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Multi-criteria decision model for multicircular flight control of unmanned aerial vehicles through a hybrid approach

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Scientific reports 2025年第1期15卷 18962页

作者： Noorulden Basil Hamzah M Marhoon Bayan Mahdi Sabbar Abdullah Fadhil Mohammed Osamah Albahri Ahmed Albahri Abdullah Alamoodi Iman Mohamad Sharaf Amare Merfo Amsal Mahrous Ahmed Enas Ali Sherif S M Ghoneim Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq. noorulden@uomustansiriyah.edu.iq. Department of Automation Engineering and Artificial Intelligence College of Information Engineering Al-Nahrain University Jadriya Baghdad Iraq. College of Engineering and Engineering Techniques Al-Mustaqbal University Babylon Iraq. Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq. Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq. Victorian Institute of Technology Melbourne Australia. Technical Engineering College Imam Ja'afar Al-Sadiq University (IJSU) Baghdad Iraq. University of Information Technology and Communications (UOITC) Baghdad Iraq. Applied Science Research Center Applied Science Private University Amman Jordan. GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University for Scienceand Technology Mishref Kuwait. Department of Basic Sciences Higher Technological Institute 10th of Ramadan City Egypt. Department of Mechanical Engineering Faculty of Technology Debre Markos University P. O. Box 269 Debre Markos Ethiopia. amare_merfo@dmu.edu.et. Department of Electrical Engineering College of Engineering Taif University 21944 Taif Saudi Arabia. Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India.

This study presents a novel approach for optimizing UAV (unmanned aerial vehicle) Multicircular flight control by developing a fractional order proportional integral derivative (FOPID)-based hybrid Eagle strategy particle swarm optimization ant lion optimizer (HESPSOALO). The proposed algorithm combines the strengths of particle swarm optimization (PSO) and the ant lion optimizer (ALO), which are enhanced by the Eagle strategy to systematically fine-tune the FOPID controller parameters. This hybrid optimization method aims to improve system stability, responsiveness, and disturbance rejection in UAVs, particularly in challenging dynamic flight conditions. The proposed approach was validated against traditional control methods that utilize FOPID (Base), the Base HESPSOALO algorithm, the FOPID-based HPSOGWO (Hybrid Particle Swarm Optimization-Gray Wolf Optimizer), and the FOPID-based HGWOALO (Hybrid Gray Wolf Optimization-Ant Lion Optimizer) with a set of benchmark functions used in the analysis. The results demonstrate a minimization of position and angular errors, reduced oscillations, and overall improved control stability for the FOPID-based HESPSOALO compared with the other methods. Furthermore, a multicriteria decision-making (MCDM) framework is applied to evaluate the overall performance of alternative control strategies utilizing the CRiteria importance through intercriteria correlation (CRITIC) and technique of order preference by similarity to ideal solution (TOPSIS) techniques. The MCDM analysis demonstrates that among the evaluated criteria, [Formula: see text] has the highest importance, with a weight of 0.244019, whereas [Formula: see text] is deemed the least significant, with a weight of 0.161023. The ranking results reveal that the HESPSOALO algorithm (Base) is the best-performing controller method, with a ranking score of 0.571161, indicating its superior control performance across major metrics. In contrast, the FOPID + HPSOGWO controller method ran

关键词： CRITIC FOPID Hybrid optimization TOPSIS UAV multicircular flight control

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Digital Modeling of Microwave Filters With Coupled-Line Sections

Digital Modeling of Microwave Filters With Coupled-Line Sect...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： José-María Muñoz-Ferreras Dimitra Psychogiou Roberto Gómez-García Department of Signal Theory and Communications University of Alcalá Madrid Spain Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder CO USA

A digital-modeling technique for the transmission scattering parameters of frequency-periodic RF/microwave passive filters has been recently proposed. By means of the direct matching of the ideally-synthesized transfer function of the filter and that of its associated discrete-time linear time-invariant (LTI) system, the corresponding coefficients a k and b k are identifiable. Hence, zero-pole diagrams in the Z-plane are derived, which enables a reinterpretation of these frequency-periodic microwave filters. This work demonstrates that this digital matching is also possible for frequency-periodic filters based on coupled-line sections. In particular, this discrete-time modeling is applied to a classic third-order coupled-line bandpass filter and to a signal-interference ultra-wideband (UWB) bandpass filtering section. In addition, their zero-pole diagrams are analyzed and discussed in detail.

关键词： Filtering theory Numerical models Linear systems Microwave circuits Scattering parameters Transfer functions

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GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning

arXiv

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arXiv 2021年

作者： Kwak, Bumju Park, Jiwon Kang, Taewon Jo, Jeonghee Lee, Byunghan Yoon, Sungroh Recommendation Team Kakao Corporation Gyeonggi13529 Korea Republic of LG Chem Seoul07795 Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of Daejeon34141 Korea Republic of Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of Department of Electronic Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Artificial Intelligence Institute Seoul National University Seoul08826 Korea Republic of

In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric information of molecular structures, resulting in less intuitive representations. Moreover, the widely used message-passing mechanism is limited to provide the interpretation of experimental results from a chemical perspective. To address these challenges, we introduce a novel Transformer-based framework for molecular representation learning, named the Geometry-aware Transformer (GeoT). GeoT learns molecular graph structures through attention-based mechanisms specifically designed to offer reliable interpretability, as well as molecular property prediction. Consequently, GeoT can generate attention maps of interatomic relationships associated with training objectives. In addition, GeoT demonstrates comparable performance to MPNN-based models while achieving reduced computational complexity. Our comprehensive experiments, including an empirical simulation, reveal that GeoT effectively learns the chemical insights into molecular structures, bridging the gap between artificial intelligence and molecular sciences. © 2021, CC BY.

关键词： Geometry

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Smart radio environments

arXiv

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arXiv 2021年

作者： Gradoni, Gabriele Renzo, Marco Di Díaz-Rubio, Ana Tretyakov, Sergei A. Caloz, Christophe Gradoni, Gabriele Peng, Zhen Alu, Andrea Lerosey, Geoffroy Fink, Mathias Galdi, Vincenzo Cui, Tie Jun Frazier, Benjamin W. Anlage, Steven M. Salucci, Marco Massa, Andrea Cheng, Qiang Wang, Jinghe Jin, Shi Dardari, Davide Decarli, Nicoló Yurduseven, Okan Matthaiou, Michail Kenney, Mitchell Gordon, George Georgiou, Orestis Nguyen, Cam Ly Martini, Enrica Maci, Stefano Wakatsuchi, Hiroki Phang, Sendy School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Laboratoire des Signaux et Systémes CNRS CentraleSupélec Université Paris-Saclay Gif-sur-Yvette Paris France Department of of Electronics and Nanoengineering Aalto University Espoo Finland META Research Group KU Leuven Belgium School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham University Park NG72RD United Kingdom Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL United States Advanced Science Research Center City University of New York United States Greenerwave 6 rue Jean Calvin Paris75005 France Institut Langevin 1 rue Jussieu Paris75005 France Fields & Waves Lab Department of Engineering University of Sannio Benevento Italy State Key Laboratory of Millimeter Waves Southeast University Nanjing China Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Department of Electrical and Computer Engineering Department of Physics Quantum Materials Center University of Maryland College ParkMD20742 United States Trento Italy Chengdu China Beijing China Institute of Electromagnetic Space State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China University of Bologna Bologna Italy Bologna Italy Centre for Wireless Innovation Institute of Electronics Communications and Information Technology Queen’s University Belfast Belfast United Kingdom Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Wireless System Laboratory Corporate Research & Development Center Toshiba Corporation Kawasaki Japan Department of Information Engineering an

This Roadmap takes the reader on a journey through the research in electromagnetic wave propagation control via reconfigurable intelligent surfaces. Meta-surface modelling and design methods are reviewed along with physical realisation techniques. Several wireless applications are discussed, including beam-forming, focusing, imaging, localisation, and sensing, some rooted in novel architectures for future mobile communications networks towards 6G. © 2021, CC BY.

关键词： Electromagnetic wave propagation

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Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation

arXiv

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arXiv 2021年

作者： Chen, Chen Qin, Chen Ouyang, Cheng Li, Zeju Wang, Shuo Qiu, Huaqi Chen, Liang Tarroni, Giacomo Bai, Wenjia Rueckert, Daniel Department of Computing Imperial College London United Kingdom Institute for Digital Communications School of Engineering University of Edinburgh United Kingdom Digital Medicine Research Centre School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of MICCAI Shanghai China CitAI Research Centre Department of Computer Science City University of London United Kingdom Department of Brain Sciences Imperial College London United Kingdom Data Science Institute Imperial College London United Kingdom Klinikum Rechts der Isar Technical University of Munich Germany

The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impractical due to data sharing and privacy issues. To address this challenge, we propose AdvChain, a generic adversarial data augmentation framework, aiming at improving both the diversity and effectiveness of training data for medical image segmentation tasks. AdvChain augments data with dynamic data augmentation, generating randomly chained photo-metric and geometric transformations to resemble realistic yet challenging imaging variations to expand training data. By jointly optimizing the data augmentation model and a segmentation network during training, challenging examples are generated to enhance network generalizability for the downstream task. The proposed adversarial data augmentation does not rely on generative networks and can be used as a plug-in module in general segmentation networks. It is computationally efficient and applicable for both low-shot supervised and semi-supervised learning. We analyze and evaluate the method on two MR image segmentation tasks: cardiac segmentation and prostate segmentation with limited labeled data. Results show that the proposed approach can alleviate the need for labeled data while improving model generalization ability, indicating its practical value in medical imaging applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

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Physical layer encryption using a Vernam cipher

arXiv

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arXiv 2019年

作者： Mirsky, Yisroel Fedidat, Benjamin Haddad, Yoram Georgia Institute of Technology School of Computer Science College of Computing Department of Software and Information Systems Engineering Ben-Gurion University Department of Communications Engineering Jerusalem College of Technology

Secure communication is a necessity. However, encryption is commonly only applied to the upper layers of the protocol stack. This exposes network information to eavesdroppers, including the channel's type, data rate, protocol, and routing information. This may be solved by encrypting the physical layer, thereby securing all subsequent layers. In order for this method to be practical, the encryption must be quick, preserve bandwidth, and must also deal with the issues of noise mitigation and synchronization. In this paper, we present the Vernam Physical Signal Cipher (VPSC): A novel cipher which can encrypt the harmonic composition of any analog waveform. The VPSC accomplished this by applying a modified Vernam cipher to the signal's frequency magnitudes and phases. This approach is fast and preserves the signal's bandwidth. In the paper, we offer methods for noise mitigation and synchronization, and evaluate the VPSC over a noisy wireless channel with multi-path propagation interference. Copyright © 2019, The Authors. All rights reserved.

关键词： Cryptography

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Planetary nebulae in Gaia EDR3: Central Star identification, properties and binarity

arXiv

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arXiv 2021年

作者： González-Santamaría, Iker Manteiga, M. Manchado, A. Ulla, A. Dafonte, C. López Varela, P. Department of Computer Science and Information Technologies Campus Elviña s/n Coruña 15071 A Spain CIGUS CITIC Centre for Information and Communications Technologies Research Universidade da Coruña Campus de Elviña s/n A Coruña15071 Spain Department of Nautical Sciences and Marine Engineering Paseo de Ronda 51 A Coruña15011 Spain Instituto de Astrofísica de Canarias Tenerife La Laguna38200 Spain Astrophysics Department Tenerife La Laguna38206 Spain CSIC Spain Applied Physics Department Campus Lagoas-Marcosende s/n Vigo36310 Spain

Context. The Gaia Early Data Release 3 (EDR3), published in December 2020, features improved photometry and astrometry compared to that published in the previous DR2 file and includes a substantially larger number of sources, of the order of 2,000 million, making it a paradigm of big data astronomy. Many of the central stars of planetary nebulae (CSPNe) are inherently faint and difficult to identify within the field of the nebula itself. Gaia measurements may be relevant not only in identifying the ionising source of each nebula, but also in the study their physical and evolutionary properties. Aims. We demonstrate how Gaia data mining can effectively help to solve the issue of central star misidentification, a problem that has plagued the field since its origin. As we did for DR2, our objective is to present a catalogue of CSPNe with astrometric and photometric information in EDR3. From that catalogue, we selected a sample of stars with high-quality astrometric parameters, on which we carried out a more accurate analysis of CSPNe properties. Methods. Gaia GBP - GRP colours allow us to select the sources with sufficient temperatures to ionise the nebula. In order to estimate the real colour of a source, it is important to take into account interstellar extinction and, in the case of compact nebulae, nebular extinction when available. In addition, distances derived from EDR3 parallaxes (combined with consistent literature values) can be used to obtain nebular intrinsic properties from those observed. With this information, CSPNe can be plotted in an Hertzsprung-Russell (HR) diagram. From information on the spectral classification of the CS (from the literature) and evolutionary models for post-AGB stars, their evolutionary state can then be analysed. Furthermore, EDR3 high-quality astrometric data enable us to search for objects comoving with CSs in the field of each nebula by detecting sources with parallaxes and proper motions similar to those of the CS. Results. W

关键词： Galaxies

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Secured CBIR with Anonimity Preserving for Images and Users in Cloud Environment

Secured CBIR with Anonimity Preserving for Images and Users ...

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International Conference for Convergence of Technology (I2CT)

作者： Praveen. Y. Chitti K. Prabhushetty Department of Computer Science and Engineering Jain College of Engineering Belagavi Karnataka India Department of Electronics and Communications S.G. Balekundri instiute of Technology Belagavi Karnataka India

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

With advancement in imaging devices, the images are playing a vital role in everyday life. Multimedia image usage across various platforms are increased by major usage of communication technologies. The images in medical, automotive and various other domain carries the sensitive information in images. Handling images on large scale is expensive and computationally tedious. Major organization outsource the centralized image repository and other computations on image to cloud server. The cloud being the cheapest way of handling operations with images but lacks the concern of security and privacy. Example, the images of patients in hospitals and other personal images have to be encrypted before outsourcing to the cloud vendor, Content Based Image Retrieval (CBIR) then can be used for querying image. The Paper presents a novel method of outsourcing image repository without revealing information about images to cloud vendors by encrypting image dataset. The extracted feature vectors are used to classify the images are also encrypted, and a local hashing table is developed to improve the search efficiency. The generated feature vector values are secured using kNN. The experiment results have shown acceptable level of efficiency.

关键词： Cryptography Image retrieval Feature extraction Image color analysis Cloud computing Servers Indexes

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Reconfigurable intelligent surface assisted mobile edge computing with heterogeneous learning tasks

arXiv

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arXiv 2020年

作者： Huang, Shanfeng Wang, Shuai Wang, Rui Wen, Miaowen Huang, Kaibin The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The Department of Electrical and Electronic Engineering The Department of Computer Science and Engineering The Sifakis Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China The Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since it is with rich computation resources to train machine learning (ML) models, as well as low-latency access to the data generated by mobile and internet of things (IoT) devices. In this paper, we present an infrastructure to perform ML tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criterions are to maximize the throughput, we aim at maximizing the learning performance. Specifically, we minimize the maximum learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station (BS), and the phase-shift matrix of the RIS. An alternating optimization (AO)-based framework is proposed to optimize the three terms iteratively, where a successive convex approximation (SCA)-based algorithm is developed to solve the power allocation problem, closed-form expressions of the beamforming vectors are derived, and an alternating direction method of multipliers (ADMM)-based algorithm is designed together with an error level searching (ELS) framework to effectively solve the challenging nonconvex optimization problem of the phase-shift matrix. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks. Lastly, a unified communication-training-inference platform is developed based on the CARLA platform and the SECOND network, and a use case (3D object detection in autonomous driving) for the proposed scheme is demonstrated on the developed platform. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Towards motion invariant authentication for on-body IoT devices

arXiv

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arXiv 2019年

作者： Huang, Yong Xu, Mengnian Wang, Wei Wang, Hao Jiang, Tao Zhang, Qian School of Electronic Information and Communications Huazhong University of Science and Technology Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Department of Computer Science and Engineering Hong Kong University of Science and Technology

As the rapid proliferation of on-body Internet of Things (IoT) devices, their security vulnerabilities have raised serious privacy and safety issues. Traditional efforts to secure these devices against impersonation attacks mainly rely on either dedicated sensors or specified user motions, impeding their widescale adoption. This paper transcends these limitations with a general security solution by leveraging ubiquitous wireless chips available in IoT devices. Particularly, representative time and frequency features are first extracted from received signal strengths (RSSs) to characterize radio propagation profiles. Then, an adversarial multi-player network is developed to recognize underlying radio propagation patterns and facilitate on-body device authentication. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances. We build a prototype of our system using universal software radio peripheral (USRP) devices and conduct extensive experiments with both static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 90.4%, with a high area under the receiver operating characteristic curve (AUROC) of 0.958 and better generalization performance in comparison with the conventional non-adversarial-based approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

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