Journal of Physics: Conference Series最新文献_第6页

Research on Bursapherenchus Xylophophilus Disease Recognition Based on HSV Space 基于 HSV 空间的 Xylophilus Bursapherenchus 疾病识别研究

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012012

Xuejiao Luo, Yage Deng, Yunxia Hu, Fangfang Xu, Tong Ye

引用次数: 0

Design and implementation of dynamic balance in ultra-high speed IP network 超高速 IP 网络动态平衡的设计与实现

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012018

Yining Ge, Shikang Chen

引用次数: 0

Flexible and High-Efficiency LDPC Encoder Architecture for CCSDS Standard 适用于 CCSDS 标准的灵活高效 LDPC 编码器架构

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012006

Jing Kang, Junshe An, Yan Zhu

{"title":"Flexible and High-Efficiency LDPC Encoder Architecture for CCSDS Standard","authors":"Jing Kang, Junshe An, Yan Zhu","doi":"10.1088/1742-6596/2833/1/012006","DOIUrl":"https://doi.org/10.1088/1742-6596/2833/1/012006","url":null,"abstract":"The Consultative Committee for Space Data Systems (CCSDS) has adopted quasi-cyclic low-density parity-check (QC-LDPC) codes for use in near-Earth (C2) and deep space (AR4JA) communications. Existing encoder architectures for C2 codes, however, fall short in efficiency for high-throughput applications. This paper introduces a comprehensive approach combining algorithmic and architectural optimizations to enhance hardware usage efficiency (HUE) while offering flexibility. We propose an integrated inter-block and intra-block parallel (IIB-IBP) encoding algorithm that leverages the unique matrix structure to significantly enhance performance. Additionally, a matrix-specific command register pretreatment (MSCRP) technique is developed to effectively handle the special dimensions of the generator matrix. Furthermore, we detail an offline design process for the automated generation of the encoder core’s HDL description, facilitating fine-tuning of encoding parallelism, latency, FPGA resource utilization, and overall throughput. Hardware implementation on a Virtex XC5VLX110T FPGA demonstrates that our encoder reaches an impressive throughput of 10.6 Gb/s with only 2531 LUTs and 1040 FFs, achieving a HUE of 2.97 Mbps/logic unit. This performance marks a 70.6% increase in HUE when compared to state-of-the-art designs.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatio-Temporal-Interaction Graph Neural Networks for Multi-Agent Trajectory Prediction 用于多代理轨迹预测的时空交互图神经网络

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012010

Zhoujuan Cui, Wenshuo Peng, Yaqiang Zhang, Yiping Duan, Xiaoming Tao

{"title":"Spatio-Temporal-Interaction Graph Neural Networks for Multi-Agent Trajectory Prediction","authors":"Zhoujuan Cui, Wenshuo Peng, Yaqiang Zhang, Yiping Duan, Xiaoming Tao","doi":"10.1088/1742-6596/2833/1/012010","DOIUrl":"https://doi.org/10.1088/1742-6596/2833/1/012010","url":null,"abstract":"For intelligent transportation systems, accurately forecasting the future trajectories of multiple agents is pivotal. Considering the increased diversity of agents within a scene, in order to capture and model the variations in their appearance, motion status, behavioral patterns, and interrelationships, we propose a simple yet effective framework based on Spatio-Temporal-Interaction Graph Neural Networks. Specifically, a Multi-Class Agent Encoder is meticulously tailored to the specific class of each agent to distill pertinent information from their motion attributes and historical trajectories. Subsequently, a Spatio-Temporal-Interaction Graph Attention Module is constructed to productively represent and learn the complex, dynamic interactions. Finally, a Multimodal Trajectory Generation Module is customized, and a learnable diversity sampling function is introduced to map the features of each agent to a set of potential variables, so as to capture the multimodal distribution of future trajectories. Empirical evaluations on the ETH/UCY and KITTI datasets reveal that our method can efficiently improve the accuracy of trajectory prediction.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Active Adaptive Composite Fault Tolerant Controller Design For Nonlinear Systems 非线性系统的主动自适应复合容错控制器设计

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012004

Xiuli Ning, Xiaowei Lu, Yingcheng Xu, Zhenduo Fu

引用次数: 0

Plaid Fabric Image Retrieval Based on Wavelet Transform and SIFT Features 基于小波变换和 SIFT 特征的格子织物图像检索

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012013

Pengyu Zhao, Yuan Liu, Xiaoting Zhang

引用次数: 0

Strong Single Frequency Jamming Detection Method based on Adaptive Equalization Coefficients 基于自适应均衡系数的强单频干扰检测方法

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012003

Yanhui Qi, Xiaolu Yan, Weican Meng, Qingju He, Guangluan Xu, Xiao Deng

引用次数: 0

Peer Review Statement 同行评审声明

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/011002

引用次数: 0

Study on ECG Signal Classification and Athlete Health Analysis Based on Attention Mechanism 基于注意力机制的心电信号分类和运动员健康分析研究

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012014

Dong Zhu, Haiyan Zhu

{"title":"Study on ECG Signal Classification and Athlete Health Analysis Based on Attention Mechanism","authors":"Dong Zhu, Haiyan Zhu","doi":"10.1088/1742-6596/2833/1/012014","DOIUrl":"https://doi.org/10.1088/1742-6596/2833/1/012014","url":null,"abstract":"In the training and competition process of athletes, their bodies are subjected to various levels of load and stress. As an important diagnostic tool, ECG signals can provide deep insights into the cardiac function of athletes, including heart rate, rhythm, and changes in cardiac electrical activity. By conducting a thorough examination of ECG readings, we are able to quickly identify possible heart conditions or irregularities, which is essential for preserving the heart health of athletes. However, ECG signals are highly complex and multidimensional. To accurately classify these signals, it is necessary to select the most representative and discriminative features. However, this is not an easy task, and the selection of effective features remains a pressing issue. To address this problem, this paper proposes the CSNet classification network model. This framework eradicates disruptions in electrocardiogram signals, performs attribute extraction via a direct network configuration, and combines channel focus mechanisms and spatial focus mechanisms to enhance attribute representation and categorization capabilities. Furthermore, to retain the temporal information of ECG signals, we introduce the Gated Recurrent Unit (GRU), which helps to better capture temporal patterns and dependencies in the signals, thus enabling more accurate classification of ECG signals.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bi-DAUnet: Leveraging BiFormer in a Unet-like Architecture for Building Damage Assessment Bi-DAUnet：在类 Unet 架构中利用 BiFormer 进行建筑物损坏评估

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012015

Chao Dong, Xi Zhao

{"title":"Bi-DAUnet: Leveraging BiFormer in a Unet-like Architecture for Building Damage Assessment","authors":"Chao Dong, Xi Zhao","doi":"10.1088/1742-6596/2833/1/012015","DOIUrl":"https://doi.org/10.1088/1742-6596/2833/1/012015","url":null,"abstract":"In recent years, Convolutional Neural Networks (CNNs) have become an important research direction in the field of building damage assessment. Particularly, deep neural networks based on the U-shaped architecture and skip connections have achieved significant breakthroughs in the task of architectural damage assessment. Despite the impressive performance of CNNs, effectively capturing global and long-range semantic information remains a challenge due to the local nature of their convolutional operations. To address this issue, we propose a novel architectural damage assessment model called Bi-DAUnet, which adopts a BiFormer structure similar to U-Net. In this model, we employ a U-shaped encoder-decoder architecture based on BiFormer and combine it with skip connections to achieve global semantic feature learning. Specifically, we utilize a hierarchical BiFormer with a dual-layer routing attention mechanism as the encoder to extract contextual features of architectural images. In the symmetric decoder, a BiFormer Block is introduced to fuse shallow and deep features of the feature maps and learn the correlation between pixels at distant locations. Experimental results indicate that the U-shaped encoder-decoder network based on BiFormer achieves superior performance in the task of architectural damage assessment compared to fully convolutional methods.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0