检索结果-内蒙古大学图书馆

2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Wang, Chuangchuang Jia, Zhenhong Huang, Xiaohui Song, Sensen Wang, Jiajia Zhou, Gang Shi, Fei Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (纸本)9798350390254

Video captured by surveillance equipment will jitter due to the shaking of the equipment, this jitter will affect the detection results of moving target detection algorithms that rely on stable video frames. This paper proposes an improved ORB algorithm to solve the video jitter problem, so that the moving target detection algorithm can accurately detect moving targets in jittery videos. First, wavelet transform is used to locate the high-frequency area of the image, and then feature extraction is performed on the area, which improves the efficiency of ORB feature extraction. And use Boosted Efficient Binary Local Image Descriptor (BEBLID) to replace the descriptors of directional FAST and rotation BRIEF (ORB) to improve matching accuracy. In the feature matching stage, a neighborhood query method is proposed to replace the global search of the traditional BFMatcher, which improves the matching speed. Finally, Progressive Sample Consistency (PROSAC) is employed to ensure accurate matching of point pairs, resulting in a motion matrix for video stabilization. Finally, a Gaussian mixture model with adaptive distribution numbers is combined to quickly detect moving targets. Comparative experiments with scale-invariant feature transform(SIFT), Accelerated-KAZE(AKAZE), ORB, Qtree _ ORB and SIRB prove the superior accuracy and speed of this algorithm. © 2024 IEEE.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Improved Self-Consistency Training with Selective Feature Fusion for Sound Event detection 6

Improved Self-Consistency Training with Selective Feature Fu...

引用

6th International Conference on Information Communication and signal Processing, ICICSP 2023

作者： Wang, Mingyu Li, Yunlong Hu, Ying Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (纸本)9798350339994

Sound event detection (SED) is a joint task of identifying the categories and time boundaries of sound events within an audio clip. In this paper, we propose an improved self-consistency training (ISCT) strategy for semi-supervised SED based on Mean Teacher (MT) method. For teacher and student models, each adopts two branches with the same CRNN structure, the two branches help training the model by means of consistency regularization. ISCT strategy incorporates self-consistency loss on the basis of MT loss to improve the generalization performance of the model. A selective feature fusion (SFF) module is designed for applying in the shallow layers of the feature extraction part to selectively fuse the features with different scales. A parallel attention (PA) module is designed for applying in the deep layers of the feature extraction part to obtain much richer high-level features by the channel and spatial-wise attention. Ablation experiments verify the effectiveness of our proposed ISCT strategy, SFF and PA modules. In addition, compared with four methods, our proposed method achieves competitive performance on the DCASE 2020 task4 dataset. © 2023 IEEE.

关键词： attention selective feature fusion self-consistency training sound event detection

来源：评论

学校读者我要写书评

暂无评论

Online radar screening pulse width allocation Strategy Based on Non-stationary Bandit

引用

Digital signal Processing 2025年

作者： Hongyu Zhu Yefei Wang Rong Xie Zheng Liu Shuwen Xu Lei Ran National Key Laboratory of Radar Signal Processing Xidian University Xi’an 710071 China Multisensor Intelligent Detection and Recognition Technologies R&D Center of CASC Chengdu 610100 China

With the development of jamming technologies, jammers can respond to radar signals within a short period, causing radar frequency-hopping strategies to fail. In the face of jammers with instantaneous frequency measurements (IFM) capabilities, radio frequency (RF) screening techniques are an effective countermeasure. However, in practice, the jamming strategy is usually unknown to the radar. Moreover, when the jammer's strategy changes, failing to promptly adjust the shielding pulse parameters will result in a significant degradation of the RF screening anti-jamming effectiveness. To overcome the limitations caused by the lack of information about the jammer and the non-stationary environment due to changes in the jammer's IFM time, this paper proposes an online radar screening pulse width allocation method based on a non-stationary multi-armed bandit (MAB). This method combines discounted historical rewards and sliding window rewards, allowing it to better adapt to the non-stationary jamming environment. Simulation results show that the proposed method has a faster convergence speed than the discounted method and a stronger exploration capability than the sliding window method. It can effectively enhance radar performance in countering instantaneous frequency measuring jammers in non-stationary jamming environments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Localized Target detection Algorithms in Low Illumination Wide-Field of View High-Resolution Environments 24

Localized Target Detection Algorithms in Low Illumination Wi...

引用

4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Xie, Chengyou Huang, Xiaohui Jia, Zhenhong Song, Sensen Wang, Jiajia Zhou, Gang Shi, Fei Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang China

ISBN: (纸本)9798400710247

To solve the problem of difficult detection of small targets in wide-field videos captured by eagle-eye equipment in low-illumination environments. we propose a local moving target detection algorithm based on sample consistency. First, the initial background model is established using the first ten frames of the video. The contrast and texture features between the current and background frames are calculated, and the S component in the HSV space is extracted and fused to obtain the final features. Finally, The target area is located through the global segmentation map and the local tracking detection module is activated. Experimental results demonstrate that Compared to other algorithms, the proposed algorithm reduces the running time by implementing local detection. It effectively minimizes the interference caused by light sources and dark spot noise in low-light environments, thereby enhancing the accuracy of the algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Residual Graph Convolution Collaborative Filtering with Asymmetric neighborhood aggregation

引用

Neural Computing and Applications 2024年第22期36卷 13989-14003页

作者： Wang, Tao Qin, Jiwei Ma, Chao School of Computer Science and Technology Xinjiang University Urumqi830046 China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830046 China

Due to the superior performance of graph convolutional networks (GCNs) in feature extraction and representation, researchers have introduced GCNs to collaborative filtering (CF) to improve the accuracy of recommendations. However, existing GCN-based CF models still have three shortcomings: degrading the model performance due to symmetric normalization in neighborhood aggregation, underutilizing the node (user or item) representations at lower layers, and failing to alleviate the over-smoothing effect in graph convolution operations. We present a Residual Graph Convolution Collaborative Filtering model with Asymmetric neighborhood aggregation (ARGCCF) to tackle the above shortcomings. Firstly, ARGCCF utilizes asymmetric normalization in neighborhood aggregation, enabling better preservation of high-degree node information for more precisely learning node representations. Secondly, the propagation of ARGCCF embeddings proceeds in two steps. During the initial propagation, a residual connection is established between the node representation of the current layer and the previous layer to utilize the low-level layer’s node representation fully. The embeddings generated after the initial propagation are propagated again in the same graph convolutional layer, which obtains more refined embedding representations to alleviate the over-smoothing problem. Finally, experimentations on five truthful datasets exhibit that ARGCCF has better-improved performance over several mainstream methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

AMIMC: Based on Improved Meta-Learning Contrast Network with Attention Mechanism for Few Shot Text Classification 3

AMIMC: Based on Improved Meta-Learning Contrast Network with...

引用

3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Li, Senyan Dong, Ye Qin, Xizhong Xinjiang University Xinjiang Signal Detection and Processing Key Laboratory Urumqi830049 China

ISBN: (纸本)9798331506582

Currently, meta-learning is the mainstream approach to solving the problem of scarce data in few-shot text classification. Still, challenges remain, such as embedding vectors not being compact enough, suboptimal meta-task sampling strategies, and the rigidity of convolution operations in traditional Text CNNs. To address this, we propose the Attention Mechanism-based Improved Meta-learning Contrast Network (AMIMC), which enhances intra-class aggregation, increases inter-class separation and improves embedding quality. Additionally, Double Dynamic Similar Sampling (DDSS) generates more challenging meta-tasks, and the attention mechanism enhances the flexibility of Text CNNs, significantly boosting accuracy on five few-shot text classification datasets. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Design and Application of Pocket Experiment System Based on STM32F4 8

Design and Application of Pocket Experiment System Based on ...

引用

8th International Conference on Information Technology: IoT and Smart City, ICIT 2020

作者： Zhou, Yanyun Shi, Fei Chen, Juan School of Information Science and Engineering Xinjiang University and Key Laboratory of Signal Detection and Processing China

ISBN: (纸本)9781450388559

Aiming at the disadvantages of embedded system in practical teaching, a pocket experiment system based on STM32F4 is developed. The system includes input unit, output unit, logic control unit, power supply unit, peripheral unit and data transmission unit. It has the characteristics of rich resources, low cost, and easy to carry, which meets the needs of students' mobility experiments and extra-curricular innovation practice training. Practical application shows that the system not onlystimulates students' enthusiasm for learning, but also trains their creative ability and practical application ability. © 2020 ACM.

关键词： Computer programming

来源：评论

学校读者我要写书评

暂无评论

The detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

引用

Computers, Materials & Continua 2023年第12期77卷 4073-4087页

作者： Xuanchen Zhou Wenzhong Yang Liejun Wang Fuyuan Wei KeZiErBieKe HaiLaTi Yuanyuan Liao College of Software Xinjiang UniversityUrumqi830000China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumqi830000China Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Urumqi830000China

With the increasing popularity of Ethereum,smart contracts have become a prime target for fraudulent activities such as Ponzi,honeypot,gambling,and phishing *** some researchers have studied intelligent fraud detection,most research has focused on identifying Ponzi contracts,with little attention given to detecting and preventing gambling or phishing *** are three main issues with current ***,there exists a severe data imbalance between fraudulent and non-fraudulent ***,the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent ***,most prior studies have used contract source code as raw features,but many smart contracts only exist in *** address these issues,we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet(ECA-EfficientNet)and data *** method begins by converting bytecode into Red Green Blue(RGB)three-channel images and then applying channel exchange data *** then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB *** proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi,honeypot,gambling,and phishing smart *** results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract *** research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.

关键词： Fraud detection smart contract ECA-EfficientNet Ethereum

来源：评论

学校读者我要写书评

暂无评论

End-To-End Residual Stage Network for COVID-19 detection 4

End-To-End Residual Stage Network for COVID-19 Detection

引用

4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Chen, Hongwei Huang, Zhihua Lu, Kexin Liu, Hanzheng Xinjiang University Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumchi China

ISBN: (纸本)9798350324303

Currently, the COVID-19 virus is in a low-level epidemic state but still spreading in society. To timely cut off the transmission chain, early screening for COVID-19 is crucial. However, adding extra network modules during training may lead to module stacking errors and increased complexity. In this paper, we propose an optimization method based on residual stage networks (ResStage) and polarized self-Attention mechanism to improve the detection ability of the model. This method provides better paths for information propagation across different layers of the network and extraction more refined and high-quality features. Results show that the fusion model based on ResStage and polarized self-Attention can improve the detection accuracy of COVID-19 without increasing excessive parameters, achieving an AUC score of 86.53%. Moreover, this paper also subdivides the training tasks, including COVID-19 positive patients, COVID-19 positive patients with coughing, healthy individuals with coughing, asthma patients with coughing, and healthy asymptomatic individuals. These different tasks help to evaluate the performance of model and provide more references for practical applications. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

A Bit-Filp Joint Algorithm of Polar-SCMA Systems for mMTC

A Bit-Filp Joint Algorithm of Polar-SCMA Systems for mMTC

引用

2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Qiao, Wei Ge, Wenping Zhang, Shiwei Sun, Yin Xinjiang University Key Laboratory of Signal Detection and Processing College of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350331790

The conflict between limited spectrum resources and the need for massive machine-type communications (mMTC) is becoming more significant. To address these challenges, Sparse Code Multiple Access (SCMA) has emerged as a promising solution for mMTC in 5G. SCMA allows many users to share time and frequency resources, increasing spectral efficiency and receiving increased attention in the mMTC domain. As a control channel coding scheme in 5G communication systems, polar codes (PC) offer dependable data transmission capabilities. This paper presents a joint algorithm for an uplink PC-SCMA receiver that yields a reasonable block error rate (BLER) performance and an overload rate of 150% for mMTC. Generally, the previous error bit leads to much decoding error propagation in the following information bits for the iterative algorithm. We propose a bit-flip-assisted joint iterative detection decoding (BF-JIDD) algorithm for the error propagation problem in JIDD. Based on the simulation results, it can be concluded that the BF-JIDD algorithm outperforms the JIDD algorithm in terms of BLER performance. © 2023 IEEE.

关键词： Spectrum efficiency

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：