Search Results (1,733)

This research seeks to identify optimised screen façade patterns and ratios for the effective management of daylight ingress and glare effects. It employs generative adversarial networks (GANs) to generate pattern variations and further evaluates the resultant variations through daylight simulations for application in screen façades. The generated pattern data were classified by hierarchical clustering to distinguish distinct feature groups, and they were subsequently utilised as façade configurations. The pattern data were assessed through daylight performance metrics: spatial daylight autonomy (sDA), annual sunlight exposure (ASE), and daylight glare probability (DGP). The results of the annual-based simulations indicate that façade patterns with frame ratios in the range of 50–65% are useful in reducing the areas exposed to intensive glare on the façade side while maintaining the minimum required lighting conditions. The overall influence of screen façades on interior daylighting in a large space (e.g., 10 m × 10 m) was found to be limited. Their performance is notable in reducing glare discomfort areas within approximately 2.5 m of south-facing façades. This study supports an application strategy in which screen façades are used to manage the extent of areas exposed to daylight ingress within an interior space. Full article

In real-world industrial settings, obtaining class-balanced fault data is often difficult. Imbalanced data across categories can degrade diagnostic accuracy. Time-series Generative Adversarial Network (TimeGAN) is an effective tool for addressing one-dimensional data imbalance; however, when dealing with multiple fault categories, it faces issues such as unstable training processes and uncontrollable generation states. To address this issue, from the perspective of data augmentation and classification, a gradient-penalized Conditional Time-series Generative Adversarial Network with a Multi-Scale Importance-aware Network (CTGAN-MSIN) is proposed in this paper. Firstly, a gradient-penalized Conditional Time-Series Generative Adversarial Network (CTGAN) is designed to alleviate data imbalance by controllably generating high-quality fault samples. Secondly, a Multi-scale Importance-aware Network (MSIN) is constructed for fault classification. The MSIN consists of the Multi-scale Depthwise Separable Residual (MDSR) and Scale Enhanced Local Attention (SELA): the MDSR network can efficiently extract multi-scale features, while the SELA network is capable of screening out the most discriminative scale features from them. Finally, the proposed method is validated using the HUST bearing dataset and the axial piston pump dataset. The results show that under the data imbalance ratio of 15:1, the CTGAN-MSIN achieves diagnostic accuracies of 98.75% and 96.50%, respectively, on the two datasets and outperforms the comparison methods under different imbalance ratios. Full article

Machine vision (MV) is reshaping numerous industries by giving machines the ability to understand what they “see” and respond without human intervention. This review brings together the latest developments in deep learning (DL), image processing, and computer vision (CV). It focuses on how these technologies are being applied in real operational environments. We examine core methodologies such as feature extraction, object detection, image segmentation, and pattern recognition. These techniques are accelerating innovation in key sectors, including healthcare, manufacturing, autonomous systems, and security. A major emphasis is placed on the deepening integration of artificial intelligence (AI) and machine learning (ML) into MV. We particularly consider the impact of convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer architectures on the evolution of visual recognition capabilities. Beyond surveying advances, this review also takes a hard look at the field’s persistent roadblocks, above all the scarcity of high-quality labeled data, the heavy computational load of modern models, and the unforgiving time limits imposed by real-time vision applications. In response to these challenges, we examine a range of emerging fixes: leaner algorithms, purpose-built hardware (like vision processing units and neuromorphic chips), and smarter ways to label or synthesize data that sidestep the need for massive manual operations. What distinguishes this paper, however, is its emphasis on where MV is headed next. We spotlight nascent directions, including edge-based processing that moves intelligence closer to the sensor, early explorations of quantum methods for visual tasks, and hybrid AI systems that fuse symbolic reasoning with DL, not as speculative futures but as tangible pathways already taking shape. Ultimately, the goal is to connect cutting-edge research with actual deployment scenarios, offering a grounded, actionable guide for those working at the front lines of MV today. Full article

We propose a novel hybrid deep learning framework that synergistically integrates Convolutional Neural Networks (CNNs), Spiking Neural Networks (SNNs), and Generative Adversarial Networks (GANs) for robust and accurate classification of high-resolution frontal and sagittal human gait video sequences—capturing both lower-limb kinematics and upper-body posture—from subjects with Knee Osteoarthritis (KOA), Parkinson’s Disease (PD), and healthy Normal (NM) controls, classified into three disease-type categories. Our approach first employs a tailored CNN backbone to extract rich spatial features from fixed-length clips (e.g., 16 frames resized to 128 × 128 px), which are then temporally encoded and processed by an SNN layer to capture dynamic gait patterns. To address class imbalance and enhance generalization, a conditional GAN augments rare severity classes with realistic synthetic gait sequences. Evaluated on the controlled, marker-based KOA-PD-NM laboratory public dataset, our model achieves an overall accuracy of 99.47%, a sensitivity of 98.4%, a specificity of 99.0%, and an F1-score of 98.6%, outperforming baseline CNN, SNN, and CNN–SNN configurations by over 2.5% in accuracy and 3.1% in F1-score. Ablation studies confirm that GAN-based augmentation yields a 1.9% accuracy gain, while the SNN layer provides critical temporal robustness. Our findings demonstrate that this CNN–SNN–GAN paradigm offers a powerful, computationally efficient solution for high-precision, gait-based disease classification, achieving a 48.4% reduction in FLOPs (1.82 GFLOPs to 0.94 GFLOPs) and 9.2% lower average power consumption (68.4 W to 62.1 W) on Kaggle P100 GPU compared to CNN-only baselines. The hybrid model demonstrates significant potential for energy savings on neuromorphic hardware, with an estimated 13.2% reduction in energy per inference based on FLOP-based analysis, positioning it favorably for deployment in resource-constrained clinical environments and edge computing scenarios. Full article

With the widespread application of generative adversarial networks (GANs) in image generation and content creation, their model architectures and training outcomes have become valuable intellectual property assets. However, in practical deployment, image generative models are vulnerable to surrogate model attacks, posing significant risks to copyright ownership and commercial interests. To address this issue, this paper proposes a novel copyright protection scheme for image generative models with a symmetric embedding–retrieval watermark architecture in GANs focused on defending against surrogate model attacks. Unlike traditional model encryption or architectural constraint strategies, the proposed approach integrates a watermark embedding module directly into the image generative network, enabling generated images to implicitly carry copyright identifiers. Leveraging a symmetric design between the embedding and retrieval processes, the system ensures that, under surrogate model attacks, the original model’s identity can be reliably verified by extracting the embedded watermark from the generated outputs. The implementation comprises three key modules—feature extraction, watermark embedding, and watermark retrieval—forming an end-to-end, balanced embedding–retrieval pipeline. Experimental results demonstrate that this approach achieves efficient and stable watermark embedding and retrieval without compromising generation quality, exhibiting high robustness, traceability, and practical applicability, thereby offering a viable and symmetric solution for intellectual property protection in image generative networks. Full article

A method for super-resolution reconstruction of sonograms based on Residual Dense Conditional Generative Adversarial Network (RDC-GAN) is proposed in this paper. It is well known that the resolution of medical ultrasound images is limited, and the single-frame image super-resolution algorithms based on a convolutional neural network are prone to losing texture details, extracting much fewer features, and then blurring the reconstructed images. Therefore, it is very important to reconstruct high-resolution medical images in terms of retaining textured details. A Generative Adversarial Network could learn the mapping relationship between low-resolution and high-resolution images. Based on GAN, a new network is designed, where the generation network is composed of dense residual modules. On the one hand, low-resolution (LR) images are input into the dense residual network, then the multi-level features of images are learned, and then are fused into the global residual features. On the other hand, conditional variables are introduced into a discriminator network to guide the process of super-resolution image reconstruction. The proposed method could realize four times magnification reconstruction of medical ultrasound images. Compared with classical algorithms including Bicubic, SRGAN, and SRCNN, experimental results show that the super-resolution effect of medical ultrasound images based on RDC-GAN could be effectively improved, both in objective numerical evaluation and subjective visual assessment. Moreover, the application of super-resolution reconstructed images to stage the diagnosis of cirrhosis is discussed and the accuracy rates prove the practicality in contrast to the original images. Full article

The deployment of deep learning models in Internet of Things (IoT) systems is increasingly threatened by adversarial attacks. To address the challenge of effectively detecting adversarial examples generated by Generative Adversarial Networks (AdvGANs), this paper proposes an unsupervised detection method that integrates spatial statistical features and multidimensional distribution characteristics. First, a collection of adversarial examples under four different attack intensities was constructed on the CIFAR-10 dataset. Then, based on the VGG16 and ResNet50 classification models, a dual-module collaborative architecture was designed: Module A extracted spatial statistics from convolutional layers and constructed category prototypes to calculate similarity, while Module B extracted multidimensional statistical features and characterized distribution anomalies using the Mahalanobis distance. Experimental results showed that the proposed method achieved a maximum AUROC of 0.9937 for detecting AdvGAN attacks on ResNet50 and 0.9753 on VGG16. Furthermore, it achieved AUROC scores exceeding 0.95 against traditional attacks such as FGSM and PGD, demonstrating its cross-attack generalization capability. Cross-dataset evaluation on Fashion-MNIST confirms its robust generalization across data domains. This study presents an effective solution for unsupervised adversarial example detection, without requiring adversarial samples for training, making it suitable for a wide range of attack scenarios. These findings highlight the potential of the proposed method for enhancing the robustness of IoT systems in security-critical applications. Full article

Bayesian optimization (BO) struggles with data scarcity and poor scalability in high-dimensional many-objective optimization problems. To address this, we propose Co-Evolutionary GAN–Bayesian Optimization (CoEGAN-BO), a novel framework that synergizes generative adversarial networks (GANs) with Bayesian co-evolutionary search for data-driven optimization. The GAN module generates synthetic samples conditioned on promising regions identified by BO, while a co-evolutionary mechanism maintains two interacting populations: one explores the GAN’s latent space for diversity, and the other exploits BO’s probabilistic model for convergence. A bi-stage infilling strategy further enhances efficiency: early iterations prioritize exploration via $L_p$-norm-based candidate selection, later switching to a max–min distance criterion for Pareto refinement. Experiments on expensive multi/many-objective benchmarks show that CoEGAN-BO outperforms four state-of-the-art surrogate-assisted algorithms, achieving superior convergence and diversity under limited evaluation budgets. Full article

The rapid development of Generative Adversarial Networks (GANs) has transformed medical image processing, enabling realistic image synthesis, augmentation, and restoration. This study presents a comparative evaluation of three representative GAN architectures, Pix2Pix, SPADE GAN, and Wasserstein GAN (WGAN), across multiple medical imaging tasks, including segmentation, image synthesis, and enhancement. Experiments were conducted on three benchmark datasets: ACDC (cardiac MRI), Brain Tumor MRI, and CHAOS (abdominal MRI). Model performance was assessed using Fréchet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Dice coefficient, and segmentation accuracy. Results show that SPADE-inpainting achieved the best image fidelity (PSNR ≈ 36 dB, SSIM > 0.97, Dice ≈ 0.94, FID < 0.01), while Pix2Pix delivered the highest segmentation accuracy (Dice ≈ 0.90 on ACDC). WGAN provided stable enhancement and strong visual sharpness on smaller datasets such as Brain Tumor MRI. The findings confirm that no single GAN architecture universally excels across all tasks; performance depends on data complexity and task objectives. Overall, GANs demonstrate strong potential for medical image augmentation and synthesis, though their clinical utility remains dependent on anatomical fidelity and dataset diversity. Full article

The fusion of infrared and visible images provides critical value in computer vision by integrating their complementary information, especially in the field of industrial detection, which provides a more reliable data basis for subsequent defect recognition. This paper presents STGAN, a novel Generative Adversarial Network framework based on a Swin Transformer for high-quality infrared and visible image fusion. Firstly, the generator employs a Swin Transformer as its backbone for feature extraction, which adopts a U-Net architecture, and the improved W-MSA is introduced into the bottleneck layer to enhance local attention and improve the expression ability of cross-modal features. Secondly, the discriminator uses a Markov discriminator to distinguish the difference. Then, the core GAN framework is leveraged to guarantee the retention of both infrared thermal radiation and visible-light texture details in the generated image so as to improve the clarity and contrast of the fused image. Finally, simulation verification showed that six out of seven indicators ranked in the top two, especially in key indicators such as PSNR, VIF, MI, and EN, which achieved optimal or suboptimal values. The experimental results on the general dataset show that this method is superior to the advanced method in terms of subjective vision and objective indicators, and it can effectively enhance the fine structure and thermal anomaly information in the image, which gives it great potential in the application of industrial surface defect detection. Full article

Accurate delineation of glacier extent is crucial for monitoring glacier degradation in the context of global warming. Satellite remote sensing with high spatial and temporal resolution offers an effective approach for large-scale glacier mapping. However, persistent cloud cover limits its application on the Tibetan Plateau, leading to substantial omissions in glacier identification. Therefore, this study proposed a novel sub-cloudy glacier extraction model (SCGEM) designed to extract glacier boundaries from cloud-affected satellite images. First, the cloud-insensitive characteristics of topo-graphic (Topo.), synthetic aperture radar (SAR), and temporal (Tempo.) features were investigated for extracting glaciers under cloud conditions. Then, a transformer-based generative adversarial network (GAN) was proposed, which incorporates an image reconstruction and an adversarial branch to improve glacier extraction accuracy under cloud cover. Experimental results demonstrated that the proposed SCGEM achieved significant improvements with an IoU of 0.7700 and an F1 score of 0.8700. The Topo., SAR, and Tempo. features all contributed to glacier extraction in cloudy areas, with the Tempo. features contributing the most. Ablation studies further confirmed that both the adversarial training mechanism and the multi-task architecture contributed notably to improving the extraction accuracy. The proposed architecture serves both to data clean and enhance the extraction of glacier texture features. Full article

The rapid expansion of the Internet of Things (IoT) has introduced new vulnerabilities that traditional security mechanisms often fail to address effectively. Signature-based intrusion detection systems cannot adapt to zero-day attacks, while rule-based solutions lack scalability for the diverse and high-volume traffic in IoT environments. To strengthen the security framework for IoT, this paper proposes a deep learning-based anomaly detection approach that integrates Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRUs). The model is further optimized using the Moth–Flame Optimization (MFO) algorithm for automated hyperparameter tuning. To mitigate class imbalance in benchmark datasets, we employ Generative Adversarial Networks (GANs) for synthetic sample generation alongside Z-score normalization. The proposed CNN–BiGRU + MFO framework is evaluated on two widely used datasets, UNSW-NB15 and UCI SECOM. Experimental results demonstrate superior performance compared to several baseline deep learning models, achieving improvements across accuracy, precision, recall, F1-score, and ROC–AUC. These findings highlight the potential of combining hybrid deep learning architectures with evolutionary optimization for effective and generalizable intrusion detection in IoT systems. Full article

High-resolution meteorological satellite cloud imagery plays a crucial role in diagnosing and forecasting severe convective weather phenomena characterized by suddenness and locality, such as tropical cyclones. However, constrained by imaging principles and various internal/external interferences during satellite data acquisition, current satellite imagery often fails to meet the spatiotemporal resolution requirements for fine-scale monitoring of these weather systems. Particularly for real-time tracking of tropical cyclone genesis-evolution dynamics and capturing detailed cloud structure variations within cyclone cores, existing spatial resolutions remain insufficient. Therefore, developing super-resolution techniques for meteorological satellite cloud imagery through software-based approaches holds significant application potential. This paper proposes a Multi-scale Residual Deformable Attention Model (MRDAM) based on Generative Adversarial Networks (GANs), specifically designed for satellite cloud image super-resolution tasks considering their morphological diversity and non-rigid deformation characteristics. The generator architecture incorporates two key components: a Multi-scale Feature Progressive Fusion Module (MFPFM), which enhances texture detail preservation and spectral consistency in reconstructed images, and a Deformable Attention Additive Fusion Module (DAAFM), which captures irregular cloud pattern features through adaptive spatial-attention mechanisms. Comparative experiments against multiple GAN-based super-resolution baselines demonstrate that MRDAM achieves superior performance in both objective evaluation metrics (PSNR/SSIM) and subjective visual quality, proving its superior performance for satellite cloud image super-resolution tasks. Full article

Off-system bridges are critical components of the United States’ transportation infrastructure, providing essential access to rural communities and enabling residents to reach vital services such as employment, education, and healthcare. Many of these bridges are structurally deficient, functionally obsolete, and unmaintained. This disproportionately hinders the mobility of underserved populations, worsening socioeconomic disparities. Despite existing research, there is insufficient focus on the unique challenges posed by off-system bridges, including handling the class imbalanced nature of the bridge condition rating dataset. This paper predicts bridge deck conditions by using Generative Adversarial Networks with Focal Loss (GAN-FL) to generate synthetic data which enhances precision–recall balance in imbalanced datasets. Results show that integrating GAN-FL with random forest (RF) classifiers significantly enhances the performance of minority classes, improving their precision, recall, and F1 scores. The study finds that augmenting training data using GAN-FL greatly enhances minority class prediction, thereby assisting in accurate bridge condition modeling. Full article

Attenuation correction (AC) is an essential process in Single Photon Emission Computed Tomography (SPECT) myocardial perfusion imaging (MPI), an established imaging method for assessing coronary artery disease. Conventional AC approaches typically require CT scans, supplementary hardware, intricate reconstruction, or segmentation processes, which can hinder their clinical applicability. Recently, deep learning (DL) techniques have emerged as alternatives, allowing for the direct learning of attenuation patterns from non-AC (NAC) imaging data. This review explores the existing literature on DL-based AC methods for SPECT MPI. We highlight high-performing models, including attention-gated U-Net conditional Generative Adversarial Networks (GANs), and evaluate their validation methods. Although significant advancements have been achieved, numerous challenges persist, which are thoroughly discussed. Full article