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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The International Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 IEEE.

关键词： automatic ICD coding cascaded convolutional network multi-label classification

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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video Datasets 9

Temporal Transformation Network Based On Scale Sequences for...

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9th International Conference on computer and Communications, ICCC 2023

作者： Zhu, Xiaoke Zhao, Bo Dong, Zhiwei Dong, Lingyun Li, Danyang School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9798350317251

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline. © 2023 IEEE.

关键词： Video recording

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An Efficient Phase Noise Elimination Method to Facilitate Cost-effective 100/200G Coherent PON

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Journal of Lightwave technology 2025年

作者： Chen, Yifan Wang, Chen Yu, Jianjun Long, Jianyu Sang, Bohan Li, Fan Sun Yat-Sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Guangzhou510275 China Fudan University State Key Laboratory of ASIC and System Shanghai200433 China

In the field of passive optical networks (PONs), coherent sinking has emerged as a significant topic, particularly in the context of cost and complexity improvements. We experimentally demonstrate a cost-effective 100-/200-Gbits/s/λ coherent PON system employing dual-polarization quadrature phase shift keying (DP-QPSK) and 16-ary quadrature amplitude modulation (DP-16QAM). To achieve cost reductions in coherent PON implementations, we employ low-cost distributed feedback lasers (DFBs) as the sole active light sources, replacing the much more expensive external cavity lasers (ECLs) at both the optical line terminal (OLT) and the optical network units (ONUs). To effectively address the performance penalty associated with phase noise (PN) of DFBs characterized by MHz-class linewidths, a straightforward carrier phase recovery (CPR) is proposed via embedding a one-tap carrier phase estimator (CPE) with gradient-descent algorithm into a 4×4 multiple-input multiple-output (MIMO) equalizer following the least-mean square algorithm (LMS). This approach simplifies the structure and enables more precise cancellation of PN. We conduct a transmission performance comparison at 32 Gbaud between our proposed scheme and the blind phase searching algorithm (BPS) for single-carrier (SC) signals, as well as between our method and the frequency-domain pilot-tone carrier phase recovery (FPT-CPR) for two-sub-band digital subcarrier multiplexing (DScM) signals. Experimental results demonstrate the corresponding power budgets of 38.1, 30.8, 36.5, and 29.1 dB for SC-QPSK/16QAM and DScM-QPSK/16QAM at the 20% soft-decision forward error correction (SD-FEC) threshold of 2×10-2. Furthermore, complexity analysis of these phase noise estimation (PNE) methods reveals the superiority of our proposed scheme in balancing performance and computational complexity. © 1983-2012 IEEE.

关键词： Passive optical networks

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SC$^{2}$2-PCR++: Rethinking the Generation and Selection for Efficient and Robust Point Cloud Registration

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 12358-12376页

作者： Zhi Chen Kun Sun Fan Yang Lin Guo Wenbing Tao National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan Hubei China Hubei Key Laboratory of Intelligent Geo-Information Processing School of Computer Science China University of Geosciences Wuhan Hubei China

Outlier removal is a critical part of feature-based point cloud registration. In this article, we revisit the model generation and selection of the classic RANSAC approach for fast and robust point cloud registration. For the model generation, we propose a second-order spatial compatibility (SC $^{2}$ ) measure to compute the similarity between correspondences. It takes into account global compatibility instead of local consistency, allowing for more distinctive clustering between inliers and outliers at an early stage. The proposed measure promises to find a certain number of outlier-free consensus sets using fewer samplings, making the model generation more efficient. For the model selection, we propose a new Feature and Spatial consistency constrained Truncated Chamfer Distance (FS-TCD) metric for evaluating the generated models. It considers the alignment quality, the feature matching properness, and the spatial consistency constraint simultaneously, enabling the correct model to be selected even when the inlier rate of the putative correspondence set is extremely low. Extensive experiments are carried out to investigate the performance of our method. In addition, we also experimentally prove that the proposed SC $^{2}$ measure and the FS-TCD metric are general and can be easily plugged into deep learning based frameworks.

关键词： Measurement Computational modeling Estimation Point cloud compression Three-dimensional displays Deep learning Pipelines

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Logical Implication of Structural Integrity Constraints for XML

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Chinese Journal of Electronics 2023年第2期18卷 243-248页

作者： Jianmei Zhang Shiqun Tao Jiye Liang Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan China Changzhi University Changzhi China

For tree XML, constraints that specify structural relationships among nodes or paths are very natural. In this paper, we introduce the concept of structural integrity constraints for XML (XSICs), which specify path implication, path cooccurrence, path mutual-exclusion, element obligatory inclusion and exclusive inclusion, and define the syntax and semantics of XSICs. For reasoning about XSICs, we rewrite all the other constraints into path implication constraints, and develop a sound and complete set of inference rules for path implication constraints. Meanwhile, we propose the concept of path implication closure. By using the path implication closure, we prove the completeness of inference rules, and determine the implication decision about XSICs.

关键词： Semantics XML Medical services Syntactics Cognition

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ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions

arXiv

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arXiv 2024年

作者： Huang, Wenfeng Xu, Guoan Jia, Wenjing Perry, Stuart Gao, Guangwei The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

Images captured in challenging environments–such as nighttime, foggy, rainy weather, and underwater–often suffer from significant degradation, resulting in a substantial loss of visual quality. Effective restoration of these degraded images is critical for the subsequent vision tasks. While many existing approaches have successfully incorporated specific priors for individual tasks, these tailored solutions limit their applicability to other degradations. In this work, we propose a universal network architecture, dubbed "ReviveDiff", which can address a wide range of degradations and bring images back to life by enhancing and restoring their quality. Our approach is inspired by the observation that, unlike degradation caused by movement or electronic issues, quality degradation under adverse conditions primarily stems from natural media (such as fog, water, and low luminance), which generally preserves the original structures of objects. To restore the quality of such images, we leveraged the latest advancements in diffusion models and developed ReviveDiff to restore image quality from both macro and micro levels across some key factors determining image quality, such as sharpness, distortion, noise level, dynamic range, and color accuracy. We rigorously evaluated ReviveDiff on seven benchmark datasets covering five types of degrading conditions: Rainy, Underwater, Low-light, Smoke, and Nighttime Hazy. Our experimental results demonstrate that ReviveDiff outperforms the state-of-the-art methods both quantitatively and visually. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

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A Biological Echo Identification Method with Single Polarized Weather Radar Using Super-Pixel Segmentation

A Biological Echo Identification Method with Single Polarize...

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IET International Radar Conference 2023, IRC 2023

作者： Yan, Zujing Cui, Kai Zhang, Jingmin Wang, Rui Hu, Cheng Liu, Jiaxing Zhang, Duo Sun, Zhuoran Mao, Huafeng Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China No. 208 Research Institute of China Ordnance Industries Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Laboratory School of Information and Electronic Beijing Institute of Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781839539954

Weather radar networks, given their advantages of continental coverage and year-round operation, show great potential in the study of large-scale aerial biological migration. The precise identification and extraction of biological echoes from vast amounts of weather data serve as the essential metrics for assessing the biological monitoring capability of weather radar systems. However, complex weather conditions pose an obstacle to extracting biological information accurately. This study introduces super-pixel segmentation approach in the domain of weather radar-based biological recognition. A cluster-based method is proposed to extract biological information from localized spatial weather radar rendered images. From the perspective of colour space, we confirm the necessity and evident benefits of differentiating them at local spatial level. We collected 750,000 historical records from 108 weather radars nationwide and handpicked 36,000 representative migration cases for labelling, creating a large-scale trainable dataset for studying aerial ecological migration patterns with weather radars. We apply the proposed method to two typical scenes, and the results demonstrate that the accuracy of the proposed method is above 89.38 % in the whole scenes. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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Enhancing Programming Course Quality through Human Posture Recognition and Screen Content Detection 4

Enhancing Programming Course Quality through Human Posture R...

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4th International Conference on information Science and Education, ICISE-IE 2023

作者： Chen, Xiaopan Bai, Haiyang Zhu, Xiaoke Dong, Lingyuan School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

The evaluation of programming course instruction is a key component of educational management. However, relying solely on supervising teachers for assessment poses challenges in comprehensive evaluation and providing feedback on students' classroom learning status. Additionally, a significant amount of monitoring video data from programming courses in Chinese universities remains underutilized. To address these challenges, this paper combines traditional educational management with artificial intelligence and proposes an intelligent algorithm for detecting students' learning status in programming courses. We first establish a dual YOLO network model to analyze students' postures and screen content in programming classroom monitoring videos, thereby identifying their behaviors. Subsequently, we develop a Programming Classroom Student Behavior Recognition System (PCSBS) that integrates data collection, student behavior detection, and result visualization functionalities. Experimental results demonstrate that the system can accurately provide feedback on students' programming course learning status, offering graded results that assist supervising teachers in their classroom instructional evaluation tasks to enhance educational management efficiency. © 2023 IEEE.

关键词： Students

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Improving Classroom Quality by Analyzing Student Behaviors Through computer Vision 4

Improving Classroom Quality by Analyzing Student Behaviors T...

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4th International Conference on information Science and Education, ICISE-IE 2023

作者： Chen, Xiaopan Li, Boyuan Zhu, Xiaoke Zhang, Tao School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

This paper aims to enhance the efficiency of teaching and learning by leveraging computer vision technology for automated analysis of student behavior in the classroom. We propose a feature-enhanced method for recognizing student behavior, which integrates an Upsampling module (UPS) and a convolutional block layer (CBL) module into the YOLOv5 model. This innovative approach significantly improves the detection and feature extraction capabilities for student targets, effectively addressing challenges associated with small-sized targets and lowresolution images. Through algorithm optimization, it achieves a balance in sample diversity, enhancing the model's adaptability and generalizability in complex classroom environments. Building upon these technological advancements, we have developed a Classroom Student Posture Perception System (CSP2S). This system integrates functions for data collection, student behavior detection, and result visualization. The CSP2S operates in realtime by capturing and analyzing students' behavioral dynamics while providing teachers with immediate feedback. Experimental results demonstrate that this system substantially reduces teachers' workload in student assessment, offering significant potential to improve teaching effectiveness. These robust technical advancements provide strong support for future applications in real educational environments. © 2023 IEEE.

关键词： Students

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