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Image anomaly detection plays an important role in various fields, such as industrial defect detection and medical image analysis. Although the use of image restoration has led to significant progress in image anomaly detection, challenges persist in insufficient image feature extraction and inadequate integration of low-level and high-level features. In this study, a framework that combines reconstruction methods with knowledge distillation is designed. To address the challenge of insufficient image convolution feature extraction, a multi-branch feature extraction module is introduced, extracting both local and global features using convolution and self-attention modules, respectively. Additionally, to overcome the inadequate integration of low-level and high-level features in the skip connection part, a straightforward skip connection is designed to fuse semantic information from high-level features with finer details from low-level features through element-wise multiplication. Extensive experiments on industrial image datasets, such as Mvtec AD and BTAD, validate the effectiveness of the proposed method in improving anomaly detection localization performance. The Mvtec AD dataset yielded impressive results, with image-level and pixel-level ROC_AUC achieving average accuracy values of 99.16% and 98.09%, respectively. Similarly, on the BTAD dataset, our method exhibited remarkable performance, achieving average accuracy values of 93% and 96% for image-level and pixel-level ROC_AUC, respectively. These notable improvements across both datasets underscore the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Industrial image anomaly detection achieves automated detection and localization of defects or abnormal regions in images through image processing and deep learning techniques. Currently, utilizing the approach of reverse knowledge distillation has yielded favourable outcomes. However, it is still a challenge in terms of the feature extraction capability of the image and the robustness of the decoding of the student network. This study first addresses the issue that the teacher network has not been able to extract global information more effectively. To acquire more global information, a vision transformer network is introduced to enhance the model's global information extraction capability, obtaining better features to further assist the student network in decoding. Second, for anomalous samples, to address the low similarity between features extracted by the teacher network and features restored by the student network, Gaussian noise is introduced. This further increases the probability that the features decoded by the student model match normal sample features, enhancing the robustness of the student model. Extensive experiments were conducted on industrial image datasets AeBAD, MvtecAD, and BTAD. In the AeBAD dataset, under the PRO performance metric, the result is 89.83%, achieving state‐of‐the‐art performance. Under the AUROC performance metric, it reaches 83.35%. Similarly, good results were achieved on the MvtecAD and BTAD datasets. The proposed method's effectiveness and performance advantages were validated across multiple industrial datasets, providing a valuable reference for the application of industrial image anomaly detection methods. [ABSTRACT FROM AUTHOR]

Industrial defect detection plays an important role in smart manufacturing and is widely used in various scenarios such as smart inspection and product quality control. Currently, although utilizing a framework for knowledge distillation to identify industrial defects has achieved great progress, it is still a significant challenge task to extract better image features and prevent overfitting for student networks. In this study, a reverse knowledge distillation framework with two teachers is designed. First, for the teacher network, two teachers with different architectures are used to extract the diverse features of the images from multiple models. Second, considering the different contributions of channels and different teacher networks, the attention mechanism and iterative attention feature fusion idea are introduced. Finally, to prevent overfitting, the student network is designed with a network architecture that is inconsistent with the teacher network. Extensive experiments were conducted on Mvtec and BTAD datasets, which are industrial defect detection datasets. On the Mvtec dataset, the average accuracy values of image-level and pixel-level ROC achieved 99.43% and 97.87%, respectively. On the BTAD dataset, the average accuracy values of image-level and pixel-level ROC reached 94% and 98%, respectively. The performance on both datasets is significantly improved, demonstrating the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Due to the high cost of mobile nodes and the low cost of fixed nodes, in some applications of wireless sensor networks, in order to reduce the use cost, the mixed mode is used for node deployment. The deployed nodes include both fixed and mobile nodes. After the nodes are deployed, the fixed nodes do not move, and the mobile nodes move reasonably according to the network coverage to improve the network coverage effect. Due to the differences in equipment structure and use cost between fixed nodes and mobile nodes, in order to complete the monitoring task under the condition of reducing the use cost of wireless sensor networks, the number and position of mobile nodes can be reasonably adjusted as needed to improve the effective coverage area of wireless sensor networks. In the process of adjusting the position of mobile nodes, the moving distance of mobile nodes is fully considered. Simulation results show that this algorithm can improve the coverage effect of mixed wireless sensor networks and reduce the moving distance of mobile nodes. [ABSTRACT FROM AUTHOR]

Extraction and analysis of the EEG (electroencephalograph) information features generated during behavioral decision-making can provide a better understanding of the state of mind. Previous studies have focused more on the brainwave features after behavioral decision-making. In fact, the EEG before decision-making is more worthy of our attention. In this study, we introduce a new index based on the reaction time of subjects before decision-making, called the Prestimulus Time (PT), which have important reference value for the study of cognitive function, neurological diseases, and other fields. In our experiments, we use a wearable EEG feature signal acquisition device and a systematic reward and punishment experiment to obtain the EEG features before and after behavioral decision-making. The experimental results show that the EEG generated after behavioral decision due to loss is more intense than that generated by gain in the medial frontal cortex (MFC). In addition, different characteristics of EEG signals are generated prior to behavioral decisions because people have different expectations of the outcome. It will produce more significant negative-polarity event-related potential (ERP) in the forebrain area when the humans are optimistic about the outcomes. [ABSTRACT FROM AUTHOR]

High-quality network coverage can quickly and accurately collect the data in the monitoring area and complete the task of target monitoring in wireless sensor networks. The existing related work focuses on improving the effect of network coverage and reducing the consumption of network resources. Aiming at the problems of coverage blind area and node redundancy in network coverage, a monitoring area coverage algorithm based on an improved virtual algorithm is proposed by improving the existing virtual force algorithm. This algorithm controls the moving direction and distance of nodes by adjusting the distance between nodes. Make the node move to a reasonable position, improve the coverage effect of the network, and reduce the moving distance of the node. The simulation results show that compared with the DNDVF algorithm and VFADP algorithm, this algorithm has a better application effect, can improve the coverage effect, and reduce the moving distance of nodes. [ABSTRACT FROM AUTHOR]

Wireless sensor networks deploy a large number of wireless sensor nodes in the monitoring area to complete the target monitoring task. The coverage effect is an important index to evaluate wireless sensor networks. The effect of network coverage is realized by the wireless sensor nodes deployed in the monitoring area, because the reasonable deployment of sensor nodes can affect the accuracy, reliability, real time, and integrity of monitoring target data collection. The main purpose of network coverage is to use the least number of wireless sensor nodes to complete the monitoring task of targets in the monitoring area and to avoid the emergence of coverage blind areas. Random node deployment, electronic equipment failure, software error, fire spread, flood, and other phenomena may lead to large-scale failure of wireless sensor nodes. For the failure of some nodes, the phenomenon of coverage blind area appears in the network. In order to repair the coverage blind area in the network, a coverage blind area repair algorithm for wireless sensor networks is proposed in this article. The coverage blind area attracts the nodes, makes the nodes move, and repairs the coverage blind area in the network. [ABSTRACT FROM AUTHOR]

Summary: Coverage is one of the basic problems that any wireless sensor network application must solve properly to achieve the expected quality of service. The communication distance of wireless sensor network nodes is limited, the initial energy is low, and many optimization objectives need to be considered in the deployment, which makes the network deployment optimization very complicated, and many problems are difficult. In essence, the deployment problem is a multidimensional nonlinear optimization problem with continuous discrete variables. In order to meet the needs of network monitoring, this article establishes a reasonable mathematical model according to the research objectives. By adjusting node position to obtain better network coverage effect, an optimization algorithm of monitoring area coverage based on control node position is proposed. By calculating the new location of nodes, more monitoring areas can be covered by the nodes. The simulation results show that the proposed algorithm can improve the coverage effect rapidly. [ABSTRACT FROM AUTHOR]

Vehicle detection in aerial images is an interesting and challenging task. Traditional methods are based on sliding‐window search and handcrafted features, which limits the representation power and has heavy computational costs. Recent research have shown that deep‐learning algorithms are widely used in the field of object detection. However, the deep‐learning algorithms still face many difficulties and challenges in the object detection under the aerial scene. Meanwhile, the high computational cost of detection algorithms lead to low‐detection efficiency. In this study, we build a fast and accurate lightweight detection framework for vehicle detection in aerial scenes. The proposed detection method improves the expressive ability of detection network and significantly reduces the amount of calculations in the model. Meanwhile, setting suitable anchor boxes according to the size of the object vehicles have been introduced in our model, which also effectively improves the performance of the detection. In addition, we have published a new aerial vehicle image dataset and verified the effectiveness of our method. In the Munich dataset and our dataset, our method achieves 85.8% and 91.2% of the mean average precision (mAP), and its detection time is 1.78 and 0.048 s on Nvidia Titan XP. Our results show that the proposed framework achieves significant improvement over several alternatives and state‐of‐the‐art schemes with higher accuracy and less detection time. [ABSTRACT FROM AUTHOR]

With the rapid development of the electronic industry, the defect detection of printed circuit board (PCB) components is becoming more and more important. The types of PCB components are diverse and accompanied by complex character information, which is difficult to identify. The traditional detection method is inefficient, and it is unable to effectively perform the diversified category detection of PCB components and character recognition in complex scenes. The deep convolutional neural network has obvious advantages in object detection and character recognition, which can be used to implement a PCB component defect detection system. In this study, the authors have established a lightweight PCB type detection model called LD‐PCB, which can perform real‐time detection while improving detection accuracy. In addition, in the character detection of PCB, they have established a fast and robust character recognition model, called CR‐PCB. This model can effectively improve the accuracy of irregular character recognition. Finally, they established and published a dataset of PCB components, and combined with LD‐PCB and CR‐PCB to realise the PCB defect detection system. This system can realise the functions of defect detection, wrong insertion, missing insertion, and character recognition in industrial PCB production. The results show that the method proposed in this study can effectively detect defects on PCB components. [ABSTRACT FROM AUTHOR]

In recent years, QR code has been widely used in various society fields, and has provided great convenience in information exchange, commodity payment and website registration. However, the degradation of QR code images can cause difficulty in reading the code information. In this paper, we propose an alternating minimization restoration model for QR code images. Based on the binary prior, we use the binary characteristic and L 0 norm as the regularization terms, and introduce different auxiliary variables to make the complex model become solvable. The bi-level constraint of bar code image maintains the sparseness of image pixels and gradient. The experimental result shows that our algorithm has excellent effect on common blur kernels and multiple noise, and its performance is better than the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, numerous incentive mechanisms of mobile crowdsensing have been designed to attract extensive user participation, but most of these mechanisms focus only on independent task scenarios, where the sensing tasks are independent of each other. On the contrary, we focus on a periodical task scenario, where each user participates in the same type of sensing tasks periodically. In this paper, we consider the long‐term user participation incentive in a general periodical mobile crowdsensing system from a frugality payment perspective. We explore the issue under both semi‐online (the intraperiod interactive process is synchronous while the interperiod interactive process is sequential and asynchronous during each period) and online user arrival models (the previous 2 interactive processes are sequential and asynchronous). In particular, we first propose a semi‐online frugal incentive mechanism by introducing a Lyapunov method. Moreover, we also extend it to an online frugal incentive mechanism, which satisfies the long‐term participation constraint and approximate optimality. Finally, extensive simulations show that our mechanisms satisfy the above theoretical properties. [ABSTRACT FROM AUTHOR]

Machine learning techniques require an enormous amount of high-quality data labeling for more naturally simulating human comprehension. Recently, mobile crowdsensing, as a new paradigm, makes it possible that a large number of instances can be often quickly labeled at low cost. Existing works only focus on the single labeling for supervised learning problems of traditional machine learning, where one instance associates with only label. However, in many real world applications, an instance may have more than one label. To the end, in this paper, we explore an incremental multi-labeling issue by incentivizing crowd users to label instances under the budget constraint, where each instance is composed of multiple labels. Considering both uncertainty and diversity of the number of each instance's labels, this paper proposes two mechanisms for incremental multi-labeling crowdsensing by introducing both uncertainty and diversity. Through extensive simulations, we validate their theoretical properties and evaluate the performance. [ABSTRACT FROM AUTHOR]

For the demand of the collimated system improvement of a kind of helicopter, a detection algorithm for the cross line of the collimated system is proposed. Firstly, after image denoising, Kirsch edge detection is used to obtain the edge points of the cross. Then the chain code of these edge points is obtained. After analyzing the chain code, the line of the cross is detected based on a line similarity algorithm. Finally, the cross of collimated system is detected based on the combination of line feature, site feature and angle feature, which improves the reliability of the result greatly. Experiments show that the performance of the proposed algorithm is excellent, and it can meet the requirement of the practical use. [ABSTRACT FROM AUTHOR]

Abstract: When a two-dimensional bar code is away from a camera’s focus, the image is blurred by the convolution of the point spread function. In the presence of noise, the out-of-focus deblurring is an ill-posed problem. The two-dimensional bar code image has a very special form, making deblurring feasible. This paper proposes a fast deblurring algorithm called the Increment Constrained Least Squares filter that is specifically designed for two-dimensional bar code images. After analyzing the bar code image, the standard deviation of the Gaussian blur kernel is obtained. Then, the bar code image is restored through on iterative computations. In each iteration, the bi-level constraint of the bar code image is efficiently incorporated. Experimental results show that our algorithm can obtain better bar code image quality compared with existing methods. Our method can also improve the reading depth of field, which is an important performance parameter for bar code readers. [Copyright &y& Elsevier]

Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain. Deep Adaptation Network (DAN) is one of these efficient frameworks, it utilizes Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to align the feature distribution in a reproducing kernel Hilbert space. However, DAN does not perform very well in feature level transfer, and the assumption that source and target domain share classifiers is too strict in different adaptation scenarios. In this paper, we further improve the adaptability of DAN by incorporating Domain Confusion (DC) and Classifier Adaptation (CA). To achieve this, we propose a novel domain adaptation method named C2DAN. Our approach first enables Domain Confusion (DC) by using a domain discriminator for adversarial training. For Classifier Adaptation (CA), a residual block is added to the source domain classifier in order to learn the difference between source classifier and target classifier. Beyond validating our framework on the standard domain adaptation dataset office-31, we also introduce and evaluate on the Comprehensive Cars (CompCars) dataset, and the experiment results demonstrate the effectiveness of the proposed framework C2DAN. [ABSTRACT FROM AUTHOR]

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results. [ABSTRACT FROM AUTHOR]