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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Isaac Skog Manon Kok Gustaf Hendeby Chuan Huang Thomas Edridge School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Delft Center for Systems and Control Delft University of Technology Delft The Netherlands Dept. of Electrical Engineering Linköping University Linköping Sweden

ISBN: (数字)9798331523176

ISBN: (纸本)9798331523183

Magnetic-field simultaneous localization and mapping (SLAM) using consumer-grade inertial and magnetometer sensors offers a scalable, cost-effective solution for indoor localization. However, the rapid error accumulation in the inertial navigation process limits the feasible exploratory phases of these systems. Advances in magnetometer array processing have demonstrated that odometry information, i.e., displacement and rotation information, can be extracted from local magnetic field variations and used to create magnetic-field odometry-aided inertial navigation systems. The error growth rate of these systems is significantly lower than that of standalone inertial navigation systems. This study seeks an answer to whether a magnetic-field SLAM system fed with measurements from a magnetometer array can indirectly extract odometry information — without requiring algorithmic modifications — and thus sustain longer exploratory phases. The theoretical analysis and simulation results show that such a system can extract odometry information and indirectly create a magnetic field odometry-aided inertial navigation system during the exploration phases. However, practical challenges related to map resolution and computational complexity remain significant.

关键词： Phased arrays Magnetic field measurement Simultaneous localization and mapping Magnetometers Simulation Inertial navigation Odometry Magnetic fields Data mining Sensor arrays

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Visualizing the Path of a Photo Taker from Image Metadata

Visualizing the Path of a Photo Taker from Image Metadata

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2023 4th International Informatics and Software engineering Conference (IISEC)

作者： Rasty Shakhawan Majid Cihan Varol Asaf Varol Ahad Nasab Department of Software Engineering Firat University Erbil Iraq Computer Science Department Sam Houston State University Huntsville TX USA Dept. of Engineering Management and Technology College of Engineering and Computer Science Chattanooga TN USA

One of the most challenging investigations in forensic analysis is the work conducted on smartphones. Specifically using smartphone data to provide evidence for a case is becoming a norm nowadays. Many Android device applications use and work with location services that assist investigators in working with log files that store valuable information. Images captured on Android devices will save geolocation information, date, and time in the properties. In this work, a tool is developed to acquire the date and geolocation for each taken photo so that the path of the photo taker can be displayed on Google Maps. The work showed that the application can take about 4 and a half seconds to process four images and display the map to the investigator.

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Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG 3

Evaluation of Deep Training and Adapting Algorithms in Medic...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Abdulwahid, Ali Hadi Karetla, Girija Rani Ganesh, K.V.B. Kumar, A Suresh Radha, K. Shivakumar, K. Southern Technical University Engineering Technical College Control and Automation Engineering Department Basra Iraq Western Sydney University School of Computer Data and Mathematical Sciences Australia Koneru Lakshmaiah Education Foundation KL Business School Guntur Andhra Pradesh India Graphic Era Deemed to Be University Department of CSE India St. Martin's Engineering College Dept. of IT Secunderabad Telangana India SR University School of Computer Science and Artificial Intelligence Warangal Telangana India

ISBN: (纸本)9798350399264

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean Square (LMS) technique, the Connected Neural Network, and also the Recursive Least-square (RLS) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMSE and signal-to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods. © 2023 IEEE.

关键词： Electroencephalography

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MDI-QKD with resource-efficient polarization compensation

MDI-QKD with resource-efficient polarization compensation

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Bedroya, Olinka Li, Chenyang Wang, Wenyuan Hu, Jianyong Lo, Hoi-Kwong Qian, Li University of Toronto Centre for Quantum Information and Quantum Control Dept. of Physics TorontoONM5S 3G4 Canada University of Toronto Centre for Quantum Information and Quantum Control Dept. of Electrical & Computer Engineering TorontoONM5S 3G4 Canada University of Hong Kong Department of Physics Pokfulam Hong Kong 100 College St TorontoON1M5G 1L5 Canada

ISBN: (纸本)9781957171180

We implemented MDI-QKD with a novel polarization compensation scheme using discarded bits without reducing the key-sharing cycle or demanding additional resources. Polarization drift was maintained below 0.13 rad over a 40 km unisolated fibre spool for four hours, and the average secret key rate generated was 7.45×10-6bits per pulse. © 2023 The Author(s).

关键词： Polarization

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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Advanced Machine Learning Techniques for Optimizing Renewable Energy Systems in Healthcare Facilities

Advanced Machine Learning Techniques for Optimizing Renewabl...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： S. Pradeep Kalpana P G. Sreeram Amarja Adgaonkar Devika Rani Roy Arpit Namdev Dept. of CSE Malla Reddy Engineering College for Women (UGC Autonomous) Secunderabad Telangana India Dept. of Computer Science CHRIST University Bangalore Karnataka India Dept. of CSE Vignana Bharathi Institute of Technology Hyderabad Telangana India K.C. College of Engineering & Management Studies & Research Thane India Dept. of IT University Institute of Technology RGPV Bhopal MP India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Optimizing renewable energy systems in healthcare facilities via use of modern ML algorithms to improve energy efficiency along with sustainability is goal of this research. Integrating machine learning provides a potential solution in case of optimizing energy consumption along with management, which is particularly relevant for healthcare institutions that are looking to decrease their operating expenses and carbon footprint by using renewable energy sources. Optimal deployment of renewable ES like solar along with wind may be achieved by the analysis of consumption patterns, prediction of future energy demands, and the investigation of different ML methods, such as neural networks, reinforcement learning, and predictive analytics. Research in this area aims to increase hospital renewable energy systems' efficiency and dependability by creating models that take use of environmental factors, real-time energy consumption indicators, and historical data. The results imply that healthcare facilities may save money, have less of an effect on the environment, and be more resilient if they use sophisticated machine learning methods to perk upcompetence of their renewable energy systems. The study emphasizes the importance of hyperparameter configuration in improving machine learning model accuracy, with a proposed approach achieving 99 % training and 96 % testing accuracy, outperforming conventional methods and outperforming other techniques.

关键词： Training Renewable energy sources Energy consumption Accuracy Reinforcement learning Real-time systems Wind forecasting Sustainable development Predictive analytics Testing

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Preserving Topology of Network Systems: Metric, Analysis, and Optimal Design

arXiv

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arXiv 2023年

作者： Li, Yushan Wang, Zitong He, Jianping Chen, Cailian Guan, Xinping Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize the non-asymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this paper proposes a novel metric named trace-based variance-expectation ratio. This metric effectively captures the decaying rate of the topology inference error, where a slower rate indicates better non-asymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are non-increasing and independent (milder than the i.i.d. condition). Then, the optimal noise design that produces the slowest decaying rate for the error is obtained. More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multi-lag time dependence are provided. Comprehensive simulations verify the theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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CPT Manager: An Open Environment for Constrained Path-Based Testing 18

CPT Manager: An Open Environment for Constrained Path-Based ...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Klima, Matej Bures, Miroslav Holotik, Daniel Herczeg, Maximilian Miltner, Marek Zanocco, Chad FEE Czech Technical University in Prague Dept. of Computer Science Prague Czech Republic Stanford University Civil and Environmental Engineering StanfordCA United States The Czech Technical University in Prague FEE Dept. of Economics Management and Humanities Prague Czech Republic

ISBN: (纸本)9798331534677

Path-based Testing is a common technique to test System Under Test (SUT) processes. Generally, a directed graph that models a system's workflow is input to the test path generation process, as well as the selected test coverage criterion. Several algorithms are proposed in the literature that traverse the graph and facilitate the generation of test cases for the selected coverage criterion. However, a plain directed graph used for modeling SUT processes does not allow for the capture of real-life dependencies and constraints between actions in the tested processes, which might pose a limit in application of this technique. Therefore, we defined an extended model that allows the specification of constraints upon the graph's elements and a set of algorithms that allow the generation of the set of test cases that satisfy the given constraints with the edge coverage. Considering the fact that in path-based testing, there is no platform in which engineers and researchers can share SUT models to be further assembled into open datasets to test performance of evolved path-based testing MBT algorithms, especially for the given problem of test paths generation with the constraints, this paper presents a summary of the problem and a novel management for creation and management of SUT models with constraints that allows the generation of test paths as well as to serve as a platform for creation of such benchmark datasets. © 2025 IEEE.

关键词： Data flow graphs

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Feedforward control in the Presence of Input Nonlinearities: With Application to a Wirebonder

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IFAC-PapersOnLine 2023年第2期56卷 1895-1900页

作者： Maurice Poot Jilles van Hulst Kai Wa Yan Dragan Kostić Jim Portegies Tom Oomen CST Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands Center of Competency ASMPT Beuningen The Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

The increasing demands on throughput and accuracy of semiconductor manufacturing equipment necessitates accurate feedforward motion control that includes compensation of input nonlinearities. The aim of this paper is to develop a data-driven feedforward approach consisting of a Wiener feedforward, i.e., linear parameterization with an output nonlinearity, to achieve high tracking accuracy and task flexibility for a class of Hammerstein systems. The developed approach exploits iterative learning control to learn a feedforward signal from data that minimizes the error and utilizes a control-relevant cost function to learn the parameters of a Wiener feedforward parameterization. Experimental validation on a wirebonder shows that the developed approach enables high tracking accuracy and task flexibility.

关键词： Iterative and repetitive learning control Identification and control methods Nonlinear system identification Data-driven control Learning for control

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Optimal Linear Attack in Cyber-Physical Systems with Periodic Detection

Optimal Linear Attack in Cyber-Physical Systems with Periodi...

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IEEE Conference on Decision and control

作者： Jia Qi Chongrong Fang Jianping He Dept. of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Security issues are of significant importance for cyber-physical systems (CPS), where the attack design is a major concern. Most related studies on attack design implicitly consider that the control period and detection period are the same. However, the two periods could be different in practical systems with remote detection such as supervisory control and data acquisition (SCADA) systems, which could lead to new vulnerabilities for attackers. In this paper, we consider the design of innovation-based linear attack strategies for CPS when the control period and detection period are inconsistent. Specifically, we propose an attack framework that consists of attack strategies for detection and non-detection instants under the period discrepancy. On this basis, we design the optimal stealthy innovation-based linear attack strategies for state estimation and LQG control to maximize the estimation error or control cost, respectively. Simulations are given to demonstrate the effectiveness of the proposed attack strategies.

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