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Mediterranean Electrotechnical Conference (MELECON)

作者： Nathan Hiruy Vidya Sumathy Jakub Haluska George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

In this work, a real-time electro-pneumatic interface framework for emulating human muscular motion on a pneumatic-actuated muscle-based robot is presented. Pneumatic-actuated muscles are flexible actuators that contract and expand using pressure input and are widely used in medical and robotic research to generate motions that mimic human muscles. Electromyographic signals that measure the nerve stimulation of the muscles in an organism, can be used to emulate real-time muscular motion in Pneumatic Artificial Muscle based robots. Here, the proposed electro-pneumatic interface framework takes a real-time electromyographic signal as input and outputs corresponding pneumatic signals for actuating the pneumatic artificial muscles. The electromyographic sensors, attached to the human body, pick up the signals during motion, and the software electro-pneumatic interface computes the required differential pressure for the robot’s motion. In this approach, experimental data is used to model the transfer functions between variables such as the electromyographic signal, robot deflection angle, and differential pressure for the pneumatic artificial muscles. The proposed framework is experimentally validated on a humanoid robot made of pneumatic artificial muscles. The simulation and experiment results show the virtues of the proposed methodology.

关键词： Artificial muscles Humanoid robots Pneumatic systems Muscles Robot sensing systems Electromyography Real-time systems

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Visual recognition of wheel hubs with convolutional neural network 3

Visual recognition of wheel hubs with convolutional neural n...

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3rd International Conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Dai, Yining Fang, Zaojun Zhong, Caiming Ningbo University Faculty of Electrical Engineering and Computer Science NingBo Zhejiang China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology NingBo Zhejiang China

ISBN: (纸本)9798350331578

The traditional recognition methods of wheel hubs are mainly based on extracted feature matching. In practical production, their accuracy, robustness and processing speed are usually greatly affected. To overcome these problems, this paper proposes a recognition method based on convolutional neural network. The basic steps include two parts: wheel image pre-processing and wheel model classification. The image processing method, mainly using the detection algorithm of hough circles, obtains the center coordinates and radius of the wheel. Then it maps the ring-shaped wheel in right-angle coordinates to polar coordinates by the center coordinates and radius. This stepcan extract the ring-shaped feature information of the wheel image and reduce the influence generated by redundant features. Then a network architecture with an improved Resnet is designed to classify the wheel models. Finally, the wheel model recognition algorithm is evaluated, and the effectiveness of the method is verified through the comparison experiments of SVM, KNN and other models. The experiments show that the recognition accuracy can reach about 99.8% for 10 kinds of wheels. © 2023 IEEE.

关键词： Wheels

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An Interactive Platform for Farmers with Plant Disease Recognition Using Deep Learning Approach 1

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International Conference on Intelligent Systems and Human-Machine Collaboration, ICISHMC 2022

作者： Murthy, I. Aryan Mhatre, Niraj Ashish Oroskar, Rohit Vasudev Voriganti, Surya Kharatmol, Keerti Das, Poulami Department of Computer Engineering K.C. College of Engineering and Management Studies and Research Mumbai400603 India Automation and Robotics Specialist GlobalMed Inc TrentonK8V 5R5 Canada

ISBN: (数字)9789811984778

ISBN: (纸本)9789811984761

India’s primary sector is mainly contributed by the agriculture industry. The GDP from agriculture in India increased to 6630 billion INR in the fourth quarter of 2021 from 4076 billion INR in the third quarter of 2021. Due to the varied conditions of the subcontinent, the farmers may face different problems affecting the crop yield which ultimately affects the economy. Also, in case of the crops the decisions need to be immediate since if the crops are affected by a disease, then it can spread over the whole field affecting other crops. In order to help the farmers, come up with solutions to these problems, it has been aimed to create a platform on which the farmers can post their queries and other individuals can respond to those questions with answers. A deep learning approach has also been used to detect plant diseases. This will help the farmers to detect the disease of 14 different plant species with 38 sub-classes and also to post their queries in the proposed platform. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding 16th

A Model of the Contribution of Interneuron Diversity to ...

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16th International Conference on Brain Informatics, BI 2023

作者： Lorenz, Gabriel Matías Martínez-Cañada, Pablo Panzeri, Stefano Istituto Italiano di Tecnologia Rovereto Italy Department of Pharmacy and Biotechnology University of Bologna Bologna Italy Hamburg Germany Department of Computer Engineering Automation and Robotics University of Granada Granada Spain

ISBN: (纸本)9783031430749

Spiking neural network models that have studied how oscillations are generated by recurrent cortical circuits and how they encode information have been focused on describing the encoding of information about external sensory stimuli carried by feed-forward inputs in a two-population circuit configuration that includes excitatory cells and fast-spiking interneurons. Here we extend these models to explore the contribution of different classes of cortical interneurons to cortical oscillations. We found that in our extended model, the feed-forward stimulus is still mainly encoded in the gamma frequency range, consistent with earlier works using a single interneuron type. However, we also found that the information carried by different regions of the gamma frequency range was larger than the sum of the information carried by the two individual frequencies. This shows that the power values at different frequencies carried information about the feedforward input in a synergistic way. This is in contrast to previous models with only one interneuron type, which mainly led to redundant information between frequencies in the gamma range. These results suggest that interneuron diversity has useful properties for enriching the encoding of information in the gamma frequency range. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural network models

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MoCap-to-Visual Domain Adaptation for Efficient Human Mesh Estimation from 2D Keypoints

arXiv

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arXiv 2024年

作者： Uguz, Bedirhan Suat, Ozhan Karagoz, Batuhan Akbas, Emre Department of Computer Engineering METU ROMER Robotics Center Middle East Technical University Ankara Turkey

This paper presents Key2Mesh, a model that takes a set of 2D human pose keypoints as input and estimates the corresponding body mesh. Since this process does not involve any visual (i.e. RGB image) data, the model can be trained on large-scale motion capture (MoCap) datasets, thereby overcoming the scarcity of image datasets with 3D labels. To enable the model’s application on RGB images, we first run an off-the-shelf 2D pose estimator to obtain the 2D keypoints, and then feed these 2D keypoints to Key2Mesh. To improve the performance of our model on RGB images, we apply an adversarial domain adaptation (DA) method to bridge the gap between the MoCap and visual domains. Crucially, our DA method does not require 3D labels for visual data, which enables adaptation to target sets without the need for costly labels. We evaluate Key2Mesh for the task of estimating 3D human meshes from 2D keypoints, in the absence of RGB and mesh label pairs. Our results on widely used H3.6M and 3DPW datasets show that Key2Mesh sets the new state-of-the-art by outperforming other models in PA-MPJPE for both datasets, and in MPJPE and PVE for the 3DPW dataset. Thanks to our model’s simple architecture, it operates at least 12× faster than the prior state-of-the-art model, LGD [44]. Additional qualitative samples and code are available on the project website: https://***/. © 2024, CC BY.

关键词： Image enhancement

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A Traffic Model Integrating Long Short-Term Memory Networks with Multi-class Macroscopic Equations

A Traffic Model Integrating Long Short-Term Memory Networks ...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： K. Binjaku C. Pasquale S. Sacone E. K. Meçe Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Computer Engineering Polytechnic University of Tirana Albania

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Accurate traffic state estimation is crucial for effective transportation management and urban planning. In recent years, deep learning techniques, particularly Long Short-Term Memory (LSTMs) networks, have shown promise in capturing the complex temporal dynamics of traffic systems. This paper proposes a novel approach by integrating LSTM networks with the multi-class METANET model, considering on/off ramps, to enhance traffic state prediction accuracy. We develop both univariate and multivariate versions of the physics-informed multi-class LSTM model, enabling comprehensive exploration of traffic variables such as speed and flow for passenger and heavy vehicles. Experimental results demonstrate the superior predictive capabilities of our approach compared to traditional LSTM models, particularly in capturing the intricate relationship between speed and flow variables. Our findings underscore the significant benefits of incorporating physics-based principles into deep learning architectures for traffic prediction, facilitating more robust and reliable traffic state estimations for real-world applications.

关键词： Accuracy Urban planning Transportation Predictive models Reliability engineering Mathematical models Robustness State estimation Vehicle dynamics Long short term memory

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BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization

BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detecti...

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Mediterranean Conference on Control and Automation (MED)

作者： Mario A.V. Saucedo Nikolaos Stathoulopoulos Vidya Sumathy Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Object detection and global localization play a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to multi-layered 3D Scene Graphs for semantic scene understanding. This article proposes $BOX3D$ , a novel multi-modal and lightweight scheme for localizing objects of interest by fusing the information from RGB camera and 3D LiDAR. $BOX3D$ is structured around a three-layered architecture, building up from the local perception of the incoming sequential sensor data to the global perception refinement that covers for outliers and the general consistency of each object's observation. More specifically, the first layer handles the low-level fusion of camera and LiDAR data for initial 3D bounding box extraction. The second layer converts each LiDAR's scan 3D bounding boxes to the world coordinate frame and applies a spatial pairing and merging mechanism to maintain the uniqueness of objects observed from different viewpoints. Finally, BOX3D integrates the third layer that supervises the consistency of the results on the global map iteratively, using a point-to-voxel comparison for identifying all points in the global map that belong to the object. Benchmarking results of the proposed novel architecture are showcased in multiple experimental trials on public state-of-the-art large-scale dataset of urban environments.

关键词： Location awareness Point cloud compression Three-dimensional displays Laser radar Urban areas Merging Object detection

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Authentication using Biometric Data from Mobile Cloud Computing in Smart Cities 3

Authentication using Biometric Data from Mobile Cloud Comput...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Near East University Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near Artificial Intelligence Engineering Dept Near East University Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them. © 2023 IEEE.

关键词： Mobile cloud computing

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Cloud-Based Scheduling Mechanism for Scalable and Resource-Efficient Centralized Controllers

arXiv

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arXiv 2024年

作者： Seisa, Achilleas Santi Satpute, Sumeet Gajanan Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden

This paper proposes a novel approach to address the challenges of deploying complex robotic software in large-scale systems, i.e., Centralized Nonlinear Model Predictive Controllers (CNMPCs) for multi-agent systems. The proposed approach is based on a Kubernetes-based scheduling mechanism designed to monitor and optimize the operation of CNMPCs, while addressing the scalability limitation of centralized control schemes. By leveraging a cluster in a real-time cloud environment, the proposed mechanism effectively offloads the computational burden of CNMPCs. Through experiments, we have demonstrated the effectiveness and performance of our system, especially in scenarios where the number of robots is subject to change. Our work contributes to the advancement of cloud-based control strategies and lays the foundation for enhanced performance in cloud-controlled robotic systems. © 2024, CC BY.

关键词： Multi agent systems

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