检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Huang, Yuhao Chen, Chen Chang, Chenjie Cheng, Zhiyuan Liu, Yang Chen, Cheng Lv, Xiaoyi College of Software Xinjiang University Xinjiang Urumqi830046 China College of Information Science and Engineering Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang Cloud Computing Engineering Technology Research Center Karamay834000 China

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for Systemic Lupus Erythematosus (SLE), the relatively weak signals from a single Raman spectroscopic data make it difficult to obtain desirable classification results. Although the surface enhancement technique can enhance the scattered signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the non-uniform distribution of hotspots degrade some of the signals. To fully utilize both types of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique that allows the original spectra to complement the degraded portion, improving the model's classification accuracy. The residual module is used to extract features, which preserves the original features while extracting the deep ones. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight assignment of the two modal features more efficiently. The experimental results demonstrated that both the low-level fusion method and the intermediate-level fusion method significantly improved the diagnostic accuracy of SLE disease classification when compared with a single modality, and the intermediate-level fusion DBRAN achieved 100% classification accuracy, sensitivity, and specificity. Compared with the raw Raman spectrum single mode and SERS single mode, the improvement is 10% and 7%, respectively. This experiment realized the rapid diagnosis of SLE disease by fusing the spectral data of multiple modes, which provides a reference idea in the Raman spectroscopy field. © 2023, The Authors. All rights reserved.

关键词： Raman spectroscopy

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LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Verification

LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Ver...

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International Symposium on Chinese Spoken Language processing

作者： Shuanghong Liu Zhida Song Zhihua Fang Liang He School of Computer Science and Technology Xinjiang University Urumqi Xinjiang Key Laboratory of Signal Detection and Processing Urumqi School of Intelligence Science and Technology Xinjiang University Urumqi Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Due to its superior performance and fewer parameters, CAM++ has become the state-of-the-art model for speaker verification tasks. This model uses 2D convolutional blocks to extract front-end features, which are then fed into a densely connected time-delay neural network backbone to extract deep features. However, the simple stacking of 2D convolutions may lead to the generation of a significant amount of redundant features, which is detrimental to efficient feature extraction. Furthermore, although CAM++ already has a relatively small number of parameters, there is still room for further optimization. To address these issues, this paper first employs depthwise separable convolutions to replace the dilated convolutions in the back-end network of CAM++, making the model more lightweight. Next, we introduce spatial and channel reconstruction convolution (SCConv) in the ResBlock module of CAM++ to reduce redundant features and optimize the feature extraction process. Finally, after SCConv, we apply squeeze and excitation attention mechanism to model the interdependencies between channels and recalibrate each channel, further enhancing the model's representational capacity. We name the resulting model LE-CAM++. Our proposed model achieves an EER of 0.686 and a minDCF of 0.084 on the VoxCeleb1–O dataset. Compared to the baseline model CAM ++, the EER is reduced by 11%, and the minDCF is reduced by 28%. Additionally, the model parameters are reduced by 8%.

关键词： Attention mechanisms Stacking Neural networks Feature extraction Optimization

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Simplified Skip-Connected UNet for Robust Speaker Verification Under Noisy Environments

Simplified Skip-Connected UNet for Robust Speaker Verificati...

引用

International Symposium on Chinese Spoken Language processing

作者： Zonghui Wang Zhihua Fang Zhida Song Liang He School of Computer Science and Technology Xinjiang University Urumqi Xinjiang Key Laboratory of Signal Detection and Processing Urumqi School of Intelligence Science and Technology Xinjiang University Urumqi Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

In recent years, deep neural network based methods for speaker verification have made remarkable progress in clean environments. However, background noise significantly reduces the accuracy and reliability of speaker verification systems by masking or changing the voice characteristics of the speaker. In this paper, we propose a cascaded framework optimized with multiobjective loss to mitigate the interference of different levels and types of noise on the speaker verification task. The proposed architecture consists of two components: a speech enhancement module based on improved 2D-UNet, which reduces the structural limitations of directly using classical UNet for noise reduction, and a back-end speaker embedding extraction module. We carry our experiments on the VoxCeleb1 and VOiCES datasets, as well as in the presence of out-of-domain noise conditions. The evaluations have demonstrated this method shows great potential for speaker verification in noisy environments.

关键词： Training Semantics Noise reduction Interference Speech enhancement Complexity theory Noise measurement Reliability Optimization signal to noise ratio

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STransFuse: Fusing swin transformer and convolutional neural network for remote sensing image semantic segmentation

TechRxiv

引用

TechRxiv 2021年

作者： Gao, Liang Liu, Hui Yang, Minhang Chen, Long Wan, Yaling Qian, Yurong Xiao, Zhengqing College of Software Xinjiang University Urumqi830008 China Key Laboratory of signal detection and processing in Xinjiang Uygur Autonomous Region Urumqi830014 China the Key Laboratory of Software Engineering Urumqi830008 China College of Information Science and Engineering Xinjiang University Urumqi830014 China College of Mathematics and Systems Science Xinjiang University Urumqi830014 China

The convolutional neural network (CNN) bolstered the application study of remote sensing images. However, the fixed size of the receptive field limits CNN’s ability in semantic modelling. Although the Transformer model based on self-attention may model global semantic information, it remains a challenge to work effectively in the remote sensing field with limited data. Leveraging the benefits of both Transformer and CNN, a new model is proposed in this article as a semantic segmentation method for remote sensing images which is constructed by fusing Swin Transformer and CNN and is thus named as STransFuse model. On the one hand, the STransFuse model makes use of the capability of the transformer network to model global semantic information in remote sensing images. On the other hand, it utilized the capability of the CNN with pre-training weights to solve the problem of low performance of the Transformer network when the amount of remote sensing image data is small and to provide the contextual location information of the image. An Adaptive Fusion Module (AFM) is created to adaptively fuse the feature maps produced by the Transformer network and CNN to improve the model’s feature representation capacity. The OA (Overall Accuracy) of the STransFuse model is 1.36% higher than the baseline on the Vaihingen dataset, and 1.27% higher than baseline on the Potsdam dataset. When compared with other state-of-the-art models, the STransFuse model performed competitively. © 2021, CC BY.

关键词： Semantic Segmentation

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Performance Analysis of Heterogeneous Optical Beams based MIMO Visible Light Communication System

Performance Analysis of Heterogeneous Optical Beams based MI...

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International Conference on Computer and Communications (ICCC)

作者： Jupeng Ding Chih-Lin I Lili Wang Kai Zhao Jiong Zheng Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region School of Information Science and Engineering Xinjiang University Uygur China China Mobile Research Institute Beijing China School of Information and Electrical Engineering Ludong University Yantai China

ISBN: (纸本)9781665409513

To a large extent, the multiple input multiple output (MIMO) visible light communication (VLC) is still restricted in homogeneous Lambertian research paradigm. In this work, the heterogeneous optical beams configuration is proposed for the MIMO VLC system. Specifically, two typical non-Lambertian optical beams i.e. LUXEON Rebel UB and LB patterns are introduced to heterogeneous MIMO beams configurations. In typical indoor scenario, the channel capacity of the proposed heterogeneous MIMO VLC system is evaluated and compared with the counterpart of the homogeneous Lambertian MIMO VLC system. Numerical results illustrate that the LUXEON Rebel UB based heterogeneous configuration could provide more than 9.7% average MIMO VLC capacity gain. Respectively, the capacity dynamic range is elevated to about 74.0-156.2 Mbps from the original about 75.9-116.2 Mbps of homogeneous configuration.

关键词： Integrated optics Graphical models Channel capacity Dynamic range Performance analysis Optical beams MIMO communication

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A High Isolation Design Used Between Broadband Dual Polarization Transceiver Antennas

A High Isolation Design Used Between Broadband Dual Polariza...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yuke Guo Xiaosheng Zhang Luyu Zhao National Key Laboratory of Radar Detection and Sensing Xidian University Xi'an China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

We proposed a high isolation design between two broadband dual polarization transceiver antennas in this paper. It employs periodic metal isolation walls with equal spacing and height to strongly suppress the low-frequency band (LB) coupling. Covering the vertical and horizontal surfaces of the metal with flexible graphene high resistive metasurfaces that easily conform to the structure improves 40 dB in isolation between different polarizations at 6–18 GHz. This device has less impact on the radiation pattern of the antenna. This design not only enhances the isolation between different polarizations but also optimizes equipment heat dissipation and improves device performance.

关键词： Performance evaluation Heating systems Couplings Graphene Metals Metasurfaces Transceivers Broadband communication Broadband antennas Antenna radiation patterns

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Speech Topic Classification Based on Pre-trained and Graph Networks

Speech Topic Classification Based on Pre-trained and Graph N...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Fangjing Niu Tengfei Cao Ying Hu Hao Huang Liang He School of Information Science and Engineering Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Beijing China

Speech Topic Classification (STC) automatically classifies audio clips into predefined categories, which is widely used in short video, personalized recommendation and other fields. At present, the common system is composed of two parts: first, the speech is converted into text by automatic speech recognition (ASR), and then the text topic is classified by natural language processing (NLP). Most of them have problems such as error propagation and lack of global structure. So in this paper, we propose a new end-to-end framework based on a pre-trained model and graph network. The pre-trained model is used to extract the semantic features with sequential structure instead of acoustic features, and the combination with the global features of conversational context constructed by graph network has achieved good results on the Fisher dataset.

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MFUnetr: A Transformer-Based Multi-Task Learning Network for Multi-Organ Segmentation from Partially Labeled Datasets

SSRN

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SSRN 2022年

作者： Hao, Qin Tian, Shengwei Yu, Long Wang, Junwen College of Software Xinjiang University Urumqi830000 China Key Laboratory of Software Engineering Technology Xinjiang University Urumqi830000 China College of Network Center Xinjiang University Urumqi830000 China Signal and Signal Processing Laboratory College of Information Science and Engineering Xinjiang University Urumqi830000 China

As multi-organ segmentation of CT images is crucial for clinical applications, most state-of-the-art models rely on a fully annotated dataset with strong supervision to pursue higher accuracy. However, these models have weak generalization when applied to various CT images due to the small scale and single source of training data. To utilize existing partially labeled datasets to obtain full organ segmentation and improve accuracy and robustness, we create a transformer-based multi-task learning network called MFUnetr. By directly feeding a union of datasets, MFUnetr trains an encoder-decoder network on two tasks in parallel. The main task is to produce full organ segmentation by using a particular training strategy. The auxiliary task is to segment organs of each dataset by using labels prior. Additionally, we offer a new weighted combined loss function to optimize the model. Compared to the base model trained on the fully annotated dataset BTCV, our network model, trained on a combination of three datasets, achieved mean Dice on overlapping organs: spleen +2.5%, esophagus +8.9%, and aorta +0.5%. The generalization ability was enhanced, with spleen +4.1%, esophagus +37.4%, and aorta +21.4%. Importantly, without fine-tuning, the mean Dice calculated on 13 organs of BTCV remained +0.6% when all 15 organs were segmented. Experimental results show that our proposed method can effectively use the large existing partially annotated datasets to alleviate the problem of data hunger in multi-organ segmentation. © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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Exploring Turkish Speech Recognition via Hybrid CTC/Attention Architecture and Multi-feature Fusion Network

arXiv

引用

arXiv 2023年

作者： Ren, Zeyu Yolwas, Nurmement Wang, Huiru Slamu, Wushour Xinjiang Multilingual Information Technology Laboratory College of Information Science and Engineering Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection College of Information Science and Engineering Xinjiang University Urumqi China Xinjiang Multilingual Information Technology Research Center College of Information Science and Engineering Xinjiang University Urumqi China

In recent years, End-to-End speech recognition technology based on deep learning has developed rapidly. Due to the lack of Turkish speech data, the performance of Turkish speech recognition system is poor. Firstly, this paper studies a series of speech recognition tuning technologies. The results show that the performance of the model is the best when the data enhancement technology combining speed perturbation with noise addition is adopted and the beam search width is set to 16. Secondly, to maximize the use of effective feature information and improve the accuracy of feature extraction, this paper proposes a new feature extractor LSPC. LSPC and LiGRU network are combined to form a shared encoder structure, and model compression is realized. The results show that the performance of LSPC is better than MSPC and VGGnet when only using Fbank features, and the WER is improved by 1.01% and 2.53% respectively. Finally, based on the above two points, a new multi-feature fusion network is proposed as the main structure of the encoder. The results show that the WER of the proposed feature fusion network based on LSPC is improved by 0.82% and 1.94% again compared with the single feature (Fbank feature and Spectrogram feature) extraction using LSPC. Our model achieves performance comparable to that of advanced End-to-End models. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech recognition

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Robust Training for Speaker Verification against Noisy Labels

arXiv

引用

arXiv 2022年

作者： Fang, Zhihua He, Liang Ma, Hanhan Guo, Xiaochen Li, Lin School of Information Science and Engineering Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Tsinghua University Beijing100084 China School of Electronic Science and Engineering Xiamen University Xiamen361005 China

The deep learning models used for speaker verification rely heavily on large amounts of data and correct labeling. However, noisy (incorrect) labels often occur, which degrades the performance of the system. In this paper, we propose a novel two-stage learning method to filter out noisy labels from speaker datasets. Since a DNN will first fit data with clean labels, we first train the model with all data for several epochs. Then, based on this model, the model predictions are compared with the labels using our proposed the OR-Gate with top-k mechanism to select the data with clean labels and the selected data is used to train the model. This process is iterated until the training is completed. We have demonstrated the effectiveness of this method in filtering noisy labels through extensive experiments and have achieved excellent performance on the VoxCeleb (1 and 2) with different added noise rates. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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