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arXiv 2025年

作者： Liang, Pengchen Shi, Leijun Yao, Huiping Pu, Bin Chen, Jianguo Zhao, Lei Huang, Haishan Chen, Zhuangzhuang Xu, Zhaozhao Xu, Lite Chang, Qing Li, Yiwei Department of Nuclear Medicine Shanghai Children’s Hospital School of Medicine Shanghai Jiao Tong University Shanghai200062 China School of Microelectronics Shanghai University Shanghai201800 China Department of Ophthalmology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai201800 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong School of Software Engineering Sun Yat-sen University Guangdong Province Zhuhai519000 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China Department Shanghai Key Laboratory of Gastric Neoplasms Department of Surgery Shanghai Institute of Digestive Surgery Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai201800 China

Rapid bone scintigraphy is an essential tool for diagnosing skeletal diseases and tumor metastasis in pediatric patients, as it reduces scan time and minimizes patient discomfort. However, rapid scans often result in poor image quality, potentially affecting diagnosis due to reduced resolution and detail, which make it challenging to identify and evaluate finer anatomical structures. To address this issue, we propose the first application of SAM-based semantic priors for medical image restoration, leveraging the Segment Anything Model (SAM) to enhance rapid bone scintigraphy images in pediatric populations. Our method comprises two cascaded networks, fIR1 and fIR2, augmented by three key modules: a Semantic Prior Integration (SPI) module, a Semantic Knowledge Distillation (SKD) module, and a Semantic Consistency Module (SCM). The SPI and SKD modules incorporate domain-specific semantic information from a fine-tuned SAM, while the SCM maintains consistent semantic feature representation throughout the cascaded networks. In addition, we will release a novel Rapid Bone Scintigraphy dataset called RBS, the first dataset dedicated to rapid bone scintigraphy image restoration in pediatric patients. RBS consists of 137 pediatric patients aged between 0.5 and 16 years who underwent both standard and rapid bone scans. The dataset includes scans performed at 20 cm/min (standard) and 40 cm/min (rapid), representing a 2× acceleration. We conducted extensive experiments on both the publicly available endoscopic dataset and RBS. The results demonstrate that our method outperforms all existing methods across various metrics, including PSNR, SSIM, FID, and LPIPS. Copyright © 2025, The Authors. All rights reserved.

关键词： Pediatrics

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Block-Based Projection Matrix Design for Compressed Sensing

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Chinese Journal of Electronics 2016年第3期25卷 551-555页

作者： LI Zhetao XIE Jingxiong ZHU Gengming PENG Xin XIE Yanrong CHOI Youngjune The College of Information Engineering Xiangtan University School of Computer National University of Defense Technology Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University School of Computer Science and Engineering Hunan University of Science and Technology College of Information and Communication Engineering Hunan Institute of Science and Technology Department of Information and Computer Engineering Ajou University

The objective of optimizing a projection matrix is to decrease the mutual coherence between a projection matrix and a basis matrix. In this paper, a novel block-based method is proposed to design a projection matrix in compressed sensing. Here, the projection matrix is divided into two blocks. The relationship between the two blocks was obtained by reasoning and proving. Theoretical analysis demonstrates that the mutual coherence between the whole projection matrix and the whole basis matrix keeps as good as the mutual coherence between the block matrix and blocked basis matrix. Experimental results show that the proposed method obtains better performance compared to existing methods.

关键词： Compressed sensing Projection matrix Block optimization Mutual coherence

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Modular chaotification model with FPGA implementation

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science China(Technological sciences) 2021年第7期64卷 1472-1484页

作者： HUA Zhong Yun ZHOU BingHang ZHANG YinXing ZHOU YiCong School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen518055China College of Electrical and Information Engineering Hunan UniversityChangsha410082China Department of Computer and Information Science University of MacaoMacao999078China

Chaotic systems are an effective tool for various applications, including information security and internet of things. Many chaotic systems may have the weaknesses of incomplete output distributions, discontinuous chaotic regions, and simple chaotic *** may result in many negative influences in practical applications utilizing chaos. To deal with these issues, this study introduces a modular chaotification model(MCM) to increase the dynamic properties of current one-dimensional(1 D) chaotic maps. To exhibit the effect of the MCM, three 1 D chaotic maps are improved using the MCM as examples. Studies of the resulting properties show the robust and complex dynamics of these improved chaotic maps. Moreover, we implement these improved chaotic maps of MCM in a field-programmable gate array hardware platform and apply them to the application of PRNG. Performance analyses verify that these chaotic maps improved by the MCM have more complicated chaotic behaviors and wider chaotic ranges than the existing and several new chaotic maps.

关键词： nonlinear system chaotic system field-programmable gate array(FPGA) pseudorandom number generator(PRNG) hardware implementation

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Large language model-driven probabilistic trajectory prediction in the Internet of Things using spatio-temporal encoding and normalizing flows

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Digital Communications and Networks 2025年

作者： Xiaoliang Wang Liming Xu Lian Zhou Yuzhen Liu Neal Xiong Kuan-Ching Li School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan 411201 China Sanya Institute of Hunan University of Science and Technology Sanya 572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan 411201 China Department of Computer Mathematical and Physical Sciences Sul Ross State University Alpine TX 79830 USA Dept of Computer Science and Information Engineering Providence University Taichung 43301 Taiwan Republic of China

The convergence of the Internet of Things (IoT) and foundation models, particularly Large Language Models (LLMs), heralds a new era of intelligent and adaptive systems. This investigation explores the integration of LLMs and IoT for probabilistic vehicle trajectory prediction, a critical component in autonomous driving. Traditional deterministic models often fail to capture the complexity and uncertainty of real-world driving environments. To address these limitations, we propose an innovative approach that leverages LLM-driven spatio-temporal encoding and normalizing flows. The proposed method extracts latent motion patterns from historical trajectory data using LLMs, providing a nuanced understanding of vehicle dynamics. Advanced spatio-temporal encoders are employed for comprehensive feature extraction, capturing temporal dependencies and spatial relationships. Additionally, scaled dot-product attention is utilized for effective multimodal feature fusion, enhancing the integration of diverse data sources. The normalizing flows framework enhances the model's ability to capture complex, multimodal probability distributions of future trajectories by constructing an invertible transformation network. The network systematically maps simple base distributions to intricate target distributions through reversible transformations, significantly improving prediction accuracy. Evaluation of the nuScenes dataset demonstrates substantial improvements over existing methods. The results underscore the model's capability to generate diverse and realistic trajectory distributions, paving the way for safer and more efficient autonomous driving systems.

关键词： Internet of things Large language models Normalizing flows Spatio-temporal encoding Trajectory prediction

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Prolonging the lifetime of sensor networks using non-uniform sensor distribution

Prolonging the lifetime of sensor networks using non-uniform...

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2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Yuan, Huiyong Yang, Siqing Xie, Jiansheng Department of Computer Science Hunan Institute of Humanities Science and Technology Loudi China Network Center Loudi Vocational and Technical College Hunan China

ISBN: (纸本)9781479916603

In wireless sensor networks, sensors close to the sink drain their energy much faster than sensors far away from the sink due to the unevenly distributed forwarding workloads among sensors, which is known as energy-hole problem. To address this problem, we first privodea analytical model for data gathering. We then propose a non-uniform sensor distribution scheme for sensor networks, where more sensors are added to those areas with heavier energy load. Finally, we conduct extensive experiments and show that the proposed sensor distribution scheme outperforms random distribution better scalability and longer network lifetime. © 2013 IEEE.

关键词： Wireless sensor networks

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Mixed communication method for data gathering and aggregation in wireless sensor networks

Mixed communication method for data gathering and aggregatio...

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2013 IEEE Conference Anthology, ANTHOLOGY 2013

ISBN: (纸本)9781479916603

Clustering provides an effective way for prolonging the lifetime of a sensor network. However, when cluster heads transmit their data to the sink via multi-hop communication, the cluster heads closer to the sink are burdened with heavy relay traffic and tend to die early. Alternatively, if all cluster heads transmit data to the sink via single-hop communication, the cluster heads further from the sink will die much more quickly than those closer to the sink. To balance the cluster heads energy consumption, we present a mixed communication method where the cluster heads can transmit data to the sink in either single-hop or multi-hop. The simulation results show that our method outperforms LEACH and HEED in terms of network lifetime by balancing energy consumption. © 2013 IEEE.

关键词： Wireless sensor networks

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Prolonging the lifetime of heterogeneous wireless sensor networks via non-uniform node deployment

Prolonging the lifetime of heterogeneous wireless sensor net...

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International Conference on Internet technology and Applications

作者： Yuan, Huiyong Liu, Weiqun Xie, Jiansheng Department of Computer Science Hunan Institute of Humanities Science and Technology Loudi Hunan China Network Center Loudi Vocational and Technical College Loudi Hunan China

ISBN: (纸本)9781424472550

Deployment of sensor nodes in a sensor network is a critical task as node deployment should be optimum to increase network lifetime. In this paper, we first investigate the theoretical aspects of the non-uniform node deployment strategy in multi-hop heterogeneous wireless sensor networks. We then predict optimum deployment of micro-server nodes and propose a nonuniform node deployment scheme by calculating nodes energy consumption. We finally conduct extensive experiments and show that our non-uniform deployment scheme performed better in prolonging the network lifetime and improving energy utilization rate than the other conventional deployment schemes. © 2011 IEEE.

关键词： Energy utilization

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Node deployment strategy for a mobile sink in wireless sensor networks

Node deployment strategy for a mobile sink in wireless senso...

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IEEE International Conference on Communication Software and Networks

作者： Yuan, Huiyong Xie, Jiansheng Department of Computer Science Hunan Institute of Humanities Science and Technology Loudi Hunan China Network Center Loudi Vocational and Technical College Loudi Hunan China

ISBN: (纸本)9781612844855

In multi-hop wireless sensor networks, the nodes closest to the sink tend to deplete their energy faster than other nodes, which is known as energy-hole problem. To address this problem, we first study the mobile sink model, and conclude the best mobility strategy. We then consider jointly sink mobility and node deployment, and formulate optimization problems that maximize the network lifetime subject to energy depletion balance. Finally, we propose a non-uniform node deployment strategy for periodical data gathering in sensor networks. Simulation experiments show that the proposed deployment strategy outperforms random deployment better scalability and longer network lifetime. © 2011 IEEE.

关键词： Sensor nodes

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REDUCTION OF CODE SPACE IN PARALLEL LOGIC PROGRAMMING SYSTEMS 3rd

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BIANNUAL CONF ON PARALLEL ARCHITECTURES AND LANGUAGES EUROPE ( PARLE 91 )

作者： HWANG, ZY HU, SR SUN, CZ GAO, YQ Department of Computer Science Changsha Institute of Technology Changsha Hunan China

ISBN: (纸本)3540541527

This paper presents the problem of the explosion of code space in methods for exploiting independent AND-paralleiism in parallel logic programming systems, and proposes a scheme to solve it. The scheme can largely reduce the code space with minimal loss of parallelism. Some key algorithms in the scheme are presented in this paper. Finally, we give the experimental results and the preliminary performance analysis on the scheme. © Springer-Verlag Berlin Heidelberg 1991.

关键词： LOGIC PROGRAMMING PARALLEL PROCESSING COMPILE AND-PARALLELISM ABSTRACT INTERPRETATION MODE INFERENCE PROLOG computer LANGUAGES

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