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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Huang, Chenyu Zhang, Yu Zhao, Yi Dong, Heng Li, Zhuoming School of Electron. and Inf. Eng Harbin Institute of Technology Harbin China Faculty of Computing Harbin Institute of Technology Harbin China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

SiamFC is equipped with a fully-convolutional Siamese network trained end-To-end on the ILSVRC15. Despite the success of the algorithm, it's still susceptible to interference from background clutter. In order to solve the problem, we propose an object tracking algorithm based on SiamFC. We introduce a spatial displacement penalty module into the tracker, to spatially weight the position where multiple peaks may occur on the score map. Using the correlation between two consecutive frames, tracker can reduce the probability of tracking the wrong analogues. SiamFC is difficult to track the object after deformation, so we define the confidence degree to measure the trustworthiness of tracking results. When the degree reaches the threshold, tracker updates the object appearance model selectively. We exploit the spatial and temporal information of the object and background to optimize the tracking process. Our tracker has higher robustness compared to SiamFC algorithm with the same complexity. We test our tracker on TC128 and VOT dataset, the simulation results show that our tracker achieves better performance in precision, AUPC and IoU than SiamFC. © 2022 IEEE.

关键词： Statistical tests

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Vision Transformers(ViT) Pretraining on 3D ABUS Image and Dual-CapsViT: Enhancing ViT Decoding via Dual-Channel Dynamic Routing

Vision Transformers(ViT) Pretraining on 3D ABUS Image and Du...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xu, Mingwang Wang, Wei Wang, Kuanquan Dong, Suyu Sun, Pengzhong Sun, Jinwei Luo, Gongning Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Instrumentation Science and Engineering Harbin China

ISBN: (纸本)9798350337488

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs' average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder's performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT's potential to enhance breast cancer diagnostics. © 2023 IEEE.

关键词： abus breast cancer capsule network classification vit

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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

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Computers, Materials & Continua 2022年第1期70卷 1683-1697页

作者： Mohamed Esmail Karar Marwa Ahmed Shouman Claire Chalopin Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Computer Engineering and Networks College of Computing and Information TechnologyShaqra UniversityShaqra11961Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Innovation Center for Computer Assisted Surgery(ICCAS) Universit鋞LeipzigLeipzig04103Germany

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide *** X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians *** this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary *** one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested *** tests have been successfully conducted on public dataset of US images acquired with a convex US *** study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 *** results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent ***,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

关键词： COVID-19 medical imaging machine learning applications adversarial neural networks ultrasound

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Path Planning with Potential Field-Based Obstacle Avoidance in a 3D Environment by an Unmanned Aerial Vehicle

arXiv

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arXiv 2023年

作者： Batinovic, Ana Goricanec, Jurica Markovic, Lovro Bogdan, Stjepan University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 Zagreb10000 Croatia

In this paper we address the problem of path planning in an unknown environment with an aerial robot. The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 3D sensors, such as LiDARs. It performs obstacle avoidance in real time and on an on-board computer. We present a novel algorithm based on the conventional Artificial Potential Field (APF) that corrects the planned trajectory to avoid obstacles. To this end, our modified algorithm uses a rotation-based component to avoid local minima. The smooth trajectory following, achieved with the MPC tracker, allows us to quickly change and re-plan the UAV trajectory. Comparative experiments in simulation have shown that our approach solves local minima problems in trajectory planning and generates more efficient paths to avoid potential collisions with static obstacles compared to the original APF method. © 2023, CC BY-SA.

关键词： Motion planning

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Design and Validation of a Wireless Drone Docking Station

arXiv

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arXiv 2023年

作者： Stuhne, Dario Vasiljević, Goran Bogdan, Stjepan Kovačić, Zdenko LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb10000 Croatia

Drones are increasingly operating autonomously, and the need for extending drone power autonomy is rapidly increasing. One of the most promising solutions to extend drone power autonomy is the use of docking stations to support both landing and recharging of the drone. To this end, we introduce a novel wireless drone docking station with three commercial wireless charging modules. We have developed two independent units, both in mechanical and electrical aspects: the energy transmitting unit and the energy receiving unit. We have also studied the efficiency of wireless power transfer and demonstrated the advantages of connecting three receiver modules connected in series and parallel. We have achieved maximum output power of 96.5 W with a power transfer efficiency of 56.6% for the series connection of coils. Finally, we implemented the system in practice on a drone and tested both energy transfer and landing. Copyright © 2023, The Authors. All rights reserved.

关键词： Drones

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Flexible production line product R&D and manufacturing cloud platform based on intelligent data collaboration 3

Flexible production line product R&D and manufacturing cloud...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Xie, Yanyin Yang, Rui Hu, Ruihan Gan, Lin Ke, Hualin Guangdong HEGII Sanitary Wares Co. Ltd Chaozhou528000 China Guangdong Institute of Intelligent Manufacturing Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Faculty of Computing National Pilot School of Software Harbin Institute of Technology Harbin150000 China

ISBN: (纸本)9781510662964

This paper focuses on how to ensure the availability and effectiveness of massive cloud data for industrial robots in the flexible production line, address the technical challenge in building a massive data cloud platform for industrial robots, and resolve the engineering problem of cloud based industrial robot cloud service application. To achieve this purpose, research is conducted on industrial robot hybrid cloud platform architecture, network technology, industrial robot big data system, autonomous learning cloud data processing and other technologies, which provides support for cloud service applications. It is suggested to combine knowledge atlas, digital twins, deep neural network, migration learning and other artificial intelligence technologies, which is conducive to remote monitoring and fault diagnosis cloud service applications. This has been verified and promoted in the handling, polishing, stacking, welding, assembly and other robots in 3C, mold, household appliances, automobile, furniture, electronic equipment manufacturing and other industries. © 2023 SPIE.

关键词： Industrial robots

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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Robust Length of Stay Prediction Model for Indoor Patients

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Computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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Synergistically Learning Class-specific Tokens for Multi-class Whole Slide Image Classification

Synergistically Learning Class-specific Tokens for Multi-cla...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pengzhong Sun Wei Wang Xiangyu Li Suyu Dong Shuo Li Kuanquan Wang Gongning Luo Faculty of Computing Harbin Institute of Technology Harbin China Faculty of Computing Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China Case School of Engineering Case Western Reserve University Cleveland USA

The application of transformer architecture in analyzing whole slide images (WSIs) has become increasingly popular due to its remarkable ability to learn complex associations. Nevertheless, a significant drawback emerges in the multiclass analysis of WSIs. The majority of the transformer-based methods available currently rely primarily on a single, class-agnostic token. This approach might not ideally capture the subtleties of class-discriminative information. To address this challenge, we present an innovative approach tailored for multi-class WSI analysis that harnesses the power of class-specific tokens. Central to our method is a novel attention mechanism designed to foster a synergistic learning relationship between patch and class tokens, enhancing the granularity of information captured and ensuring a more comprehensive representation of the WSI. Complementing this, we introduce a dynamic class-centric training strategy designed to optimize token representation learning, ensuring each token is informatively aligned with its corresponding class. Through extensive experimentation on three challenging multi-class WSI analysis datasets, our method consistently demonstrates superior performance, underscoring its potential as a robust solution for multi-class WSI analysis tasks.

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Towards Instance Segmentation-Based Litter Collection with Multi-Rotor Aerial Vehicle

Towards Instance Segmentation-Based Litter Collection with M...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Filip Zoric Antonio Franchi Matko Orsag Zdenko Kovacic Chiara Gabellieri Laboratory for Robotics and Intelligent Control Systems (LARICS) at the Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Robotics and Mechatronics Group from the University of Twente Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.

关键词： Instance segmentation Reviews Neural networks Laboratories Estimation Trajectory Aircraft

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