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The Kullback-Leibler divergence (KLD), which is widely used to measure the similarity between two distributions, plays an important role in many applications. In this article, we address the KLD metric-learning task, which aims at learning the best KLD-type metric from the distributions of datasets. Concretely, first, we extend the conventional KLD by introducing a linear mapping and obtain the best KLD to well express the similarity of data distributions by optimizing such a linear mapping. It improves the expressivity of data distribution, which means it makes the distributions in the same class close and those in different classes far away. Then, the KLD metric learning is modeled by a minimization problem on the manifold of all positive-definite matrices. To deal with this optimization task, we develop an intrinsic steepest descent method, which preserves the manifold structure of the metric in the iteration. Finally, we apply the proposed method along with ten popular metric-learning approaches on the tasks of 3-D object classification and document classification. The experimental results illustrate that our proposed method outperforms all other methods.

Geoscience Australia's geomagnetic observatory network covers one-eighth of the Earth. The first Australian geomagnetic observatory was established in Hobart in 1840. This almost continuous 180-year period of magnetic-field monitoring provides an invaluable dataset for scientific research. Geomagnetic storms induce electric currents in the Earth that feed into power lines through substation neutral earthing, causing instabilities and sometimes blackouts in electricity transmission systems. Power outages to business, financial and industrial centres cause major disruption and potentially billions of dollars of economic losses. The intensity of geomagnetically induced currents is closely associated with geological structure. We modelled peak geoelectric field values induced by the 1989 Quebec storm for south-eastern Australian states using a scenario analysis. Modelling shows the 3D subsurface geology had a significant impact on the magnitude of induced geoelectric fields, with more than three orders of magnitude difference across conductive basins to resistive cratonic regions in south-eastern Australia. We also estimated geomagnetically induced voltages in the Australian high-voltage power transmission lines by using the scenario analysis results. The geomagnetically induced voltages may exhibit local maxima in the transmission lines at differing times during the course of a magnetic storm depending on the line's spatial orientation and length with respect to the time-varying inducing field. Real-time forecasting of geomagnetic hazards using Geoscience Australia's geomagnetic observatory network and magnetotelluric data from the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) helps develop national strategies and risk assessment procedures to mitigate space weather hazard., Open-Access Online Publication: May 22, 2023

Recently, deep convolution neural networks (DCNNs) have obtained remarkable performance in exploring single image super-resolution (SISR). However, most of the existing CNN-based SISR methods only focus on increasing the width and depth of the network to improve SR performance, which makes them face a heavy computing burden. In this paper, we propose a lightweight dynamic dual attention iteration network (DDAIN) for SISR. Specifically, to better realize the attention of the channel and the convolution kernel, we design a dynamic convolution unit (DYCU) at the head of the network. It improves the SR performance by enhancing the complexity of the model without increasing the width and depth of the network. Compared with the traditional static convolution, it can extract more abundant high and low-frequency image features according to different input images. Moreover, to recover the high-frequency detail features of images with different resolutions as much as possible, we embed multiple dual residual attention (DRA) in the feature refinement unit (FRU). Finally, to alleviate the height discomfort caused by SR, we introduce iterative loss Liter to optimize the training process further. Extensive experimental results on benchmark show that the performance of the DDAIN in different degradation models exceeds some existing classical methods.

Caching and transcoding at multi-access edge computing (MEC) server and wireless resource allocation in eNodeB interact with each other and together determine the quality of experience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients. However, the relationship among the three factors has not been explored, which has led to limited improvement in clients’ QoE. Therefore, we propose a joint optimization framework of video segment caching and transcoding in MEC servers and resource allocation to improve the QoE of DASH clients. Based on the established framework, we develop an MEC caching management mechanism that consists of the MEC caching partition, video segment deletion, and MEC caching space transfer. Then, a joint optimization algorithm that combines the video segment caching and transcoding in the MEC server and resource allocation is proposed. In the algorithm, the clients’ channel state and the playback status and cooperation among MEC servers are employed to estimate the client's priority, video segment representation switch and continuous playback time. Considering the above four factors, we develop a utility function model of clients’ QoE. Then, we formulate a mixed-integer nonlinear programming mathematical model to maximize the total utility of DASH clients, where the video segment caching and transcoding strategy and resource allocation strategy are jointly optimized. To solve this problem, we propose a low-complexity heuristic algorithm that decomposes the original problem into multiple subproblems. The simulation results show that our proposed algorithms efficiently improve client's throughput, received video quality and hit ratio of video segments while decreasing the playback rebuffering time, video segment representation switch and system backhaul traffic.

Artificial intelligence including deep learning and 3D reconstruction methods is changing the daily life of people. Now, an unmanned aerial vehicle that can move freely in the air and avoid harsh ground conditions has been commonly adopted as a suitable tool for 3D reconstruction. The traditional 3D reconstruction mission based on drones usually consists of two steps: image collection and offline post-processing. But there are two problems: one is the uncertainty of whether all parts of the target object are covered, and another is the tedious post-processing time. Inspired by modern deep learning methods, we build a telexistence drone system with an onboard deep learning computation module and a wireless data transmission module that perform incremental real-time dense reconstruction of urban cities by itself. Two technical contributions are proposed to solve the preceding issues. First, based on the popular depth fusion surface reconstruction framework, we combine it with a visual-inertial odometry estimator that integrates the inertial measurement unit and allows for robust camera tracking as well as high-accuracy online 3D scan. Second, the capability of real-time 3D reconstruction enables a new rendering technique that can visualize the reconstructed geometry of the target as navigation guidance in the HMD. Therefore, it turns the traditional path-planning-based modeling process into an interactive one, leading to a higher level of scan completeness. The experiments in the simulation system and our real prototype demonstrate an improved quality of the 3D model using our artificial intelligence leveraged drone system.

In recent years, the hyperspectral classification algorithm based on deep learning has received widespread attention, but the existing network models have higher model complexity and require more time consumption. In order to further improve the accuracy of hyperspectral image classification and reduce model complexity, this paper proposes an asymmetric coordinate attention spectral-spatial feature fusion network (ACAS2F2N) to capture distinguishing hyperspectral features. Specifically, adaptive asymmetric iterative attention was proposed to obtain the discriminative spectral-spatial features. Different from the common feature fusion method, this feature fusion method can adapt to most skip connection tasks. In addition, there is no manual parameter setting. Coordinate attention is used to obtain accurate coordinate information and channel relationship. The strip pooling module was introduced to increase the network’s receptive field and avoid irrelevant information brought by conventional convolution kernels. The proposed algorithm is tested on the mainstream hyperspectral datasets (IP, KSC, and Botswana), experimental results show that the proposed ACAS2F2N can achieve state-of-the-art performance with lower time complexity.

Image-text matching aims to find the relationship between image and text data and to establish a connection between them. The main challenge of image-text matching is the fact that images and texts have different data distributions and feature representations. Current methods for image-text matching fall into two basic types: methods that map image and text data into a common space and then use distance measurements and methods that treat image-text matching as a classification problem. In both cases, the two data modes used are image and text data. In our method, we create a fusion layer to extract intermediate modes, thus improving the image-text processing results. We also propose a concise way to update the loss function that makes it easier for neural networks to handle difficult problems. The proposed method was verified on the Flickr30K and MS-COCO datasets and achieved superior matching results compared to existing methods.

Smoke detection based on video monitoring is of great importance for early fire warning. However, most of the smoke detection methods based on neural network only consider the normal weather. The harsh weather such as the fog environment is ignored. In this paper, we propose a smoke detection in normal and fog weather, which combines attention mechanism and feature-level and decision-level fusion module. First, a new fog smoke dataset with diverse positive and hard negative samples dataset is established through online collection and offline shooting. Then, an attention mechanism module combining spatial attention and channel attention is proposed to solve the problem of small smoke detection. Next, a lightweight feature-level and decision-level fusion module is proposed, which can not only improve the discrimination of smoke, fog and other similar objects, but also ensure the real-time performance of the model. Finally, a large number of comparative experiments on the existing dataset and our self-created dataset, show that our method can obtain higher detection accuracy rate, precision rate, recall rate, and F1 score from the perspective of overall, each category, small smoke and hard negative samples detection than the existing methods.

U-Net has become baseline standard in the medical image segmentation tasks, but it has limitations in explicitly modeling long-term dependencies. Transformer has the ability to capture long-term relevance through its internal self-attention. However, Transformer is committed to modeling the correlation of all elements, but its awareness of local foreground information is not significant. Since medical images are often presented as regional blocks, local information is equally important. In this paper, we propose the GPA-TUNet by considering local and global information synthetically. Specifically, we propose a new attention mechanism to highlight local foreground information, called group parallel axial attention (GPA). Furthermore, we effectively combine GPA with Transformer in encoder part of model. It can not only highlight the foreground information of sample, but also reduce the negative influence of background information on the segmentation results. Meanwhile, we introduce the sMLP block to improve the global modeling capability of network. Sparse connectivity and weight sharing are well achieved by applying it. Extensive experiments on public datasets confirm the excellent performance of our proposed GPA-TUNet. In particular, on Synapse and ACDC datasets, mean DSC reached 80.37% and 90.37% respectively, mean HD95 reached 20.55% and 1.23% respectively.

Learning-based single image super-resolution (SISR) aims to learn a versatile mapping from low resolution (LR) image to its high resolution (HR) version. The critical challenge is to bias the network training towards continuous and sharp edges. For the first time in this work, we propose an implicit boundary prior learnt from multi-view observations to significantly mitigate the challenge in SISR we outline. Specifically, the multi-image prior that encodes both disparity information and boundary structure of the scene supervise a SISR network for edge-preserving. For simplicity, in the training procedure of our framework, light field (LF) serves as an effective multi-image prior, and a hybrid loss function jointly considers the content, structure, variance as well as disparity information from 4D LF data. Consequently, for inference, such a general training scheme boosts the performance of various SISR networks, especially for the regions along edges. Extensive experiments on representative backbone SISR architectures constantly show the effectiveness of the proposed method, leading to around 0.6 dB gain without modifying the network architecture.

When taking pictures of electronic screens or objects with high-frequency textures, people often run across colorful rainbow patterns that are known as “moire”, seriously affecting the image quality and subsequent processing. Current methods for removing moire patterns mostly extract multiscale information by downsampling pooling layers, which may inevitably cause information loss. To address this issue, this paper proposes a demoireing method in the wavelet domain. By employing both discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) instead of traditional downsampling and upsampling, this method can effectively increase the network receptive field without information loss. In addition, to further reconstruct more details of moire patterns, this paper proposes an efficient attention fusion module (EAFM). With a combination of efficient channel attention, spatial attention and local residual learning, this module can self-adaptively learn various weights of feature information at different levels and inspire the network to focus more on effective information such as moire details to improve learning and demoireing performance. Extensive experiments based on public datasets have shown that this suggested method can efficiently remove moire patterns and has a good quantitative and qualitative performance.

Frequent vehicle thefts have a highly detrimental impact on public safety. Thanks to surveillance equipment distributed throughout a city, a large number of videos that can be used to recognize vehicle theft are available. However, vehicle theft behavior has the characteristics of a small criminal target and small movement. Hence, the existing action recognition algorithms cannot be directly applied for the recognition of vehicle theft. In this paper, we propose a method for vehicle theft recognition based on a spatiotemporal attention mechanism. First, a database of vehicle theft is established by collecting videos from the Internet and an existing dataset. Then, we establish a vehicle theft recognition network and introduce a spatiotemporal attention mechanism for application when extracting the spatiotemporal features of theft. Through the learning of adaptive feature weights, the features that contribute most greatly to recognition are emphasized. Simulation experiments show that our proposed algorithm can achieve 97.04% accuracy on the collected vehicle theft database.

In view of the significant intensity difference between remote sensing image pairs, weak robustness, and insufficient key point correspondence, the novel remote sensing image registration method is proposed. Firstly, a nonlinear scale space is established by means of the anisotropic diffusion equation and fast explicit diffusion. Then, an improved gradient calculation method is used to calculate the gradient amplitude of the nonlinear scale-space image to establish the gradient amplitude space of the nonlinear scale space, and the multiscale Harris method is used to detect the feature points in the gradient amplitude space. The experimental results show that this feature extraction method can consider the boundaries and smoothness of objects and reduce the problem of gray-level difference to increase the number of feature points with potential of being correctly matched, and the distribution of feature points is relatively uniform. In addition, the improved gradient calculation method can effectively reduce the impact of nonlinear intensity differences on image registration. Overall, the algorithm can effectively solve the problem of registration difficulties caused by the significant grayscale difference between multisource remote sensing images and enhance the robustness. Compared with other advanced algorithms, this one has higher accuracy and more correct correspondence relations, and the registration performance has been significantly improved.

To address the problem that some current algorithms suffer from the loss of some important features due to rough feature distillation and the loss of key information in some channels due to compressed channel attention in the network, we propose a progressive multistage distillation network that gradually refines the features in stages to obtain the maximum amount of key feature information in them. In addition, to maximize the network performance, we propose a weight-sharing information lossless attention block to enhance the channel characteristics through a weight-sharing auxiliary path and, at the same time, use convolution layers to model the interchannel dependencies without compression, effectively avoiding the previous problem of information loss in channel attention. Extensive experiments on several benchmark data sets show that the algorithm in this paper achieves a good balance between network performance, the number of parameters, and computational complexity and achieves highly competitive performance in both objective metrics and subjective vision, which indicates the advantages of this paper’s algorithm for image reconstruction. It can be seen that this gradual feature distillation from coarse to fine is effective in improving network performance. Our code is available at the following link: https://github.com/Cai631/PMDN.

Deep hashing method is widely applied in the field of image retrieval because of its advantages of low storage consumption and fast retrieval speed. There is a defect of insufficiency feature extraction when existing deep hashing method uses the convolutional neural network (CNN) to extract images semantic features. Some studies propose to add channel-based or spatial-based attention modules. However, embedding these modules into the network can increase the complexity of model and lead to over fitting in the training process. In this study, a novel deep parameter-free attention hashing (DPFAH) is proposed to solve these problems, that designs a parameter-free attention (PFA) module in ResNet18 network. PFA is a lightweight module that defines an energy function to measure the importance of each neuron and infers 3-D attention weights for feature map in a layer. A fast closed-form solution for this energy function proves that the PFA module does not add any parameters to the network. Otherwise, this paper designs a novel hashing framework that includes the hash codes learning branch and the classification branch to explore more label information. The like-binary codes are constrained by a regulation term to reduce the quantization error in the continuous relaxation. Experiments on CIFAR-10, NUS-WIDE and Imagenet-100 show that DPFAH method achieves better performance.

Geoscience Australia's geomagnetic observatory network covers one-eighth of the Earth. The first Australian geomagnetic observatory was established in 1840 in Hobart. This almost continuous 180-year period of magnetic-field monitoring provides an invaluable dataset for scientific research. Geomagnetic storms induce electric currents in the Earth that feed into power lines through substation neutral earthing, causing instabilities and sometimes blackouts in electricity transmission systems. Power outages to business, financial and industrial centres cause major disruption and potentially billions of dollars of economic losses. The intensity of geomagnetically induced currents is closely associated with geological structure. Geomagnetic storm events across three decades have been analysed to develop a statistical model of geomagnetic storm activity in Australia and the model used to predict the intensity of geomagnetically induced currents in Australia's modern-day power grids. Modelling shows the induced electric fields in South Australia, Victoria and New South Wales caused by an intense magnetic storm that occurred in 1989. Real-time forecasting of geomagnetic hazards using Geoscience Australia's geomagnetic observatory network and magnetotelluric data from the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) helps develop national strategies and risk assessment procedures to mitigate space weather hazard., Open-Access Online Publication: March 01, 2023

Change detection (CD) is one of the essential tasks in remote sensing image processing and analysis. Remote sensing CD is a process of determining and evaluating changes in various surface objects over time. The impressive achievements of deep learning in image processing and computer vision provide an innovative concept for the task of CD. However, existing methods based on deep learning still have problems detecting small changed regions correctly and distinguishing the boundaries of the changed regions. To solve the above shortcomings and improve the efficiency of CD networks, inspired by the fact that an attention mechanism can refine features effectively, we propose an attention-based network for remote sensing CD, which has two important components: an asymmetric convolution block (ACB) and a combined attention mechanism. First, the proposed method extracts the features of bi-temporal images, which contain two parallel encoders with shared weights and structures. Then, the feature maps are fed into the combined attention module to reconstruct the change maps and obtain refined feature maps. The proposed CANet is evaluated on the two publicly available datasets for challenging remote sensing image CD. Extensive empirical results with four popular metrics show that the designed framework yields a robust CD detector with good generalization performance. In the CDD and LEVIR-CD datasets, the F1 values of the CANet are 3.3% and 1.3% higher than those of advanced CD methods, respectively. A quantitative analysis and qualitative comparison indicate that our method outperforms competitive baselines in terms of both effectiveness and robustness.

In the SAR change detection algorithm based on self-supervised learning, speckle noise reduces the difference image (DI) quality. Therefore, the contrast of the DI is low, and its change area is not significant. Moreover, the preclassification algorithm with the poor robustness makes the classification results of the low-quality DI inaccurate. When the wrong labels are sent into the classification network, the accuracy of the final detection results is reduced. First, to improve the quality of the initial DI, we design an adaptive gamma correction algorithm that adjusts the contrast according to the mean value of the initial DI and the variation coefficient $\beta$ . The contrast of the new DI generated by this algorithm is higher. Furthermore, to suppress the noise, we adopt a new algorithm based on popular ranking to obtain the saliency map of the new DI. Combining the initial DI with this saliency map, a high-quality DI with a low noise level is obtained. After that, we introduce the structure tensor into the fuzzy local information c-means clustering algorithm (FLICM) to classify the DI more accurately. The new clustering algorithm improves the accuracy of preclassification, especially its hierarchical version. Besides, we use the structure tensor to generate the structure maps of the original images. Finally, according to the prior information obtained from the preclassification, we use a convolution wavelet neural network (CWNN) to supervise and train the structure maps of the original images. Experimental results show that the DI generated by us is closer to the ground-truth than other methods. Our preclassification algorithm performs better. Our algorithm shows higher detection accuracy for SAR images with strong noise than some advanced change detection algorithms.

The diversity of network attacks poses severe challenges to intrusion detection systems (IDSs). Traditional attack recognition methods usually adopt mining data associations to identify anomalies, which has the disadvantages of a high false alarm rate (FAR), low recognition accuracy (ACC) and poor generalization ability. To ameliorate the comprehensive capabilities of IDS and strengthen network security, we propose a novel intrusion detection method based on the adaptive synthetic sampling (ADASYN) algorithm and an improved convolutional neural network (CNN). First, we use the ADASYN method to balance the sample distribution, which can effectively prevent the model from being sensitive to large samples and ignore small samples. Second, the improved CNN is based on the split convolution module (SPC-CNN), which can increase the diversity of features and eliminate the impact of interchannel information redundancy on model training. Then, an AS-CNN model mixed with ADASYN and SPC-CNN is used for intrusion detection tasks. Finally, the standard NSL-KDD dataset is selected to test AS-CNN. The simulation illustrates that the accuracy is 4.60% and 2.79% higher than that of the traditional CNN and RNN models, and the detection rate (DR) increased by 11.34% and 10.27%, respectively. Additionally, the FAR decreased by 15.58% and 14.57%, respectively, compared with the two models.

At present, occlusion and appearance similarity pose severe challenges to person re-identification tasks. Although many robust deep convolutional neural networks alleviate these problems, convolutional layers with limited receptive fields cannot model global semantic information well. In addition, in the person re-identification model, many metric losses ignore or destroy the intra-class structure of the sample, which makes the model difficult to be optimized. Therefore, we design a discriminative Re-identification model with global-local attention and adaptive weighted rank list loss (GLWR). Specifically, our global-local attention (GL-Attention) learns the semantic context in the channel and spatial dimensions. By learning the dependencies between features, GL-Attention integrates global semantic information into local features to extract discriminative features. Unlike rank list loss, our adaptive weighted rank list loss (WRLL) adaptively assigns weights according to the metric distance between the negative sample and the input image, which further improves the performance of the model. Experimental studies on three public datasets (Market-1501, DukeMTMC-ReID and CUHK03) indicate that the performance of our GLWR is significantly superior to many of the latest algorithms.

This paper proposes an improved method for extracting NMI features. This method uses Particle Swarm Optimization in advance to optimize the two-dimensional maximum class-to-class variance (2OTSU) in advance. Afterwards, the optimized 2OUSU is introduced into the Pulse Coupled Neural Network (PCNN) to automatically obtain the number of iterations of the loop. We use an improved PCNN method to extract the NMI features of the image. For the problem of low accuracy of single feature, this paper proposes a new method of multi-feature fusion based on image retrieval. It uses HSV colour features and texture features, where, the texture feature extraction methods include: Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Improved PCNN. The experimental results show that: on the Corel-1k dataset, compared with similar algorithms, the retrieval accuracy of this method is improved by 13.6%; On the AT&T dataset, the retrieval accuracy is improved by 13.4% compared with the similar algorithm; on the FD-XJ dataset, the retrieval accuracy is improved by 17.7% compared with the similar algorithm. Therefore, the proposed algorithm has better retrieval performance and robustness compared with the existing image retrieval algorithms based on multi-feature fusion.

Computer-aided diagnosis of retinopathy is a research hotspot in the field of medical image classification. Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common ocular diseases that can result in partial or complete loss of vision. Optical coherence tomography imaging (OCT) is widely applied to the diagnosis of ocular diseases including DME and AMD. In this paper, an automatic method based on deep learning is proposed to detect AME and AMD lesions, in which two publicly available OCT datasets of retina were adopted and a network model with effective feature of reuse feature was applied to solve the problem of small datasets and enhance the adaptation to the difference of different datasets of the approach. Several network models with effective feature of reusable feature were compared and the transfer learning on networks with pre-trained models was realized. CliqueNet achieves better, classification results compared with other network models with a more than 0.98 accuracy and 0.99 of area under the curve (AUC) value finally.