ISSN: 2790-7511
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2024
Issue 1
2023
Issue 1
2022
Issue 1
Editor(s)-in-Chief: Shuai (Steven) Li

EAI Endorsed Transactions on AI and Robotics (eISSN: 2790-7511) covers all aspects of robotics and knowledge-based AI systems along with interdisciplinary approaches to computer science, control systems, computer vision, machine learning, electrical engineering, intelligent machines, mathematics, and other disciplines. An important goal of this journal is to extend cutting-edge technologies in the control and learning of both symbolic and sensory robots with regard to smart systems. Our journal contains articles on the theoretical, mathematical, computational, and experimental aspects of robotics and intelligent systems.

  • Editor-in-Chief
  • Shuai (Steven) Li, College of Engineering, Swansea University, United Kingdom.
  • Administrative Editor
  • Yiguo Yang, School of Computer Science, Shanghai University, China.
  • Advisory Board
  • Oubay Hassan, Department of Civil Engineering, Swansea University, UK. Fellow of UK Royal Academy of Engineering.
  • Michael Navon, Department of Scientific Computing, Florida State University, USA. Fellow of the American Meteorological Society; Honorary Member of Romanian Academy of Science. 
  • Jianming Shi, School of Mathematical Sciences, Soochow University, China. School of Management, Tokyo University of Science, Japan. Foreign Member, Engineering Academy of Japan (EAJ).   
  • Editors
  • Dunhui Xiao, School of Mathematical Sciences, Tongji University, China. 
  • Lining Sun, School of Mechanical and Electric Engineering, Soochow University, China. Foreign member of Russian Academy of Engineering.
  • Yuhua Zheng, Zhejiang Lab, Hangzhou, China.
  • Xingye Yue, School of Mathematical Sciences, Soochow University, China.
  • Editorial Board Members
  • Ivona Brajević, University Business Academy in Novi Sad, Serbia.
  • Xinwei Cao, School of Management, Shanghai University, China.
  • JinLong Fu，Faculty of Science and Engineering, Swansea University, United Kingdom.
  • Hewa Majeed Zangana, Duhok Polytechnic University and Nawroz University
  • Christian Griffiths, College of Engineering, Swansea University, United Kingdom.
  • Hongliang Guo, Agency for Science, Technology and Research, Singapore. 
  • Wojciech Hunek, Opole University of Technology, Opole, Poland.
  • T. Jarin, Department of Electrical Engineering Jyothi Engineering College India.
  • Chao Jiang, Department of Electrical Engineering, University of Wyoming, USA.
  • Vasilios N. Katsikis, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Greece.
  • Ameer Hamza Khan, Department of Computing, Hongkong Polytechnic University, Hongkong China.
  • Wenqiang Lu, Chongqing Institute of Green and Intelligent Technology, China.
  • Zhan Li, Department of Computer Sciences, Swansea University, UK.
  • Jianfeng Li, College of Information Science and Engineering, Jishou University, China.
  • Bolin Liao, College of Information Science and Engineering, Jishou University, China.
  • Zhenzhong Liu, Tianjin University of Technology, China.
  • Md. Asraful Haque, Zakir Hussain College of Engineering & Technology, India.
  • Huanfei Ma, School of Mathematical Sciences, Soochow University, China.
  • Ata Jahangir Moshayedi, College of Computing, Jiangxi University of Science and Technology, China.
  • Muhammad Rehan, Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Pakistan.
  • Mingsheng Shang, Chongqing Institute of Green and Intelligent Technology, China.
  • Predrag S. Stanimirović, Faculty of Sciences and Mathematics, University of Nis, Serbia.
  • Xuefeng Zhou, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, China.
  • Weiwei Jiang, Beijing University of Posts and Telecommunications, China.
  • Ameer Tamoor Khan, Computing Department, University of Copenhagen, Denmark.
  •  

  • Prediction of short circuit current of wind turbines based on artificial neural network model

    Appears in:
    airo 24(1):

    Authors:
    Ebrahim Aghajari, Ali AbdulKarim AbdulRahim

    Published:
    4th Dec 2024

    Abstract:
    The growth of renewable energy on a global scale is making significant strides in power plants. This is due to the increasing concern about climate change, the rising demand for electricity, and the necessity to reduce reliance on fossil fuels. Ensuring the successful integration of new energy resources into the existing network is just as crucial as it requires the system to be reliable and adaptable. For instance, wind energy, which is one of the renewable sources, has an intermittent nature that necessitates the ability to synchronize its actions to achieve the desired system performance. The objective of this study is to utilize a new neural network system to calculate the short circuit current of power plants. Specifically, the focus is on identifying and categorizing the short circuit faults that occur between the stator coils of the squirrel cage induction generator used in wind power generation. To achieve this, a system was developed to simulate turbine data. Subsequently, four feature extraction techniques and machine learning algorithms were employed to enable early detection of short circuit faults. The numerical results obtained from the simulation demonstrated the high efficiency and accuracy of the proposed model.

  • A Comprehensive Survey of Text Encoders for Text-to-Image Diffusion Models

    Appears in:
    airo 24(1):

    Authors:
    Shun Fang

    Published:
    4th Dec 2024

    Abstract:
    In this comprehensive survey, we delve into the realm of text encoders for text-to-image diffusion models, focusing on the principles, challenges, and opportunities associated with these encoders. We explore the state-of-the-art models, including BERT, T5-XXL, and CLIP, that have revolutionized the way we approach language understanding and cross-modal interactions. These models, with their unique architectures and training techniques, enable remarkable capabilities in generating images from textual descriptions. However, they also face limitations and challenges, such as computational complexity and data scarcity. We discuss these issues and highlight potential opportunities for further research. By providing a comprehensive overview, this survey aims to contribute to the ongoing development of text-to-image diffusion models, enabling more accurate and efficient image generation from textual inputs.

  • A Comprehensive Review of Electromyography in Rehabilitation: Detecting Interrupted Wrist and Hand Movements with a Robotic Arm Approach

    Appears in:
    airo 24(1):

    Authors:
    Ata Jahangir Moshayedi, Kimia Savoji

    Published:
    4th Dec 2024

    Abstract:
    Electromyography (EMG) is a diagnostic technique that measures the electrical activity generated by skeletal muscles. Utilizing electrodes placed either on the skin (surface EMG) or inserted directly into the muscle(intramuscular EMG), it detects electrical signals produced during muscle contractions. EMG is widely employed in clinical and research settings to assess muscle function, diagnose neuromuscular disorders,and guide rehabilitation therapy. Over the years, EMG has evolved from a basic measurement tool into an essential technology within clinical and research environments, propelled by advances in recording techniques and digital innovations. The integration of wearable technology and artificial intelligence (AI) has significantly expanded its applications, particularly in rehabilitation and sports science. By capturing muscle electrical activity through surface or intramuscular electrodes, EMG benefits from enhanced signal processing that improves accuracy and data analysis. Despite challenges such as signal interference and the complexities of movement patterns—especially in wrist and hand rehabilitation—EMG combined with robotic systems offers real-time feedback for precise and personalized therapy. However, obstacles like cost, complexity, and variability among patients still remain. Future advancements aim to make EMG more accessible and to integrate AI for tailored rehabilitation strategies, alongside improvements in sensors and wireless communication to enhance reliability and performance. This review explores various facets of EMG,from its fundamental principles to its application in detecting disrupted wrist and hand movements through robotic approaches. It provides a comprehensive analysis of EMG’s historical and technological evolution, recent innovations like AI and wearable devices, and its extensive applications in rehabilitation and sports science. Detailed case studies illustrate its effectiveness in areas such as stroke recovery and spinal cord injury rehabilitation. Additionally, the review addresses challenges like technical limitations and patient variability while emphasizing the integration of EMG with robotic systems for personalized therapy. It also discusses the significance of real-time feedback, future enhancements in AI and sensor technology, and the pressing need for more affordable, user-friendly solutions to improve therapeutic outcomes.

  • Robots in Agriculture: Revolutionizing Farming Practices

    Appears in:
    airo 24(1):

    Authors:
    Jiandong Hu, Amir Sohail Khan, Amin Kolahdooz, Yiguo Yang, Ata Jahangir Moshayedi

    Published:
    4th Dec 2024

    Abstract:
    The integration of robotics in modern agriculture represents a revolutionary paradigm shift, enhancing efficiency and sustainability in food production. Agricultural robots, designed to automate various tasks, play a pivotal role in addressing the challenges faced by the industry. These robots are purpose-built for activities such as precision planting, weeding, and harvesting, streamlining processes that were traditionally labor-intensive. Their implementation leads to increased productivity, reduced operational costs, and minimized environmental impact through optimized resource utilization. This paper delves into the intricate landscape of robotic structures employed in agriculture, unraveling the diverse mechanisms and designs that underpin their functionality. It meticulously examines and elucidates the structural nuances of agricultural robots, shedding light on the engineering marvels that enable precision farming. From articulated arms to autonomous drones, the paper navigates through a spectrum of robot architectures, dissecting their roles in automating tasks critical to modern agriculture.

  • Hybrid Image Denoising Using Wavelet Transform and Deep Learning

    Appears in:
    airo 24(1):

    Authors:
    Hewa Majeed Zangana, Firas Mahmood Mustafa

    Published:
    4th Dec 2024

    Abstract:
    In this paper, we propose a hybrid image denoising method that combines wavelet transform and deep learning techniques to effectively remove noise from digital images. The wavelet transform is applied to each color channel of the noisy image, decomposing it into different frequency components. The approximation coefficients are then denoised using a convolutional neural network (CNN), specifically designed for this task. The denoised coefficients are subsequently reconstructed to form the final denoised image. Our experimental results demonstrate that this hybrid approach outperforms traditional denoising methods, achieving superior noise reduction while preserving image details. The proposed method is validated using synthetic noisy images, and the results are visually and quantitatively evaluated to confirm its effectiveness.

  • Methods and Strategies for 3D Content Creation Based on 3D Native Methods

    Appears in:
    airo 24(1):

    Authors:
    Shun Fang, Yanna Lv, Xing Feng

    Published:
    4th Dec 2024

    Abstract:
    The present paper provides a comprehensive overview of three neural network models, namely Point·E, 3DGen, and Shap·E, with a focus on their overall processes, network structures, loss functions, as well as their strengths, weaknesses, and potential future research opportunities. Point·E, an efficient framework, generates 3D point clouds from complex text prompts, leveraging a text-to-image diffusion model followed by 3D point cloud creation. 3DGen, a novel architecture, integrates a Variational Autoencoder with a diffusion model to produce triplane features for conditional and unconditional 3D object generation. Shap·E, a conditional generative model, directly generates parameters of implicit functions, enabling the creation of textured meshes and neural radiance fields. While these models demonstrate significant advancements in 3D generation, areas for improvement include enhancing sample quality, optimizing computational efficiency, and handling more complex scenes. Future research could explore further integration of these models with other techniques and extend their capabilities to address these challenges.

  • Systematic review on Artificial Intelligence in the editorial management of scientific journals

    Appears in:
    airo 24(1):

    Authors:
    Alejandro Rodríguez Urrutia, María del Carmen Roche Madrigal, Carlos Rafael Araujo Inastrilla, Mayelin Llosa Santana, Dayami Gutiérrez Vera, Alejandro Araujo Inastrilla

    Published:
    4th Dec 2024

    Abstract:
    INTRODUCTION: scientific journals play a crucial role in the dissemination and validation of scientific knowledge, and editorial management ranges from conceptualization to post-publication of content. Artificial intelligence (AI) has had a great impact on scientific communication and editorial management of scientific journals. AI can offer solutions and benefits for editorial management, but it also poses technical, economic, social and ethical challenges that should be considered and approached with caution and responsibility. OBJECTIVE: to describe the benefits and limitations of the use of AI in the editorial management of scientific journals through a systematic literature review. METHOD: a systematic literature review was conducted based on the PRISMA methodology. An information search was carried out in different bibliographic database systems, indexing systems and search engines, and inclusion and exclusion criteria were applied to the identified studies. RESULTS: the information search allowed retrieving a total of 2750 sources, of which 10 articles that met the stated criteria were included. Benefits such as the facilitation of writing, translation, review and editing tasks were identified, as well as limitations related to ethical issues, bias, errors and plagiarism generated by AI. CONCLUSIONS: While AI can streamline the production and analysis of information distribution, it also poses challenges in terms of reliability, ethics and authenticity of published content. It requires the critical involvement of human intelligence for proper exploitation.

  • Comparison of the Performance of Six Machine Learning Algorithms for Fake News

    Appears in:
    airo 24(1):

    Authors:
    Hasan Abdulkader, Rafah H Al-Furaiji

    Published:
    4th Dec 2024

    Abstract:
    INTRODUCTION: This research focuses on the increasing importance of social media websites as versatile platforms for entertainment, work, communication, commerce, and accessing global news. However, it emphasizes the need to use this power responsibly. OBJECTIVES: The objective of the study is to evaluate the performance of artificial intelligence algorithms in detecting fake news. METHODS: Through a comparison of six machine learning algorithms and the use of natural language processing techniques, RESULTS: The study identifies four algorithms with a 99% accuracy rate in detecting fake news. CONCLUSION: The results demonstrate the effectiveness of the proposed method in enhancing the performance of artificial intelligence algorithms in addressing the problem of fake news detection.

  • Empirical Analysis of Widely Used Website Automated Testing Tools

    Appears in:
    airo 24(1):

    Authors:
    Sadaqat Jan, Balqees Sani

    Published:
    10th Oct 2024

    Abstract:
    In today's software development, achieving product quality while minimising cost and time is critical. Automated testing is crucial to attaining these goals by lowering inspection efforts and discovering faults more effectively. This paper compares widely used automated testing tools, such as Selenium, Appium, Java Unit (JUnit), Test Next Generation (TestNG), Jenkins, Cucumber, LoadRunner, Katalon Studio, Simple Object Access Protocol User Interface (SoapUI), and TestComplete, based on functionality, ease of use, platform compatibility, and integration capabilities. Our findings show that no single tool is inherently superior, with each excelling in certain areas such as online, mobile, Application Programming Interface (API), or performance testing. While Selenium and Appium are the dominant online and mobile testing frameworks, TestComplete and Katalon Studio offer complete, user-friendly cross-platform testing solutions. Despite the benefits of automation, obstacles such as tool maintenance, scalability, and cost issues remain. The report finishes with advice for picking the best tool for the project and offers potential approaches for enhancing testing frameworks, such as AI-driven optimisation, cloud-based testing, and greater Continuous Integration/ Continuous Deployment (CI/CD) integration. This study offers useful information for developers and testers looking to optimise their testing methods and increase software quality.

  • A Hybrid Approach for Robust Object Detection: Integrating Template Matching and Faster R-CNN

    Appears in:
    airo 24(1):

    Authors:
    Hewa Majeed Zangana, Firas Mahmood Mustafa, Marwan Omar

    Published:
    7th Oct 2024

    Abstract:
    Object detection is a critical task in computer vision, with applications ranging from autonomous vehicles to medical imaging. Traditional methods like template matching offer precise localization but struggle with variations in object appearance, while deep learning approaches such as Faster R-CNN excel in handling diverse and complex datasets but often require extensive computational resources and large amounts of labeled data. This paper proposes a hybrid approach that integrates template matching with Faster R-CNN to leverage the strengths of both techniques. By combining the accuracy of template matching with the robustness and generalization capabilities of Faster R-CNN, our method achieves superior performance in challenging scenarios, including objects with occlusions, varying scales, and complex backgrounds. Extensive experiments demonstrate that the hybrid model not only enhances detection accuracy but also reduces computational load, making it a practical solution for real-world applications.

  • Deep Learning Application Pros And Cons Over Algorithm Deep Learning Application Pros And Cons Over Algorithm

    Appears in:
    airo 22(1): 7

    Authors:
    Atanu Shuvam Roy, Yang Shuxin, Ata Jahangir Moshayedi, Amin Kolahdooz

    Downloads:
    2553

    Abstract:
    Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods. Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods.

  • The Object Detection, Perspective and Obstacles In Robotic: A Review

    Appears in:
    airo 22(1): e13

    Authors:
    Ata Jahangir Moshayedi, Ge Xu, Amir Sohail Khan, Yang Shuxin, Xiaohong Zhang

    Downloads:
    1234

    Abstract:
    A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.

  • Review On: The Service Robot Mathematical Model

    Appears in:
    airo 22(1): 8

    Authors:
    Liefa Liao, Ata Jahangir Moshayedi, Atanu Shuvam Roy, Yangwan Zhong, Sithembiso Khaya Sambo

    Downloads:
    1202

    Abstract:
    After nearly 30 years of development, service robot technology has made important achievements in the interdisciplinary aspects of machinery, information, materials, control, medicine, etc. These robot types have different shapes, and mainly in some are shaped based on application. Till today various structure are proposed which for the better analysis’s need to have the mathematical equation that can model the structure and later the behaviour of them after implementing the controlling strategy. The current paper discusses the various shape and applications of all available service robots and briefly summarizes the research progress of key points such as robot dynamics, robot types, and different dynamic models of the differential types of service robots. The current review study can be helpful as an initial node for all researchers in this topic and help them to have the better simulation and analyses. Besides the current research shows some application that can specify the service robot model over the application.

  • The Role of Biometric in Banking: A Review

    Appears in:
    airo 23(1):

    Authors:
    Masoumeh Soleimani, Mehdi Marani, Atajahangir Moshayedi, Mina Bahadori

    Downloads:
    1175

    Abstract:
    Biometrics plays a pivotal role in enhancing security, ensuring accurate identification, and offering convenient solutions across diverse industries. Its uniqueness, reliability, and potential for future advancements establish it as a crucial and valuable field in today's digital landscape. Fingerprint authentication in ATMs presents primary advantages such as heightened security through distinctive identification and user convenience by eliminating the reliance on PINs or passwords. This research paper focuses on conducting a comprehensive review and comparative analysis of various approaches for fingerprint identification, aiming to contribute to the understanding of effective and efficient methods in the context of ATM authentication.

  • Peaks Detector Algorithm after CFAR for Multiple Targets Detection

    Appears in:
    airo 22(1): 11

    Authors:
    Syed Waqar Hameed

    Downloads:
    801

    Abstract:
    The constant false alarm rate (CFAR) algorithm is a strong technique to detect and track dynamic targets in an environment of an unknown noise floor. Multiple reflections of a pulse from a target and different signal processing techniques applied to the received pulse, make it spread along the range and/or Doppler axis. Spreading of a pulse results in a cluster of targets detection for a single target when the CFAR technique is applied to it. This causes difficulties in calculating those target’s parameters which require only a single maximum peak for a target, such as Radar cross-section (RCS), relative phase, etc. This manuscript proposes a solution, which extracts a single independent peak for a target that had clusters of peaks after CFAR. The novelty of the algorithm is that it works well to extract a single peak for each of all targets in the multiple targets environment, as compared to the conventional global maxima finding techniques which outputs only one target of the maximum amplitude while suppressing the rest of the small targets. The algorithm is basically a local maxima finder algorithm termed as peaks detector algorithm. An attractive feature of this algorithm is that it neither disturbs the Probability of false alarm rate (Pfa) of CFAR nor it affects the probability of detection (Pd) of a target. The algorithm is tested and its performance is evaluated in a multiple targets environment on the output of 1D and 2D CFAR.

  • Review of Image Classification Algorithms Based on Graph Convolutional Networks

    Appears in:
    airo 23(1):

    Authors:
    Wenhao Tang

    Downloads:
    767

    Abstract:
    In recent years, graph convolutional networks (GCNs) have gained widespread attention and applications in image classification tasks. While traditional convolutional neural networks (CNNs) usually represent images as a two-dimensional grid of pixels when processing image data, the classical model of graph neural networks (GNNs), GCNs, can effectively handle data with graph structure, such as social networks, recommender systems, and molecular structures. In this paper, we will introduce the problems that graph convolutional networks have had, such as over-smoothing, and the methods to solve them, and suggest some possible future directions.

  • A Brief Analysis of “ChatGPT” – A Revolutionary Tool Designed by OpenAI

    Appears in:
    airo 23(1): e15

    Authors:
    Md. Asraful Haque

    Downloads:
    633

    Abstract:
    The ChatGPT, a powerful conversational tool trained by OpenAI is considered to be a revolutionary model in the field of artificial intelligence and natural language processing. It has received a lot of attention because of its potential to automate a variety of tasks and possibly have an impact on sectors like translation, customer service, and content creation. It uses GPT-3 (Generative Pre-training Transformer 3) language model to process user queries. GPT-3 has been trained on a very large dataset, which includes a wide range of texts from the internet and other sources. This has given it a broad knowledge base and has allowed it to generate responses to a wide range of prompts that are coherent and human-like. GPT-3 is one of the largest and most powerful language models to date, and it has the ability to perform a wide range of natural language processing tasks. After its release, ChatGPT has become a trending tool for the public to experiment and explore what it is capable of. In this article, we want to clarify what ChatGPT is. How does it work? What makes it different from other chatbots or search engines like Google? What are the major challenges and future prospects for it?

  • LQG, PID controller, ANN for single axis gimbal actuator

    Appears in:
    airo 22(1): e12

    Authors:
    Nguyen Cong Danh

    Downloads:
    572

    Abstract:
    Gimbal or other stable platforms have structures that move according to its functions. This is for the purpose of keeping track of the goals to the fullest. Tracking targets can become difficult as the subject moves further and further away and they are out of the gimbal’s allowable viewing range. Besides, under the influence of noise signals form outside space, it becomes even more difficult to observe the gimbal’s targets. To overcome above disadvantages, this paper is presented an adjustment method to limit above risks. Adjusting Linear Quadratic Gaussian (LQG) for expensive gimbal systems, noise signals are processed purely by Kalman filters to improve the function of observing targets. In addition, proportional- integral-derivative (PID) controller, artificial neural network in this case is also considered to verify the effectiveness of  control methods listed below. In particular, ANN is the most effective control method today to deal with unwanted signals. These unwanted signals can cause worsening conditions during the operation of systems.Therefore, artificial network (ANN) is a solution to information and communication security problems. Simulation is done by Matlab. Novelty of the work: no previous research has been published for this  genre. The study of this genre with the use of artificial intelligence is suggestive of the study of artificial intellligence technologies at a higher level. This category is also a suggestion for studying a smoother control method based on existing data.

  • The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

    Appears in:
    airo 22(1): e14

    Authors:
    Ata moshayedi, Nafiz Md Imtiaz Uddin, Yang Shuxin, Hong lan

    Downloads:
    543

    Abstract:
    Facial recognition research is one of the different types of research in this world today. In recent years, facial recognition in robots has attracted increased study interest. Robotic platforms now utilize a variety of object detection methods, with face detection being a viable use. Face detection in robotics is a computer technique that recognizes human faces in digital pictures and is used in a range of applications. Different authors have performed their research in different ways on the use of detection systems. This paper aims to give future researchers a better idea of using facial recognition systems in robotics. In this study, we reviewed research by various authors over recent years to facilitate future facial recognition research. In addition, scholars have addressed the topics, how they have done so, and the specifics of their approaches are described. This paper reviewed an overview of hardware implementation and software implementation by various authors. It can automatically focus cameras or count the number of people who have entered a location. Commercial applications of the method include displaying tailored advertisements in response to a recognized face along with the algorithms, functions and architectures used in facial recognition and giving the opinions of various authors mentioned. The comparative analysis of facial recognition and its architecture system is highlighted.

  • Immersive Horizons: Exploring the Transformative Power of Virtual Reality Across Economic Sectors

    Appears in:
    airo 23(1): e6

    Authors:
    Abdullah Nawaz, Amin Kolahdooz, Ata Jahangir Moshayedi, Abiodun Durojaye

    Downloads:
    539

    Abstract:
    The scholarly discourse surrounding the manifold advantages, applications, and limitations of implementing Virtual Reality (VR) in the contemporary milieu has burgeoned over time. VR holds immense potential, attracting fervent interest from governmental and private entities alike. Nevertheless, the existing body of literature pertaining to the expanding utilization of VR in diverse economic sectors remains scant. Therefore, the primary objective of this study is to furnish a comprehensive literature review encompassing VR applications across various economic domains while elucidating concerns surrounding its integration within engineering education. A total of 108 publications were extracted from prominent databases such as Scopus, Elsevier, Science Direct, and Google Scholar, with a subsequent review of 51 relevant works. These scrutinized journals were published between 2015 and 2022 and were predominantly authored in English. The reviewed publications encompassed VR applications in education, robotics, healthcare, transportation, sports, agriculture, governance, security, and media. The study’s findings unveiled significant advancements in VR implementation within engineering education, medical training, cognitive augmentation, aircraft assembly, governance, and diverse other spheres. Notwithstanding these achievements, impediments to VR deployment were identified, stemming from financial exigencies, cultural and conventional norms, with scant evidence of VR’s prevalence in underdeveloped nations, given that all the assessed research originated from developed economies. Additionally, the limitations of this review encompassed a small sample size and a narrowly focused demographic in the examined articles. Nevertheless, despite these constraints, the research highlights substantial progress in VR utilization over the preceding decade.
Publisher
EAI
ISSN
2790-7511
Number of Volumes
3
Last Published
2024-01-08
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