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NANO-BOTS: Designing and Manufacturing 14

NANO-BOTS: Designing and Manufacturing

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14th IEEE international conference on Computational Intelligence and communication networks, CICN 2022

作者： Rathi, Kritika Khetan, Sonika Samarah, Ashraf Bagwari, Jyotshana Bagwari, Ashish Women Institute of Technology Uttarakhand Technical University Department of Electronics and Communication Engineering Dehradun India Al Balqa Applied University Department of Electrical Energy Engineering Salt Jordan Advanced and Innovative Research Laboratory India

ISBN: (纸本)9781665487719

this report highlight the process of the nano-technology for the nano-robots which includes the literature review, molecular machines, how the sensors work in nano-robotics, its applications and the future scope. As the nano-robots also helps in the field of nano-medicine like cancer treatment and several tools for the treatment of human body. the carbon atom is used in a diamond structure for the exterior of the nano-robots because of its properties and strength and it also helps in the drug delivery inside the human body. As far as the scientist predict, the current study of the nano-technology involves the great achievement in the field of the nano-robots. © 2022 IEEE.

关键词： Drug delivery

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Depth-Awareness Shared Self-Supervised Bronchial Orifice Segmentation for Center Detection in Vision-Based Robotic Bronchoscopy 14

Depth-Awareness Shared Self-Supervised Bronchial Orifice Seg...

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14th IEEE international conference on Cyber Technology in Automation, Control, and Intelligent systems, CYBER 2024

作者： Xu, Siang Wang, Xiangyu Qin, Yanding Wang, Hongpeng Yu, Ningbo Han, Jianda Institute of Robotics and Automatic Information System Nankai University Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

ISBN: (纸本)9798331506056

Limited by the complex porous structure of the bronchi, supervised segmentation of the bronchial orifice is difficult to perform. Since there is no gold standard for definition of the edges of bronchial orifices, manual labeling can lead to inconsistencies in calibration standards. this paper presents a self-supervised bronchial orifice segmentation method based on depth information and synthetic data. the method uses synthetic data and synthetic depth maps to train CNN and can be directly applied to real image data. We also propose an Attention Guidance Module that uses depth information to guide the neural network attention distribution. We manually segmented the bronchial orifice region as ground truth and counted the offset of the center point between the segmentation result and ground truth. the results show that the mIoU of the segmentation result is 79.52, the dice coefficient of the result is 0.86 which is better than 0.77 of the previous method, and the pixel deviation of the center point in a 128 × 128 image is 3.92 which is better than 4.35 of the previous method. © 2024 IEEE.

关键词： Neural networks

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Learning Environment for Digital Twin Technologies and M2M communication 14th

Learning Environment for Digital Twin Technologies and M2M C...

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14th international conference on Learning Factories (CLF)

作者： Triltsch, U. Brueggemann, H. Ostfalia Univ Appl Sci Fac Mech Engn Salzdahlumer St 46-48 D-33302 Wolfenbuttel Germany

ISBN: (纸本)9783031654107;9783031654114

this article discusses the practical application of digital twin and machine-to-machine (M2M) communication technologies using a case study involving a FDM printer. the case study serves as a platform for a wide range of practical applications and learning examples within the Ostfalia Learning and Innovation Factory (OLIF). the digital twin was created and dynamized using Siemens' Mechatronic Concept Designer. the primary focus of this article is on the integration of the digital twin with the physical machine and the technologies employed for M2M communication modelling, specifically Asset Administration Shell (AAS) and OPC Unified Architecture (OPC/UA). the article demonstrates how AAS can be leveraged to manage the digital twin and facilitate the connection with the physical machine. the central question addressed here is how core digital twin technologies can be effectively taught to learners using a continuous example. this pedagogical challenge takes into account varying levels of prior knowledge and the limited time available for in person activities. To address these issues, blended learning approaches are applied, bridging knowledge gaps and accommodating individual learning paces in preparation for in-person sessions. this work represents a practical approach to enhancing understanding and proficiency in digital twin and M2M communication technologies through hands-on learning experiences using learning-factory sessions as well as self-organized online teaching on a learning platform. To assess learning progress, we employed a multifaceted approach, including pre- and post-assessments, learner feedback, and performance metrics. Additionally, a comparative study was conducted with a control group from earlier workshops receiving solely theoretical instruction, revealing significant advantages in comprehension and retention for the group with direct practical exposure.

关键词： Digital Twins M2M communication Asset Administration Shell OPC/UA 3D Printing

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Review of Resource Allocation Strategies for Handoff in 5G Mobile communication System 14

Review of Resource Allocation Strategies for Handoff in 5G M...

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14th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Rani, Seema Charaya, Saurabh Deptt of CSE Om Sterling Global University Hisar India

Machine learning-based handoff management has emerged as a promising approach to improve handover performance and quality of service in 5G networks. this approach involves using machine learning algorithms and models to automate handover prediction, decision-making, and resource allocation. Machine learning models can be trained on historical network data to predict when and where a handover may occur, which can help trigger proactive handovers or allocate resources in anticipation of a handover. By analysing current network conditions and user preferences, machine learning algorithms can make intelligent decisions on whether to perform a handover or not, and which target cell or network to handover to. Additionally, machine learning models can be used to optimize the allocation of network resources, such as bandwidth and power, for handover management, which can improve network capacity and reduce energy consumption. However, there are also some challenges associated with machine learning-based handoff management. the availability and quality of data can affect the accuracy of the models, and the complexity of the models may make it difficult to identify the root cause of handover problems or to debug the models. In summary, machine learning-based handoff management has the potential to improve handover performance and quality of service in 5G networks. this paper presents a review of ML based resource allocation strategies for Handoff in 5G Mobile communication System along with a proposed ML based model. By leveraging the power of machine learning algorithms and models, handover prediction, decision-making, and resource allocation can be optimized to improve network performance, capacity, and efficiency. © Grenze Scientific Society, 2023.

关键词： 5G mobile communication systems

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TraveltheLake: Promoting Sustainable Lake Development Using Augmented Reality 14th

TravelTheLake: Promoting Sustainable Lake Development Using ...

引用

14th international conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Dimou, Victoria Styliaras, Georgios Kehayias, George Agelada, Androniki University of the Aegean Mitilini Greece Department of Food Science and Technology University of Patras Agrinio Greece Department of Cultural Technology and Communication University of the Aegean Mitilini Greece

ISBN: (纸本)9783031674259

this paper presents an augmented reality mobile application that promotes sustainable food and touristic development in a complex lake environment, by affecting the socioeconomic relations of local inhabitants, food producers and visitors. the application is based on previous research showing that appropriate fishing methods in the lake could have a positive impact on the quality of fishing population, on the income of fishermen and the broader development of the surrounding area and its population. After an introduction, related work defines what constitutes sustainable development in a complex lake environment. Findings of related work and previous research led to the design of the application and the implementation of a Unity3D application. the application has been evaluated by end users and results are positive. Evaluator remarks will be considered towards a future implementation of the application. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Augmented reality

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Maximum Entropy Deep Reinforcement Learning Based Power Allocation for NOMA Maritime Network 14th

Maximum Entropy Deep Reinforcement Learning Based Power Allo...

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14th EAI international conference on Wireless and Satellite systems, WiSATS 2024

作者： He, Jiayi Zhang, Yakai Liu, Zhiyong School of Information Science and Engineering of HIT Weihai264209 China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai264209 China Key Laboratory of Cross Domain Synergy and Comprehensive Support for Unmanned Marine Systems of Ministry of Industry and Information Technology Weihai264209 China

ISBN: (纸本)9783031862021

In order to address the challenges of high propagation delays and limited service capabilities in maritime satellite communications, unmanned aerial vehicles have been proposed as an airborne backhaul solution to enhance communications between satellites and maritime base stations. the non-orthogonal multiple access (NOMA) framework can solve the user sparsity problem in maritime networks. In this paper, a deep reinforcement learning algorithm is used to solve the nonconvex power allocation problem under NOMA. In order to mitigate the risk of overestimation of Q values and local optimal convergence of Deep Q Network (DQN) algorithm, we propose an algorithm called Soft Agent Critical Ocean Satellite communication Power Allocation (SAC-OSCPA) based on the idea of maximum entropy and compare it with the traditional DQN algorithm. the main goal of this research is to maximize network throughput in scenarios with randomly distributed users. Simulation results show that the average system throughput is improved by 13.18% with the SAC-OSCPA algorithm, and the average throughput of the worst performing user is significantly improved by 41.59%. these results demonstrate the efficacy of the proposed algorithm in optimizing the communication performance of maritime satellite networks. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Deep reinforcement learning

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A Wide Band Annular-Ring Loaded Circularly Polarized Microstrip Antenna 14th

A Wide Band Annular-Ring Loaded Circularly Polarized Microst...

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14th EAI international conference on Broadband communications, networks, and systems, BROADNETS 2024

作者： Muthyala, Samuel Nishant Rao, Pasumarthy Nageswara Mannem, Kiran Aruna, E.R. Department of Electronics and Communication Engineering Vardhaman College of Engineering Hyderabad India Department of Electronics and Communication Engineering GRIET Hyderabad India

ISBN: (纸本)9783031811678

the wireless device market is expanding rapidly nowadays as a result of the rapid advancements in technology in recent years. the antenna is the most crucial part of a wireless device since it facilitates communication and data transfer. In this paper a small microstrip antenna with dual circularly polarized bands is presented. the antenna is small and compact with dimensions 24 mm × 22 mm × 1 mm. the patch is loaded with an annular-ring slot and contains a quarter circle cut on one end. Two slits of varying length and width are also etched in the radiating patch on one side of patch opposite to each other for fine tuning to achieve better circular polarization. A rectangular defected ground structure is used for improving the radiation characteristics. the designed antenna shows good wide bandwidth, gain and circular polarization with dual circularly polarized bands for use in wireless communication (WLAN, WiMax) and satellite communication applications. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Circular polarization

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Lifetime Maximization for UAV-Enabled Integrated Localization and communication networks in Emergency Scenarios 14

Lifetime Maximization for UAV-Enabled Integrated Localizatio...

引用

14th IEEE international conference on Wireless communications and Signal Processing (WCSP)

作者： Yu, Xinyu Liu, Zhenyu Xu, Lianming Wang, Li Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China BUPT Natl Pilot Software Engn Sch Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665450850

the flexible and controllable mobility property makes Unmanned Aerial Vehicles (UAVs) valuable for constructing temporary networks in a harsh environment. However, existing works always focus on enhancing the coverage under the assumption of given positions of users, which degrades the coverage performance in environments without prior locations. In this paper, we propose a divide-conquer UAV deployment strategy for integrated localization and communication networks. Specifically, we first model the UAV deployment problem with the objective of maximizing the regional localization accuracy to locate the users. then, based on the positions obtained, the lifetime maximization problem is modeled under the constraints of communication and localization requirements. An enhanced genetic algorithm (EGA) is proposed to maximize the UAVs' residual lifetime while meeting the service requirements. Simulation results demonstrate that the proposed deployment strategy can provide more stable communication coverage and obtain longer residual lifetime for the UAVs compared with the existing strategies.

关键词： emergency communication the deployment of UAVs localization enhanced genetic algorithm residual lifetime

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AdaKron: an Adapter-based Parameter Efficient Model Tuning with Kronecker Product 30

AdaKron: an Adapter-based Parameter Efficient Model Tuning w...

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Joint 30th international conference on Computational Linguistics and 14th international conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Braga, Marco Raganato, Alessandro Pasi, Gabriella University of Milano-Bicocca Department of Informatics Systems and Communication Italy

ISBN: (纸本)9782493814104

the fine-tuning paradigm has been widely adopted to train neural models tailored for specific tasks. However, the recent upsurge of Large Language Models (LLMs), characterized by billions of parameters, has introduced profound computational challenges to the fine-tuning process. this has fueled intensive research on Parameter-Efficient Fine-Tuning (PEFT) techniques, usually involving the training of a selective subset of the original model parameters. One of the most used approaches is Adapters, which add trainable lightweight layers to the existing pretrained weights. Within this context, we propose AdaKron, an Adapter-based fine-tuning with the Kronecker product. In particular, we leverage the Kronecker product to combine the output of two small networks, resulting in a final vector whose dimension is the product of the dimensions of the individual outputs, allowing us to train only 0.55% of the model's original parameters. We evaluate AdaKron performing a series of experiments on the General Language Understanding Evaluation (GLUE) benchmark, achieving results in the same ballpark as recent state-of-the-art PEFT methods, despite training fewer parameters. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Adapters Kronecker Product Parameter Efficient Tuning

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Robustness Verification of Deep Reinforcement Learning Based Control systems Using Reward Martingales 38

Robustness Verification of Deep Reinforcement Learning Based...

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38th AAAI conference on Artificial Intelligence (AAAI) / 36th conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Zhi, Dapeng Wang, Peixin Chen, Cheng Zhang, Min East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China Univ Oxford Oxford England

ISBN: (纸本)1577358872

Deep Reinforcement Learning (DRL) has gained prominence as an effective approach for control systems. However, its practical deployment is impeded by state perturbations that can severely impact system performance. Addressing this critical challenge requires robustness verification about system performance, which involves tackling two quantitative questions: (i) how to establish guaranteed bounds for expected cumulative rewards, and (ii) how to determine tail bounds for cumulative rewards. In this work, we present the first approach for robustness verification of DRL-based control systems by introducing reward martingales, which offer a rigorous mathematical foundation to characterize the impact of state perturbations on system performance in terms of cumulative rewards. Our verified results provide provably quantitative certificates for the two questions. We then show that reward martingales can be implemented and trained via neural networks, against different types of control policies. Experimental results demonstrate that our certified bounds tightly enclose simulation outcomes on various DRL-based control systems, indicating the effectiveness and generality of the proposed approach.

关键词： Reinforcement learning

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