EAI Endorsed Transactions on AI and Robotics

Editor(s)-in-Chief: Shuai (Steven) Li

Aims & Scope
Editorial Board

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.
Deputy Editor-in-Chief
Dunhui Xiao, School of Mathematical Sciences, Tongji University, China.
Advisory Board (alphabetically)
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).
Associate Editor-in-Chief
Lining Sun, School of Mechanical and Electric Engineering, Soochow University, China. Foreign member of Russian Academy of Engineering.
Xingye Yue, School of Mathematical Sciences, Soochow University, China.
Editor Assistants
Ameer Tamoor Khan, Computing Department, The Hong Kong Polytechnic University, Hong Kong, China.
Yiguo Yang, School of Computer Science, Shanghai University, China.
Associate Editors (alphabetically)
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.
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.
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.
Editorial board members

Recently Published
Most Popular

Machine Learning in Robotics with Fog/Cloud Computing and IoT
Appears in:
airo 23(1):

Authors:
Kiran Deep Singh, Prabh Deep Singh

Published:
4th Dec 2023

Abstract:
Robotics has been transformed by machine learning (ML), enabling intelligent and adaptive autonomous systems. By delivering massive computational resources and real-time data, fog/cloud computing and the Internet of Things boost ML-based robotics. Intelligent and linked robotics have emerged from …Robotics has been transformed by machine learning (ML), enabling intelligent and adaptive autonomous systems. By delivering massive computational resources and real-time data, fog/cloud computing and the Internet of Things boost ML-based robotics. Intelligent and linked robotics have emerged from fog/cloud computing, IoT, and machine learning. Robots using distributed computing, real-time IoT data, and advanced machine learning algorithms could alter industries and improve automation. To maximize its potential, this revolutionary combination must overcome several obstacles. This paper discusses the benefits and drawbacks of integrating technologies. It offer rapid model training and deployment for robots ML algorithms like deep learning and reinforcement learning. Case studies demonstrate how this combination might enhance robotics across industries. This study discusses the benefits and drawbacks of fog/cloud computing, IoT, and machine learning in robots. We propose solutions for security and privacy, resource management, latency and bandwidth, interoperability, energy efficiency, data quality, and bias. By proactively addressing these difficulties, we can establish a secure, efficient, and privacy-conscious robotic ecosystem where robots seamlessly interact with the physical world, improving productivity, safety, and human-robot collaboration. As these technologies progress, appropriate integration and ethical principles are needed to maximize their benefits to society.
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A Study Towards Building Content Aware Models in NLP using Genetic Algorithms
Appears in:
airo 23(1):

Authors:
Umesh Tank, Saranya Arirangan, Anwesh Reddy Paduri, Narayana Darapaneni

Published:
7th Nov 2023

Abstract:
INTRODUCTION: With the advancement in the large language models, often called LLMs, there has been increasing concerns around the usage of these models. As they can generate human-like text and can also perform a number of tasks such as generating code, question answering, essay writing and even ge…INTRODUCTION: With the advancement in the large language models, often called LLMs, there has been increasing concerns around the usage of these models. As they can generate human-like text and can also perform a number of tasks such as generating code, question answering, essay writing and even generating text for research papers.
OBJECTIVES: The generated text is subject to the usage of the original data (using which models are trained) which might be protected or may be personal/private data. The detailed description of such concerns and various potential solutions is discussed in ‘Generative language models and automated influence operations: Emerging threats and potential mitigations’. METHODS: Addressing these concerns becomes the paramount for LLMs usability. There are several directions explored by the researchers and one of the interesting works is around building content aware models. The idea is that the model is aware of the type of content it is learning from and aware what type of content should be used to generate a response to a specific query.
RESULTS: In our work we explored direction by applying poisoning techniques to contaminate data and then applying genetic algorithms to extract the non-poisoned content from the poisoned content that can generate a good response when paraphrased.
CONCLUSION: While we demonstrated the idea using poisoning techniques and tried to make the model aware of copyrighted content, the same can be extended to detect other types of contents or any other use cases where content awareness is required.
more »
Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach
Appears in:
airo 23(1):

Authors:
Rafid Kaysar Shagor, Fahim Faisal, Mirza Muntasir Nishat, Sayka Afreen Mim, Hafsa Akter

Published:
31st Oct 2023

Abstract:
This paper represents the idea of implementing bio-inspired algorithms in designing an optimized PID controller to investigate the stability and improve the performance of the closed-loop CUK converter. Bio-inspired algorithms (BIA) such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO) …This paper represents the idea of implementing bio-inspired algorithms in designing an optimized PID controller to investigate the stability and improve the performance of the closed-loop CUK converter. Bio-inspired algorithms (BIA) such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO) and, Artificial Bee Colony (ABC) are stochastic optimization techniques and have been increasingly applied to attain optimal solution for designing and optimizing power converters in current times. The closed-loop transfer function of the CUK converter has been developed by the State Space Average (SSA) technique. This paper assesses both the cases of converter stability when it is integrated with the conventional PID controller and the BIA-PID controllers (FA-PID, PSO-PID, ABC-PID) and eventually compares the outcomes from all the controllers. For examining the stability of the system, three objective functions (IAE, ITAE, and ISE) and various performance specifications such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated. MATLAB and Simulink are used to carry out the simulations meticulously. Hence, a comparative analysis is illustrated in this paper to state a clear-sighted evaluation of the performances.
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Security and Privacy in Fog/Cloud-based IoT Systems for AI and Robotics
Appears in:
airo 23(1):

Authors:
Prabh Deep Singh, Kiran Deep Singh

Published:
28th Aug 2023

Abstract:
Integration of Internet of Things (IoT) systems based on the fog or the cloud with Artificial Intelligence (AI) and Robotics has prepared the way for breakthrough advancements in a variety of different fields of business. However, these cross-disciplinary technologies present significant difficulti…Integration of Internet of Things (IoT) systems based on the fog or the cloud with Artificial Intelligence (AI) and Robotics has prepared the way for breakthrough advancements in a variety of different fields of business. However, these cross-disciplinary technologies present significant difficulties in terms of maintaining confidentiality and safeguarding data. This article digs into the issues of establishing robust security and protecting user privacy in IoT systems that are based in the fog or the cloud and are utilized for AI and robotics applications. This study gives insights into the possible hazards encountered by such interconnected systems by conducting an in-depth review of existing security threats, vulnerabilities, and privacy concerns. In addition, the study investigates cutting-edge security mechanisms, encryption approaches, access control strategies, and privacy-preserving solutions that can be utilized to safeguard data, communications, and user identities. The results of this study highlight the demand for comprehensive security and privacy solutions to support the mainstream deployment of Fog/Cloud-based Internet of Things systems in the field of artificial intelligence and robotics.
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The Role of Biometric in Banking: A Review
Appears in:
airo 23(1):

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

Published:
21st Aug 2023

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. Fingerp…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.
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Advancing Robot Perception in Non-Spiral Environments through Camera-based Image Processing
Appears in:
airo 23(1):

Authors:
Hamid Hoorfar, Alireza Bagheri

Published:
17th Aug 2023

Abstract:
Robot perception heavily relies on camera-based visual input for navigating and interacting in its environment. As robots become integral parts of various applications, the need to efficiently compute their visibility regions in complex environments has grown. The key challenge addressed in this pa…Robot perception heavily relies on camera-based visual input for navigating and interacting in its environment. As robots become integral parts of various applications, the need to efficiently compute their visibility regions in complex environments has grown. The key challenge addressed in this paper is to devise an innovative solution that not only accurately computes the visibility region V of a robot operating in a polynomial environment but also optimizes memory utilization to ensure real-time performance and scalability.
The main objective of this research is to propose an algorithm that achieves optimal-time complexity and significantly reduces memory requirements for visibility region computation. By focusing on sub-linear memory utilization, we aim to enhance the robot's ability to perceive its surroundings effectively and efficiently.
Previous approaches have provided solutions for visibility region computation in non-spiral environments, but most were not tailored to memory limitations. In contrast, the proposed algorithm is designed to achieve optimal time complexity that is O(n) while reducing memory usage to O(c/log n) variables, where c < n represents the number of critical corners in the environment. Leveraging the constant-memory model and memory-constrained algorithm, we aim to strike a balance between computational efficiency and memory usage.
The algorithm's performance is rigorously evaluated through extensive simulations and practical experiments. The results demonstrate its linear-time complexity and substantial reduction in memory usage without compromising the accuracy of the visibility region computation. By efficiently handling memory constraints, the robot gains a cost-effective and reliable perception mechanism, making it well-suited for a wide range of real-world applications.
The constant-memory model and memory-constrained algorithm presented in this paper offer a significant advancement in robot perception capabilities. By optimizing the visibility region computation in polynomial environments, our approach contributes to the efficient operation of robots, enhancing their performance and applicability in complex real-world scenarios. The results of this research hold promising potential for future developments in robotics, computer vision, and related fields.
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Efficiently Guiding K-Robots Along Pathways with Minimal Turns
Appears in:
airo 23(1):

Authors:
Hamid Hoorfar, Nedasadat Taheri, Houman Kosarirad, Alireza Bagheri

Published:
12th Jul 2023

Abstract:
INTRODUCTION: This paper addresses the navigation of a team of k protector robots within pathways, focusing on minimizing the total number of turns. These robots utilize orthogonal routes known as watchman routes, which prioritize finding the shortest path while maintaining visibility of all points…INTRODUCTION: This paper addresses the navigation of a team of k protector robots within pathways, focusing on minimizing the total number of turns. These robots utilize orthogonal routes known as watchman routes, which prioritize finding the shortest path while maintaining visibility of all points in the environment from at least one robot on its designated route. The main objective of this research is to optimize robot navigation by reducing the overall number of turns.
OBJECTIVES: The primary objective of this study is to develop a linear-time algorithm that efficiently processes and determines routes for k robots within a specified area. By minimizing the number of turns, this algorithm aims to enhance the navigation capabilities of watchman robots, enabling them to effectively traverse complex environments.
METHODS: This research employs techniques derived from computational geometry to investigate the navigation of protector robots. The focus is on developing an algorithm that can efficiently process and determine the optimal routes for the robots, considering factors such as visibility and shortest path length. The algorithm is designed to minimize the number of turns while ensuring efficient coverage of the environment.
RESULTS: The main results of this paper include the development of a linear-time algorithm for determining routes for a team of k protector robots. The algorithm efficiently processes the input data and produces separate routes for each robot. By minimizing the number of turns, the algorithm improves the overall navigation efficiency of the robots. The results demonstrate the effectiveness of the algorithm in optimizing robot paths and reducing the complexity of navigation in real-world scenarios.
CONCLUSION: In conclusion, this research contributes to the field of robotic systems by addressing the navigation challenges faced by a team of protector robots. The introduced linear-time algorithm optimizes the routes for k robots, aiming to minimize the total number of turns. The outcomes of this study have significant implications for the advancement of watchman robots, enhancing their coverage and surveillance capabilities in real-world applications. The algorithm’s efficiency and effectiveness in minimizing turns open new opportunities for developing efficient navigation strategies in complex environments.
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Multi-class Classification of Imbalanced Intelligent Data using Deep Neural Network
Appears in:
airo 23(1):

Authors:
Masoumeh Soleimani, Akram Sadat Mirshahzadeh

Published:
12th Jul 2023

Abstract:
In recent years, studies in the field of deep learning have made significant progress. These studies have focused more on datasets with balanced classification, and less research has been done on imbalanced datasets, which are of great importance in the real world and present significant challeng…In recent years, studies in the field of deep learning have made significant progress. These studies have focused
more on datasets with balanced classification, and less research has been done on imbalanced datasets, which
are of great importance in the real world and present significant challenges for classification. This article
studies the problem of classifying imbalanced data, introduces dynamic sampling for deep neural networks,
investigates the imbalanced multiclass problem, and proposes a dynamic sampling method for deep learning.
In our proposed method, all samples are fed to the current deep neural network for each training iteration,
and the accuracy, precision, and mean error of the deep neural network are estimated. The proposed method
dynamically selects informative data for training the deep neural network. Comprehensive experiments were
conducted to evaluate and understand its strengths and weaknesses. The results of 13 imbalanced multiclass
datasets show that the proposed method outperforms other methods, such as initial sampling techniques,
active learning, cost-sensitive learning, and reinforcement learning.
more »
Review of Image Classification Algorithms Based on Graph Convolutional Networks
Appears in:
airo 23(1):

Author:
Wenhao Tang

Published:
6th Jul 2023

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 o…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.
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Immersive Horizons: Exploring the Transformative Power of Virtual Reality Across Economic Sectors
Appears in:
airo 23(1): e6

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

Published:
9th Jun 2023

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…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.
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Deep Learning Application Pros And Cons Over Algorithm Deep Learning Application Pros And Cons Over Algorithm
Appears in:
airo 22(1): 7

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

Downloads:
1306

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 fa…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.
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Review On: The Service Robot Mathematical Model
Appears in:
airo 22(1): 8

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

Downloads:
591

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 vario…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.
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The Object Detection, Perspective and Obstacles In Robotic: A Review
Appears in:
airo 22(1): e13

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

Downloads:
476

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 soluti…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.
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Peaks Detector Algorithm after CFAR for Multiple Targets Detection
Appears in:
airo 22(1): 11

Author:
Syed Waqar Hameed

Downloads:
304

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 rang…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.
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Feedback Control Systems Stabilization Using a Bio-inspired Neural Network
Appears in:
airo 22(1): 5

Authors:
Spyridon Mourtas, Vasilios Katsikis, Chrysostomos Kasimis

Downloads:
258

Abstract:
The proportional–integral–derivative (PID) control systems, which have become a standard for technical and industrial applications, are the fundamental building blocks of classical and modern control systems. In this paper, a three-layer feed-forward neural network (NN) model trained to replicate t…The proportional–integral–derivative (PID) control systems, which have become a standard for technical and industrial applications, are the fundamental building blocks of classical and modern control systems. In this paper, a three-layer feed-forward neural network (NN) model trained to replicate the behavior of a PID controller is employed to stabilize control systems through a NN feedback controller. A novel bio-inspired weights-and-structure-determination (BIWASD) algorithm, which incorporates a metaheuristic optimization algorithm dubbed beetle antennae search (BAS), is used to train the NN model. More presicely, the BIWASD algorithm identifies the ideal weights and structure of the BIWASD-based NN (BIWASDNN) model utilizing a power sigmoid activation function while handling model fitting and validation. The results of three simulated trials on stabilizing feedback control systems validate and demonstrate the BIWASDNN model’s exceptional learning and prediction capabilities, while achieving similar or better performance than the corresponding PID controller. The BIWASDNN model is compared to three other high-performing NN models, and a MATLAB repository is accessible in public through GitHub to encourage and enhance this work.
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The Role of Biometric in Banking: A Review
Appears in:
airo 23(1):

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

Downloads:
225

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. Fingerp…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.
more »
Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot
Appears in:
airo 22(1): 10

Authors:
Mubashir Usman Ijaz, Ameer Tamoor Khan, Shuai Li

Downloads:
224

Abstract:
Trajectory tracking and obstacle avoidance lies at the heart of autonomous navigation for mobile robots. In this paper, a control architecture for trajectory tracking while avoiding obstacles and controller tuning is proposed for a differential drive mobile robot (DMR). The framework of optimizatio…Trajectory tracking and obstacle avoidance lies at the heart of autonomous navigation for mobile robots. In this paper, a control architecture for trajectory tracking while avoiding obstacles and controller tuning is proposed for a differential drive mobile robot (DMR). The framework of optimization algorithm is inspired by the food search behavior of beetles using their antennae. Path planning and controller tuning remain computationally demanding tasks despite of the proposed algorithms existing today. Our bio inspired approach unifies these two problems by minimizing the respective cost functions and solving the optimization problem efficiently. Trajectory tracking problem is based on the difference of the current and next pose of the robot while obstacle avoidance is achieved on the principle of maximizing the minimum distance between the robot and obstacle in the path of the robot. The proposed architecture is simulated in V-REP environment using MATLAB. Simulation results have verified that beetle antennae search can successfully plan and track the reference path by tuning the PID controller efficiently.
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The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review
Appears in:
airo 22(1): e14

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

Downloads:
223

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 detectio…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.
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LQG, PID controller, ANN for single axis gimbal actuator
Appears in:
airo 22(1): e12

Author:
Nguyen Cong Danh

Downloads:
221

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. …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.
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A Brief Analysis of “ChatGPT” – A Revolutionary Tool Designed by OpenAI
Appears in:
airo 22(1): e15

Author:
Md. Asraful Haque

Downloads:
210

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 …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?
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Publisher: EAI
ISSN: 2790-7511
Number of Volumes: 2

Last Published: 2023-04-20