results demonstrated that the GPUs provided sufficient performance to achieve WBA even when using budget GPUs, indicating that the GPUs are advantageous for practical applications of WBA. This study also implies that although budget GPUs are left unused, they can potentially be great resources for wavelet-based processing. Full article

We address the inverse spectral problem for a PT-symmetric Dirac operator with discontinuity conditions imposed along the entire real axis—a configuration that has not been explicitly solved in prior literature. Our approach constructs fundamental solutions via convergent recursive series expansions and establishes their linear independence through a constant Wronskian. We derive explicit formulas for transmission and reflection coefficients, assemble them into a PT-symmetric scattering matrix, and demonstrate how both spectral and scattering data uniquely determine the underlying complex-valued, discontinuous potentials. Unlike classical treatments, which assume smoothness or limited discontinuities, our framework handles full-axis discontinuities within a non-Hermitian setting, proving uniqueness and providing a constructive recovery algorithm. This method not only generalizes existing inverse scattering theory to PT-symmetric discontinuous operators but also offers direct applicability to optical waveguides, metamaterials, and quantum field models where gain–loss mechanisms and zero-width resonances are critical. Full article

Accurate parameter estimation of solar cells is critical for early-stage fault diagnosis in photovoltaic (PV) power systems. A physical model based on three-diode configuration has been recently introduced to improve model accuracy. However, nonlinear and recursive relationships between internal parameters and PV output, along with parameter drift and PV degradation due to long-term operation, pose significant challenges. To address these issues, this study proposes a deep learning-based ensemble framework that integrates outputs from multiple optimization algorithms to improve estimation precision and robustness. The proposed method consists of three stages. First, the collected data were preprocessed using some data processing techniques. Second, a PV power generation system is modeled using the three-diode structure. Third, several optimization algorithms with distinct search behaviors are employed to produce diverse estimations. Finally, a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is used to learn from these results. Experimental validation on a 733 kW PV power generation system demonstrates that the proposed method outperforms individual optimization approaches in terms of prediction accuracy and stability. Full article

Accurate and timely estimation of the root-mean-square (RMS) voltage is essential for grid-connected inverter systems, where it underpins reference generation, synchronization, and protection functions. Conventional RMS estimation methods, based on squaring, averaging, and taking the square root of values over full-cycle windows, achieve high accuracy but incur significant latency and computational overhead, thereby limiting their suitability for real-time control. Frequency-domain approaches, such as the FFT or wavelet analysis offer harmonic decomposition but are too complex for cost-sensitive embedded controllers. To address these challenges, this paper proposes an averaging-based RMS estimation method that exploits the proportionality between the mean absolute value of a sinusoidal waveform and its RMS. The method computes a moving average of the absolute voltage over a half-cycle window synchronized to the phase-locked loop (PLL) frequency, followed by a fixed scaling factor. This recursive implementation reduces the computational burden to a few arithmetic operations per sample while maintaining synchronization with off-nominal frequencies. Time-domain simulations under nominal (60 Hz) and deviated frequencies (57 Hz and 63 Hz) demonstrate that the proposed estimator achieves steady-state accuracy comparable to that of conventional and adaptive methods but with convergence within a half-cycle, thereby reducing latency by nearly 50%. These results confirm the method’s suitability for fast, reliable, and resource-efficient real-time inverter control in modern distribution grids. To provide a comprehensive evaluation, the paper first reviews conventional RMS estimation methods and their inherent limitations, followed by a detailed presentation of the proposed averaging-based approach. Simulation results under both nominal and off-nominal frequency conditions are then presented, along with a comparative analysis highlighting the advantages of the proposed method. Full article

Let $q_1+\cdots +q_n+m$ objects be arranged in n rows with $q_1,\dots ,q_n$ objects and one last row with m objects. The Janjić–Petković counting function denotes the number of $(n+k)$-insets, defined as subsets containing $n+k$ objects such that at least one object is chosen from each of the first n rows, generalizing the binomial coefficient that is recovered for $q_1$ = … = $q_n$ = 1, as then only the last row matters. Here, we discuss two explicit forms, combinatorial interpretations, recursion relations, an integral representation, generating functions, convolutions, special cases, and inverse pairs of summation formulas. Based on one of the generating functions, we show that the Janjić–Petković counting function, like the binomial coefficients that it generalizes, may be regarded as a Riordan array, leading to additional identities. As an application to a physical system, we calculate the heat capacity of a many-body system for which the configurations are constrained as described by the Janjić–Petković counting function, resulting in a modified Schottky anomaly. Full article

Learning and implementing algorithms is a fundamental but challenging aspect of Computer Science education. One of the key tools used in teaching algorithms is pseudocode, which serves as an abstract representation of the logic behind a given algorithm. This study explores the educational value of the FLoCIC (Few Lines of Code for Image Compression) algorithm, which is designed to teach lossless image compression through algorithmic implementation, particularly within the context of multimedia data. Image compression represents a typical multimedia task that combines algorithmic thinking with practical problem-solving. By analysing questionnaire responses (N = 121) from undergraduate and graduate students, this study identifies critical challenges in pseudocode-based learning, including understanding complex algorithmic components and debugging recursive functions. This paper highlights the influence of prior knowledge in areas such as data structures, compression, and algorithms in general on the success of students in completing the task, with graduate students demonstrating stronger results compared to undergraduates. The study analyses the role of external resources and online code repositories, further revealing their utility in supporting implementation efforts but highlighting the need for a fundamental understanding of the algorithm for successful implementation. The findings highlight the importance of promoting conceptual understanding and practical problem-solving skills to improve student learning in algorithmic tasks. Full article

As a new generation 3D video coding standard, 3D-HEVC offers highly efficient compression. However, its recursive quadtree partitioning mechanism and frequent rate-distortion optimization (RDO) computations lead to a significant increase in coding complexity. Particularly, intra-frame coding in depth maps, which incorporates tools like depth modeling modes (DMMs), substantially prolongs the decision-making process for coding unit (CU) partitioning, becoming a critical bottleneck in compression encoding time. To address this issue, this paper proposes a fast CU partitioning framework based on hierarchical feature fusion convolutional neural networks (HFF-CNNs). It aims to significantly accelerate the overall encoding process while ensuring excellent encoding quality by optimizing depth map CU partitioning decisions. This framework synergistically captures CU’s global structure and local details through multi-scale feature extraction and channel attention mechanisms (SE module). It introduces the wavelet energy ratio designed for quantifying the texture complexity of depth map CU and the quantization parameter (QP) that reflects the encoding quality as external features, enhancing the dynamic perception ability of the model from different dimensions. Ultimately, it outputs depth-corresponding partitioning predictions through three fully connected layers, strictly adhering to HEVC’s quad-tree recursive segmentation mechanism. Experimental results demonstrate that, across eight standard test sequences, the proposed method achieves an average encoding time reduction of 48.43%, significantly lowering intra-frame encoding complexity with a BDBR increment of only 0.35%. The model exhibits outstanding lightweight characteristics with minimal inference time overhead. Compared with the representative methods under comparison, this method achieves a better balance between cross-resolution adaptability and computational efficiency, providing a feasible optimization path for real-time 3D-HEVC applications. Full article

Accurately predicting future activities in egocentric (first-person) videos is a challenging yet essential task, requiring robust object recognition and reliable forecasting of action patterns. However, the limited number of observable frames in such videos often lacks critical semantic context, making long-term predictions particularly difficult. Traditional approaches, especially those based on recurrent neural networks, tend to suffer from cumulative error propagation over extended time steps, leading to degraded performance. To address these challenges, this paper introduces a novel framework, Virtual Frame-Augmented Guided Forecasting (VFGF), designed specifically for long-term egocentric activity prediction. The VFGF framework enhances semantic continuity by generating and incorporating virtual frames into the observable sequence. These synthetic frames fill the temporal and contextual gaps caused by rapid changes in activity or environmental conditions. In addition, we propose a Feature Guidance Module that integrates anticipated activity-relevant features into the recursive prediction process, guiding the model toward more accurate and contextually coherent inferences. Extensive experiments on the EPIC-Kitchens dataset demonstrate that VFGF, with its interpolation-based temporal smoothing and feature-guided strategies, significantly improves long-term activity prediction accuracy. Specifically, VFGF achieves a state-of-the-art Top-5 accuracy of 44.11% at a 0.25 s prediction horizon. Moreover, it maintains competitive performance across a range of long-term forecasting intervals, highlighting its robustness and establishing a strong foundation for future research in egocentric activity prediction. Full article

The proliferation of sensor technology has led to an explosion in data volume, making the retrieval of specific information from large repositories increasingly challenging. While Retrieval-Augmented Generation (RAG) can enhance Large Language Models (LLMs), they often lack precision in specialized domains. Taking the Civil IoT Taiwan Data Service Platform as a case study, this study addresses this gap by developing a dialogue engine enhanced with a GraphRAG framework, aiming to provide accurate, context-aware responses to user queries. Our method involves constructing a domain-specific knowledge graph by extracting entities (e.g., ‘Dataset’, ‘Agency’) and their relationships from the platform’s documentation. For query processing, the system interprets natural language inputs, identifies corresponding paths within the knowledge graph, and employs a recursive self-reflection mechanism to ensure the final answer aligns with the user’s intent. The final answer transformed into natural language by utilizing the TAIDE (Trustworthy AI Dialogue Engine) model. The implemented framework successfully translates complex, multi-constraint questions into executable graph queries, moving beyond keyword matching to navigate semantic pathways. This results in highly accurate and verifiable answers grounded in the source data. In conclusion, this research validates that applying a GraphRAG-enhanced engine is a robust solution for building intelligent dialogue systems for specialized data platforms, significantly improving the precision and usability of information retrieval and offering a replicable model for other knowledge-intensive domains. Full article

Recent studies suggest a role for the gut microbiota in the onset, progression, and prognosis of prostate cancer (PCa), one of the most common neoplasms in males. PCa screening relies on PSA testing, whose usefulness remains controversial due to its low specificity. This study was aimed at investigating the differences in the gut microbiota of PCa patients and healthy controls (HCs) and finding correlations between gut microbes and the clinical laboratory parameter assessed in the evaluation of PCa, to identify bacteria which could be used as diagnostic and prognostic biomarkers. Fecal samples collected from 18 PCa patients and 18 HCs were used to isolate bacterial DNA. 16S rRNA gene sequencing provided the gut microbial profiles of the enrolled subjects, whose functional impact was also predicted. A recursive partitioning tree method allowed us to identify a bacterial signature discriminating PCa from HC. A correlation analysis was performed between gut bacteria and the clinical laboratory parameters assessed in the evaluation of PCa. Differential bacterial patterns emerged between PCa patients and HCs, together with significant differences in beta-diversity, alpha-diversity, and richness. The functional prediction of the microbial profiles revealed several metabolic processes differentially regulated, including an enrichment in the Krebs cycle and in steroid hormone synthesis in PCa patients. A bacterial signature based on the abundance of Lactobacillus and Collinsella was found to discriminate between the two groups. Significant correlations were found between gut bacteria and the clinical laboratory parameters generally assessed in the evaluation of PCa. These results indicate that gut microbiota profiles may, in the future, represent potential biomarkers associated with prostate cancer risk or progression; however, further prospective studies and clinical validation are needed before considering their use as diagnostic or prognostic tools. Full article

Rapid and non-destructive acquisition of soil nutrient information is crucial for precision fertilization and soil quality monitoring. This study aims to establish a Raman spectroscopy-based framework for predicting key soil fertility indicators, including alkali-hydrolyzable nitrogen (AN), total nitrogen (TN), total phosphorus (TP), and organic matter (OM). The framework systematically integrates three typical spectral preprocessing methods (Standard Normal Variate transformation (SNV), Savitzky–Golay first derivative (SG_D1), and wavelet transform (Wavelet)), three feature selection strategies (Recursive Feature Elimination, XGBoost importance, and Random Forest importance), and 14 mainstream regression models to construct a multi-combination modeling system. Model performance was evaluated using five-fold cross-validation, with 80% of samples used for training and 20% for validation in each fold. Preprocessed Raman spectral features served as input variables, while the corresponding nutrient contents were used as outputs. Results showed significant differences in prediction performance across various combinations of preprocessing methods and regression algorithms for the four soil nutrient indicators. For AN prediction, the combination of Raw_SNV preprocessing with ElasticNet and BayesianRidge models achieved the best performance, with Test R² values of 0.713 and 0.721, and corresponding Test NRMSE as low as 0.092. For OM prediction, the same Raw_SNV preprocessing with ElasticNet and BayesianRidge also performed well, yielding Test R² values of 0.825 and 0.832, and Test NRMSE of 0.100 and 0.098, respectively. In TN prediction, both ElasticNet and BayesianRidge under Raw_SNV preprocessing achieved consistent Test R² of 0.74 and Test NRMSE around 0.20, indicating stable reliability. For TP prediction, the BayesianRidge model with Raw_SNV preprocessing outperformed all others with a Test R² of 0.71 and Test NRMSE of just 0.089, followed closely by ElasticNet (Test R² = 0.70, Test NRMSE = 0.092). Overall, the Raw_SNV preprocessing method demonstrated superior performance compared to SG_D1_SNV and Wavelet_SNV. Both BayesianRidge and ElasticNet consistently achieved high R² and low NRMSE across multiple targets, showcasing strong generalization and robustness, making them optimal model choices for Raman spectroscopy-based soil nutrient prediction. This study demonstrates that Raman spectroscopy, when combined with appropriate preprocessing and modeling techniques, can effectively predict soil organic matter and nitrogen in specific soil types under laboratory conditions. These results provide initial methodological insights for future development of intelligent soil nutrient diagnostics. Full article

In this paper, we address the problem of increasing the number of regenerations in the simulation of the workload process in a single-server queueing system. To this end, we extend the splitting technique developed for the Markov workload process in the M/M/1 queue to the more general GI/M/1 queueing systems. This approach is based on a minorization condition for the transition kernel of the workload process, which is a Markov chain defined by the Lindley recursion. The proposed method increases the number of regenerations during the simulation and potentially reduces the time required to estimate stationary performance metrics with a given level of precision. Full article

Hybrid-electric aircraft require a reliable power distribution architecture. The electrical drive system is connected to the power source via a DC-link composed mostly of capacitors—one of the faultiest power electronic components. In order to ensure the safe operation of the aircraft, DC-link capacitor condition monitoring is needed. The main requirements for such an algorithm are low data consumption and the possibility to use it in generator- or battery-powered systems. The proposed discharge-based repetitive recursive least squares (RRLS) method provides satisfactory estimates utilizing small data packages. Its execution during capacitor discharge makes it independent from the power source type. Based on the capacitor’s physical parameters, the computational complexity of the estimation process is reduced. Simulation validation and experimental tests were conducted. An analysis was carried out in a capacitance range between 705 μF and 1175 μF. The effective range of the algorithm is 881 μF–1044 μF, with an estimation error of less than 5%. Additionally, a range of changes in the time constant of the multiplier of 0.1–10 was tested in the simulation study. Full article

Spherical shells exhibit superior strength-to-geometry efficiency, making them ideal for industrial applications such as fluid storage tanks, architectural domes, naval vehicles, nuclear containment systems, and aeronautical and aerospace components. Given their critical role, careful attention to the design parameters and engineering constraints is essential. The present paper investigates the buckling responses of bio-inspired helicoidal laminated composite spherical shells under normal and torsional loading, including the effects of a Winkler elastic medium. The pre-buckling equilibrium equations are derived using linear three-dimensional (3D) elasticity theory and the principle of virtual work, solved via the classical finite element method (FEM). The buckling load is computed using a nonlinear Green strain formulation and a generalized geometric stiffness approach. The shell material employed in this study is a T300/5208 graphite/epoxy carbon fiber-reinforced polymer (CFRP) composite. Multiple helicoidal stacking sequences—linear, Fibonacci, recursive, exponential, and semicircular—are analyzed and benchmarked against traditional unidirectional, cross-ply, and quasi-isotropic layups. Parametric studies assess the effects of the normal/torsional loads, lamination schemes, ply counts, polar angles, shell thickness, elastic support, and boundary constraints on the buckling performance. The results indicate that quasi-isotropic (QI) laminate configurations exhibit superior buckling resistance compared to all the other layup arrangements, whereas unidirectional (UD) and cross-ply (CP) laminates show the least structural efficiency under normal- and torsional-loading conditions, respectively. Furthermore, this study underscores the efficacy of bio-inspired helicoidal stacking sequences in improving the mechanical performance of thin-walled composite spherical shells, exhibiting significant advantages over conventional laminate configurations. These benefits make helicoidal architectures particularly well-suited for weight-critical, high-performance applications in aerospace, marine, and biomedical engineering, where structural efficiency, damage tolerance, and reliability are paramount. Full article

Time-varying lumped disturbance and measurement noise are primary obstacles that restrict the control performance of permanent magnet synchronous motor (PMSM) drives. To tackle these obstacles, an adaptive nonsingular terminal sliding mode (ANTSM) algorithm is combined with augmented recursive sliding mode observer (ARSMO) for PMSM speed regulation system in this paper. Generally, conventional nonsingular terminal sliding mode (NTSM) controller adopts a fixed and conservative control gain to suppress the time-varying disturbance, which will lead to unsatisfactory steady-state performance. Without requiring any information of the time-varying disturbance in advance, a novel barrier function adaptive algorithm is utilized to adjust the gain of NTSM controller online according to the amplitude of disturbance. In addition, the ARSMO is emoloyed to estimate the total disturbance and motor speed simultaneously, thereby alleviating the negative impact of measurement noise and excessive control gain. Comprehensive experimental results verify that the proposed enhanced ANTSM strategy can optimize the dynamic performance of PMSM system without sacrificing its steady-state performance. Full article