检索结果-内蒙古大学图书馆

DP-GenFL: a local differentially private federated learning system through generative data

science China(information sciences) 2023年第8期66卷 275-276页

作者： Jun LI Kang WEI Chuan MA Feng SHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Information and Communication Engineering Hainan University

With the rapid development of the Internet of Things(Io T),the amount of data from intelligent devices is propagating at unprecedented scales. Meanwhile, machine learning(ML),which relies heavily on such data, is revolutionizing many aspects of our lives [1]. However, conventional centralized ML offers little scalability for efficiently processing this huge amount of data.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Near-field Beam Training with Sparse DFT Codebook

引用

IEEE Transactions on Communications 2024年第6期73卷 4394-4408页

作者： Zhou, Cong Wu, Chenyu You, Changsheng Zhou, Jiasi Shi, Shuo Harbin Institute of Technology School of Electronic and Information Engineering Harbin150001 China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China Shenzhen Key Laboratory of Optoelectronics and Intelligent Sensing Shenzhen518055 China Xuzhou Medical University School of Medical Information and Engineering Xuzhou221004 China

Extremely large-scale arrays (XL-arrays) have emerged as one promising technology to improve the spectral efficiency and spatial resolution in future sixth generation (6G) wireless systems. The drastic increase in the number of antennas renders the communication users more likely to be located in the near-field region, which requires a more accurate spherical (instead of planar) wavefront propagation modeling. However, this also inevitably incurs unaffordable beam training overhead when performing a two-dimensional (2D) beam-search in both the angular and range domains. To address this issue, we first introduce in this paper a new sparse discrete Fourier transform (DFT) codebook, which exhibits the angular periodicity in the received beam pattern at the user. This thus motivates us to propose a three-phase beam training scheme. Specifically, in the first phase, we utilize the sparse DFT codebook for beam sweeping in an angular subspace and estimate candidate user angles according to the received beam pattern. Then, a central subarray is activated to scan specific candidate angles for resolving the issue of angular ambiguity for identifying the user angle. In the third phase, the polar-domain codebook is applied in the estimated angle to search the best effective user range. Finally, numerical results show that our proposed beam training scheme enabled by the sparse DFT codebook achieves 98.67% beam training overhead reduction as compared to the exhaustive-search scheme, yet without compromising rate performance in the high signal-to-ratio (SNR) regime. © 1972-2012 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Recognizing Liquids Using a Multi-resonance Microwave Sensor

Deep Learning-based Recognizing Liquids Using a Multi-resona...

引用

2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Zhou, Yifan Yong, Jinglei Peng, Jinyang Li, Peng Tong, Meisong Zhang, Yunjing Soochow University School of Electronic and Information Engineering China Tongji University Department of Electronic Science and Technology China

ISBN: (纸本)9798350375909

In this paper, a microwave sensor is investigated by etching multiple different split ring resonators (SRRs) on the ground layer, thus obtaining multiple complementary split ring resonators (CSRRs). In order to improve the sensitivity, we control the size of the CSRRs between λ/4 to λ/2, so that the the stored energy is large near the resonances. The model uses a two-port microstrip line coupled with the CSRRs to extract the insertion loss (S21) in the range of 5-8 GHz, rather than the usual analysis just near a single resonant points. In addition, we find that the size of the CSRRs and the distance between the CSRRs and microstrip line have great influences on its resonant frequency and Q-factor of the sensor. We detect unknown liquids in two cases. First, different types of liquids including water, ethanol, ether, and methanol are investigated, and we find that the characteristics of S21 are distinctive. Secondly, a challenge task that detecting different concentrations of ethanol solutions is carried out. In this case, the changes in their permittivity are small, indicating small changes in the characteristics of S21. Considering the fact that most of liquid containers on the market have grooves, hollow bottom, and different thickness of walls, the measured S21 of liquids is inconsistent, let alone the placement position error between bottles and the sensing area in rapid test. To solve the problem, we use a feedforward neural network (FNN) by the backpropagation method to process the measured S21 in a wide band. The results show that the accuracy of detecting different liquids by the deep learning method is 100% and of detecting the same liquid with different concentrations is 99%. Compared with the traditional method, the proposed one is insusceptible to random errors. The nondestructive liquid detection proposed in this work has good future in detecting suspicious liquids in the market. © 2024 IEEE.

关键词： Microwave sensors

来源：评论

学校读者我要写书评

暂无评论

A Rearrangement and Restore-Based Mixer Model for Target-Oriented Multimodal Sentiment Classification

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3109-3119页

作者： Jia, Li Ma, Tinghuai Rong, Huan Sheng, Victor S. Huang, Xuejian Xie, Xintong Nanjing University of Information Science and Technology School of Computer Science Nanjing Jiangsu210044 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing Jiangsu210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang Jiangsu222005 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing Jiangsu210044 China Texas Tech University Department of Computer Science LubbockTX79409 United States

With the development of fine-grained multimodal sentiment analysis tasks, target-oriented multimodal sentiment (TMSC) analysis has received more attention, which aims to classify the sentiment of target with the help of textual and associated image features. Existing methods focus on exploring fine-grained image features and incorporate transformer-based complex fusion strategies, while ignoring the heavy computational burden. Recently, some lightweight multilayer perceptrons (MLP)-based methods have been successfully applied to multimodal sentiment classification tasks. In this article, we propose an effective rearrangement and restore mixer model (RR-Mixer) for TMSC, which dedicates the interaction of image, text, and targets along the modal-axis, sequential-axis, and feature channel-axis through rearrangement and restore operations. Specifically, we take vision transformer (ViT) and robustly optimized BERT (RoBERTa) pretrained models to extract image and textual features, respectively. Further, we adopt cosine similarity to select the most semantically relevant image features. Then, an RR-Mixer module is designed for mixed multimodal features, with the core technology consisting of rolling, grouping rearrangement and restore operations. Moreover, we introduce MLP Unit to learn the information of different modalities for intermodal interaction. The results show that our model achieves superior performance on two benchmark multimodal datasets, TWITTER-15 and TWITTER-17, with a significant improvement of 4.66% and 1.26% in terms of macro-F1. © 2020 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Simulation on the Membrane Potential Variations by THz Unipolar Stimulation with Triangle Envelope

Simulation on the Membrane Potential Variations by THz Unipo...

引用

2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Bo, Wenfei Che, Rong Guo, Lemeng Zhang, Xiaobo Liu, Qiang Tao, Bo Duan, Baosong Li, Yuansheng Gong, Yubin National University of Defense Technology College of Information and Communication China University of Electronic Science and Technology of China School of Electronic Science and Engineering China

ISBN: (纸本)9798350375909

Terahertz has recently shown new application prospects in non-invasive brain-machine communications. Terahertz stimulation can induce the information communication of cells. Cell membrane potentials are significant electrical variable in cell communication. However, the membrane potential variations are far from being understood fully under the terahertz stimulation. This paper studies the membrane potential variations of a cell under terahertz unipolar stimulation with triangle envelope, by means of the cell model of ion flow considering Na+, K+-ATPase and hydrophilic pores in cell membrane. From the numerical results, except where the cell membrane in the cell is nearly parallel to the terahertz electric fields, the absolute values of the membrane potentials increase first and then decrease towards zero under the stimulation. During those variation processes, the membrane potentials vibrate at the terahertz frequency the same as the stimulation. Besides, the variation rates of the membrane potentials show quasi pulse train forms at the same terahertz frequency during the stimulation, and the envelopes of the variation rates show triangle envelope, and additionally the triangle envelopes of the variation rates at some polar angles of the cell are out of shape near the end of the terahertz unipolar stimulation. This indicates that the triangle envelope of the terahertz unipolar stimulation can be delivered into the membrane potentials of the biological cell, and shows a potential way of transferring the triangle envelope signal in terahertz unipolar picosecond pulse train into biological cells for the applications in the brain-machine communications. © 2024 IEEE.

关键词： Cell membranes

来源：评论

学校读者我要写书评

暂无评论

Prediction and Analysis of Stroke Risk Based on Ensemble Learning 15th

Prediction and Analysis of Stroke Risk Based on Ensemble Lea...

引用

15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Zuo, Xiuji Guo, Xin Yin, Zilong Tseng, Shih-Pang School of Information Science and Technology Sanda University Shanghai201209 China Department of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Software and Big Data Changzhou College of Information Technology Changzhou213164 China

ISBN: (纸本)9789819994113

With the development of science and technology, the application of data mining in medical field is becoming more and more popular. Machine learning methods also plays an important role in disease prediction. Stroke is characterized by high incidence rate, high disability rate, high mortality rate and high recurrence rate, and it is also likely to cause other kinds of complications. In this paper, each feature in the stroke dataset was analyzed in order to find out the factors affecting stroke and conducts classification and prediction research on whether there is a disease risk. Specifically, the PCA (Principal Component Analysis) algorithm is used to extract the main feature components of data, the SMOTE (Synthetic Minority Oversampling Technique) algorithm is used to adjust imbalanced feature categories. Traditional machine learning classification algorithms, such as decision tree, SVM(support vector machines), and various ensemble learning algorithms are used for the prediction of stroke risk, so as to study the relationship between stroke disease and each feature, and the classification prediction model, so that we can prevent strokes in time and reduce the risk of stroke. Among all the models, Bagging (Bootstrap aggregating) has the best performance with an ROC value of 0.97. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Voxel-RCNN Improved Algorithm Based on Atrous Spatial Pyramid Pooling 4

Voxel-RCNN Improved Algorithm Based on Atrous Spatial Pyrami...

引用

4th International Conference on electronic information engineering and Computer science, EIECS 2024

作者： Yao, Kai Zhao, Haili Zhao, Dongjie Meng, Jiahe Liang, Meihui School of Electronic Information Engineering Changchun University of Science and Technology Changchun China School of Optoelectronic Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

In order to solve the problem of poor bicycle and pedestrian detection under sparse point cloud conditions in autonomous driving, an improved Voxel-RCNN algorithm is proposed to optimize the performance by introducing a dilated spatial pyramid structure while minimizing the computational cost and model complexity. Voxel-RCNN uses 3D sparse convolution with a fixed-size kernel to extract target features, which may not be suitable for the detection of objects of different sizes and limits the information interaction of long-distance features. To this end, we design a sparse atrous spatial pyramid pooling (SASPP) module to expand the receptive field of the model and fuse multi-scale features. For the same purpose, a ASPP module is integrated in the 2D backbone network. The experimental results show that the improved algorithm has achieved significant improvement in the detection of bicycles and pedestrians, especially on difficult samples. The pedestrian detection accuracy is improved by 4.65%, and the bicycle detection accuracy is improved by 4.23%. These results demonstrate that our algorithm is effective for difficult-to-detect objects such as bicycles and pedestrians. © 2024 IEEE.

关键词： Bicycles

来源：评论

学校读者我要写书评

暂无评论

Robust Detector Designing for FDA-MIMO Radar With Training Data in Gaussian Noise

引用

IEEE Transactions on Aerospace and electronic Systems 2025年第3期61卷 7787-7802页

作者： Gui, Limin Huang, Bang Wang, Wen-Qin division Thuwal23955 Saudi Arabia University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Huzhou313001 China

This paper presents the design of robust detectors for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar in the presence of Gaussian noise with an unknown covariance matrix. It aims to address the challenge of detecting the target of a coherent return from an appointed cell under test. Our primary focus is on the case of signal mismatch, specifically considering the two-dimensional mismatch, including the target and the Doppler steering vectors. To tackle these issues, we introduce a fictitious signal with a specified structure in addition to the nominal steering vector, thereby forming a new hypothesis testing model. By making use of this model, we further derive robust detectors utilizing the generalized likelihood ratio test (GLRT) and Wald test in the presence of training data. Besides, a parametric detector is implemented, which includes robust GLRT as a particular case. The proposed detectors own constant false alarm rate (CFAR) properties under the null hypothesis. The effectiveness of designed algorithms is verified by comparing their performance with those of their given counterparts. Moreover, numerical examples illustrate that the functionality of presented detectors is better compared to GLRT, adaptive matched filter (AMF), Wald, and multiple-input multiple-output-Wald (MIMO-Wald) detectors in terms of robustness against signal mismatches. © 1965-2011 IEEE.

关键词： Covariance matrix

来源：评论

学校读者我要写书评

暂无评论

Topology-Aware Keypoint Detection via Skeleton-Based Shape Matching

引用

IEEE Transactions on Consumer electronics 2024年第1期71卷 367-378页

作者： Li, Yushi Li, Pengfei Xu, Meng Wang, Yunzhe Ji, Chengtao Han, Yu Chen, Rong Xi'an Jiaotong-Liverpool University Department of Intelligent Science China Dalian Maritime University College of Information Science and Technology China Suzhou University of Science and Technology School of Electronic and Information Engineering China Xi'an Jiaotong-Liverpool University Department of Computing China Shenzhen University School of Mathematical Sciences China

3D keypoint detection endeavors to identify well-aligned and semantically consistent elements that reflect object shapes within point clouds, which plays a significant role in wide-ranging applications such as navigation and object tracking based on mobile devices. While existing approaches prioritize either salient features or statistic distributions for alignment, they overlook the underlying spatial topology of shapes. Although some recent methods take potential skeletons into account, they fail to associate this representation with local and global topology, thus reconciling comprehensive coverage and semantic awareness. To address this, we reckon keypoint detection as the skeleton-based shape matching and propose a two-branch framework that explicitly localizes the keypoints with broad coverage and semantic coherence in an unsupervised manner. Specifically, one branch incorporates the keypoint detector with a skeleton generator to infer the coarse skeletons that represent the global topology. Meanwhile, another branch leverages skeletal sphere estimation to generate the skeletal point set that sustains the local structures, serving as the foundation for optimizing the skeletons formed by keypoints. Since these skeletal representations capture both the structural essence and semantic attributes of a shape, our model is capable of extracting semantically rich keypoints with good alignment. We extensively evaluate our method on different datasets to demonstrate its effectiveness and competitiveness in 3D keypoint detection. © 1975-2011 IEEE.

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

引用

Journal of Artificial Intelligence and technology 2024年第2期4卷 179-187页

作者： Tianke Fang Zhenxing Hui William P.Rey Aihua Yang Bin Liu Zhiying Xie School of Information Technology Mapua UniversityManilaPhilippines School of Computing and Information Engineering Xiamen University of TechnologyXiamenChina School of Information Media Zhangzhou Vocational College of Science and TechnologyZhangzhouChina College of Software Engineering Xiamen University of TechnologyXiamenChina

Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural ***,the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human ***,an artificial intelligence repair technology based on three-dimensional(3D)point cloud(PC)reconstruction and generative adversarial networks(GANs)was proposed to improve the precision and efficiency of repair ***,in-depth research on the principles and algorithms of 3D PC data processing and GANs should be ***,a digital restoration frameworkwas constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning *** experimental results showed that the errors in the restoration of palace buildings,defense walls,pagodas,altars,temples,and mausoleums were 0.17,0.12,0.13,0.11,and 0.09,*** technique can significantly reduce the error while maintaining the high-precision repair *** technology with artificial intelligence as the core has excellent accuracy and stability in the digital *** provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.

关键词： 3D PC reconstruction artificial intelligence repair GAN algorithm stereoscopic vision historical buildings

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：