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International Conference on Applied system Innovation (ICASI)

作者： You-Rong Chen Pei-Yin Chen Yu-Ting Lee Department of Computer Science and Information Engineering Digital Integrated Circuit Design Laboratory National Cheng Kung University Tainan Taiwan MediaTek Inc. Hsinchu Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

In this paper, a contact-free human-computer interaction interface is developed. The system recognizes the hand gestures from the user and transmits corresponding control signals to the end-user side. Subsequently, the end-user side manipulates cursor movements or clicks based on the received control signals. To ensure the operability on the embedded system development board, the system adopts low-complexity methods and integrates an external vision processing unit. The experiment results demonstrate the successful implementation of the system on Raspberry Pi 4.

关键词： Human computer interaction Technological innovation Embedded systems Computational modeling Graphics processing units User experience Artificial intelligence

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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An Embedded system-Based Keypoint Detection Method for the Barthel Index Measurement

An Embedded System-Based Keypoint Detection Method for the B...

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International Conference on Applied system Innovation (ICASI)

作者： You-Rong Chen Kuan-Yu Huang Pei-Yin Chen Yu-Ting Lee Department of Computer Science and Information Engineering Digital Integrated Circuit Design Laboratory National Cheng Kung University Tainan Taiwan MediaTek Inc. Hsinchu Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

In this paper, an embedded system-based keypoint detection method for the Barthel index measurement is proposed. The proposed system can detect important joints on the human body. By transmitting only the coordinates of joints for the subsequent Barthel index measurement process, the privacy concerns are significantly reduced. The experimental results show that the proposed system can be successfully implemented on embedded system development boards and achieves effective keypoint detection.

关键词： Privacy Technological innovation Analytical models Embedded systems Coordinate measuring machines Predictive models Aging

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Plasmonic nanoprinting:thinner,brighter&swifter

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Light(Science & Applications) 2023年第1期12卷 39-39页

作者： Lei Jin Cheng-Wei Qiu Key Laboratory of RF Circuits&System of Ministry of Education School of Electronics and InformationHangzhou Dianzi UniversityXiasha High Education ParkHangzhou 310018China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore

The plasmonic nanoprinting displays fade-resistant colours with ultrahigh resolution and opens a new avenue toward thedigital colour *** ideal product of nanoprinting requires low-cost production,high-saturation perfo... 详细信息

关键词： printing saturation colour

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Identification of soybean in Argentina using Sentinel-2 composite images

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International Journal of Agricultural and Biological engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google Earth Engine

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Modified Kelvin-Voigt fractional derivative model for viscoelasticity measurement in optical coherence elastography

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Chinese Optics Letters 2025年第1期23卷 93-101页

作者： Chenming Yang Zhongliang Li Nan Nan Teng Liu Yaoli Luo Xiangzhao Wang Advanced Light Source and System R&D Center Department of Advanced Optical and Microelectronic EquipmentShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China

Optical coherence elastography(OCE)can quantitatively obtain the viscoelasticity of tissues using rheological models and is widely applied to the clinical diagnosis of ***,commonly used rheological models in OCE do not account for the distinctive dependence of high-frequency storage and loss moduli on frequency in tissues,which results in the rheological models failing to accurately measure the viscoelastic properties of *** this paper,a modified Kelvin-Voigt fractional derivative model is presented based on the power-law behavior of soft tissues and the dependence of high-frequency complex shear modulus on frequency in living *** the rheometer and OCE tests,the modified model can provide the prediction of the power-law relationship between the low-frequency shear viscosity and frequency;compared with the Kelvin-Voigt and Kelvin-Voigt fractional derivative models,the modified model has a higher goodness-of-fit(accuracy>96%)for the high-frequency storage moduli of gelatin ***,the proposed model can reduce the root mean square error of fit by approximately 83%for the high-frequency(1–128 kHz)storage modulus of the polydimethylsiloxane phantoms obtained from publicly available ***,the modified model accurately predicts the mechanical properties of biomimetic materials over a wide frequency range,with the potential to more accurately reflect pathological changes in tissues.

关键词： optical coherence tomography optical coherence elastography viscoelasticity rheological model

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

arXiv

引用

arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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Statistical approach and territorial analysis on the Covid-19 emergency: the case study of the municipality of Tito

Statistical approach and territorial analysis on the Covid-1...

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Metrology for Living Environment (MetroLivEn), IEEE International Workshop on

作者： Fausto Bisaccia Valentina Santarsiero Luigi Santopietro Simone Corrado Antonella Iuliano Beniamino Murgante Department of Mathematics Computer Science and Economics University of Basilicata Potenza Italy School of Engineering Laboratory of Urban and Regional System Engineering (LISUT) University of Basilicata Potenza Italy

This paper analyzes the impact of the Covid-19 pandemic on Tito, a small municipality in Basilicata, Italy. Descriptive and inferential statistical models were used to study the course of the pandemic and predict its future development. The paper examines the possible consequences of proximity to a Site of National Interest on the spread of infection. The linear regression model was used to analyze the above phenomena. The paper also illustrates the multiple linear regression model and how it was constructed, considering the qualitative explanatory variables and ANOVA. The models were applied to data collected in the Municipality of Tito to test the trend of infection within the municipal area, with particular attention to the variables of gender, age, area of residence and vaccination status.

关键词：

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Muhammad Umair Raza Jie Chen Adil Nawaz Faisal Saeed Victor C.M. Leung Jianqiang Li Zhaoxia Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Gastroenterology Shenzhen Children’s Hospital China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network’s prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB’s input regions with high mutual information with the network’s output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets—CNMC, ISBI2016, and BreaKHis—demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains.

关键词： Pathology Accuracy Biological system modeling Noise Predictive models Reliability Decision trees Biological cells Cancer Biomedical imaging

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PointFaceFormer: Local and Global Attention Based Transformer for 3D Point Cloud Face Recognition

PointFaceFormer: Local and Global Attention Based Transforme...

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International Conference on Automatic Face and Gesture Recognition

作者： Ziqi Gao Qiufu Li Gui Wang Linlin Shen Computer Vision Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science University of Nottingham Ningbo China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Existing 3D point cloud-based facial recognition struggles to fully leverage both global and local information inherent in the 3D point cloud data. In this paper, we introduce the PointFaceFormer, the first Transformer model designed for 3D point cloud face recognition. It incorporates an attention mechanism based on dot product and cosine functions to construct a similarity Transformer architecture, which effectively extracts both local and global features from the point cloud data. Experimental results demonstrate that PointFaceFormer achieves a recognition accuracy of 89.08% and a verification accuracy of 76.93% on the large-scale facial point cloud dataset Lock3DFace, which is a new state-of-the-art in 3D face recognition. Furthermore, PointFaceFormer exhibits excellent generalization performance on cross-quality datasets. Additionally, we validate the effectiveness of the attention mechanism through ablation experiments, which justify the effectiveness of the proposed modules.

关键词： Point cloud compression Solid modeling Three-dimensional displays Attention mechanisms Accuracy Face recognition Transformers

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