Search Results (3,876)

This paper proposes a novel outpainting technique for quadri-wave lateral shearing interferograms which employs Quadri-Wave Generative Adversarial Networks (QW-GANs). QW-GAN is adversarially trained to capture the high-order statistics of real interferograms. It thereby avoids the Gibbs-ringing artifacts and spectral leakage caused by deterministic interpolation or single-frame extrapolation. The recovered fringes remain physically consistent with preserved Fourier support, enabling more accurate wavefront phase retrieval. Numerical simulations and comparisons with aberrations (defocus, astigmatism, coma, spherical, and random), along with real experimental results, demonstrate the enhanced reconstruction precision of our method, providing an efficient solution for wavefront reconstruction in optical applications. Full article

Deep neural networks (DNNs) are widely deployed in safety-critical applications such as medical diagnosis and autonomous driving. Adversarial training (AT) is among the most effective defenses, casting robust optimization as a min–max problem over a defender-specified ${\ell }_p$-ball of fixed radius $ϵ$. Bounded defenses of this kind are known to generalize poorly to test-time perturbations larger than $ϵ$. In this paper, we revisit this failure mode on MNIST and FashionMNIST and make three of its properties explicit. The degradation is abrupt rather than gradual, and its location is indexed by the training budget, so that enlarging the budget translates the drop instead of removing it. The translation is, in turn, capped by trainability, since training stops converging once $ϵ$ becomes too large. Decision-surface visualization exhibits the same failure geometrically, as adversarially trained models form a plateau whose edge coincides with the training boundary. Together, these properties suggest that the failure follows from concentrating training on a single radius. We therefore present Density-Weighted Adversarial Training (DWAT), a plug-in framework that spreads training over a set of sampled $ϵ$-balls and reweights each candidate adversarial example by a Gaussian density of its distance from the benign sample. We derive its objective as a self-normalized importance-sampling estimate of an expected adversarial risk under a perturbation prior, and we show that this risk is upper-bounded by the standard adversarial risk, so that the in-bound objective of DWAT relaxes rather than replaces that of AT. We further prove that the population objective upper-bounds the worst-case adversarial risk at every radius, including radii beyond the training budget, at an explicit cost that grows with the radius. Experiments with PGD-AT, TRADES, and MART as base defenses indicate that DWAT alleviates the out-of-bound degradation in the settings that we study, at a cost inside the training budget that we report and discuss. Full article

Battery prognosis is a critical component of battery management systems, enabling the prediction of end of life (EoL) and remaining useful life (RUL). However, obtaining sufficiently large labelled datasets for data-driven prognosis is challenging because battery ageing experiments are time-consuming and expensive. To address this data scarcity, we propose a conditional generative adversarial network (GAN) framework for targeted synthetic battery-data generation, in which degradation regime is explicitly used as conditioning information. The framework generates samples from three degradation regions—early, pre-knee, and post-knee—and is investigated using two representations of the same underlying battery data: direct time series and Gramian Angular Fields (GAFs). The generated data are evaluated using representation-specific quantitative metrics together with qualitative distributional analyses. As an additional validation of synthetic-data utility, GAN-generated samples are incorporated as unlabelled data in a Mean Teacher semi-supervised EoL prediction framework. Across 10 matched random-seed runs, augmentation reduces the mean EoL prediction error for both representations. For the time-series workflow, MAE and RMSE decrease by 6.37% and 3.66%, respectively, while the GAF-based workflow shows larger reductions of 15.93% and 16.20%. The improvements in both metrics are statistically significant for the GAF-based workflow, and after augmentation no statistically significant difference is detected between the aggregate EoL prediction errors of the GAF and time-series-based models. These findings demonstrate the potential of targeted conditional GANs for battery-data augmentation and highlight GAF-based generation as a promising complementary approach to conventional time-series-based augmentation for battery prognosis. Full article

Background: Internet of Things (IoT) deployments face complex network dependencies and persistent data limitations that constrain machine learning (ML) intrusion detection systems (IDS). Objective: To synthesise peer-reviewed machine learning IDS research for IoT, focusing on dependency-driven failure propagation and chronic data scarcity, positioned against 2024–2025 EU regulatory requirements (NIS2, the Cyber Resilience Act). Eligibility criteria: Peer-reviewed empirical studies proposing or evaluating a machine learning or deep learning IoT intrusion detection method published in English from January 2018 (limited pre-2018 exceptions for seminal works). Information sources: IEEE Xplore, SpringerLink, Elsevier ScienceDirect, Scopus, Web of Science, and Google Scholar, searched on 12 February 2025. Risk of bias: Each candidate was scored against four criteria (objectives clarity, methodological soundness, reproducibility, IoT-security relevance); studies scoring at least 3 out of 4 were retained. Screening and scoring were performed by one reviewer, with a second reviewer independently checking 20 percent of records. Synthesis methods: Narrative thematic synthesis; heterogeneous metrics and incompatible datasets across studies precluded quantitative meta-analysis. Included studies: Of 427 records identified, 52 studies initially met inclusion criteria; a post hoc independently validated reconstruction of individual QA1–QA4 scores subsequently found that six did not meet the threshold or topical eligibility criteria, yielding a final 46-study corpus. Main findings: Generative adversarial networks (GANs) dominate dataset augmentation work, graph-based communication analysis addresses dependency modelling, and methods based on transformers or federated learning emerge from 2023 onward. Certainty of evidence: No formal grading (GRADE) applies to this narrative synthesis. Confidence in the corpus composition is high, following independent QA1–QA4 validation, while confidence in the thematic findings is moderate given single-reviewer screening and judgment-based classification. Conclusions: We identify three recurring gaps: real-time detection under resource constraints, dependency-aware detection, and regulatory compliance. Closing these gaps requires detection methods that treat IoT security as a networked and regulated system rather than an isolated device classification problem. Registration: Open Science Framework, 10.17605/OSF.IO/NMAK4 (registered retrospectively). No external funding supported this review. Full article

In the complex problem of air-to-ground mission planning, multi-UAV systems face significant challenges such as system complexity and heterogeneity, insufficient target observability, and difficulties in collaborating information sharing. To address these issues, this paper proposes a novel learnable sparse attention graph architecture (SAGA). This architecture deeply integrates graph reasoning and policy optimization within the MAPPO framework and includes three innovative mechanisms: (i) a GATv2-based graph neural network encoder that performs multi-round distributed consensus on the communication graph among UAVs via a multi-head attention mechanism, enabling selective aggregation of tactical information; (ii) an edge predictor that learns to prune low-value communication links, generating a sparse and mission-adaptive communication topology; and (iii) an L1 sparsity penalty term that further enhances communication efficiency. In a self-developed simulation environment for heterogeneous multi-UAV mission planning, comprehensive comparative experiments were conducted against the following baseline reinforcement learning algorithms: MADDPG, MATD3, QMIX, MAPPO, TarMAC, DGN, and G2ANet. The experimental results show that SAGA achieves reward values of 390 and 1100 in small-scale and large-scale scenarios, and outperforms the best-performing baseline algorithm by more than 20% across all operational performance metrics. Generalization experiments validate the model’s robust transfer capability under unknown defense deployment modes. Ablation experiments further confirmed the individual contributions of the three components. This study provides an innovative and effective method for mission planning of heterogeneous multi-UAV systems in partially observable adversarial environments. Full article

The aquatic products industry has experienced rapid development due to the growing global demand for high-quality proteins. However, conventional production models remain constrained by multifaceted impediments, including disease outbreaks, environmental stressors, microbial contamination, and operational inefficiencies in processing, quality control, and logistics. Artificial intelligence (AI) offers targeted methodological solutions to these challenges. From a functional perspective, this review categorizes artificial intelligence into four major types: perception, prediction, control, and generation, and systematically evaluates its application progress in aquaculture, processing, quality inspection, and traceability fields. Perception AI constitutes the data acquisition and digitization layer, utilizing computer vision, sonar, and multimodal fusion technologies to establish digital mappings from environmental parameters to biological indicators. Prediction AI employs machine learning and deep learning algorithms to transform historical datasets into quantitative forecasts regarding water quality dynamics, disease risks, and production trends. Control AI translates decision-making protocols into precise, autonomous regulatory actions for aquaculture environments and processing workflows through fuzzy logic and model-based predictive control. Generation AI leverages large language models and generative adversarial networks to demonstrate innovative capabilities in data augmentation, solution optimization, and virtual simulation. Collectively, these applications optimize core production processes while significantly enhancing product quality, processing efficiency, safety management, and traceability systems. Future research directions will prioritize the development of robust, interdisciplinary AI technologies with superior integration capabilities. Full article

Smart systems, the Industrial Internet of Things, and cyber-physical networks increasingly make decisions on the devices where data is generated, on nodes short of memory, compute, energy, and bandwidth, and exposed to real adversaries. Two research currents have grown to meet this: one makes artificial intelligence small and distributed (quantization, pruning, distillation, TinyML, federated and split learning), the other makes it safe (defenses against poisoning, backdoors, inversion, and evasion). This review argues that the two are entangled rather than parallel. Operators that shrink a model or scatter it across nodes also redraw its attack surface, each carrying a security dividend and a security liability, and because a node’s resources are finite and shared, model capacity and defense strength compete for one multi-dimensional budget. We formalize this as an efficiency-decentralization-security (EDS) design tension, explicitly a tension and not an impossibility, and show with published measurements that the coupling is non-monotonic. Around this thesis we build three artifacts, following an explicit design-science research process: an evidence-graded scoring matrix that separates each operator’s security dividend from its liability across seven axes and reports the direction of every effect separately from the confidence in the evidence behind it; a resource-aware threat model that judges attack and defense feasibility against a tiered device, gateway, network, and server budget with stated units; and a co-design framework whose decision workflow terminates in a defense-selection program and a verification step under adaptive attack. We work the framework through an industrial predictive-maintenance scenario with the resource arithmetic computed line by line, and evaluate it retrospectively against six published edge-AI systems. The result is a decision-support guide for building edge AI that is efficient, decentralized, and secure at once. Full article

Continuous maritime surveillance increasingly relies on optical remote sensing, yet small ships are difficult to detect in low-resolution imagery because limited spatial resolution, complex sea backgrounds, and sensor noise suppress the fine-grained cues on which detectors depend, while routine acquisition of high-resolution imagery is prohibitively costly. To address this problem, a hybrid framework (RGT-YOLOv5Det) is proposed that couples transformer-based super-resolution (SR) with a lightweight object detector so that fine spatial detail is restored before detection. Methodologically, a paired benchmark (Ship-HRRSI/Ship-LRRSI) was constructed from the public TGRS-HRRSD dataset by standardising images to 800 × 800 pixels and generating 200 × 200 pixel counterparts via 4× bicubic down-sampling; three SR models (RGT, HAT, and Real-ESRGAN) and four detectors (YOLOv5s, YOLOv8s, YOLOv10s, and Faster R-CNN-MobileNetV3-Large-FPN) were fine-tuned and compared under identical training settings, and the best-performing components were integrated into the proposed two-stage pipeline. In the results, RGT delivered the best reconstruction quality (PSNR 22.038 dB, SSIM 0.3502) with the fewest parameters (13.37 M), YOLOv5s proved the most resolution-robust detector, and the integrated RGT-YOLOv5Det achieved mAP@0.5 of 0.947 and mAP@0.5:0.95 of 0.768 on low-resolution imagery, exceeding the best standalone detector score on each metric by 0.033 and 0.089, respectively. It is concluded that restoring structural detail prior to detection offers an accurate and acquisition cost-efficient alternative to high-resolution imaging, providing a practical route to reliable small ship detection in degraded optical remote sensing imagery. Full article

Adversarial attacks misled deep neural networks by injecting perturbations into input images. Training networks with adversarial examples defended against adversarial attacks. However, training with specific adversarial examples only defended against the corresponding attacks. To generalize the defense effect from one specific attack to other attacks, we proposed a method called Gradient Vicinity Adversarial Training (GVAT), which generated adversarial examples along directions sampled in the vicinity of the gradient. The defense effects of GVAT were evaluated using three attack methods: fast gradient sign method (FGSM), projected gradient descent (PGD), and Carlini–Wagner (CW) under the $L_2$-norm constraint. A three-layer convolutional network was trained on the MNIST dataset, and two WideResNet-28-10 networks were trained on the CIFAR-10 and CIFAR-100 datasets respectively. Under the transfer-based black-box setting, the results showed that GVAT not only defended against the corresponding attacks that generated adversarial examples but also defended against other attacks. In other words, the defense effect of GVAT was generalized to other attacks under the transfer-based black-box setting. Full article

Millimeter wave (mmWave) Massive MIMO is a cornerstone technology for sixth-generation (6G) wireless networks, providing the directional gain necessary to overcome high path loss. However, the acquisition of high-fidelity Channel State Information (CSI) and the associated beamforming overhead remain significant bottlenecks, particularly in dynamic environments with frequent blockages. In this paper, we propose a fast and robust beamforming strategy enabled by a digital twin (DT) framework. Specifically, we develop a Conditional Generative Adversarial Network (cGAN)-based DT module that serves as a high-fidelity virtual surrogate for site-specific ray-tracing. By processing environmental 3D geometry and dynamic obstacle data, the cGAN predicts real-time Beam-Power Maps (BPM) with minimal computational latency. Building upon these predictions, we introduce a Graph Neural Network (GNN)-based resource allocation agent that models the network as a spatial interference graph to perform coordination and power control. Numerical results demonstrate that our proposed DT-assisted approach significantly reduces online interaction overhead by shifting the computational burden of ray-tracing to an offline generative phase. Furthermore, the framework achieves superior sum-rate performance and link robustness under dynamic blockages compared to conventional deep learning and heuristic benchmarks. Full article

This paper presents a simulation-based cyber-quantum Internet of Things (IoT) security framework for modeling, securing, and detecting attacks in QKD-secured IoT communication environments. The proposed framework integrates heterogeneous IoT traffic generation, gateway-assisted routing, edge processing, QKD key-pool management, Quantum-Synchronized Ephemeral Encryption (Q-SEE), cross-layer adversarial attack injection, and AI-based multiclass detection. Unlike conventional IoT intrusion datasets that mainly capture packet- or flow-level abnormalities, the generated dataset represents the joint behavior of IoT sessions, network delay, queue pressure, QKD state, key consumption, encryption-mode transitions, ciphertext metadata, and cyber-quantum risk. A Python/SimPy/NetworkX simulation was developed using 80 IoT devices, 3 gateways, 2 edge servers, 4 cyber-quantum control-plane nodes, and 1 adversarial orchestrator. The final simulation produced 46,351 records with 76 features covering normal traffic, five traditional IoT attacks, and six novel cyber-quantum attacks, including QKD key-pool starvation, QBER camouflage, false QKD-health injection, encryption downgrade induction, queue–key coupling, and multi-vector cyber-quantum orchestration. Q-SEE adaptively selects among QKD-OTP, QKD-synchronized AES-256 ephemeral mode, PQC fallback, degraded mode, and blocked mode according to QBER, secret key rate, key availability, device criticality, downgrade pressure, and risk. A leakage-aware Quantum-Aware Kolmogorov–Arnold Network (QKAN) was then trained using deployable cyber-quantum evidence. The final nonrisk QKAN achieved 98.79% test accuracy, 98.61% macro-F1, 98.85% weighted-F1, and 99.78% macro-AUC, demonstrating effective detection of traditional and cyber-quantum IoT attacks. Full article

In smart manufacturing, efficient job shop scheduling (JSS) and resource management remain critical challenges, especially in dynamic production environments. Traditional methods often struggle to adapt to real-time changes and unexpected events. To address these limitations, this paper proposes a novel Generative Adversarial Network (GAN)-based generative AI framework that augments scheduling data with realistic synthetic scenarios and integrates Local Outlier Factor (LOF)-enhanced Q-learning-based reinforcement learning (QRL) for adaptive JSS optimization in Industry 5.0 environments. The GAN is trained to generate realistic synthetic scheduling scenarios, which are combined with real-world data from a state-of-the-art Festo Didactics Cyber Physical Lab to augment the diversity and coverage of training samples. The LOF algorithm enables real-time bottleneck detection, while the QRL agent learns robust scheduling policies that minimize makespan and prioritize bottleneck mitigation. Experimental results demonstrate that the proposed GAN-LOF-QRL approach achieves an average makespan reduction of 70.8% across varying production volumes (12, 15, and 18 orders), significantly improving scheduling efficiency and resource utilization compared to traditional RL and heuristic methods. This research advances smart manufacturing initiatives and Industry 5.0 goals by providing a scalable, adaptive scheduling solution that leverages generative AI to address the complexities of modern supply networks. Full article

Accurate three-dimensional (3D) characterization of geothermal reservoirs is crucial for resource assessment and development, yet it remains a significant challenge due to sparse direct observations and the complex, heterogeneous nature of subsurface geology. While deep learning has emerged as a powerful tool for subsurface modeling, existing approaches often struggle to preserve critical fine-scale structural details and adaptively focus on geologically informative regions, limiting their effectiveness for geothermal applications. To address these limitations, we propose Gen-LEP, a novel deep generative neural network specifically designed for geothermal reservoir characterization. The proposed framework integrates a key component of local edge pattern (LEP) learning module to enhance the preservation of lithological boundaries and structural discontinuities. The LEP learning module is embedded within a conditional generative framework to effectively learn the nonlinear relationships between sparse conditioning data and complex 3D reservoir structures. We evaluate our method on a geothermal reservoir modeling dataset. Experimental results demonstrate that Gen-LEP can achieve accurate reconstruction and preserve complex geological boundaries. Gen-LEP can provide an effective deep learning framework that improves the fidelity of geothermal reservoir reconstructions by explicitly addressing the specific spatial characteristics of subsurface geological systems. Full article

Goal: Deep learning-based motor imagery EEG classification is limited by data scarcity, which constrains model generalization and performance. Methods: We propose a dual-cascade generative adversarial network (dcGAN) framework with a variable focused attention (VFA) module for MI-EEG data augmentation. The first stage learns latent frequency-domain priors from random noise through an adversarial training scheme; the second stage then synthesizes artificial EEG samples with a U-Net generator conditioned on these priors, augmented by the VFA module and a time-domain consistency loss. A VFA-enhanced EEGNet is subsequently trained on the combination of real and generated samples for classification. Results: On the BCI Competition IV 2a and 2b datasets, the proposed method achieves classification accuracies of 84.92% and 91.79%, with Cohen’s Kappa coefficients of 0.79 and 0.81, respectively, outperforming baseline methods. Conclusions: The integration of structured frequency-domain priors and attention mechanisms improves the fidelity of generated EEG samples, which in turn enhances downstream classification performance. Full article

Precipitation nowcasting aims to predict short-term precipitation evolution over forecast lead times of 1–6 h and can support hydrological-risk and disaster-prevention applications when near-real-time observations are available. However, precipitation forecasting remains challenging because of rapid spatiotemporal evolution, spatial displacement, and the difficulty of representing localized high-intensity precipitation. To address these issues, this study proposes STAMP-GAN, a spatiotemporal attention-modulated generative adversarial network for regional precipitation sequence prediction. STAMP-GAN combines an AM-ConvLSTM temporal evolution module with spatial attention, efficient channel attention, large-receptive-field context modeling, and temporal-index-conditioned feature modulation. A spatially aligned two-dimensional digital elevation model (DEM) field is retained as static auxiliary geographical information. The STAMP-Net generator uses hierarchical multi-scale feature extraction to reconstruct precipitation structures at different spatial scales while a dual-branch temporal PatchGAN provides adversarial supervision for both the complete forecast sequence and the final three forecast frames. A hybrid objective combines regression, event-based, structural, temporal, and adversarial constraints. Experiments on the ERA5 and CMA-S datasets show that, compared with the best-performing baseline for each metric, STAMP-GAN achieves relative CSI improvements of approximately 6.5% and 9.5%, respectively. The proposed framework provides a data-driven approach for retrospective hourly regional precipitation sequence prediction under gridded meteorological-data conditions, rather than a fully validated operational real-time nowcasting system. Full article