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Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Cryptologic Research (2095-7025) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

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

Aiming at the problems of the practical Byzantine fault-tolerant algorithm (PBFT), such as insufficient system dynamics, low consensus efficiency and poor system robustness caused by the random selection of master nodes, a consensus algorithm of CRPBFT based on consistent hash and random selection is proposed. Firstly, the nodes are grouped by consistent hash, and the dynamic change mechanism of nodes is added to provide a dynamic network structure for the system. Secondly, the reputation value of the node is dynamically calculated according to the performance of the node in the consensus. At the same time, this paper defines three node reputation levels, namely, the candidate list of primary nodes, common nodes and malicious nodes. The primary node that is reliable and whose identity is difficult to be maliciously predicted, is selected through the verifiable random function, and the nodes that satisfied the reputation value requirements are selected to form a relatively stable consensus cluster. Experimental results show that CRPBFT algorithm is more reliable than consensus node cluster in PBFT algorithm, and its performance in consensus delay, throughput and system robustness is better than PBFT algorithm. [ABSTRACT FROM AUTHOR]

The crack image on the rail surface has a small target, many background interference information and a high resolution, making its target detection effect not ideal. In this regard, the paper proposes an improved target detection algorithm of YOLOV5s to improve the detection effect of rail cracks. The VOV-GSCSP module is introduced in the neck network, and the ordinary convolution is replaced by the lighter convolution method GSconv to reduce the computational effort of the network while retaining more detailed information. The feature pyramid structure is improved and a multipath cross-layer fusion structure is proposed to incorporate the information of the backbone network across layers in the process of feature pyramid downsampling to retain more original feature information and improve the accuracy of target detection. Meanwhile, the CA attention module and Transformer structure are introduced to further enhance the information extraction of higher-order semantics. The experimental results show that the improved YOLOV5s algorithm achieves an average mean accuracy (mAP) of 62.4%, which is 6.2 percentage points better than the original YOLOV5s algorithm; the recall rate (Recall) is 92.2%, which is 4.4 percentage points better. [ABSTRACT FROM AUTHOR]

The density peaks clustering (DPC) algorithm has several issues such as difficulties in accurately finding cluster centers of manifold data, and easily produce the joint errors in the allocation of residual samples. To address these issues, this paper proposes a density peaks clustering algorithm based on shared neighbor degree and probability assignment (SP-DPC). Firstly, using K-nearest neighbor and shared K-nearest neighbor, the shared neighbor degree between sample points is defined, and the local density of sample points is redefined using this information to identify the correct cluster centers. Next, the transfer probability assignment strategy and the evidence probability assignment strategy are proposed to optimize the allocation of the residual sample together based on their K-nearest neighbors, thus avoiding the joint allocation errors. Finally, SP-DPC algorithm is compared to DPC, SKM-DPC, DPC-NN, DBSCAN, and K-means algorithms using 17 synthetic datasets and 12 UCI datasets. The experimental results show that the SP-DPC algorithm achieves relative optimal values in the three evaluation indexes of AMI, ARI and FMI as a whole and outperforms other algorithms in terms of clustering efficiency. [ABSTRACT FROM AUTHOR]

Articulation disorder, as a medical difficulty, currently mainstream speech recognition technologies are not well adapted to the needs of this field. At the same time, speech recognition technology for dysarthria utilizes a combination of pre training and personalized training to further improve algorithm performance and reduce recognition word error rate through data-driven methods. However, currently, speech recognition technology for dysarthria still has a certain distance from practical commercial use, and its development is limited by data scale and technology. So far, there have been no comprehensive articles on speech recognition for dysarthria. It is urgent to compare and analyze the construction methods and advanced technologies of various datasets in this field, in order to facilitate researchers entering the field to quickly acquire knowledge in this field. This paper conducts a survey on existing datasets, mainstream algorithms, and evaluation methods, and summarizes the scale, form, and characteristics of mainstream speech impairment datasets at home and abroad. It analyzes the mainstream algorithms for speech recognition with dysarthria, and provides the performance and characteristics of different algorithms. Finally, the performance evaluation indicators of the algorithm model based on the severity level of patients with dysarthria are studied, and future research directions are discussed, in order to provide help for the researchers engaged in speech recognition with dysarthria and assist in the rapid development of this field. [ABSTRACT FROM AUTHOR]

Recommendation of point of interest (POI) is one of the most popular topics in location-based social network (LBSN). The existing recommendation methods do not fully consider the deep influence of the spatial and temporal intervals between sequences of check-in data on the recommended sequences. They ignore the global information in the checkin data and focus on the local preferences in the recent check-in sequence of a single user. To address these problems, this paper proposes a global feature fusion based spatiotemporal network (GSTN) interest point recommendation algorithm. The method uses graph neural networks to obtain global features of the heterogeneous network graph of check- in data, and incorporates spatiotemporal gating into the traditional gating structure, fuses global features to model users'mobile behavior, and then introduces a self-attentive mechanism to learn user preference vector representation. Finally, the experiments are carried out on two real datasets. The experiments show that the proposesd approach outperforms similar algorithms in terms of recommendation performance and verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The intelligent classroom is a popular application scenario in the field of artificial intelligence. This paper proposes a face detection algorithm based on improved YOLOv5, named YOLOv5-SASA, to address the issues of missed or false detection caused by small or occluded faces in images captured by cameras located far away or at an angle. The algorithm consists of three parts. Firstly, the CSPDarknet53 network is utilized in the backbone layer, and the BasicRFB module is used in the final spatial pooling layer to enhance the network's feature extraction ability. Secondly, the NWD loss function is employed to improve the model's robustness in detecting small targets. Thirdly, the independent self- attention mechanism module SASA is introduced in the head layer to address the issue of face occlusion and reduce the model's parameter count. Finally, the improved YOLOv5 network is optimized by reducing the number of neurons in the middle layer channels and adjusting the learning rate to avoid overfitting. Experimental results demonstrate that the proposed method outperforms the original network in the easy, medium, and hard levels of the WiderFace validation set, achieving accuracies of 97.5%, 96.3%, and 86.5%, respectively, which effectively improves the accuracy of face detection in classroom scenarios. [ABSTRACT FROM AUTHOR]

A multi-objective vehicle routing optimization model is established to minimize total cost and equilibrium degree for multi-objective vehicle routing problem with time windows, and a hybrid multi-objective grey wolf algorithm is proposed to solve the problem. Mainly design three strategies: (1) A new encoding and decoding method is designed to achieve the conversion of continuous grey wolf position vectors to discrete customer sequences. (2) Convergence and distribution indicators are used to select decision individuals. (3) Multiple deletion and insertion operators have been designed to implement local routing optimization. To demonstrate the effectiveness of the algorithm, some numerical examples in Solomon are used as examples to experimentally compare the proposed algorithm with MOIGA and improved ACO algorithms. Experimental results show that the hybrid multi-objective grey wolf algorithm proposed in this paper can find a better Pareto solution, and its performance is better than other evolutionary algorithm. [ABSTRACT FROM AUTHOR]

In order to counter the limitations of the original YOLOv5 human fall detection task, a highly accurate fall detection algorithm, called C2D-YOLO, is proposed in this paper. The original task struggles to effectively handle complex detail capture, deformation handling, target adaptation to different scales, and occlusion detection. To overcome these challenges, several improvements are made to the YOLOv5 model. Firstly, a new feature extraction module called C2D is introduced, which improves feature characterisation, captures complex details, and handles deformations by combining deformable convolution, standard convolution, and channel-space hybrid attention mechanisms. Secondly, in the neck network, Swin Transformer block replaces the bottleneck layer of the C3 module to retain more feature information, thereby improving target detection accuracy at different scales and enhancing performance under occlusion. Finally, the head module of YOLOv5 is enhanced based on the decoupled structure of YOLOX borrowed from YOLOv5 to optimise classification and regression performance. Experimental results show that this method achieves a 3.2 percentage points improvement in mAP0.5 and a 6.5 percentage points improvement in mAP0.5: 0.95 compared to existing YOLOv5s. These improvements significantly increase detection accuracy and reduce false alarm rates. [ABSTRACT FROM AUTHOR]

Community detection has been one of the key research directions in network analysis. Most of the current network community detection algorithms mainly use the structural information of the network to adopt a greedy algorithm to maximize a certain indicator, which cannot fully consider the node feature information, edge weight, and network community relationship asymmetry. To address this situation, this paper proposes an autoencoder-like modularity nonnegative matrix factorization (AMNMF) community detection algorithm. The algorithm expands the depth of non-negative matrix factorization by using an encoder-like structure, and introduces modularity and graph regularizer into the objective function optimization process of non-negative matrix factorization to fully mine the node and community structure information in the network. The problem of community relationship imbalance is solved by adding orthogonal constraints to the middle layer of the encoder. Experiments on multiple real networks show that: AMNMF is an effective NMF extension algorithm that uses node feature information and network structure information. Compared with the best results of baseline algorithms, it achieves an improvement of about 15% to 122%, and can accurately and effectively complete the community detection task. [ABSTRACT FROM AUTHOR]

Spatial Modulation(SM) and Orthogonal Frequency Division Multiplexing(OFDM) Multi-Mode Index Modulation (MM-OFDM-IM) techniques have significant advantages in Energy Efficiency(EE) and Spectral Efficiency(SE), respectively. The combination of multi-dimensional index modulation technology can apply multiple physical resources to meet the high demand for data transmission and system capacity in wireless communication systems. Therefore, SM and MM-OFDM-IM systems can be flexibly combined to construct an OFDM Multiple Mode Space Frequency Composite Index Modulation (MM-OFDM-SFCIM) based on space frequency combination. This system can retain the silent subcarriers in traditional subcarrier index modulation technique, expanding new composite coefficient dimensions and improving its SE and EE. Aiming at the complex characteristics of this system, this paper proposes a joint detection algorithm EL-REML (REML based on symbolic Energy and LLR) based on symbolic energy and log-likelihood. The simulation results show that the MM-OFDM-SFCIM system achieves about 30% improvement in SE compared to traditional space-frequency combination systems, and the EL-REML algorithm proposed in this system achieves a 3-4 dB improvement in BER (Bit Error Rate) compared to the LLR algorithm. Compared with traditional ML algorithm, this algorithm has lower computational complexity. [ABSTRACT FROM AUTHOR]

Synthetic minority oversampling technique (SMOTE) has become one of the mainstream methods for dealing with unbalanced data due to its ability to effectively deal with minority samples, and many SMOTE improvement algorithms have been proposed, but very little research existing considers popular algorithmic-level improvement methods. Therefore a more comprehensive analysis of existing SMOTE class algorithms is provided. Firstly, the basic principles of the SMOTE method are elaborated in detail, and then the SMOTE class algorithms are systematically analyzed mainly from the two levels of data level and algorithmic level, and the new ideas of the hybrid improvement of data level and algorithmic level are introduced. Data-level improvement is to balance the data distribution by deleting or adding data through different operations during preprocessing; algorithmic-level improvement will not change the data distribution, and mainly strengthens the focus on minority samples by modifying or creating algorithms. Comparison between these two kinds of methods shows that, data- level methods are less restricted in their application, and algorithmic-level improvements generally have higher algorithmic robustness. In order to provide more comprehensive basic research material on SMOTE class algorithms, this paper finally lists the commonly used datasets, evaluation metrics, and gives ideas of research in the future to better cope with unbalanced data problem. [ABSTRACT FROM AUTHOR]

The combination of Non-Orthogonal Multiple Access (NOMA) technology and Device-to-Device (D2D) communication technology has outstanding advantages in achieving efficient spectrum utilization and large-scale access. Aiming at the problem of single channel allocation mode and the difficulty in obtaining optimal power allocation within D2D group in existing NOMA-D2D systems, this paper constructs a joint resource allocation algorithm with D2D group and rate as the optimization target. Firstly, in terms of sub-channel allocation, this problem is converted into a bilateral matching problem, and a D2D group channel allocation algorithm based on many-to-one scenarios is proposed, Then, the difference of two convex functions (DC) programming method based on successive convex approximation is used to find the power allocation value close to the optimal one. The simulation results show that the proposed channel matching algorithm in the many-to-one scenario is superior to the one in the one-to-one scenario in terms of sum rate, and the proposed power allocation algorithm is closer to the optimal power allocation compared to the dual iterative algorithm. [ABSTRACT FROM AUTHOR]

With the popularization of smart devices and the continuous development of wireless communication technology, a large amount of trajectory data is recorded in wireless networks, and frequent item mining for trajectory data has become a research hotspot. In order to update the frequent trajectory set when new data is added to the trajectory database and the minimum support is changed, we propose the dynamic minimum support incremental updating PrefixSpan algorithm (DMSIU-PrefixSpan). A method is proposed in the algorithm to estimate the support of a sequence, in which the confidence of its suffix subsequence is approximated to replace its own confidence when estimating the support of a sequence. With this method, the sequence support can be calculated without scanning the database. DMSIU-PrefixSpan algorithm can make full use of the information of frequent trajectory set. When incremental trajectory data is added to the original trajectory database and the minimum support changes, the candidate sequence is generated by connecting frequent trajectory sequence and frequent 1 sequence, and the candidate sequence support is estimated using the support estimation method to discover the frequent sequences. The algorithm no longer needs to save the original trajectory data and achieves fast update of the frequent trajectory set. The accuracy of the support degree estimation and the effectiveness of the algorithm of this paper are verified by the mining experiments of the trajectory database. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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Copyright of Journal of Cryptologic Research (2095-7025) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Cryptologic Research (2095-7025) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Cryptologic Research (2095-7025) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] Trajectory planning is critical for the motion control of a manipulator. The motion time and joint vibration of the manipulator can be reduced by providing a fast and stable reference trajectory to the controller. The single objective trajectory optimization algorithm remains insufficient in meeting the increasing production needs; therefore, it is often necessary to use a multiobjective heuristic algorithm to optimize the motion trajectory of the manipulator while satisfying its kinematic constraints to optimize the motion time and reduce the joint impact or energy loss, improving the production efficiency of the manipulator and ensuring its stability during motion. An improved adaptive multiobjective particle swarm optimization (IAMOPSO) method is studied for the time- and impact-based optimal trajectory planning of a multidegree-of-freedom manipulator. [Methods] First, to obtain physically continuous motion trajectories, including position, velocity, and acceleration, we use a quintic B-spline curve to interpolate the joint path points based on the local controllability of the curve. The trajectory optimization process is a step-by-step approach so that the interpolated solution optimally satisfies the kinematic constraints of the manipulator, and the B-spline curve passes through all interpolation points, rendering the motion trajectory of the manipulator more adaptable. Second, a specific objective function improves the motion efficiency of the manipulator. Under certain kinematic constraints, an expression for the joint impact of the manipulator is designed to improve the trajectory tracking accuracy and ensure the stability of the manipulator during operation tasks. Finally, to increase the diversity of the nondominated solution set while avoiding the local extremum, the multi-objective particle swarm optimization (MOPSO) algorithm is improved through a hybrid strategy of mutation operators, adaptive weights, and dynamic learning factors; consequently, the objective function is optimally solved, and the average optimal solution is selected using a normalization function. A nonlinear mutation operator is adopted to encourage particles to comprehensively explore the decision space, ensuring population diversity in the initial iteration of the algorithm, and an adaptive weight strategy and dynamic learning factor are adopted to balance the exploration and development of the MOPSO algorithm, making it easy for the algorithm to determine the global optimal solution. [Results] 1 The quintic B-spline curve accurately interpolates the path points and ensures the continuity of the acceleration and jerk curves, thus meeting the requirements of smoothness and continuity of the joint trajectory of the manipulator; 2 the optimal trajectory planning method based on the IAMOPSO algorithm can yield a Pareto front with good convergence, select the time- and impact-based optimal trajectory according to the average optimal criterion, and achieve continuity of joint angles and their derivatives. [Conclusions] The optimal trajectory planning method for the manipulator based on IAMOPSO proposed in this paper can improve the motion efficiency and tracking accuracy of the manipulator, obtain Pareto front surfaces with good convergence, ensure smooth and continuous joint curves of the manipulator, and improve the operational efficiency and stability of the manipulator. [ABSTRACT FROM AUTHOR]

In the process of moving target detection, the background is complex and the target is easily occluded. This paper proposes an autonomous detection algorithm for moving targets based on unsupervised missing value prediction. The missed targets are regarded as missing values in the tag data. According to the prior knowledge of the category and number of objects to be detected, the unsupervised generative adversarial imputation networks (GAIN) are used to predict the missing values through the acquired tag data, which greatly improves the recall rate at the expense of less accuracy. The experimental results on the small sample dataset of the characteristic parts of cattle show when the missing rate of tag data is less than 40%, the accuracy of missing value prediction is about 95%, and the average F1 score of detection is 0.92 for different degrees of occluded targets. This method has good detection performance for moving targets under the condition of small samples, which can reduce the uncertainty in practical application and the dependence of the algorithm on sample data, and improve the problem of missing detection in the process of moving target detection. [ABSTRACT FROM AUTHOR]

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

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

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

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

[Objective] With the rapid popularization of new energy vehicles and the vigorous development of autonomous driving technology, 3D object detection algorithms play a pivotal role in real road scenes. The LiDAR point clouds contain precise position and geometric structure information of the object, which can accurately describe the 3D space position of the target. Moreover, LiDAR makes environmental perception and route planning of unmanned vehicles a reality. However, cars in real scenes often fall into complex and difficult situations, such as occlusion and truncation of objects, which contribute to highly sparse clouds and incomplete contours. Therefore, the effective use of disordered and unevenly distributed point clouds for accurate 3D object detection has important research significance and practical value for the safety of autonomous driving. [Methods] This paper uses LiDAR point clouds in an autonomous driving scene to conduct in-depth research on a high-performance 3D object detection algorithm based on deep learning. The 3D object detection algorithm based on multibranch feature fusion (PSANet) is designed to improve the capacity and viability of autonomous driving technology. After the disordered point clouds are divided into regular voxels, the voxel feature coding module and convolutional neural network are used to learn the voxel features, and the sparse 3D data are compressed into a dense 2D bird's-eye view. Furthermore, the multiscale bird's-eye view features are deeply fused through the coarse and fine branches of the 2D backbone network. The splitting and aggregation feature pyramid module in the fine branch splits and aggregates the bird's-eye view features at different levels and realizes the deep fusion of semantic information, texture information, and context information of multiscale features to obtain more expressive features. The multiscale features in the coarse branch are fused after transposed convolution, and the precise original spatial location information is preserved. After the feature extraction of coarse and fine branches, the element-wise addition method can obtain more accurate and complete features for object classification, position regression, and orientation prediction. [Results] The experimental results on the KITTI dataset show that the average precision of PSANet in 3D object detection and bird's-eye view object detection tasks reach 81.72% and 88.25%, respectively. The inference speed on a single GTX 1080Ti GPU can reach 24 frames per second, and it shows strong robustness in complex scenes. Compared with the two-stage target detection algorithms MV3D, AVOD-FPN, F-PointNet, and IPOD, the average accuracy of 3D target detection of this algorithm increased by 18.21%, 5.89%, 8.94%, and 3.12%, respectively. Compared with the one-stage target detection algorithms VoxelNet, SECOND, PointPillars, and VoTr-SSD, the average accuracy of 3D target detection of this algorithm increased by 11.63%, 4.05%, 3.19%, 0.98%, 1.16%, and 0.7%, respectively. The detection speed of this algorithm improved by 14 frames per second compared with the two-stage algorithm PointRCNN with similar accuracy. [Conclusions] In comparison with other advanced algorithms, this algorithm exhibits strong performance, in which it can better balance the accuracy and speed of target detection in autonomous driving scenarios. Higher accuracy is required in the algorithm, whether one- or two-stage, for novel applications. The method with the highest efficiency must be used in an autonomous car. [ABSTRACT FROM AUTHOR]

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

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

Aiming at the problems of multi-scale, multi angle and occlusion in mask wearing detection in complex scenes, a mask wearing detection algorithm in complex scenes based on improved FCOS is proposed in this paper. Firstly, in order to improve the feature extraction ability of the network for masks with different scales, the packet residual connection structure of Res2Net is introduced into the backbone network of the algorithm, and the deformable convolution is integrated to expand its modeling ability for objects with unknown shapes. Then, a feature pyramid integrating attention mechanism is designed to give different weights to feature channels and suppress useless feature information. Finally, according to the relevant statistical characteristics of the target mask, the positive and negative samples are automatically divided to improve the sample quality of masks with different scales, and Generalized Focal Loss is introduced to jointly train the classification score and positioning quality score of samples so as to improve the performance of the algorithm. The experimental results show that the mAP of improved algorithm in this paper improves 6.7 percentage points compared with the original FCOS in the detection of mask wearing in complex scenes. Meanwhile, compared with some mainstream target detection algorithms, the improved algorithm in this paper also has better effect and robustness. [ABSTRACT FROM AUTHOR]

Object detection is currently a research hotspot in the field of computer vision. With the development of deep learning, object detection algorithms based on deep learning are increasingly applied and their performance is constantly improved. This paper summarizes the latest research progress of object detection methods based on deep learning by summarizing common problems encountered in the process of object detection and corresponding improvement methods. This paper focuses on two types of object detection algorithms based on deep learning. In addition, the latest improvement ideas of target detection algorithms are summarized from the aspects of attention mechanism, lightweight network, multiscale detection. Finally, in view of the current problems in the field of target detection, the future development trend is prospected. And the feasible solution is put forward in order to provide reference ideas and directions for the follow-up research work in this field. [ABSTRACT FROM AUTHOR]

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

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

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

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

Based on a polynomial basis, this paper constructs a finite field operation to optimize the S-box of the AES algorithm. By searching for the isomorphic mapping matrix between the fields, the inverse operation over finite field GF(28) is converted to the composite field GF((24)²). By using the SAT solver to search for the multiplication inverse operation over the finite field GF(24), two different polynomial basis optimization schemes are proposed. Scheme 1 uses the classic Class 3 gate circuit to construct the composite field. Based on Scheme 1, Scheme 2 uses a bit-level optimization method that introduces a MUX gate, and has less circuit area and smaller number of gates than those in Scheme 1. This paper uses the SMIC130 nm and SMIC65 nm processors to evaluate the circuit area. The evaluation shows that the results of this paper are better than the existing schemes using the same kind of gate circuits. [ABSTRACT FROM AUTHOR]

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

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

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