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GNSS Interference Classification Using Federated Reservoir Computing

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arXiv 2024年

作者： Ye, Ziqiang Gao, Yulan Liu, Xinyue Xiao, Yue Xiao, Ming Zammit, Saviour National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China Division of Information Science and Engineering KTH Royal Institute of Technology Stockholm100 44 Sweden SWJTU-Leeds Joint School Southwest Jiaotong University Chengdu611756 China Department of Communications and Computer Engineering University of Malta MsidaMSD 2080 Malta

The expanding use of Unmanned Aerial Vehicles (UAVs) in vital areas like traffic management, surveillance, and environmental monitoring highlights the need for robust communication and navigation systems. Particularly vulnerable are Global Navigation Satellite Systems (GNSS), which face a spectrum of interference and jamming threats that can significantly undermine their performance. While traditional deep learning approaches are adept at mitigating these issues, they often fall short for UAV applications due to significant computational demands and the complexities of managing large, centralized datasets. In response, this paper introduces Federated Reservoir Computing (FedRC) as a potent and efficient solution tailored to enhance interference classification in GNSS systems used by UAVs. Our experimental results demonstrate that FedRC not only achieves faster convergence but also sustains lower loss levels than traditional models, highlighting its exceptional adaptability and operational efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

Development of Image Processing Incorporated Real-Time Smart Water Quality Monitoring System to Detect Algae and Debris for a Sustainable Environment

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Development of Image Processing Incorporated Real-Time Smart...

Advancements in Computing (ICAC), International Conference on

作者： Madusha Harischandra Sanoj Madhushan Sachintha Dilruksha Nipun Shantha Kahatapitiya Udaya Wijenayake Bhagya Nathali Silva Ruchire Eranga Wijesinghe Dept. of Materials and Mechanical Technology Faculty of Technology University of Sri Jayewardenepura Pitipana Sri Lanka Dept. of Civil and Environmental Technology Faculty of Technology University of Sri Jayewardenepura Pitipana Sri Lanka Dept. of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Dept. of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Access to clean and safe drinking water is critical to human well-being while ensuring water quality remains an ongoing challenge. Despite the water board's best attempts at filtration, the potential for contamination remains due to factors such as inappropriate storage, deteriorating pipelines, or unintentional mixing during transit to houses. To address this issue, this study introduces an image processing incorporated real-time smart water quality Monitoring system to detect Algae and debris for a sustainable environment. This micro controller-based device consists of sensors, cameras, and image processing technologies to deliver real-time data for analysis. An object detection and localization model were trained using YOLO V5 architecture with 73.4% accuracy to assess the existence of Algae and debris. The developed user-friendly and accurate portable system will undoubtedly benefit the developing field of water quality monitoring and safeguarding public health. With further technological enhancements, the developed method will enable the assessment of numerous qualitative and quantitative parameters of water to contribute to environmental sustainability.

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A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps

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arXiv 2023年

作者： Chang, Tyler H. Elias, Jakob R. Wild, Stefan M. Chaudhuri, Santanu Libera, Joseph A. Mathematics and Computer Science Division Argonne National Laboratory United States Applied Materials Division Argonne National Laboratory United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory United States Department of Civil Materials and Environmental Engineering University of Illinois Chicago United States

In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints. For these reasons, we present an active learning process based on multiobjective black-box optimization with continuously updated machine learning models. This workflow is built on open-source technologies for real-time data streaming and modular multiobjective optimization software development. We demonstrate a proof of concept for this workflow through the autonomous operation of a continuous-flow chemistry laboratory, which identifies ideal manufacturing conditions for the electrolyte 2,2,2-trifluoroethyl methyl carbonate. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

Implementation of DNN in Cyber Security

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Implementation of DNN in Cyber Security

2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Alanya-Beltran, Joel Akram, Shaik Vaseem Ajmath, K.A. Valderrama-Zapata, Carlos Singh, Yashpal Nomani, M.Z.M. Universidad Tecnológica Del Perú Electronic Department Peru UIT Uttaranchal University Division of Research & Innovation Uttarakhand Dehradun India Sree Vidyanikethan Engineering College Department of Basic Sciences and Humanities Tirupati India Universidad San Ignacio de Loyola Peru Jain Deemed to Be University Department of Computer Science & Engineering Karnataka Bengaluru India Aligarh Muslim University Faculty of Law UP Aligarh202001 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

The arrival of cyberspace brought with it the luxury of global connection. It has also evolved into a trusted repository for all types of data. In recent years, this has also resulted in an increase in the amount of cyber risks. Innumerable individuals and businesses have been affected by data breaches, cyberterrorism, identity theft, and a variety of other threats. Cybercriminals are continually refining their skills in order to represent an even larger threat to the global community. Despite the fact that cybersecurity specialists are continuously working to avoid such errors, current security solutions have been shown to be ineffective on a number of aspects. AI skills may be a boon for all of this objective since they can be used to enhance a variety of 's cybersecurity strategies as well as perhaps produce some innovative products. AI is a revolutionary innovation that has changed the way we live. This paper's major objective is to examine various AI characteristics that may be integrated with some other security protocols to create a more safe environment and to make it a vital weapons in our arsenal to fend off assaults. © 2022 IEEE.

关键词： Cybersecurity

Learning-based Rigid Tube Model Predictive Control

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arXiv 2023年

作者： Gao, Yulong Yan, Shuhao Zhou, Jian Cannon, Mark Abate, Alessandro Johansson, Karl H. Department of Electrical and Electronic Engineering Imperial College London United Kingdom Department of Mathematics University of Stuttgart Germany Department of Electrical Engineering Linköping University Sweden Department of Engineering Science University of Oxford United Kingdom Department of Computer Science University of Oxford United Kingdom Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

This paper is concerned with model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and mixed constraints on the state and input, whereas the true disturbance set is unknown. Unlike most existing work on robust MPC, we propose an algorithm incorporating online learning that builds on prior knowledge of the disturbance, i.e., a known but conservative disturbance set. We approximate the true disturbance set at each time step with a parameterised set, which is referred to as a quantified disturbance set, using disturbance realisations. A key novelty is that the parameterisation of these quantified disturbance sets enjoys desirable properties such that the quantified disturbance set and its corresponding rigid tube bounding disturbance propagation can be efficiently updated online. We provide statistical gaps between the true and quantified disturbance sets, based on which, probabilistic recursive feasibility of MPC optimisation problems is discussed. Numerical simulations are provided to demonstrate the effectiveness of our proposed algorithm and compare with conventional robust MPC algorithms. © 2023, CC BY.

关键词： Model predictive control

A Mixed-Integer Approach for Motion Planning of Nonholonomic Robots under Visible Light Communication Constraints

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arXiv 2023年

作者： Caregnato-Neto, Angelo Maximo, Marcos R.O.A. Afonso, Rubens J.M. Electronic Engineering Division Instituto Tecnológico de Aeronáutica São José dos Campos Brazil Delft University of Technology Netherlands Computer Science Division Instituto Tecnológico de Aeronáutica São José dos Campos Brazil

This work addresses the problem of motion planning for a group of nonholonomic robots under Visible Light Communication (VLC) connectivity requirements. In particular, we consider an inspection task performed by a Robot Chain Control System (RCCS), where a leader must visit relevant regions of an environment while the remaining robots operate as relays, maintaining the connectivity between the leader and a base station. We leverage Mixed-Integer Linear Programming (MILP) to design a trajectory planner that can coordinate the RCCS, minimizing time and control effort while also handling the issues of directed Line-Of-Sight (LOS), connectivity over directed networks, and the nonlinearity of the robots’ dynamics. The efficacy of the proposal is demonstrated with realistic simulations in the Gazebo environment using the Turtlebot3 robot platform. Copyright © 2023, The Authors. All rights reserved.

关键词： Visible light communication

A discussion of statistical criteria for assessing awareness with SMR BCI after brain injury

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A discussion of statistical criteria for assessing awareness...

IEEE International Conference on Systems, Man and Cybernetics

作者： Idorenyin Amaunam Christoph Schneider Marina Lopes da Silva Jane Jöhr Karin Diserens Serafeim Perdikis Brain-Computer Interface and Neural Engineering Laboratory School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Department of Radiology Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Clinical Neurosciences Acute Neurorehabilitation Unit Division of Neurology Centre Hospitalier Universitaire Vaudois Lausanne Switzerland

This work discusses the implications of selecting particular statistical metrics and thresholds as criteria to diagnose awareness through Brain-computer Interface (BCI) technology in patients with Disorders of Consciousness (DOC). We report a first analysis of a novel dataset collected to investigate whether a motor attempt electroencephalography (EEG) paradigm coupled with Functional Electrical Stimulation (FES) can detect command following and, therefore, signs of conscious awareness in DOC. We assessed 22 DOC patients admitted to the acute rehabilitation unit after a brain lesion over one or more sessions. We extracted EEG sensorimotor rhythms and performed a standard open-loop BCI pipeline evaluation, classifying motor attempt against resting-state trials. We validate this approach by correlating classification accuracy with the established clinical scale Coma Recovery Scale Revised. We employ a machine learning (ML)-inspired diagnostic criterion based on confidence intervals over chance-level classification accuracy and show that it yields more conservative and, arguably, reliable inference of Cognitive Motor Dissociation (CMD) by means of command-following, neuroimaging-based tools, compared to diagnoses based on clinical assessments or criteria examining the statistical significance of brain features across different mental states.

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Fault Detection in Solar Power System with Internet of Things using Multi resolution Sinusoidal Neural Network - Snow Geese Optimization Approach

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Fault Detection in Solar Power System with Internet of Thing...

Sustainable Expert Systems (ICSES), International Conference on

作者： Pramod Kumar K.E. Purushothaman Arunmozhi R V Jayachitra S D. Leela Rani S. Kaliappan Department of Computer Science and Engineering Ganga Institute of Technology and Management Kablana Haryana India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronic Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Chemistry S.A. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh India Division of Research and Development Lovely Professional University Jalandhar Delhi Punjab India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Fault detection in power systems, including Photovoltaic (PV) systems, using Internet of Things (IoT) involves deploying sensors to monitor key parameters and analyzing the data identify anomalies. The accuracy of these systems can be compromised by issues such as noisy or low-quality sensor data, which may lead to false positives or missed faults. Additionally, the complexity of managing large volumes of data from numerous IoT devices can strain the system, further affecting detection accuracy. To overcome these drawbacks, this paper proposes an efficient approach for fault uncovering in solar power system grounded on IoT. The data are collected from solar power generation dataset. Subsequently, the data are nourished to preprocessing. In preprocessing segment removes the absent values in the data utilizing Reverse Lognormal Kalman Filter (RLKF). The consequence after the preprocessing data is transported to the Multiresolution Sinusoidal Neural Network (MSNN). The line-line faults, ground faults, line-ground faults and partial shading are successfully predicted and classified by using MSNN. The Snow Geese Optimization (SGO) is rummage-sale to optimize the weight structure of MSNN. The projected MSNN-SGO is utilized within the MATLAB platform. Performance metrics counting precision, accuracy, Root Mean Squared Error (RMSE), F1-score, and Mean Squared Error (MSE) were examined in order to determine the proposed method. The proposed DAGCN-COA technique yields 14.89%, 16.89%, and 18.23% higher precision, 16.65%, 18.85%, and 17.89% higher accuracy, 22.36%, 15.42% and 28.27% lower RMSE when analysed the existing methods. The proposed MSNN-SGO technique is associated with the existing methods for instance spatial-temporal recurrent graph neural network (STRGNN), convolutional neural network (CNN), and long short-term memory network (LSTMN), respectively.

关键词： Accuracy Fault detection Snow Electrical fault detection Sensor systems Power systems Sensors Internet of Things Spatial resolution Optimization

Explicit Time-Optimal Speed Profiles for Planar Paths with Monotone Curvature

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arXiv 2022年

作者： Selvaratnam, Daniel Cantoni, Michael Manzie, Chris Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Electrical and Electronic Engineering The University of Melbourne ParkvilleVIC3010 Australia

Minimum-time speed profiles are constructed for planar paths with smooth strictly-monotonic signed curvature, subject to constraints on velocity, normal acceleration and tangential acceleration. The construction is explicit and exact, and global optimality is rigorously established from first principles under mild regularity conditions on the path. Free, fixed, and inequality-constrained boundary speeds are all accommodated. Numerical implementation is straightforward. © 2022, CC BY-NC-ND.

关键词： Acceleration