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Traditional point cloud simplification methods are slow to process large point clouds and prone to losing small features, which leads to a large loss of point cloud accuracy. In this paper, a new point cloud simplification method using a three-step strategy is proposed, which realizes efficient reduction of large point clouds while preserving fine features through point cloud down-sampling, normal vector calibration, and feature extraction based on the proposed feature descriptors and neighborhood subdivision strategy. In this paper, we validate the method using measured point clouds of large co-bottomed component surfaces, visualize the errors, and compare it with other methods. The results demonstrate that this method is well-suited for efficiently reducing large point clouds, even those on the order of ten million points, while maintaining high accuracy in feature retention, refinement precision, efficiency, and robustness to noise. [ABSTRACT FROM AUTHOR]

Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved. [ABSTRACT FROM AUTHOR]

At present, the majority of techniques for assessing image quality are limited to extracting features from an image in a single space. This paper proposes a new dual-space multi-feature fusion based method for full-reference image quality assessment. This method involves simultaneously extracting features from both the YIQ and L*a*b* color spaces. First, we extract the luminance, slope, chroma, and gradient features in the spatial domain of the image to describe the salient differences in the image. Second, based on contrast sensitivity characteristics, we extract spatial frequency features in the spatial domain of the image to represent frequency differences in the image. Next, merge the features extracted in the dual space to construct a quality perception feature vector. Finally, the feature vector is input into the Random Forest model for regression prediction to obtain the predicted score of the image. Many experiments have been carried out on the four public datasets, and contrasted with other methods. The experimental confirm that the proposed method predicts image quality more accurately. The MATLAB source code and dataset of this paper will be published on GitHub, and the corresponding author can be contacted if necessary. [ABSTRACT FROM AUTHOR]

In order to realize the intelligent recognition and statistics of hydrogenation catalyst image information, this paper presents a new method to judge the active phase by image recognition, which is different from traditional methods. Firstly, considering that hydrogenation catalyst image targets are small and easy to stack, the feature extraction network in the CenterNet model is optimized by adding the multi-feature fusion module to improve the accuracy of the network in edge positioning. Secondly, according to the linear shape of the hydrogenation catalyst, the mask branch is added to the CenterNet model to train the hydrogenation catalyst stripes with unclear target to reduce the leakage rate of the hydrogenation catalyst. The experimental results show that the detection accuracy of the improved CenterNet network is 91 % , 7 % higher than that of the original one, with a decline in detection rate by 12 % . The method proposed in this paper can accurately identify and segment the hydrogenation catalyst in the electron microscope image, which can provide technical support for the statistics and analysis of the hydrogenation catalyst image. [ABSTRACT FROM AUTHOR]

Background: Emotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal's changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers' performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions. Results: The RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases' results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm's capabilities in precisely determining the emotion label for each participant's EEG features' instance. Conclusion: The findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm's performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result. [ABSTRACT FROM AUTHOR]

The object scale of a small object scene changes greatly, and the object is easily disturbed by a complex background. Generic object detectors do not perform well on small object detection tasks. In this paper, we focus on small object detection based on FocusDet. FocusDet refers to the small object detector proposed in this paper. It consists of three parts: backbone, feature fusion structure, and detection head. STCF-EANet was used as the backbone for feature extraction, the Bottom Focus-PAN for feature fusion, and the detection head for object localization and recognition.To maintain sufficient global context information and extract multi-scale features, the STCF-EANet network backbone is used as the feature extraction network.PAN is a feature fusion module used in general object detectors. It is used to perform feature fusion on the extracted feature maps to supplement feature information.In the feature fusion network, FocusDet uses Bottom Focus-PAN to capture a wider range of locations and lower-level feature information of small objects.SIOU-SoftNMS is the proposed algorithm for removing redundant prediction boxes in the post-processing stage. SIOU multi-dimension accurately locates the prediction box, and SoftNMS uses the Gaussian algorithm to remove redundant prediction boxes. FocusDet uses SIOU-SoftNMS to address the missed detection problem common in dense tiny objects.The VisDrone2021-DET and CCTSDB2021 object detection datasets are used as benchmarks, and tests are carried out on VisDrone2021-det-test-dev and CCTSDB-val datasets. Experimental results show that FocusDet improves mAP@.5% from 33.6% to 46.7% on the VisDrone dataset. mAP@.5% on the CCTSDB2021 dataset is improved from 81.6% to 87.8%. It is shown that the model has good performance for small object detection, and the research is innovative. [ABSTRACT FROM AUTHOR]

Forest fire is natural disasters that are sudden, destructive and difficult to handle and rescue, with millions of hectares of forests burned every year all over the world, causing serious ecological damage, loss of life and property. Therefore, timely detection and treatment of early fire is of positive and important significance for forest fire early control. The fire detection method based on image processing is one of the most important means of preventing the occurrence of large-scale forest fires at present by extracting the flame and smoke features in the image and quickly determining the location of the fire. The current deep learning-based forest fire early detection methods have problems such as high false alarm rate due to small detection targets and complex environmental background, and large number of detection model parameters. In view of this, this paper proposes a lightweight early forest fire and smoke detection method based on GS-YOLOv5. Firstly, this paper proposes a novel Super-SPPF structure to replace the SPPF structure in YOLOv5, by which the output of the feature extraction network is used as input, and the input is divided into two branches to retain more semantic information, and the GhostConv operation is performed separately to reduce the number of model parameters. The Super-SPPF structure performs the serial MaxPooling operation on one of the branches, which improves the computation speed by choosing a smaller pooling kernel, and then fuses the outputs of the two branches to reduce the false alarm rate of the detection model. Secondly, C3Ghost is utilized instead of the C3 module in YOLOv5 to further reduce the number of detection model parameters. Finally, the coordinate attention (CA) module is introduced in backbone of YOLOv5 to obtain the relationship between channels and space, which enables the network to obtain the location information of interest more accurately and further improves the detection accuracy of early fires. In this paper, a self-constructed DL-Fire dataset is used to verify the performance of the GS-YOLOv5 detection model by collecting environmental interference samples and combining them with the D-Fire dataset. The experimental results show that the detection accuracy of GS-YOLOv5 is 95.9%, and the model size is 10.58 mb. Compared with YOLOv5, false alarm rate is reduced from 12% to 6% and computational complexity is reduced from 16.0 GFlops to 12.8 GFlops by GS-YOLOv5. [ABSTRACT FROM AUTHOR]

Due to the large intra-class differences between the same categories and the scale imbalance between different categories in the remote sensing image dataset, the semantic segmentation task presents the problem of small-scale object information loss, the imbalance between foreground and background, and simultaneously the background dominates, which seriously affects the performance of the network model. To solve the above problems, this paper proposes an efficient bilateral branch depth neural network model based on the U-Net depth neural network, named BBU-Net. Firstly, one branch of the network learns the distribution characteristics of the original data, and the other focuses on difficult samples. Then the two branches improve the representation and classification ability of the neural network by accumulating learning strategies. Finally, considering the geometric diversity of remote sensing images, this paper adopts test time augmentation and reflection padding strategies and proposes a balanced weighted loss function named CombineLoss to alleviate the imbalance in the training process. The depth neural network proposed in this paper was first tested on the Inria Aerial Image Labeling Dataset, and 87.53% of mean intersection over union and 97.4% of mean pixel accuracy were obtained, respectively. At the same time, to verify the model's complexity, the model proposed in this paper is compared with the neural network based on integrated learning. The comparison results show that the spatial complexity of the network proposed in this paper is much lower than the neural network obtained by integrated learning, and the parameters are also much smaller than the neural network based on integrated learning. Then use the satellite building dataset I in the WHU Building Dataset and mainstream semantic segmentation methods for multiple groups of comparative experiments. The experimental results show that the method proposed in this paper can effectively extract the semantic information of remote sensing images, significantly improve the imbalance of remote sensing image data, improve the performance of the network model, and achieve a good semantic segmentation effect, which fully proves the effectiveness of this method. [ABSTRACT FROM AUTHOR]

The longitudinal tear of conveyor belts is the most common accident occurring at the workplace. Given the limitations on accuracy and stability of current single-modal approaches to detecting the longitudinal tear of conveyor belts, a solution is proposed in this paper through Audio-Visual Fusion. According to this method, a linear CCD camera is used to capture the images of the conveyor belt and a microphone array for the acquisition of sound signals from the operating belt conveyor. Then, the visual data is inputted into an improved Shufflenet_V2 network for classification, while the preprocessed sound signals are subjected to feature extraction and classification using a CNN-LSTM network. Finally, decision fusion is performed in line with Dempster-Shafer theory for image and sound classification. Experimental results show that the method proposed in this paper achieves an accuracy of 97% in tear detection, which is 1.2% and 2.8% higher compared to using images or sound alone, respectively. Apparently, the method proposed in this paper is effective in enhancing the performance of the existing detection methods. [ABSTRACT FROM AUTHOR]

With a significant number of mechanical products approaching the retirement phase, the batch recycling of discarded mechanical parts necessitates a preliminary assessment of their surface condition. However, the presence of surface rust poses a challenge to defect identification. Therefore, this paper proposes a method for detecting heavily rusted surface defects based on an improved YOLOv8n network. In the Backbone, the C2f-DBB module of re-parameterized deep feature extraction was introduced, and the attention module was designed to improve the accuracy of information extraction. In the Neck part, a Bi-Afpn multiscale feature fusion strategy is designed to facilitate information exchange between features at different scales. Finally, Focal-CIoU is employed as the bounding box loss function to enhance the network's localization performance and accuracy for defects. Experimentally, it is proved that the improved network in this paper improves the Recall, Precision, and mAP0.5 by 1.2%, 2.1%, and 1.9%, respectively, on the original basis, which is better than other network models. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) paradigm and the advanced wireless technologies of 5G and beyond are expected to enable diverse applications such as autonomous driving, industrial automation, and smart cities. These applications bring together a vast and diverse IoT device population that occupy radio frequency spectrum. Such a large number of wireless devices expose previously unheard-of threat surfaces in addition to the bandwidth shortage and throughput issues. Device identification is crucial in such scenarios not only to authenticate and authorize nodes, but also to employ different network services. One of the promising solutions for device identification is the use of radio frequency (RF) fingerprinting. Recently, wireless device identification using RF fingerprinting along with machine learning and deep learning technologies showed outstanding results in the recent contemporary domains. This paper presents a systematic literature review of RF fingerprinting identification of wireless devices by presenting the results as a graphical and tabular representation of statistical data obtained. Only experimental research papers were considered of over 130 journals and international conference papers that have been classified and evaluated from the year 2010 till date. This survey focuses on exploring the commonly used RF fingerprinting approaches, feature extraction and filtration techniques, and classification algorithm used in the device identification. Finally, open issues and challenges along with future directions have presented which were discovered during the process of analyzing the literature. [ABSTRACT FROM AUTHOR]

Optical tracking sensor technology has been widely used in the field of motion. However, there are still some challenges in the recognition of badminton players, and more accurate methods are needed to capture the dynamic characteristics of badminton players. The aim of this study is to design an optical tracking sensor system based on image feature extraction for badminton player motion recognition. In this paper, a high resolution camera is used to collect the image sequence of badminton match. Then through image processing and computer vision technology, the key image features are extracted from the image sequence. Then, machine learning algorithm is used to classify and recognize the extracted features to achieve accurate recognition of badminton players' movements. The experimental results show that the optical tracking sensor system can effectively extract the movement features of badminton players and identify their movements accurately. Compared with the traditional method, the system in this paper has higher precision and real-time performance, and can meet the needs of practical applications. [ABSTRACT FROM AUTHOR]

Existing biohashing authentication schemes usually store bioinformation in the cloud, whose storage method can face the problems of bioinformation leakage and tampering, and single point of failure. To address the above problems, a ciphertext speech biohashing authentication scheme based on chaotic system and improved public chain is proposed. First, the fusion features are extracted and generated with the inner product of 3D chaotic system to generate the biohashing sequence. Second, the original speech is encrypted and uploaded to the InterPlanetary file system and then, uploaded to the blockchain after the data are encrypted. Finally, the server matches the hash sequence queried on the chain with the to-be-authenticated hash at the user's end using the Hamming distance. The experimental results show that the biohashing algorithm in this paper has better distinguishability, robustness, security and time efficiency. The key space of the speech encryption algorithm is 2 256 , which can well resist the exhaustive attack and ensure the security during speech authentication. In this paper, the combination of on-chain and off-chain storage of speech information can prevent the problem of bioinformation leakage and tampering, as well as avoiding the problem of single point of failure. [ABSTRACT FROM AUTHOR]

Danmakus are user-generated comments that overlay on videos, enabling real-time interactions between viewers and video content. The emotional orientation of danmakus can reflect the attitudes and opinions of viewers on video segments, which can help video platforms optimize video content recommendation and evaluate users' abnormal emotion levels. Aiming at the problems of low transferability of traditional sentiment analysis methods in the danmaku domain, low accuracy of danmaku text segmentation, poor consistency of sentiment annotation, and insufficient semantic feature extraction, this paper proposes a video danmaku sentiment analysis method based on MIBE-RoBERTa-FF-BiLSTM. This paper constructs a "Bilibili Must-Watch List and Top Video Danmaku Sentiment Dataset" by ourselves, covering 10,000 positive and negative sentiment danmaku texts of 18 themes. A new word recognition algorithm based on mutual information (MI) and branch entropy (BE) is used to discover 2610 irregular network popular new words from trigrams to heptagrams in the dataset, forming a domain lexicon. The Maslow's hierarchy of needs theory is applied to guide the consistent sentiment annotation. The domain lexicon is integrated into the feature fusion layer of the RoBERTa-FF-BiLSTM model to fully learn the semantic features of word information, character information, and context information of danmaku texts and perform sentiment classification. Comparative experiments on the dataset show that the model proposed in this paper has the best comprehensive performance among the mainstream models for video danmaku text sentiment classification, with an F1 value of 94.06%, and its accuracy and robustness are also better than other models. The limitations of this paper are that the construction of the domain lexicon still requires manual participation and review, the semantic information of danmaku video content and the positive case preference are ignored. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

Microscopic image analysis technology helps solve the inadvertences of artificial traditional methods in disease, wastewater treatment, and environmental change monitoring analysis. Convolutional neural network (CNN) play an important role in microscopic image analysis. Image segmentation, in which U-Net is increasingly applied in microscopic image segmentation, is a crucial step in detection, tracking, monitoring, feature extraction, modelling, and analysis. This paper comprehensively reviews the development history of U-Net, analyses several research results of various segmentation methods since the emergence of U-Net, and conducts a comprehensive review of related papers. This paper summarised the improved methods of U-Net and then listed the existing significance of image segmentation techniques and their improvements introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can see the dynamics of the transmission of technological development and keep up with future trends in this interdisciplinary field. [ABSTRACT FROM AUTHOR]

Since its inception, salient object detection has been widely used in various image processing tasks. However, previous approaches have mainly focused on how to optimize the accuracy of detection, ignoring the computational overhead of the model itself. This makes it difficult to be used in many devices with limited hardware conditions. To address the above problems, this paper proposes a lightweight saliency detection network based on background denoising and feature generation. First, we build a lightweight feature extraction network by improving the MobileNetV3 feature extraction part and with the help of a multidimensional focus module. Subsequently, in order to fully explore the difference between the background and the salient objects. We insert a noise suppression subnet at the end of the encoder. This subnetwork uses the learned background features to reverse fuse the information from the backbone network, effectively decreasing the amount of background noise and encouraging the model to better distinguish subtle differences between salient objects and the background. Finally, in the decoding stage this paper constructs a feature generator that allows the model to efficiently extend the feature map with little increase in the number of parameters. Thereby, it compensates for the lack of expression capability of lightweight model features. The experiment results on six publicly available benchmark datasets prove that our method is more efficient and accurate than existing lightweight saliency detection methods. [ABSTRACT FROM AUTHOR]

Cancelable biometrics serves as an effective countermeasure against various template attacks launched by intruders, safeguarding the biometric system. This paper proposes a cancelable approach with a novel feature extraction technique for iris recognition, known as the hybrid architecture of the convolutional neural network (CNN) and GRU (gated recurrent unit). To provide cancelability to the system, the paper makes use of a random projection technique. The proposed method has the best outcome in terms of accurate identification. The method is validated on two Iris datasets IITD and MMU, which show promising results on the equal error rate (EER) and accuracy. The proposed model provides 0.02 and 0.045 EER for IITD and MMU, respectively, and accuracy 0.98 and 0.933%, for IITD and MMU Iris dataset, respectively, which is very high compared to other methodologies. The proposed hybrid architecture is being used for a cancelable biometric system for the first time based on literature review. The efficiency of the proposed method is high when validated on the datasets. [ABSTRACT FROM AUTHOR]

At present, in end-to-end network combined with deep learning in stereo matching, how to perceive ample image details within an acceptable range of computational cost, so as to improve the final matching accuracy, has become a hot research topic. In this paper, we design a cascaded disparity correction network based on DispNet, which can refine the initial disparity image by combining the process output results in the trunk network. The method proposed in this paper is named ETE–DC–CNN (end to end–disparity correction–convolutional neural network). In the trunk network, the mixed dilated convolution module is employed to extract the features of the original image, and the feature image is constructed into a 3D disparity space volume through depth-separable convolution, after which the initial disparity image can be obtained through 2D encoder–decoder module. In the correction network, the gradient information of the initial feature image is combined to reconstruct the modified matching cost body guided by the initial disparity image and then a smaller encoder–decoder module is trained and processed to make the final result at the sub-pixel level. The algorithm in this paper has been verified in SceneFlow and KITTI 2012/2015 data sets. The method proposed in this article achieves 3.46, 2.12, 1.19, and 0.6 results in the non-occluded area and 4.04, 2.55, 1.58, and 0.7 results in the global area on the Er (> 2 px), Er (> 3 px), Er (> 5 px), and Epe indicators in the KITTI 2012 test set, which are higher than those of the backbone network DispNet. [ABSTRACT FROM AUTHOR]

At present, the traditional martial arts teaching method has some problems, such as unstable teaching effect, difficult to quantify, etc. This study aims to use optical technology to improve the effect of martial arts movement teaching. This paper introduces the basic principle and related techniques of human body pose estimation. By analyzing the posture information of the human body, it can accurately capture the movement posture of the students during the martial arts movement, and feed back to the teachers and students in real time. In this paper, a dynamic light acquisition system is designed to capture students' posture information by installing corresponding cameras and sensors in the teaching space. By analyzing and processing the images captured by the camera, the students' posture features can be extracted effectively. Then, according to the characteristics of martial arts movement teaching, the corresponding simulation system is designed. In this system, teachers and students can use virtual reality devices to interact, observe and analyze students' gestures in real time, and make corresponding adjustments and guidance. The conclusion shows that the dynamic light acquisition system based on human body pose estimation has significant advantages in martial arts movement teaching, it can accurately capture students' movement posture and provide real-time feedback and guidance for teachers and students. The application of this technology is expected to improve the effectiveness and quality of martial arts teaching and provide students with a better learning experience. [ABSTRACT FROM AUTHOR]

The emergence of digital media has changed the way people live and learn. In the context of the new era, digital media is gradually integrating into people's life and learning. Digital media contains massive images, and fine-grained image recognition for digital media has become an important topic. The challenge of fine-grained image recognition is that the difference between different categories is small, and the difference between the same categories is sometimes large. This work designs a fine-grained image recognition based on feature enhancement (FIRFE). This extracts as much information as possible from fine-grained images under weak supervision to improve the recognition accuracy. When the existing methods extract image features, the feature extraction other than the most significant local feature is not enough. This deals with local features alone and ignores the relationship between features. First, this paper designs a feature enhancement and suppression module to process image features. Secondly, this paper designs pyramid residual convolution. This uses different scale convolution kernels to capture different levels of features in the scene. Thirdly, this paper uses the softpool method to rationally allocate the information weight in the pooling process. Fourth, this paper uses feature focus module to mine more features. This focuses on obtaining similar information in multiple local features as discriminant features to further improve the recognition. Fifthly, this paper carried out systematic experiments on the designed method. The proposed method achieves 94.3%/95.7% accuracy, 92.9%/94.1% recall, and 91.4%/92.2% F1 score on different datasets. This verified the superiority of this method for fine-grained image recognition of digital media. [ABSTRACT FROM AUTHOR]

With the rapid development of information transfer technology and the influence of complex electromagnetic environment, the signal components in communication systems are becoming more and more complex, and the spectrum congestion is further aggravated, which seriously affects the performance of communication systems. Single-channel blind source separation technology is an effective solution, but the existing algorithms are deficient in terms of estimation accuracy, time delay and computational complexity. Based on the powerful feature extraction capability and timing signal processing capability of deep learning, this paper proposes an end-to-end time-domain blind separation convolutional network structure, including three parts: encoder, separation module and decoder. The attention mechanism module is also introduced into the encoding layer of the network to enhance the deep feature extraction ability of the network, highlight the influence of important timing features of communication signals, further improve the separation performance and enhance the practicality of the network. The experimental results show that the algorithm has superior performance and can effectively separate the mixed communication signal of various modulation types, and it can also extract own-side communication signals from high-power masked signals. Compared with the existing algorithms, the network in this paper has high separation accuracy and low computational complexity, which breaks the bottleneck of traditional single-channel blind separation algorithms in complex communication electromagnetic environment. [ABSTRACT FROM AUTHOR]

Automated and accurate detection of power quality (PQ) events is important from the point of view of safety as well as maintaining the reliability of the power transmission and distribution network. However, detection of multiple PQ events in a noisy environment is challenging task. Another important issue is the choice of meaningful features that can directly influence the accuracy of PQ detection. Considering these two aforesaid facts, this paper presents a novel framework for automated classification of PQ signals in a noisy environment employing cross Stockwell Transform (XST). The XST proposed in this paper has better noise suppression capability compared to conventional Stockwell Transform. Here, XST was used to convert 1D PQ signals to 2D time–frequency (T–F) images. To improve the accuracy of PQ detection, an automated feature extraction method employing deep learning is implemented in this work. The noise free T–F images obtained using XST were fed as inputs to several pre-trained convolutional neural networks (CNNs) for deep feature extraction. Transfer learning technique was implemented to reduce the computational cost. The extracted deep features were further undergone selection using one-way analysis of variance test followed by false discovery rate correction. The statistically significant deep features were subsequently fed to three benchmark machine learning classifiers for classification of PQ signals. In addition, tests were also carried out on real-life PQ signals to verify the practicability of the proposed framework. Investigations revealed that the proposed method returned mean accuracy of 99.72% and 96.45% for classification of simulated and real-life PQ signals, respectively. [ABSTRACT FROM AUTHOR]

Based on optical image enhancement, this paper uses convolutional neural network to detect key points of swimming posture. In swimming posture analysis, key point detection can provide information about posture accuracy and evaluation. However, due to the change of illumination conditions, the reduction of image quality and occlusion, the accuracy and stability of key point detection are limited. In order to solve this problem, an optical image enhancement method based on convolutional neural network is proposed in this paper. First, a specific convolutional neural network architecture is used to extract features from input images. Then optical image enhancement technology is used to preprocess the input image to improve the image quality. Next, the trained convolutional neural network model is used to detect key points. Experimental results show that the proposed method has good performance under different illumination conditions and image quality. Therefore, this study provides a new idea and method for the application of optical image enhancement in the detection of key points in swimming posture analysis. [ABSTRACT FROM AUTHOR]

Pedestrian behavior exhibits high levels of dynamism, and pedestrian trajectories are influenced not only by the pedestrians themselves, but also by interactions with surrounding objects. Efficiently understanding pedestrian motion behavior and modeling its interactions play a crucial role in fields like autonomous driving. Addressing issues related to dynamic feature extraction and interaction modeling in pedestrian trajectory prediction tasks, this paper introduces the conditional pedestrian trajectory prediction network (CSGAT-Net) based on semantic segmentation maps and spatiotemporal graph attention. CSGAT-Net models the physical environment and pedestrian behavior information in the scene as a semantic map, and it leverages graph attention networks to extract pedestrian interaction features. Finally, it predicts pedestrian future trajectories using a variational autoencoder. Comparative experiments conducted on publicly available datasets, ETH and UCY, show that our model exhibits favorable objective evaluation metrics and subjective prediction performance. Particularly, in terms of ADE and FDE metrics, CSGAT-Net outperforms current state-of-the-art methods, indicating that our model can reasonably and accurately predict pedestrian trajectories in different scenarios. [ABSTRACT FROM AUTHOR]

Lung cancer is an uncontrolled growth of tissue causing a lump in the human lung. If lung cancer can be detected early, it can increase the survival rate. Therefore, a multi-classification approach of lung nodule detection with high computational effectiveness is required. In this paper, a multi-classification approach of lung nodule detection and classification is proposed using artificial intelligence on computed tomography (CT) scan images. Different preprocessing steps are applied for resizing, smoothing, and enhancement of the CT images. Then, two different approaches for feature extraction using VGG16 transfer learning and morphological segmentation are proposed. Morphological segmentation and feature extraction are applied for the segmentation of the region of interest and to extract the distinct features. Finally, the proposed deep learning architecture and seven different machine learning algorithms are applied on the preprocessed data and the extracted features for the classification of lung nodules into three classes: malignant, benign, and normal. It is observed that the stacked ensemble model of deep learning convolutional neural network (CNN) and VGG16 transfer learning models (CNN+VGG16) can achieve 99.55% accuracy using preprocessed data. It is also observed that all the ML algorithms perform with reasonably high accuracy using the low-dimensional morphological features. It is observed from the fivefold cross-validation results that logistic regression performs with 99.36% accuracy in 23.71 s time using the preprocessed data. Whereas, using the morphological features, k-nearest neighbor, and the support vector machine perform with the highest accuracy of 99.76% with very reduced computational time of 0.017 and 0.008 s, respectively. [ABSTRACT FROM AUTHOR]

Film cooling is an effective technology for improving the thrust-to-weight ratio of an aero-engine. The axial orientation of film cooling holes is one of the key factors determining the film cooling effect. However, the machining quality of cooling holes is still carried out by manual inspection, whose accuracy and consistency cannot be guaranteed. To address this problem, this paper proposes an axial orientation extraction algorithm based on laser point cloud processing. Firstly, the blade surface is scanned by a line laser sensor and a blade point cloud is obtained. Secondly, a single film hole can be segmented from the blade point cloud based on scanning line fitting residual. Thirdly, an improved Gaussian map is to complement the arc belt obtained by Gaussian map to obtain a complete circle along an error-sensitive direction. Finally, the axial orientation is estimated by random sample consensus with an initial value. The accuracy and consistency of the algorithm are verified by comparing the extraction result by the algorithm with those by the Geomagic software. The result shows that the axial accuracy extracted by the algorithm can reach 0.405°. [ABSTRACT FROM AUTHOR]

Ancient murals have suffered from continuous damage over time, and especially paint loss disease. Therefore, disease labeling, as the basis for ancient mural restoration, plays an important role in the protection of cultural relics. The predominant method of disease labeling is currently manual labeling, which is highly dependent on expert experience, time consuming, inefficient and results in inconsistent accuracy of the marking effect. In this paper, we propose a labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation. The proposed framework involves first the extraction of features from the hyperspectral image, and then image segmentation is performed based on the spatial features to obtain more accurate region boundaries. Then, the hyperspectral image's regions are classified based on their spatial-spectral characteristics, and the candidate areas of paint loss disease are obtained. Finally, by leveraging the true color image segmentation results, the proposed disease labeling strategy combines the results of classification and segmentation to propose the final paint loss disease labeling areas. The experimental results show that the proposed method can not only combine the hyperspectral space and spectral information effectively to obtain accurate labeling of paint loss disease, but can also mark the paint loss disease not easily observed using ordinary digital cameras. Compared with the state-of-the-art methods, the proposed framework could be promising for accurate and effective paint loss disease labeling for ancient murals. [ABSTRACT FROM AUTHOR]

Generally, the recognition performance of lightweight models is often lower than that of large models. Knowledge distillation, by teaching a student model using a teacher model, can further enhance the recognition accuracy of lightweight models. In this paper, we approach knowledge distillation from the perspective of intermediate feature-level knowledge distillation. We combine a cross-stage feature fusion symmetric framework, an attention mechanism to enhance the fused features, and a contrastive loss function for teacher and student models at the same stage to comprehensively implement a multistage feature fusion knowledge distillation method. This approach addresses the problem of significant differences in the intermediate feature distributions between teacher and student models, making it difficult to effectively learn implicit knowledge and thus improving the recognition accuracy of the student model. Compared to existing knowledge distillation methods, our method performs at a superior level. On the CIFAR100 dataset, it boosts the recognition accuracy of ResNet20 from 69.06% to 71.34%, and on the TinyImagenet dataset, it increases the recognition accuracy of ResNet18 from 66.54% to 68.03%, demonstrating the effectiveness and generalizability of our approach. Furthermore, there is room for further optimization of the overall distillation structure and feature extraction methods in this approach, which requires further research and exploration. [ABSTRACT FROM AUTHOR]

In this paper, a method based on transfer learning is proposed to recover the three-dimensional shape of cultural relics faces from a single old photo. It can simultaneously reconstruct the three-dimensional facial structure and align the texture of the cultural relics with fewer training samples. The UV position map is used to represent the three-dimensional shape in space and act as the output of the network. A convolutional neural network is used to reconstruct the UV position map from a single 2D image. In the training process, the human face data is used for pre-training, and then a small amount of artifact data is used for fine-tuning. A deep learning model with strong generalization ability is trained with fewer artifact data, and a three-dimensional model of the cultural relic face can be reconstructed from a single old photograph. The methods can train more complex deep networks without a large amount of cultural relic data, and no over-fitting phenomenon occurs, which effectively solves the problem of fewer cultural relic samples. The method is verified by restoring a Chinese Terracotta Warrior with facial cracks and a Buddha with a broken nose. Other applications can be used in the fields such as texture recovery, facial feature extraction, and three-dimensional model estimation of the damaged cultural relics or sculptures in the photos. [ABSTRACT FROM AUTHOR]

Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields. [ABSTRACT FROM AUTHOR]

Because of the limitations of current PCB detection methods, such as low detection accuracy and high rate of false and missed detection, a PCB defect detection algorithm based on improved RetinaNet is proposed, that is, RetinaNet-line. Firstly, the algorithm uses PVT v2B2-Hi as the backbone network of the whole model, which can not only solve the deep feature redundancy caused by the residual network in RetinaNet but also improve the feature extraction ability of the network. Then, the CARAFE up-sampling module is used to enrich the semantic features of defective targets and promote the discrimination ability of the algorithm to defective targets. Finally, HSV color space is used to process the input feature image, and the color distribution is expanded by random perturbation in H, S, and V channels, with the data enhancement completed. In this paper, experiments are conducted on the publicly shared PCB defect detection dataset from the Intelligent Open Laboratory of Peking University, and the results show that the improved algorithm achieves 94.2% in the mAP index, which is better than other networks. [ABSTRACT FROM AUTHOR]

Internet of Things (IoTs) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for IoT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed into machine learning models. This aims to make the detection complexity low enough for real-time operations, which is particularly vital in any intrusion detection systems. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of the modern UNSW-NB15 dataset as well as both binary and multiclass classification. For example, in general, the feature selection method not only provides better detection performance but also lower training and inference time compared to its feature extraction counterpart, especially when the number of reduced features K increases. However, the feature extraction method is much more reliable than its selection counterpart, particularly when K is very small, such as K = 4 . Additionally, feature extraction is less sensitive to changing the number of reduced features K than feature selection, and this holds true for both binary and multiclass classifications. Based on this comparison, we provide a useful guideline for selecting a suitable intrusion detection type for each specific scenario, as detailed in Table 14 at the end of Sect. 4. Note that such the comparison between feature selection and feature extraction over UNSW-NB15 as well as theoretical guideline have been overlooked in the literature. [ABSTRACT FROM AUTHOR]

The magnetic flux leakage (MFL) technique is widely employed for nondestructive testing of ferromagnetic specimens and materials, including wire ropes, bridge cables, and pipelines. As regards the MFL testing, extracting features from MFL signals is crucial for defect recognition and estimation of corresponding widths. Deep learning has been extensively used for feature extraction, but it often performs inadequately on a small sample dataset. To address this limitation, this paper develops a network framework that combines the Wavelet Scattering Transform (WST) and Neural Networks (NN) for defect width estimation. The WST is a knowledge-based feature extraction technique with a structure similar to convolutional neural networks. It offers a translation-invariant representation of signal features using a redundant dictionary of wavelets. The NN then maps the WST feature representation to the defect width information. Experiments on real steel plates with defects are carried out to validate the effectiveness of the proposed framework. Quantitative comparisons of the experimental results demonstrate that the proposed framework achieves better estimation performance in handling MFL signals and has superiority in scenarios with limited training samples. [ABSTRACT FROM AUTHOR]

In the past decades, a variety of learning-based algorithms have been emerged to try to explore a better solution for stereo matching by leveraging various machine learning algorithms. For enriching learning-based stereo matching algorithm's methodologies, we cast the disparity estimation as a regression problem by leveraging Structured Support Vector Machine (SSVM) in this paper. There are three categories of features have been extracted on account of disparity cues for training the SSVM. Particularly, one of the three feature is named as 'Coarse-to-Fine PatchMatch Filtering', which effectively exploits region and pixel disparity cues. For attaining region disparity cues, we adopt MeshStereo and MeshStereo with Cross-Scale algorithms; for attaining pixel disparity cues, PatchMatch and Cross-Scale PatchMatch stereo matching algorithms are utilized. Performance evaluations on Middlebury v.2 and v.3 stereo data sets demonstrate that the proposed algorithm reveals comparable accuracy with other challenging learning-based ones. It is worth pointing out that our proposal performs over several orders of magnitude faster than others on training time. [ABSTRACT FROM AUTHOR]

Being a significant repository of Buddhist imagery, Thangka images are valuable historical materials of Tibetan studies, which covers many domains such as Tibetan history, politics, culture, social life and even traditional medicine and astronomy. Thangka cultural element images are the essence of Thangka images. Hence, Thangka cultural element images classification is one of the most important works of knowledge representation and mining in the field of Thangka and is the foundation of digital protection of Thangka images. However, due to the limited quantity, high complexity and the intricate textures of Thangka images, the classification of Thangka images is limited to a small number of categories and coarse granularity. Thus, a novel fusion texture feature dual-branch Thangka cultural elements classification model based on the attention mechanism and self-supervised contrastive learning has been proposed in this paper. Specifically, to address the issue of insufficient labeled samples and improve the classification performance, this method utilizes a large amount of unlabeled irrelevant data to pre-train the feature extractor through self-supervised learning. During the fine-tuning stage of the downstream task, a dual-branch feature extraction structure incorporating texture features has been designed, and MS Triplet Attention proposed by us is used for the integration of important features. Additionally, to address the problem of sample imbalance and the existence of a large number of difficult samples in the Thangka cultural element dataset, the Gradient Harmonizing Mechanism Loss has been adopted, and it has been improved by introducing a self-designed adaptive mechanism. The experimental results on Thangka cultural elements dataset prove the superiority of the proposed method over the state-of-the-art methods. The source code of our proposed algorithm and the related datasets is available at https://github.com/WiniTang/MS-BiCLR. [ABSTRACT FROM AUTHOR]

This research paper aims to ensure the reliable and efficient operation of grid-connected solid-state transformers (SSTs) by detecting and evaluating various undesirable operating conditions. The study considers different types of faults, including internal faults like open switches and open capacitors, external faults such as symmetrical and asymmetrical faults occurring at various locations of the SST, and abnormalities on the grid side known as sympathetic inrush conditions. To analyze these operating conditions, the secondary current of the high-frequency transformer is normalized and decomposed using the discrete wavelet transform (DWT) and wavelet packet transform (WPT). From the DWT and WPT decomposition at multiple levels, several statistical parameters are calculated. These statistical parameters are carefully selected from different decomposition levels to enhance the effectiveness of the detection algorithm utilizing DWT and WPT. In order to quickly identify and classify all operating conditions that impact the performance of the grid-connected SST, a three-layer feedforward artificial neural network (ANN) is employed, using the selected statistical features. The accuracy and efficiency of the ANN-based classification approach are evaluated by assessing the effectiveness of the statistical features obtained from DWT and WPT. Simulation results have been altered by introducing various noise levels to systematically assess the performance of the proposed algorithms. The average accuracy of the DWT-ANN algorithm stands at 97.89%, while the WPT-ANN algorithm achieves a slightly elevated accuracy level of 98.01%. This notable similarity in accuracy curtails from the careful selection of the wavelet function, decomposition level, and feature sets. [ABSTRACT FROM AUTHOR]

Aiming to solve the problems of multiple internal power components, high fault probability, high similarity of the fault features of different power components, difficulty of traditional fault diagnosis feature extraction and low accuracy of fault identification in high-voltage multilevel cascaded H-bridge inverters, this paper presents a fault diagnosis method based on an attention-parallel multisource data fusion residual network. First, a parallel residual neural network model is established, and the extracted multilevel three-phase voltage before filtering and the three-phase current waveform after filtering are converted into two-dimensional image data using a wavelet transform. Subsequently, a feature fusion module is integrated into the network structure to adaptively extract features at different network levels. This module locates key features using the attention mechanism. Then, it fuses useful fault information into feature images using the feature fusion mechanism, enhancing the feature representation capability of the network. Finally, the fault features extracted by the feature fusion module undergo the complete convolution operation. The final enhanced features are used as classification features and classified using a softmax layer. Experimental results demonstrate that the proposed method exhibits high fault diagnosis accuracy and adaptability. [ABSTRACT FROM AUTHOR]

In remote sensing image fusion, the conventional Convolutional Neural Networks (CNNs) extract local features of the image through layered convolution, which is limited by the receptive field and struggles to capture global features. Transformer utilizes self-attention to capture long-distance dependencies in images, which has a global receptive field, but the computational cost for high-resolution images is excessively high. In response to the above issues, this paper draws inspiration from the FusionNet network, harnessing the local detail acquisition capability of CNNs and the global data procuring capacity of Transformer. It presents a novel method for remote sensing image sharpening named Guided Filtering-Cross Stage Partial Network-Transformer, abbreviated as GF-CSTNet. This solution unifies the strengths of Guided Filtering (GF), Cross Stage Partial Network (CSPNet), and Transformer. Firstly, this method utilizes GF to enhance the acquired remote sensing image data. The CSPNet and Transformer structures are then combined to further enhance fusion performance by leveraging their respective advantages. Subsequently, a Rep-Conv2Former method is designed to streamline attention and extract diverse receptive field features through a multi-scale convolution modulator block. Simultaneously, a reparameterization module is constructed to integrate the multiple branches generated during training into a unified branch during inference, thereby optimizing the model's inference speed. Finally, a residual learning module incorporating attention has been devised to augment the modeling and feature extraction capabilities of images. Experimental results obtained from the GaoFen-2 and WorldView-3 datasets demonstrate the effectiveness of the proposed GF-CSTNet approach. It effectively extracts detailed information from images while avoiding the problem of spectral distortion. [ABSTRACT FROM AUTHOR]

Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice. [ABSTRACT FROM AUTHOR]

Aiming at the problem of cartoon piracy and plagiarism, this paper proposes a method of cartoon copyright recognition based on character personality actions. This method can be used to compare the original cartoon actions with the action characteristics of pirated or copied cartoons to identify whether there is piracy or plagiarism. Firstly, an image preprocessing scheme for character extraction is designed. GrabCut interactive image segmentation algorithm was used to obtain cartoon characters, and then binarization and morphological processing were performed on the results. Secondly, a feature extraction scheme based on character profile, moving character and character pose is designed. By extracting the perimeter and area of the character contour, the length-to-width ratio of the smallest rectangle and the inclination angle of the contour, the character contour features are obtained. The three-dimensional coordinates are established by the central point position of the cartoon character in the two-dimensional image and the change of the character's zoom in and out, and the character's motion angle characteristics are calculated. By skeletonizing a character to obtain the pose characteristics, and using deburring operation to remove redundant branches, then extract the skeleton joint angle information. Finally, feature fusion is performed on the extracted features. The experimental results show that the proposed method breaks the limitation of the conventional single feature based recognition, and can better extract the character features including contour feature, motion feature and pose feature through multi-feature based extraction, so as to protect the cartoon copyright. [ABSTRACT FROM AUTHOR]

In order to solve the difficult portability problem of traditional non-invasive sleeping posture recognition algorithms arising from the production cost and computational cost, this paper proposes a sleeping posture recognition model focusing on human body structural feature extraction and integration of feature space and algorithms based on a specific air-spring mattress structure, called SPR-DE (SPR-DE is the Sleep Posture Recognition-Data Ensemble acronym form). The model combines SMR (SMR stands for Principle of Spearman Maximal Relevance) with horizontal and vertical division based on the barometric pressure signals in the human body's backbone region to reconstruct the raw pressure data into strongly correlated non-image features of the sleep postures in different parts and directions and construct the feature set. Finally, the recognit-ion of the two sleep postures is accomplished using the AdaBoost-SVM integrated classifier. SPR-DE is compared with the base and integrated classifiers to verify its performance. The experimental results show that the amount of significant features helps the algorithm to classify different sleeping patterns more accurately, and the f1 score of the SPR-DE model determined by the comparison experiments is 0.998, and the accuracy can reach 99.9%. Compared with other models, the accuracy is improved by 2.9% ~ 7.7%, and the f1-score is improved by 0.029 ~ 0.076. Therefore, it is concluded that the SMR feature extraction strategy in the SPR-DE model and the AdaBoost-SVM can achieve high accuracy and strong robustness in the task of sleep posture recognition in a small area, low-density air-pressure mattress, taking into account the comfort of the mattress structural design and the sleep posture recognition, integrated with the mattress adaptive adjustment system. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset. [ABSTRACT FROM AUTHOR]

Danmaku-enabled videos have revolutionized the way online users engage with and enjoy video content, and creating a dynamic and interactive atmosphere for users. In this paper, we investigate a new task of extract entity and relation on video-sync comments, which holds the potential to facilitate the creation of semantic-driven video knowledge graphs and video content analysis. However, the task is difficult since video-sync comments often contain intricate, ambiguous relationships, and their syntactic structure may be incomplete or inconsistent. One possible method is to extract relationships by using distant supervision or an external knowledge base system (Kbs). Regrettably, the domain of online videos suffers from a lack of comprehensive external KBs. To tackle the problem, we propose an innovative strategy that combines syntactic rule-based techniques with advanced pre-trained language models. We aim to encapsulate both syntactic cues and distinctive features within latent spaces. Our approach comprises two key facets. Firstly, we employ syntactic rules to extract open relations from the video-sync comments. Simultaneously, we introduce a BERT-based model that integrates relationship category constraints. This model not only fuses extraction features but also autonomously learns intricate relationships, enhancing the accuracy and granularity of the extraction process. For demonstration, we conduct experiments on large-scale danmaku data and results demonstrate that our proposed approach can achieve competitive performance. [ABSTRACT FROM AUTHOR]

Fabric defect detection is an important part of the textile industry to ensure product quality. To solve the problems such as the difficulty of detecting small defects and the coexistence of multi-scale defects in fabric defect detection, a fabric defect detection method based on the feature purification fusion structure is proposed in this paper. Specifically, we improve the feature extraction network to enhance the network's ability to focus on small defective features and effectively reduce the model parameters. The existing methods use direct fusion between multi-level feature maps, which will lead to feature confusion. Therefore, we propose the feature purification fusion structure (FPF), which includes the semantic information supplementation strategy (SIS) and the detail information supplementation strategy (DIS). SIS extracts valid information from the deep feature map and supplements it to the shallow feature map, weakening feature information irrelevant to the shallow feature map detection task. DIS adaptively supplements the feature information required by the deep feature map detection task from the shallow feature map. FPF improves the ability of the network to detect small defects and effectively mitigates the aliasing effect generated during feature fusion. The experimental results show that compared to the baseline model YOLOv5s algorithm, our model achieved a 6.8% improvement in detection accuracy, with an average inference frame rate of 37.6 FPS, demonstrating better detection performance in fabric defect detection. Furthermore, extending this model to aluminum profile defect datasets also demonstrates strong performance. [ABSTRACT FROM AUTHOR]

The strip steel is a common metallic material with a wide range of applications in various industries. However, the issue of surface defects that possess high concealment and low discrimination, which arises during the process of inspecting the quality of strip steel, imposes limitations on the overall quality of strip steel products. The challenging task of industrial quality inspection stems from the difficulty and inefficiency of detecting these defects. This paper addresses the aforementioned challenges by introducing a fast segmentation network model called the feature reused network (FR-Net), which aims to improve the detection of small defects and enhance real-time detection performance in the strip steel surface quality inspection process. In FR-Net, a feature fusion module is used to construct a feature reused fusion bypass to improve the segmentation accuracy of small defects. In addition, a lightweight feature refinement module is proposed to enhance the expression capability of the feature extraction network without increasing the computational effort. Finally, an atrous spatial pyramid pooling module with residual connectivity is proposed to fuse deep features of different scales and enhance the perception of objects of different scales. Experiments on the publicly available datasets showed that the proposed FR-Net achieved the mean intersection over union (mIoU) for NEU-Seg and SD-Saliency-900 datasets were 84.53% and 87.24%, respectively. Meanwhile, the detection speed on a single GPU was 58 FPS. Finally, the size of the proposed FR-Net model is only 45 MB, achieving a good trade-off between accuracy, speed, and model size, thus, providing a new solution for network model deployment on industrial equipment. [ABSTRACT FROM AUTHOR]

Image fusion refers to the process of synergistic combination of useful sensory information from multiple images to synthesize a composite image with greater information content and increased practical value. It aims to maximize pertinent information specific to a sensor while minimizing uncertainty and redundancy in the fused output. In this paper, the authors have proposed a simple yet cohesive framework for 2D greyscale image fusion using morphological differential features. The features are extracted using morphological open–close filters applied at multiple scales using an isotropic structuring element which brings out categorical bright and dark features from the source images. At each scale, the bright (and dark) differential features are mutually compared using higher-valued AC coefficients obtained in the DCT domain within a block. The scale-specific fused features are recursively added to form an image containing high-frequency information from all conceivable scales. The fused image is achieved by superimposing the cumulative feature image onto a suitable base image. The base image is obtained by using a morphological weighted version of pseudomedian filter over the source images using the largest homothetic of the structuring element. The superiority of the framework is empirically verified in different domains of fusion, i.e. multi-focus, multi-sensor, multi-exposure, and multi-spectral image fusion. The proposed approach has surpassed the state-of-the-art unified fusion algorithms in terms of qualitative and quantitative evaluation with a perfect resource-time trade-off. Furthermore, the proposed method has been extended to greyscale–colour and colour–colour image pairs qualifying it for anatomical–functional image fusion. [ABSTRACT FROM AUTHOR]

Image generation has always been one of the important research directions in the field of computer vision. It has rich applications in virtual reality, image design, and video synthesis. Our experiments proved that the proposed multi-style image generative network can efficiently generate high-quality images with different artistic styles based on the semantic images. Compared with the current state-of-the-art methods, the result generation speed of our proposed method is the fastest. In this paper, we focus on implementing arbitrary style transfer based on semantic images with high resolution ( 512 × 1024 ). We propose a new multi-channel generative adversarial network which uses fewer parameters to generate multi-style images. The network framework consists of a content feature extraction network, a style feature extraction network, and a content-stylistic feature fusion network. Our qualitative experiments show that the proposed multi-style image generation network can efficiently generate semantic-based, high-quality images with multiple artistic styles and with greater clarity and richer details. We adopt a user preference study, and the results show that the results generated by our method are more popular. Our speed study shows that our proposed method has the fastest result generation speed compared to the current state-of-the-art methods. We publicly release the source code of our project, which can be accessed at https://github.com/JuanMaoHSQ/Multi-style-image-generation-based-on-semantic-image. [ABSTRACT FROM AUTHOR]

Deep-learning-based automatic modulation recognition techniques have been extensively explored for wireless communication systems, because of their strong feature extraction and classification abilities of deep neural networks. Although the high recognition accuracy and low alarms, they also raise concerns about complexity and interpretability, which can affect the practical deployments. In this paper, we propose a randomly perturbation convolutional kernel activation mapping (RPCKAM) strategy to explain modulation recognition networks, and the RPCKAM-based filter pruning method to compress modulation recognition networks, combining the reasoning and interpretation of decisions made by modulation recognition networks with the compression of network models. The RPCKAM is used to evaluate the importance of convolutional kernels, and then the importance ranking is used to provide a basis for network pruning. Moreover, the effect of pruning is verified with VGG16 and ResNet34 as the baseline networks. The experimental results reflect that the RPCKAM-based convolutional kernel pruning can compress the model on a large scale while maintaining high accuracy, and can even improve the modulation recognition accuracy within a certain compression range. Specifically, the accuracy improvement is close to 1% at most in the VGG16, and it is also close to 0.5% at most in the ResNet34. Furthermore, by analyzing the performance changes before and after model compression, it is found that the compression effect of ResNet34 is better than that of VGG16, and the performance of the RPCKAM-based pruning method outperforms that of the Grad-CAM-based pruning method. [ABSTRACT FROM AUTHOR]

The core premise of cancelable biometrics lies in the creation of a distinct biometric template for every individual, which can be either canceled or regenerated as needed. This process requires the use of a uniquely-defined key during the generation of such template. The generated templates are tailored to be key-specific. This ensures that each distinct key will generate a unique template, while preserving the integrity and security of the original biometric data, ensuring that it remains uncompromised. In this paper, a cancelable biometric system based on electrocardiography (ECG) and photoplethysmography (PPG) signals is introduced. A signal fusion process is implemented for the two traits to generate a single template per user. In order to enhance the security of generated templates, a well-designed permutation stage is implemented according to a user-specific key. The permutation key is obtained through a well-designed look-up table created by the authors. The user verification is conducted on the cancelable template, without the need for any inversion processes. The user verification scheme depends on a two-pronged approach: robust feature extraction followed by the application of a machine learning (ML) classifier. The mel-frequency cepstral coefficients (MFCCs) extraction algorithm is employed for feature extraction due to the low frequency range of the adopted biometric signals and the nonlinearity of the filter bank used for MFCC extraction. Several ML classifiers are adopted to validate the system with cancelable templates without any inversion process. Simulation results with multilayer perceptron (MLP) and logistic regression (LR) classifiers demonstrated superior effectiveness of the proposed authentication framework, with accuracy rates up to 100% and 99.7% on the pulse transit time PPG and BIDMC datasets, respectively. Hence, the proposed system proves effective access control and user verification in the Internet-of-Things (IoT) applications. [ABSTRACT FROM AUTHOR]