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Low Latency Video Streaming System for VR Teleoperation over 5G Networks 3

Low Latency Video Streaming System for VR Teleoperation over...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial intelligence and Neural Engineering, MetroXRAINE 2024

作者： Blázovics, László Kovács, Viktor Gótzy, Márton Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Automation and Applied Informatics BudapestH-1111 Hungary

ISBN: (纸本)9798350378009

With the introduction of 5G networks, high-speed low-latency network connections became available for mobile and embedded devices without the limited range of WiFi networks. Although the source and the destination can now be hundreds of kilometres away, the communication between them can be flawless. Our goal is to have a low cost, low power, lightweight device that is able to stream video for UAV (unmanned aerial vehicles), mobile robot teleoperation over 5G networks to AR/VR (augmented/virtual reality) applications, to large distances without the need for line of sight. In this paper we compare the latency of existing low cost video streamer solutions (UAV camera streaming, single board computer) and present a custom hardware-software solution optimized for low latency, while conducting measurements over different network types (wired, campus 5G, public 5G). We have shown that the custom device was able to achieve with significantly lower latency (50-80 ms direct over direct connection, about 100-150 ms over public 5G). © 2024 IEEE.

关键词： 5G mobile communication systems

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An InceptionNet-based object detection algorithm for wind turbine inspection robot 3

An InceptionNet-based object detection algorithm for wind tu...

引用

3rd international conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Qiao, Yanjun Hao, Shouli Wang, Zhijian Kou, Zhiwei Cui, Xiaoming Kong, Zhe Northern Branch Beijing Energy International Holding Co. Ltd. Hohhot China College of Electric Power Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9781510681880

Wind power has developed rapidly in recent years, and regular inspection of wind turbines can guarantee their normal operation. The use of inspection robots can improve inspection efficiency and reduce risks. The traditional way mainly relies on the manual operation of the UAV to search the wind turbines, which still has the problem of low efficiency. For the object search problem in aerial images, this paper proposes a wind turbine object detection algorithm based on the Inception Network, which uses artificial intelligence to extract wind turbine objects in the images. For the problem of excessive parameters in the baseline algorithm, this paper replaces the original backbone network to reduce the number of parameters. For the problem of overfitting, the paper adopts the strategy of Batch Normalization to improve the generalization ability. After the test using the NAIP dataset, the algorithm achieves the accuracy of 93.10% on the wind turbine object detection task. The results show that the object detection algorithm based on Inception Network can achieve better results and has certain robustness and generalization ability for objects in different environments. © 2024 SPIE.

关键词： Aerial photography

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A non Invasive Brain-Computer-Interface for Service robotics 3

A non Invasive Brain-Computer-Interface for Service Robotics

引用

3rd IEEE international conference on Artificial intelligence, ICAI 2023

作者： Ahmed, Fatima Iqbal, Hashim Nouman, Ahmed Maqbool, Hafiz Farhan Zafar, Saqib Saleem, Muhammad Khurram University of Engineering & Technology Mechatronics Engineering Department Lahore Pakistan AASS Computer Science Department Orebro University Örebro Sweden University of Engineering & Technology Mechatronics Engineering Department Taxila Pakistan

ISBN: (纸本)9798350322125

A Brain-Computer Interface (BCI) enables individuals to control a system solely through their brain activity, without relying on physical movement. These interfaces have numerous applications, particularly in assisting individuals with paralysis. Our research paper details a BCI interface that can classify and control seven wheelchair movements: forward, backward, left, right, stair climbing upwards, stair climbing downwards, and stop. We collected raw signal data using the electroencephalog-raphy (EEG) technique from healthy volunteers, which we then filter before feeding into the feature extraction and classification stages. We evaluated our approach using three classification algorithms: Convolution Neural Network (CNN), Support Vector Machines (SVM), and Random Forest Classifier, and compared their performance. Our experimental results demonstrate that our proposed approach is highly promising for implementing BCI, with a classification accuracy of 99% using a Random Forest Classifier. © 2023 IEEE.

关键词： Brain computer interface

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Role of Artificial intelligence in the Indian Judicial System 3

Role of Artificial Intelligence in the Indian Judicial Syste...

引用

3rd IEEE international conference on Computational intelligence and Knowledge Economy, ICCIKE 2023

作者： Gorlamudiveti, Lakshmi Priya Sethu, Sagee Geetha Amity University Dubai Department of Law Dubai United Arab Emirates

ISBN: (纸本)9798350338263

As of January 2023 more than 47 million cases are pending in Indian Courts and this number is expected to be on an upward tangent. Many of these cases have been pending before the lower and higher courts since the last decade. There has been significant research conducted in identifying the reasons behind such exponential increase in cases. The main reasons for pendency of cases were identified as the lack of available judges, legal professionals or even quality of infrastructure and courts. However very little research has been carried on with a concrete solution for how the cases can be disposed at a faster rate and thereby reduce the case load. This paper proposes that through effective use of Artificial intelligence (AI), the cases that are pending before the courts can be significantly reduced and aid all the stakeholders involved, namely, the judges, lawyers, clients, and general public. The paper explores the utilization of AI in the judicial system by assessing its application in three main areas: Legal Research, Legal Reasoning and Outcome Prediction. Furthermore, the paper will examine the ways in which the Indian Judicial System can adopt AI while taking into account the three main factors i.e. bias, precedents and costs, which are all essential for effective implementation of AI. Overall, this paper provides prospective applications of AI's in law while also assessing the feasibility of such applications in the Indian judicial system. Since this study discusses the convergence of AI and law the paper will not only be helpful for engineers who are aiming to develop AI technology but also various stakeholders in law who will be the consumers of such technology. © 2023 IEEE.

关键词： Artificial intelligence

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Human Voice Artificial intelligence in Education, Illustrate and Prospective Possibilities 3

Human Voice Artificial Intelligence in Education, Illustrate...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raman, Ramakrishnan Joshi, Kapil Ravichand, M. Jain, Anupriya Gor, Mehul Sanjay, C.P. Maharashtra Pune India Uttaranchal University Uttaranchal Institute of Technology India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupathi India Mriirs Faridabad Haryana Department Computer Applications India Parul University Department of Piet India Department of It& Business Analytics India

ISBN: (纸本)9798350399264

One use of artificial intelligence is natural language processing (NLP). Voice assistants are able to have conversations with individuals in their native language and include AI by using cloud computing. Voice assistants are now included into millions of different consumer products due to the ease with which they may be used. Voice assistant-enabled smart speakers are among the most popular consumer electronics devices now available, but universities and other types of organizations have just lately started using them. The purpose of this paper is twofold: first, to offer a review of previous research on speech recognition in the classroom, and second, to provide fresh discoveries. © 2023 IEEE.

关键词： Cloud computing

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Explainable AI for Predictive Analytics on Employee Promotion 3

Explainable AI for Predictive Analytics on Employee Promotio...

引用

3rd international conference on Advanced Computing Technologies and applications, ICACTA 2023

作者： Bhattacharya, Abhirup Choudhary, Pradyumn Mukhopadhyay, Sandipan Misra, Bitan Chakraborty, Sayan Dey, Nilanjan Computer Science and Engineering Techno International New Town Kolkata India

ISBN: (纸本)9798350348347

The success of a company depends upon the strategy, technology, finance, and more importantly, the performance and competence of its employees. To efficiently manage the manpower of an organization, the founders, CEO, and managers need to determine which employees must be promoted. Promotion boosts employee morale, improves the resource allocation of the organization, and helps identify talent. In this project, we have explored various machine learning algorithms to predict the promotion of an employee based on multiple employee-related factors such as education, training, rating and length of service. The analysis focuses on creating a predictive model after thorough feature analysis and preprocessing of the training data. We have trained multiple classification models such as logistic regression, decision tree, random forest, and XGBoost. Evaluation metrics, namely, precision, recall, and Fl-score, were used to evaluate the performance of each model, and the XGBoost classifier outperformed the other algorithms. In addition, we have implemented explainable artificial intelligence methods such as local interpretable model agnostic explanations. Additive explanations to derive the reason behind the predictions made by artificial intelligence. The current work managed to predict with an accuracy of 95% and detect the features that have an impact on employee promotion. © 2023 IEEE.

关键词： Lime

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Research on the Construction of Agricultural Planting Big Data Corpus Based on Knowledge Graphs 3

Research on the Construction of Agricultural Planting Big Da...

引用

3rd IEEE international conference on Information technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Xiaohong, Ning Qian, Peng Maalla, Allam Shaoqiang, Li School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou China

ISBN: (纸本)9781665490788

Agriculture has always been an important pillar of China, and the continuous development of science and technology has always been inclusive to the people. How to help more growers more scientific and convenient planting through intelligent technology and solving problems and challenges in the field of agricultural planting have always been a problem worth thinking and practice. The purpose of this study is to build a new-style agricultural field of the new agricultural field of the conceptual system, and build a standardized, systematic knowledge corpus, agricultural planting data entity extraction corpus in the agricultural field through the structural concept of the new agricultural planting knowledge base. Agricultural planting data is due to basic data during data mining and model construction. Provide an efficient and professional crop planted big data corpus solution for agricultural knowledge graph applications. © 2023 IEEE.

关键词： Data mining

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Wearable Ear EEG Device for Emotion Recognition in Human-robot Interaction 23

Wearable Ear EEG Device for Emotion Recognition in Human-Rob...

引用

23rd international conference on Cyberworlds, CW 2024

作者： Mai, Ngoc-Dau Go, Kentaro Mao, Xiaoyang Chung, Wan-Young Pukyong National University Dept. Artificial Intelligence Convergence Busan48513 Korea Republic of University of Yamanashi Kofu Dept. Computer Science and Engineering Yamanashi400-8511 Japan

ISBN: (纸本)9798331527174

Wearable electroencephalography (EEG) devices are emerging as crucial tools in human-robot interaction (HRI), enabling intuitive and effective communication between humans and robots. These devices non-invasively measure brain activity, providing real-time insights into a user's mental state, intentions, and cognitive load. This paper explores the advancements and applications of wearable ear EEG technology in HRI, with a focus on detecting human intent, monitoring emotional and cognitive states, and delivering real-time feedback for adaptive robot behavior. The benefits of wearable EEG over traditional scalp EEG, such as enhanced user comfort, reduced setup time, and improved long-term wearability, are thoroughly examined. Additionally, the paper covers advanced applications, including emotion-aware adaptive interactions, neurofeedback training, and direct robot control via brain-computer interfaces (BCIs). A review of the latest advancements in device miniaturization, integration with other wearable sensors, and applications in virtual reality, gaming, and healthcare is provided. Future directions focus on expanding applications, enhancing human-AI collaboration, and improving accuracy and reliability in HRI. The studies discussed in this paper represent our state-of-the-art research across various fields and applications of wearable EEG systems. This study highlights the potential of wearable Ear-EEG devices to revolutionize human-robot interactions by enhancing customization, responsiveness, and overall efficacy. © 2024 IEEE.

关键词： Human robot interaction

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Exploration on Computer Vision Object Detection Algorithms under Artificial intelligence 3

Exploration on Computer Vision Object Detection Algorithms u...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Qian, Wei School of Software Taiyuan University of Technology Shanxi Jinzhong China

ISBN: (纸本)9798350317022

In computer vision object detection, traditional detection methods often have poor performance when dealing with a large amount of background interference, target scale changes, and occlusion, and cannot capture all the complexity of the target, which will result in limited detection performance. In contrast, the application of computer vision object detection algorithms based on artificial intelligence in this field has become mainstream, driving the development of a series of efficient object detection methods. This article focuses on combining the convolutional neural network (CNN) algorithm to explore the relevant research on input image and candidate region selection, feature extraction and region classification, as well as optimization and evaluation indicators. Simulation experiments have concluded that compared to traditional visual detection, the improved accuracy index is improved by about 9.3%, while the recall performance index is improved by 20.5%. This study has improved the accuracy and efficiency of object detection, enabling computers to more reliably understand and interact with the real world, thus providing a solid foundation for automation and intelligent applications, with the potential to improve quality of life and promote technological innovation. © 2023 IEEE.

关键词： Artificial intelligence Computer Vision Convolutional Neural Network Object Detection

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The Stiffness of 3-PRS PM Across Parasitic and Orientational Workspace 6

The Stiffness of 3-PRS PM Across Parasitic and Orientational...

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6th international conference on Reconfigurable Mechanisms and robots (ReMAR)

作者： Nigatu, Hassen Li Jihao Zhu, Keqi Zhang, Junhan Guo, Haotian Lu, Guodong Kim, Doik Zhejiang Univ Sch Mech Engn Robot Percept & Grasp Lab Hangzhou 310027 Peoples R China Zhejiang Univ Robot Res Ctr Yuyao Yuyao Technol Innovat Ctr Robot Inst 479 Ye Shan Rd Ningbo Zhejiang Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Sch Mech Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Engn Res Ctr Design Engn & Digital Twin Zhejiang Sch Mech Engn Hangzhou 310027 Zhejiang Peoples R China Korea Inst Sci & Technol KIST Artificial Intelligence & Robot Inst Ctr Intelligent & Interact Robot xR Lab Seoul South Korea

ISBN: (纸本)9798350395976;9798350395969

This study investigates the stiffness characteristics of the Sprint Z3 head, also known as 3-PRS Parallel Kinematics Machines, which are among the most extensively researched and viably successful manipulators for precision machining applications. Despite the wealth of research on these robotic manipulators, no previous work has demonstrated their stiffness performance within the parasitic motion space. Such an undesired motion influences their stiffness properties, as stiffness is configuration-dependent. Addressing this gap, this paper develops a stiffness model that accounts for both the velocity-level parasitic motion space and the regular workspace. Numerical simulations are provided to illustrate the stiffness characteristics of the manipulator across all considered spaces. The results indicate that the stiffness profile within the parasitic motion space is both shallower and the values are smaller when compared to the stiffness distribution across the orientation workspace. This implies that evaluating a manipulator's performance adequately requires assessing its ability to resist external loads during parasitic motion. Therefore, comprehending this aspect is crucial for redesigning components to enhance overall stiffness.

关键词： robot applications

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