检索结果-内蒙古大学图书馆

GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Attention-Based Residual Dense Shrinkage Network for ECG Denoising

引用

computer Modeling in engineering & sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

来源：评论

学校读者我要写书评

暂无评论

Quality of Control Based Resource Dimensioning for Collaborative Edge robotics 22

Quality of Control Based Resource Dimensioning for Collabora...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Roy, Neelabhro Dhullipalla, Mani H. Sharma, Gourav Prateek Dimarogonas, Dimos V. Gross, James School of Electrical Engineering and Computer Science Digital Futures Research Center Kth Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9798331508050

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably. © 2025 IEEE.

关键词： Industrial robots

来源：评论

学校读者我要写书评

暂无评论

Hardware Implementation of nmODE on FPGA

Hardware Implementation of nmODE on FPGA

引用

2023 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Chen, Yi Liu, Hanwen Zhang, Enqi Qu, Hong Yi, Zhang Computer Science & Engineering University of Electronic Science and Technology of China Chengdu China Intelligent Interdisciplinary Research Center School of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9798350309003

Neural Ordinary Differential Equations (ODEs) have attracted much attention since their introduction. They use a black-box differential equation solver instead of iterating repeatedly to calculate the network output, which makes a tradeoff between numerical precision and speed. The research on ODE mainly focused on theoretical orientation, and lacks practical applications, especially in hardware deployment. In this paper, we propose a hardware implementation of the neural memory ODE model based on Field-Programmable Gate Arrays (FPGAs). Specifically, we propose a neural memory ODE (nmODE) numerical mapping method to accelerate its differential equation solver speed and save memory consumption. After that, we design a nmODE neuron core on FPGA and expand it to support parallel computation. This work is implemented on Xilinx XC7z030 and evaluated with the MNIST dataset. The experimental results demonstrate that nmODE on FPGA achieves a comparable accuracy of 98.84% compared to the baseline nmODE on GPU while accelerating the inference speed by x31 times. © 2023 IEEE.

关键词： Ordinary differential equations

来源：评论

学校读者我要写书评

暂无评论

Xception for Tomato Leaf Disease Detection: Hyperparameter Tuning and Fine-tuning Approaches

Xception for Tomato Leaf Disease Detection: Hyperparameter T...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Maini, Yash Singh, Sanjay Kumar Saxena, Pranshu University School of Automation and Robotics Guru Gobind Singh Indraprastha University East Delhi Campus Surajmal Vihar Delhi110092 India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331517953

Tomato cultivation is essential to world agriculture, affecting food security and economic stability. Tomasto leaf diseases must be detected accurately to maintain crop health and maximize yield. This work improves disease classification by using deep learning models, notably CNN, Xception, DenseNet, and a hyper-tuned Xception model. We used transfer learning and data augmentation to improve model performance on the Kaggle dataset, which contains 10,000 training images and 1,000 validation images from the Plant Village Repository organized into ten groups. The hyper-tuned Xception model achieved the maximum performance by meticulously tweaking hyperparameters such as learning rate, batch size, and network architecture, with an accuracy of 97.23%, precision of 95.73%, recall of 94.40%, and F1-score of 95.06%. These results show a considerable improvement over the CNN, Xception, and DenseNet models. The results highlight the efficiency of sophisticated deep learning algorithms and hyperparameter tweaking in achieving high classification accuracy, making the proposed model a robust tool for practical tomato leaf disease diagnostics. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

TP-Detector: Detecting Turning Points in the engineering Process of Large-scale Projects

TP-Detector: Detecting Turning Points in the Engineering Pro...

引用

2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Wu, Qi Chao, Wenhan Zhou, Xian Luo, Zhunchen School of Computer Science and Engineering Beihang University China Information Research Center of Military Science PLA Academy of Military Science China

This paper introduces a novel task of detecting turning points in the engineering process of large-scale projects, wherein the turning points signify significant transitions occurring between phases. Given the complexities involving diverse critical events and limited comprehension in individual news reports, we approach the problem by treating the sequence of related news streams as a window with multiple instances. To capture the evolution of changes effectively, we adopt a deep Multiple Instance Learning (MIL) framework and employ the multiple instance ranking loss to discern the transition patterns exhibited in the turning point window. To facilitate comprehensive evaluation of the task, we curate a dataset comprising 80 large-scale projects. Extensive experiments consistently demonstrate the effectiveness of our proposed approach on the constructed dataset compared to baseline methods. We deployed the proposed model1 and provided a demonstration video2 to illustrate its functionality. The code and dataset are available on GitHub. © 2023 Association for Computational Linguistics.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

引用

Journal of Shanghai Jiaotong University (science) 2025年 1-9页

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Drone to Address Smart City Security Issues 37th

AI-Powered Drone to Address Smart City Security Issues

引用

37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Salama, Ramiz Al-Turjman, Fadi Culmone, Rosario Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Near East University Nicosia Mersin 10 Nicosia Turkey Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey Computer Science Division School of Science and Technology University of Camerino Camerino62032 Italy

ISBN: (纸本)9783031286933

The idea of a dazzling metropolis has drawn interest from all across the world. New innovations like blockchain, IoT, artificial intelligence, robots, and many other things were added to it. Security is one of the top issues for people living in big cities, and everyone wants to feel completely secure when traveling around every single day. In this research, we will look into how CEOs in affluent cities use and value robots, especially in terms of security. To understand the robot security the board stream, some approaches and intricacies are used. Following that is a discussion of issues with urban security and the application of artificial intelligence to drones as a management tool. The use of cutting-edge technology, such as blockchain, to support smart urban community management is covered in the last part. The smart city idea and all of its benefits for local inspection are supported by robotic use. The idea of thriving urban areas is spreading around the world and is crucial in the context of developing economies. We examined how to leverage cutting-edge technologies in this project to make it feasible. Artificial intelligence, robotics breakthroughs, and blockchain technologies all have significant effects. This research highlights their significance to analysts and how they consider them when assessing prospects. This project will be very beneficial for analysts and experts in a relevant subject. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

SpaCCC:Large Language Model-Based Cell-Cell Communication Inference for Spatially Resolved Transcriptomic Data

引用

Big Data Mining and Analytics 2024年第4期7卷 1129-1147页

作者： Boya Ji Xiaoqi Wang Debin Qiao Liwen Xu Shaoliang Peng College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China School of Computer Science Northwestern Polytechnical UniversityXi’an 710000China School of Computer and Artificial Intelligence and National Supercomputing Center in Zhengzhou Zhengzhou UniversityZhengzhou 450001China

Drawing parallels between linguistic constructs and cellular biology,Large Language Models(LLMs)have achieved success in diverse downstream applications for single-cell data ***,to date,it still lacks methods to take advantage of LLMs to infer Ligand-Receptor(LR)-mediated cell-cell communications for spatially resolved transcriptomic ***,we propose SpaCCC to facilitate the inference of spatially resolved cell-cell communications,which relies on our fine-tuned single-cell LLM and functional gene interaction network to embed ligand and receptor genes into a unified latent *** LR pairs with a significant closer distance in latent space are taken to be more likely to interact with each *** that,the molecular diffusion and permutation test strategies are respectively employed to calculate the communication strength and filter out communications with low *** benchmarked performance of SpaCCC is evaluated on real single-cell spatial transcriptomic datasets with superiority over other *** also infers known LR pairs concealed by existing aggregative methods and then identifies communication patterns for specific cell types and their signaling ***,SpaCCC provides various cell-cell communication visualization results at both single-cell and cell type *** summary,SpaCCC provides a sophisticated and practical tool allowing researchers to decipher spatially resolved cell-cell communications and related communication patterns and signaling pathways based on spatial transcriptome data.

关键词： Large Language Models(LLM) spatial transcriptome data Cell-Cell Communications(CCCs) functional gene interaction networks unified latent space

来源：评论

学校读者我要写书评

暂无评论

Anomalous Sound Detection Using Time-Frequency Feature and Mixbatch

引用

Journal of Shanghai Jiaotong University (science) 2025年 1-8页

作者： Huang, Shun Zhang, Yunxiang Fang, Zhihua Tang, Minrui Xu, Ruifeng He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The sound emitted by machines under abnormal working conditions exhibits various frequency patterns. Currently, the most advanced anomalous sound detection (ASD) approach is to apply a multi-head self-attention mechanism to the Log-Mel spectrogram for automatic frequency pattern analysis. However, the Log-Mel spectrogram may filter out high-frequency components of abnormal sounds;thus the use of self-attention mechanisms on Log-Mel spectrogram seems to have certain limitations. In this paper, we construct a simple convolutional neural network to extract comprehensive frequency features from raw audio to complement spectral-temporal information fusion. The parameters of this neural network are continuously updated during the training process to extract better frequency features for downstream classification neural networks in ASD. Additionally, a method for data augmentation in the batch dimension was developed to help the classification model learn both types of comprehensive features simultaneously. The proposed method achieved an AUC of 94.13% and a pAUC of 89.09% on the DCASE 2020 Challenge Task 2 dataset. Even when using only the features proposed in this paper, an AUC of 84.62% was achieved. (Figure presented.). © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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：