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Wireless Sensor Networks and Green Networking for 6G communication- An Overview 1

Wireless Sensor Networks and Green Networking for 6G communi...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bordoloi, Dibyahash Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Dehradun Rs Cse Graphic Era University Dept. of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Wireless sensor networks and green networking have been major research areas in the field of communication technology for the past few decades. In particular, the development of 6G communication systems has brought renewed focus on these areas, as the demands for higher data rates, more diverse applications, and lower energy consumption continue to increase. The use of wireless sensor networks in 6G communication systems has been widely explored in the literature, and has shown great potential for a wide range of applications, including environmental monitoring, industrial process control, and healthcare. However, a key challenge in the deployment of these networks is ensuring their energy efficiency, as the demands on the network will be much higher than in previous generations. To address this challenge, researchers have proposed various green networking techniques, such as energy harvesting and low-power communication protocols, which have been shown to significantly reduce the energy consumption of the sensors. In addition to improving the energy efficiency of wireless sensor networks, researchers have also studied their reliability, scalability, and adaptability. These properties are important in 6G communication systems, as the demands on the network are expected to be much higher than in previous generations. Researchers have proposed various methods to improve the reliability, scalability, and adaptability of wireless sensor networks, including the use of error-correction codes, redundant communication channels, and machine learning algorithms. © 2023 IEEE.

关键词： Wireless sensor networks

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A Novel Shared Control Framework Based on Imitation Learning 25

A Novel Shared Control Framework Based on Imitation Learning

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Hu, Hao Shi, Donghao Yang, Chenguang Si, Weiyong Li, Qinchuan School Of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou China University Of The West Of England Bristol Robotics Laboratory Bristol United Kingdom School Of Computer Science And Electronic Engineering University Of Essex London United Kingdom

ISBN: (纸本)9798350340266

Learning from Demonstration (LfD) stands out as a powerful tool for swiftly deploying tasks in robotics, distinguished by its capacity to effectively leverage the task expertise of experts. However, contemporary robotic intelligence has not yet attained a level of complete autonomy. The integration of shared control methodologies allows for the assimilation of offline experiences derived from experts, concomitant with the introduction of online professional guidance during task execution. This study augments the established framework by incorporating electromyographic signals to discern expert intentions and utilising the Gaussian Mixture Model (GMM) to learn experience. The proposed approach engenders a more efficient shared control method. Validation of this methodology is conducted on the Omni and Franka Emika Panda robotic platforms. © 2024 IEEE.

关键词： Gaussian distribution

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MOSFET Based Active Rectifier Design in Wireless Capacitive Power Transfer for Electric Vehicles 1

MOSFET Based Active Rectifier Design in Wireless Capacitive ...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Dragon, Klemens Raphael Lie, Zener Sukra Muharam, Aam Ika, Juan Luciano Perdana, Muhamad Arjuna Amin Hapid, Abdul Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Badan Riset dan Inovasi Nasional Research Center for Transportation Technology Jawa Barat Bandung Indonesia

ISBN: (纸本)9798350368918

While the rise of electric vehicles reflects a push for clean energy as envisioned by international agreements like the Paris Agreement, wireless charging's convenience can overcome limitations and hasten their adoption, making it a strategic step alongside regulations such Permenhub No. 15/2022 and SDG 9 'Industry, Innovation, and Infrastructure' for cleaner air and sustainable transportation. The aim is to convert the high frequency AC voltage from the coupling capacitor into a stable DC voltage. Further increase the system sensitivity to the load variations, by designing a MOSFET based active rectifier. By changing load parameters, the accuracy of PFC adaptation to the battery State of Charge (SOC) obtained is at 98% with 2% tolerance for errors. It is concluded that the research is said to be successful because it has met the parameters of general research hypothesis where the system is able to convert high frequency AC voltage into a stable DC voltage. © 2024 IEEE.

关键词： State of charge

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Internet of Intelligent Things (IoT) - An Overview 1

Internet of Intelligent Things (IoT) - An Overview

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Aeri, Manisha Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

The basic goal of the IoT is to permit the online connection and communication of everyday physical objects like appliances, vehicles, and other gadgets. This paves the way for effortless and automated data collection and exchange, as well as interaction between the objects themselves and with humans. The Internet of Everything (IoE) is an expansion of the Internet of Things (IoT) that aims to maximize and improve human life by connecting and making the most of all available resources (humans, information, and technology). The transportation, healthcare, agricultural, energy, and manufacturing sectors are all examples. The IoIT aims to incorporate AI and ML within the IoT's interconnected network of physical items. These objects can then independently behave in response to the information they have gathered and the instructions they have been given. This has the potential to lead to smarter, more efficient systems that can react to new information and human input in real time. The Internet of Things, the Internet of Everything, and the Industrial Internet of Things all have the same overarching goal: to pave the way for the development of more efficient and intelligent systems and processes that can enhance a wide range of human endeavors. However, in order to make sure that these technologies are used in a responsible and ethical way, it will be necessary to carefully analyze their consequences and the issues they provide. © 2023 IEEE.

关键词： Internet of things

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

引用

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Smart Cities Using Artificial Intelligence (AI) and Blockchain in the Internet of Things (IoT) Era 1st

Smart Cities Using Artificial Intelligence (AI) and Blockcha...

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1st International Conference on Smart Environment and Green Technologies, ICSEGT 2024

作者： Al-Turjman, Fadi Salama, Ramiz Sadikoglu, Gunay Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Mersin Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Mersin Turkey Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Mersin Turkey Near East University Nicosia Mersin Turkey

ISBN: (纸本)9783031815638

Around the world, modern cities are changing dramatically in order to install smart infrastructure, provide smart services to residents, improve accessibility for vulnerable populations, and provide a clean, sustainable, and secure environment. Numerous initiatives that address significant issues with urban transportation, health, water management, energy conservation, housing, safety, and accessibility are centered on the adoption of new technologies. To overcome these obstacles and enhance the resourcefulness, sustainability, and security of cities, it is imperative to embrace new and creative technologies, especially in light of the advancements in communication and sensor technology over the past few decades. This article gives a summary of the metropolitan issues that smart urban communities are now experiencing. It investigates how edge AI and blockchain, two recent technological innovations, might revolutionize urban intelligence. It also examines significant research initiatives in these two crucial areas of today’s smart cities, along with a variety of applications for edge AI and blockchain in smart energy and transportation. It discusses numerous uses of blockchain in the context of secure vehicle-to-vehicle trade and communication, along with a range of methods for processing speed detection, counting, and identification to lessen traffic congestion. The purpose of this review article is to set the stage for next research on applying blockchain technology and edge artificial intelligence to smarter city applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sustainable city

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Online 3D Bin Packing for Novel Objects Based on Deep Reinforcement Learning

Online 3D Bin Packing for Novel Objects Based on Deep Reinfo...

引用

2023 International Conference on Advanced robotics and Intelligent Systems, ARIS 2023

作者： Chien, Shao-Yu Wong, Ching-Chang Tamkang University College of Engineering Doctoral Program in Robotics New Taipei City25137 Taiwan Tamkang University Electrical and Computer Engineering Department New Taipei City25137 Taiwan

ISBN: (纸本)9798350302714

In this paper, a method using deep reinforcement learning is proposed to deal with the 3D online bin packing problem. The packing objects are not limited to several specific or fixed cuboid objects, but are composed of more than a thousand objects and randomly generated cuboids, which make the trained policy network can handle novel unknown objects. In addition, the posture of the object in the box can be any angle, not limited to horizontal and vertical. In the proposed method, four voxel maps are used as inputs, and a Soft Actor-Critic (SAC) algorithm is used to train a policy network. On the other hand, in order to deal with various objects with irregular shapes, a packing task simulator with physics engine enable the policy network to learn the state of falling and stacking objects. In terms of training environment of deep reinforcement learning, the proposed method can be applied to boxes of different sizes because of the scalable image information. Moreover, a reward function and a training strategy with gradually increasing difficulty are proposed to effectively improve the learning of policy network. In terms of experimental results, the results on a random object bin packing task in a simulator illustrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Reinforcement learning

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Autonomous Liver Ultrasound Examination Based on Imitation Learning and Stiffness Estimation 25

Autonomous Liver Ultrasound Examination Based on Imitation L...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Wang, Zihao Shi, Donghao Yang, Chenguang Si, Weiyong Li, Qinchuan School Of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou China Bristol Robotics Laboratory University Of The West Of England Bristol United Kingdom School Of Computer Science And Electronic Engineering University Of Essex London United Kingdom

ISBN: (纸本)9798350340266

In the past few decades, the demand for assistive robots in the field of healthcare has steadily increased. We conducted research on an autonomous robot system based on imitation learning to assist in liver scans guided by ultrasound. Traditional procedures for liver examination using ultrasound imaging typically require a physician to slide the ultrasound probe along the patient's lower ribs for multi-angle observation. To automate this process, we employed imitation learning to capture the physician's manipulation techniques. However, the imitation generalization system often struggles to accurately extract the starting and ending points due to the obstruction of fat and skin, and the individual variations in patient anatomy lead to inconsistencies in the positions of lower ribcage. To improve the scanning process, we introduced stiffness estimation to differentiate between the abdomen and the rib, allowing real-time adjustments for optimal alignment of lower ribcage. Furthermore, due to the need for occasional physician intervention during the detection process, we implemented mechanisms for pausing and resuming the trajectory learning system to ensure continued operation. Ultimately, the proposed approach was evaluated using the Franka Panda robot system and demonstrated autonomous liver ultrasound examination in scenarios involving multiple human subjects. © 2024 IEEE.

关键词： Stiffness

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LoopGAN: A Novel Multi-Step Generative Architecture for Sequential Stock Forecasting 29

LoopGAN: A Novel Multi-Step Generative Architecture for Sequ...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： NoorMohammadzadehMaleki, Deniz Kargar, Masoud Movassaghpour, Shahin Sharbaf Ardebili, Sondos Samadian, Ehsan Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Robotics and Soft Technologies Research Center Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Azerbaijan Charkhe Niloofari Higher Education Institue Tabriz Iran

ISBN: (纸本)9798331523114

This paper introduces "LoopGAN," an advanced generative model architecture designed for sequential stock forecasting, leveraging the strengths of Conditional GAN (CGAN) and Least Squares GAN (LSGAN) to enhance prediction accuracy in highly volatile financial time series. LoopGAN's unique recursive prediction mechanism allows each output to feed back as input, creating dynamic, extended forecasts across multiple time steps. Extensive evaluation of the Dow Jones Industrial Average (DJIA) dataset, highlights LoopGAN's superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 0.005739. This result surpasses traditional models, with LoopGAN outperforming LSTM and RNN models, which registered MAPE scores of 0.006803 and 0.008023, respectively. These findings underscore LoopGAN's robustness, offering a marked improvement in prediction accuracy and confirming its reliability for complex financial forecasting tasks. © 2025 IEEE.

关键词： Financial markets

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET 25

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khalid, Fatima Hanif, Muhammad Ul Ain, Qurat Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Faculty of Computer Science and Engineering KPK Topi Pakistan Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Aerial Robotics and Vision Lab KPK Topi Pakistan Shifa Tameer-E-Millat University Department of Computing Islamabad Pakistan

ISBN: (纸本)9798350379037

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management. © 2024 IEEE.

关键词： CD&S Dataset EffiecientNet-b0 Fused Truncated EfficientNet Maize Disease Maize Severity Level

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