Longhu Huang, Sheng Zheng^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082622 - 23 July 2026

Abstract In the field of online automated defect inspection for small-size liquid crystal display modules (LCMs), the accuracy of module loading is crucial for the subsequent lighting inspection. However, due to the physical characteristics of the module’s flexible ribbon cable, the ribbon often exhibits varying degrees of curling, causing conventional monocular vision systems to frequently encounter local underexposure or overexposure when positioning the workpiece, resulting in loss of local details and significantly affecting subsequent positioning and loading. To address the problem of local image degradation caused by abnormal exposure, this study proposes a regional image generation… More >

Yiqiao Xiang¹, Jingchun Zhou^1,2,*, Ruijie Liu¹, Dehuan Zhang¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082357 - 23 July 2026

Abstract Underwater imagery is degraded by depth-dependent absorption and scattering, which often introduce color casts and contrast attenuation. Although recent Vision Mamba models provide efficient long-range dependency modeling, their conventional 2D scanning patterns are not explicitly designed to exploit the depth-correlated structure of underwater degradation and may therefore weaken geometry-aware feature dependencies. To address this limitation, we propose Isoline-Guided Evolutionary Mamba (IG-Mamba), a physics-inspired framework that uses a depth-correlated potential prior to organize state-space token propagation. Specifically, we introduce a Topology-Preserving Isoline Scanning mechanism. By leveraging a geometric prior, this mechanism quantizes the scene into discrete… More >

Botambu Collins, Jin-Taek Seong^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082245 - 30 June 2026

Abstract Classical image denoising methods remain relevant in practical scenarios where training data or noise models are unavailable, yet their performance is highly sensitive to parameter selection. Non-Local Means (NLM) is a representative example whose effectiveness depends critically on smoothing strength, patch size, and search window configuration. This paper formulates NLM parameter selection as a black-box optimization problem under unknown noise conditions and employs adaptive metaheuristic optimization strategies for this task. We propose an adaptive optimization framework that integrates rank-based perturbation, opposition-based learning, Lévy-flight exploration, and noise-aware parameter constraints to improve robustness and convergence. The proposed More >

Sami Ullah^1,2, Najmul Hassan², Naeem Bhatti², Asad Saleem^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082467 - 15 June 2026

Abstract Underwater imaging facilitates the exploration of the underwater environment. However, irregular optical absorption and light scattering in water, ranging from clear to highly turbid conditions, often result in low visibility, color distortion, and blurriness in underwater images (UWIs). Conventional UWI enhancement methods are limited by inefficient physical modeling, while deep learning-based approaches are constrained by the scarcity of paired training datasets. In this work, we propose a hybrid learning framework for UWI enhancement that leverages the usefulness of both conventional and deep learning-based techniques. At first, we preprocess the UWIs using a revised underwater physical… More >

Rana Lateef¹, Asmaa Abdul Jabbar^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080975 - 15 June 2026

Abstract The challenges of underwater object detection are derived from complex environmental conditions, including light scattering, absorption, and turbidity. The deep learning approaches have enhanced the detection of objects in these low-visual conditions. This work presents a multi-stage object-detection framework for the underwater environment that performs well on the Semantic Segmentation of Underwater Imagery (SUIM) benchmark. To begin with, there is the adaptive Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, which improves image quality by correcting color distortions and increasing contrast. Second, an augmented YOLOv8 model (with a ResNet-50 backbone and the Convolutional Block Attention Module More >

Shih-Lin Lin^*, Yi-Hsuan Chen

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078862 - 27 April 2026

Abstract Urban intersections contain severe blind zones where buildings and roadside obstacles block line-of-sight sensing, limiting the ability of autonomous vehicles to anticipate hidden hazards. This paper presents an urban-intersection-oriented non-line-of-sight (NLOS) perception framework that exploits specular reflections from building surfaces using 77 GHz frequency-modulated continuous-wave (FMCW) automotive radar. All evaluations are conducted in a MATLAB-based simulation environment that models intersection geometry, building-induced occlusions, and specular reflection-assisted propagation, and generates 77-GHz FMCW radar echoes under controllable interference; real-world validation with measured radar data and richer multipath/material modeling is planned as future work. To improve robustness under… More >

Ryul Kim, Young Hwan Choi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077480 - 30 March 2026

Abstract Leakage events occurring at multiple locations simultaneously generate overlapping and topology-dependent pressure signatures, making reliable detection and subsequent restoration planning a persistent challenge in water distribution systems (WDSs). While recent data-driven techniques have improved the ability to identify anomalous hydraulic behavior, most approaches remain limited to the detection stage and offer little guidance on how utilities should prioritize repairs once multiple failures are identified. To bridge this gap, this study proposes an integrated framework that links topology-aware leakage detection with quantitative restoration prioritization. First, a multi-task learning framework based on Graph Attention Networks (GAT) is… More >

Oncology Research Editorial Office

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.078461 - 19 January 2026

Abstract This article has no abstract. More >

Changcheng Li^1,2, Weimeng Chang^1,2, Dahai Zhang^1,*, Jinghan He¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069389 - 27 December 2025

Abstract Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts. This paper proposes a novel partitioning method based on deep reinforcement learning. First, the partitioning decision process is formulated as a Markov decision process (MDP) model to maximize the modularity. Corresponding key partitioning constraints on parallel restoration are considered. Second, based on the partitioning objective and constraints, the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function. The soft bonus scaling mechanism… More >

Hanyong Lee^#, Ye-Chan Park^#, Jaesung Lee^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4977-4993, 2025, DOI:10.32604/cmc.2025.068156 - 23 October 2025

Abstract Restoring texts corrupted by visually perturbed homoglyph characters presents significant challenges to conventional Natural Language Processing (NLP) systems, primarily due to ambiguities arising from characters that appear visually similar yet differ semantically. Traditional text restoration methods struggle with these homoglyph perturbations due to limitations such as a lack of contextual understanding and difficulty in handling cases where one character maps to multiple candidates. To address these issues, we propose an Optical Character Recognition (OCR)-assisted masked Bidirectional Encoder Representations from Transformers (BERT) model specifically designed for homoglyph-perturbed text restoration. Our method integrates OCR preprocessing with a… More >