检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

755 篇 会议
3 册 图书
2 篇 期刊文献

馆藏范围

760 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

653 篇 工学
- 533 篇 计算机科学与技术...
- 464 篇 软件工程
- 245 篇 信息与通信工程
- 147 篇 生物工程
- 114 篇 光学工程
- 91 篇 电气工程
- 91 篇 控制科学与工程
- 90 篇 电子科学与技术（可...
- 76 篇 生物医学工程（可授...
- 54 篇 化学工程与技术
- 39 篇 机械工程
- 34 篇 仪器科学与技术
- 34 篇 材料科学与工程（可...
- 30 篇 安全科学与工程
- 27 篇 动力工程及工程热...
- 25 篇 交通运输工程
- 24 篇 土木工程
438 篇 理学
- 203 篇 数学
- 159 篇 物理学
- 152 篇 生物学
- 71 篇 统计学（可授理学、...
- 66 篇 化学
- 34 篇 系统科学
199 篇 管理学
- 123 篇 图书情报与档案管...
- 98 篇 管理科学与工程(可...
- 39 篇 工商管理
52 篇 医学
- 49 篇 基础医学(可授医学...
- 49 篇 临床医学
- 36 篇 药学(可授医学、理...
28 篇 法学
- 25 篇 社会学
15 篇 经济学
15 篇 农学
14 篇 教育学
2 篇 军事学

主题

23 篇 deep learning
19 篇 machine learning
16 篇 support vector m...
15 篇 feature extracti...
14 篇 computer vision
11 篇 convolution
11 篇 natural language...
11 篇 sentiment analys...
10 篇 internet of thin...
10 篇 object detection
10 篇 big data
10 篇 semantics
10 篇 convolutional ne...
9 篇 natural language...
7 篇 signal processin...
7 篇 visualization
6 篇 signal detection
6 篇 deep neural netw...
6 篇 graphics process...
6 篇 neural networks

机构

7 篇 key laboratory o...
7 篇 school of inform...
6 篇 school of comput...
6 篇 xinjiang key lab...
6 篇 school of inform...
6 篇 xinjiang key lab...
6 篇 key laboratory o...
4 篇 nss college of e...
4 篇 school of comput...
4 篇 college of infor...
4 篇 school of contro...
3 篇 school of comput...
3 篇 department of co...
3 篇 college of compu...
3 篇 college of intel...
3 篇 guangdong polyte...
3 篇 school of comput...
3 篇 school of comput...
3 篇 academy for engi...
2 篇 hefei university...

作者

10 篇 hamdulla askar
6 篇 ablimit mijit
5 篇 gao yanxia
5 篇 liu yiwen
4 篇 shi yuanquan
4 篇 xiao jianhua
3 篇 faisal rehman
3 篇 wang chao
3 篇 xiao hongbo
3 篇 sharif hanan
3 篇 peng xiaoning
3 篇 rehman faisal
3 篇 chen xi
3 篇 mala c.
3 篇 shi zhen-gang
3 篇 li chao
3 篇 jiang huiming
3 篇 yuan jing
3 篇 li jing
3 篇 zhao qian

语言

753 篇 英文
7 篇 其他

检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

共 760 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

2022 3rd international conference on signal processing and computer science, spcs 2022

2022 3rd International Conference on Signal Processing and C...

引用

2022 3rd international conference on signal processing and computer science, spcs 2022

The proceedings contain 21 papers. The topics discussed include: Crucian carp position and pose recognition based on an improved photo-model method;parameter measurement method of PD radar based on non-coherent accumulation;speech recognition and hardware accelerator design based on hummingbird core;ISAR imaging method for high-speed maneuvering target based on MFT-UNET3+ network;main lobe interference suppression by FDA-MIMO radar based on a novel log function frequency offset;a high dynamic weak spread spectrum signal acquisition strategy based on the transfer model;nonuniform ISRJ suppression based on time shift alignment and morphological processing;data augmentation based on contrastive learning in audio anomaly detection;main lobe interference suppression method of skywave OTHR based on fast-time STAP;design of occupational accident prevention system for transportation vehicles based on TOF;and programmable logic controller memory management vulnerability analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Pulse Feature Point Identification Based on Differential Threshold 24

Research on Pulse Feature Point Identification Based on Diff...

引用

3rd international conference on signal processing, computer Networks and Communications, SPCNC 2024

作者： Chen, Likai Zheng, Gaofeng Zeng, Ziyue Li, Haonan Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Fujian Xiamen China

ISBN: (纸本)9798400710834

Accurate recognition of pulse signal characteristics is the key to disease diagnosis, the existing methods cannot adapt to the identification of sudden pulse cycles. The dynamic threshold method is used to locate all the starting point of the ascending branch and pulse cycle. The single-cycle segmentation of the entire pulse cycle can be achieved. The results show that the starting point and the peak point of all pulse data can be identified by this method. The segment of single pulse signal and recognition of feature points can also be realized through this method. The recognition accuracy of the tidal wave points and the dichotomous wave point reaches 92.6%;it can be basically completed. The identification of characteristic points provides the basis for the analysis and digitization of traditional Chinese medicine. © 2024 Copyright held by the owner/author(s).

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Deploying Emotion Recognition on Resource Constrained Device: Utilizing Light Weight Deep Learning Models with Multi-Physiological signals 24

Deploying Emotion Recognition on Resource Constrained Device...

引用

3rd international conference on signal processing, computer Networks and Communications, SPCNC 2024

作者： Chen, Xu Liang, Dong Zhang, Jianwei Zhang, Yongjian Chengdu Medical College Sichuan Chengdu China University of International Relations Beijing China

ISBN: (纸本)9798400710834

In recent years, the field of emotion recognition has witnessed significant advancements, with applications spanning from healthcare monitoring to human-computer interaction. However, deploying such systems on resource-constrained end devices, such as wearable sensors or mobile phones, remains a challenging task due to limitations in computational power, memory, and battery life. This paper proposes a novel approach to address these challenges by utilizing lightweight deep learning models that are specifically designed to operate efficiently on such devices. The proposed system integrates multi-physiological signals, including Electrocardiogram (ECG), Photoplethysmographic (PPG) readings, Heartbeat Rate (HR), Skin Temperature measurements (SKT), and Respiratory patterns (RSP) to provide a comprehensive assessment of emotional states. We evaluate the performance of our system on a dataset of physiological signals collected from subjects undergoing video emotional stimuli. Experimental results demonstrate that our lightweight models achieve comparable accuracy to heavier models while significantly reducing computational overhead and power consumption. The findings of this study pave the way for the development of practical, real-time emotion recognition systems that can be deployed on a wide range of resource-constrained end devices. © 2024 Copyright held by the owner/author(s).

关键词： Physiological models

来源：评论

学校读者我要写书评

暂无评论

Voice Spoofing Detection using Feature Adaptive One-Class Classification Based on Graph Neural Network 24

Voice Spoofing Detection using Feature Adaptive One-Class Cl...

引用

3rd international conference on signal processing, computer Networks and Communications, SPCNC 2024

作者： Lan, Haiyan Li, Yike Wang, Yuhua Dong, Linfan Han, Zhiying Yang, Kai College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400710834

To address the problems of complex real voice feature distribution, easy overfitting by learning only one classification boundary, and poor generalization ability of existing voice spoofing detection methods for unknown attacks, this paper combines the representation ability of graph neural networks with the learning objective of one-class classification, and proposes a feature adaptive one-class voice detection framework based on graph neural networks. The core idea of the feature adaptive one-class classification loss (FAOC) in the framework is to cluster the real voices affected by similar factors more closely, learn several classification boundaries of the real voices, and optimize the distance of the real voices with different distributions, so that the model can obtain more general features of the real voices. At the same time, the relationship between different frequency bands or time segments of the voice is modeled by using graph neural networks, and the graph embedding features with strong recognition ability are obtained, which can better exploit the performance advantages of FAOC. A series of experiments are conducted on the ASVspoof2019 LA and ASVspoof2021 LA datasets, and the results on the ASVspoof2021 LA show that the EER and min-tDCF are improved by 45.8% and 48.2% compared to the baseline. The results show that the proposed method has good performance for voice synthesis attack detection. © 2024 Copyright held by the owner/author(s).

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Convolutional Models for Indoor Radio Mapping via Ray Marching

Enhancing Convolutional Models for Indoor Radio Mapping via ...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wu, Mengfan Skocaj, Marco Boban, Mate Munich Research Center Huawei Technologies Germany School of Electrical Engineering and Computer Science TU Berlin Germany

ISBN: (纸本)9798350368741

In this paper, we present an enhanced convolutional model for indoor radio map generation, focusing on the integration of a novel ray-marching feature. We describe our machine learning pipeline developed for the ICASSP 2025 signal processing Grand Challenge, specifically the First Indoor Pathloss Radio Map Prediction Challenge. Our method incorporates a ray-marching feature that, combined with a UNet architecture enhanced by dilated convolution layers, significantly improves indoor pathloss prediction accuracy. Our approach achieved a 3rd-place ranking in the challenge with a weighted RMSE of 10.33 on the test dataset. © 2025 IEEE.

关键词： feature engineering machine learning radio maps Wireless communications

来源：评论

学校读者我要写书评

暂无评论

SSVEP EEG signal classification model based on EEMD-FastICA-TRCA 24

SSVEP EEG signal classification model based on EEMD-FastICA-...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Liu, Yunfeng Zhang, Xueru Wan, Sijia Zhan, Weijian Liu, Yongming Zhao, Zhuanzhe School of Mechanical Engineering Anhui Polytechnic University Anhui Wuhu China School of Artificial Intelligence Anhui Polytechnic University Anhui Wuhu China

ISBN: (纸本)9798400707308

In order to retain as many effective signals in EEG as possible and improve the accuracy of a signal classification and the performance of the brain-computer interface system, a steady-state visual evoked potential EEG classification model combining ensemble empirical mode decomposition, fast, independent component analysis and task-related component analysis was proposed to solve the problem that noise would reduce the accuracy of signal classification and recognition. Firstly, it is proved that the proposed EEMD-FastICA noise reduction model has certain advantages over the standard noise reduction methods, and the performance of the model is verified by using the Benchmark dataset of the brain-computer interface group of Tsinghua University. On this basis, the EEMD-FastICA noise reduction model is combined with the TRCA algorithm to classify EEG signals, and the classification results are compared with other classification models. The comparison results show that under different time Windows, the classification accuracy and information transmission rate of the proposed model are higher than those of other models. Within 1.5 S, the highest classification recognition accuracy was 85.83%, and the highest ITR was 238.34 bits/min. © 2024 Copyright held by the owner/author(s).

关键词： Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

A Spatio-Temporal Multi-Subgraph Convolutional Network for Parkinson’s Disease Detection Using Gait Data 24

A Spatio-Temporal Multi-Subgraph Convolutional Network for P...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wu, Huimin Liu, Yongcan Wang, Xianquan Yu, Xinglin Zhao, Aite College of Computer Science and Technology Qingdao University Shandong Qingdao China Institute of Advanced Technology University of Science and Technology of China Anhui Hefei China

ISBN: (纸本)9798400707308

Gait disturbance is a key motor symptom of Parkinson’s disease (PD) and has been widely studied for diagnosis and severity assessment using non-invasive sensor data. However, most existing methods for PD detection struggle to adaptively model sensor positions and inter-signal correlations within a multi-sensor system. Additionally, these sensors form a complex system with multiple subsystems monitoring local regions, and their interrelations are often overlooked. In this work, we propose a spatio-temporal multi-subgraph convolutional network (ST-MSGCN), a hybrid framework using graph neural networks to capture gait differences between healthy individuals and PD patients from multi-sensor data. We first decouple the overall sensor system and encode inter-subsystem correlations into multiple subgraphs. The ST-MSGCN then employs a spatio-temporal graph convolutional network to analyze 3D-graph representations of foot force signals, exploring spatial and temporal characteristics within each subgraph. We also introduce channel-wise topology and temporal attention mechanisms into the graph convolutional layers to dynamically learn discriminative features. Finally, a decision-making layer using a voting strategy fuses observations and state estimations from multiple graphs. The proposed method is validated on the PDgait dataset and compared with several existing models. Additionally, it is evaluated on a normal skeleton-based dataset to explore the relationships among body parts. © 2024 Copyright held by the owner/author(s).

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Bayesian networks based on differential privacy for financial data privacy 3

Bayesian networks based on differential privacy for financia...

引用

3rd international conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Li, Ni Yang, Jianwei Ren, Xiaoli Ma, Xin Li, Xiangyang School of Computer Baoji University of Arts and Science Baoji China

ISBN: (纸本)9781510688179

As the demand for privacy protection continues to grow, ensuring effective financial data processing while preventing sensitive information leakage has become a pressing issue in the financial industry and related research fields. We propose a financial data processing method based on Bayesian networks and differential privacy, innovatively introducing an exponential mechanism to optimize the Bayesian network and adding Laplace noise to disrupt the data, thereby reducing the risk of sensitive information leakage. Through the evaluation of data usability using support vector machines, experimental results show that the processed synthetic data is highly similar to the original data in key metrics, demonstrating that this method maintains data usability while protecting privacy. © The Authors.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Generative Replay and Multi-steps Knowledge Distillation in Class-Incremental Learning 24

Generative Replay and Multi-steps Knowledge Distillation in ...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Meng, Yicheng Ping, Jinming Shi, Jingye Zhi, Ruicong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400707308

Data-free generative modeling (GM) methods have gained much attention because pseudo data avoids security and privacy issues, and no amount limitation for storing real data in Class-Incremental Learning (CIL). However, GM-based methods have three drawbacks: un stable quality of generated image, semantic sifting, and domain bias for knowledge and distillation. To tackle with these problems, we propose an effective two-stage training method based on generative replay distillation, namely GA-RCT in this paper. It mainly consists of three modules. The Generative Adaptive Replay (GAR) module in stage one obtainsa well-trained generator for every task, then freezes generator for generating stable images, and trains feature extractor with mixture of real data and generated data. The Global Average Class (GAC) module is designed to reserve class prototypes to reduce semantic drift in the training stage. And the Generative Auxiliary Teaching (GAT) module in stage two performs generative distillation in dual-teacher to student way, for solving domain bias. Experiments on four datasets have shown that GA-RCT performs better than other generative replay methods. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

3D-CNN for Breast Cancer Detection on Angular IR Images 3rd

3D-CNN for Breast Cancer Detection on Angular IR Images

引用

3rd international conference on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2024

作者： de Freitas Oliveira Baffa, Matheus Neves, Thamiris Gire Zine Tulha, Carinna Nunes Conci, Aura SP Ribeirão Preto Brazil SP Santo André Brazil SP Campinas Brazil RJ Niterói Brazil

ISBN: (纸本)9783031765834

Breast cancer detection depends on accurate imaging techniques that capture the full complexity of the breast tissue. Angular breast images, particularly the mediolateral oblique and mediolateral projections, play a vital role in traditional mammography, offering comprehensive views that include difficult-to-visualize areas. However, infrared imaging, which measures surface temperature distribution, provides a complementary approach by highlighting thermal anomalies that may indicate underlying malignancies. While frontal infrared images are commonly used, they often fail to capture the entire thermal profile of the breast, particularly in areas obscured from the frontal view. To address this limitation, angular infrared images can be integrated to provide a more complete analysis, although this approach introduces challenges in image processing and interpretation. In this paper, we present a model to address the problem of breast cancer detection specifically on angular images, consisting of a 3D convolutional neural network (CNN) that integrates lateral and oblique views into a single volumetric representation, allowing for comprehensive analysis of the breast. The results of our ablation experiment demonstrates an architecture that effectively classifies breast cancer, achieving 93.82% accuracy, 93.82% sensitivity, and an area under the receiver operating characteristic curve of 97.22%, which highlights the model’s efficacy in distinguishing between healthy and cancerous cases. The use of a 3D CNN for this problem is well-suited given the volumetric nature of the data, making it a promising approach for further exploration in medical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共76页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：