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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5208-5222页

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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A New Semi-supervised Approach for Network Encrypted Traffic Clustering and Classification 25

A New Semi-supervised Approach for Network Encrypted Traffic...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Lin, Kunda Xu, Xiaolong Jiang, Yu School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781665405270

Encrypted network traffic classification is an essential task in modern communications, which is used in a wide range of applications, such as network resource allocation, QoS (Quality of Service), malicious detection, etc. With the continuous evolution of network technology, approaches used to classify network encrypted traffic have become increasingly complex, and model training relies heavily on a large amount of labeled data. However, the acquisition of correct and massive labeled data of network traffic in the real environment remains a major challenge in this field. On the opposite, unlabeled traffic is extremely easy to obtain in a network. Therefore, the effective use of unlabeled data is of great significance to the development of modern communications. In this paper, we propose the Sauce model, which can effectively use unlabeled information to obtain high-quality clustering space. It is composed of Aux (auxiliary network) and AE (auto encoder), where Aux is designed for collaborative training with AE, affecting the distribution of samples in the latent space generated by AE. Sauce uses t-SNE (t- distributed stochastic neighbor embedding) to perform secondary dimensionality reduction on the latent code, and then uses a clustering algorithm for cluster analysis. Besides, sauce applies the self-training technique, replacing the real labeled data with pseudo-labeled data to reduce the reliance on labeled data and improve the utilization of unannotated data. We conduct experiments on two real network datasets. The experiments show that Sauce can achieve clustering accuracies of 98.4% (VPN dataset) and 98.0% (TOR dataset), outperforming other unsupervised or semi-supervised learning methods. © 2022 IEEE.

关键词： Cluster analysis

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Reversible Data Hiding for 3D Mesh Model Based on Block Modulus Encryption and Multi-MSB Prediction 10th

Reversible Data Hiding for 3D Mesh Model Based on Block Modu...

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10th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Fu, Zihao Gao, Yang Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819787456

Reversible data hiding in encrypted domain (RDH-ED) fortifies data security and privacy safeguards while upholding the original data’s integrity and accessibility. Current research on RDH-ED focuses on 2D images, while research on 3D mesh models is still immature. This paper introduces an RDH-ED method using block modulus encryption and multi-MSB prediction. Initially, the original mesh model is partitioned into non-overlapping subblocks of equal size, and then the vertices in each subblock are encrypted with the same key for additive modulus encryption, ensuring that the spatial correlation present in the original mesh subblocks remains preserved within the encrypted subblocks. Subsequently, the subblocks are disrupted one by one using the 3D Arnold Transform to enhance security. The vertices in each embeddable subblock are divided into a reference vertex and several embeddable vertices, where the multi-MSB prediction strategy is employed to allocate embedding room for each embeddable vertex. Finally, the secret information is embedded into the allocated room. Since the proposed method almost completely preserves the spatial correlation within each subblock, the achieved embedding rate surpasses that of all previous outstanding methods that rely on vacating room after encryption (VRAE). The experimental findings demonstrate that the proposed approach achieves an average embedding rate of 45.55 bits per vertex (bpv), surpassing the state-of-the-art method that achieves 25.63 bpv. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mesh generation

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RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model 31

RGR-KBQA: Generating Logical Forms for Question Answering Us...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Feng, Tengfei He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798891761964

In the field of natural language processing, Knowledge Base Question Answering (KBQA) is a challenging task that involves accurately retrieving answers from structured knowledge. Existing methods often face issues when generating query statements using LLMs, as the knowledge introduced may be imprecise and the models themselves may exhibit hallucination problems, leading to low accuracy, particularly when dealing with complex questions. To address these challenges, we introduce a novel semantic parsing approach called RGR-KBQA, which adopts a Retrieve-Generate-Retrieve framework. The first retrieval step introduces factual knowledge from a knowledge graph to enhance the semantic understanding capabilities of LLMs, thereby improving generation accuracy of logical form. The second step uses a fine-tuned model to generate the logical form, and the final step involves unsupervised relation and entity retrieval to further enhance generation accuracy. These two retrieval steps help alleviate the hallucination problems inherent in LLMs. Experimental results show that RGR-KBQA demonstrate promising performance on CWQ and WebQSP datasets. © 2025 Association for Computational Linguistics.

关键词： Question answering

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LLaMA-Unidetector: An LLaMA-Based Universal Framework for Open-Vocabulary Object Detection in Remote Sensing Imagery

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xie, Jianlin Wang, Guanqun Zhang, Tong Sun, Yikang Chen, He Zhuang, Yin Li, Jun Beijing100081 China Peking University National Key Laboratory for Multimedia Information Processing Beijing100871 China China University of Geosciences School of Computer Science Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430078 China

The object detection is a crucial task in the computer vision for remote sensing applications. However, the reliance of traditional methods on predefined and trained object categories limits their applicability in open-world scenarios. A key challenge in open-vocabulary object detection lies in accurately identifying unseen objects. Existing approaches often focus solely on detecting object locations, struggling to recognize the categories of previously unseen targets. To address this issue, we propose a novel benchmark, where models are trained on known base classes and evaluated on their performance in detecting and recognizing unseen or novel classes. To this end, we introduce LLaMA-Unidetector, a universal framework that incorporates textual information into a closed-set detector, enabling the generalization to open-set scenarios. Our LLaMA-Unidetector leverages a decoupled learning strategy that separates localization and recognition. In the first stage, a class-agnostic detector identifies objects, distinguishing only between foreground and background. In the second stage, the detected foreground objects are passed through TerraOV-LLM, a multimodal large language model (MLLM), for recognition, utilizing the strong generalization capabilities of large language models to infer the correct categories. We propose a self-built vision question answering (VQA) remote sensing dataset, TerraVQA, and conduct extensive experiments on the NWPU-VHR10, DOTA1.0, and DIOR datasets. The LLaMA-Unidetector achieves impressive results, with a performance of 75.46% AP, 50.22% AP, and 51.38% AP on the zero-shot detection benchmarks for the NWPU-VHR10, DOTA1.0, and DIOR datasets, respectively. © 1980-2012 IEEE.

关键词： Object detection

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Exploration and Practice of Course Homework Metaverse Based on Extended Reality Under Edge Computing 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Fu, Jinrong Liu, Yiwen Yan, Haobo Gao, Yanxia Xiao, Jianhua School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

The metaverse originated from science fiction at first, but it gradually came into reality with the continuous power of technology. Aiming at the problems of course work in Chinese universities, such as its function is weakened or suppressed, its form is too abstract, and it lacks elaborate design, this paper proposes a design method of course work meta-universe based on extended reality under edge computing. XR technology is used to realize the interactive immersion of the coursework meta-universe, in order to improve students’ learning enthusiasm and ability to solve abstract problems. Based on the international perspective, this paper systematically combs and analyzes the integration architecture, application scenarios and future trends of the meta-universe education application based on Edge computing (MEC). Four key processes are described: teacher-student interaction, data collection, data feedback and comprehensive evaluation. In order to promote the application of the metaverse in the field of education to provide mirror. The validity of the method is illustrated by the data. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

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Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

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

作者： Chen, Yangdong Chen, Mohan Zhang, Yuejie Feng, Rui Zhang, Tao Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (纸本)9798350368741

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research. © 2025 IEEE.

关键词： Diffusion Models Point Cloud Generation Self-supervised Contrastive Learning Sketch

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A Ship Power Sharing Strategy Based on Shapley Value Method and Cartel Mechanism

A Ship Power Sharing Strategy Based on Shapley Value Method ...

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2024 International Annual Conference on Complex Systems and intelligent Science, CSIS-IAC 2024

作者： Zhang, Zhengxu Yang, Jie Ma, Kai Guo, Shiliang Li, Hui School of Electrical Engineering Yanshan University Qinhuangdao China Yanshan University Key Laboratory of Power Electronics for Energy Conservation and Drive Control of Hebei Province Qinhuangdao China Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing of the Ministry of Education Qinhuangdao China

ISBN: (纸本)9798331504755

With the development of the maritime industry, the number and capacity of existing port cold-ironing (CI) equipment can no longer meet the power demand of ships during berthing, resulting in significant carbon emissions from ships within the port area. Therefore, a ship power sharing strategy based on Shapley value method and Cartel mechanism is proposed. Firstly, the rolling optimization model of ship power scheduling is established to adjust the ship power scheduling strategy according to the real-time information. Secondly, the cooperative game model of ship power sharing is established, and the cost sharing among cooperative ships is realized by Shapley value method. Finally, in order to reduce the situation that ships increase their own profits by deviating from cooperation and affect the overall profits, this paper introduces Cartel mechanism to punish the behavior of deviating from cooperation. The simulation results show that the power sharing strategy proposed in this paper can reduce the total ship cost and carbon emission by 5.28% and 3.78%, respectively. The introduction of Cartel mechanism can increase the cost of non-cooperative behavior, thus ensuring that all ships take cooperative behavior. The power sharing strategy based on Shapley method and Cartel mechanism proposed in this paper is helpful to the construction of green ports. © 2024 IEEE.

关键词： Profitability

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Highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Changkai, Lu Yiwen, Liu Yanxia, Gao Yuanquan, Shi Xiaoning, Peng School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

In recent years, gestures have been widely used in many fields. For example, human-computer interaction, virtual reality, gesture translation, etc. The reason for its rapid development is due to the emergence of deep learning and artificial intelligence under today’s society. Due to dynamic gestural interactions, such large intelligent models are often characterized by many parameters, large sample size, frequent parameter updates, and high communication volume. Based on this feature, we propose a cloud-based edge design architecture approach for gesture recognition based on an improved YOLOv5 network model optimization by changing different gestures, background interference, which uses a 21-layer model for the neural network. With the use of edge intelligence, the computational accuracy can be improved by 10.6% over the traditional YOLOv5 with a MAP value of 93.3%. The final recall rate is also improved by 3.6%. The parameter model is only 43.6% of the original one. This shows the practicality and operability of using edge computing as a technique in gesture recognition, as well as the small improvement cost and obvious effect. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Gesture recognition

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RETRACTED ARTICLE: ZpZp2 -linear codes: rank and kernel

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Designs, Codes, and Cryptography 2023年第2期91卷 693-693页

作者： Shi, Minjia Wang, Shukai Li, Xiaoxiao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei China

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