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

作者： Hamed Hamzeh Kiarash Shahroozi Ahmad Nabipour Parham Kazemi Mohammad Malek-zahedi Mehdi Hallajian Hadi Moradia Advanced Robotics and Intelligent Systems Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Traditional puppet manipulation systems often require human operators to be physically present in tight and cramped locations. This leads to challenges in the positioning and effective operation of puppets, particularly in the presence of children in educational settings. In this paper, we present the design, development, and integration of a cost-effective robotic system for remotely manipulating puppets in real-time. Additionally, the system integrates a YOLOv8 Pose Detection model, trained on a custom dataset with an accuracy of 91.4%, enabling real-time gesture recognition and allowing the robot to be controlled by imitating the motions of a manually operated puppet. The system demonstrates the potential for many applications, including interactive entertainment, teleoperation, and education.

关键词： Hands Mechatronics Education Entertainment industry Manuals Gesture recognition Real-time systems Maintenance Robots Intelligent control

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Finding Optimal Nash Equilibria in Multiplayer Games via Correlation Plans 22

Finding Optimal Nash Equilibria in Multiplayer Games via Cor...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Zhang, Youzhi An, Bo Subrahmanian, V.S. Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation CAS Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science Northwestern University Evanston United States

Designing efficient algorithms to compute a Nash Equilibrium (NE) in multiplayer games is still an open challenge. In this paper, we focus on computing an NE that optimizes a given objective function. Finding an optimal NE in multiplayer games can be formulated as a mixed-integer bilinear program by introducing auxiliary variables to represent bilinear terms, leading to a huge number of bilinear terms, making it hard to solve. To overcome this challenge, we propose a novel algorithm called CRM based on a novel mixed-integer bilinear program with correlation plans for some subsets of players, which uses Correlation plans with their Relations to strictly reduce the feasible solution space after the convex relaxation of bilinear terms while Minimizing the number of correlation plans to significantly reduce the number of bilinear terms. Experimental results show that our algorithm can be several orders of magnitude faster than the state-of-the-art baseline. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Computation theory

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RoboRover - A Jetson Nano Powered Robot Car for Object Following, Obstacle Avoidance, and Wall Following 14

RoboRover - A Jetson Nano Powered Robot Car for Object Follo...

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Huynh, Trinh Walter, James Aveta, Federica University of Michigan Robotics Department Ann ArborMI48109 United States Wentworth Insitute of Technology Electrical and Computer Engineering Program BostonMA02115 United States

ISBN: (纸本)9798350304138

An intelligent and self-sufficient robot is essential across a wide range of fields, including transportation, industry, space exploration, and defense. Mobile robots possess the capability to undertake diverse tasks such as handling materials, aiding in disaster scenarios, conducting patrols, and executing rescue operations. As a result, the development of an autonomous robot that can navigate through both unchanging and ever-changing surroundings has become important. The primary objective of mobile robot navigation revolves around ensuring the seamless and secure traversal of the robot through complex environments, starting from an initial position, and reaching a designated goal position. This paper presents the design and implementation of a Jetson Nano powered robot car which uses local sensors to interact with an unknown environment. Object following, obstacle avoidance, and wall following features are built for the car to navigate to reach its desired destinations. © 2023 IEEE.

关键词： Mobile robots

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

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SN computer Science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN computer vision Deep learning Smart glasses Smoke monitoring

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A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

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computers, Materials & Continua 2023年第2期74卷 2817-2838页

作者： Noureldin Eissa Uswah Khairuddin Rubiyah Yusof Ahmed Madani Centre for Artificial Intelligence&Robotics Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Computer Engineering Arab Academy for ScienceTechnology and Maritime TransportCairo2033Egypt Department of Mechanical Precision Engineering Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia

Epigenetics is the study of phenotypic variations that do not alter DNA *** epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human *** of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in ***,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA ***,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying *** each DNA methylation site,a beta value(β)is used to reflect the methylation ***,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user *** study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation *** efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research *** performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data *** NBDC-GA outperformed the IGA in terms of convergence ***,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,*** proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities th

关键词： Cancer clustering DNA methylation feature selection metaheuristic technique the cancer genome atlas

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Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments

Care3D: An Active 3D Object Detection Dataset of Real Roboti...

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2023 IEEE International Symposium on Multimedia, ISM 2023

作者： Adam, Michael G. Eger, Sebastian Piccolrovazzi, Martin Iskandar, Maged Vogel, Joern Dietrich, Alexander Bien, Seongjien Skerlj, Jon Naceri, Abdeldjallil Steinbach, Eckehard Albu-Schaeffer, Alin Haddadin, Sami Burgard, Wolfram Technical University of Munich Media Technology Department of Computer Engineering School of Computation Information and Technology Munich Germany Wessling Germany Robotics and Systems Intelligence Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Munich Germany University of Technology Nuremberg Engineering Department Nuremberg Germany

ISBN: (纸本)9798350395761

As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We further provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot. © 2023 IEEE.

关键词： Object detection

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A Semiautomatic Image Processing-Based Method for Binary Segmentation of Lungs in Computed Tomography Images

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SN computer Science 2024年第6期5卷 689页

作者： Ramos, Leo Pineda, Israel School of Mathematical and Computational Sciences Yachay Tech University Deep Learning for Autonomous Driving Robotics and Computer Vision (DeepARC) Research Group Yachay Tech University Kauel Inc. Computer Vision Center Universitat Autònoma de Barcelona College of Sciences and Engineering Universidad San Francisco de Quito

Precise biomedical image segmentation is pivotal in medical diagnosis and treatment. Among various methodologies, image processing-based techniques are useful due to their swift and consistent results, free from the dependency on extensive training data required by deep learning approaches. This study introduces a tri-phase, semi-automated pipeline for lung computed tomography image segmentation, beginning with a preprocessing phase that includes contrast enhancement, noise reduction, and edge detection to improve image quality. This is followed by the application of the region growing method for segmentation and is completed with morphological closing to refine the final output. Comparative tests reveal that our technique surpasses prevalent methods like split and merge, Otsu’s technique, and basic region growing in terms of the dice coefficient (DC) and intersection over union (IoU). Specifically, our approach achieved an average DC of 0.9633, contrasting with 0.4668, 0.005, and 0.4067 recorded by Otsu’s, split and merge, and basic region growing techniques respectively. Similarly, the average IoU for our method was 0.9341, in comparison to 0.3105, 0.0660, and 0.2605 for the aforementioned methods. Additionally, comparative analysis with contemporary segmentation models demonstrates that our approach is competitive, even without relying on large training datasets. These results highlight our method’s efficiency and practicality, distinguishing it as a compelling option in medical scenarios where access to extensive training data is limited. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image analysis Image processing Image segmentation Medical image Pattern recognition Region growing

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Convergence Analysis for Delay Dependent LFC Scheme for Power Systems Networks Over Open Communication Networks with Disturbances

Convergence Analysis for Delay Dependent LFC Scheme for Powe...

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Islam, Shafiqul Ncf 263 Xavier University of Louisiana Dual-Degree Computer Science and Engineering Intelligent Robotics Mechatronics and Autonomous Systems Lab New OrleansLA70125 United States

ISBN: (纸本)9798350336504

This paper presents a delay-dependent convergence criterion for load frequency control (LFC) for smart power grid networks with the presence of disturbances and time-varying delays. The LFC scheme comprises delayed remote measurement and control signals with local state feedback signals. The convergence criteria are developed by using Lyapunov-Krasovskii functional for both symmetrical and asymmetrical time-varying delays associated with remote measurement and control channel. First, the analysis shows that all the power system states are bounded and asymptotically stable without the presence of disturbances. Then, the convergence analysis proves that all the power systems states are bounded and their bounds guarantee the convergence property of the H∞ control with the presence of bounded disturbances. These convergence properties are achieved by using well know Linear Matrix Inequalities (LMIs). Compared with the existing design, the proposed analysis uses both symmetrical and asymmetrical time-varying delays in the measurement channel from RTU to the control center and in the control channel from the control center to the power generation unit. In contrast with reported designs, the proposed design and analysis uses the disturbances associated with the closed loop systems appearing from modeling errors and other external load disturbances and faults. © 2023 IEEE.

关键词： Closed loop systems

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Design and Model Predictive Control of a Mars Coaxial Quadrotor

Design and Model Predictive Control of a Mars Coaxial Quadro...

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2022 IEEE Aerospace Conference, AERO 2022

作者： Patel, Akash Banerjee, Avijit Lindqvist, Bjorn Kanellakis, Christoforos Nikolakopoulos, George Robotics and Ai Team Lulea University of Technology Department of Computer Electrical and Space Engineering LuleaSE-97187 Sweden

ISBN: (纸本)9781665437608

Mars has been a prime candidate for planetary explo-ration of the solar system because of the science discoveries that support chances of future habitation on this planet. The Mars exploration landers and rovers have laid the foundation of our understanding of the planet's atmosphere and terrain. However, the rovers have presented limitations in terms of their pace, travers ability, and exploration capabilities from the ground and thus, one of the main field of interest for future robotic mission to Mars is to enhance the autonomy of this exploration vehicles. Martian caves and lava tubes like terrains, which consists of uneven ground, poor visibility and confined space, makes it impossible for wheel based rovers to navigate through these areas. In order to address these limitations and advance the exploration capability in a Martian terrain, this article presents the design and control of a novel coaxial quadrotor Micro Aerial Vehicle (MAV). As it will be presented, the key contributions on the design and control architecture of the proposed Mars coaxial quadrotor, are introducing an alternative and more enhanced, from a control point of view concept, when compared in terms of autonomy to Ingenuity. Based on the presented design, the article will introduce the mathematical modelling and automatic control framework of the vehicle that will consist of a linearised model of a co-axial quadrotor and a corresponding Model Pre-dictive Controller (MPC) for the trajectory tracking. Among the many models, proposed for the aerial flight on Mars, a reliable control architecture lacks in the related state of the art. The MPC based closed loop responses of the proposed MAV will be verified in different conditions during the flight with additional disturbances, induced to replicate a real flight scenario. For the model validation purpose, the Mars coaxial quadrotor is sim-ulated inside a Martian environment with related atmospheric conditions in the Gazebo simulator, which will

关键词： Controllers

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