检索结果-内蒙古大学图书馆

Toward Throughput Optimization: A Congestion Control Scheme for CAM Delivery via NR Sidelink

IEEE Internet of Things Journal 2025年第9期12卷 12206-12218页

作者： Fu, Qiang Liu, Jiajia Wang, Jiadai Kong, Jun Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology The School of Cybersecurity Shaanxi Xi’an710072 China

In the new radio (NR) vehicle-to-everything (V2X),congestion control (CC) is adopted to reduce channel occupancy and improve delivery performance. However, most existing CC schemes decide to adjust the number of messages while ignoring the throughput requirements, which may lead to serious traffic accidents due to the loss of information. In light of this, we investigate the performance of cooperative awareness messages (CAMs) delivery via NR sidelink under different CC configurations, including the modulation and coding scheme, the semi-persistent scheduling-based resource allocation, as well as the CAM transmission period. Specifically, taking into account these configurations, we develop a theoretical framework to capture two metrics of CAM delivery, namely the effective throughput and the channel busy ratio, based on the stochastic geometry approach. With the help of this framework, we propose the CC-(m,n,τ ) scheme, which is able to optimize the two metrics simultaneously by properly tuning the parameters m, n, and τ . Extensive numerical results show that the proposed scheme is highly beneficial for efficient and reliable CAM delivery via NR sidelink, particularly in scenarios with high vehicle densities. © 2014 IEEE.

关键词： Congestion control (communication)

来源：评论

学校读者我要写书评

暂无评论

Multi-lab.l MambaOut for Quality Assessment of Low-Field Pediatric Brain MR Images 1st

Multi-Label MambaOut for Quality Assessment of Low-Field P...

引用

1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2024

作者： Zhu, Yueyue Jiang, Haotian Cai, Rongqing Chen, Geng National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031830105

Magnetic Resonance Imaging (MRI) can be utilized to study the structure of pediatric brains non-invasively. In practice, low-field MRI scanners are widely adopted for pediatric brain imaging. However, the corresponding acquired MRI data usually suffers from severe artifacts, such as noise and motion. Therefore, an effective Quality Assessment (QA) method is essential. To this end, we design a Multi-lab.l MambaOut (MLMambaOut) model for the low-field pediatric brain MRI QA challenge. Specifically, we view this challenge as a multi-lab.l classification task, utilizing four stages of gated convolution neural network blocks and ML-Decoder to finish the classification with class balance loss. Furthermore, we explore the performance of Mamba and some advanced models for this challenge. We performed extensive experiments on the challenge data, which is low-field and corrupted with seven kinds of artifacts. The results show that our MLMambaOut achieves superior classification results compared with other methods. © The Author(s) 2025.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Research of photon counting imaging detector based on delay line anode 10

Research of photon counting imaging detector based on delay ...

引用

10th Symposium on Novel Optoelectronic Detection technology and applications

作者： Liu, Yong'an Zhang, Yalong Yang, Xianghui Zhang, Ruili Liu, Zhe Sheng, Lizhi State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

Photon counting imaging detector based on microchannel plate (MCP) and position-sensitive anode readout has the ability to detect single photon, and has been widely used in space detection, high-energy physics and bioluminescence imaging. Traditionally, the detector consists of photocathode, electron multiplication part and position decoding part, in which position decoding is accomplished by position sensitive anode. The delay line anode not only has high spatial resolution and large detection area, but also has high count rate. Based on the principle of delay line decoding, an induction readout cross delay line anode (XDL) is designed and fabricated. And the performance of the detector is tested by using the experimental system. The results show that the width of XDL anode output pulse (FWHM) is about 4ns, the spatial resolution of the detector is better than 100μm, and the dark count rate is 2s). © 2025 SPIE.

关键词： Photocathodes

来源：评论

学校读者我要写书评

暂无评论

A Revisit of Landsat-Observed Macroalgal Blooms in the Yellow Sea and East China Sea Before 2008

引用

Journal of ocean University of China 2025年第2期24卷 525-531页

作者： YUAN Chao HUANG Juan WU Lingjuan XIAO Jie ZHANG Xuelei GAO Song LI Yifei XU Jiangling WANG Zongling Shandong Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation North China Sea Marine Forecasting and Hazard Mitigation CenterMinistry of Natural ResourcesQingdao 266061China Laboratory for Marine Ecology and Environmental Science Qingdao Marine Science and Technology CenterQingdao 266061China First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an 710129China

Harmful macroalgal blooms caused by Ulva prolifera and Sargassum horneri are increasing in the Yellow Sea and East China ***,our understanding of macroalgal blooms before 2008 is fragmented and *** time-series Landsat imagery from 1984 to 2008,we examined macroalgal bloom events and their evolutionary *** results suggest that no macroalgal blooms were observed before *** blooms could be traced back to 1999 and occurred on a small scale in 2000,2004 and 2005,before escalating into large green tides in 2007 and ***,these Ulva blooms were confined to the southern Yellow Sea from May to *** comparison,Sargassum blooms were first detected in the East China Sea in March 2000 and occurred almost every year thereafter,although the size of the blooms showed significant interannual *** distribution areas generally moved northwards from March until the bloom dissipated in May or June,suggesting the influence of the monsoon and *** investigation provided some insight into the bloom history of these two harmful macroalgal blooms in the Yellow Sea and East China Sea.

关键词： macroalgal bloom green tide golden tide remote sensing Landsat Yellow Sea East China Sea

来源：评论

学校读者我要写书评

暂无评论

TAPFixer: automatic detection and repair of home automation vulnerabilities based on negated-property reasoning 24

TAPFixer: automatic detection and repair of home automation ...

引用

Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yinbo Yu Yuanqi Xu Kepu Huang Jiajia Liu National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Northwestern Polytechnical University China and Research & Development Institute of Northwestern Polytechnical University in Shenzhen China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Northwestern Polytechnical University China

ISBN: (纸本)9781939133441

Trigger-Action Programming (TAP) is a popular end-user programming framework in the home automation (HA) system, which eases users to customize home automation and control devices as expected. However, its simplified syntax also introduces new safety threats to HA systems through vulnerable rule interactions. Accurately fixing these vulnerabilities by logically and physically eliminating their root causes is essential before rules are deployed. However, it has not been well studied. In this paper, we present TAPFixer, a novel framework to automatically detect and repair rule interaction vulnerabilities in HA systems. It extracts TAP rules from HA profiles, translates them into an automaton model with physical and latency features, and performs model checking with various correctness properties. It then uses a novel negated-property reasoning algorithm to automatically infer a patch via model abstraction and refinement and model checking based on negated-properties. We evaluate TAPFixer on market HA apps (1177 TAP rules and 53 properties) and find that it can achieve an 86.65% success rate in repairing rule interaction vulnerabilities. We additionally recruit 23 HA users to conduct a user study that demonstrates the usefulness of TAPFixer for vulnerability repair in practical HA scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An M3RSMA-Based Roadside Cooperative Message Delivery Scheme for Complex Intersection

引用

IEEE Transactions on Wireless Communications 2025年

作者： Shi, Zhenjiang Liu, Jiajia School of Information Engineering Chang’an University Xi’an710064 China Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710072 China

Traditional single-vehicle intelligence system faces challenges such as blind spots and perception performance bottlenecks due to limitations in sensor perception angles, ranges, and accuracy, which are particularly pronounced in complex intersection. Vehicle-infrastructure cooperative mechanism has been widely recognized as a promising solution to address challenges faced by single-vehicle intelligence. However, against the backdrop of limited spectrum resources and the sharply rising in the number of connected vehicles, how to efficiently deliver cooperative messages from roadside unit to vehicles is often overlooked. Towards this end, we propose a roadside cooperative message delivery scheme based on multicarrier multigroup multicast rate-splitting multiple access, considering the rarely explored case of transmitting messages with limited size under delay constraint. Then we focus on the critical joint optimization problem of message size and power allocation, with consideration for imperfect channel state information at the transmitter. Subsequently, a multi-agent deep reinforcement learning based resource allocation algorithm is designed to solve this joint optimization problem, exhibiting robustness to dynamic changes in vehicle density and message size. Finally, we analyze through extensive numerical results the impacts of various factors on message delivery success probability. © 2002-2012 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Depth-Enhanced Panoramic Image Stitching for Real-Time Longwall Face Monitoring

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Nie, Jiayang Li, Jing Zhang, Guodong Jiang, Yuqi Zhang, Canbin Song, Jianxing Liu, Jing Yang, Tao Xidian University School of Telecommunications Engineering Xian710071 China Northwestern Polytechnical University School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xian710129 China

This paper presents a novel panoramic stitching solution for real-time monitoring of longwall mining faces, addressing significant parallax issues inherent in underground environments. Using a depth camera array and leveraging a local point cloud registration method, our method enhances real-time monitoring, ensuring precise scene alignment measurement and object placement in dynamic environments. Furthermore, we introduce depth completion to resolve issues related to incomplete depth data and propose a scan projection method to maintain image integrity in constrained, tunnel-like spaces. Our system has been validated in both real and simulated environments. The experimental results demonstrate superior performance compared to existing state-of-the-art methods, with an operational speed of 25 fps. © 1963-2012 IEEE.

关键词： Longwall mining

来源：评论

学校读者我要写书评

暂无评论

Robust Multi-Object Tracking Using Vision Sensor with Fine-Grained Cues in Occluded and Dynamic Scenes

引用

IEEE Sensors Journal 2025年

作者： Hu, Yaoqi Sun, Jinqiu Jin, Hao Niu, Axi Yan, Qingsen Zhu, Yu Zhang, Yanning School of Computer Science China School of Astronautics China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Dongxiang Road Shaanxi Xi'an710129 China

Multi-object tracking (MOT) using vision sensors remains a challenging problem, particularly in dynamic backgrounds and severe occlusions. Existing methods, relying on holistic appearance or spatial cues, fail to capture detailed information within object regions and the background, resulting in inaccurate and inconsistent tracking. To address these issues, we propose a novel Detail-Driven Multi-Object Tracking (DD-MOT) method that revisits and leverages fine-grained cues to discover the dynamics of both object regions and the background, facilitating the recovery and association of trajectories. Specifically, the proposed method consists of three key modules: (i) Points Trajectories Generator, (ii) Camera Motion and Occlusion Compensation, and (iii) Fine- and Coarsegrained Association. The Points Trajectories Generator is responsible for generating fine-grained cues by sampling an initial set of points, generating point trajectories, and refining the initial set of points. The Camera Motion and Occlusion Compensation module utilizes the background and object point trajectories to correct background motion and recover occluded object bounding boxes. Finally, the Fine- and Coarse-grained Association module leverages point trajectory cues to assist in establishing a more effective association strategy, combining both fine-grained and coarse-grained spatial cues from the object bounding boxes. We evaluate our DD-MOT method on several benchmark datasets, including MOT17, MOT20, demonstrating that it consistently outperforms state-of-the-art (SOTA) methods in key metrics such as HOTA (64.2, 62.6), MOTA (80.8, 78.1), and IDF1 (78.8, 77.1). © 2025 IEEE.

关键词： Motion compensation

来源：评论

学校读者我要写书评

暂无评论

Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Proactive Anomaly Detection for 5G Core Control Plane Network Function Interactions

引用

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Tan, Yawen Liu, Jiajia Li, Yuanhao Wang, Jiadai Xidian University State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xi'an710071 China Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China

The Service-Based Architecture (SBA) introduced by 3GPP allows the control plane of 5G Core Network (CN) to function through a set of interconnected Network Functions (NFs), which offers significant benefits such as improved scalab.lity, simplified operations, and efficient resource utilization. However, such a new transformation could also make critical NFs in 5G CN become more susceptible to attacks due to both internal vulnerabilities within the software-implemented NFs or the expanded interfaces brought by the SBA. Existing studies targeting 5G network security pay little attention to the detection of abnormal behaviors from control plane NFs. Therefore, in this paper, we propose 5GCGuard, an anomaly detection scheme aiming at identifying abnormal NF interactions in 5G CN. It employs a deep learning-based sequence model to learn normal interaction patterns and detects anomalies based on deviations from the learned pattern. Special designs including attention mechanism, multi-task learning, probabilistic lab.ling and automatic threshold decision are made to enhance its detection capabilities. We establish a cloud-native 5G testbed to evaluate the effectiveness of 5GCGuard. Extensive evaluation results show its superiority over comparison schemes. Case studies also demonstrate its ability to proactively detect various NF interaction anomalies, enabling swift action to block attacks and prevent further damage. © 2015 IEEE.

关键词： 5G mobile communication systems

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：