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Number of results: 4
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Abstract

Traditional sports and esports benefit from the development of Information and Communications Technologies (ICT), including gaming, 4D image/video processing, augmented reality (AR), virtual reality (VR), machine learning (ML), artificial intelligence (AI), big data, high-performance computing (HPC), and cloud computing. On the fuzzy border between the areas of physical and modified reality, both types of sports can coexist. The hardware layer of esports includes PC, consoles, smartphones, and peripherals used to interface with computers, including sensors and feedback devices. The IT layer of esports includes algorithms required in the development of games, online platforms, and virtual reality. The esports community includes amateur and professional players, spectators, esports organizers, sponsors, and other stakeholders. Esports and gaming research spans throughout law (intellectual rights, insurance, safety, and age restrictions), administration (teams, clubs, organizations, league regulations, and tournaments) biology (medicine, psychology, addiction, training and education) Olympic and non- Olympic disciplines, ethical issues, game producers, finance, gambling, data acquisition and analysis. Our article aims to presents selected research issues of esports in the ICT virtualization layer.
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Authors and Affiliations

Andrzej Białecki
1
Jan Gajewski
2
Ryszard Romaniuk
1

  1. Warsaw University of Technology
  2. Józef Piłsudski University of Physical Education
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Abstract

This study aims to investigate the impact of hand gesture recognition techniques on the efficiency of American Sign Language (ASL) interpretation, addressing a critical gap in the existing literature. The research seeks new insights into the challenges of automated sign language recognition, contributing to a deeper understanding of accessibility in communication for the deaf and hard-of-hearing community. The study employs a quantitative approach, using a dataset comprising hand gesture images representing the static letters of the ASL alphabet collected from multiple users. Data were collected from various individuals to ensure diversity and analyzed using machine learning models to evaluate their effectiveness in recognizing ASL signs. The results reveal that the machine learning models implemented achieved a high accuracy rate in recognizing hand gestures, indicating that person-specific variations do not significantly hinder performance. These findings provide evidence that the proposed dataset and methodologies can improve the reliability of sign language recognition systems, offering significant implications for the development of more inclusive communication technologies. This research offers a novel perspective on sign language recognition, providing valuable insight that extends the current understanding of gesture-based communication systems. The study’s findings contribute to advancements in accessibility technologies, highlighting areas for future research and practical applications in improving communication for the Deaf and hard of hearing community.
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Authors and Affiliations

Michał Chwesiuk
1
Piotr Popis
1

  1. Warsaw University of Technology, Poland
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Abstract

Quality evaluation is very important for haptic rendering. In this paper, an objective evaluation method for a haptic rendering system based on haptic perception features is proposed. In the method, the haptic rendering process is compared to the real world perception process in a simple standardized procedure based on feature extraction and data analysis. A complete evaluation process for a simple haptic rendering task of pressing a virtual spring is presented as an example to explain the method in detail. Compared with the traditional objective method based on error statistics, the method is more concerned about the consistency of human subjective feelings rather than physical parameters, which makes the evaluation process more consistent with the haptic perception mechanism. The results of comparative analysis show that the method presented in this paper is simple, gives reliable results reflecting the consistency with subjective feeling and has a better discrimination ability for different kinds of devices and algorithms compared with the traditional evaluation methods.

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Authors and Affiliations

Zhiyu Shao
Juan Wu
Qiangqiang Ouyang
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Abstract

Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR's can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed.
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Authors and Affiliations

Ravi Raj
1
ORCID: ORCID
Andrzej Kos
1

  1. Faculty of Computer Science, Electronics, and Telecommunications, AGH University of Science and Technology, Krakow, Poland

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