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arXiv 2021年

作者： Shahbandeh, Mobina Ghaffarpour, Fatemeh Vali, Sina Haghpanah, Mohammad Amin Torkamani, Amin Mousavi Masouleh, Mehdi Tale Kalhor, Ahmad School of Electrical and Computer Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory University of Tehran Tehran Iran Department of Computational Engineering Lappeenranta University of Technology Lappeenranta Finland School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran

Hand hygiene is crucial for preventing viruses and infections. Due to the pervasive outbreak of COVID-19, wearing a mask and hand hygiene appear to be the most effective ways for the public to curb the spread of these viruses. The World Health Organization (WHO) recommends a guideline for alcohol-based hand rub in eight steps to ensure that all surfaces of hands are entirely clean. As these steps involve complex gestures, human assessment of them lacks enough accuracy. However, Deep Neural Network (DNN) and machine vision have made it possible to accurately evaluate hand rubbing quality for the purposes of training and feedback. In this paper, an automated deep learning based hand rub assessment system with real-time feedback is presented. The system evaluates the compliance with the 8-step guideline using a DNN architecture trained on a dataset of videos collected from volunteers with various skin tones and hand characteristics following the hand rubbing guideline. Various DNN architectures were tested, and an Inception-ResNet model led to the best results with 97% test accuracy. In the proposed system, an NVIDIA Jetson AGX Xavier embedded board runs the software. The efficacy of the system is evaluated in a concrete situation of being used by various users, and challenging steps are identified. In this experiment, the average time taken by the hand rubbing steps among volunteers is 27.2 seconds, which conforms to the WHO guidelines. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes

Scene Understanding in Pick-and-Place Tasks: Analyzing Trans...

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Iranian Conference on electrical engineering (ICEE)

作者： Seraj Ghasemi Hamed Hosseini MohammadHossein Koosheshi Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

With robots increasingly collaborating with humans in everyday tasks, it is important to take steps toward robotic systems capable of understanding the environment. This work focuses on scene understanding to detect pick-and-place tasks given initial and final images from the scene. To this end, a dataset is collected for object detection and pick-and-place task detection. A YOLOv5 network is subsequently trained to detect the objects in the initial and final scenes. Given the detected objects and their bounding boxes, two methods are proposed to detect the pick-and-place tasks which transform the initial scene into the final scene. A geometric method is proposed which tracks objects' movements in the two scenes and works based on the intersection of the bounding boxes which moved within scenes. Contrarily, the CNN-based method utilizes a Convolutional Neural Network to classify objects with intersected bounding boxes into 5 classes, showing the spatial relationship between the involved objects. The performed pick-and-place tasks are then derived from analyzing the experiments with both scenes. Results show that CNN-based method, by VGG-16 backbone, outscores the geometric method by roughly 12% in certain scenarios, with an overall success rate of 84.3%.

关键词： YOLO electrical engineering Accuracy Tracking Transforms Feature extraction Manipulators

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Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes

arXiv

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arXiv 2024年

作者： Ghasemi, Seraj Hosseini, Hamed Koosheshi, MohammadHossein Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran

With robots increasingly collaborating with humans in everyday tasks, it is important to take steps toward robotic systems capable of understanding the environment. This work focuses on scene understanding to detect pick-and-place tasks given initial and final images from the scene. To this end, a dataset is collected for object detection and pick-and-place task detection. A YOLOv5 network is subsequently trained to detect the objects in the initial and final scenes. Given the detected objects and their bounding boxes, two methods are proposed to detect the pick-and-place tasks which transform the initial scene into the final scene. A geometric method is proposed which tracks objects’ movements in the two scenes and works based on the intersection of the bounding boxes which moved within scenes. Contrarily, the CNN-based method utilizes a Convolutional Neural Network to classify objects with intersected bounding boxes into 5 classes, showing the spatial relationship between the involved objects. The performed pick-and-place tasks are then derived from analyzing the experiments with both scenes. Results show that CNN-based method, by VGG-16 backbone, outscores the geometric method by roughly 12% in certain scenarios, with an overall success rate of 84.3%.MSC Codes computer Vision and Pattern Recognition (***), Systems and Control (***) © 2024, CC BY.

关键词： Convolutional neural networks

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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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A 2D Geometry Based Grasping Pose Generation Algorithm for a Two-Finger robot Hand

A 2D Geometry Based Grasping Pose Generation Algorithm for a...

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Iranian Conference on electrical engineering (ICEE)

作者： Arash Akbari Arman Akbari Mehdi Tale Masouleh Department of Computer Science University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran

robot grasping is of paramount importance in industrial and service robotics. In recent years, various data-driven algorithms have been proposed to solve the problem of grasp detection and a part of them are based on reinforcement learning (RL) approaches. In a variety of proposed algorithms, random key points are being employed which will make the learning process inefficient and time-consuming. In this paper, a geometry-based algorithm is presented which can find grasp poses based on the geometry of the unknown object and propose the ones which may lead to successful grasping. For the grasp contacts computation part, the presented algorithm produces a finite number of key points based on the 2D shape of the object from a specific point of view. Afterward, it will narrow down the candidate points and output a finite number of successful grasp poses based on three grasp quality metrics for various unknown objects. Three approaches are proposed in order to achieve center points which can describe different parts of a 2D shape. Then, the obtained points are used as the center of circles which are tangent to the 2D shape contour. Also, a new grasp quality metric is proposed. The time of the grasp and the amount of object disorientation after grasping are considered as a metric to evaluate the successfulness of the grasp. Simulation results demonstrate that the proposed algorithm for unknown object grasping can find a finite number of successful grasp poses for different seen or unseen objects without using any random point,

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Enhancing Impulse Response-Based SSVEP Decoding Method by Incorporating Temporal Local Correlation 7

Enhancing Impulse Response-Based SSVEP Decoding Method by In...

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7th International Conference on Biological Information and Biomedical engineering, BIBE 2024

作者： Huang, Jiayang Wang, Jiaxi Yang, Pengfei Xiong, Bang Zhang, Zhi-Qiang School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xidian University China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds United Kingdom

ISBN: (纸本)9783800762910

Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of SSVEPs, leading to decreased classification performance. Therefore, we propose an SSVEP decoding method that calculates template signals and spatial filters using the subject's impulse responses and periodic impulses, incorporating the temporally local correlation coefficient in the SSVEP decoding process. Then, we employ a combined correlation coefficient for SSVEP classification. The proposed method addresses the temporal instability of SSVEPs, with template signals that exhibit strong individual characteristics. Utilizing a public benchmark dataset, we achieved a classification accuracy of 87.43% and an ITR of 256.1 bits/min at 1s time window, demonstrating the performance of this method. Compared to other classical spatial filtering methods, the proposed method demonstrates superior classification performance, which can enhance the practical usability of SSVEP-BCI systems. © VDE VERLAG GMBH.

关键词： Wiener filtering

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Practical Implementation of Real-Time Waste Detection and Recycling based on Deep Learning for Delta Parallel robot

Practical Implementation of Real-Time Waste Detection and Re...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Hasan Jalali Shaya Garjani Ahmad Kalhor Mehdi Tale Masouleh Parisa Yousefi School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran School of Computer Engineering Imam Reza International University Mashhad Iran

Intelligent robots play an essential role in waste management and recycling due to their high speed and a wide variety of applications. In this paper, two methods for waste detection and accurate pick-and-place based on computer vision and neural networks are presented. The suggested methods have been put into practical application on a 3-DOF Delta parallel robot to show the accuracy and fastness of the foregoing method for real intelligence systems. The first method, Multi-Stage Detection, consists of two stages to detect the waste objects, namely, object localization and segmentation, and classification. The second method, known as One-Stage object detectors, such as YOLOv5, has the capability to simultaneously localize and classify the waste objects. The dataset utilized in this paper relies on the TrashNet dataset as its foundation. In order to improve the classification capabilities in the multi-stage method, a larger dataset was created by utilizing data augmentation. Also, for the one-stage method, a new multi-label dataset is constructed based on the TrashNet dataset. Additionally, the results of the experimental implementation were compared based on time and evaluation metrics for detection and classification. The ResNet50 model achieved the highest accuracy in the multi-stage method, with 99.31% accuracy. In the one-stage detection method, the YOLOv5x model achieved the best mAP (@IoU = 0.75) of 97.4%, which outperformed the YOLOv5s model by 0.8 percent; however, the inference speed of the YOLOv5x in comparison with the YOLOv5s models was six times as slow. Therefore, the YOLOv5s model was employed in real-time online waste detection, which resulted in 82.1% mAP (@IoU = 0.5) after being trained on real images from the waste-sorting platform.

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Experimental Study on Automatically Assembling Custom Catering Packages With a 3-DOF Delta robot Using Deep Learning Methods

Experimental Study on Automatically Assembling Custom Cateri...

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Iranian Conference on electrical engineering (ICEE)

作者： Reihaneh Yourdkhani Arash Tavoosian Navid Asadi Khomami Mehdi Tale Masouleh Electrical and Computer Engineering Department Human and Robot Interaction Laboratory University of Tehran Mechanical Engineering Department University of Tehran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper introduces a pioneering experimental study on the automated packing of a catering package using a two-fingered gripper affixed to a 3-degree-of-freedom Delta parallel robot. A distinctive contribution lies in the application of a deep learning approach to tackle this challenge. A custom dataset, comprising 1,500 images, is meticulously curated for this endeavor, representing a noteworthy initiative as the first dataset focusing on Persian-manufactured products. The study employs the YOLOV5 model for object detection, followed by segmentation using the FastSAM model. Subsequently, rotation angle calculation is facilitated with segmentation masks, and a rotated rectangle encapsulating the object is generated. This rectangle forms the basis for calculating two grasp points using a novel geometrical approach involving eigenvectors. An extensive experimental study validates the proposed model, where all pertinent information is seamlessly transmitted to the 3-DOF Delta parallel robot. The proposed algorithm ensures real-time detection, calibration, and the fully autonomous packing process of a catering package, boasting an impressive over 80% success rate in automatic grasping. This study marks a significant stride in advancing the capabilities of robotic systems for practical applications in packaging automation.

关键词： Deep learning YOLO Parallel robots Image segmentation 3-DOF Focusing Grasping

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Experimental Study on Neural Network-ARX and ARMAX Actuation Identification of a 3-DoF Delta Parallel robot for Accurate Motion Controller Design

Experimental Study on Neural Network-ARX and ARMAX Actuation...

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

作者： Saeed Rahimi Hasan Jalali Mohammad Reza Hairi Yazdi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665420952

This paper investigates the problem of actuator identification of a 3-DoF Delta parallel robot, by means of linear AutoRegressive Moving Average with eXogenous input (ARMAX) and nonlinear dynamic Neural Network AutoRegressive with eXogenous input (NN-ARX) methods. To this end, the ARMAX and NN-ARX approaches are used to develop a scheme which is capable of identifying a model for each actuator. Based on the ARMAX structure, an accurate model of the actuation system is derived. The model is then trained and tested using the data collected from a real robotic setup. Using a dynamic neural network capabilities, an identification and prediction scheme is designed for modeling the nonlinear dynamic behavior of the system. The NN-ARX is trained based on the collected data from the system, and the new trajectory data is used to validate both methods. By considering the results of experimental implementations, three servo motors are demonstrated to have different dynamical behavior which was expected to happen from the outset, due to uncertainty in fabrication of motors component and gearbox. In the identification and prediction stages, the Root Mean Square Errors (RMSE) index is used to validate and analyze the performance of each method using the validation data from new trajectories. In terms of predicting the output of the system, NN-ARX performed better than ARMAX with RMSE of 0.001441, compared to ARMAX with RMSE of 0.0886. Due to the high accuracy of the obtained models. thus they can be used in the design of motion controllers and modelling disturbances in the system.

关键词： Actuators Parallel robots Artificial neural networks Predictive models Mathematical models Trajectory Nonlinear dynamical systems

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Design a 3-DOF Delta Parallel robot by One Degree Redundancy along the Conveyor Axis, A Novel Automation Approach 5

Design a 3-DOF Delta Parallel Robot by One Degree Redundancy...

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5th IEEE Conference on Knowledge Based engineering and Innovation, KBEI 2019

作者： Ahangar, Sajjad Mehrabani, Mehdi Valizadeh Pouransari Shorijeh, Alireza Masouleh, Mehdi Tale Mechanical Eng. Dept. of Islamic Azad University Central Tehran Branch Tehran Iran School of Mechanical Eng. University of Tehran Tehran Iran Human Robot Interaction Laboratory School of Electrical and Computer Eng. University of Tehran Tehran Iran

ISBN: (纸本)9781728108728

This paper addresses the design of a 3 degree-of-freedoms (DOF) Delta parallel robot with one DOF redundancy attached to its base. This paper aims at presenting the state-of-the-art of the design and control for the proposed redundant robot for improving time and saving energy consumption. The main idea behind the proposed design consists in examining the performance of a movable Delta robot for pick-and-place purpose. To this end, some improvement has been made on the design of the robot. Spherical magnetic joints are used for the sake of easing the assembling and disassembling of components. At the end, kinematic properties of the robot are investigated. © 2019 IEEE.

关键词： Energy utilization

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