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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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Computer Modeling in engineering & Sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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Sensitivity and Distance Based Performance Analysis for Batteryless Tags with Transmit Beamforming and Ambient Backscattering

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China Communications 2022年第2期19卷 109-117页

作者： Minzheng Jia Chaochao Yao Wei Liu Ruyi Ye Tutun Juhana Bo Ai Department of Information Engineering Beijing Polytechnic CollegeBeijing 100042China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Communication and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China Chinese National Institute of Standalization Beijing 100088China School of Electrical Engineering and Informatics Institut Teknologi BandungIndonesia State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China

Ambient backscatter is a new green technology for Internet of Things(IoT)that utilizes surrounding wireless signals to enable batteryless devices to communicate with other *** battery-free devices first harvest energy from ambient wireless signals and then backscatter the signals for ***,sensitivity and distance are two important parameters for system ***,most existing studies on ambient backscatter communication systems do not consider the impact of the sensitivity of the energy-harvesting nodes and the distances between these *** this paper,we first provide a literature review for ambient communication technology and then take sensitivity and distance as two key parameters and investigate the sensitivity and distance based performance for ambient backscatter communication ***,we establish the mathematical model based on distances between transceivers and backscattering nodes,extract a parameter that can differentiate the direct path and the backscattering path,evaluate the effects of transmit beamforming,design an energy detector for the reader,and analyze the outage probability of energy harvesting at the tag and the bit error rate(BER)at the *** are then provided to corroborate the proposed studies.

关键词： ambient backscatter battery-free tag Internet of Things(IoT) outage probability performance analysis sensitivity transmission distance

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SE-COTR: A Novel Fruit Segmentation Model for Green Apples Application in Complex Orchard

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Plant Phenomics 2022年第1期4卷 12-25页

作者： Zhifen Wang Zhonghua Zhang Yuqi Lu Rong Luo Yi Niu Xinbo Yang Shaoxue Jing Chengzhi Ruan Yuanjie Zheng Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinan 250358China State Key Laboratory of Biobased Materials and Green Papermaking Qilu University of Technology(Shandong Academy of Science)Jinan 25035China School of Informatics University of LeicesterLeicester LE17RHUK Department of Engineering Design and Mathematics University of the West of EnglandBristol BS161QYUK Fujian Key Laboratory of Intelligent Control and Manufacturing of Agricultural Machinery Wuyishan 354300China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Jiangsu UniversityZhenjiang 212013China

Because of the unstructured characteristics of natural orchards,the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent ***,an innovative fruit segmentation method based on deep learning,termed SE-COTR(segmentation based on coordinate transformer),is proposed to achieve accurate and real-time segmentation of green apples.

关键词： Application segmentation Complex

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Computing Challenges of UAV Networks: A Comprehensive Survey

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Computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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An Efficient medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Heterogeneity Effect of Human Disturbances on Landscape Patterns in the Yellow River Delta Wetland,China

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Journal of Geodesy and Geoinformation Science 2024年第4期7卷 75-93页

作者： HAN Zheng LING Ziyan DONG Li ZHANG Aizhu JING Cheng LI Xinyan Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China Ministry of Natural ResourcesGuangzhou 510663China Surveying and Mapping Institute of Land and Resources Department of Guangdong Province Guangzhou 510663China College of Oceanography and Space Informatics China University of Petroleum(East China)Qingdao 266580China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520China GEOVIS Wisdom Technology Co. Ltd.Qingdao 266100China

Wetland landscapes have undergone tremendous changes and the spatial heterogeneity of wetlands has ***’s a huge challenge to accurately assess the effect of human disturbance on the landscape patterns in such a complex *** paper,taking the Yellow River Delta as a case,proposed a new framework to evaluate the heterogeneity effect of human disturbances on landscape patterns.A pixel-based Human Disturbance Index(HDI)with the addition of ecological conditions and the buffer influence is first established to quantify the spatial difference of human ***,Geographically Weighted Regression(GWR)model was introduced to analyze the spatial correlation between HDI and landscape indices,i.e.,Shannon’s Diversity Index(SHDI),Contagion Index(CONTAG),and Area-Weighted Mean Shape Index(SHAPEAM),which strongly correlated with *** results show that HDI in the Yellow River Delta has increased gradually and its spatial heterogeneity has continued to increase in the past 30 *** increase of human disturbances mainly occurred in coastal areas due to H-level human disturbances in Dongying Port and M-H level human disturbances along the *** in most areas inland of study area,the HDI reduced slightly benefit from the wetland conservation *** landscape pattern in the Yellow River Delta wetland has changed *** spatial heterogeneity of landscapes is enhanced,and the connectivity is *** tend to be *** levels of human disturbance have different effects on the landscape *** M-H level HDI leads to low landscape different and more *** the H level HDI leads to stronger landscape heterogeneity,complex patch shapes and decreased spatial *** implicate the proposed framework is efficient for evaluating the heterogeneity effect of human disturbance and landscape patterns in a complex wetland *** methods and findings will provide suggestions and guidan

关键词： human disturbance Yellow River Delta wetland landscape pattern Geographically Weighted Regression(GWR)

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NURBS Modeling and Curve Interpolation Optimization of 3D Graphics

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Computers, Materials & Continua 2021年第2期66卷 1799-1811页

作者： Hao Zhu Mulan Wang Kun Liu Weiye Xu School of Information and Communication Engineering Nanjing Institute of TechnologyNanjing211167China Jiangsu Key Laboratory of Advanced Numerical Control Technology Nanjing Institute of TechnologyNanjing211167China Department of informatics University of LeicesterLeicesterLE17RHUK

In order to solve the problem of complicated Non-Uniform Rational B-Splines(NURBS)modeling and improve the real-time performance of the high-order derivative of the curve interpolation process,the method of NURBS modeling based on the slicing and layering of triangular mesh is *** research and design of NURBS curve interpolation are carried out from the two aspects of software algorithm and hardware *** on the analysis of the characteristics of traditional computing methods with Taylor series expansion,the Adams formula and the Runge-Kutta formula are used in the NURBS curve interpolation process,and the process is then optimized according to the characteristics of NURBS *** can ensure accuracy,and avoid the calculation of higher-order ***,the hardware modules for the Adams and Runge-Kutta formulas are designed by using the parallel hardware construction technology of Field Programmable Gate Array(FPGA)*** parallel computing process using FPGA is compared with the traditional serial computing process using *** and experimental results show that this scheme can improve the computational speed of the system and that the algorithm is feasible.

关键词： NURBS modeling interpolation Adams Runge-Kutta FPGA

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Towards Data-Driven Artificial Intelligence Models for Monitoring, Modelling and Predicting Illicit Substance Use 7th

Towards Data-Driven Artificial Intelligence Models for Monit...

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Mbunge, Elliot Batani, John Chitungo, Itai Moyo, Enos Musuka, Godfrey Muchemwa, Benhildah Dzinamarira, Tafadzwa Department of Information Technology Faculty of Accounting and Informatics Durban University of Technology P O Box 1334 Durban4000 South Africa Kwaluseni Manzini Swaziland Faculty of Engineering and Technology Botho University Maseru100 Lesotho Department of Medical Laboratory Sciences University of Zimbabwe Harare Zimbabwe Medical Centre Oshakati Oshakati Namibia ICAP at Columbia University Harare Zimbabwe School of Health Systems and Public Health University of Pretoria Pretoria South Africa

ISBN: (纸本)9783031548192

Illicit substance use (ISU) is a major public health problem and a significant cause of morbidity and mortality globally. Early assessment of risk behaviour, predicting, identifying risk factors, and detecting illicit substance use become imperative to reduce the burden. Unfortunately, current digital tools for early detection and modelling ISU are largely ineffective and sometimes inaccessible. Data-driven artificial intelligence (AI) models can assist in alleviating the burden and tackling illicit substance use but their adoption and use remain nascent. This study applied the PRISMA model to conduct a systematic literature review on the application of artificial intelligence models to tackle illicit substance use. The study revealed that elastic net, artificial neural networks support vector machines, random forest, logistic regression, KNN, decision trees and deep learning models have been used to predict illicit substance use. These models were applied to tackle different substance classes, including alcohol, cannabis, hallucinogens, tobacco, opioids, sedatives, and hypnotics among others. The models were trained and tested using various substance use data from social media platforms and risk factors such as socioeconomic and demographic data, behavioural, phenotypic characteristics, and psychopathology data. Understanding the impact of these risk factors can assist policymakers and health workers in effective screening, assessing risk behaviours and, most importantly, predicting illicit substance use. Using AI models and risk factors to develop data-driven intelligent applications for monitoring, modelling, and predicting illicit substance use can expedite the early implementation of interventions to reduce the associated adverse consequences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Benefits and drawbacks of radiofrequency ablation via percutaneous or minimally invasive surgery for treating hepatocellular carcinoma

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World Journal of Gastrointestinal Surgery 2024年第11期16卷 3400-3407页

作者： Ching-Lung Hsieh Cheng-Ming Peng Chun-Wen Chen Chang-Hsien Liu Chih-Tao Teng Yi-Jui Liu Department of Computer Science and Information Engineering Feng Chia UniversityTaichung 40724Taiwan Department of Surgery Chung Shan Medical University HospitalTaichung 40201Taiwan School of Medicine Chung Shan Medical UniversityTaichung 40201Taiwan Department of Radiology Taichung Armed Forces General HospitalTaichung 406Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and TechnologyTaichung 406Taiwan Department of Radiology School of MedicineNational Defense Medical CenterTaipei 11490Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital and China Medical UniversityHsinchu 302Taiwan Institute of Nuclear Engineering and Science National Tsing Hua UniversityHsinchu 300Taiwan Department of Radiology Nantou Hospital of the Ministry of Health and WelfareNantou 540Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia UniversityTaichung 407Taiwan Department of Automatic Control Engineering Feng Chia UniversityTaichung 407Taiwan

The management of early stage hepatocellular carcinoma(HCC)presents significant *** radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant ***,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent *** is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC *** enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor *** invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical *** studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its ***,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA *** article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.

关键词： Percutaneous radiofrequency ablation Minimally invasive surgery Hepatocellular carcinoma Intraoperative radiofrequency ablation Contrast-enhanced ultrasound

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