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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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AI-Powered Drone to Address Smart City Security Issues 37th

AI-Powered Drone to Address Smart City Security Issues

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Salama, Ramiz Al-Turjman, Fadi Culmone, Rosario Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Near East University Nicosia Mersin 10 Nicosia Turkey Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey Computer Science Division School of Science and Technology University of Camerino Camerino62032 Italy

ISBN: (纸本)9783031286933

The idea of a dazzling metropolis has drawn interest from all across the world. New innovations like blockchain, IoT, artificial intelligence, robots, and many other things were added to it. Security is one of the top issues for people living in big cities, and everyone wants to feel completely secure when traveling around every single day. In this research, we will look into how CEOs in affluent cities use and value robots, especially in terms of security. To understand the robot security the board stream, some approaches and intricacies are used. Following that is a discussion of issues with urban security and the application of artificial intelligence to drones as a management tool. The use of cutting-edge technology, such as blockchain, to support smart urban community management is covered in the last part. The smart city idea and all of its benefits for local inspection are supported by robotic use. The idea of thriving urban areas is spreading around the world and is crucial in the context of developing economies. We examined how to leverage cutting-edge technologies in this project to make it feasible. Artificial intelligence, robotics breakthroughs, and blockchain technologies all have significant effects. This research highlights their significance to analysts and how they consider them when assessing prospects. This project will be very beneficial for analysts and experts in a relevant subject. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

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Control Synthesis Problem of Traffic Flow in Urban Network

Control Synthesis Problem of Traffic Flow in Urban Network

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Elena Sofronova Askhat Diveev Department of Robotics Control Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The paper deals with the problem of traffic flow control in the urban road networks. The control is performed by traffic lights at intersections. The statement of the optimal control problem is given. A model-based approach is applied. The mathematical model of traffic flow based on the theory of controlled networks is used. A quality criterion is set. It is pointed out that a feature of the control of this object is its cyclical nature. Control is repeated at a certain time interval, which is called a control cycle. The duration of traffic light phases is considered as control. The phases switch sequentially in a certain order. Additional constraints are given that must be taken into account when choosing phase duration. As a result of solution of the optimal control problem the coordination plan for traffic lights is obtained. Coordination plan is cyclically repeated. The control synthesis problem is formulated that accounts for change in initial state. It is proposed to solve the control synthesis problem as a problem of selecting an optimal coordination plan from a set of plans calculated in advance for various initial flow states and model parameters.

关键词： Roads Decision making Optimal control Machine learning Switches Mathematical models Information technology Optimization

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Problem of Optimal Area Monitoring and Universal Motion Stabilisation System for its Practical Realisation

Problem of Optimal Area Monitoring and Universal Motion Stab...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Askhat Diveev Elena Sofronova Department of Robotics Control Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The area monitoring problem is considered as optimization task. The problem belongs to the class of global optimization. First, the problem of finding optimal trajectories is solved so that the control objects, when moving along these trajectories, observe the entire area, do not collide with each other and do not violate phase constraints. One of the main quality criteria when searching for trajectories is their minimum total length, and the closeness of the trajectory lengths of each control object. Subsequently, the optimal control problem is solved in order to ensure the movement of the object along the obtained optimal trajectory. To solve the problem in class of implemented in real object control functions the stabilisation system of control object motion along a given trajectory is synthesized. When solving the optimal control problem, the same criteria are considered as when finding optimal trajectories, with the addition of the accuracy of passing points on trajectories. An illustrative example of solving the optimal area monitoring problem by two quadcopters with one circular phase constraint is provided. Symbolic regression is employed to synthesise a system for stabilising the movement of an object along the optimal trajectory.

关键词： Accuracy Perturbation methods Optimal control Search problems Trajectory Feedback control Information technology Monitoring Optimization Quadrotors

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Deep learning-based magnetic resonance image super-resolution: a survey

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Neural Computing and Applications 2024年第21期36卷 12725-12752页

作者： Ji, Zexin Zou, Beiji Kui, Xiaoyan Liu, Jun Zhao, Wei Zhu, Chengzhang Dai, Peishan Dai, Yulan School of Computer Science and Engineering Central South University Changsha410083 China Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South University Changsha410083 China Department of Radiology The Second Xiangya Hospital Central South University Changsha410011 China The College of Literature and Journalism Central South University Changsha410083 China

Magnetic resonance imaging (MRI) is a medical imaging technique used to show anatomical structures and physiological processes of the human body. Due to limitations like image acquisition time, hardware capabilities, or uncooperative patients, the resolution of MR images is insufficient. Super-resolution (SR) is a crucial method to enhance the resolution of images without expensive scanning equipment. Recent years have witnessed significant progress in MR image super-resolution. Therefore, this survey presents a thorough overview of current developments in deep learning-based MR image super-resolution methods. In general, we can roughly divide the MRI super-resolution methods into single-contrast MR image SR methods and multi-contrast MR image SR methods. Additionally, we introduce the multi-task learning approaches about the MR image super-resolution. We also summarize other crucial topics, such as the degradation model, the definition of the super-resolution problem, the dataset, loss functions, and image quality assessment. Lastly, we indicate the challenges in the field of super-resolution and draw a conclusion to our survey. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset

M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dat...

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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Chaney, Kenneth Cladera, Fernando Wang, Ziyun Bisulco, Anthony Hsieh, M. Ani Korpela, Christopher Kumar, Vijay Taylor, Camillo J. Daniilidis, Kostas University of Pennsylvania GRASP Laboratory PA19104 United States Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States

ISBN: (纸本)9798350302493

We present M3ED, the first multi-sensor event camera dataset focused on high-speed dynamic motions in robotics applications. M3ED provides high-quality synchronized and labeled data from multiple platforms, including ground vehicles, legged robots, and aerial robots, operating in challenging conditions such as driving along off-road trails, navigating through dense forests, and performing aggressive flight maneuvers. Our dataset also covers demanding operational scenarios for event cameras, such as scenes with high egomotion and multiple independently moving objects. The sensor suite used to collect M3ED includes high-resolution stereo event cameras (1280×720), grayscale imagers, an RGB imager, a high-quality IMU, a 64-beam LiDAR, and RTK localization. This dataset aims to accelerate the development of event-based algorithms and methods for edge cases encountered by autonomous systems in dynamic environments. © 2023 IEEE.

关键词： Cameras

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FreqMixFormerV2: Lightweight Frequency-aware Mixed Transformer for Human Skeleton Action Recognition

arXiv

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arXiv 2024年

作者： Wu, Wenhan Wang, Pengfei Chen, Chen Lu, Aidong Department of Computer Science University of North Carolina at Charlotte Charlotte United States Center for Research in Computer Vision University of Central Florida Orlando United States

Transformer-based human skeleton action recognition has been developed for years. However, the complexity and high parameter count demands of these models hinder their practical applications, especially in resource-constrained environments. In this work, we propose FreqMixForemrV2, which was built upon the Frequency-aware Mixed Transformer (FreqMixFormer) [18] for identifying subtle and discriminative actions with pioneered frequency-domain analysis. We design a lightweight architecture that maintains robust performance while significantly reducing the model complexity. This is achieved through a redesigned frequency operator that optimizes high-frequency and low-frequency parameter adjustments, and a simplified frequency-aware attention module. These improvements result in a substantial reduction in model parameters, enabling efficient deployment with only a minimal sacrifice in accuracy. Comprehensive evaluations of standard datasets (NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets) demonstrate that the proposed model achieves a superior balance between efficiency and accuracy, outperforming state-of-the-art methods with only 60% of the parameters. Our project is publicly available at: https://***/wenhanwu95/FreqMixFormer. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning

Exploring Parameter-Efficient Fine-Tuning to Enable Foundati...

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IEEE International Conference on Big Data

作者： Guangyu Sun Umar Khalid Matias Mendieta Pu Wang Chen Chen Center for Research in Computer Vision University of Central Florida Orlando FL USA Department of Computer Science University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the typical FL paradigm (e.g., FedAvg), model weights are sent to and from the server each round to participating clients. Recently, the use of small pre-trained models has been shown to be effective in federated learning optimization and improving convergence. However, recent state-of-the-art pre-trained models are getting more capable but also have more parameters, known as the "Foundation Models." In conventional FL, sharing the enormous model weights can quickly put a massive communication burden on the system, especially if more capable models are employed. Can we find a solution to enable those strong and readily available pre-trained models in FL to achieve excellent performance while simultaneously reducing the communication burden? To this end, we investigate the use of parameter-efficient fine-tuning in federated learning and thus introduce a new framework: FedPEFT. Specifically, we systemically evaluate the performance of FedPEFT across a variety of client stability, data distribution, and differential privacy settings. By only locally tuning and globally sharing a small portion of the model weights, significant reductions in the total communication overhead can be achieved while maintaining competitive or even better performance in a wide range of federated learning scenarios, providing insight into a new paradigm for practical and effective federated systems.

关键词： Training Technological innovation Federated learning Foundation models Transformers Data models Stability analysis Robustness Servers Tuning

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Machine Learning In Sustainable Development-An Overview 1

Machine Learning In Sustainable Development-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 Altrjman, Chadi Gupta, Richa 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. Turkey University of Kyrenia Faculty of Engineering Kyrenia Turkey Waterloo University Canada Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338027

I have studied how artificial intelligence (AI) or machine learning (ML) is applied in a sustainable system called "Eugenie"for my work, project, and thesis. In this project, I get to discuss Eugenie's role in ensuring the sustainability of large environmental business organizations. I also get to discuss how it aids operation managers in making predictions about when their machines will break down days, weeks, and months in advance, allowing them to schedule maintenance cycles more effectively. In other words, Eugenie provides plant operators with data so they can prioritize repairs. Eugenie executes this process by utilizing real-time data from numerous sensors attached to large machinery and equipment in the process industry, metals and mining, oil and gas, and smart cities industries. Two products make up this program;the first is named RAY-FINN and the second is called PAPILLON. Eugenie assists businesses in a variety of ways, including:•Aids in the attainment of operational excellence by process organizations•Always raise overall profitability.•Contributes to a greener and safer future. © 2023 IEEE.

关键词： Sustainable development

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Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance

Cross-View Meets Diffusion: Aerial Image Synthesis with Geom...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Ahmad Arrabi Xiaohan Zhang Waqas Sultani Chen Chen Safwan Wshah Department of Computer Science Vermont Artificial Intelligence Lab University of Vermont Intelligent Machines Lab Information Technology University Center for Research in Computer Vision University of Central Florida

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Aerial imagery analysis is critical for many research fields. However, obtaining frequent high-quality aerial images is not always accessible due to its high effort and cost requirements. One solution is to use the Ground-to-Aerial (G2A) technique to synthesize aerial images from easily collectible ground images. However, G2A is rarely studied, because of its challenges, including but not limited to, the drastic view changes, occlusion, and range of visibil-ity. In this paper, we present a novel Geometric Preserving Ground-to-Aerial (G2A) image synthesis (GPG2A) model that can generate realistic aerial images from ground images. GPG2A consists of two stages. The first stage predicts the Bird's Eye View (BEV) segmentation (referred to as the BEV layout map) from the ground image. The second stage synthesizes the aerial image from the predicted BEV layout map and text descriptions of the ground image. To train our model, we present a new multimodal cross-view dataset, namely VIGORv2, built upon VIGOR [64] with newly collected aerial images, maps, and text descriptions. Our extensive experiments illustrate that GPG2A synthesizes better geometry-preserved aerial images than existing models. We also present two applications, data augmentation for cross-view geo-localization and sketch-based region search, to further verify the effectiveness of our GPG2A. The code and dataset are available at https://***/AhmadArrabi/GPG2A.

关键词： Geometry Image segmentation computer vision Costs Image synthesis Geology Computational modeling Layout Diffusion models Data augmentation

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