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

8,736 篇 会议
439 篇 期刊文献
19 篇 学位论文
3 篇 科技报告
2 册 图书
1 篇 专利

馆藏范围

9,200 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

5,627 篇 工学
- 2,389 篇 信息与通信工程
- 2,336 篇 电气工程
- 2,204 篇 计算机科学与技术...
- 1,245 篇 软件工程
- 627 篇 控制科学与工程
- 610 篇 电子科学与技术（可...
- 600 篇 仪器科学与技术
- 448 篇 航空宇航科学与技...
- 361 篇 测绘科学与技术
- 236 篇 机械工程
- 229 篇 生物医学工程（可授...
- 213 篇 光学工程
- 163 篇 生物工程
- 91 篇 化学工程与技术
- 90 篇 土木工程
- 85 篇 船舶与海洋工程
- 84 篇 材料科学与工程（可...
- 76 篇 建筑学
- 76 篇 交通运输工程
2,785 篇 理学
- 1,660 篇 物理学
- 777 篇 数学
- 301 篇 统计学（可授理学、...
- 245 篇 系统科学
- 193 篇 生物学
- 167 篇 化学
- 95 篇 地球物理学
816 篇 医学
- 747 篇 临床医学
- 73 篇 基础医学(可授医学...
312 篇 管理学
- 177 篇 管理科学与工程(可...
- 140 篇 图书情报与档案管...
58 篇 军事学
30 篇 经济学
30 篇 农学
28 篇 法学
18 篇 文学
15 篇 教育学
5 篇 艺术学

主题

1,271 篇 signal processin...
583 篇 signal processin...
464 篇 signal to noise ...
350 篇 target tracking
318 篇 object detection
305 篇 feature extracti...
275 篇 training data
231 篇 data mining
220 篇 radar
212 篇 training
202 篇 data processing
197 篇 array signal pro...
191 篇 neural networks
189 篇 sensors
164 篇 signal resolutio...
159 篇 radar tracking
153 篇 signal detection
152 篇 synthetic apertu...
152 篇 data models
150 篇 speech processin...

机构

59 篇 national laborat...
35 篇 school of inform...
25 篇 univ connecticut...
24 篇 nanjing research...
24 篇 duke univ dept e...
24 篇 school of electr...
21 篇 xidian univ natl...
20 篇 national key lab...
20 篇 xidian universit...
20 篇 national lab of ...
17 篇 school of inform...
17 篇 university of el...
17 篇 department of el...
16 篇 national key lab...
16 篇 beijing institut...
15 篇 xidian universit...
14 篇 college of elect...
13 篇 xidian univ natl...
13 篇 university of ch...
12 篇 univ chinese aca...

作者

28 篇 bar-shalom yaako...
23 篇 willett peter
21 篇 kirubarajan t.
19 篇 bar-shalom y
17 篇 jun wang
17 篇 blair wd
15 篇 drummond oliver ...
14 篇 xu shuwen
14 篇 liu hongwei
13 篇 collins leslie m...
13 篇 shui penglang
13 篇 bin yang
12 篇 wang wei
12 篇 torrione peter a...
12 篇 mengdao xing
12 篇 liao guisheng
11 篇 drummond oe
11 篇 abdelhak m. zoub...
10 篇 morton kenneth d...
10 篇 sinha a

语言

8,949 篇 英文
156 篇 中文
58 篇 其他
34 篇 土耳其文
2 篇 法文
2 篇 俄文

检索条件"任意字段=Conference on Signal and Data Processing of Small Targets"

共 9200 条记录，以下是61-70 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：

Leveraging IPA and Articulatory Features as Effective Inductive Biases for Multilingual ASR Training

Leveraging IPA and Articulatory Features as Effective Induct...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jaeyoung Lee Masato Mimura Tatsuya Kawahara Kyoto University NTT Corporation

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent advancements in end-to-end ASR, large-scale self-supervised or weakly supervised models have achieved a significant milestone. However, it remains challenging to train consistently high-performing multilingual models, transferable to languages without much resource. In this study, we propose embedding universal phonological knowledge to multilingual ASR by predicting international phonetic alphabet (IPA) targets and universal articulatory features alongside primary grapheme targets. These additions are expected to provide effective inductive bias or regularization for predicting grapheme targets across various languages. In the experiments, which involve fine-tuning a pre-trained XLS-R model using 10,400 hours of data across 120 languages from the Common Voice corpus, our proposed method achieved a 6.81% relative reduction in character error rate.

关键词： Training Error analysis Speech enhancement signal processing Phonetics data models Acoustics Multilingual

来源：评论

学校读者我要写书评

暂无评论

Guitar-TECHS: An Electric Guitar dataset Covering Techniques, Musical Excerpts, Chords and Scales Using a Diverse Array of Hardware

Guitar-TECHS: An Electric Guitar Dataset Covering Techniques...

引用

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

作者： Pedroza, Hegel Abreu, Wallace Corey, Ryan M. Roman, Iran R. Dept. of Art & Humanities UAM Estado de México Lerma Mexico Signals Media & Telecom. Lab UFRJ Rio de Janeiro Brazil Discovery Partners Institute UIC Chicago United States School of EECS QMUL London United Kingdom

ISBN: (纸本)9798350368741

Guitar-related machine listening research involves tasks like timbre transfer, performance generation, and automatic transcription. However, small datasets often limit model robustness due to insufficient acoustic diversity and musical content. To address these issues, we introduce Guitar-TECHS, a comprehensive dataset featuring a variety of guitar techniques, musical excerpts, chords, and scales. These elements are performed by diverse musicians across various recording settings. Guitar-TECHS incorporates recordings from two stereo microphones: an egocentric microphone positioned on the performer's head and an exocentric microphone placed in front of the performer. It also includes direct input recordings and microphoned amplifier outputs, offering a wide spectrum of audio inputs and recording qualities. All signals and MIDI labels are properly synchronized. Its multi-perspective and multi-modal content makes Guitar-TECHS a valuable resource for advancing data-driven guitar research, and to develop robust guitar listening algorithms. We provide empirical data to demonstrate the dataset's effectiveness in training robust models for Guitar Tablature Transcription. © 2025 IEEE.

关键词： Automatic Guitar Transcription Egocentric & Exocentric Audio Guitar signal processing Guitar Techniques Multimodal Audio datasets Music Information Retrieval

来源：评论

学校读者我要写书评

暂无评论

A study of image recognition intelligent small car system 16

A study of image recognition intelligent small car system

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Zhang, Zhixi Wang, Zhilei Advanced Perception and Intelligent Equipment Engineering Research Center of Jiangsu Province Suzhou City University Jiangsu China Yi Jin Luo power supply branch of Ordos power supply company Inner Mongolia Ordos China

ISBN: (纸本)9781510689251

With the rapid development of modern science and technology, control technology and theory have made great progress, the development of industrial automation is more and more rapid, the degree of automation is also higher and higher, and the scope of use has also been greatly expanded. Under the influence of the rapidly developing microcontroller technology, the technology covers many disciplines including sensor technology. This design is under the background of hardware selection and software design to realize the image recognition intelligent car system. The intelligent car system designed by the design can realize a variety of functions, including image recognition, Bluetooth remote control, voice broadcast, ultrasonic obstacle avoidance, speech recognition, autonomous tracking, gesture recognition, ultrasonic speed measurement, OLED screen display and other functions. The design of the camera on the OpenMV module are used to collect images, and identify and process them. If the target image is identified, send data to the microcontroller through the established serial port communication protocol, and the microcontroller performs the next *** car has added a LD3320 voice recognition module, which can distinguish the sound of people, and can control the car driving through the sound. The car can be remotely controlled through the HC-05 Bluetooth module, and each module can be selected through the *** the ATK-PAJ7620 gesture recognition module, the car can recognize our gestures and realize the corresponding functions. The TCRT5000L tracking sensor can detect the black track on the ground, and the ultrasonic HC-SR 04 sensor is added in the front position for distance detection. The module transmits the collected relevant data to the main control, so as to realize the autonomous tracking of the car and the function of of avoiding obstacles. Speed measurement can also be performed by the ultrasonic module, and the speed results can be display

关键词： Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

Deep Support Vein Machine for Lung Parcellation

Deep Support Vein Machine for Lung Parcellation

引用

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

作者： Peng, Haichao Fang, Hao Fan, Wenkang Wang, Yong Ke, Sunkui Luo, Jie Luo, Xiongbiao Department of Computer Science and Technology Xiamen University Xiamen361102 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China Zhongshan Hospital Xiamen University Xiamen361004 China Massachusetts General Hospital Harvard Medical School Boston02114 United States

ISBN: (纸本)9798350368741

Pulmonary segments parcellation is essential to thoracoscopic segmentectomy. Surgeons manually outline pulmonary segments from preoperative images before surgery, which is a time-consuming, labor-intensive and mental-stress procedure. This work proposes a novel small learning model of deep support vein machine without using annotated pulmonary segments data for automatic lung parcellation. Specifically, this machine can learn anatomical structures of pulmonary lobe, bronchus, artery, and vein by two cascade multilayer perceptrons to automatically divides the lung into eighteen segments. The perceptron module typically smooths the boundary of the pulmonary segment to attain robust and precise parcellation. Additionally, three new metrics are defined to quantitatively evaluate the quality of lung parcellation. We validate our methods on 108 clinical pulmonary computed tomography scans, with the experimental results showing that our proposed machine certainly outperforms current methods and provides a promising way to fully automated lung parcellation. Particularly, the dice similarity coefficient of lung parcellation was significantly improved from 0.886 to 0.918. © 2025 IEEE.

关键词： Lung Segmentectomy Multilayer Perceptron Pulmonary Segments Video-Assisted Thoracoscopic Surgery

来源：评论

学校读者我要写书评

暂无评论

In-Context Multitask Learning for Few-shot Fine-tuning of Large Language Models in Traditional Chinese Medicine Tongue Diagnosis

In-Context Multitask Learning for Few-shot Fine-tuning of La...

引用

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

作者： Fu, Changzeng Fu, Zelin Yan, Shaojun Lyv, Xiaoyong Zhao, Yuliang Northeastern University China Osaka University Japan Hebei Key Laboratory of Marine Perception Network and Data Processing @ NEUQ China

ISBN: (纸本)9798350368741

Tongue diagnosis is integral to Traditional Chinese Medicine (TCM) for evaluating a patient's body constitution. Yet, this field faces challenges such as indirect constitution diagnosis, a dearth of labeled datasets, and the complexities of few-shot learning. Existing studies focus mainly on analyzing tongue color and coating, rather than directly analyzing the body constitution from the patient's tongue. Moreover, the lack of publicly available datasets with constitution labels impedes model development for constitution diagnosis. The resource-intensive process of creating large-scale labeled datasets calls for efficient training methods on small datasets. Addressing these issues, this paper presents an In-Context Multitask Learning approach to improve few-shot fine-tuning accuracy for Large Language Models (LLMs) in constitution diagnosis. We created a dataset for color-coating analysis and a few-shot dataset with constitution labels and a structured prompt-label framework, allowing LLMs to learn from varied datasets. Our method shows improved performance over traditional methods in constitution diagnosis and significantly boosts LLMs' generalization and robustness in TCM's complex diagnostic tasks, providing a viable path for automating tongue diagnosis. © 2025 IEEE.

关键词： Few-shot Fine-Tuning Large Language Models Multitask Learning Tongue Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Driver Fatigue Recognition Based on EEG signal and Semi-supervised Learning 6th

Driver Fatigue Recognition Based on EEG Signal and Semi-supe...

引用

6th IFIP TC 12 International conference on Intelligence Science, ICIS 2024

作者： Chen, Lin Chen, Xiaobo Shandong Technology and Business University Yantai264005 China

ISBN: (纸本)9783031712524

Driving fatigue has become a serious hidden danger to road traffic safety. Drivers in a fatigued state often have problems such as delayed reactions and lack of concentration, which increases the risk of traffic accidents. In fatigued driving, the brain activity of the driver undergoes a series of changes, such as a decrease in the frequency of brain waves and a decrease in the amplitude of electroencephalogram (EEG) signals. Therefore, we propose a novel Semi-supervised Label Propagation with Optimal Graph Learning (SOGL) model that for identifying the fatigue state of drivers. This model uses class information from a small amount of labeled EEG data to assist the learning of unlabeled data and uses soft projection matrix learning to handle non-linear data structures. In addition, we also introduce a partially labeled graph learning method that extracts potential data structure information through graph structure learning techniques to improve the robustness and generalization ability of the model. Experimental results show that the model has good performance on a driving fatigue dataset. © IFIP International Federation for Information processing 2025.

关键词： Semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Practical Radar Sensing Using Two Stage Neural Network for Denoising OTFS signals

Practical Radar Sensing Using Two Stage Neural Network for D...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ashok S Kumar Sheetal Kalyani Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Our objective is to derive the range and velocity of multiple targets from the delay-Doppler domain for radar sensing using orthogonal time frequency space (OTFS) signaling. Noise contamination affects the performance of OTFS signals in real-world environments, making radar sensing challenging. This work introduces a two-stage approach to tackle this issue. In the first stage, we use a generative adversarial network to denoise the corrupted OTFS samples, significantly improving data quality. Following this, the denoised signals are passed to a convolutional neural network model to predict the values of the velocities and ranges of multiple targets. The proposed two-stage approach can predict the range and velocity of multiple targets, even in very low signal-to-noise ratio scenarios, with high accuracy and outperforms existing methods.

关键词： Time-frequency analysis Spaceborne radar Noise reduction Neural networks Predictive models Generative adversarial networks Sensors Convolutional neural networks Speech processing signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Leveraging Deep Learning Technique with Meta-data-Featured Images Captured by Drone for Real-Time Victim Positioning in Flood-Affected Areas 19

Leveraging Deep Learning Technique with Meta-Data-Featured I...

引用

19th International conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Do, Trong Tuan Nguyen, Dinh Duy Ho, Xuan Hai Nguyen, Van Duc School of Electrical and Electronic Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9798331507817

Flooding has long been a major concern for nations frequently affected by natural disasters. The detection and localization of isolated victims for immediate temporary rescue have been a focal point for many researchers aiming to demonstrate the practical value of their solutions. However, accessing flood-affected areas poses significant challenges, as floods severely disrupt local transportation and damage infrastructure, rendering access nearly impossible. In such scenarios, the most feasible rescue approach is aerial intervention using Unmanned Aerial Vehicles (UAVs). Nevertheless, accurately detecting and localizing targets from a UAV’s perspective is a significant challenge. UAVs operate at various altitudes and speeds, resulting in motion blur of targets against densely populated backgrounds. Additionally, target localization is made difficult due to the limited contextual understanding of the surroundings. To address these challenges, we propose training the YOLOv10 model to enhance the detection of small objects, integrated with a GPS-based target localization system. Furthermore, to enable the practical, real-world application of this solution, we introduce a communication infrastructure that facilitates the timely and effective deployment of both the detection and localization models. Field experiments have yielded promising results, with the artificial intelligence model achieving an Average Precision (AP) between 85-95%, and the localization model processing targets at an impressive speed of under 1 ms. The combined detection and localization approach demonstrated a positioning error of only 7-9 meters. Moreover, integrating the AI model and the localization algorithm into a low-latency communication system has shown promising results, providing near-instantaneous outputs with delays ranging from 0.6 to 0.9 seconds from the moment a target enters the frame. © 2025 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced fMRI Visual Decoding

TROI: Cross-Subject Pretraining with Sparse Voxel Selection ...

引用

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

作者： Wang, Ziyu Pan, Tengyu Li, Zhenyu Wu, Ji Li, Xiuxing Wang, Jianyong Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350368741

fMRI (functional Magnetic Resonance Imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding performance. Additionally, the lack of automated ROI labeling methods hinders the practical application of fMRI visual decoding technology, especially for new subjects. This work presents TROI (Trainable Region of Interest), a novel two-stage, data-driven ROI labeling method for cross-subject fMRI decoding tasks, particularly when subject samples are limited. TROI leverages labeled ROIs in the dataset to pretrain an image decoding backbone on a cross-subject dataset, enabling efficient optimization of the input layer for new subjects without retraining the entire model from scratch. In the first stage, we introduce a voxel selection method that combines sparse mask training and low-pass filtering to quickly generate the voxel mask and determine input layer dimensions. In the second stage, we apply a learning rate rewinding strategy to fine-tune the input layer for downstream tasks. Experimental results on the same small sample dataset as the baseline method for brain visual retrieval and reconstruction tasks show that our voxel selection method surpasses the state-of-the-art method MindEye2 with an annotated ROI mask. © 2025 IEEE.

关键词： Computational neuroscience multi-modal learning region of interest analysis small sample learning

来源：评论

学校读者我要写书评

暂无评论

Exploring Prediction targets in Masked Pre-Training for Speech Foundation Models

Exploring Prediction Targets in Masked Pre-Training for Spee...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Li-Wei Chen Takuya Higuchi He Bai Ahmed Hussen Abdelaziz Shinji Watanabe Alexander Rudnicky Tatiana Likhomanenko Barry-John Theobald Zakaria Aldeneh Carnegie Mellon University Apple

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech data and then used for a range of downstream tasks. These models use a masked prediction objective, where the model learns to predict information about masked input segments from the unmasked context. The choice of prediction targets in this framework impacts their performance on downstream tasks. For instance, models pre-trained with targets that capture prosody learn representations suited for speaker-related tasks, while those pre-trained with targets that capture phonetics learn representations suited for content-related tasks. Moreover, prediction targets can differ in the level of detail they capture. Models pre-trained with targets that encode fine-grained acoustic features perform better on tasks like denoising, while those pre-trained with targets focused on higher-level abstractions are more effective for content-related tasks. Despite the importance of prediction targets, the design choices that affect them have not been thoroughly studied. This work explores the design choices and their impact on downstream task performance. Our results indicate that the commonly used design choices for HuBERT can be suboptimal. We propose approaches to create more informative prediction targets and demonstrate their effectiveness through improvements across various downstream tasks.

关键词： Foundation models Noise reduction Predictive models Phonetics signal processing Acoustics Speech processing Context modeling

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500页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：