检索结果-内蒙古大学图书馆

Mining Statistically Significant Communities From Weighted Networks

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第6期35卷 6073-6084页

作者： He, Zengyou Chen, Wenfang Wei, Xiaoqi Liu, Yan Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116024 Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China

As one of the most important issues in data mining and network science, the community detection problem has been extensively investigated during the past decades. Despite of the success achieved by existing methods, how to directly access the statistical significance of an individual community in a weighted network remains unsolved. To address this issue, we present a new method to calculate the analytical p-value of an individual community in weighted networks. The proposed analytical p-value is able to assess the statistical significance that one target community appears in a random weighted graph in a straightforward manner. To verify the effectiveness of the proposed p-value in community evaluation, it is utilized as the objective function in a local search procedure to derive a new community detection algorithm. Experimental results show that the new algorithm is able to achieve comparable performance to those state-of-the-art algorithms for identifying communities from weighted networks.

关键词： Probability Measurement detection algorithms Software Machine learning algorithms Linear programming Image edge detection Community detection random graphs statistical significance weighted networks

来源：评论

学校读者我要写书评

暂无评论

Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping

引用

IEEE ACCESS 2023年 11卷 13690-13703页

作者： Tian, Chengjun Liu, Haobo Liu, Zhe Li, Hongyang Wang, Yuyu Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun 130022 Peoples R China

Simultaneous Localization and Mapping (SLAM) is the core technology of the intelligent robot system, and it is also the basis for its autonomous movement. In recent years, it has been found that SLAM using a single sensor has certain limitations, such as Inertial Measurement Unit (IMU) noise and serious drift, and 2D radar can only detect environmental information on the same horizontal plane. In this regard, this paper constructs a multi-sensor back-end fusion SLAM algorithm that combines vision, laser, encoder and IMU information. Experiments have proved that compared with using a single sensor, the application of a multi-sensor fusion system makes the edges of the constructed map clearer and the noise reduced. Aiming at the problem of increased calculation caused by particle degradation and too many particles, this paper improves the Gmappping algorithm, and uses the combination of selective resampling and Kullback-Leibler Distance (KLD) sampling to complete resampling. It has been proved by experiments that compared with the original algorithm of Gmapping, the application of the improved algorithm increases the particle convergence speed by 39.85% in the process of indoor mapping. Aiming at the problems that the traditional loop detection algorithm is easily affected by environmental factors, resulting in low detection accuracy, and the loop detection algorithm based on deep convolutional neural network has a large amount of calculation and takes a long time to detect. The main research of this paper is to apply a deep learning-based loop detection algorithm on the multi-sensor fusion framework, and use the combination of high-dimensional and low-dimensional features of the image for loop detection. This paper uses different algorithms to conduct comparative experiments on the dataset CityCentre. The experimental results show that compared with the traditional algorithms Bag of Words (BoW), AlexNet algorithm, VGG19 algorithm, and ResNet32 algorithm, the accuracy

关键词： Simultaneous localization and mapping Robots Laser radar Laser fusion detection algorithms Measurement by laser beam Degradation Laser SLAM Gmapping algorithm multi-sensor fusion SLAM system loop closure detection

来源：评论

学校读者我要写书评

暂无评论

Lossdetection: Real-Time Packet Loss Monitoring System for Sampled Traffic Data

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第1期20卷 30-45页

作者： Wu, Hua Liu, Ya Ni, Shanshan Cheng, Guang Hu, Xiaoyan Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Nanjing 210096 Peoples R China Purple Mt Labs Network & Commun Secur Nanjing 211111 Peoples R China Jiangsu Prov Engn Res Ctr Secur Ubiquitous Network Nanjing 211102 Peoples R China

Packet loss is common in networks, which leads to network quality of service degradation. Packet loss is an essential and concerning symptom when the quality of service is degraded. Therefore, real-time passive packet loss detection is conducive to estimating network services. Existing passive packet loss detection methods mainly study the packet loss for TCP using header information from full traffic. However, it cannot infer packet loss status for UDP due to its limited header information and is too costly to perform full acquisition in real networks. To address these problems, we propose a framework called Lossdetection based on packet sampling and Feature-Sketch to detect packet loss in real time for both TCP and UDP. The result shows that our methodology can detect packet loss with an accuracy of 98%-100% at a sampling rate of 1/16. Furthermore, our extensive evaluation demonstrates that Lossdetection is easy to implement in a software router and achieves low memory and detection latency while providing real-time information about packet loss.

关键词： Packet loss Real-time systems Feature extraction Monitoring Systematics detection algorithms Current measurement Packet loss detection sketch real-time detection machine learning

来源：评论

学校读者我要写书评

暂无评论

Algorithm of detection and Tracking for Laboratory Unsafe Behavior Based on Computer Vision

Algorithm of Detection and Tracking for Laboratory Unsafe Be...

引用

2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Gao, Lu Bohai University College of Physical Science and Technology Jinzhou China

ISBN: (纸本)9798350313413

Computer vision gives computers the ability of visual perception similar to people, so that computers can feel the environment in the field of vision, understand the content of the feeling, and take corresponding actions. Object tracking is a problem of creating corresponding matching between successive image frames based on position, speed, shape, texture and color. Many security problems in laboratories are caused by unsafe behaviors, and most laboratories have built a complete video surveillance system based on computer vision, using these video surveillance data to identify unsafe behaviors. In this paper, image denoising based on morphological filter algorithm, moving target detection based on optical flow algorithm and moving target tracking based on Mean-Shift algorithm are used to provide key technical solutions for the study of unsafe behaviors in laboratories. The results show that the optical flow algorithm has good adaptability and is suitable for both static and dynamic background. If a more accurate similarity measurement function is used, the tracking time is shorter and the speed is faster. © 2023 IEEE.

关键词： computer vision detection algorithms Mean-Shift algorithm morphological filtering optical flow algorithm tracking algorithms unsafe behavior

来源：评论

学校读者我要写书评

暂无评论

Broad Learning System-Based Detector for OFDM With Index Modulation

引用

IEEE COMMUNICATIONS LETTERS 2023年第1期27卷 224-228页

作者： Du, Ruiyan Wang, Shiyi Liu, Fulai Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

This letter presents a broad learning system (BLS) based orthogonal frequency division multiplexing with index modulation (OFDM-IM) detector called BLS-IM, which achieves bit-error-rate performance improvement with less training time. In BLS-IM, in order to help the network detect the index of the active subcarrier, the received data needs to be preprocessed into polar coordinates first. The processed data is then fed into the BLS network. In particular, the BLS is built in the form of a flat network where the initial signal bits are transferred among the feature nodes. At the same time, the structure is extended in a broad sense in terms of enhanced nodes. The proposed BLS-IM detector has an advantage in training time because it avoids the time-consuming training process caused by a large number of connection parameters in the layers. Simulation results demonstrate that BLS-IM is capable of achieving better detection performance with less training time than the existing algorithms.

关键词： Detectors Feature extraction Training Symbols Indexes Learning systems detection algorithms Broad learning system incremental learning OFDM-IM activated subcarriers index modulation detection

来源：评论

学校读者我要写书评

暂无评论

A New Index Modulation for LoRa

引用

IEEE INTERNET OF THINGS JOURNAL 2023年第13期10卷 11938-11939页

作者： Kang, Jae-Mo Kyungpook Natl Univ Dept Artificial Intelligence Daegu 41566 South Korea

Long range (LoRa) is a widely adopted Internet of Things (IoT) technique, but its fatal limit is a low-data rate. In this letter, we propose a new index modulation for LoRa to increase the data rate, in which multiple quasi-orthogonal chirps modulated under different spreading factors (SFs) are concurrently transmitted, thereby leveraging the SF domain as a means to carry more information. Both the coherent and noncoherent detection algorithms for the proposed index modulation are also developed in efficient forms. Numerical results demonstrate that the proposed scheme notably outperforms the state-of-the-art techniques.

关键词： Chirp Modulation Indexes Signal to noise ratio detection algorithms Symbols Sensitivity Chirp spread spectrum index modulation Internet of Things (IoT) long-range (LoRa)

来源：评论

学校读者我要写书评

暂无评论

Sensitivity and Specificity of Wearables for Atrial Fibrillation in Elderly Populations: A Systematic Review

引用

CURRENT CARDIOLOGY REPORTS 2023年第7期25卷 761-779页

作者： Babar, Faiza Cheema, Abdul Manan Ahmad, Zubair Sarfraz, Azza Sarfraz, Zouina Ashraff, Huma Tayyab, Hamnah Bajwa, Ali Mustafa Ali, Haider Sarfraz, Muzna Shahzad, Anum Sabzwari, Syed Rafay Ali CMH Lahore Med Coll Lahore Pakistan Sargodha Med Coll Sargodha Pakistan Independent Med Coll Faisalabad Pakistan Aga Khan Univ Karachi Pakistan Fatima Jinnah Med Univ Lahore Pakistan Shalamar Med & Dent Coll Lahore Pakistan King Edward Med Univ Lahore Pakistan Bahria Univ Karachi Pakistan Albert Einstein Med Ctr Philadelphia PA USA Univ Colorado Aurora CO USA

Purpose of ReviewThis study aims to evaluate the sensitivity and specificity of wearable devices for AF detection in older adults, as well as examine the incidence of AF across various studies, contextual factors impacting AF detection, and safety and adverse events associated with wearable *** FindingsA systematic search of three databases identified 30 studies on wearables for AF detection in older adults, encompassing 111,798 participants. Both PPG-based and single-lead electrocardiography-based wearables show scalable potential for screening and managing *** results of this systematic review demonstrate that wearable devices, such as smartwatches, can effectively identify arrhythmias like AF in older adults, with scalable potential in PPG-based and single-lead electrocardiography-based wearables. As wearable technologies continue to gain prominence in healthcare, it is crucial to understand their challenges and incorporate them as preventative and monitoring tools for AF detection in elderly populations to improve patient care and prevention techniques.

关键词： Atrial fibrillation Wearable technology Elderly population Sensitivity and specificity detection algorithms Cardiovascular disease Stroke risk Electrocardiogram Systematic review Adverse events

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Difference-Guided Adaptive Fusion Network for Change detection in PolSAR Images

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Yang, Zhifei Wu, Yan Li, Ming Hu, Xin Li, Zhikang Xidian Univ Sch Elect Engn Remote Sensing Image Proc & Fus Grp Xian 710071 Peoples R China Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

It is challenging to accurately extract change features in polarimetric synthetic aperture radar (PolSAR) image change detection. Recently, many existing convolutional neural network (CNN)-based change detection algorithms show satisfactory performance, but error detection still exists in the acquired change features. In this article, we propose an unsupervised difference-guided adaptive fusion network (DGAFN), which contains three key modules, i.e., adaptive difference extraction module (ADEM), difference feature enhancement module (DFEM), and adaptive weighted fusion module (AWFM). First, ADEM extracts initial multiscale difference features through nonlocal attention mechanisms guided by bitemporal feature differences, which helps the network to capture the correlation and difference between bitemporal features more accurately. Second, in order to further refine multiscale difference features and reduce pseudo-changes, DFEM uses class activation maps (CAMs) and contextual feature extraction modules (CFEMs) to perceive more polarization contextual information in a larger scale range. Finally, in order to improve the recognition ability of change targets with different sizes, AWFM adaptively fuses enhanced multiscale difference features in spatial and channel dimensions by introducing attention mechanisms to obtain change features with multiscale complementary information. Comparative experimental results on five measured PolSAR datasets obtained by GaoFen-3 show that DGAFN is superior to other relevant advanced algorithms in change detection performance.

关键词： Feature extraction Adaptive systems Data mining Convolutional neural networks detection algorithms Remote sensing Fuses Adaptive multiscale feature fusion difference-guided feature extraction polarimetric synthetic aperture radar (PolSAR) image unsupervised change detection

来源：评论

学校读者我要写书评

暂无评论

Trained and Robust Parameter Based Path Sampling for Low Complexity MIMO detection in 5G-NR 95

Trained and Robust Parameter Based Path Sampling for Low Com...

引用

IEEE 95th Vehicular Technology Conference: (VTC-Spring)

作者： Qian, Jing Wang, Hao Huawei Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781665482431

ISBN: (纸本)9781665482431

In this paper, a low complexity MIMO detection algorithm based on path sampling is proposed to achieve near-optimal performance with limited number of paths. Conventional list sphere decoders (LSD) have good detection performance but at cost of high computation complexity, while the random sampling (RS) based MIMO detectors may suffer from inevitable performance loss and sample repetition. To overcome these issues, we first propose a data-driven method, called the trained parameter based path sampling (TPbPS), to optimize the parameter of sampling distribution, and design an approach to improve the generalization performance. Further, we take advantages of the well-trained sampling distribution and derandomize the RS to develop the robust parameter based path sampling (RPbPS), while all the sampling paths are determined by the trained sampling distribution as well as the fixed and uniformly-distributed numbers. Combining these techniques yields an enhanced MIMO detection design that non-trivially advances the state-of-the-art, and can provide significant performance and complexity gains over the traditional LSD and RS methods.

关键词： Vehicular and wireless technologies Costs Detectors Performance gain Parity check codes Decoding detection algorithms

来源：评论

学校读者我要写书评

暂无评论

MMW-YOLOv5: A Multi-Scale Enhanced Traffic Sign detection Algorithm

引用

IEEE ACCESS 2024年 12卷 148880-148892页

作者： Wang, Tong Zhang, Juwei Ren, Bingyi Liu, Bo Henan Univ Sci & Technol Coll Informat Engn Luoyang 471023 Henan Peoples R China Henan New Energy Vehicle Power Elect & Elect Drive Luoyang 471023 Peoples R China Henan Univ Sci & Technol Sch Mechatron Engn Luoyang 471003 Henan Peoples R China Zhengzhou Univ Aeronaut Sch Elect & Informat Zhengzhou 450046 Henan Peoples R China

Traffic sign detection is a crucial component of the autonomous driving field, where real-time performance and accuracy play a significant role in ensuring vehicle safety. This paper aims to improve the detection performance of multi-scale traffic sign targets and proposes an enhanced multi-scale traffic sign detection algorithm MMW-YOLOv5 based on the YOLOv5 algorithm. The algorithm first uses a multi-scale fusion network (MSFNet) on the neck, which significantly enhances the algorithm's fusion capabilities for multi-scale features and its ability to detect small-sized targets. Secondly, the C3 bottleneck structure in the trunk and neck used to process small-scale feature maps is replaced with the multi-scale feature extraction bottleneck module (MSFEBM) to obtain rich multi-scale feature information and facilitate multi-scale target detection. Finally, the positioning regression function Wise-MPDIoU (WMPDIoU) is used to further improve the overall accuracy of the model and accelerate the convergence speed of the network. Experimental results show that the detection accuracy of the MMW-YOLOv5 algorithm on the TT100K data set reached 87.1% mAP@0.5 and 53.7% mAP@0.5:0.95, which were improved by 6.6% and 5.1% respectively compared with the baseline model.

关键词： Feature extraction YOLO Accuracy detection algorithms Convolutional neural networks Real-time systems Convergence Roads Optimization Traffic control Object detection Traffic sign detection deep learning YOLOv5 multi-scale fusion object detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：