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The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]

This systematic review aims to assess the efficacy of Artificial Intelligence (AI) applications in orthopedic surgery, with a focus on diagnostic accuracy and outcome prediction. In this review, we expose the findings of a systematic literature review awning the papers published from 2016 to October 2023 where authors worked on the application of an AI techniques and methods to an orthopedic purpose or problem. After application of inclusion and exclusion criteria on the extracted papers from PubMed and Google Scholar databases, 75 studies were included in this review. We examined, screened, and analyzed their content according to PRISMA guidelines. We also extracted data about the study design, the datasets included in the experiment, the reported performance measures and the results obtained. In this report, we will share the results of our survey by outlining the key machine and Deep Learning (DL) techniques, such as Convolutional Neural Network (CNN), Autoencoders and Generative Adversarial Network, that were mentioned, the various application domains in orthopedics, the type of source data and its modality, as well as the overall quality of their predictive capabilities. We aim to describe the content of the articles in detail and provide insights into the most notable trends and patterns observed in the survey data. [ABSTRACT FROM AUTHOR]

Deepfakes, hyper-realistic synthetic media created using artificial intelligence, pose a growing threat to trust in information and online interactions, serious challenge to the integrity of digital content. This paper delves into the evolving landscape of deepfake detection. Our primary objectives are analyzing the techniques used to detect fake videos, tracing their development alongside the advancements in deep learning that enable increasingly sophisticated deepfakes, assessing societal impact by investigating the social, political, and psychological implications of deepfakes. This includes exploring how they can manipulate public perception and potentially disrupt societal harmony and evaluating detection methods used for fake video detection. Through a comparative analysis, we evaluate their effectiveness, identify limitations, and highlight potential areas for improvement. By examining both the detection methods and the evolving nature of deepfakes, this paper aims to provide a comprehensive understanding of this critical challenge. Through this analysis, we hope to contribute to the development of more robust solutions for identifying and mitigating the negative impacts of deepfakes. Finally, we are trying to contribute to a deeper understanding of the dynamic challenges posed by deepfake videos and inform strategies to fortify the digital ecosystem against malicious content. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a widely used technology for pipeline detection due to its fast detection speed and high resolution. However, the presence of complex underground media often results in strong ground clutter interference in the collected B-scan echoes, significantly impacting detection performance. To address this issue, this paper proposes an improved clutter suppression network based on a cycle-consistency generative adversarial network (CycleGAN). By employing the concept of style transfer, the network aims to convert clutter images into clutter-free images. This paper introduces multiple residual blocks into the generator and discriminator, respectively, to improve the feature expression ability of the deep learning model. Additionally, the discriminator incorporates the squeeze and excitation (SE) module, a channel attention mechanism, to further enhance the model's ability to extract features from clutter-free images. To evaluate the effectiveness of the proposed network in clutter suppression, both simulation and measurement data are utilized to compare and analyze its performance against traditional clutter suppression methods and deep learning-based methods, respectively. From the result of the measured data, it can be found that the improvement factor ( I m ) of the proposed method has reached 40.68 dB, which is a significant improvement compared to the previous network. [ABSTRACT FROM AUTHOR]

Tomb murals are different from cave temple murals and temple murals, as they are underground cultural relics, their painting style is unique, solemn, and austere, and the performance image is characterised by simple colours, low contrast, and fewer survivors. During the digital restoration process, it is important to have sufficient reference samples to ensure the accuracy of the restoration. In addition, the style of mural paintings in the tombs varies greatly from other styles of murals and types of word paintings. Therefore, learning the unique artistic style of tomb murals, providing stylistically consistent training samples for digital restoration, and overcoming the problems of dim lighting and complex surface granularity of tomb murals are all necessary for research. This paper proposes a generative adversarial network algorithm that separates and fuses style features to enhance the generative network's ability to acquire image information. The algorithm extracts underlying and surface style feature details of the image to be tested and conducts fusion generation experiments. The generative network's parsing layer modifies the input noise tensor and optimises the corresponding weights to prevent misalignment between drawing lines and fresco cracks. Finally, to optimise the fresco generation effect, we add the corresponding loss function in the discriminator. The tomb murals dataset was established for experiments and tests, and quantitatively and qualitatively analysed with other style migration models, and SSIM, FID, LPIPS and NIQE were used as evaluation indexes. The results were 0.97, 269.579, 0.425 and 3.250, respectively, and the effect of style migration of this paper's method was significantly higher than that of the control group model. [ABSTRACT FROM AUTHOR]

In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects. [ABSTRACT FROM AUTHOR]

In response to the issue of short-term fluctuations in photovoltaic (PV) output due to Cloud movement, this paper proposes a method for forecasting short-term PV output based on a Depthwise Separable Convolution Visual Geometry Group (DSCVGG) and a Deep Gate Recurrent Neural Network (DGN). Initially, a cloud motion prediction model is constructed using a DSCVGG, which achieves edge recognition and motion prediction of clouds by replacing the previous convolution layer of the pooling layer in VGG with a depthwise separable convolution. Subsequently, the output results of the DSCVGG network, along with historical PV output data, are introduced into a Deep Gate Recurrent Unit Network (DGN) to establish a PV output prediction model, thereby achieving precise prediction of PV output. Through experiments on actual data, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of our model are only 2.18% and 5.32 x 10-5, respectively, which validates the effectiveness, accuracy, and superiority of the proposed method. This provides new insights and methods for improving the stability of PV power generation. [ABSTRACT FROM AUTHOR]

The rapid development of deep learning in medical imaging has significantly enhanced the capabilities of artificial intelligence while simultaneously introducing challenges, including the need for vast amounts of training data and the labor-intensive tasks of labeling and segmentation. Generative adversarial networks (GANs) have emerged as a solution, offering synthetic image generation for data augmentation and streamlining medical image processing tasks through models such as cGAN, CycleGAN, and StyleGAN. These innovations not only improve the efficiency of image augmentation, reconstruction, and segmentation, but also pave the way for unsupervised anomaly detection, markedly reducing the reliance on labeled datasets. Our investigation into GANs in medical imaging addresses their varied architectures, the considerations for selecting appropriate GAN models, and the nuances of model training and performance evaluation. This paper aims to provide radiologists who are new to GAN technology with a thorough understanding, guiding them through the practical application and evaluation of GANs in brain imaging with two illustrative examples using CycleGAN and pixel2style2pixel (pSp)-combined StyleGAN. It offers a comprehensive exploration of the transformative potential of GANs in medical imaging research. Ultimately, this paper strives to equip radiologists with the knowledge to effectively utilize GANs, encouraging further research and application within the field. [ABSTRACT FROM AUTHOR]

Reinforced concrete corrosion inspection methods based on deep learning have been widely used in the engineering field to monitor the service status of reinforced concrete. However, in engineering practice, it is difficult to obtain a large amount of reinforced concrete corrosion data of different types, which greatly hinders the improvement of the accuracy of neural network models in predicting corrosion conditions. The classic generative adversarial network (GAN) model gives poor model quality for datasets with small amounts of data and high concentration. This paper proposes an improved generative adversarial network approach to optimise reinforced concrete corrosion data. Firstly, a quantum particle swarm optimisation (QPSO) algorithm is used to improve the generative adversarial network. Then, existing corrosion characteristic data is used to train the improved generative adversarial network until the ideal equilibrium state is reached. Next, the feature data generated by the generator are fused with the original data and the fused data are input into several common machine learning models for training. Experimental results show that compared with other conventional results obtained by directly inputting corrosion data into a neural network model for training, the improved method makes full use of multi-source signal data and achieves better classification performance. [ABSTRACT FROM AUTHOR]

Robust object detection and weather classification are essential for the safe operation of autonomous vehicles (AVs) in adverse weather conditions. While existing research often treats these tasks separately, this paper proposes a novel multi objectives model that treats weather classification and object detection as a single problem using only the AV camera sensing system. Our model offers enhanced efficiency and potential performance gains by integrating image quality assessment, Super-Resolution Generative Adversarial Network (SRGAN), and a modified version of You Only Look Once (YOLO) version 5. Additionally, by leveraging the challenging Detection in Adverse Weather Nature (DAWN) dataset, which includes four types of severe weather conditions, including the often-overlooked sandy weather, we have conducted several augmentation techniques, resulting in a significant expansion of the dataset from 1027 images to 2046 images. Furthermore, we optimize the YOLO architecture for robust detection of six object classes (car, cyclist, pedestrian, motorcycle, bus, truck) across adverse weather scenarios. Comprehensive experiments demonstrate the effectiveness of our approach, achieving a mean average precision (mAP) of 74.6%, underscoring the potential of this multi objectives model to significantly advance the perception capabilities of autonomous vehicles' cameras in challenging environments. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (or GANs) are a specific deep learning architecture often used for different usages, such as data generation or image-to-image translation. In recent years, this structure has gained increased popularity and has been used in different fields. One area of expertise currently in vogue is the use of GANs to produce synthetic aperture radar (SAR) data, and especially expand training datasets for SAR automatic target recognition (ATR). In effect, the complex SAR image formation makes these kind of data rich in information, leading to the use of deep networks in deep learning-based methods. Yet, deep networks also require sufficient data for training. However, contrary to optical images, we generally do not have a substantial number of available SAR images because of their acquisition and labelling cost; GANs are then an interesting tool. Concurrently, how to improve explainability for SAR ATR deep neural networks and how to make their reasoning more transparent have been increasingly explored as model opacity deteriorates trust of users. This paper aims at reviewing how GANs are used with SAR images, but also giving perspectives on how GANs could be used to improve interpretability and explainability of SAR classifiers. [ABSTRACT FROM AUTHOR]

The presence of defects in substation equipment is a major factor affecting the safety of power transmission. Therefore, timely and accurate detection of these defects is crucial. As intelligent inspection robots advance, using mainstream object detection models to diagnose surface defects in substation equipment has become a focal point of current research. However, the lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. To address the issue of insufficient training data for defect images with complex backgrounds, such as rust and surface oil leakage in substation equipment, which leads to the poor performance of detection models, this paper proposes a novel adversarial deep learning model for substation defect image generation: the Abnormal Defect Detection Generative Adversarial Network (ADD-GAN). Unlike existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model uses a joint discriminator for both overall images and defect images to address the issue of low attention to local defect areas, thereby reducing the loss of image features. This approach enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves a mean average precision (mAP) of 81.5% on the test dataset, and outperforms other image data augmentation and generation methods. This confirms that the ADD-GAN method can generate a high-fidelity image dataset of substation equipment defects. [ABSTRACT FROM AUTHOR]

Providing the completely automated public test to tell computers and humans apart (CAPTCHA) services that are not vulnerable to learning is an important issue. Image-based CAPTCHA services have strengthened authentication by taking advantage of the fact that it is more difficult for computers to understand images, but the rapid growth of deep learning has made it possible to break and attack authentication. Image-based CAPTCHA uses pre-stored data, which enables learning and results in vulnerability. This paper presents an adaptive generative adversarial network (GAN) selection scheme using time-average image quality maximization subject to system/buffer stability. By using GAN, it can generate and provide new images for CAPTCHA authentication every time, preventing deep learning from learning images and enhancing security. In the image generation process, the trade-off exists between image quality and generation time, and in consideration of this trade-off, delay aware image-based authentication Lyapunov-based algorithm is proposed for stable and maximized performance. Moreover, through the performance evaluation, we investigate and show the existence of trade-off between generation time and generated image quality in the image generation process in both quantitative and qualitative manner. [ABSTRACT FROM AUTHOR]

Introduction: Optical and SAR image matching is one of the fields within multisensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images. Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network. Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice. [ABSTRACT FROM AUTHOR]

With the development of the field of deep learning, image recognition, enhancement and other technologies have been widely used.However, dark lighting environments in reality, such as insufficient light at night, cause or block photographic images in low brightness, severe noise, and a large number of details are lost, resulting in a huge loss of image content and information, which hinders further analysis and use. Such problems not only exist in the traditional deep learning field, but also exist in criminal investigation, scientific photography and other fields, such as the accuracy of low-light image. However, in the current research results, there is no perfect means to deal with the above problems. Therefore, the study of low-light image enhancement has important theoretical significance and practical application value for the development of smart cities. In order to improve the quality of low-light enhanced images, this paper tries to introduce the luminance attention mechanism to improve the enhancement efficiency. The main contents of this paper are summarized as follows: using the attention mechanism, we proposed a method of low-light image enhancement based on the brightness attention mechanism and generative adversarial networks. This method uses brightness attention mechanism to predict the illumination distribution of low-light image and guides the enhancement network to enhance the image adaptiveness in different luminance regions. At the same time, u-NET network is designed and constructed to improve the modeling process of low-light image. We verified the performance of the algorithm on the synthetic data set and compared it with traditional image enhancement methods (HE, SRIE) and deep learning methods (DSLR). The experimental results show that our proposed network model has relatively good enhancement quality for low-light images, and improves the overall robustness, which has practical significance for solving the problem of low-light image enhancement. [ABSTRACT FROM AUTHOR]

A large amount of sensitive information is generated in today's evolving network environment. Some hackers utilize low-frequency attacks to steal sensitive information from users. This generates minority attack samples in real network traffic. As a result, the data distribution in real network traffic is asymmetric, with a large number of normal traffic and a rare number of attack traffic. To address the data imbalance problem, intrusion detection systems mainly rely on machine-learning-based methods to detect minority attacks. Although this approach can detect minority attacks, the performance is not satisfactory. To solve the above-mentioned problems, this paper proposes a novel high-performance multimodal deep learning method. The method is based on deep learning. It captures the features of minority class attacks based on generative adversarial networks, which in turn generate high-quality minority class sample attacks. Meanwhile, it uses the designed multimodal deep learning model to learn the features of minority attacks. The integrated classifier then utilizes the learned features for multi-class classification. This approach achieves 99.55% and 99.95% F-measure, 99.56% and 99.96% accuracy on the CICIDS2017 dataset and the NSL-KDD dataset, respectively, with the highest false positives at only 3.4%. This exceeds the performance of current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data-enhanced virtual texture generation network for use in deep learning detection systems. The current methods of data enhancement, such as image flipping, scaling ratios, or Generative Adversarial Networks, have limitations as they cannot determine characteristics beyond the training data. The proposed system uses the texture characteristics of a learning surface to generate surface textures through the Open Graphics Library, which can simulate material textures, light sources, and shadow effects. This enables the generation of required texture parameters for the Reinforcement Learning Network to conduct parameter search. The generated image is authenticated by a discriminator, and the reward score is fed back into the critic network to update the value network. The proposed system can complement the imbalance of defective data types, generate large quantities of random and non-defective data, and automatically classify and label during the generation process, reducing labor consumption and improving labeling accuracy. The study found that the proposed data enhancement method can increase the diversity of data characteristics, and the generated data can increase the recall rate of test and verification data sets. Specifically, the proposed system increased the recall rate of test data sets with different distributions from 78.21% to 82.40% and the recall rate of verification data sets with the same distribution from 81.64 to 91.94%. [ABSTRACT FROM AUTHOR]

The simple and efficient network structure of U-Net is widely used in medical image segmentation, and many scholars have made various researches on the U-Net structure. This paper elucidates in the following: firstly, the paper summarizes the key challenges of the U-Net network in the field of medical image segmentation; next, it elaborates the formats and characteristics of medical image datasets that are commonly used in the U-Net network; then, it summarizes the six improvement mechanism of U-Net: skip connection mechanism, generative adversarial network, residual connection mechanism, 3D-UNet, Transformer mechanism, and dense connecting mechanism. Finally, the paper discusses the relationship between these improvement mechanisms and commonly used medical data formats, and points out the ideas and directions for future improvement, so as to stimulate the unlimited potential of U-Net in medical image segmentation. [ABSTRACT FROM AUTHOR]

Weld stability is directly related to the safety and reliability of engineering, and continuous improvement of its detection technology has great research significance. This paper presents a novel method for weld stability detection based on color digital holography. A color digital holography optical path was designed to capture the holograms of welds under varying loads. Several common denoising algorithms were used to deal with speckle noise in the wrapped phase, among which the 4-f optical simulation integrated cycle-consistent generative adversarial network (4f-CycleGAN) denoising algorithm based on deep learning was more suitable for the color digital holographic detection system. The three-dimensional deformation fields of three samples (lap-jointed, butt-jointed, and defective butt-jointed aluminum alloy plates) under different loads were calculated. The center profile of the deformation field in the direction of load and holographic reconstruction images are used to determine the position of the weld. The coefficient of variation near the weld was used to evaluate the weld stability. The coefficient of variation for lap-jointed, butt-jointed and defective butt-jointed plates are 0.0335 (<0.36, good stability), 0.1240 (<0.36, good stability) and 0.3965 (>0.36, poor stability), respectively. The research results of this paper provide a new strategy for detecting weld stability. [ABSTRACT FROM AUTHOR]

Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time–frequency domain-based deep learning methods are relatively rare and usually employ single loss function, resulting in suboptimal performance on low SNR signals and potential damage to P wave. This paper proposes a method based on generative adversarial networks (GANs). Compared to convolutional neural networks, the discriminator in GANs helps retain more true signal details by judging denoising performance. Additionally, an attention mechanism is introduced to fully extract signal features, and a perceptual loss is employed to evaluate the difference between the denoised result and the target's high-level features. Experimental results show that this method can effectively improve SNR and ensure that the denoised result is close to the true signal. Furthermore, by comparing DeepDenoiser and ARDU, it is proven that the proposed method achieves better denoising performance, especially for low SNR signals, while causing less damage to the seismic signals. [ABSTRACT FROM AUTHOR]

This document is an editorial published in the journal Frontiers in Marine Science, discussing the application of deep learning technology in marine science research. It highlights the potential of deep learning in various research fields, including biology, ecosystems, climate, energy, and physical and chemical interactions. The document also provides a summary of 39 research papers published in the journal, covering topics such as marine image enhancement, underwater visual recognition, dataset and labeling, and marine process prediction. The papers explore different methods and techniques to improve the quality and analysis of underwater images and data. The studies aim to enhance our understanding of marine life and ecosystems while also addressing challenges in data collection and analysis. [Extracted from the article]

Cracks in tunnel lining surfaces directly threaten structural integrity; therefore, regular inspection of cracks is essential. Lightweight convolutional neural networks (LCNNs) have recently offered a promising alternative to conventional manual inspection. However, the effectiveness of LCNNs is still adversely affected by the lack of sufficient crack images, which limits the potential detection performance. In this paper, transfer learning was used to optimize deep convolutional generative adversarial networks (DCGANs) for crack image synthesis to significantly improve the accuracy of LCNNs. In addition, an improved LCNN model named ShuffleNetV2-1.0-SE was proposed, incorporating the squeeze–excitation (SE) attention mechanism into ShuffleNetV2-1.0 and realizing highly accurate classification results while maintaining lightness. The results show that the DCGAN-based data enhancement method can significantly improve the classification accuracy of ShuffleNetV2-1.0-SE for tunnel lining cracks. ShuffleNetV2-1.0-SE achieves an accuracy of 98.14% on the enhanced dataset, which is superior to multiple advanced LCNN models. [ABSTRACT FROM AUTHOR]

Convenient and accurate blood pressure (BP) measurement is of great importance in both clinical and daily life. Although deep learning (DL) can achieve cuff-less BP measurement based on Photoplethysmography (PPG), the performance of DL is affected by few-shot data. Data augmentation becomes an effective way to enhance the size of the training data. In this paper, we use cropping, flipping, DTW barycentric averaging (DBA), generative adversarial network (GAN) and variational auto-encoder (VAE) for the data augmentation of PPG. Furthermore, a PE–CNN–GRU model is designed for cuff-less BP measurement applying position encoding (PE), convolutional neural networks (CNNs) and gated recurrent unit (GRU) networks. Experiment results based on real-life datasets show that VAE is the most suitable method for PPG data augmentation, which can reduce the mean absolute error (MAE) of PE–CNN–GRU when measuring systolic blood pressure (SBP) and diastolic blood pressure (DBP) by 18.80% and 19.84%. After the data augmentation of PPG, PE–CNN–GRU achieves accurate and cuff-less BP measurement, thus providing convenient support for preventing cardiovascular diseases. [ABSTRACT FROM AUTHOR]

In pursuit of cost-effective manufacturing, enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips. To ensure consistent chip orientation during packaging, a circular marker on the front side is employed for pin alignment following successful functional testing. However, recycled chips often exhibit substantial surface wear, and the identification of the relatively small marker proves challenging. Moreover, the complexity of generic target detection algorithms hampers seamless deployment. Addressing these issues, this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips, termed Van-YOLOv8. Initially, to alleviate the influence of diminutive, low-resolution markings on the precision of deep learning models, we utilize an upscaling approach for enhanced resolution. This technique relies on the Super-Resolution Generative Adversarial Network with Extended Training (SRGANext) network, facilitating the reconstruction of high-fidelity images that align with input specifications. Subsequently, we replace the original YOLOv8smodel's backbone feature extraction network with the lightweight VanillaNetwork (VanillaNet), simplifying the branch structure to reduce network parameters. Finally, a Hybrid Attention Mechanism (HAM) is implemented to capture essential details from input images, improving feature representation while concurrently expediting model inference speed. Experimental results demonstrate that the Van-YOLOv8 network outperforms the original YOLOv8s on a recycled chip dataset in various aspects. Significantly, it demonstrates superiority in parameter count, computational intricacy, precision in identifying targets, and speed when compared to certain prevalent algorithms in the current landscape. The proposed approach proves promising for real-time detection of recycled chips in practical factory settings. [ABSTRACT FROM AUTHOR]

A major research subject in recent times is Alzheimer's disease (AD) due to the growth and considerable societal impacts on health. So, the detection of AD is essential for medication care. Early detection of AD is critical for effective treatment, and monitoring the time period between normal aging's unavoidable cognitive loss and dementia's more catastrophic degradation is common practice. The deep learning method for early diagnosis and automated categorization of AD has suddenly gained a lot of attention since rapid advancement in the field of GANs approaches has now been used in the clinical research sector. Many recent studies using brain MRI images and convolutional neural networks (CNNs) to identify Alzheimer's disease have yielded promising results. Instead of adequately engaging with the lack of real data, many research papers have focused on prediction. The main purpose of this paper is to do this by generating synthetic MRI images using a series of DCGANs. This paper demonstrates the effectiveness of this concept by cascading DCGANs that imitate different stages of Alzheimer's disease and utilizing SRGANs to enhance the resolution of MRI scans. The purpose of this research is to come forward and tell if an individual might just get Alzheimer's disease. CNN, DCGANs, and SRGANs are used in this paper to present a deep learning-based approach that improves classification and prediction accuracy to 99.7% and also handles the lack of data and the resolution of data. [ABSTRACT FROM AUTHOR]

The accurate and reliable acquisition of measurement information is very important for the stable operation of power systems, especially the operation status information of new energy stations. With the increasing proportion of new energy stations in power systems, the quality issues of data from these stations, caused by communication congestion, interference, and network attacks, become more pronounced. In this paper, to deal with the issue of low accuracy and poor performance of bad data restoration in new energy stations, a novel deep learning approach by combining the modified long short-term memory (LSTM) neural network and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is proposed. The proposed method can be implemented in a parallel ensemble way. First, the normal data set acquired from multiple sections of new energy stations is utilized to train the modified LSTM and WGAN-GP model. Secondly, according to the data characteristics and rules captured by each model, the two models are systematically integrated and the bad data repair model pool is constructed. Subsequently, the results of model repair are screened and merged twice by the parallel integration framework to obtain the final repair result. Finally, the extensive experiments are carried out to verify the proposed method. The simulative results of energy stations in a real provincial power grid demonstrate that the proposed method can effectively repair bad data, thereby enhancing the data quality of new energy stations. [ABSTRACT FROM AUTHOR]

Deep learning has made significant advancements in recent years. The technology is rapidly evolving and has been used in numerous automated applications with minimal loss. With these deep learning methods, medical image analysis for disease detection can be performed with minimal errors and losses. A survey of deep learning-based medical image classification is presented in this paper. As a result of their automatic feature representations, these methods have high accuracy and precision. This paper reviews various models like CNN, Transfer learning, Long short term memory, Generative adversarial networks, and Autoencoders and their combinations for various purposes in medical image classification. The total number of papers reviewed is 158. In the study, we discussed the advantages and limitations of the methods. A discussion is provided on the various applications of medical imaging, the available datasets for medical imaging, and the evaluation metrics. We also discuss the future trends in medical imaging using artificial intelligence. [ABSTRACT FROM AUTHOR]

Currently, generative applications are reshaping different fields, such as art, computer vision, speech processing, and natural language. The computer science personalization area is increasingly relevant since large companies such as Spotify, Netflix, TripAdvisor, Amazon, and Google use recommender systems. Then, it is rational to expect that generative learning will increasingly be used to improve current recommender systems. In this paper, a method is proposed to generate synthetic recommender system datasets that can be used to test the recommendation performance and accuracy of a company on different simulated scenarios, such as large increases in their dataset sizes, number of users, or number of items. Specifically, an improvement in the state-of-the-art method is proposed by applying the Wasserstein concept to the generative adversarial network for recommender systems (GANRS) seminal method to generate synthetic datasets. The results show that our proposed method reduces the mode collapse, increases the sizes of the synthetic datasets, improves their ratings distributions, and maintains the potential to choose the desired number of users, number of items, and starting size of the dataset. Both the baseline GANRS and the proposed Wasserstein-based WGANRS deep learning architectures generate fake profiles from dense, short, and continuous embeddings in the latent space instead of the sparse, large, and discrete raw samples that previous GAN models used as a source. To enable reproducibility, the Python and Keras codes are provided in open repositories along with the synthetic datasets generated to test the proposed architecture (https://github.com/jesusbobadilla/ganrs.git). [ABSTRACT FROM AUTHOR]

Crack detection at the pixel level across complex scenarios (structural interference and adverse working conditions) is a critical consideration in the maintenance of ballastless track slab (BTS). Although existing deep learning models achieve acceptable accuracy on cracks with a monotonous background, the ground truth with high labor cost is inevitable and their performance in complex scenarios may fall far below their theoretical bounds or even cause "all-black images." A hybrid algorithm based on synthetic data from digital twin model and weakly supervised style transfer is proposed in this paper for addressing the above challenges. The algorithm uses a region-attention strategy to enable the uncontrolled generative adversarial network (GAN) focusing its attention on weak labels containing crack regions, directly obtaining segmentation results with the same style as the ground truth of the crack forest dataset. In addition, a digital twin model that can simulate the real inspection working conditions is established to generate a synthetic crack dataset, enabling the hybrid algorithm to extract the most discriminative features. The results show that the performance of the hybrid algorithm on inspection images across complex scenarios is nearly 25% higher than that of the DeepLabv3+ network, while the time cost consumed is only 0.5% of the latter. The deployment of the region attention strategy also enables the hybrid algorithm to achieve a mean intersection of union (MIoU) of 79.38%, which is nearly twice as much as that of GAN. It not only eliminates the oversegmentation caused by structures such as rails and fastener systems but also overcomes "all-black images." In addition, synthetic data can greatly enlarge the range, type, and number of discriminative crack features compared with data augmentation based on limited real data, thus enhancing the performance of the hybrid algorithm for uncertain inspection data. Particularly, the fully trained hybrid algorithm based on the synthetic dataset shows good adaptability and generalization to adverse working conditions such as uneven lighting, noise, and blur. [ABSTRACT FROM AUTHOR]

Inadequate training data is a significant challenge for deep learning techniques, particularly in applications where data is difficult to get, and publicly available datasets are uncommon owing to ethical and privacy concerns. Various approaches, such as data augmentation and transfer learning, are employed to address this problem, which help to some extent in removing this limitation. However, after a certain amount of data augmentation, the quality of the generated data stalls, and transfer learning suffers from the issue of negative transfer. This paper proposes a novel generative adversarial network-based synthetic data training (GAN-ST) model to generate synthetic data for training a lightweight convolutional neural network (CNN). An enhanced generator is proposed to quickly saturate and cover the colour space of the training distribution. The GAN-ST model is based on Deep Convolutional Generative Adversarial Network(s) (DCGAN) and Conditional Generative Adversarial Network(s) (CGAN) models, which consist of an enhanced generator. The study evaluates the accuracy of a CNN model on the MNIST and CIFAR 10 datasets using both original and synthetic data. The results revealed an impressive classifier accuracy on the MNIST dataset, achieving an accuracy of 99.38% on GAN-ST-generated synthetic training data, which is only 0.05% lower than the performance on original data-based training. The classifier performance on the CIFAR dataset is also remarkable, achieving an accuracy of 90.23%. The performance of CNN trained using GAN-ST-based synthetic data is notable, with the most considerable improvement of 0.66% and 7.06%, over a single GAN-based synthetic data training for the MNIST and CIFAR datasets, respectively. By training two GANs independently, the GAN-ST model covers different parts of the original data distribution, resulting in a more diverse and realistic training data set for the classifier. This diverse set of synthetic data, when used to train a CNN, shows better generalization to new data, leading to improved classification accuracy. [ABSTRACT FROM AUTHOR]

In response to the rapid advancements in facial manipulation technologies, particularly facilitated by Generative Adversarial Networks (GANs) and Stable Diffusion-based methods, this paper explores the critical issue of deepfake content creation. The increasing accessibility of these tools necessitates robust detection methods to curb potential misuse. In this context, this paper investigates the potential of Vision Transformers (ViTs) for effective deepfake image detection, leveraging their capacity to extract global features. Objective: The primary goal of this study is to assess the viability of ViTs in detecting multiclass deepfake images compared to traditional Convolutional Neural Network (CNN)-based models. By framing the deepfake problem as a multiclass task, this research introduces a novel approach, considering the challenges posed by Stable Diffusion and StyleGAN2. The objective is to enhance understanding and efficacy in detecting manipulated content within a multiclass context. Novelty: This research distinguishes itself by approaching the deepfake detection problem as a multiclass task, introducing new challenges associated with Stable Diffusion and StyleGAN2. The study pioneers the exploration of ViTs in this domain, emphasizing their potential to extract global features for enhanced detection accuracy. The novelty lies in addressing the evolving landscape of deepfake creation and manipulation. Results and Conclusion: Through extensive experiments, the proposed method exhibits high effectiveness, achieving impressive detection accuracy, precision, and recall, and an F1 rate of 99.90% on a multiclass-prepared dataset. The results underscore the significant potential of ViTs in contributing to a more secure digital landscape by robustly addressing the challenges posed by deepfake content, particularly in the presence of Stable Diffusion and StyleGAN2. The proposed model outperformed when compared with state-of-the-art CNN-based models, i.e., ResNet-50 and VGG-16. [ABSTRACT FROM AUTHOR]

Yoga is a popular practice that promotes physical and mental well-being. The lack of regulation and standardization in yoga teaching and learning can lead to unsafe practices and health issues for practitioners, such as injury or exacerbation of pre-existing medical conditions. Accurate classification of yoga postures is essential for effective teaching and learning of yoga. Deep Learning (DL) techniques have shown significant potential in automating the process of posture classification, leading to improve accuracy and efficiency. This paper provides a comprehensive review of the use of DL techniques for yoga posture classification. Different DL algorithms, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Generative Adversarial Network are explored. Additionally, various datasets used for training and testing deep learning models for yoga posture classification are discussed. Challenges involved in yoga posture classification, such as variability in posture performance and the lack of standardized datasets, are also highlighted. A comparison of different DL techniques for yoga posture classification is presented, mentioning their respective strengths and weaknesses. This review emphasizes the potential benefits of DL for accurate yoga posture classification and provides insights into future research directions in this field. [ABSTRACT FROM AUTHOR]

Digital watermarking plays a vital role in protecting the rights and integrity of images, preventing unauthorized use, and promoting a safer and more accountable digital environment. Conventional watermarking schemes often suffer from limited robustness and susceptibility to various image processing operations, while deep learning-based watermarking schemes provide enhanced resilience to attacks and greater adaptability to complex image transformations, making them more effective in maintaining the integrity and ownership of digital content. This paper provides a concise analysis of commonly utilized deep learning networks and watermarking methods. The article begins by discussing the general framework, applications, and classification of watermarking based on different use cases. It then delves into deep learning network models such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Deep Neural Networks (DNN), along with associated watermarking techniques. Lastly, the paper addresses the challenges and offers guidance for future developers and researchers in this field. [ABSTRACT FROM AUTHOR]

Deep learning is a subfield of artificial intelligence that has gained significant attention in recent years due to its remarkable success in various domains. The main objective of this research paper is to provide a comprehensive study of deep learning algorithms, including their architecture, training techniques, and applications. In this paper, we have reviewed the literature on deep learning algorithms, highlighting their strengths and limitations. We have also discussed the current trends in deep learning research and provided insights into the future scope of deep learning algorithms. Our findings suggest that deep learning algorithms have the potential to revolutionize various industries and bring significant advancements in the field of artificial intelligence. [ABSTRACT FROM AUTHOR]

The quality of weld seams is a core factor affecting the performance of welded components, significantly impacting the safety and reliability of weldment structures. It holds considerable importance in quality control and cost management. Therefore, weld seam inspection is an indispensable part of industrial manufacturing and a key topic in non-destructive testing. This paper comprehensively summarises the main research results in weld seam feature extraction and defect identification based on systematic and detailed literature research. Firstly, the paper compares current mainstream non-destructive testing technologies, such as ultrasonic testing, radiographic testing, and infrared thermography, regarding detection principles, performance accuracy, and efficiency. It summarises their applicable scopes, as well as their advantages and disadvantages, and briefly introduces image processing-based methods for weld seam feature extraction. Secondly, a discussion and analysis of weld defect recognition are carried out, focusing on intelligent recognition methods based on deep learning, such as convolutional neural networks, generative adversarial networks, YOLO, etc. Finally, the outlook and future research trends are presented from various perspectives, such as real-time, adaptability, intelligence, etc. to provide a theoretical basis and technical support for welding quality control and structural safety assessment. [ABSTRACT FROM AUTHOR]

Generative design refers to a methodology that not only simulates the characteristics of a given data or system but also creates artificial data for various purposes. It's a significant research area encompassing diverse issues such as privacy preservation, data distribution analysis, and the development of surrogate models. Initially, research in this field primarily employed stochastic models or basic machine learning methods. However, with the advancement of deep learning technology, numerous studies have emerged, showcasing developed mechanisms using artificial neural network-based methods like variational autoencoders (VAEs) and generative adversarial networks (GANs). These studies extend across different data types, including images and texts, tailored to specific objectives. This paper presents a systematic review of generative design research focused on tabular data. We begin by elucidating the characteristics of tabular data within generative design, followed by a discussion on the goals and challenges in this area. Subsequently, the paper introduces various generative design studies on tabular data, categorized according to their methodological development and unique objectives. Finally, we address the benchmark methods used in generative design for tabular and how their performance is evaluated. [ABSTRACT FROM AUTHOR]

Accurate tool wear state identification models are essential to ensure manufacturing reliability and efficiency. Tool wear state recognition systems establish a mapping relationship with the tool state by extracting signal features. Therefore, this paper proposes an architecture for identifying the actual wear state of data unbalanced machining tools by applying the power of minimalism in deep learning networks, namely, VanillaNet, combined with recurrence plot encoding technology (RP). In this paper, the signal is preprocessed by RP, and the nonlinear one-dimensional time-sequential digital signal embedded in variable time-lag delay coordinate space in the phase space is converted into a two-dimensional (2D) color texture image, thereby achieving the feature extraction of tool wear. Then, the data-enhanced 2D recurrence coded image is used as the input to VanillaNet, and its minimalist network architecture is applied to establish the mapping relationship between tool wear states and wear features. This process reduces the state recognition time and achieves the fast recognition of tool wear states. The model in this paper achieves more than 95 % on all four classification metrics: accuracy, recall, F1 score, and precision in three sets of crossover experiments while reducing misclassification in the sharp wear phase. The proposed model also outperforms three DL-based methods, namely, CNN-Attention, AlexNet, and ResNet. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) have achieved remarkable success in various tasks, including image generation, editing, and reconstruction, as well as in unsupervised and representation learning. Despite their impressive capabilities, GANs are often plagued by challenges such as unstable training dynamics and limitations in generating complex patterns. To address these challenges, we propose a novel image style transfer method, named C3GAN, which leverages CycleGAN architecture to achieve consistent and stable transformation of image style. In this context, "image style" refers to the distinct visual characteristics or artistic elements, such as the color schemes, textures, and brushstrokes that define the overall appearance of an image. Our method incorporates cyclic consistency, ensuring that the style transformation remains coherent and visually appealing, thus enhancing the training stability and overcoming the generative limitations of traditional GAN models. Additionally, we have developed a robust and efficient image style transfer system by integrating Flask for web development and MySQL for database management. Our system demonstrates superior performance in transferring complex styles compared to existing model-based approaches. This paper presents the development of a comprehensive image style transfer system based on our advanced C3GAN model, effectively addressing the challenges of GANs and expanding application potential in domains such as artistic creation and cinematic special effects. [ABSTRACT FROM AUTHOR]

Developing precise deep learning (DL) models for predicting tool wear is challenging, particularly due to the scarcity of experimental data. To address this issue, this paper introduces an innovative approach that leverages the capabilities of tabular generative adversarial networks (TGAN) and conditional single image GAN (ConSinGAN). These models are employed to generate synthetic data, thereby enriching the dataset and enhancing the robustness of the predictive models. The efficacy of this methodology was rigorously evaluated using publicly available milling datasets. The pre-processing of acoustic emission data involved the application of the Walsh-Hadamard transform, followed by the generation of spectrograms. These spectrograms were then used to extract statistical attributes, forming a comprehensive feature vector for model input. Three DL models—encoder-decoder long short-term memory (ED-LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN)—were applied to assess their tool wear prediction capabilities. The application of 10-fold cross-validation across these models yielded exceptionally low RMSE and MAE values of 0.02 and 0.16, respectively, underscoring the effectiveness of this approach. The results not only highlight the potential of TGAN and ConSinGAN in mitigating data scarcity but also demonstrate significant improvements in the accuracy of tool wear predictions, paving the way for more reliable and precise predictive maintenance in manufacturing processes. [ABSTRACT FROM AUTHOR]

Tomographic synthetic aperture radar is an advanced multi‐channel interferometric technique for retrieving 3‐D spatial information. It can be regarded as an inherently sparse reconstruction problem and can be solved using compressive sensing algorithms. However, the performances are limited by the number of acquisitions and suffer from computational burdens in practice. This paper proposes a novel method based on deep learning, which is carried out and optimized in an end‐to‐end manner by the generative adversarial neural networks. The proposed method applies the cascaded U‐Net architectures to achieve the reconstruction of full‐channel synthetic aperture radar images and the refinement of obtained tomographic results, respectively. The proposed network is trained using simulated data and validate the technique on simulated and real data. The tests show promising results with the limited number of acquisitions while reducing the computation time. [ABSTRACT FROM AUTHOR]

Data enhancement methods need to be carefully considered and studied for the widespread application of machine vision and deep learning in the mining field. Generative adversarial networks (GANs) prove successful at generating data. However, training a high-resolution image generation network depends on a large-scale dataset and takes a long time. For coal gangue detection, this paper proposes a stride-and-transpose-based progressive generative adversarial network (STP-GAN), which can achieve fast training on a few samples and generate high-resolution images in size of 10242. We employ stride convolutions, up-sampling, and average pooling to construct the model progressively and introduce noise and style optimization. We propose a hidden-layer-frozen progressive training scheme according to the model construction. Compared with other test GANs, STP-GAN generates more authentic and diverse images. The test results of advanced object detection models show that after the auxiliary training of STP-GAN, the mean average precision and average recall of coal–gangue detection are increased by up to 6.92% and 20.39%, respectively. The proposed method can effectively improve the accuracy of coal–gangue detection through data optimization. [ABSTRACT FROM AUTHOR]

Missing data imputation is a key topic in healthcare that covers the issues and strategies involved in dealing with partial data in medical records, clinical trials, and health surveys. Data in healthcare might be missing for a variety of reasons, including non-response in surveys, data entry problems, or unrecorded information during therapeutic appointments. This paper introduces a novel approach to impute missing data utilizing a hybrid model that integrates denoising autoencoders with generative adversarial networks (GANs). We begin by highlighting the prevalence of missing data in health care datasets and the potential impact on analytical outcomes. The proposed methodology leverages the denoising autoencoder's ability to reconstruct data from noisy inputs, coupled with the GAN's proficiency in generating synthetic data that is indistinguishable from real data. By combining these two neural network architectures, our model demonstrates an enhanced capability to predict and fill in missing data points effectively. To validate our approach, we conducted experiments on several large-scale health care datasets with varying degrees of artificially introduced missingness. The performance of our model was benchmarked against traditional imputation methods such as mean imputation and k-nearest neighbors, as well as against standalone denoising autoencoders and GANs. Our results indicate a significant improvement in imputation accuracy, as measured by root mean square error (RMSE) and mean absolute error (MAE), confirming the efficacy of the hybrid model in handling missing data in a robust manner. [ABSTRACT FROM AUTHOR]

Introduction: Lung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment. Methods: This paper explores the lung CT image segmentation method by generative adversarial networks. We employ a variety of generative adversarial networks and used their capability of image translation to perform image segmentation. The generative adversarial network is employed to translate the original lung image into the segmented image. Results: The generative adversarial networks-based segmentation method is tested on real lung image data set. Experimental results show that the proposed method outperforms the state-of-the-art method. Discussion: The generative adversarial networks-based method is effective for lung image segmentation. [ABSTRACT FROM AUTHOR]

In autonomous driving, Frequency-Modulated Continuous-Wave (FMCW) radar has gained widespread acceptance for target detection due to its resilience and dependability under diverse weather and illumination circumstances. Although deep learning radar target identification models have seen fast improvement, there is a lack of research on their susceptibility to adversarial attacks. Various spoofing attack techniques have been suggested to target radar sensors by deliberately sending certain signals through specialized devices. In this paper, we proposed a new adversarial deep learning network for spoofing attacks in radar target detection (RTD). Multi-level adversarial attack prevention using deep learning is designed for the coherence pulse deep feature map from DAALnet and Range-Doppler (RD) map from TDDLnet. After the discrimination of the attack, optimization of hybrid deep learning (OHDL) integrated with enhanced PSO is used to predict the range and velocity of the target. Simulations are performed to evaluate the sensitivity of AOHDL for different radar environment configurations. RMSE of AOHDL is almost the same as OHDL without attack conditions and it outperforms the earlier RTD implementations. [ABSTRACT FROM AUTHOR]

Three-dimensional reconstruction based on optical satellite images has always been a research hotspot in the field of photogrammetry. In particular, the 3D reconstruction of building areas has provided great help for urban planning, change detection and emergency response. The results of 3D reconstruction of satellite images are greatly affected by the input images, and this paper proposes an improvement method for 3D reconstruction of satellite images based on the generative adversarial network (GAN) image enhancement. In this method, the perceptual loss function is used to optimize the network, so that it can output high-definition satellite images for 3D reconstruction, so as to improve the completeness and accuracy of the reconstructed 3D model. We use the public benchmark dataset of satellite images to test the feasibility and effectiveness of the proposed method. The experiments show that compared with the satellite stereo pipeline (S2P) method and the bundle adjustment (BA) method, the proposed method can automatically reconstruct high-quality 3D point clouds. [ABSTRACT FROM AUTHOR]

Computer vision has advanced much in recent years. Several tasks, such as image recognition, classification, or image restoration, are regularly solved with applications using artificial intelligence techniques. Image restoration comprises different use cases such as style transferring, improvement of quality resolution, or completing missing parts. The latter is also known as image inpainting, virtual image inpainting in this case, which consists of reconstructing missing regions or elements. This paper explores how to evaluate the performance of a deep learning method to do virtual image inpainting to reconstruct missing architectonical elements in images of ruined Greek temples to measure the performance of different activation functions. Unlike a previous study related to this work, a direct reconstruction process without segmented images was used. Then, two evaluation methods are presented: the objective one (mathematical metrics) and an expert (visual perception) evaluation to measure the performance of the different approaches. Results conclude that ReLU outperforms other activation functions, while Mish and Leaky ReLU perform poorly, and Swish's professional evaluations highlight a gap between mathematical metrics and human visual perception. [ABSTRACT FROM AUTHOR]

Deep learning‐based models have become ubiquitous across a wide range of applications, including computer vision, natural language processing, and robotics. Despite their efficacy, one of the significant challenges associated with deep neural network (DNN) models is the potential risk of copyright leakage due to the inherent vulnerability of the entire model architecture and the communication burden of the large models during publishing. So far, it is still challenging for us to safeguard the intellectual property rights of these DNN models while reducing the communication time during model publishing. To this end, this paper introduces a novel approach using knowledge distillation techniques aimed at training a surrogate model to stand in for the original DNN model. To be specific, a knowledge distillation generative adversarial network (KDGAN) model is proposed to train a student model capable of achieving remarkable performance levels while simultaneously safeguarding the copyright integrity of the original large teacher model and improving communication efficiency during model publishing. Herein, comprehensive experiments are conducted to showcase the efficacy of model copyright protection, communication‐efficient model publishing, and the superiority of the proposed KDGAN model over other copyright protection mechanisms. [ABSTRACT FROM AUTHOR]

Drones have become essential tools across various industries due to their ability to provide real-time data and perform automated tasks. However, integrating multiple sensors on a single drone poses challenges such as payload limitations and data management issues. This paper proposes a comprehensive system that leverages advanced deep learning techniques, specifically an attention-based generative adversarial network (GAN), to address data scarcity in drone-collected time-series sensor data. By adjusting sensing frequency based on operational conditions while maintaining data resolution, our system ensures consistent and high-quality data collection. The spatiotemporal The attention mechanism within the GAN enhances the generation of synthetic data, filling gaps caused by reduced sensing frequency with realistic data. This approach improves the efficiency and performance of various applications, such as precision agriculture, environmental monitoring, and surveillance. The experimental results demonstrated the effectiveness of our methodology in extending the operational range and duration of drones and providing reliable augmented data utilizing a variety of evaluation metrics. Furthermore, the superior performance of the proposed system was verified by comparing it with various comparative GAN models. [ABSTRACT FROM AUTHOR]

Surface defect inspection based on deep learning has demonstrated outstanding performance in improving detection accuracy and model generalization. However, the small scale of defect datasets always limits the application of deep models in industry. Generative models can obtain realistic samples in a very cheap way, which can effectively solve this problem and thus has received widespread attention in recent years. This paper provides a comprehensive analysis and summary of the current studies of surface defect inspection methods proposed between 2022 and 2024. First, according to the use of generative models, these methods are classified into four categories: Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and multi-models. Second, the research status of surface defect inspection based on generative models in recent years is discussed from four aspects: sample generation, detection objective, inspection task, and learning model. Then, the public datasets and evaluation metrics that are commonly used for surface defect inspection are discussed, and a comparative evaluation of defect inspection methods based on generative models is provided. Finally, this study discusses the existing challenges for the defect inspection methods based on generative models, providing insights for future research. [ABSTRACT FROM AUTHOR]

Although modulation classification with deep learning has been widely explored, this is challenging when the training data is limited. In this paper, we meet this challenge by data augmentation based on a semi-supervised generative model, named semi-supervised variational auto-encoder GAN (SS-VAEGAN). The proposed model has two novel aspects: first, the ensemble prediction technique is presented to establish a semi-supervised framework, and thus few labeled data and abundant unlabeled data can be used simultaneously to train the generative model; second, a modified feature matching strategy is utilized to ensure the diversity of the generated samples and contribute to the final classification under limited labeled data. With the aim of the constellation diagram with density and the ResNet-18 network, we have worked out a complete solution of modulation classification with data augmentation using SS-VAEGAN. In the experiments, we investigate the classification accuracy and robustness of the proposed data augmentation method and then compare the proposed SS-VAEGAN with other deep generative models. It is shown that our method can achieve the classification accuracy of 0.968 in the extreme absence of training data. [ABSTRACT FROM AUTHOR]