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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Yuana, Kumara Ari Kusrini Setyanto, Arief Laksito, Arif Dwi Maruf, Zauvik Rizaldi Johari, Muhammad Zuhdi Fikri Adninda, Gardyas Bidari Kartikakirana, Renindya Azizza Suprapto, Rhisa Aidilla Mimboro, Prasetyo Widayani, Wiwi Universitas Amikom Yogyakarta Faculty of Computer Science Yogyakarta Indonesia Universitas Amikom Yogyakarta Magister of Informatic Engineering Yogyakarta Indonesia Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Science and Technology Yogyakarta Indonesia

ISBN: (纸本)9798350399615

Fire spreading or fire propagation is the transfer of heat from the fire to the fuel bed, and heating it to its ignition point. Wildland fire management needs information about the characteristics and areas on the map. The map is always available in complicated geometry and difficult to calculate the area. The Monte Carlo technique gives an easy but powerful and accurate method to handle that problem. This study prepares the methodology for complicated area calculation with a large area range ratio. Hypothetic map areas with different shapes and sizes ensure the applicability of the real map. For validation purposes, the hypothetic map is also ready for theoretical calculation due to its border involving ideal geometry. The results show that the Monte Carlo technique give 1 % error for small size area (1 % of total area), 0.2% - 0.05% error for mid-size area (20% - 30% of total area), and 0.025% error for big-size area (50% of the total area). The accuracy level of this Monte Carlo technique indicate the readiness for wild land map area that always involved as complicated shape maps. © 2022 IEEE.

关键词： accuracy disaster management error Monte Carlo wildland fire

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WildGaussians: 3D Gaussian splatting in the wild 24

WildGaussians: 3D Gaussian splatting in the wild

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jonas Kulhanek Songyou Peng Zuzana Kukelova Marc Pollefeys Torsten Sattler Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague and Faculty of Electrical Engineering Czech Technical University in Prague and Department of Computer Science ETH Zurich Department of Computer Science ETH Zurich Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague

ISBN: (纸本)9798331314385

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework.

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, Science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms 15

Enhancing Security and Scalability of Metaverse with Blockch...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Rajawat, Anand Singh Goyal, S.B. Goyal, Aarti Rajawat, Kavita Raboaca, Maria Simona Verma, Chaman Mihaltan, Traian Candin School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Mind Intellect Technology Indore India National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania

ISBN: (纸本)9798350321388

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world. © 2023 IEEE.

关键词： Scalability

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Frame Elimination for Recordings of Bat Flight Kinematics from a High-Speed Video Array

Frame Elimination for Recordings of Bat Flight Kinematics fr...

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Jayathunga, D.P. Ramashini, M. Zaini, Juliana De Silva, Liyanage C. Müller, R. Uva Wellassa University of Sri Lanka Department of Computer Science and Informatics Sri Lanka Universiti Brunei Darussalam Faculty of Integrated Technologies Brunei School of Digital Science Faculty of Integrated Technologies Universiti Brunei Darussalam Brunei Virginia Tech Department of Mechanical Engineering BlacksburgVA24060 United States

ISBN: (纸本)9798350341072

Bat flight is complex because they have the ability to change the shape of their wings while in flight to perform manoeuvres. To accurately capture the subject's complexity, we have set up a synchronized array of 50high-speed cameras. This array is capable of capturing over 50,000 images per second. The video array is designed to cover a large imaging volume that allows bats to perform full-flight manoeuvres. As a result, many video frames do not show the entire bat. Hence, this study uses deep learning techniques to develop an accurate frame elimination process. Until the controlled environment is set up, we used simulated data created from the blender. For the frame elimination purpose, images needed to be classified based on the location of the bat, where it could be fully or partially presented within the frame or maybe not presented. We used different deep learning techniques, including the ordinary CNN, pretrained models, and object detection model - YOLOv5, to achieve this target. Among all the models, we noted that YOLOv5 gave the best accurate unwanted frame elimination results, where the mAP0.5 value is 0.99. © 2023 IEEE.

关键词： Object detection

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Augmented Node Placement Model in t-WSN Through Multiobjective Approach

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computers, Materials & Continua 2021年第12期69卷 3629-3644页

作者： Kalaipriyan Thirugnansambandam Debnath Bhattacharyya Jaroslav Frnda Dinesh Kumar Anguraj Jan Nedoma School of Computer Science and Engineering VIT UniversityChennai CampusTamilnaduIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturIndia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of Ostrava70833 Ostrava-PorubaCzech Republic

In Wireless Sensor Network(WSN),coverage and connectivity are the vital challenges in the target-based *** linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement *** this paper,a multiobjective problem on target-based WSN(t-WSN)is derived,which minimizes the number of deployed nodes,and maximizes the cost of coverage and sensing *** Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II(NSGA-II)is incorporated to tackle this multiobjective problem *** problems are intended to solve different objectives of a problem ***-inspired algorithms address the NP-hard problem most effectively in recent *** NSGA-II,the Non-Dominated sorting preserves the better solution in different objectives simultaneously using dominance *** the diversity maintenance phase,density estimation and crowd comparison are the two components that balance the exploration and exploitation phase of the *** of NSGA-II on this multiobjective problem is evaluated in terms of performance indicators Overall Non-dominated Vector Generation(ONGV)and Spacing(SP).The simulation results show the proposed method performs outperforms the existing algorithms in different aspects of the model.

关键词： Focused wireless sensor network m-coverage k-connectivity problem non-dominated sorting NSGA-II

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Development of Automotive-mobility Training in the TRIREME project

Development of Automotive-mobility Training in the TRIREME p...

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International Symposium for Design and Technology of Electronics Packages (SIITME)

作者： Bálint Medgyes Csaba Farkas Marek Spanyik Stefano Danieli Elena Cantiani Jakub Stolfa Department of Electronics Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava Ostrava Czechia Mylia – Adecco Formazione Milan Italy

ISBN: (数字)9798331539511

ISBN: (纸本)9798331539528

To achieve climate neutrality by 2050, the consistent transformation of the mobility sector is one of the most important pillars of the European Green deal. As a result of green and sustainable approaches, novel automotive concepts and massive digitalisation in the mobility ecosystem is undergoing a continuous development towards eco-friendly solutions and novel business models, and the changing landscape of automotive industry comes with a significant social impact as well. The overall horizontal collaboration on skills agenda in the Automotive-Mobility Ecosystem is assured throughout the Automotive Skills Alliance (ASA) [1], which is the Large-scale Pact for Skills Partnership. The latest strategic project of the partnership to support its activities is the Digital & Green Skills Towards Future of the Mobility Ecosystem (TRIREME) which promotes a collaboration between industry stakeholders and training providers including Vocational education Trainings (VETs), Higher Educations (HEs) and universities), moreover, also emphasizing the widespread public representation of different project segments and evaluating the social impacts and regional impact of the changes in the Automotive-Mobility Ecosystem. In parallel to the development of sectoral skills strategy, our department (Department of Electronics Technology - ETT) is contributing to deliver specific training materials and courses within Work Package 4 (WP4), which is based on the gathered and processed intelligence from previous desk research, survey activity and participation in professional events. The methodology of course development relies on the concept of modularity, our professional competences in the field of Automotive-mobility, and the fulfillment of requirements to cover the needed skills, the competence and the occupational profile subsets.

关键词： Training Industries Surveys Ecosystems Green products Collaboration Tutorials Stakeholders Automotive engineering Meteorology

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A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization 7

A Study on Text Feature Selection Using Ant Colony and Grey ...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Widians, Joan Angelina Wardoyo, Retantyo Hartati, Sri Universitas Gadjah Mada Doctoral Program Dept. of Computer Science and Electronics Indonesia Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Faculty of Mathematics and Natural Sciences Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) for FS are the main topics of our thorough assessment of the literature on the Swarm Intelligence (SI) algorithm. Furthermore, it illustrates how the hybrid SI technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms. © 2022 IEEE.

关键词： Ant colony optimization

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Organising unstructured data using Double Net Self-Organising Map (DNSOM) model

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Soft Computing 2025年第7期29卷 1-22页

作者： You, Cheng Chun Lim, Seng Poh Lee, Chen Kang Tan, Joi San Lim, Seng Chee Department of Computer Science Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Jalan University Bandar Barat Perak Kampar31900 Malaysia Department of Computing Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Jalan Sungai Long Bandar Sungai Long Cheras Selangor Kajang43000 Malaysia School of Computing and Informatics ViTrox College 746 Persiaran Cassia Selatan 3 Batu Kawan Industrial Park Penang Bandar Cassia14110 Malaysia

Surface reconstruction is the process of representing surfaces using the data obtained from scanning devices. When the data obtained is unstructured during data collection, it can cause problems in presenting the surface due to the lack of connectivity information for the data. It shows that data collection is a crucial task in surface reconstruction. Previous works have proved that Self-Organising Map (SOM) models can be used to organise unstructured data. However, the 2-D SOM model will generate a surface with holes for the closed surfaces. Besides, the 3-D SOM model will generate an incorrect surface due to the connectivity of the internal neurons. In addition, the Cube Kohonen SOM (CKSOM) model is limited to the same grid size. Hence, a SOM model known as Double Net SOM (DNSOM) is proposed via the merging of two 2-D SOMs to overcome the issues. Three data sets (cube, sphere, and talus bone) with different grid sizes are applied to test the models. When the grid size of all the models increases, the surface becomes smoother. The DNSOM contains a lower quantisation error than the 2-D SOM and the lowest topographic error among the other SOM models. It performs faster than the 3-D SOM and CKSOM. It presents the correct closed surface with a smaller number of neurons and different grid sizes. Microsoft Visual Studio 2022 with the C++ programming language is used to develop the models, while GNUPlot is used to visualise the results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Visualization

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Deploying YOLOV10 for Affordable Real-Time Handgun Detection

Deploying YOLOV10 for Affordable Real-Time Handgun Detection

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International Symposium on Intelligent Systems and informatics (SISY)

作者： Nikola Žigulić Matko Glučina Domagoj Frank Ivan Lorencin Zoran Šverko Dario Matika Faculty of Engineering University of Rijeka Rijeka Croatia Department of Engineering Istrian University of Applied Sciences Pula Croatia Department of Computer Science and Informatics University North Koprivnica Croatia Faculty of Informatics Juraj Dobrila University of Pula Pula Croatia Department of Electrical engineering University North Varaždin Croatia

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

The development of artificial intelligence is one of the most significant technological innovations that contributes to humanity with its characteristics and facilitates, secures, and improves everyday life. However the challenge arises when the programmer-engineer learns the algorithm of artificial intelligence to perform the proposed task, that is, the challenge lies in the issue of available hardware resources. Artificial intelligence algorithms perform many human-impossible tasks, such as detection and counting, i.e. calculating the interrelationships of individual objects, segmentation of tumors and other malignant diseases, i.e. tissues, classification of specific states of classes of a scene, and many other similar technologies. This research paper examines the possibility of implementing the You Only Look Once algorithm of the tenth generation on certain devices such as an affordable Raspberry Pi, and will discuss the advantages and disadvantages of changes in detection parameters, i.e. inferences to the applied model. In addition, a mock-up of the device will be shown, which will serve to provide timely information about criminals and suspicious persons who possess firearms in different situations, such as normal weather conditions in a populated place or in shops where petty robberies are frequent. The testing will be done using recorded videos in real-time scenarios. Finally, real-time inference or detection in real-time will be tested and the actions that Raspberry will perform will be simulated. The results indicate that the optimal model achieved a precision of 0.938, recall of 9.863, mAP50 of 0.91, and mAP50-95 of 0.739. This was achieved using an image size of 640, IOU threshold of 0.7, confidence threshold of 0.6, and training for 600 iterations with the Stochastic Gradient Descent optimizer, without augmentations, and employing the ONNX inference format.

关键词： YOLO Training Technological innovation Real-time systems Hardware Classification algorithms Delays Videos Tumors Testing

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