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arXiv 2023年

作者： Merlo, Elena Lagomarsino, Marta Lamon, Edoardo Ajoudani, Arash Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

Automatic Interaction and Activity Recognition from Videos o...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Elena Merlo Marta Lagomarsino Edoardo Lamon Arash Ajoudani Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

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MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation

arXiv

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arXiv 2020年

作者： Liu, Jun Li, Qing Cao, Rui Tang, Wenming Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham United Kingdom

Predicting depth from a single image is an attractive research topic since it provides one more dimension of information to enable machines to better perceive the world. Recently, deep learning has emerged as an effective approach to monocular depth estimation. As obtaining labeled data is costly, there is a recent trend to move from supervised learning to unsupervised learning to obtain monocular depth. However, most unsupervised learning methods capable of achieving high depth prediction accuracy will require a deep network architecture which will be too heavy and complex to run on embedded devices with limited storage and memory spaces. To address this issue, we propose a new powerful network with a recurrent module to achieve the capability of a deep network while at the same time maintaining an extremely lightweight size for real-time high performance unsupervised monocular depth prediction from video sequences. Besides, a novel efficient upsample block is proposed to fuse the features from the associated encoder layer and recover the spatial size of features with the small number of model parameters. We validate the effectiveness of our approach via extensive experiments on the KITTI dataset. Our new model can run at a speed of about 110 frames per second (fps) on a single GPU, 37 fps on a single CPU, and 2 fps on a Raspberry Pi 3. Moreover, it achieves higher depth accuracy with nearly 33 times fewer model parameters than state-of-the-art models. To the best of our knowledge, this work is the first extremely lightweight neural network trained on monocular video sequences for real-time unsupervised monocular depth estimation, which opens up the possibility of implementing deep learning-based real-time unsupervised monocular depth prediction on low-cost embedded devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Unsupervised learning

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Integration of Convolutional Neural Networks for Real-Time Monitoring of Soil Health in Precision Agriculture

Integration of Convolutional Neural Networks for Real-Time M...

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International conference of electronics, Communication and Aerospace Technology (ICECA)

作者： Tummapudi Sunil Krishnagandhi Pachiappan S. Senthilrajan Y. Nagendar Renato R. Maaliw C. Pavin Department of CSE Malla Reddy Engineering College Malkajgiri Hyderabad India Electrical and Electronics Engineering Nandha Engineering College Erode Tamil Nadu India Department of Robotics& Automation Rajalakshmi Engineering College Chennai Tamil Nadu India Department of School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of CSE Southern Luzon State University Lucban Quezon Philippines Biomedical Engineer YAR Tech Services Hyderabad India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Convolutional Neural Networks (CNNs) represent a revolutionary breakthrough in improving crop productivity and sustainability when integrated into real-time monitoring systems for soil health in precision agriculture. CNNs are used in a novel way to analyze soil parameters directly from high-resolution photos taken by in-field sensors, such as moisture content, $\mathbf{p H}$ levels, and nutrient concentration. To forecast soil health measures, a CNN model was trained and validated using a collection of soil samples. The model achieved an accuracy rate of approximately $\mathbf{9 2 \%}$. The CNN-based technique shortens the time needed for soil health evaluation by almost $80 \%$ when compared to conventional approaches that involve manual sampling and laboratory analysis, allowing for quick corrective actions in the field. Additionally, more accurate and timely soil health monitoring was found to boost crop output prediction accuracy by $15 \%$. An up to $20 \%$ decrease in the amount of resources used, especially water and fertilizers, can be achieved by using real-time data processing to make educated decisions about irrigation, fertilizing, and pest management. The CNN model offers scalability and adaptability across a range of crop varieties and soil conditions, and it can be easily integrated into current agricultural IoT frameworks. All things considered, using CNNs for soil health monitoring offers an economical and effective way to improve the accuracy and output of contemporary farming methods.

关键词： Precision agriculture Irrigation Accuracy Soil Predictive models Real-time systems Data models Convolutional neural networks Monitoring Farming

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computer Vision and Image Understanding

SSRN

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SSRN 2022年

作者： Tang, Wenming Gong, Yuanhao Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science The University of Nottingham United Kingdom

Graph neural networks (GNNs) are ideally suited for mesh denoising. However, existing solutions such as those based on graph convolutional networks (GCNs) are built for a fixed graph thus making them not naturally generalisable to unseen meshes. Furthermore, their graph Laplacian based global node embedding algorithms can cause excessive smoothing while achieving feature preserving mesh denoising requires a GNN to posses local processing capability. This paper presents a mesh denoising method via a new Dense lOcal Graph neural NETwork (DOGNET). DOGNET implements a local node embedding algorithm that generates node embeddings through aggregating information from a node’s connected local neighbours which automatically make DOGNET inductive as well as effective for feature preserving mesh denoising. We present extensive experimental results to demonstrate quantitatively and qualitatively that DOGNET is superior to SOTA meshing denoising techniques. © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Dense graph convolutional neural networks on 3D meshes for 3D object segmentation and classification

arXiv

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arXiv 2021年

作者： Tang, Wenming Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence Robotics for Society Shenzhen China School of Computer Science The University of Nottingham United Kingdom

This paper presents new designs of graph convolutional neural networks (GCNs) on 3D meshes for 3D object segmentation and classification. We use the faces of the mesh as basic processing units and represent a 3D mesh as a graph where each node corresponds to a face. To enhance the descriptive power of the graph, we introduce a 1-ring face neighbourhood structure to derive novel multi-dimensional spatial and structure features to represent the graph nodes. Based on this new graph representation, we then design a densely connected graph convolutional block which aggregates local and regional features as the key construction component to build effective and efficient practical GCN models for 3D object classification and segmentation. We will present experimental results to show that our new technique outperforms state of the art where our models are shown to have the smallest number of parameters and consietently achieve the highest accuracies across a number of benchmark datasets. We will also present ablation studies to demonstrate the soundness of our design principles and the effectiveness of our practical models. © 2021, CC BY.

关键词： Graph neural networks

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5G Light-Emitting Biomedical Robots for Hospital Disinfection

5G Light-Emitting Biomedical Robots for Hospital Disinfectio...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Ugochukwu O. Matthew Renata L. Rosa Nwamaka U. Okafor Jazuli S. Kazaure Ogobuchi Daniel Okey Matthew Abiola Oladipupo Lateef Olawale Fatai Victor Nosakhare Oriakhi Demostenes Z. Rodriguez Computer Science Dept Federal University of Lavras Minas Gerais Brazil School of Elect Electronics Engineering University College Dublin Dublin Ireland Electrical Engineering Dept Hussaini Adamu Federal Polytechnic Kazaure Nigeria Center for Engineering Federal University of ABC Sao Paulo Brazil Data Science Department University of Salford Manchester England UK Robotics and Automation University of Salford Manchester England UK

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Hospital-acquired infections (HAIs) pose a significant challenge to healthcare systems worldwide, exacerbated by the COVID-19 pandemic. Current disinfection methods often fall short in ensuring comprehensive sterilization of high-contact areas, leading to persistent risks. This study addresses this gap by leveraging 5G biomedical internet of things (IoT) robots equipped with ultra-violet light emitting diodes (UV-LEDs) for effective disinfection. Our approach focuses on destroying the genetic material of pathogens, thereby preventing their spread within medical facilities. By integrating autonomous IoT 5G disinfection robots, we aim to enhance hospital sanitation and continuous infectious disease surveillance. A thorough review of nine biomedical studies underscores the efficacy of UV-based disinfection methods. We demonstrate that advancements in 5G infrastructure will significantly boost IoT and 5G medical robotic innovations, facilitating their application in both hospital settings and industrial healthcare automation. Thus, this paper presents a robust framework for utilizing 5G and IoT technologies in medical robotics to improve healthcare safety and efficacy.

关键词： COVID-19 5G mobile communication Hospitals Pandemics Service robots Robot kinematics Surveillance Light emitting diodes Safety Standards

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IoT Intervention in the monitoring of aquaponics metrics and control of input schedule

IoT Intervention in the monitoring of aquaponics metrics and...

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Smart Devices (ICSD), International Conference on

作者： Pooja Malik Praveen Kumar Malik Gotte Ranjith Kumar Jyoti S. Bali Anshul Mahajan M. Muhsen Hassan School of Electronics and Electrical Engineering Lovely Professional University Phagwara Punjab India School of Computer Science & Artificial Intelligence SR University Warangal Dept of Robotics and Automation Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune The Islamic University Najaf Iraq

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

The extensive use of technology across several Modern civilization is clearly impacted by technology, with cutting-edge techniques and tools playing critical roles in sectors including healthcare, business, agriculture, and education. Processes have been transformed by these improvements, which have increased their dependability and efficiency. With the incorporation of technology, agriculture has made enormous strides, with techniques like aquaponics developing as extremely effective and sustainable systems. Aquaponics is a novel farming method that combines hydroponics (soilless plant cultivation) and aquaculture (fish farming) to grow vegetables and fish at the same time. But the performance of this incorporated system relies upon on careful observation of a number of vital elements, such as water high-quality, pH tiers, temperature, and dissolved oxygen levels. To keep the health and growth of both fish and flora in an aquaponics device, it's miles critical to hold perfect situations. This highlights the significance of technological intervention, especially via the integration of Internet of Things (IoT) era, and calls for precision in regulating the input time table for fish feed and plant vitamins.

关键词： Technological innovation Schedules Urban areas Vegetation Water conservation Fish Safety Internet of Things Sustainable development Farming

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Modelling and Control of a Trailer Sprayer for Precision Spraying

Modelling and Control of a Trailer Sprayer for Precision Spr...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： André Baltazar Filipe N. Santos Antόnio Paulo Moreira Salviano Pinto Soares M. J. C. S. Reis José Boaventura Cunha CRIIS-Centre for Robotics in Industry and Intelligent Systems INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto Portugal ECT—School of Science and Technology UTAD—University of Trás-os-Montes and Alto Douro Vila Real Portugal FEUP—Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) Portugal Intelligent Systems Associate Laboratory (LASI) Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Precision spraying in agriculture is crucial for optimizing the application of pesticides while minimizing environmental impact. Despite significant advancements in control models for spraying systems, predictive control algorithms were not used. This paper addresses this gap by proposing a real-time control framework that integrates predictive control strategies to ensure consistent pressure output in a trailer sprayer. Based on information from various sensors, the framework anticipates and adapts to dynamic environmental conditions, enhancing accuracy and sustainability in spraying practices. A methodology is developed to define a proportional valve model. Based on this valve model, the predictive control model optimizes valve movements to minimize errors between predicted and reference pressures, thereby improving spraying efficiency. This study demonstrates the viability of predictive control in improving precision spraying systems applicable to autonomous robots, encouraging future advances in agricultural spraying technologies.

关键词： Adaptation models Spraying Predictive models Valves Real-time systems Agriculture Sensors

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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Liu, Huan Li, Liangyan Wu, Zijun Dong, Yubo Li, Yuyan Qiu, Tian He, Yu Lu, Yonghong Wu, Yinwei Jiang, Zhenxiang Liu, Songhua Yang, Xingyi Jing, Yongcheng Benjdira, Bilel Ali, Anas M. Koubaa, Anis Yang, Hao-Hsiang Chen, I-Hsiang Chen, Wei-Ting Huang, Zhi-Kai Chen, Yi-Chung Hsieh, Chia-Hsuan Chang, Hua-En Chiang, Yuan-Chun Kuo, Sy-Yen Guo, Yu Gao, Yuan Liu, Ryan Wen Lu, Yuxu Qu, Jingxiang He, Shengfeng Ren, Wenqi Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Yang, Lehan Wu, Alex Jiahao Mai, Tiancheng Cong, Xiaofeng Yin, Xuemeng Yin, Xuefei Emad, Hazim Abdallah, Ahmed Yasser, Yahya Elshahat, Dalia Elbaz, Esraa Li, Zhan Kuang, Wenqing Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhang, Zhao Wei, Yanyan Wang, Junhu Zhao, Suiyi Zheng, Huan Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Jiang, Kui Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph ETcTI Universitatea Politehnica Timisoara Romania ICTEAM UCL Belgium Computer Vision Lab University of Wuerzburg Germany Computer Vision Lab ETH Zurich Switzerland Department of Electrical and Computer Engineering McMaster University Canada Department of Electrical and Computer Engineering University of Alberta Canada McMaster University Canada Xidian University China Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Wuhan University of Technology China Singapore Management University Singapore Singapore Sun Yat-sen University China Electrical Engineering Department Pennsylvania State University United States The University of Sydney Australia Southeast University China University of California Los Angeles United States Beijing Jiaotong University China Mansoura Univeristy Egypt College of Information Science and Technology Jinan University China Department of Information Technology Uppsala University Sweden Hefei University of Technology China Zhejiang Dahua Technology China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology. © 2023 IEEE.

关键词： Demulsification

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