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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Farooq, Adil Nain Zukhraf, Syeda Zillay Shafi, Sadman Kamal, Mohsin Chumuang, Narumol University Of Cyprus Department Of Electrical And Computer Engineering Cyprus Penn State University Department Of Electrical Engineering United States National University Of Sciences And Technology School Of Electrical Engineering And Computer Science Pakistan Muban Chombueng Rajabhat University Department Of Digital Media Technology Thailand

ISBN: (纸本)9798350370096

Autonomous drone racing competitions serve as a testing ground for enhancing the perceptual, planning, and control aspects of micro unmanned aerial vehicles (MAVs). This study thoroughly outlines the strategy, methodology, and technical development of a lightweight collision avoidance module for small drones, employed during our participation in the IMAV 2022 Nanocopter AI Challenge held at TU Delft, The Nether-lands. Tailored for nano-sized drones, this competition presented challenges related to agile maneuvers, obstacle avoidance, and navigation in a dynamic indoor arena. We provide detailed insights into our obstacle avoidance algorithm, employing an AI computer vision approach and showcasing its robust performance in dynamic environments. The accuracy of the proposed strategy was verified through simulation testing, highlighting the effectiveness of our approach in a simulated Webots environment across diverse parameter settings. The shared insights aim to contribute significantly to the ongoing evolution of autonomous drone navigation. Moreover, the simulation of our proposed strategy can be seen on the YouTube video link: https://***/HVZPAfyf57M. © 2024 IEEE.

关键词： Drones

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HoLens:A visual analytics design for higher-order movement modeling and visualization

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Computational Visual media 2024年第6期10卷 1079-1100页

作者： Zezheng Feng Fang Zhu Hongjun Wang Jianing Hao Shuang-Hua Yang Wei Zeng Huamin Qu Department of Computer Science and Engineering The Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Thrust of Computational Media and Arts(CMA) The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511458China Department of Computer Science University of ReadingBerkshire RG66AHUK Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement *** methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the ***,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban *** overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban *** mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral ***,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state *** real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using *** also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.

关键词： data visualization movement modeling state sequence visualization movement visualization urban visual analytics

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Multidimensional image morphing-fast image-based rendering of open 3D and VR environments

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 155-172页

作者： Simon SEIBT Bastian KUTH Bartosz von Rymon LIPINSKI Thomas CHANG Marc Erich LATOSCHIK Game Tech Laboratory Faculty of Computer ScienceNuremberg Institute of TechnologyNuremberg 90489Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and ArtsCoburg 96450Germany Human-Computer Interaction Group Institute of Computer ScienceUniversity of WuerzburgWuerzburg 97074Germany

Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains *** This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and ***,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image *** this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of *** rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image *** detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in *** addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light *** Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,*** achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering *** Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR ***,the handling of morphing artifacts in the parallax image regions remains a topic for future resea

关键词： computer graphics 3D real-time rendering computer vision Image morphing Virtual reality

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An Intelligent Diagnostic Framework Based on Digital Twins and Partial Transfer Learning: Methodology and Industrial Application

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 1-13页

作者： Azari, Mehdi Saman Ricci, Liana Santini, Stefania Flammini, Francesco Linnaeus University Department of Computer Science and Media Technology Växjo352 52 Sweden GE Power Sweden Steam Power Unit Norrköping602 23 Sweden University of Naples Federico II Department of Electrical Engineering and Information Technology Naples80125 Italy Mälardalen University School of Innovation Design and Engineering Eskilstuna632 17 Sweden Linnaeus University Department of Computer Science and Media Technology Växjö352 52 Sweden

In Industry 4.0, efficient fault diagnosis is crucial for predictive maintenance but is often hindered by significant domain shifts between training and testing domains and lack of training datasets, limiting the effectiveness of machine learning in practice. Transfer learning has been used to address these challenges by utilizing knowledge from similar source domains. However, the scarcity of faulty data from real machines and the difficulty of obtaining labeled datasets from lab machines as source domain pose significant obstacles. This paper presents a novel diagnostic framework that integrates digital twins and transfer learning to overcome these limitations. Digital twins generate training datasets, while a model update strategy based on parameter sensitivity analysis improves adaptability. The framework also incorporates a partial transfer diagnostic model with a double attention mechanism to handle data distribution discrepancies and label inconsistencies between digital twins and real machines. Validated on an industrial rotating machine case study using real data, the proposed approach improves diagnostic accuracy by over 10% compared to state-of-the-art methods. © 2023 IEEE.

关键词： Predictive maintenance

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd Science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Effective Supervision for Enhancing Quality of Doctoral Research in computer Science and engineering

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SN computer Science 2023年第5期4卷 678页

作者： Malekian, Reza Department of Computer Science and Media Technology Malmö University Malmö 205 06 Sweden Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria 0083 South Africa

This article reflects on effective supervision and possible guidance for enhancing quality of doctoral research in the computer science and engineering field. The aims of this study are (1) to understand supervision and the role of supervisors in the quality of doctoral research, (2) to elaborate on effective supervision in the computer science and engineering field and challenges in effective supervision, and (3) to identify key indicators for evaluating effective supervision with a view to improving the quality of doctoral research. After studying various pieces of literature and conducting interviews with experienced supervisors and doctoral students, the article concludes by describing important characteristics in effective supervision. Some of the features for effective supervision are common to other areas of research;however, in computer science and engineering and similar fields, it is important that a supervisor takes the role of a team member by giving proper advice on the reports, algorithm and mathematical modeling developed in the research, and demonstrating the ability to provide advice on complex problems with practical approaches. © 2023, The Author(s).

关键词： computer science and engineering Doctoral research Doctoral students Effective supervision Quality Supervisor

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Investigating Haptic Co-creation with Reinforcement Learning 14th

Investigating Haptic Co-creation with Reinforcement Learnin...

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14th International Conference on Human Haptic Sensing and Touch Enabled computer Applications, EuroHaptics 2024

作者： Regimbal, Juliette Cooperstock, Jeremy R. Department of Electrical and Computer Engineering McGill University Montréal Canada Centre for Interdisciplinary Research in Music Media and Technology McGill University Montréal Canada

ISBN: (纸本)9783031700606

Co-creative computer agents can be used to support designers they work alongside. However, this approach to co-creativity has not been studied in haptics. In this paper, we investigate this topic by applying an interactive reinforcement learning technique developed for sound synthesis to a two degree-of-freedom force-feedback design space. We developed a prototype authoring tool to study the interaction between designers and this type of co-creative agent. As these agents are trained on the designer’s feedback in real-time, this method is flexible and could be applied to other hardware and software configurations. Five participants were recruited to use the tool in two artistic tasks and were interviewed about their experiences. An analysis of the participants’ comments and actions indicated that co-creation in haptics can help designers explore new effects if they are sufficiently able to control the extent and direction of exploration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Crops Leaf Diseases Recognition:A Framework of Optimum Deep Learning Features

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computers, Materials & Continua 2023年第1期74卷 1139-1159页

作者： Shafaq Abbas Muhammad Attique Khan Majed Alhaisoni Usman Tariq Ammar Armghan Fayadh Alenezi Arnab Majumdar Orawit Thinnukool Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai50200Thailand

Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely *** couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial ***,they still faced some challenges such as similarity in disease symptoms and irrelevant features *** this article,we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases *** proposed architecture consists of five *** the first step,data augmentation is performed to increase the numbers of training *** the second step,pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer *** features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search *** best selected features are finally classified using machine learning classifiers such as SVM,and named a few more for final classification *** proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant *** proposed architecture achieved an accuracy of 100.0%,92.9%,and 99.2%,*** with recent techniques is also performed,revealing that the proposed method achieved improved accuracy while consuming less computational time.

关键词： Crops diseases preprocessing convolutional neural network features optimization machine learning

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Machine Vision Based Local Hyacinth Bean Breed Recognition Using Convolutional Neural Network

Machine Vision Based Local Hyacinth Bean Breed Recognition U...

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2024 International Conference on Advances in Computing, Communication, electrical, and Smart Systems, iCACCESS 2024

作者： Khan, Md. Abbas Ali Rahman, Md. Ataur Hossain, Md Liton Habib, Md. Tarek Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Electrical and Electronic Engineering Center for Innovation and Technology University of Liberal Arts Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798350350289

The classification is a significant one. This paper proposes a CNN-based Local Hyacinth Bean Breed Recognition (CNN-LHBR) approach along with a machine vision approach. Among the 52 breeds, we have taken only eight categories, namely Bashpaki Faridpur, Chaina Sada Patla Chela, Gochi, Kajoli, Katla, Lati, Noldub, and Pudi Aishna. There are many works done before about breed detection of different fruits as well as disease recognition. But no research has done such a work as LHBR, especially in Bangladesh. The interest of this research is the reason for the high protein and vitamin B complex;besides, each bean has a separate test, yield, seed, and nutrition level. More importantly, we have implemented 3 CNN models for the experiment of the breed recognition of 8 Hyacinth Bean specimens. For model accuracy, we have considered training, validation, and testing accuracy. As for performance evaluation, the confusion matrix has bean applied. Among the models, the customized CNN model gives the best accuracy. The customized CNN model's accuracy is 97.50%. © 2024 IEEE.

关键词： computer vision

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Hierarchical Transfer Multi-task Learning Approach for Scene Classification 27th

Hierarchical Transfer Multi-task Learning Approach for Scen...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Khoshkangini, Reza Tajgardan, Mohsen Jamali, Mahtab Ljungqvist, Martin Georg Mihailescu, Radu-Casian Davidsson, Paul Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Malmö Sweden Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Axis Communications AB Lund Sweden

ISBN: (纸本)9783031781063

This paper presents a novel Hierarchical Transfer and Multi-task Learning (HTMTL) approach designed to substantially improve the performance of scene classification networks by leveraging the collective influence of diverse scene types. HTMTL is distinguished by its ability to capture the interaction between various scene types, recognizing how context information from one scene category can enhance the classification performance of another. Our method, when applied to the Places365 dataset, demonstrates a significant improvement in the network’s ability to accurately identify scene types. By exploiting these inter-scene interactions, HTMTL significantly enhances scene classification performance, making it a potent tool for advancing scene understanding and classification. Additionally, this study explores the contribution of individual tasks and task groupings on the performance of other tasks. To further validate the generality of HTMTL, we applied it to the Cityscapes dataset, where the results also show promise. This indicates the broad applicability and effectiveness of our approach across different datasets and scene types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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