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Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder

IEEE Transactions on Vehicular technology 2025年第6期74卷 9165-9179页

作者： Krishnan, Sivaram Park, Jihong Sherman, Gregory Campbell, Benjamin Choi, Jinho The University of Adelaide School of Electrical and Mechanical Engineering Adelaide5005 Australia Singapore University of Technology and Design Information Systems Technology and Design pillar 487372 Singapore Defence Science and Technology Group Mission Autonomy Platforms Division Canberra2610 Australia

Low Probability of Detection (LPD) communication aims to obscure the presence of radio frequency (RF) signals to evade surveillance. In the context of mobile surveillance utilizing unmanned aerial vehicles (UAVs), achieving LPD communication presents significant challenges due to the UAVs' rapid and continuous movements, which are characterized by unknown nonlinear dynamics. Therefore, accurately predicting future locations of UAVs is essential for enabling real-time LPD communication. In this paper, we introduce a novel framework termed predictive covert communication, aimed at minimizing detectability in terrestrial ad-hoc networks under multi-UAV surveillance. Our data-driven method synergistically integrates graph neural networks (GNN) with Koopman theory to model the complex interactions within a multi-UAV network and facilitating long-term predictions by linearizing the dynamics, even with limited historical data. Extensive simulation results substantiate that the predicted trajectories using our method result in at least 63%-75% lower probability of detection when compared to well-known state-of-the-art baseline approaches, showing promise in enabling low-latency covert operations in practical scenarios. © 2025 IEEE.

关键词： Graph neural networks

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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Neural Computing and Applications 2024年第30期36卷 18727-18759页

作者： Kassaymeh, Sofian Al-Betar, Mohammed Azmi Rjoubd, Gaith Fraihat, Salam Abdullah, Salwani Almasri, Ammar Software Engineering Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Artificial Intelligence Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Management Information Systems Department Al-Balqa Applied University Amman Jordan

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Global optimization

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software fault localization based on eigenvector centrality in complex network theory 24

Software fault localization based on eigenvector centrality ...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wu, Wentao Wang, Shihai Shao, Yuanxun Xie, Wandong School of Reliability and Systems Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory China State Key Laboratory of Software Development Environment China Aerospace Science and Engineering Network Information Development CO. Ltd Beijing China Information Center of China North Industries Group Corporation Beijing China

ISBN: (纸本)9798350365658

software debugging plays a crucial role in fault localization tasks, and spectrum-based fault localization (SBFL) is a hot topic in software automation debugging research. However, existing SBFL technologies are generally limited by tie within ranks, where a large number of elements share the same suspiciousness, which in turn severely limits the performance of SBFL. To this end, we propose an SBFL model based on eigenvector centrality (FLEC). This algorithm first utilizes the statement coverage information in the program spectrum to construct a statement network, and adopts the correlation between statements as edge weights. Then, FLEC takes statement suspiciousness as node weight. On this basis, the algorithm utilizes the eigenvector centrality to calculate the weighted suspiciousness of each statement while considering both node importance and correlation between nodes. Finally, FLEC conducted experimental validation on 3 datasets of Defects4J, and the results showed an average improvement of 10.7% in ACC@N compared to the optimal baseline. © 2024 IEEE.

关键词： Program debugging

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Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea

Effectiveness of Using Deep Learning for Blister Blight Iden...

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2023 International Research Conference on Smart Computing and systems engineering, SCSE 2023

作者： Hewawitharana, G.H.A.U. Nawarathne, U.M.L.A. Hassan, A.S.F. Wijerathna, Lochana M. Sinniah, G.D. Vidhanaarachchi, Samitha P. Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Tea Research Institute of Sri Lanka Plant Pathology Division Talawakelle Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Military Technological College Systems Engineering Department Muscat Oman

ISBN: (纸本)9798350341454

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase. © 2023 IEEE.

关键词： Convolutional neural networks

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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Computer systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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On deployment of Eclipse Kuksa as a framework for an intelligent moving test platform for research of autonomous vehicles 2

On deployment of Eclipse Kuksa as a framework for an intelli...

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2nd Eclipse Research International Conference on Security, Artificial Intelligence, Architecture and Modelling for Next Generation Mobility, SAAM Mobility 2021

作者： Hirvonsalo, Harri Seppänen, Pertti Faculty of Information Technology and Electrical Engineering Empirical Software Engineering in Software Systems and Services M3S Group University of Oulu Oulu Finland

After an era of huge propagation within the field of mobile communications, digitalization has been spreading during the latest years at an accelerating speed to automotive technology and business. Following the developments experienced earlier in the mobile communications branch, open solutions and platforms are emerging in the automotive industries, challenging the traditional proprietary systems. Like in mobile communications, open platforms enable development of a wide variety of novel applications and systems that speed up the digitalization of the automotive and traffic ecosystems and offer the developers new attractive business opportunities. One of such open approaches is the Eclipse Kuksa framework developed in a consortium of European research institutions and automotive industries. In this study, we explored how the Kuksa framework could be used as a technology basis for an automotive data system combining in-vehicle functionality to cloud-based services. The study was carried out as a part of the SMAD research project of the University of Oulu aiming at an intelligent moving test platform for research of autonomous vehicles built on top of a Toyota Rav4 hybrid car. Our study covered subsystems of the Kuksa framework, the in-vehicle subsystem, cloud subsystems and the data communications between them. Our results indicate that the Kuksa framework is a feasible basis for the development of open, intelligent automotive data systems, though with a considerable learning needs. The results and experiences of our study provide besides additional knowledge for our continuing research on automotive software, also new, valuable contribution to the Eclipse Kuksa community and the practitioners planning to deploy open approaches in their automotive software development. Copyright © 2021 for this paper by its authors.

关键词： Open systems

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Strategies for Semi-Automated Registration of Historic Aerial Photographs Utilizing Street and Roof Segmentations as Durable Landmarks 34

Strategies for Semi-Automated Registration of Historic Aeria...

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34th European Modeling and Simulation Symposium, EMSS 2022

作者： Zwettler, Gerald A. Ono, Yuta Stradner, Michael Praschl, Christoph Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Graduate School of Software and Information Science Iwate Prefectural University Japan Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria

Historical and current aerial photographs are only of great value if the geolocation or address of the photographed areas is also available. In Western Europe, especially Austria, Germany and Czech Republic, there is a market for the sale of aerial photographs of one’s own private residential building. Automated geolocation is a feasible way to enable the sales agents to assign the addresses for the sale more quickly. In the course of this research work, a process chain is modeled that allows the assignment of aerial photographs to residential addresses using machine vision. After model-based rectifying the aerial images to compensate for perspective distortions, larger image blocks get assembled using image stitching. The assignment to a 2D reference map, such as satellite imagery via Google Maps, is done by applying a U-Net CNN after extracting durable image features such as roads or buildings. The mapping of aerial imagery to two-dimensional cartography is either automated via registration approaches or based on manually placed corresponding landmarks and homography. Test runs on imagery between the years 1969 and 2020 show that the labor-intensive process of geolocation of aerial imagery can be solved by the proposed process model in a hybrid way. © 2022 The Authors.

关键词： Antennas

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Towards an Automated System for Reverse Geocoding of Aerial Photographs 30

Towards an Automated System for Reverse Geocoding of Aerial ...

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30th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2022

作者： Praschl, Christoph Stradner, Michael Ono, Yuta Zwettler, Gerald Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Austria Graduate School of Software and Information Science Iwate Prefectural University Japan Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Austria

ISBN: (纸本)9788086943336

Aerial photographs of buildings are often used as memorabilia sold by trading companies. Such photographs come with an issue regarding the address of the shown buildings, since the recording location of the camera may be known, but shows a spatial distance to the actual subject of the image. In addition to that, also this recording location is often not known in detail but only roughly in the form of the flight route/area. To address this problem, a methodology for reverse geocoding is proposed, allowing to identify the position of buildings that are photographed from aerial vehicles. This is done using a process for extending recording locations and a second process based on the registration of invariant features within aerial shots compared to maps. © 2022 University of West Bohemia. All rights reserved.

关键词： Aerial photography

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Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 307-315页

作者： Wang, Wei Chen, Jian Hu, Yuzhu Liu, Han Chen, Junxin Gadekallu, Thippa Reddy Garg, Lalit Guizani, Mohsen Hu, Xiping Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518000 China School of Software Dalian University of Technology Dalian116621 China Division of Research and Development Lovely Professional University Phagwara India Center of Research Impact and Outcome Chitkara University Punjab India Faculty of Information and Communication Technology University of Malta Msida Malta United Arab Emirates

The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently. © 2024 The Authors

关键词： Internet of things

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Data-informativity conditions for structured linear systems with implications for dynamic networks

arXiv

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arXiv 2024年

作者： Van den Hof, Paul M.J. Shi, Shengling Fonken, Stefanie J.M. Ramaswamy, Karthik R. Hjalmarsson, Håkan Dankers, Arne G. Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Department of Chemical Engineering Massachusetts Institute of Technology United States ASML Veldhoven Netherlands Division of Decision and Control Systems Royal Institute of Technology Stockholm Sweden Department of Electrical and Software Engineering University of Calgary Canada

When estimating models of a multivariable dynamic system, a typical condition for consistency is to require the input signals to be persistently exciting, which is guaranteed if the input spectrum is positive definite for a sufficient number of frequencies. In this paper it is investigated how such a condition can be relaxed by exploiting prior structural information on the multivariable system, such as structural zero elements in the transfer matrix or entries that are a priori known and therefore not parametrized. It is shown that in particular situations the data-informativity condition can be decomposed into different MISO (multiple input single output) situations, leading to relaxed conditions for the MIMO (multiple input multiple output) model. When estimating a single module in a linear dynamic network, the data-informativity conditions can generically be formulated as path-based conditions on the graph of the network. The new relaxed conditions for data-informativity will then also lead to relaxed path-based conditions on the network graph. Additionally the new expressions are shown to be closely related to earlier derived conditions for (generic) single module identifiability. © 2024, CC BY-NC-ND.

关键词： MIMO systems

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