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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors

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Heliyon 2025年第1期11卷 e41409页

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Nouraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Information Technology Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Radiation Oncology Research Center (RORC) Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. Radiomics features were extracted from the area segmented which included the right and left optic nerves and chiasm. We integrated CT image features with dosimetric and clinical data subsequently, ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31 % of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97 % and 95 % respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision was able to predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024 The Authors

关键词： Computed tomography Machine learning Radiation-induced optic neuropathy Radiomics Visual evoked potential

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Multi-loop Sliding-Mode control for a Battery Charger Using a Quadratic Buck Converter

Multi-loop Sliding-Mode Control for a Battery Charger Using ...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Oswaldo López-Santos F. Flores-Bahamonde C. A. Torres-Pinzón Reham Haroun Hugo Valderrama-Blavi Juan A. Garriga Luis Martínez-Salamero Group of Automatic Control and Industrial Electronics Rovira i Virgili University Tarragona Spain Energy Transformation Center Faculty of Enigineering Universidad Andres Bello Chile Faculty of Electronic Engineering Universidad Santo Tomás Colombia Department of Computer Science and Industrial Engineering University of Lleida Lleida Spain

In this paper, the quadratic buck converter (QBC) is proposed as competitive alternative to implement a battery charger. Since, QBC is a high order system, the required control is designed to follow the conventional constant-current constant-voltage regime by means of three loops. Namely, i) an inner-loop operating in sliding mode to control the current of the closest inductor to the input port providing the properly stability of the system, ii) a first outer loop designed to regulate the battery voltage providing the reference of the inner loop, and finally iii) a second outer loop to regulate the battery current modifying the reference of the voltage loop. Proportional Integral (PI) controllers are used in both outer loops. Simulation results are presented validating the theoretical study.

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A Novel Fractional-Order Tabu Learning Neuron Model and its Dynamics with External Excitation

SSRN

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SSRN 2024年

作者： Yu, Yajuan Gu, Zhenhua Xu, Quan Wang, Feng Department of Mathematics Changzhou University Changzhou213164 China School of Computer Science and Artificial Intelligence Changzhou University Changzhou213159 China School of Microelectronics and Control Engineering Changzhou University Changzhou213159 China Changzhou University Advanced Cat alysis and Green Manufacturing Collaborative Innovation Center Changzhou213164 China

In this study, a novel fractional-order tabu learning single neuron model is proposed by replacing the exponential memory kernel function with the power-law memory kernel function. In the new model, the memory decay rate of the neuron is measured by the order of the fractional-order derivative. According to the stability analysis, the order of the fractional-order derivative at that the model happens Hopf bifurcationis is determined and is numerically smaller than that of integer-order tabu learning neuron model. Set the time as a new state variable, the non-autonomous fractional-order tabu learning neuron model with the sinusoidal excitation is autonomized. The autonomized fractional-order tabu learning neuron model has no equilibriums. For different fractional order, hidden attractors such as quasi-periodic, multi-periodic, and 1-periodic orbits are shown numerically. Excited by the quite stronger external stimuli, the bigger the memory decay rate of neurons is, the more obvious the neuron's firing behavior is. © 2024, The Authors. All rights reserved.

关键词： Neurons

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Synchronization of a Fourth-order Hyperchaotic System by Intermittent control with Double Impulses

Synchronization of a Fourth-order Hyperchaotic System by Int...

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2022 International Conference on Intelligent Systems and Computational Intelligence, ICISCI 2022

作者： Lu, Zequn Onasanya, B.O. Feng, Yuming Xie, Xianyang Tang, Ning Zhang, Wei Chongqing Three Gorges University School Of Computer Science And Engineering Chongqing404100 China University Of Ibadan Department Of Mathematics Ibadan Nigeria Chongqing Three Gorges University School Of Three Gorges Artificital Intelligence Chongqing404100 China Chongqing Three Gorges University School Of Electronical And Information Engineering Chongqing404100 China Chongqing Three Gorges University Key Laboratory Of Intelligent Information Processing And Control Chongqing404100 China Chongqing Three Gorges University Chongqing Engineering Research Center Of Internet Of Things And Intelligent Control Technology Chongqing404100 China

ISBN: (数字)9781665456197

ISBN: (纸本)9781665456197

In this paper, we describe a hyperchaotic system with time-delay. The process of the system is composed of a drive system and a response system, which is controlled by an intermittent mode. Double impulses are added to enhance the control, which is closer to the actual situation. According to Lyapunov stability theory, Linear Matrix Inequality (LMI), double impulses and intermittent control theory, and considering the approximation method of delay equation, an exponential synchronization condition of error system is given. The validity of the conclusion is realized by computer simulation. © 2022 IEEE.

关键词： Time delay

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Extracting Spatial High Utility Co-location Patterns Based on Fuzzy Feature Clusters 9th

Extracting Spatial High Utility Co-location Patterns Based o...

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9th China National Conference on Big Data and Social Computing, BDSC 2024

作者： Jin, Peijie Wang, Xiaoxuan Xiong, Wen Wang, Lizhen Gao, Song School of Information Science and Technology Yunnan Normal University Kunming650500 China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province Kunming650500 China Dianchi College Yunnan University Kunming650500 China School of Software Yunnan University Kunming650500 China

ISBN: (纸本)9789819758029

Sets of spatial features whose instances frequently appear together in nearby areas are regarded as spatial co-location patterns (SCPs). Spatial high utility co-location pattern(SHUCP), which is an extended research of SCPs, can better reflect the high quality aggregation phenomenon among spatial features. Utility participation index (UPI) is a commonly used adaptive utility measurement in most of the existing methods. Unfortunately, UPI does not take into account the impacts of fuzziness and overlap of proximity relationships on the calculation of utility. Additionally, UPI mainly relies on the computation of the instance layer and does not satisfy the downward closure property. In this paper, we combines fuzzy set theory to establish fuzzy neighbor relationships and discuss the issue of instance overlap between neighbor relationships. Then, a new calculation method for the utility index between spatial features has been design with considering fuzzy contribution score of instances. Using Fuzzy Chameleon Clustering algorithm (FCC) based on fuzzy utility index, this paper further extracts SHUCPs from different high utility fuzzy feature clusters. Experiments are conducted on three synthetic data sets and two real data sets to prove the rationality and effectiveness of the proposed algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fuzzy set theory

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Ring Formation Maneuvering with Double Integrator Dynamics

Ring Formation Maneuvering with Double Integrator Dynamics

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Tran, Dzung Casbeer, David Garcia, Eloy Weintraub, Isaac E. Milutinovic, Dejan Control Science Center of Excellence at the Air Force Research Laboratory Wright-Patterson Air Force Base Ohio45433 United States University of California Santa Cruz Department of Electrical and Computer Engineering Santa CruzCA95064 United States

ISBN: (纸本)9780738131153

Conventional leader-follower formations restrict the follower to a single desired position relative to the leader. To give the follower more flexibility in motion and to replicate typical human pilot operations, in this paper we propose a control architecture allowing the follower to converge to a ring, which is a set of desired points, relative to the leader. The follower is considered subject to a point-mass aircraft model, which can be transformed into the double integrator kinematics. For that reason, the nonlinear backstepping method is first utilized to design the controller for the double integrator kinematics with input saturation constraints being taken into account. The controller is then converted into control variables for the point-mass model. The stability of the proposed architecture is analyzed. Finally, a numerical example is presented to illustrate the efficacy of the proposed controller. © 2021 IEEE.

关键词： controllers

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A Novel Pose Estimation Method of Object in Robotic Manipulation Using Vision-Based Tactile Sensor 5th

A Novel Pose Estimation Method of Object in Robotic Manipula...

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Zhao, Dan Sun, Fuchun Zhou, Quan Wang, Zongtao Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing100083 China AnHui Province Key Laboratory of Special Heavy Load Robot Anhui University of Technology Ma Anshan China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066000 China

ISBN: (纸本)9789811623356

The computer vision techniques have been widely used in the robotic manipulation for perception and positioning. However, duo to the inaccuracy of camera calibration and measurement, there is a greatly need for more accurate method to estimate the object pose especially for robotic precise manipulation. In this paper, we propose a novel pose estimation method of object in robotic manipulation using a vision-based tactile sensor. The structure of the tactile sensor and the proposed model are introduced in detail, and verification experiments have been performed. The results show the advantages and better performance of the proposed method. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Robot vision

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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computer Systems science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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Visuotactile Sensor Enabled Pneumatic Device Towards Compliant Oropharyngeal Swab Sampling

Visuotactile Sensor Enabled Pneumatic Device Towards Complia...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shoujie Li Mingshan He Wenbo Ding Linqi Ye Xueqian Wang Junbo Tan Jinqiu Yuan Xiao-Ping Zhang Tsinghua Shenzhen International Graduate School Shenzhen China Department of Mechanical Engineering Seoul National University Seoul Korea Department of Intelligent Control Qianyuan Institute of Science Hangzhou China Clinical Research Center The Seventh Affiliated Hospital Sun Yat-sen University Shenzhen Guangdong China Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto ON Canada

Manual oropharyngeal (OP) swab sampling is an intensive and risky task. In this article, a novel OP swab sampling device of low cost and high compliance is designed by combining the visuotactile sensor and the pneumatic actuator-based gripper. Here, a concave visuotactile sensor called CoTac is first proposed to address the problems of high cost and poor reliability of traditional multi-axis force sensors. Besides, by imitating the doctor's fingers, a soft pneumatic actuator with a rigid skeleton structure is designed, which is demonstrated to be reliable and safe via finite element modeling and experiments. Furthermore, we propose a sampling method that adopts a compliant control algorithm based on the adaptive virtual force to enhance the safety and compliance of the swab sampling process. The effectiveness of the device has been verified through sampling experiments as well as in vivo tests, indicating great application potential. The cost of the device is around 30 US dollars and the total weight of the functional part is less than 0.1 kg, allowing the device to be rapidly deployed on various robotic arms.

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