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28th International Conference on Optical Fiber Sensors, OFS 2023

作者： Hu, Zihe Zhao, Can Tang, Ming National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

We propose a phase-sensitive reflectometry using a continuous probe generated by fractional Fourier transform (FrFT) of a direct current signal. High-performance pulse compression is achieved by a single FrFT operatio... 详细信息

ISBN: (纸本)9781957171302

关键词： Reflection

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High Accuracy Curve Reconstruction Based on Twisted Multicore Fiber and Twist Bias Calibration

High Accuracy Curve Reconstruction Based on Twisted Multicor...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Yang, Keyuan Gui, Zhiyuan Ke, Changjian Xu, Zikang Liu, Deming School of Optical and Electronic Information Huazhong University of Science and Technology National Engineering Research Center of Next Generation Internet Access-system Wuhan China

A high accuracy curve reconstruction method based on twisted multicore fiber and twisted bias calibration is proposed. The reconstruction error is optimized from 7.43 mm to 1.41 mm after twist bias calibration. ©... 详细信息

ISBN: (纸本)9798350312614

关键词： Curve reconstruction RMSE twist bias calibration twisted MCF

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Optimal voltage compensation method for focal plane avalanche detector based on improved random forest 2

Optimal voltage compensation method for focal plane avalanch...

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2nd International Conference on Electronic information and Mechanical Manufacturing, EIMM 2022

作者： Chen, Mengqiang Yang, Jiazhi Yu, Guangwang Shen, Jie College of Information Science and Engineering Guilin University of Technology Guangxi Guilin541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guangxi Guilin541006 China

Working distance and background radiation greatly affect the signal-to-noise ratio of avalanche photodiode (APD) in the lidar detection system. The traditional method cannot adapt to a complex environment by offline compensation or pre-compensation according to the influence factors of the external environment. In this paper, an avalanche photodiode voltage compensation method based on the improved random forest is designed. Firstly, the distance image data is de-noised. Then the weight of each decision tree in the random forest was changed to improve the classification performance. The particle swarm optimization (PSO) algorithm was used to search for the optimal combination of parameters affecting classification accuracy and performance. Finally, the improved random forest algorithm is used to judge the current working state of APD at different distances, compensate for the bias voltage, and make APD work in the optimal state. The proposed method is compared with the k-nearest neighbor, support vector machine, and other commonly used classification algorithms, and the results verify the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Particle swarm optimization (PSO)

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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A Lightweight Network for Detecting Small Targets in the Air 6th

A Lightweight Network for Detecting Small Targets in the Air

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6th International Conference on Innovative Computing, IC 2023

作者： Li, Jiaxin Li, Hui Yong, Ting Hou, Xingyu Institute of System Engineering Nanjing China The Institute of North Electronic Equipment Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (纸本)9789819922864

Fast and accurate detection and identification of small airborne targets are of great importance, to security in the air. Unmanned aerial vehicle detection algorithms are mostly deployed on edge devices, and a yolov5-based aerial target lightweight detector is proposed by compressing channels and network cropping for the limited resource characteristics on edge devices. Firstly, the shallow cross-stage partial module is extended and optimized when designing the feature ex-traction network to maximize the use of shallow features. Secondly, the network is cropped to reduce the number of down-sampling, which makes the computation faster. Finally, the pyramid network used for feature fusion is simplified by modifying from two upsampling operations and two downsampling operations to only one upsampling operation. On the homemade dataset, the proposed Yolo-mini achieves 94.44% mean average accuracy on the test set and the Giga floating-point operations per second of the model is only 3.2, which achieves a better balance of accuracy and computation compared to other lightweight algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Inter-Channel Correlation-Based EMI Noise Removal (ICER) for Shielding-Free Low-Field MRI

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IEEE Transactions on Biomedical engineering 2025年第7期72卷 2095-2104页

作者： Huang, Yiman Qu, Shuxian Xie, Yushu Wang, Hanlei Zhang, Xinlin Zhang, Xiaotong Zhejiang University College of Electrical Engineering MOE Frontier Science Center for Brain Science and Brain-machine Integration Hangzhou China Zhejiang University College of Biomedical Engineering & Instrument Science MOE Frontier Science Center for Brain Science and Brain-machine Integration Hangzhou China Fuzhou University College of Physics and Information Engineering Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology Fuzhou China Zhejiang University College of Electrical Engineering the Second Affiliated Hospital of Zhejiang University School of Medicine the State Key Lab of Brain-Machine Intelligence MOE Frontier Science Center for Brain Science and Brain-machine Integration Hangzhou China

Objective: For low-field magnetic resonance imaging (MRI) in unshielded environment, existing methods have been proposed to eliminate electromagnetic interference (EMI) noise in each single radio-frequency (RF) receive coil. In the present study, we propose to use the EMI information from multiple MRI receive coils collectively in EMI denoising. Methods: The proposed method leverages the information of inter-channel correlation, including EMI detectors and RF receive coils to remove EMI noise. Calibration signals from both EMI detectors and RF receivers are concatenated to determine a de-correlation matrix, which is then used to denoise MRI signals. Results: Saline phantom and in vivo experiments demonstrated the efficacy of the proposed method in EMI elimination, showing that the proposed method outperformed advanced EMI elimination methods with up to 16.34% improvement in EMI noise removal percentage (NRP) and 1.58dB improvement in signal-to-noise ratio (SNR), along with reduced computational times. Conclusion: The proposed method effectively removes EMI noise and shows improved performance by using information from all receive coils. Significance: This method allows for the design of multi-receive coils for low-field MRI such as phased-arrays, which have the potential to enhance the performance in noise removal and improve the SNR in MRI signal acquisition. © 2025 IEEE.

关键词： Signal to noise ratio

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Predicting Car Accidents with YOLOv7 Object Detection and Object Relationships

Predicting Car Accidents with YOLOv7 Object Detection and Ob...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Wu, Ming-Xuan Chang, Chi-Sheng Miao, Jr-Ming Lee, Chia-Yen National Pingtung University of Science and Technology Department of Information Management System Pingtung Taiwan National United University Department of Electrical Engineering Miaoli Taiwan National Pingtung University of Science and Technology Department of Biomechatronics Engineering Pingtung Taiwan

ISBN: (纸本)9798350313154

In this paper, we propose a method for predicting potential accidents using object detection and object relationship analysis. We use YOLOv7 object detector to identify various objects on roads and highways, and analyze the relationships between them to predict potential accidents. Our model is trained on a large dataset from Kaggle competitions, which includes driving record videos of different types of vehicles such as buses, cars, trailers, trucks, and lorries. We analyze the patterns of detected accident vehicle objects to determine whether an accident occurred in consecutive frames. Experimental results show that our method can reasonably predict whether an accident will occur in the next 20 frames. The use of object detection allows for the identification of multiple objects, thus improving the accuracy of accident prediction. Although we did not achieve better performance or accuracy, this approach has the potential to improve the safety performance of autonomous vehicles and reduce the occurrence of traffic accidents. © 2023 IEEE.

关键词： Object recognition

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Low-loading gold in situ doped with sulfur by biomolecule-assisted approach for promoted electrochemical carbon dioxide reduction

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Nano Research 2023年第2期16卷 2059-2064页

作者： Meijie Tan Xiaoqian Han Sen Ru Chao Zhang Zhouru Ji Zhaolin Shi Guomeng Qiao Yunying Wang Ruixue Cui Qiquan Luo Jiqing Jiao Yaguang Li Tongbu Lu MOE International Joint Laboratory of Materials Microstructure Institute for New Energy Materials and Low Carbon TechnologiesSchool of Materials Science and EngineeringTianjin University of TechnologyTianjin 300384China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China Haihe Laboratory of Sustainable Chemical Transformations Tianjin 300192China College of Materials Science and Engineering State Key Laboratory of Bio-Fibers and Eco-TextilesQingdao UniversityQingdao 266071China Hebei Key Lab of Optic-electronic Information and Materials College of Physics Science and TechnologyInstitute of Life Science and Green DevelopmentHebei UniversityBaoding 071002China

For electrochemical carbon dioxide reduction(CO_(2)RR),CO_(2)-to-CO conversion is considered an ideal route towards carbon neutrality for practical ***(Au)is known as a promising catalyst with high selectivity for CO;however,it suffers from high cost and low mass-specific *** this study,we design and prepare a catalyst featuring uniform S-doped Au nanoparticles on N-doped carbon support(denoted as S-Au/NC)by an in situ synthesis strategy using *** S-Au/NC displays high activity and selectivity for CO in CO_(2)RR with a Au loading as low as 0.4 wt.%.The Faradaic efficiency of CO(FECO)for S-Au/NC is above 95%at−0.75 V(***);by contrast,the FECO of Au/NC(without S)is only 58%.The Tafel slope is 77.4 mV·dec−1,revealing a favorable kinetics ***,S-Au/NC exhibits an excellent long-term stability for CO_(2)*** functional theory(DFT)calculations reveal that the S dopant can boost the activity by reducing the free energy change of the potential-limiting step(formation of the*COOH intermediate).This work not only demonstrates a model catalyst featuring significantly reduced use of noble metals,but also establishes an in situ synthesis strategy for preparing high-performance catalysts.

关键词： electrochemical CO_(2)reduction nanocomposites heterogeneous catalyst in situ sulfured heteroatoms

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A DVS signal recognition method with high generalization based on FrFT 28

A DVS signal recognition method with high generalization bas...

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28th International Conference on Optical Fiber Sensors, OFS 2023

作者： Ge, Zhao Zhao, Can Wu, Hao Tang, Ming National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

We propose a DVS signal recognition method based on FrFT, which enhances the learning effectiveness of neural network by exploring more feature dimensions of the signal, achieving high recognition accuracy with good g... 详细信息

ISBN: (纸本)9781957171302

关键词： Signal processing

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Features for Action Recognition

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第3期47卷 327-334页

作者： Le, Tri Duc, Nham Huynh Nguyen, Chung Thai Tran, Minh Triet Faculty of Information Technology University of Science VNU-HCM Viet Nam Software Engineering Lab University of Science VNU-HCM Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

The demand for human activity recognition (HAR) from videos has witnessed a significant surge in various real-life applications, including video surveillance, healthcare, elderly care, among others. The explotion of short-form videos on social media platforms has further intensified the interest in this domain. This research endeavors to focus on the problem of HAR in general short videos. In contrast to still images, video clips offer both spatial and temporal information, rendering it challenging to extract complementary information on appearance from still frames and motion between frames. This research makes a two-fold contribution. Firstly, we investigate the use of motion-embedded images in a variant of two-stream Convolutional Neural Network architecture, in which one stream captures motion using combined batches of frames, while another stream employs a normal image classification ConvNet to classify static appearance. Secondly, we create a novel dataset of Southeast Asian Sports short videos that encompasses both videos with and without effects, which is a modern factor that is lacking in all currently available datasets used for benchmarking models. The proposed model is trained and evaluated on two benchmarks: UCF-101 and SEAGS-V1. The results reveal that the proposed model yields competitive performance compared to prior attempts to address the same problem. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Network architecture

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