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1st IEEE International Conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Abhang, L.B. Changala, Ravindra Ghosh, Anudeb Manage, Prabhakar S Rao, Vuda Sreenivasa Kiran Bala, B. Loni Under. SPPU University Pravara Rural Engineering College Automation and Robotics Dept Pune India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Siksha - Bhavana Department of Environmental Studies Visva-Bharati Santiniketan India Kle Technological University's Msscet Belagavi Campus Department of Electronics and Communication Karnataka Belagavi India Education Foundation Department of Computer Science and Engineering Koneru Lakshmaiah A.P Vaddeswaram522302 India K.Ramakrishnan College of Engineering The Department Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350372748

The study explores the intersection of robotics and neuro-cognitive rehabilitation to offer personalized assistance for individuals with neurological disorders, emphasizing autism spectrum disorder (ASD). The proposed methodology integrates Genetic Algorithms (GAs) to optimize the performance of robotic interventions. The data collection phase involves compiling a comprehensive image dataset capturing facial expressions, gestures, and relevant visual cues associated with ASD. Leveraging robotic platforms designed for therapeutic interventions, feature extraction techniques identify intricate patterns within the data. Advanced algorithms, including GAs, classify the dataset into positive (ASD) and negative (non-ASD) categories. The framework introduces a Diagnosis Matrix for enhanced diagnostic precision, correlating observed robotic interactions with clinical assessments. An Ontology Knowledge base adapts responses based on evolving patient needs. The proposed method surpasses all others, achieving an accuracy of 95.08% and demonstrating superior precision, recall, and F1-score metrics. This indicates the efficacy of the proposed approach in achieving a well-balanced performance with high accuracy and robustness in correctly identifying positive instances. The results underscore the potential of the proposed method for classification tasks, showcasing its superiority in comparison to traditional SVM, CNN, and even a well-established deep learning architecture like VGG-16. The Receiver Operating Characteristic curve validates the model's discriminatory power. © 2024 IEEE.

关键词： Genetic algorithms

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Automatic strain sensing measurement on steel beam using strain gauge

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Materials Today: Proceedings 2021年 45卷 2578-2580页

作者： L.D. Vijay Anand D. Hepsiba Shanthi Palaniappan B. Sumathy P. Vijayakumar S. Sheeba Rani Department of Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore 641114 Tamilnadu India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore 641114 Tamilnadu India Department of Computer Applications Sri Krishna College of Engineering and Technology Coimbatore 641008 Tamilnadu India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai 600044 Tamilnadu India School of Electronics Engineering Vellore Institute of Technology Chennai 600127 Tamilnadu India Department of EEE Sri Krishna College of Engineering and Technology Coimbatore 641008 Tamilnadu India

The metal beams are used in various fields such as industries, construction and vehicle structure. The capacity of load in metal beam depends on the metal and applications. The measurement of strain on the beam is difficult using conventional method. In recent years, strain gauge is used to measure the strain on mild steel beams. It is a sensor whose resistance values are changed with respect to the applied load. The change in resistance is small in magnitude which its effect can be sensed by a wheatstone bridge. It is directly proportional to the applied strain. The different process parameters and its effects are studied through optimization and variance analysis.

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Spoken dialogue-based remote control of educational mobile robot with mecanum wheels 17

Spoken dialogue-based remote control of educational mobile r...

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17th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2019

作者： Chivarov, Nayden Chikurtev, Denis Pleva, Matus Ondas, Stanislav Juhar, Jozef Liao, Yuan-Fu Institute of Information and Communication Technologies Bulgarian Academy of Sciences BAS Acad. G. Bonchev St. Sofia1113 Bulgaria Robotics Lab European Polytechnical University 23 Sv. Sv. Kiril i Metodiy Str. Pernik2300 Bulgaria Department of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University of Kosice Letna 9 Kosice04200 Slovakia Department of Electronic Engineering College of Electrical Engineering &computer Science National Taipei University of Technology Zhongxiao E. Rd. 406 Taipei10608 Taiwan

ISBN: (纸本)9781728149677

This paper describes a new platform of educational mobile robot with Mecanum wheels and voice enabled dialogue-based human robot interaction model was established. Moreover, new trends in using deep neural networks for cloud-and edge-based speech recognition was described in detail. Finally, the progress of both the cloudand edge-based speech recognition was reported. Especially, promising usability test result on resisting room reverberation and background noise had been achieved by the proposed convolutional neural network (CNN)-based wake-up word (WUW) spotting system. © 2019 IEEE.

关键词： Mobile robots

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Gait control of modular climbing caterpillar robot

Gait control of modular climbing caterpillar robot

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： W. Wang H. X. Zhang K. Wang J. W. Zhang W. H. Chen Robotics Institute School of Mechanical Engineering and Automation Beijing Aeronautics and Astronautics University Beijing China Dept of Informatics University of Hamburg Beijing Germany Department of Computer Science University of Hamburg Hamburg Germany Robotics Institute Beijing University of Aeronautics and Astronautics Beijing China Center of Electrical and Electronics Engineering Beijing University of Aeronautics and Astronautics Beijing China

The concept of a modular climbing caterpillar robot is inspired by the kinematics of the real caterpillar. Due to the fixed constraints between the suckers and wall, the gait of the caterpillar robot engages a changing kinematic chain which is from an open chain to a closed chain, and then to an open chain orderly. This paper investigates the joint control methods of the gait in detail. During the open chain periods, an Unsymmetrical Phase Method (UPM) is applied to ensure the reliable attachment of the passive suckers to the wall. But in the closed chain state, a four links kinematics model is adopted to fulfill the fixed constraints produced by the attached suckers. By combining the two methods together, we acquire complete joint control trajectories for a modular caterpillar robot with seven joints. At the end of this paper, on-site tests confirm the above principles and the validity of the climbing gait.

关键词： Legged locomotion Kinematics Climbing robots robotics and automation Mobile robots Intelligent robots Mechanical engineering computer science Joining processes Tail

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Automated classification of hand gestures using a wristband and machine learning for possible application in pill intake monitoring

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computer METHODS AND PROGRAMS IN BIOMEDICINE 2022年 219卷 106753-106753页

作者： Moccia, Sara Solbiati, Sarah Khornegah, Mahshad Bossi, Federica F. S. Caiani, Enrico G. [a]The Biorobotics Institute Scuola Superiore Sant'Anna Pisa Italy [b]Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy [c]Department of Electronics Information and Biomedical Engineering Politecnico di Milano P.zza L. da Vinci 32 Milan 20133 Italy [d]Institute of Electronics Computer and Telecommunication Engineering (IEIIT) National Research Council of Italy (CNR) Milan Italy

Background: Thanks to the increased interest towards health and lifestyle, a larger adoption in wearable devices for activity tracking is present among the general population. Wearable devices such as smart wristbands integrate inertial units, including accelerometers and gyroscopes, which can be utilised to perform automatic classification of hand gestures. This technology could also find an important application in automatic medication adherence monitoring. Accordingly, this study aims at comparing the performance of several Machine-Learning (ML) and Deep-Learning (DL) approaches for the automatic identification of hand gestures, with a specific focus on the drinking gesture, commonly associated to the action of oral intake of a pill-packed ***: A method to automatically recognize hand gestures in daily living is proposed in this work. The method relies on a commercially available wristband sensor (MetaMotionR, MbientLab Inc.) integrating tri-axial accelerometer and gyroscope. Both ML and DL algorithms were evaluated for both multi gesture (drinking, eating, pouring water, opening a bottle, typing, answering a phone, combing hair, and cutting) and binary gesture (drinking versus other gestures) classification from wristband sensor signals. Twenty-two participants were involved in the experimental analysis, performing a 10 min acquisition in a laboratory setting. Leave-one-subject-out cross validation was performed for robust performance assessment. Results: The highest performance was achieved using a convolutional neural network with long-short term memory (CNN-LSTM), with a median f1-score of 90.5 [first quartile: 84.5;third quartile: 92.5]% and 92.5 [81.5;98.0]% for multi-gesture and binary classification, ***: Experimental results showed that hand gesture classification with ML/DL from wrist accelerometers and gyroscopes signals can be performed with reasonable accuracy in laboratory settings, paving the way for a new genera

关键词： Hand-gesture classification Monitoring medical adherence Wearable sensors Machine learning Deep learning CNN-LSTM

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Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification

Ensemble of CNN classifiers using Choquet Fuzzy Integral Tec...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Boonchana Purahong Woranidtha Krungseanmuang Kasi Tenghongsakul Tuanjai Archevapanich Parkpoom Khunthawiwone Nitjaree Satayarak Attasit Lasakul Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electrical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.

关键词： Measurement Accuracy Electronic equipment Printed circuits Predictive models Feature extraction Internet

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Distributed Model Predictive Formation Control of Robots with Sampled Trajectory Sharing in Cluttered Environments

Distributed Model Predictive Formation Control of Robots wit...

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

作者： Sami Satir Yasin Furkan Aktaş Simay Atasoy Mert Ankarali Erol Şahin ASELSAN Research Center Ankara Turkey Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Center for Robotics & AI Middle East Technical University Ankara Turkey Department of Computer Engineering Middle East Technical University Ankara Turkey

In this paper, we propose a Model Predictive Control (MPC) based distributed formation control method for a multi-robot system (MRS) that would move them among dynamic obstacles to a desired goal position. Specifically, after formulating the formation control, as a distributed version of MPC, we propose and evaluate three information-sharing schemes within the MRS; namely sharing (i) positions, (ii) complete predicted trajectories, and (iii) exponentially-sampled predicted trajectories. Using a simplified kinematic model for robots, we conducted systematic simulation experiments in (a) scenarios, where the robots are instructed to switch places, as one of the most challenging forms of formation changes, and in (b) scenarios where robots are instructed to reach a goal, within environments containing dynamic obstacles. In a set of systematic experiments conducted in simulation and with mini quadcopters, we have shown that sharing of exponentially-sampled trajectories (as opposed to positions, or complete trajectories) among the robots provides near-optimal paths while decreasing the required computation cost and communication bandwidth. Surprisingly, in the presence of noise, sharing exponentially-sampled trajectories among the robots decreased the variance in the final paths. The proposed method is demonstrated on a group of Crazyflie quadcopters.

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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Detection of Gender and Age Category from Speech

Detection of Gender and Age Category from Speech

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World Symposium on Digital Intelligence for Systems and Machines (DISA)

作者： Renát Haluška Michal Popovič Matus Pleva Matthew Frohman Department of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University Kosice Kosice Slovakia Department of computer science Perception and Robotics Group University of Maryland College Park MD United States

The main goal of this paper was to find out how the gender and age group acoustical models behave on audio data that is in no way related to the data corpora used to train and evaluate the models. These models could be used for early predator detection in minors’ audio chats. The datasets used for training were mainly composed of English speakers. One part was made up of speakers whose native language is English and the rest use English as a secondary language.

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Implementation of IoT Concept for Early Diagnostic of Subacute Rumen Acidosis in Cows

Implementation of IoT Concept for Early Diagnostic of Subacu...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Anatolijs Zabasta Nadezda Kunicina Uldis Grunde Janis Judvaitis Ilga Sematovica Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga Latvia Robotics and Machine Perception laboratory Institute of Electronics and Computer Science Riga Latvia Cyber-Physical Systems laboratory Institute of Electronics and Computer Science Riga Latvia Faculty of Veterinary Medicine of Latvia University of Life Sciences and Technologies Jelgava Latvia

ISBN: (数字)9781728169491

ISBN: (纸本)9781728169507

Highly productive dairy cow' ration under intensive production conditions causes the development of subacute rumen acidosis (SARA). In this paper we discuss a reticulo-ruminal long-acting cyber-physical diagnostic system' prototype, which applies Internet of Things (IoT) for monitoring rumen parameters of cows. The new diagnostic system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes gateways for data collection from boluses, an MQTT (Message Queuing Telemetry Transport) broker, a web server and a database. The diagnostic system' prototype provides timely data on cow health status to the users, so they can evaluate the cow' health status and implement further actions. Therefore, the use of diagnostic system provides opportunity to increase cow productivity, longevity and to save maintenance cost of milk farms. The results of the first stage of the research are discussed in this paper.

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