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

作者： Ahat, Arzigul Firkat, Eksan Mijit, Tayir Hamdulla, Askar Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350324303

Mapping off-road terrain is a challenging task, especially when compared to urban terrain. The complexity and roughness of off-road terrain make it difficult to achieve accurate mapping results. Obstacle detection in such environments is also a challenging task, as the performance of LiDAR-based methods depends on the point cloud density at the location of the obstacle. High line beam LiDAR is expensive, while low line beam LiDAR is sparse, making it difficult to obtain dense point clouds. In this paper, we propose a multi-LiDAR fusion approach that uses roof LiDAR as the primary sensor and front LiDAR as a supplement. The approach employs point cloud filtering, dedistortion, and point cloud registration to fuse data from multiple sensors. This results in a richer view of the environment and more intensive measurement data. We combine the voxel map with a twodimensional grid map and accelerate the voxel map using a GPU to generate accurate maps. Experimental results show that our proposed method achieves the highest accuracy and recall rates, improving the accuracy and robustness of obstacle detection in off-road environments. © 2023 IEEE.

关键词： Optical radar

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COT2DNet: Contextual 2D Attention for Scene Text Recognition 2

COT2DNet: Contextual 2D Attention for Scene Text Recognition

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2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Qian, Yefei Ibrayim, Mayire Hamdulla, Askar Zhang, Chunhu Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Computer Science and Technology Xinjiang University Xinjiang China

ISBN: (纸本)9798350360363

Although scene text recognition (STR) methods have made great progress, reading the text of irregularly shaped scenes remains a challenge. The current conversion of two-dimensional (2D) images to one-dimensional (1D) feature maps for sequence recognition may lose a large amount of spatial information and cannot recognize text with a large degree of curvature. In this paper, we propose an network infrastructure to recognize texts of arbitrary shapes, named COT2D Attention Text Recognition Network (COT2Net). Firstly, deformable convolution is added to the feature extraction module, which can perform feature extraction according to the shape of the character and expand the receptive field of feature extraction;Finally, in the Transformer encoding stage, the multi-head attention is changed to Contextual 2D Multi-head Attention (COT2D), which can make full use of the relationship between the input keys Q, K, and V to guide the learning of dynamic attention, thereby enhancing the visual expression ability of text images. The COT2DNet uses COT2D to learn the relevance of each character in a scene text image and achieves excellent results on public datasets. © 2023 IEEE.

关键词： Feature extraction

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Network Intrusion Traffic detection Based on Feature Extraction

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Computers, Materials & Continua 2024年第1期78卷 473-492页

作者： Xuecheng Yu Yan Huang Yu Zhang Mingyang Song Zhenhong Jia College of Information Science and Engineering Xinjiang UniversityUrumqi830046China Network Department China Mobile Communications Group Xinjiang CoLtd.UrumqiUrumqi830011China Xinjiang Signal Detection and Processing Key Laboratory Xinjiang UniversityUrumqi830000China

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal ***,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion *** method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after *** network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between *** datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the ***,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic *** empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing *** results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

关键词： Network intrusion traffic detection PCA Hotelling’s T^(2) BiLSTM

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A Lightweight UAV Visual Obstacle Avoidance Algorithm Based on Improved YOLOv8

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Computers, Materials & Continua 2024年第11期81卷 2607-2627页

作者： Zongdong Du Xuefeng Feng Feng Li Qinglong Xian Zhenhong Jia College of Computer Science and Technology Xinjiang UniversityUrumqi830046China Signal Detection and Processing Autonomous Region Key Laboratory Xinjiang UniversityUrumqi830046China Xinjiang Uygur Autonomous Region Research Institute of Measurement and Testing Urumqi830000China

The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and *** propose UAD-YOLOv8,a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle *** algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2(DCNv2)to optimize the cross stage partial bottleneck with 2 convolutions and fusion(C2f)***,it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution(UGDConv)series of lightweight convolutions and a lightweight detection *** on this,we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different *** particular,we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance *** verify the effectiveness of the algorithm,the UAV obstacle detection(UAD)dataset was *** experimental results show that UAD-YOLOv8 improves mAP50 by 3.4%and reduces GFLOPs by 34.5%compared to YOLOv8n while reducing the number of parameters by 77.4%and the model size by 73%.These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments,demonstrating its wide range of applications.

关键词： Unmanned aerial vehicle obstacle detection obstacle avoidance algorithm

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

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

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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

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A Lightweight Music Source Separation Model with Graph Convolution Network 1

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18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Zhu, Mengying Wang, Liusong Hu, Ying School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing in Xinjiang Urumqi China

ISBN: (数字)9789819706013

ISBN: (纸本)9789819706006

With the rapid advancement of deep neural networks, there has been a significant improvement in the performance of music source separation methods. However, most of them primarily focus on improving their separation performance, while ignoring the issue of model size in the real-world environments. For the application in the real-world environments, in this paper, we propose a lightweight network combined with the Graph convolutional network Attention (GCN_A) module for Music Source Separation (G-MSS), which includes an Encoder and four Decoders, each of them outputs a target music source. The G-MSS network adopts both time-domain and frequency-domain L1 losses. The ablation study verifies the effectiveness of our designed GCN Attention (GCN_A) module and multiple Decoders, and also make a visualization analysis of the main components in the G-MSS network. Comparing with the other 13 methods on the MUSDB18 dataset, our proposed G-MSS achieves comparable separation performance while maintaining the lower amount of parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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Enhancing Relational Classification Model Fusing Entity and Sentence Semantic Information 4

Enhancing Relational Classification Model Fusing Entity and ...

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

作者： Wei, Chunji Qin, Xizhong Yuan, Taiping Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi830017 China

ISBN: (纸本)9798350324303

Relationship classification aims at mining the relationship between two entities in a sentence and is an essential basis for the information extraction task. However, traditional relationship classification methods, which only use the information of words in a sequence to predict the relationship between entities, cannot use the semantic information of entities and sentences for relationship classification. To solve these problems, this paper proposes a new model, ES-BERT: this model uses the word-to-word association in the BERT model to obtain the semantic information of entities by the positional encoding of entity pairs in a sentence. In addition, we use the rich semantic knowledge in the internal hidden layer of Bert to obtain the semantic information of the sentence, which can better help the model understand the sentence. Finally, the semantic information of entities and the semantic information of sentences in the hidden layer of BERT are fused, and the fused information is used for relationship classification. The experimental results show that the model proposed in this paper achieves an excellent F1 value of 88.87% on the SemEva1-2010 Task 8 dataset, which is better than other Bert-based Sota models. © 2023 IEEE.

关键词： Classification (of information)

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Sound source localization and detection based on parameter transfer learning 24

Sound source localization and detection based on parameter t...

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24th International Congress on Acoustics, ICA 2022

作者： Sun, Xinghao Ma, Mengzhen Hu, Ying Department of Information Science and Engineering Xinjiang University Urumqi830000 China Key Laboratory of Signal Detection and Processing in Xinjiang China

Sound source localization and detection is a joint task of identifying the presence of individual sound events and locating the sound sources in space. In order to promote the combination of two different tasks, we propose a sound source localization and detection method based on parameter transfer learning. Firstly, to solve the problem of uneven distribution of time-frequency dimension features, we propose a time-frequency dimension feature extractor, which extracts the features of time dimension and frequency dimension respectively and superimposes them together. Secondly, in order to promote feature interaction between different resolutions, we propose a refined feature learning module that can fuse features with different resolutions. Then, in order to reduce the training and reasoning time, we explore a temporal context representation module for learning temporal context information. The time context indicates that the module has better global feature capture capability and better parallelism capability compared with the recurrent neural network and gated recurrent unit. Finally, the rationality of our proposed model was verified by ablation experiments, and the effectiveness of our proposed model was verified by comparison with the best methods. © 2022 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Recurrent neural networks

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Improving Speech Perceptual Quality and Intelligibility Through Sub-band Temporal Envelope Characteristics 18th

Improving Speech Perceptual Quality and Intelligibility Thr...

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18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Wu, Ruilin Huang, Zhihua Song, Jingyi Liang, Xiaoming School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing in Xinjiang Urumqi China

ISBN: (纸本)9789819706006

In the speech enhancement (SE) model, using auxiliary loss based on acoustic parameters can improve enhancement effects. However, currently used acoustic parameters focus on frequency domain information, neglecting the importance of time domain information. This study explores the effects of training the SE model utilizing temporal envelope parameters as auxiliary losses on perceptual quality and intelligibility, primarily focusing on accurately extracting temporal envelope parameters and optimizing losses. Initially, the effectiveness and robustness of the temporal envelope parameters in optimizing the SE model were verified. Subsequently, a temporal envelope loss based on sub-band weighted (ENVLoss) is proposed based on the perceptual characteristics of human hearing. Then, a method for constructing a joint loss function is proposed, integrating temporal and spectral acoustic features to promote the enhancement effect. Experimental results show that temporal envelope characteristics improve both the time domain and the time-frequency domain SE models. Compared to other acoustic parameter losses, the SE model using a sub-band temporal envelope auxiliary loss shows improvement in the PESQ, STOI, and MOS estimation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Speech intelligibility

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MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screening Attention Network for Urban Carbon Emission Prediction

MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screenin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Ben Qin, Xizhong Qin, Jiwei Zhang, Xiaoyu Ma, Haodong College of Computer Science and Technology Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing Ürümqi830046 China

ISBN: (纸本)9781665410205

In order to cope with the increasingly severe global energy conservation and emission reduction problems, research on urban carbon emission prediction is of great significance. The existing methods mainly use time series analysis to predict urban carbon emission, but there is a strong spatial correlation between the carbon emission data of several cities. Therefore, this paper designs a multi-scale spatial-temporal feature screening attention network to predict target cities' future carbon emission data. Firstly, this paper combines the daily carbon emission data of the near-neighbouring cities and the daily homologous emission data of the target city to analyze the urban carbon emission data from a spatio-temporal perspective. Then, this paper designs a multi-scale spatial interactive convolution module and a multi-scale temporal convolution module to extract multi-scale spatio-temporal features effectively. In addition, the feature screening module is designed to reduce the adverse effects of redundant features. Finally, the multi-scale features are used to predict the future carbon emissions of the target city through a predictor. The experimental results show that our prediction model is superior to the existing methods in six datasets. © 2024 IEEE.

关键词： Low emission

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