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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Wen Zhao Weikai Lu Chang'en Zhou Zuoyong Li Haoyi Fan Xuejuan Lin Zhaoyang Yang Candong Li School of Traditional Chinese Medicine Fujian University of Traditional Chinese Medicine Fuzhou China School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781728194202

Objective: To develop a neural network model that recommends traditional Chinese medicine (TCM) herbal prescriptions. Methods: We constructed a new dataset of diagnosis and treatment knowledge from the Treatise on Febrile Diseases. Based on TCM's logical principles of “syndrome differentiation” and “state recognition”, a back-propagation neural network model is proposed that simulates clinical diagnosis and treatment. Results: The proposed model is a four-layer BP neural network. Experiments on the constructed dataset show that the proposed method achieved the best precision, recall, and F1-scores. Conclusion: The proposed method provides much more accurate herbal prescription recommendations than logistic regression.

关键词： Neural networks Education Clinical diagnosis Information technology Logistics Diseases

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Risk Identification Method for Smuggled Frozen Meat in Urban Public Safety Awareness and Early Warning

Risk Identification Method for Smuggled Frozen Meat in Urban...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Xueru Li Yan Zhang Lingjie Liu Ye Tian Yue Zhang College of Computer Science and Technology Harbin Engineering University Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Hangzhou Institute of Technology Xidian University Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat.

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Quantum Advantage of One-Way Squeezing in Enhancing Weak-Force Sensing

arXiv

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arXiv 2024年

作者： Wang, Jie Zhang, Qian Jiao, Ya-Feng Zhang, Sheng-Dian Lu, Tian-Xiang Li, Zhipeng Qiu, Cheng-Wei Jing, Hui Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Academy for Quantum Science and Technology Zhengzhou University of Light Industry Zhengzhou450002 China College of Physics and Electronic Information Gannan Normal University Jiangxi Ganzhou341000 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Cavity optomechanical (COM) sensors, featuring efficient light-motion couplings, have been widely used for ultrasensitive measurements of various physical quantities ranging from displacements to accelerations or weak forces. Previous works, however, have mainly focused on reciprocal COM systems. Here, we propose how to further improve the performance of quantum COM sensors by breaking reciprocal symmetry in purely quantum regime. Specifically, we consider a spinning COM resonator and show that by selectively driving it in opposite directions, highly nonreciprocal optical squeezing can emerge, which in turn provides an efficient way to surpass the standard quantum limit that otherwise exists in conventional reciprocal devices. Our work confirms that breaking reciprocal symmetry, already achieved in diverse systems well beyond spinning systems, can serve as a new strategy to further enhance the abilities of advanced quantum sensors, for applications ranging from testing fundamental physical laws to practical quantum metrology. Copyright © 2024, The Authors. All rights reserved.

关键词： Well testing

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From sensing to energy savings: A comprehensive survey on integrating emerging technologies for energy efficiency in WBANs

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Digital Communications and Networks 2024年

作者： Shumaila Javaid Hamza Fahim Sherali Zeadally Bin He Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai 201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai 201210 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai 201210 China Department of Computer Science Faculty of Computer Sciences Ilma University Karachi 75190 Pakistan College of Communication and Information University of Kentucky Lexington KY USA Academy of Computer Science & Software Engineering University of Johannesburg Johannesburg South Africa

Energy is essential for human existence, and its high consumption is a growing concern in today's technology-driven society. Global initiatives aim to reduce energy consumption and pollution by developing and deploying energy-efficient sensing technologies for long-term monitoring, control, automation, security, and interactions. Wireless Body Area Networks (WBANs) benefit a lot from the continuous monitoring capabilities of these sensing devices, which include medical sensors worn on or implanted in the human body for healthcare monitoring. Despite significant advancements, achieving energy efficiency in WBANs remains a significant challenge. A deep understanding of the WBAN architecture is essential to identify the causes of its energy inefficiency and develop novel energy-efficient solutions. We investigate energy efficiency issues specific to WBANs. We discuss the transformative impact that artificial intelligence and Machine Learning (ML) can have on achieving the energy efficiency of WBANs. Additionally, we explore the potential of emerging technologies such as quantum computing, nano-technology, biocompatible energy harvesting, and Simultaneous Wireless Information and Power Transfer (SWIPT) in enabling energy efficiency in WBANs. We focus on WBANs' architecture, hardware, and software components to identify key factors responsible for energy consumption in the WBAN environment. Based on our comprehensive review, we introduce an innovative, energy-efficient three-tier architecture for WBANs that employs ML and edge computing to overcome the limitations inherent in existing energy-efficient solutions. Finally, we summarize the lessons learned and highlight future research directions that will enable the development of energy-efficient solutions for WBANs.

关键词： Artificial intelligence Energy Healthcare Sensors Wireless body area networks

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A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

arXiv

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arXiv 2022年

作者： Zhang, Jiawei Li, Chen Rahaman, Md Mamunur Yao, Yudong Ma, Pingli Zhang, Jinghua Zhao, Xin Jiang, Tao Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States School of Resources and Civil Engineering Northeastern University Shenyang110004 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China Institute of Medical Informatics University of Luebeck Luebeck23538 Germany School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia

With the acceleration of urbanization and living standards, microorganisms play an increasingly important role in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image analysis methods with time. This study has high research significance and application value, which can be referred to as microbial researchers to comprehensively understand microorganism biovolume measurements using digital image analysis methods and potential applications. © 2022, CC0.

关键词： Microorganisms

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A Novel Sensitization Mechanism of Stress-Electric-Coupled Biosensor Originated in Magnetostrictive Effect

SSRN

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SSRN 2022年

作者： Sang, Shengbo Ge, Yang Ji, Jianglong Yuan, Zhongyun Dong, Xiushan Zhou, Chuangang Xiao, Pengli Tan, Qiuyun Zhao, Dong MicroNano System Research Center Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education College of Information and Computer Taiyuan University of Technology Taiyuan030024 China School of Electrical Engineering College of Engineering Science Hanyang University Seoul04763 Korea Republic of Shanxi Bethune Hospital Taiyuan030024 China Six-dimensional Artificial Intelligence Biomedical Research Institute Taiyuan030024 China

The stress-electric coupling based on receptor functionalization provides a new paradigm for the development of biosensor. However, the gravity factor of sensitive element usually shows difficult to be avoided due to its suppression to biosensor sensitivity. Herein, a magnetic-sensitization biosensing system with multi-physical field coupling of stress, magnetism and electricity was proposed to highly detect biomolecules. The gravitational interference of sensitive membrane was conquered by doping the TbDyFe with magnetostrictive effect into polydimethylsiloxane (PDMS) and applying magnetic field, showing great contributions to magnetic sensitization. The magnetic sensitization of the biosensor was proved by the collaboration of experiment and simulation. The detections of urine protein and urine sugar associated with kidney disease were realized with limits of detection (LOD) of 0.21 ng/mL and 0.45 nM by using the magnetic-sensitization biosensor. The prepared biosensor exhibited the high specificity, selectivity, stability and repeatability. Especially, the HSA and glucose in real samples can be accurately detect, showing good practicability of the prepared biosensor. This study provides a way to improve the sensitivity of the mechanical biosensor. © 2022, The Authors. All rights reserved.

关键词： Silicones

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Artificial Intelligence Based Segmentation for Skin-Layer Visualization from Optical Coherence Tomography Images

Artificial Intelligence Based Segmentation for Skin-Layer Vi...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Ramadhan, Gilang Titah Pusparani, Yori Prisilla, Ardha Ardea Shen, Wei-Cheng Cheng, Hsu-Tang Liau, Ben-Yi Jan, Yih-Kuen Chang, Wen-Thong Lin, Chih-Yang Lung, Chi-Wen Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Visual Communication Design Budi Luhur University Jakarta Indonesia Department of Digital Media Design Asia University Taichung Taiwan Department of Fashion Design LaSalle College Jakarta Jakarta Indonesia Department of Creative Product Design Asia University Taichung Taiwan Division of Plastic Surgery Asia University Hospital Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana United States Department of Mechanical Engineering National Central University Taoyuan Taiwan

ISBN: (纸本)9798331521165

Changes in epidermal thickness are linked to various skin diseases, such as diabetic foot. Optical Coherence Tomography (OCT), a noninvasive imaging technology, enables detailed visualization of skin layers. This study employed a deep learning approach using the U-Net architecture to analyze OCT images, specifically focusing on the stratum corneum (SC) and epidermis (ED) layers. Utilizing a dataset of 10,000 images, the U-Net model achieved 92% accuracy in predicting SC and 95% accuracy for ED. These results highlight the potential of this method for accurate 3D visualization of SC and ED layers, paving the way for precise assessment of skin layer thickness and its changes in medical applications. © 2025 IEEE.

关键词： Optical coherence tomography

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Mixture-of-Experts as Continual Knowledge Adapter for Mobile Vision Understanding

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IEEE Transactions on Mobile Computing 2025年

作者： Guo, Bicheng Zhou, Conghao He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering Zhejiang Hangzhou310027 China Xidian University School of Telecommunications Engineering China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Continual machine learning in the context of limited computational resources and data availability is critical in the connected digital world. Current intelligent applications predominantly rely on deep learning models requiring labor/computation-intensive training. These models often struggle to adapt effectively to new data while preserving performance on previously learned knowledge. In this paper, we introduce a lightweight method for continual knowledge adaptation that can address these challenges. To prevent disruption of the existing services, we propose a Mixture-of-Experts (MoE) adapter that integrates seamlessly with the existing vision model to encode new data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. The MoE technique enables scaling up the parameters of the adapter while maintaining a relatively low computation, making it fit for constrained devices in mobile computation scenarios. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between the existing knowledge and the information extracted from new data. The timing of employing the fusion module is further investigated. We find that it is conducive in scenarios where the task's performance requirements are enhanced. The MoE adapter and knowledge fusion module are integrated at each stage with minimal trainable parameters, efficiently optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed method. Specifically, the proposed method prevents an accuracy drop of 43.02% on the previous data compared to the continual train method, while achieving an accuracy of 44.81% on the new data, which is even 0.34% higher than fully training a new model. © 2002-2012 IEEE.

关键词： Deep learning

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A PCA-Based Internet Delay Space Dividing Algorithm

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China Communications 2019年第4期16卷 172-185页

作者： Zhanfeng Wang Guang Cheng Huaping Cao Ming Chen Chao Hu Li Han School of Computer Science and Engineering Southeast University Nanjing Lake Bell Information Technology Co. Ltd. school of cyber science ang engineering Southeast University National Computer Network Emergency Response Technique Team/Coordination Center of China Army Egineering University of PLA College of Comannd Control Engineering

It's a hot issue to improve the accuracy of network coordinate systems(NCS). By analyzing the error source of NCSes, it's emphasized that guaranteeing the low dimensionality of embedding datasets is the principle on the designing of NCSes. Based on this observation, a PCA based Internet delay space dividing algorithm(IDSD-PCA) is proposed. Beginning at choosing an optimized sub dataset randomly, IDSD-PCA partitions the Internet delay dataset into several sub datasets with low dimensionality by iterations. After the partition, the whole delay dataset and sub datasets are embedded into a hierarchical NCS. The experimental results show that with this framework both the metric space based models and the non-metric space based models can predict the end-to-end delays more accurately.

关键词： Internet delay space end-to-end delay low dimensionality network coordinate system

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Tigc-Net: Transformer-Improved Graph Convolution Network for Spatio-Temporal Prediction

SSRN

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SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

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