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Securing IoT Surveillance Airport Infrastructure

Securing IoT Surveillance Airport Infrastructure

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international conference on Smart Communications and Networking (SmartNets)

作者： Catalina Aranzazu-Suescun Luis Felipe Zapata-Rivera Owen Guerra-Mondragon Saenz Jacob Michael Christensen Department of Cyber Intelligence and Security Embry-Riddle Aeronautical University USA Department of Computer Electrical and Software Engineering Embry-Riddle Aeronautical University USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Over the last three decades, airports have adopted the Internet of Things (loT) technology in their core services. Luggage scanning, ticketing, and security checkpoints are be-coming increasingly automated, providing a more efficient and reliable process. However, loT merchants disregard the impor-tance of securing their devices, focusing only on the improvements following automation. This oversight has opened the door to numerous opportunities for attackers to exploit vulnerabilities in their system, resulting in damage to the company's reputation. Probable attacks are identified and ranked according to their respective damage to the system through threat modeling. After system implementation, cybersecurity professionals require peri-odic vulnerability assessments that outline potential shortcomings of the system's security to propose solutions to palliate the risks further. This project presents a test implementation of an loT-based camera system using open-source technologies and protocols commonly implemented in commercial solutions. The prototype monitors a given area in a remote location using servo motors that are connected to Raspberry Pis to support pan and tilt (PT) functionality for the cameras. This project aims to apply forensics methodologies typically used in information technology (IT) enterprise networks in the context of loT systems. OWASP's Risk Rating Methodology is used to identify and rate the likelihood and technical impacts of potential risks on the loT surveillance testbed network. Well-known and trusted tools were used to assess vulnerabilities in the system's implementation. Based on the results of the vulnerability assessment, a series of attacks were performed on the system to compromise the confidentiality, integrity, and availability of the system and data. The findings from these attacks were used to propose recommendations to secure loT systems in surveillance applications.

关键词： Threat modeling Protocols Surveillance Prototypes Airports Internet of Things Reliability

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Computer simulation of Three-Dimensional Gas Filtration Process in Porous Media

Computer Simulation of Three-Dimensional Gas Filtration Proc...

引用

2021 international conference on Information Science and Communications technologies, ICISCT 2021

作者： Kurbonov, Nozim Research Institute for Development of Digital Technologies and Artificial Intelligence Laboratory 'Modeling of Complex Systems' Tashkent Uzbekistan

ISBN: (纸本)9781665432580

The paper deals with development of multidimensional model of gas filtration in porous media and numerical algorithm for solving the initial-boundary value problem. The model consists of nonlinear differential equation in partial derivatives with the appropriate initial and boundary conditions. The model makes it possible to evaluate impact of main factors affecting the filtration process such as permeability, porosity, thickness of layers, gas recovery coefficient, viscosity etc. That gives us opportunity to solve practical problems of forecasting the process of gas production as well as making decisions on exploitation of gas deposits. © 2021 IEEE.

关键词： Numerical methods

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New Filter-based Supervised Learning Approach for State of Charge Estimation of Li-Ion Battery for EV applications

New Filter-based Supervised Learning Approach for State of C...

引用

modeling, simulation & Intelligent Computing (MoSICom), international conference on

作者： M. Rizwan Deepak Kumar Amrish K. Panwar Department of Electrical Engineering Centre of Excellence for Electrical Vehicles and Related Technologies Delhi Technological University Delhi India Department of Applied Physics Lithium-Ion Battery Technology Laboratory Delhi Technological University Delhi India

The estimation of the state of charge of Li-ion batteries is an important metric. The appropriate estimation plays an important role in assessing the performance of the batteries. The limited instruments are available for such purpose with certain limitations such as large time requirements etc. Numerous other immeasurable elements, such as battery chemistry, the surrounding environment, the aging factor, etc., may also have an impact on the SOC. A new approach based on a filter technique with supervised learning is proposed in this paper. A long-short-term memory and hybrid neural network combining the benefits of a convolutional neural network (CNN) and a bidirectional long-short-term memory (BiLSTM) neural network is proposed. CNN automatically decreases the dimensionality and extracts the deep features from the battery dataset. The obtained data is fed into the LSTM. As a result, minimizes overfitting and speeds up the learning process. Training the dataset model takes considerably less time, and the estimation results are more precise. The results demonstrate that the proposed hybrid model is computationally efficient and provides better estimation results with the single model. The estimation performances are below 1% and have superior estimation accuracy and adaptability in comparison to existing literature.

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simulation platform to design and validate control laws for a space manipulator system performing on-orbit servicing

Simulation platform to design and validate control laws for ...

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Space Robotics (iSpaRo), international conference on

作者： Sofiane Kraïem Mathieu Rognant Sérgio Waitman ONERA - The French Aerospace Lab Toulouse France

ISBN: (数字)9798350367232

ISBN: (纸本)9798350367249

Space robotics require high-precision autonomous control technologies to meet the demands of On-Orbit Servicing (OOS) applications. In such scenarios, robotic manipulators must address the challenge of managing flexible vibrations in large and lightweight structures, which significantly impact control performance. Given the high level of difficulty associated with experimental testing and validating control approaches, this paper introduces recent advancements in a real-time simulation platform. This platform is designed to facilitate rapid conception, prototyping, and testing of model-based control solutions at a low time-scale. The software/hardware architecture of the platform ensures tight and high-fidelity space robot dynamics and flexible structures, incorporating visual environment models for state observation, computer vision processing, virtual sensor data fusion, and full robot control. To showcase the utility of the platform, a robust joint-space control framework is introduced, implemented, and contrasted with a separated base/manipulators control approach for an OOS scenario. The control structure consists of a Nonlinear Dynamic Inversion (NDI) for system linearization and decoupling, together with a structured H ∞ controller to provide robustness against flexible disturbances, model uncertainties, and sensor noise.

关键词： Vibrations Visualization Uncertainty Computational modeling Soft sensors Aerospace electronics Robot sensing systems

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Multiport Bidirectional Converter with Resonant and PWM Modes for Drives applications

Multiport Bidirectional Converter with Resonant and PWM Mode...

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Intelligent Systems, Smart and Green technologies (ICISSGT), IEEE international conference on

作者： Piyush Singh Kalpana Chaudhary Department of Electrical Engineering Indian Institute of Technology (BHU) Varanasi INDIA

ISBN: (数字)9798350304329

ISBN: (纸本)9798350304336

This paper introduces a novel multiport DC-DC converter topology specifically designed for drive applications. The proposed topology offers the flexibility to operate in reso-nant mode during forward operation, achieving both buck and boost modes by adjusting the switching frequency. A significant advantage of this converter is its bidirectional functionality, allowing seamless buck and boost conversions in reverse operation. Governed by control commands to wide bandgap (WBG) devices, this versatility highlights the converter's adaptability to various needs. Notably, the primary power source is a Fuel cell stack, with a secondary Source as LiFePO4 battery for storing regenerative power. The design of magnetics, control algorithms are essential for ensuring performance across all operational modes and suitable ratings up to several kilowatts, presents a major challenge that this work aims to address. The comprehensive range of modes and operational flexibility represent the most notable outcomes of this article, which is verified via detailed simulation results. The structure of the paper is organized as follows: Section 1 provides an introduction, setting the context for the study. Section 2 delves into the proposed topology and its various possible operating modes. Section 3 presents the modeling of the converter and discusses the various obtained plots, which is followed by the results and conclusion.

关键词： Performance evaluation Photonic band gap Switching frequency Fuel cells DC-DC power converters Pulse width modulation Topology Magnetics Fuels Intelligent systems

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Enhancing Road Safety and ITS Efficiency: A Comprehensive Evaluation of Traffic Sign Detection using YOLOv8

Enhancing Road Safety and ITS Efficiency: A Comprehensive Ev...

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Emerging technologies in Computer Science for Interdisciplinary applications (ICETCS), international conference on

作者： Richa Gupta Aditya Verma Vinay Kukreja Rishabh Sharma Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Smart transportation systems (ITS) and autonomous vehicles hereby require up-to-date, highly accurate, and essential tools for sign detection with efficiency. This research paper presents a detailed evaluation of the You Only Look Once version 8 (YOLOv8) deep learning model for the detection and classification of traffic signs into four pivotal categories: red lights, stop signs, speed cameras, as well as crosswalks. The current study introduces a dataset from the Kaggle repository for a thorough investigation of model performance that simulates practical situations in which the weather is unstable and advanced signs vary in shape. The YOLOv8 model used sophisticated methodologies, which encompassed dataset preparation, model training, and solid evaluation metrics; which at the end of the day, showed that this model was better in sensitivity, specificity, and f1-scores, and at the same time, enabled real-time processing that applies to mobility and traffic management. It shows that YOLOv8 is much better than all the previous standard algorithms, as well, it is placed to be one of their best solutions in traffic sign detection. Regularly, conducted by the researchers contribute to the academic field because they present real-world evidence of the YOLOv8 model influence as well as providing practical implications for the future development of road safety and the operational efficiency of ITS and autonomous vehicles. The occurrence of such a location sets the scene for the development of novel traffic sign detection techniques, stressing the prospects of inputting improved approaches and the compilation of heterogeneous data to help in the categorization of the complex surrounding driving environments.

关键词： YOLO Deep learning Training Solid modeling Visualization Urban areas Road safety

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Advancements in single-image 3D modeling: a deepmesh fusion approach

Advancements in single-image 3D modeling: a deepmesh fusion ...

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2024 international conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Liu, Guanglei Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510689176

DeepMesh is an innovative image-to-3D reconstruction framework that significantly enhances model reconstruction quality and detail accuracy, especially when processing single images through the application of multi-light point technology. This study introduces an advanced light source simulation method that simulates multiple light sources illuminating the object, enhancing the capture of surface details and thus achieving higher visual fidelity and geometric accuracy in the generated 3D models. DeepMesh combines Transformer-based sparse view reconstruction technology with advanced mesh processing algorithms, effectively extracting and reconstructing 3D data from complex lighting environments. Experimental results on standard datasets demonstrate that DeepMesh outperforms existing technologies in terms of Structural Similarity (SSIM) and Perceptual Image Quality Index (LPIPS), proving its potential in practical applications. Moreover, the framework's multi-light point processing technique provides a new direction for future research in 3D reconstruction under complex lighting conditions. © 2025 SPIE.

关键词： 3D reconstruction

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Enhancing Sign Language Classification through Reservoir Computing and Depth Camera Technology

Enhancing Sign Language Classification through Reservoir Com...

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Image Processing and Robotics (ICIP), international conference on

作者： Witchuda Thongking Chinthaka Premachandra Ardi Wiranata Shingo Maeda Department of Engineering and Science Shibaura Institute of Technology Tokyo Japan Department of Electronic Engineering Shibaura Institute of Technology Tokyo Japan Department of Mechanical and Industrial Engineering University of Gadjah Mada Yogyakarta Indonesia Department of Mechanical Engineering Tokyo Institute of Technology Tokyo Japan Living Systems Materialogy (LiSM) Research Group International Research Frontiers Initiative (IRFI) Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350374766

ISBN: (纸本)9798350374773

The depth camera has emerged as an efficacious tool for facilitating interactions between individuals and machines. Its advantageous features, including a lightweight design, high durability, completeness, superior image quality, and versatility, have prompted researchers to explore its integration into advanced applications. Currently, the prevailing methods for classifying sign language exhibit certain limitations, such as suboptimal accuracy and inefficiency in gesture recognition technologies. In this study, we conduct a comprehensive examination of hand language characteristics rooted in American Sign Language (ASL), utilizing gesture-based interactions and augmenting sensing performance through preprocessing and the application of reservoir computing in the training model. We conduct a comprehensive evaluation of the gesture characteristics captured by the depth camera, elucidating a methodology encompassing preprocessing, machine learning, and the technical prediction of salient gesture features derived from conventional depth camera video footage. This endeavor aims to establish a robust signal model for enhanced understanding and representation of the analyzed gestures. Our machine-learning models adeptly predict American Sign Language signs. Correlation values derived from the depth camera data demonstrate a pronounced alignment with naturally occurring variations in metrics observed within specific gestures. Our methodologies for quantifying gestures with depth cameras contribute to heightened accessibility in quantitative motion analysis. The outcomes of the reservoir computing implementation exhibit the successful classification of three hand language signs, thereby manifesting high precision and recall. The robust performance of the classification is pivotal for practical applications and underscores the efficacy of our approach.

关键词： Training Sign language Tracking Computational modeling Machine learning Assistive technologies Cameras

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Critical Analysis of Doctrine Application in Exceptional Cases for Crime Reduction Insights from Bangalore Law Practitioners using Hybrid DL-Optimization Model

Critical Analysis of Doctrine Application in Exceptional Cas...

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Electronics and Renewable Systems (ICEARS), international conference on

作者： H.S Adithya Dr.S.K.Kamalakhannan Asha Sundaram Saveetha School of Law Saveetha Institute of Medical and Technical Sciences Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The integration of data-driven technologies in legal systems offers transformative potential for optimizing judicial decisions. This study presents a Hybrid Deep Learning-Optimization Model designed to analyze doctrine application in exceptional cases, with a focus on reducing crime through enhanced judicial insights. Leveraging annotated data from legal practitioners and court records in Bangalore, the model categorizes doctrines and predicts their applicability across diverse crime categories. Achieving an overall accuracy of 98.76%, the model demonstrated exceptional reliability in aligning predictions with judicial outcomes, particularly in violent crimes and institutional offenses. Metrics such as precision (98.91%), recall (98.72%), and F1-score (98.8%) highlight the model’s balanced performance. Key findings revealed patterns in doctrine application, with doctrines like Self-Defense achieving 98.76% effectiveness in violent crimes. Expert surveys further validated the model’s utility, with 96.43% of respondents supporting the use of data-driven tools in judicial processes. This research bridges the gap between conventional legal practices and advanced computational methodologies, offering a scalable solution for enhancing judicial efficiency and fairness. Future applications could extend this framework to broader legal systems and global datasets.

关键词： Surveys Measurement Analytical models Renewable energy sources Law Computational modeling Optimization models Predictive models Natural language processing Reliability

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The simulation of indoor object positioning processes

The simulation of indoor object positioning processes

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2021 international conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2021

作者： Lvovich, I.Y. Preobrazhenskiy, A.P. Choporov, O.N. Faculty of Informatics Voronezh Institute of High Technologies Voronezh Russia Faculty of Information Technology and Computer Security Voronezh State Technical University Voronezh Russia

ISBN: (纸本)9781728145877

The paper discusses the possibilities of modeling the scattering of electromagnetic waves by diffraction structures. These structures have a complex form. The results of determining the sectors of the viewing angles are given to represent the initial structure by its approximate model. The features of data processing in the CAD subsystem are considered. The possibilities of using the approximation of scattering characteristics are shown. An approach, based on which the prediction of the characteristics of the scattering of electromagnetic waves in the framework of the genetic algorithm is carried out, is given. The results may be useful for the developers of electrodynamic objects with the required scattering characteristics. © 2021 IEEE.

关键词： Genetic algorithms

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