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Single-image haze removal is an essential preprocessing phase in many object detection and segmentation approaches. Recently, end-to-end deep learning-based approaches have dominated the field of single-image dehazing because of their superiority in recovering clear images corrupted by different types of degradation. However, training an effective dehazing network remains challenging, particularly in the absence of high-quality realistic training datasets. In this paper, a novel approach called a subspace-based dehazing generative adversarial network (SuDGAN) is proposed. Traditional training methods attempt to apply changes to pixel intensities, whereas SuDGAN adopts a novel training approach using existing synthetic datasets to learn the adjustment of subspace components related to haze. This approach enables the network to learn more discriminative haze-aware features and focus on adjusting the components that are more affected by haze (luminance) while preserving those that are less influenced by haze (structure). The proposed SuDGAN, along with several state-of-the-art approaches, is evaluated on various challenging synthetic and realistic datasets using haze-related and traditional evaluation metrics. The experimental results demonstrate the efficiency of SuDGAN in removing haze and producing visually pleasing results. Furthermore, the results show that SuDGAN has clear quantitative and qualitative improvements over most state-of-the-art dehazing approaches. [ABSTRACT FROM AUTHOR]

Advancements in Machine Learning (ML) and Computer Vision have led to notable improvements in the detection of breast cancer. However, the accuracy of the classifier is limited due to imbalanced datasets that cause overfitting. Thus, additional images are needed to improve the classifier's performance. Generative Adversarial Networks (GAN) are used for image augmentation. Still, its limitations, such as the random use of different types of GAN, can lead to a too-good discriminator, causing generator training to fail due to vanishing gradients. Therefore, selecting the appropriate GAN model for the given scenario is crucial for optimal performance. This paper proposes a novel Smart Generative Adversarial Network (Smart GAN) architecture to develop an efficient and computational classification model for a limited imbalanced dataset. Smart GAN uses a three-fold approach. Different types of GAN augment the dataset in the first phase of experimental work, and their evaluation metrics are calculated. In the second phase, the metric scores are used as rewards for the Reinforcement learning model (Q-learning approach). The best augmentation is chosen based on the best Q-values for each metric score. It compares three different Convolutional Neural Networks (CNN) and selects the best-suited network to classify the augmented datasets. The proposed Smart GAN architecture outperforms other existing approaches by giving a better accuracy of 89.62% and 89.91% on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) augmented datasets, respectively, representing an approximately 10% increment in detection rate compared to non-augmented datasets. [ABSTRACT FROM AUTHOR]

With space electromagnetic environments becoming increasingly complex, the direction of arrival (DOA) estimation based on the point source model can no longer meet the requirements of spatial target location. Based on the characteristics of the distributed source, a new DOA estimation algorithm based on deep learning is proposed. The algorithm first maps the distributed source model into the point source model via a generative adversarial network (GAN) and further combines the subspace-based method to achieve central DOA estimation. Second, by constructing a deep neural network (DNN), the covariance matrix of the received signals is used as the input to estimate the angular spread of the distributed source. The experimental results show that the proposed algorithm can achieve better performance than the existing methods for a distributed source. [ABSTRACT FROM AUTHOR]

Contract bridge is a competitive-cooperative multiplayer game. In the bidding phase, the decision-making process is complex, given the extensive range of inaccessible information from the partner and opponents. Inferring partner private information seems to be a viable way to promote cooperation. However, extant research has neglected the reliability and effective utilization of inferred information, consequently limiting adaptability in the face of unpredictable policies and hidden hands of opponents. This paper introduces a novel bidding decision algorithm for contract bridge, utilizing generative adversarial inferring (GAI) and alternate inference-decision reinforcement learning (AID-RL). Specifically, GAI advances the reliability and rationality of partner information inference, factoring in stochastic noises and utilizing adversarial training. Concurrently, AID-RL strengthens the effective and explicit exploitation of inference by integrating a reinforcement objective that increases decision-making payoff into the inference model training alongside the original generative objective. Moreover, a novel self-play approach, augmented by the counterfactual difference reward (CDR) mechanism is proposed to expedite policy updates and ensure robust decision-making against inference biases and unknown opponents. Empirical results demonstrate the superior performance of our algorithm, attaining a noteworthy victory of + 0.359 IMPs against Wbridge5, outperforming EPNN and SiB + 0.596 and + 0.062 IMPs. [ABSTRACT FROM AUTHOR]

Defect detection is a crucial technology that is extensively employed in the manufacturing industry to monitor and ensure the quality of output. Deep learning models have shown remarkable potential for defect detection. However, the success of these models heavily relies on voluminous training data. Collecting substantial amounts of defect data is challenging in practical settings, and the tedious process of pixel-level defect annotation further complicates the task. Among the common defects encountered in manufacturing, scratches are particularly significant. To address these challenges, this study proposes a two-phase generative adversarial network (GAN) approach for synthesizing defect images and generating semi-automatic pixel-wise labels for anomaly detection. The first phase primarily focuses on synthesizing images, while the second phase involves the pixel-wise labeling of the images. The synthesized paired images generated by the GANs serve as input to the semantic network. Notably, the proposed methodology requires only a few real defect samples for training and a small amount of annotated data, making it practical and computationally efficient for implementation in the manufacturing industry. Experimental results indicate the effectiveness of the proposed deep-learning solution in defect detection, specifically in identifying scratches on various textured and patterned surfaces. A notably high detection accuracy is achieved, validating the potential of the approach in real-world manufacturing scenarios. [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]

Lung cancer is relying as one of the significant and leading cause for the deaths which are based on cancer. So, an effective diagnosis is a crucial step to save the patients who are all dying due to lung cancer. Moreover, the diagnosis must be performed based on the severity of lung cancer and the severity can be addressed with the help of an optimal classification approach. So, this research introduced an Attention based Capsule Network (A‐Caps Net) with dispersed dynamic routing to perform in‐depth classification of the disease affected partitions of the image and results in better classification results. The attention layer with dispersed dynamic routing evaluates the digit capsule from feature vector in a constant manner. As the first stage, data acquisitioned from datasets such as Lung Nodule Analysis‐16 (LUNA‐16), The Cancer Imaging Archive (TCIA) dataset and Lung Image Database Consortium and Image Database Resource Initiative (LIDC‐IDRI). After acquisitioning data, pre‐processing is done to enhance the resolution of the image using Generative Adversarial Network. The pre‐processed output is given as output for extraction of features that takes place using GLCM and VGG‐16 which extracts the low level features and high level features respectively. Finally, categorization of lung cancer is performed using Attention based Capsule Network (A‐Caps Net) with dispersed dynamic routing which categorize the lung cancer as benign and malignant. The results obtained through experimental analysis exhibits that proposed approach attained better accuracy of 99.57%, 99.91% and 99.29% for LUNA‐16, LIDC‐IDRI and TCIA dataset respectively. The classification accuracy achieved by the proposed approach for LUNA‐16 dataset is 99.57% which is comparably higher than DBN, 3D CNN, Squeeze Nodule Net and 3D‐DCNN with multi‐layered filter with accuracies of 99.16%, 97.17% and 94.1% respectively. [ABSTRACT FROM AUTHOR]

The rapid booming of information and communication technologies (ICT) and artificial intelligence has promoted the development of maintenance practices in the building industry towards a data-driven paradigm, of which the digital twin (DT) serves as the fundamental framework to strengthen data transit ability and interoperability. Among the state-of-the-art approaches in the maintenance industry, predictive maintenance (PdM) is a prominent approach by anticipating unexpected failures and unscheduled maintenance tasks. However, most current PdM frameworks are facility-specific, lacking generality and scalability. Besides, existing solutions mainly concentrate on condition monitoring and fault identification rather than failure prediction. Moreover, good prediction results rely heavily on sufficient labelled data sets, which are costly and labour-intensive to collect. To address these issues, the author developed a unified PdM framework for the building industry from the DT perspective. Next, a novel failure prediction method utilising the Semi-supervised Generative Adversarial Network (GAN) has been proposed in this article, which makes effective utilisation of both labelled and unlabelled data. Finally, an online platform has been developed to present the monitoring and prediction information. Experimental findings show the effectiveness and superiority of the proposed method for failure prediction through public data sets of building facilities. [ABSTRACT FROM AUTHOR]

This paper studies generative adversarial networks (GANs) from the perspective of statistical inference. A GAN is a popular machine learning method in which the parameters of two neural networks, a generator and a discriminator, are estimated to solve a particular minimax problem. This minimax problem typically has a multitude of solutions and the focus of this paper are the statistical properties of these solutions. We address two key statistical issues for the generator and discriminator network parameters, consistent estimation and confidence sets. We first show that the set of solutions to the sample GAN problem is a (Hausdorff) consistent estimator of the set of solutions to the corresponding population GAN problem. We then devise a computationally intensive procedure to form confidence sets and show that these sets contain the population GAN solutions with the desired coverage probability. Small numerical experiments and a Monte Carlo study illustrate our results and verify our theoretical findings. We also show that our results apply in general minimax problems that may be nonconvex, nonconcave, and have multiple solutions. [ABSTRACT FROM AUTHOR]

The rise in power consumption (PC) is caused by several factors such as the growing global population, urbanization, technological advances, economic development, and growth of businesses and commercial sectors. In these days, intermittent renewable energy sources (RESs) are widely utilized in electric grids to meet the need for power. Data-driven techniques are essential to assuring the steady operation of the electric grid and accurate power consumption and generation forecasting. Conversely, the available datasets for time series electric power forecasting in the energy industry are not as large as those for other domains such as in computer vision. Thus, a deep learning (DL) framework for predicting PC in residential and commercial buildings as well as the power generation (PG) from RESs is introduced. The raw power data obtained from buildings and RESs-based power plants are conceded by the purging process where absent values are filled in and noise and outliers are eliminated. Next, the proposed generative adversarial network (GAN) uses a portion of the cleaned data to generate synthetic parallel data, which is combined with the actual data to make a hybrid dataset. Subsequently, the stacked gated recurrent unit (GRU) model, which is optimized for power forecasting, is trained using the hybrid dataset. Six existent power data are used to train and test sixteen linear and nonlinear models for energy forecasting. The best-performing network is selected as the proposed method for forecasting tasks. For Korea Yeongam solar power (KYSP), individual household electric power consumption (IHEPC), and advanced institute of convergence technology (AICT) datasets, the proposed model obtains mean absolute error (MAE) values of 0.0716, 0.0819, and 0.0877, respectively. Similarly, its MAE values are 0.1215, 0.5093, and 0.5751, for Australia Alice Springs solar power (AASSP), Korea south east wind power (KSEWP), and, Korea south east solar power (KSESP) datasets, respectively. [ABSTRACT FROM AUTHOR]

Generative art has drawn increased attention in recent AI applications. Traditional approaches of robot calligraphy have faced challenges in achieving style consistency, line smoothness and high-quality structural uniformity. To address the limitation of existing methods, we propose a dual generator framework based on deep adversarial networks for robotic calligraphy reproduction. The proposed model utilizes a encoder-decoder module as one generator for style learning and a robot arm as the other generator for motion learning to optimize the networks and obtain the best robot calligraphy works. Based on the enhanced datasets, multiple evaluation metrics including coverage rate, structural similarity index measure, intersection over union and Turing test are employed to perform the experimental validation. The evaluations demonstrate that the proposed method is highly effective and applicable in robot calligraphy and achieves state-of-the-art results with the average structural similarity index measure 75.91% , coverage rate 70.25%, and intersection over union 80.68%, which provides a paradigm for evaluation in the field of art. [ABSTRACT FROM AUTHOR]

The appearance of the log cross-section provides important information when assessing the quality of the log, where properties to consider include pith location and density of annual rings. This makes tasks like estimation of pith location and annual ring detection of great interest. However, creating labeled training data for these tasks can be time-consuming and subject to misjudgments. For this reason, we aim to create generated training data with controlled properties of pith location and amount of annual rings. We propose a two-step generator based on generative adversarial networks in which we can completely avoid manual labeling, not only when generating training data but also during training of the generator itself. This opens up the possibility to train the generator on other types of log end data without the need to manually label new training data. The same method is used to create two generated training datasets; one of entire log ends and one of patches of log ends. To evaluate how the generated data compares to real data, we train two deep learning models to perform estimation of pith location and ring counting, respectively. The models are trained separately on real and generated data and evaluated on real data only. The results show that the performance of both estimation of pith location and ring counting can be improved by replacing real training data with larger sets of generated training data. [ABSTRACT FROM AUTHOR]

Floods, a common natural disaster, it affects more than half of all natural disasters, primarily due to high floods, high tides, heavy rainfall, and human activity. Distinguishing between the flooded and non-flood areas during the disaster is dependent on the quality of images collected from different sources. Since synthetic aperture radar (SAR) images can see through cloud cover and capture photos of the Earth's surface during bad weather, they are excellent for detecting floods. Image Enhancement is the approach that helps to increase the resolution of the SAR images, which helps to more accurately categorize the flooded and non-flooded areas. The quality of satellite and aerial imagery can be enhanced to recognize flooded and non-flooded areas. Our proposed approach uses a super-resolution technique to enhance the resolution of SAR images. We develop a super resolution-based generative adversarial network, or SSR-GAN (Satellite Super Resolution-based Generative adversarial network). In our approach to estimating generative models through an adversarial process, we concurrently train two different models: a generative model G and a discriminative model D. An image with low resolution is transformed into high pixel density. The peak signal-to-noise ratio (PSNR), the structural similarity index(SSIM), Multiscale Structural Similarity (MSSIM) and mean squared error (MSE) are the performance metrics used to compare the interpolation methods for enhancing resolution. In comparison to the existing approaches with our proposed model SSR-GAN, the values of PSNR, SSIM, and MSSIM are greater, and the MSE error value is lower. [ABSTRACT FROM AUTHOR]

Automated brain tumor detection and classification systems have gained popularity in recent years because traditional diagnosis procedures are time-consuming and costly in nature. Deep learning(DL) methods, specifically pre-trained convolutional neural networks (CNNs), have shown promising results in accurately and rapidly classifying brain tumors. However, the lack of diverse magnetic resonance imaging (MRI) datasets has hindered the ability of DL algorithms to generalize effectively. To address this issue, this paper proposes a DL model using generative adversarial networks (GANs) in conjunction with data augmentation strategies and a structural similarity loss function employed for generating annotated images. A novel DL model inspired from Vision Transformer, Shrinking Linear Time Vision Transformer (SL(t)-ViT) network model is proposed for disease data classification. The model underwent extensive evaluation across multiple datasets, employing standard performance metrics to assess its efficacy in brain tumor identification. The proposed model achieved remarkable testing accuracies of 0.995, 0.996, 0.9954, 0.998, and 0.997 for binary classification tasks across multiple datasets, and 0.986, 0.982, 0.985, and 0.993 for multi-class classification tasks. These results underscore the superior performance of our proposed model, showcasing its capability to outperform state-of-the-art techniques. Specifically, it demonstrated a substantial margin of improvement, ranging from 1-2% for binary classification and 9-10% for multi-class classification, solidifying its position as a leading approach in brain tumor identification. Results demonstrate the efficacy of the proposed model, outperforming other models. Overall, this study highlights the potential of SL(t)-ViT and GANs in improving the accuracy, resource consumption, and efficiency of brain tumor diagnosis. [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]

Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response. [ABSTRACT FROM AUTHOR]

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose a Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE), to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress-detection methodologies in terms of precision, recall, and interpretability. The results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on. [ABSTRACT FROM AUTHOR]

The oil and gas industry employs numerical simulation tools extensively in reservoir analysis and strategic planning. This study presents a machine-learning proxy model, employing a Few-shot Learning approach with a Deep Convolutional Generative Adversarial Network (DC-GAN) to reduce computational costs using fewer training samples. The DC-GAN generates new training samples by synthesizing spatial attributes, reservoir parameters, and time series data, thus enhancing the sample variability and diversity needed for accurate production prediction. Also, the study proposes a straightforward and efficient method for data augmentation that primarily involves replicating the initial training dataset. The accumulated production forecast generated from geostatistical realizations enables intelligent reservoir management through risk analysis. The technique can reduce the processing footprint by 70%. Unlike most reservoir studies that employ synthetic datasets, this investigation adopts a real, high-dimensional, and complex reservoir model. [ABSTRACT FROM AUTHOR]

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction. [ABSTRACT FROM AUTHOR]

The nowcasting of strong convective precipitation is highly demanded and presents significant challenges, as it offers meteorological services to diverse socio-economic sectors to prevent catastrophic weather events accompanied by strong convective precipitation from causing substantial economic losses and human casualties. With the accumulation of dual-polarization radar data, deep learning models based on data have been widely applied in the nowcasting of precipitation. Deep learning models exhibit certain limitations in the nowcasting approach: The evolutionary method is prone to accumulate errors throughout the iterative process (where multiple autoregressive models generate future motion fields and intensity residuals and then implicitly iterate to yield predictions), and the "regression to average" issue of autoregressive model leads to the "blurring" phenomenon. The evolution method's generator is a two-stage model: In the initial stage, the generator employs the evolution method to generate the provisional forecasted data; in the subsequent stage, the generator reprocesses the provisional forecasted data. Although the evolution method's generator is a generative adversarial network, the adversarial strategy adopted by this model ignores the significance of temporary prediction data. Therefore, this study proposes an Adversarial Autoregressive Network (AANet): Firstly, the forecasted data are generated via the two-stage generators (where FURENet directly produces the provisional forecasted data, and the Semantic Synthesis Model reprocesses the provisional forecasted data); Subsequently, structural similarity loss (SSIM loss) is utilized to mitigate the influence of the "regression to average" issue; Finally, the two-stage adversarial (Tadv) strategy is adopted to assist the two-stage generators to generate more realistic and highly similar generated data. It has been experimentally verified that AANet outperforms NowcastNet in the nowcasting of the next 1 h, with a reduction of 0.0763 in normalized error (NE), 0.377 in root mean square error (RMSE), and 4.2% in false alarm rate (FAR), as well as an enhancement of 1.45 in peak signal-to-noise ratio (PSNR), 0.0208 in SSIM, 5.78% in critical success index (CSI), 6.25% in probability of detection (POD), and 5.7% in F1. [ABSTRACT FROM AUTHOR]

Background: Magnetic particle imaging (MPI) is a recently developed, non‐invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X‐space are two commonly used MPI reconstruction methods, where the former is extremely time‐consuming and the latter usually produces blurry images. Purpose: Currently, we proposed an end‐to‐end machine learning framework to reconstruct high‐resolution MPI images from 1‐D voltage signals directly and efficiently. Methods: The proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291 597 pairs of high‐resolution 2‐D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high‐resolution MPI image reconstruction. Results: Experiment results showed that, MPIGAN exhibited remarkable abilities in high‐resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X‐space in recovering the fine‐scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high‐quality MPI images. Conclusion: Our study provides a promising AI solution for end‐to‐end, efficient, and high‐resolution magnetic particle imaging reconstruction. [ABSTRACT FROM AUTHOR]

Faced with dynamic and increasingly diversified public transport requirements, bus operators are urged to propose operational innovations to sustain their competitiveness. In particular, ordinary bus operations are heavily constrained by well-established route options, and it is challenging to accommodate dynamic passenger flows effectively and with a good level of resource utilization performance. Inspired by the philosophy of sharing economy, many of the available transport resources on the road, such as minibuses and private vehicles, can offer opportunities for improvement if they can be effectively incorporated and exploited. In this regard, this paper proposes a metric learning-based prediction algorithm which can effectively capture the demand pattern and designs a route planning optimizer to help bus operators effectively deploy fixed routing and cooperative buses with traffic dynamics. Through extensive numerical studies, the performance of our proposed metric learning-based Generative Adversarial Network (GAN) prediction model outperforms existing ways. The effectiveness and robustness of the prediction-supported routing planner are well demonstrated for a real-time case. Further, managerial insights with regard to travel time, bus fleet size, and customer service levels are revealed by various sensitivity analysis. [ABSTRACT FROM AUTHOR]

We propose a physics-constrained generative adversarial network, PCSAGAN, based on the self-attention mechanism for high-fidelity flow field reconstruction, which can generate high-resolution and high-precision volumes for two-dimensional time-varying datasets. PCSAGAN consists of two discriminators and one generator, capable of preserving temporal coherence and spatial characteristics. It avoids the heavy computation of multilayer convolution and obtains spatial flow evolution information by using the self-attention mechanism. A soft-constraint loss function is further employed to effectively utilize the underlying physical properties. Moreover, the dataset is generated from real-world simulations rather than artificial synthesis, which ensures generalizability to practical applications. We compare PCSAGAN against volume upscaling methods using BI, ESPCN and SSR-TVD, and demonstrate its effectiveness with several time-varying flow fields through quantitative and qualitative evaluations. Meanwhile, a re-analysis framework is introduced to enable potential later visualization and exploratory analysis of the reconstructed flow field data. [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]

Generative artificial intelligence (AI) has developed as an effective tool for time series predicting, revolutionizing the typical methods of prediction. Different classical approaches that depend on existing approaches and assumptions, generative AI controls advanced deep learning (DL) approaches like generative adversarial networks (GANs) and recurrent neural networks (RNNs), to identify designs and connections in time series data. DL has accomplished major success in optimizing performances connected with AI. In the financial area, it can be extremely utilized for the stock market predictive, trade implementation approaches, and set of optimizers. Stock market predictive is the most important use case in this field. GANs with advanced AI approaches have become more significant in recent times. However, it can be utilized in image‐image‐translation and other computer vision (CV) conditions. GANs could not utilized greatly for stock market prediction because of their effort to establish the proper set of hyperparameters. This study develops an integrated spotted hyena optimization algorithm with generative artificial intelligence for time series forecasting (SHOAGAI‐TSF) technique. The purpose of the SHOAGAI‐TSF technique is to accomplish a forecasting process for the utilization of stock price prediction. The SHOAGAI‐TSF technique uses probabilistic forecasting with a conditional GAN (CGAN) approach for the prediction of stock prices. The CGAN model learns the data generation distribution and determines the probabilistic prediction from it. To boost the prediction results of the CGAN approach, the hyperparameter tuning can be performed by the use of the SHOA. The simulation result analysis of the SHOAGAI‐TSF technique takes place on the stock market dataset. The experimental outcomes determine the significant solution of the SHOAGAI‐TSF algorithm with other compared methods in terms of distinct metrics. [ABSTRACT FROM AUTHOR]

Latent fingerprint enhancement is paramount as it dramatically influences matching accuracy. This process is often challenging due to varying structured noise and background patterns. The prints may be of arbitrary sizes and scales with a high degree of occlusion. There is a need for creating an end-to-end system that handles different conditions reliably to streamline this often lengthy and tricky process. In this work, we propose a Generative Adversarial Network (GAN) based architecture that effectively captures multi-scale context using Atrous Spatial Pyramid Pooling (ASPP). We have trained the network on a synthetically generated dataset, carefully designed to represent real-world latent prints. By avoiding the reconstruction of spurious ridges and only enhancing valid ridges, we avoid the generation of false minutiae, leading to better matching performance. We obtained state-of-the-art results in Sensor to Latent matching using the IIITD MOLF and Latent to Latent Matching using IIITD Latent datasets. • Handles fingerprints of multiple scales without the need for any pre-processing. • The network learns the fingerprint features through the generation of auxiliary maps. • Robust against various noise patterns due to well representative synthetic dataset. • Avoiding the creation of the false positive ridges increases matching accuracy. • State of the art matching accuracies are obtained on IIITD Latent and MOLF database. [ABSTRACT FROM AUTHOR]

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Latent Out is a recently introduced algorithm for unsupervised anomaly detection which enhances latent space-based neural methods, namely (Variational) Autoencoders, GANomaly and ANOGan architectures. The main idea behind it is to exploit both the latent space and the baseline score of these architectures in order to provide a refined anomaly score performing density estimation in the augmented latent-space/baseline-score feature space. In this paper we investigate the performance of Latent Out acting as a one-class classifier and we experiment the combination of Latent Out with GAAL architectures, a novel type of Generative Adversarial Networks for unsupervised anomaly detection. Moreover, we show that the feature space induced by Latent Out has the characteristic to enhance the separation between normal and anomalous data. Indeed, we prove that standard data mining outlier detection methods perform better when applied on this novel augmented latent space rather than on the original data space. [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]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Optical fiber sensors have been widely applied for their advantages such as small size, lightweight, and strong electronic interference robustness. Compared with current electronic sensors, optical fiber sensors perform better in measuring parameters in harsh environments, which makes them suitable for more and more applications, such as target tracing and detection and monitoring of health signs in medical services. However, due to fiber optic sensor failure, improper transmission and storage, or other reasons, missing data occur from time to time. Therefore, effective missing value processing methods are desirable as they can be used to facilitate data processing or analysis. In the present study, gated recurrent unit (GRU) interpolation is performed by using the generative adversarial network (GAN) model to process the irregular delay relationship between the data before and after the collection of incomplete vital signs data. Furthermore, a data interpolation model based on VS-E2E-GAN is proposed to reconstruct vital signs signals. The ROC curve (AUC), metrics including mean squared error (MSE), and accuracy (ACC) of experiments reach 0.901, 0.777, and 0.908, respectively, which indicates that the proposed VS-E2E-GAN model performs well in terms of vital signs data imputation and repairment, has strong robustness when compared with other works, and has potential clinical application in health monitoring, smart home, and so on. [ABSTRACT FROM AUTHOR]

Existing magnetic resonance imaging translation models rely on generative adversarial networks, primarily employing simple convolutional neural networks. Unfortunately, these networks struggle to capture global representations and contextual relationships within magnetic resonance images. While the advent of Transformers enables capturing long-range feature dependencies, they often compromise the preservation of local feature details. To address these limitations and enhance both local and global representations, we introduce DBGAN, a novel dual-branch generative adversarial network. In this framework, the Transformer branch comprises sparse attention blocks and dense self-attention blocks, allowing for a wider receptive field while simultaneously capturing local and global information. The convolutional neural network branch, built with integrated residual convolutional layers, enhances local modeling capabilities. Additionally, we propose a fusion module that cleverly integrates features extracted from both branches. Extensive experimentation on two public datasets and one clinical dataset validates significant performance improvements with DBGAN. On Brats2018, it achieves a 10% improvement in MAE, 3.2% in PSNR, and 4.8% in SSIM for image generation tasks compared to RegGAN. Notably, the generated MRIs receive positive feedback from radiologists, underscoring the potential of our proposed method as a valuable tool in clinical settings. [ABSTRACT FROM AUTHOR]

Face swapping or face replacement is a challenging task that involves transferring a source face to a target face while maintaining the target's facial motion and expression. Although many studies have made a lot of encouraging progress, we have noticed that most of the current solutions have the problem of blurred images, abnormal features, and unnatural pictures after face swapping. To solve these problems, in this paper, we proposed a composite face-swapping generation network, which includes a face extraction module and a feature fusion generation module. This model retains the original facial expression features, as well as the background and lighting of the image while performing face swapping, making the image more realistic and natural. Compared with other excellent models, our model is more robust in terms of face identity, posture verification, and image quality. [ABSTRACT FROM AUTHOR]

Chinese calligraphy is a significant aspect of traditional culture, as it involves the art of writing Chinese characters. Despite the development of numerous deep learning models for generating calligraphy characters, the resulting outputs often suffer from issues related to stroke accuracy and stylistic consistency. To address these problems, an end-to-end generation model for Chinese calligraphy characters based on dense blocks and a capsule network is proposed. This model aims to solve issues such as redundant and broken strokes, twisted and deformed strokes, and dissimilarity with authentic ones. The generator of the model employs self-attention mechanisms and densely connected blocks to reduce redundant and broken strokes. The discriminator, on the other hand, consists of a capsule network and a fully connected network to reduce twisted and deformed strokes. Additionally, the loss function includes perceptual loss to enhance the similarity between the generated calligraphy characters and the authentic ones. To demonstrate the validity of the proposed model, we conducted comparison and ablation experiments on the datasets of Yan Zhenqing's regular script, Deng Shiru's clerical script, and Wang Xizhi's running script. The experimental results show that, compared to the comparison model, the proposed model improves SSIM by 0.07 on average, reduces MSE by 1.95 on average, and improves PSNR by 0.92 on average, which proves the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In oncology, melanoma is a serious concern, often arising from DNA changes caused mainly by ultraviolet radiation. This cancer is known for its aggressive growth, highlighting the necessity of early detection. Our research introduces a novel deep learning framework for melanoma classification, trained and validated using the extensive SIIM‐ISIC Melanoma Classification Challenge‐ISIC‐2020 dataset. The framework features three dilated convolution layers that extract critical feature vectors for classification. A key aspect of our model is incorporating the Off‐policy Proximal Policy Optimization (Off‐policy PPO) algorithm, which effectively handles data imbalance in the training set by rewarding the accurate classification of underrepresented samples. In this framework, the model is visualized as an agent making a series of decisions, where each sample represents a distinct state. Additionally, a Generative Adversarial Network (GAN) augments training data to improve generalizability, paired with a new regularization technique to stabilize GAN training and prevent mode collapse. The model achieved an F‐measure of 91.836% and a geometric mean of 91.920%, surpassing existing models and demonstrating the model's practical utility in clinical environments. These results demonstrate its potential in enhancing early melanoma detection and informing more accurate treatment approaches, significantly advancing in combating this aggressive cancer. [ABSTRACT FROM AUTHOR]

AbstractIndividual trajectory data play a pivotal role in various application fields, such as urban planning, traffic control, and epidemic simulation. Despite the diverse means for data collection in current times, the real-world trajectory data in practical application remains severely limited due to concerns over personal privacy. In this study, we designed a Spatiotemporal-knowledge enhanced multi-TAsk GEnerative adversarial network (GAN), named STAGE, to generate synthetic trajectories that statistically resemble the real data without recycling personal information. In STAGE, we designed a multi-task generator with three stages of spatio-temporal generation tasks, i.e. activity-sequence generation task, township-level trajectory generation task, and neighborhood-level trajectory generation task, with the last one as the main task while the other two as auxiliary tasks. Meanwhile, we designed a spatial consistency loss in the adversarial training process to assess the spatial consistency of generated trajectories at different spatial scales. Experiment results show that compared to the baselines, trajectories generated by our method have closer data distributions to the real ones. We argued that the designs of spatiotemporal-knowledge enhanced generation tasks and training loss benefit the spatiotemporal generation processes, which help reproduce the temporal patterns of human daily activities and spatial distribution of human movements. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image super-resolution (SR) is a critical task in computer vision and image processing, with a wide range of real-life applications. Its goal is to reconstruct high-resolution (HR) images from low-resolution (LR) images. In recent years, deep neural networks have made significant advancements in this field. However, existing super-resolution algorithms have a large number of parameters and the reconstructed image details are too smooth and blurred. To address this problem, we propose a dynamic adaptive weight-based generative adversarial network (DAW-GAN). Our network introduces a dynamic attention module (DAM), in which the weights of attention and non-attention branches can be adaptively adjusted by a dynamic weight module (DWM). Additionally, we supplement the network with a "distance" information loss function to optimize the training of the discriminator, in addition to perceptual loss. Our experimental results demonstrate that the reconstructed images from our network exhibit finer texture details and are more consistent with human visual perception. [ABSTRACT FROM AUTHOR]

Generative adversarial network (GAN) architecture is employed to tackle the inverse scattering problem of buried dielectric objects in half-space. Traditional iterative methods aimed at resolving the inverse scattering problem of buried dielectric objects have encountered a variety of difficulties, such as highly nonlinear phenomenon, high computational costs for halfspace Green’s function, and missing measured scattered field information at the lower half of the object. The generator of GAN learns to generate more realistic images, while the discriminator of GAN improves its ability to identify fake images through a game-like process. The iterative process stops when the image generated by the generator is indistinguishable from the real image. In addition, we also analyze and compare the reconstruction outcomes obtained using both GAN and U-Net. Numerical outcomes show that GAN can efficiently reconstruct images with higher reliability than U-Net for buried objects with different dielectric permittivities and handwritten shapes. In summary, our proposed method has opened up a new avenue for imaging buried objects by adopting a deep learning network technique. [ABSTRACT FROM AUTHOR]

Infrared images hold significant value in applications such as remote sensing and fire safety. However, infrared detectors often face the problem of high hardware costs, which limits their widespread use. Advancements in deep learning have spurred innovative approaches to image super-resolution (SR), but comparatively few efforts have been dedicated to the exploration of infrared images. To address this, we design the Residual Swin Transformer and Average Pooling Block (RSTAB) and propose the SwinAIR, which can effectively extract and fuse the diverse frequency features in infrared images and achieve superior SR reconstruction performance. By further integrating SwinAIR with U-Net, we propose the SwinAIR-GAN for real infrared image SR reconstruction. SwinAIR-GAN extends the degradation space to better simulate the degradation process of real infrared images. Additionally, it incorporates spectral normalization, dropout, and artifact discrimination loss to reduce the potential image artifacts. Qualitative and quantitative evaluations on various datasets confirm the effectiveness of our proposed method in reconstructing realistic textures and details of infrared images. [ABSTRACT FROM AUTHOR]

Aiming at the problems of traditional fire alarm can not be warned before the occurrence of fire, the effect is not good in extreme weather such as night, and it is limited by complex environment, a fire alarm algorithm based on infrared and visible light images fusion is proposed. A two-branch attention structure is designed and proposed in a generative adversarial network (GAN). One branch extracts more robust feature information through the dense residual network, and the other branch makes up for the lack of spatial information through the efficient coordinate channel attention group (ECCAG) to maximize the acquisition of more high-frequency detail features, and designs and proposes a regulation loss as a loss function, and obtains the fusion image by improving the GAN algorithm. Finally, according to the proposed fire warning algorithm, whether there is a fire hazard is judged. The experimental results show that, the average accuracy of object detection in the fusion dataset obtained by the improved GAN algorithm is 96.19%, which is improved by 11.09 percentage points and 6.2 percentage points compared with the infrared dataset and the original GAN algorithm dataset, respectively, and the accuracy of flame hazard detection on the TNO and LLVIP datasets of the public dataset is 97.45%. The results show that the fire warning algorithm can warn in time when no fire occurs, and can obtain significant detection effects for different scenarios. [ABSTRACT FROM AUTHOR]

The rise of comics and games has led to increased artistic processing of portrait photos. With growing commercial demand and advancements in deep learning, neural networks for rapid facial style transfer have become a key research area in computer vision. This involves converting face photos into different styles while preserving content. Face images are more complex than regular images, requiring extensive modification. However, current methods often face issues such as unnatural color transitions, loss of detail in highlighted areas, and noticeable artifacts along edges, resulting in low-quality stylized images. In this study, an enhanced generative adversarial network (GAN) is proposed, which is based on Adaptive Layer Instance Normalization (AdaLIN) + Laplacian. This approach incorporates the AdaLIN normalization method, allowing for the dynamic adjustment of Instance Normalization (IN) and Layer Normalization (LN) parameters' weights during training. By combining the strengths of both normalization techniques, the model selectively preserves and alters content information to some extent, aiming to strike a balance between style and content. This helps address problems such as unnatural color transitions and loss of details in highlights that lead to color inconsistencies. Furthermore, the introduction of a Laplacian regularization term aids in denoising the image, preventing noise features from interfering with the color transfer process. This regularization also helps reduce color artifacts along the face's edges caused by noise while maintaining the image's contour information. These enhancements significantly enhance the quality of the generated face images. To compare our method with traditional CycleGAN and recent algorithms such as XGAN and CariGAN, both subjective and objective evaluations were conducted. Subjectively, our method demonstrates more natural color transitions and superior artifact elimination, achieving higher scores in Mean Opinion Score (MOS) evaluations. Objectively, experiments using our method yielded better scores across three metrics: FID, SSIM, and MS-SSIM. The effectiveness of the proposed methods is validated through both objective and subjective evaluations. [ABSTRACT FROM AUTHOR]

Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the predictive accuracy of zone-to-zone direct demand models, they lack inference ability due to their complex model structure. Furthermore, zone-to-zone direct demand models suffer from limited sample size for machine learning due to fixed number of origin-destination (OD) pairs. To overcome these limitations, we propose a linear regression coupled Wasserstein generative adversarial network (LR-WGAN) for direct demand modeling of zone-to-zone ride-hailing trips that captures both linear explanatory components and non-linear patterns and improves the predictive accuracy by generating data to expand the OD sample size. The proposed LR-WGAN is found to significantly improve the predictive accuracy of OD ride-hailing demand in Chicago and Austin. Furthermore, the linear explanatory component of the model is utilized to draw inferences regarding the relationship between OD ride-hailing demand and predictor variables. [ABSTRACT FROM AUTHOR]

Magnetic resonance images (MRIs) of different modalities have different reference values for pathological diagnosis. The difficulty of obtaining multimodal MRIs makes it effective to synthesize medical images of missing modalities from existing one. To train a one-for-all synthesizer with limited number of unpaired MRIs, a novel cross-dimensional knowledge-guided generative adversarial network (CKG–GAN) is proposed. In CKG–GAN, a cross-dimensional knowledge transfer network is utilized to measure the perceptual similarity of 2D images (slices of MRIs) from source and synthesized modalities, the knowledge of which is transferred from a pre-trained 3D network without accessing its private training data set. We evaluate the proposed model for three tasks on BraTs2018 and BraTs2021 data sets, using one modality of T1, T2 or Flair to synthesize the other two modalities without changing the content. The results show that compared with the current state-of-the-art methods, our method improves the performance by 2–7%. [ABSTRACT FROM AUTHOR]

Rolling bearings are essential parts for manufacturing machines. Vast quantities of features are often extracted from measured signals to comprehensively reflect the conditions of bearings, which may cause high dimensionality, information redundancy, and time consumption. In addition, it is extremely difficult, expensive, and time-consuming to collect samples with label information during the bearing fault diagnosis in real-world scenarios. In this study, a novel bearing defect diagnosis method for small sample size is proposed based on modified local joint sparse marginal embedding (MLJSME) and Wasserstein generative adversarial networks (WGANs). MLJSME can effectively extract intrinsic sparse discriminant features of high-dimensional dataset by preserving both global and local structures. Graph embedding and Gaussian kernel function are adopted to preserve the locality structure of dataset. The global structure and discriminate information are preserved by maximum margin criterion which can also avoid small sample-size problem. Moreover, joint sparsity is applied to preserve the sparse property and improve the robustness to noise and outliers. An abundance of artificial samples can be obtained with WGAN and a few labeled samples. Firstly, a high-dimensional feature dataset consisting of time-domain and frequency-domain features is extracted from original vibration signals, then MLJSME is utilized to extract sensitive low-dimensional features, and a small number of low-dimensional features are fed into WGAN to generate a large number of artificial samples that used to train the classifier, and the bearing fault types can be finally identified. The effectiveness and feasibility of the proposed method is validated by analyzing the different experimental cases. [ABSTRACT FROM AUTHOR]

Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, are indispensable. However, deep learning models are plagued by overfitting owing to the limited sample size relative to the large number of genes. Consequently, the latest style-transfer deep generative models have been implemented to generate gene expression data. However, these models are limited in their applicability for clinical purposes because they generate only transcriptomic data. Therefore, this study proposes ctGAN, which enables the combined transformation of gene expression and survival data using a generative adversarial network (GAN). ctGAN improves survival analysis by augmenting data through style transformations between breast cancer and 11 other cancer types. We evaluated the concordance index (C-index) enhancements compared with previous models to demonstrate its superiority. Performance improvements were observed in nine of the 11 cancer types. Moreover, ctGAN outperformed previous models in seven out of the 11 cancer types, with colon adenocarcinoma (COAD) exhibiting the most significant improvement (median C-index increase of ~15.70%). Furthermore, integrating the generated COAD enhanced the log-rank p -value (0.041) compared with using only the real COAD (p -value = 0.797). Based on the data distribution, we demonstrated that the model generated highly plausible data. In clustering evaluation, ctGAN exhibited the highest performance in most cases (89.62%). These findings suggest that ctGAN can be meaningfully utilized to predict disease progression and select personalized treatments in the medical field. [ABSTRACT FROM AUTHOR]

Objective: Designing physiologically adequate microvascular trees is of crucial relevance for bioengineering functional tissues and organs. Yet, currently available methods are poorly suited to replicate the morphological and topological heterogeneity of real microvascular trees because the parameters used to control tree generation are too simplistic to mimic results of the complex angiogenetic and structural adaptation processes in vivo. Methods: We propose a method to overcome this limitation by integrating a conditional deep convolutional generative adversarial network (cDCGAN) with a local fractal dimension‐oriented constrained constructive optimization (LFDO‐CCO) strategy. The cDCGAN learns the patterns of real microvascular bifurcations allowing for their artificial replication. The LFDO‐CCO strategy connects the generated bifurcations hierarchically to form microvascular trees with a vessel density corresponding to that observed in healthy tissues. Results: The generated artificial microvascular trees are consistent with real microvascular trees regarding characteristics such as fractal dimension, vascular density, and coefficient of variation of diameter, length, and tortuosity. Conclusions: These results support the adoption of the proposed strategy for the generation of artificial microvascular trees in tissue engineering as well as for computational modeling and simulations of microcirculatory physiology. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) technology can improve the resolution of images and provide clearer and more reliable remote sensing images of high quality to better serve the subsequent applications. However, when reconstructing high-frequency feature areas of remote sensing images, existing SR reconstruction methods are prone to artifacts that affect visual effects and make it difficult to generate real texture details. In order to address this issue, a texture-detail-enhanced dense generative adversarial network (TDEGAN) for remote sensing image SR is presented. The generator uses multi-level dense connections, residual connections, and Shuffle attention (SA) to improve the feature extraction ability. A PatchGAN-style discrimination network is designed to effectively perform local discrimination and helps the network generate rich, detailed features. To reduce the impact of artifacts, we introduce an artifact loss function, which is combined with the exponential moving average (EMA) technique to distinguish the artifacts generated from the actual texture details through local statistics, which can help the network reduce artifacts and generate more realistic texture details. Experiments show that TDEGAN can better restore the texture details of remote sensing images and achieves certain advantages in terms of evaluation indicators and visualization. [ABSTRACT FROM AUTHOR]

Surgical reconstruction of mandibular defects is a clinical routine manner for the rehabilitation of patients with deformities. The mandible plays a crucial role in maintaining the facial contour and ensuring the speech and mastication functions. The repairing and reconstruction of mandible defects is a significant yet challenging task in oral–maxillofacial surgery. Currently, the mainly available methods are traditional digitalized design methods that suffer from substantial artificial operations, limited applicability and high reconstruction error rates. An automated, precise, and individualized method is imperative for maxillofacial surgeons. In this paper, we propose a Stage-wise Residual Attention Generative Adversarial Network (SRA-GAN) for mandibular defect reconstruction. Specifically, we design a stage-wise residual attention mechanism for generator to enhance the extraction capability of mandibular remote spatial information, making it adaptable to various defects. For the discriminator, we propose a multi-field perceptual network, consisting of two parallel discriminators with different perceptual fields, to reduce the cumulative reconstruction errors. Furthermore, we design a self-encoder perceptual loss function to ensure the correctness of mandibular anatomical structures. The experimental results on a novel custom-built mandibular defect dataset demonstrate that our method has a promising prospect in clinical application, achieving the best Dice Similarity Coefficient (DSC) of 94.238% and 95% Hausdorff Distance (HD95) of 4.787. [ABSTRACT FROM AUTHOR]

The aim of image inpainting is to fill in damaged areas according to certain rules based on information about the adjacent positions of missing areas and the overall structure of the image, a technique that plays a key role in various tasks in computer vision. With the rapid development of deep learning, researchers have combined it with image inpainting and achieved excellent performance. To gain insight into the techniques involved, this paper summarizes the latest research advances in the field of image inpainting. Firstly, existing classical image inpainting methods are reviewed, and traditional image inpainting methods and their advantages and disadvantages are introduced. Secondly, three classical network models are outlined, and the image inpainting methods are classified into single-stage, multi-stage and a priori condition-guided approaches according to different network types and model structures. Representative algorithms among them are selected and their important technical improvement ideas are analyzed and summarized. Then, the common datasets commonly used in image inpainting tasks and the evaluation metrics used to evaluate inpainting results are introduced. The paper presents a comprehensive summary of the various algorithms in terms of network models and inpainting methods, and selects representative algorithms for quantitative and qualitative comparative analysis. Finally, the future development trends and research directions have prospected, and the current problems of image inpainting are summarized. [ABSTRACT FROM AUTHOR]