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Iranian Conference on electrical engineering (ICEE)

作者： Fatemeh Zargarbashi Hamed Nazemi Harandi Amin Hamed Mehdi Tale Masouleh School of Mechanical Engineering Sharif University of Technology Tehran Iran School of Mechanical Engineering Amirkabir University of Technology Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

This paper presents the state of the art of the design and construction of a torque measurement unit based on a new modification in balancing method. This unit consists of six soft tactile sensors that each include a MEMS barometer covered by silicone rubber. Sensors are mounted in an array under the body of a servo motor to read the force from which motor torque can be obtained. Correct performance of sensor was validated in experimental tests by applying different loads on a Kinco servo motor and comparison of sensor output with value of actual torque obtained from motor driver software. The sensor was able to measure torque with less than 0.1 N.m uncertainty. The main advantage of this torque measurement method is its accuracy, simple construction, precision, real-time measurement and low cost compared to expensive strain gauge based sensors. Due to its soft surface, this torque sensor can be safely employed in human-robot interaction purposes and one can instantly find the force exerted on the environment by the end effector of robot due to the low delay time of the sensors.

关键词： Torque Sensors Torque measurement Force Force measurement Mathematical model Tactile sensors

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Design and Prototyping of One Degree-of-Freedom Rehabilitation robotic Arm with Variable Weights

Design and Prototyping of One Degree-of-Freedom Rehabilitati...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Mahdi Atashi Gw.olestan Amin Hamed Mehdi Tale Masouleh School of Electrical & Computer Engineering University of Tehran Human & robot interaction Laboratory Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

In this paper, a novel concept is proposed to design a robotic arm for rehabilitating individuals with muscle disability. Wearable robots are used for dealing with this challenge. Many of these robots can only reduce certain amounts of hand weight and whatever it grabs, and the system force does not change with the weight of hand. The existing systems are designed for certain weights and with any variation in length, the device should be set to static balance manually; a process which takes time and expertise. The proposed mechanism, when the device is connected to the individual's hand, it synchronizes itself with the weight and torque automatically using a novel force sensor, which is based on a barometer device, and the reaches statistic balance. In addition, if a weight is lifted, the device rebalances with the new weight and reaches statistic balance accordingly. In order to provide statistic balance in this system, counterweight is used which neutralizes 98% of sum of weight of hand and the weight.

关键词： robot sensing systems Manipulators Force Torque Skin Force sensors

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Generating Synthetic Medical Images by Using GAN to Improve CNN Performance in Skin Cancer Classification

Generating Synthetic Medical Images by Using GAN to Improve ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Pooyan Sedigh Rasoul Sadeghian Mehdi Tale Masouleh Science and Research Branch Islamic Azad University Tehran Iran Human and Robot Interaction Laboratory Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

One of the main reasons of slow progress in using deep learning methods for cancer detection is the lack of data, especially the annotated data which is usually used for supervised learning algorithms. This paper presents a Convolutional Neural Network (CNN) to detect skin cancer. The primary database which is used to train the designed CNN algorithm has 97 members (50 benign and 47 malignant), which are collected from the International Skin Imaging Collaboration (ISIC). In order to compensate the lack of data for training the proposed CNN algorithm, a Generative Adversarial Network (GAN) is designed to produce synthetic skin cancer images. The classification performance of the designed trained CNN without the obtained synthetic images is near 53%, but by adding the synthetic images to the primary database the performance of the model is increased to 71%.

关键词： Gallium nitride Generative adversarial networks Databases Skin cancer Medical diagnostic imaging

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Kinematics and Control of a 4-DOF Delta Parallel Manipulator

Kinematics and Control of a 4-DOF Delta Parallel Manipulator

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Mehdi Azmoun Ali Rouhollahi Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory Faculty of New Sciences and Technologies University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781728101286;9781728101279

In this paper, the design, kinematic problem and control of a 4-DOF Delta parallel manipulator are investigated. Delta is a parallel manipulator which is widely used for pick-and-place applications. A simulation of this manipulator is designed in SimMechanic environment. The verification of the inverse kinematic problem has been done in simulation environment. Afterwards, different controllers like PID and Sliding-Mode based on the inverse kinematic problem are designed in simulation environment and their results are compared with each other. According to value of the root mean square error, PID controller exhibits better performance and tracked the desired path with less error compared to other controllers. Then the Sliding-Mode control is designed and based to the obtained results, leads to less chattering in tracking the desired path.

关键词： Kinematics End effectors Mathematical model Legged locomotion Solid modeling

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Design and Control of a Suspended Cable-Driven Parallel robot with Four Cables

Design and Control of a Suspended Cable-Driven Parallel Robo...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Ramin Mersi Sina Vali Morteza Shahamiri haghighi Ghasem Abbasnejad Mehdi Tale Masouleh Human and Robot Interaction Laboratory Mechanical and Energy Eng. Department Shahid Beheshti University Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Eng. University of Tehran Tehran Iran

ISBN: (纸本)9781728101286;9781728101279

This paper addresses design and control of a new suspended under-constrained cable-driven parallel robot with four cables. In order to improve the estimation of the cable lengths, an auxiliary roller screw mechanism is utilized to move the winch linearly for proper cable coiling on one layer. Also, for the sake of obtaining the pose of the robot in real-time, an angular rate sensor is embedded on the end-effector. The collected data from the angular rate sensor is used to make equivalent the solution of the forward geometric-static problem to the solution of linear system of equations. The foregoing reduced system can be readily solved and leads to the pose of the moving platform in real-time which is a definite asset in control. Moreover, kinematic problem of the cable-driven parallel robot is provided and a new method for obtaining the forward geometric-static problem is proposed.

关键词： Kinematics robot sensing systems Parallel robots Winches Real-time systems Global Positioning System

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Design, Development and Control of a Three Flexible-Fingers Gripper Based On Hand Gesture

Design, Development and Control of a Three Flexible-Fingers ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Rasoul Sadeghian Pooyan Sedigh Parastoo Azizinezhad Shahrooz Shahin Mehdi Tale Masouleh Human and Robot Interaction Laboratory Tehran Iran Department of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran Faculty of Electrical and Computer Science Robotic Engineering Tehran North Branch Islamic Azad University Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781728101286;9781728101279

In this paper, the process of design, and construction of a flexible gripper with three fingers is presented. The fingers of the proposed gripper are fabricated using a 3D printing device. The gripper is controlled remotely by an autonomous glove which is designed to help people with disability. In order to ameliorate the movement accuracy of the gripper a Fuzzy-PID controller is implemented on the system. The result of experimental tests demonstrate a decrease of the gripper movement error to less than 2 degrees in average. An ultrasonic sensor is set on the gripper's body to improve the grasping process by detecting the distance of the object to the gripper. The fabricated fingers are flexible and provides the ability of grasping the objects with irregular shapes. The experimental test approves the significant performance of the gripper in term of grasping complex shape objects.

关键词： Grippers Grasping Flexible printed circuits robot sensing systems Shape Kalman filters

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Push Recovery Methods Based on Admittance Control Strategies for a NAO-H25 humanoid

Push Recovery Methods Based on Admittance Control Strategies...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Fatemeh Sadat Mousavi Mehdi Tale Masouleh Ahmad Kalhor Payam Ghassemi Human and Robot Interaction Laboratory School of Electrical nad Computer Engineering University of Tehran Tehran Iran Faculty of New Sciences and Technologies University of Tehran Tehran Iran

ISBN: (纸本)9781728101286;9781728101279

This paper discusses two main investigations regarding push recovery of a NAO humanoid robot. The first part indicates the simulation results of different strategies retrieved from human behaviors, namely: Ankle, Hip, and Hip-ankle strategies. These methods are separately assessed and their efficiencies are discussed. To the end of converting torque parameter to robot's movement with a reasonable approximation in the aforementioned strategies' obtained models, a new approach, the so-called Virtual leg model, is introduced. The second discussion assesses the admittance control strategy, which relies on disturbances' energy absorption. Individual control algorithms are introduced for each disturbances' categories based on NAO's real conditions. This paper proposes a new strategy, which investigates the influence of admittance control coefficients against external disturbances, by considering a virtual interaction between robot's feet and the ground, and introducing a Kalman-filter-based estimator. All proposed methods are simulated in NAO-H25's simulation environment.

关键词： Legged locomotion Hip Admittance robot kinematics Measurement uncertainty robot sensing systems

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Accuracy Comparison of Spherical Parallel Manipulators Based on Joint Clearance 5

Accuracy Comparison of Spherical Parallel Manipulators Based...

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5th RSI International Conference on robotics and Mechatronics, IcRoM 2017

作者： Mojtaba, Seyed Mousavi, Seyed Khoogar, Ahmadreza Masouleh, Mehdi Tale Mechanical Engineering Department Malek Ashtar University of Technology Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Iran

ISBN: (纸本)9781538657034

In this paper, three types of Spherical Parallel Manipulators (SPM) are compared from accuracy point of view based on joint clearances. The 3-RRR SPM is an overconstraint parallel mechanism and one can presume that it may exhibits an accurate motion. But beside the foregoing advantage, this structure has some drawbacks such as requiring high manufacturing precision and the difficulty of assembling the mechanism. Two other types, 3-RRS and 3-RSR, are non-overconstrained and do not have the above disadvantages, therefore these two types with 3- RRR are put into contrast from accuracy point of view. First, a method to obtain a model of moving platform pose (position and orientation) error is introduced which leads to a standard convex optimization problem. Since error calculation depends on the end-effector configuration, more than 1000 configurations are taken into account in order to cover SPMs workspaces. Then maximum values of six components of the pose error in each configuration are computed for each type. Finally, the three SPMs are compared based on maximum position and orientation error for given joint clearances. The obtained results revealed that the 3- RRR SPM has better position accuracy while in the case of orientation, the 3-RRS SPM has lowest maximum error between SPMs under study in the prescribed workspace. © 2017 IEEE.

关键词： Mechanisms

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A Scalable Deep Attention Mechanism of Instance Segmentation for the Investigation of Chromosome

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SLAS technology 2025年 33卷 100306页

作者： Neelam Umbreen Sara Ali Hasan Sajid Yasar Ayaz Shrooq Alsenan Yunyoung Nam So Yeon Kim Muhammad Baber Sial National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: neelamumbreen.phd@smme.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan Suzhou Industrial Park (SIP) Monash Research Institute of Science and Technology Monash University 8Q9C+R6W Suzhou Jiangsu China. School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad NUST-COVENTRY Human-Robot Interaction (NC-HRI) Laboratory Pakistan Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: sarababer@smme.nust.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: hasan.sajid@smme.nust.edu.pk. Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: yasar@smme.nust.edu.pk. Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Electronic address: shalsenan@pnu.edu.sa. Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang University Asan 31538 Korea. Electronic address: ynam@sch.ac.kr. Department of Artificial Intelligence Department of Software and Computer Engineering Ajou University. Electronic address: jebi1771@ajou.ac.kr. School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: babersial@***.

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi

关键词： Chromosome segmentation Instance segmentation, cytogenetics Mask R-CNN attention mechanism biomedical imaging multi-scale feature fusion

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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