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WAM systems: Comparative Analysis of information Support Quality

WAM Systems: Comparative Analysis of Information Support Qua...

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2020 IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2020

作者： Obod, Ivan Svyd, Iryna Maltsev, Oleksandr Zavolodko, Ganna Leonov, Sergiy Kharkiv National University of Radio Electronics Dept. of Microprocessor Technologies and Systems Kharkiv Ukraine National Technical University khPI Dept. of Information Systems Kharkiv Ukraine National Technical University khPI Dept. of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (纸本)9781728191775

The paper considers ways to improve the quality of information support for airspace control systems and air traffic control with the help of wide-area multi-position monitoring systems. A comparative analysis has shown that the accuracy of estimating air object coordinates by the wide area multilateration system under consideration is significantly higher than that provided by currently available wide area multilateration systems. It is shown that the introduction of active and passive methods for creating wide-area multi-position observation systems significantly broadens the number of available air object coordinate estimation techniques and, as a result, enhances the quality of information support by providing its users with the opportunity of choosing an optimal estimation method for various primary measurements of received signal parameters. Active wide area multilateration systems significantly expand the number of methods available for solving the problem of adequate information support to airspace control system users;use of secondary surveillance radar aircraft responders allows estimating air object coordinates by both single-position and multi-position method. © 2020 IEEE.

关键词： Air traffic control

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Seasonal-Trend Time Series Decomposition on Graphics Processing Units

Seasonal-Trend Time Series Decomposition on Graphics Process...

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IEEE International Conference on Big Data

作者： Dmitry Serykh Stefan Oehmcke Cosmin Oancea Dainius Masiliūnas Jan Verbesselt Yan Cheng Stéphanie Horion Fabian Gieseke Nikolaj Hinnerskov Dept. of Computer Science University of Copenhagen Dept. of Information Systems Wageningen University Dept. of Geosciences and Natural Resource Management University of Copenhagen Dept. of Information Systems University of Mūnster

In many domains, large amounts of time series data are being collected and analyzed in a semi-automatic manner. A prominent approach is the seasonal and trend decomposition using locally estimated scatterplot smoothing (STL) technique, which has been applied extensively in the past. However, STL quickly becomes computationally very expensive when applied to large data sets. In this work, we propose the first parallel implementation for the STL decomposition approach, which is tailored to the specific needs of graphics processing units (GPU). Our experimental evaluation on two global-scale case studies in temperature and vegetation trend analysis exhibits at least three-to-four orders of magnitude speed-up, demonstrating the effectiveness of the overall approach and the immense potential of the implementation in spatio-temporal data analyses. The source code is publicly available at https://***/diku-dk/hastl. An artifact that allows the experimental results to be reproduced is available at https://***/sharelink/hOUrqJJ FfA.

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Common Object Request Broker-based Publisher-Subscriber Middleware for Internet of Things - Edge Computing

Common Object Request Broker-based Publisher-Subscriber Midd...

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2022 International Research Conference on Smart Computing and systems Engineering, SCSE 2022

作者： Perera, Hansani Jayakody, Anuradha Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (数字)9781665473750

ISBN: (纸本)9781665473750

The edge computing layer in IoT reduces the flow of a massive amount of data directly to the cloud by processing some data in the local network. The middleware in the layer enables this processing of data and the communication between heterogeneous devices and services in the nearby layers. CORBA, which uses as a powerful middleware technology in developing middleware solutions in enterprise-level distributed applications, has been abandoned in the current generation. The paper presents the design, and the performance evaluation of a publisher-subscriber middleware implemented using CORBA that was studied when exploring the applicability of CORBA as an IoT edge computing middleware. The evaluation was continued in two steps to analyse several parallel connections (Load test) and handle requests in a unit time (burst test) via simulating an IoT environment in a cloud environment. © 2022 IEEE.

关键词： Middleware

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Advanced Deadlock Detection and Resolution in Distributed File systems Using AI and Daemon Processes

Advanced Deadlock Detection and Resolution in Distributed Fi...

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IEEE International Conference on Big Data

作者： Sushanth S. Manakhari Ajinkya P. Jadhav Twinkle Paraye Anurag Gate Dept. of Computer Science Emory University Atlanta GA USA Dept. of Computer and Information Science Gannon University Country Erie PA USA Dept. of Information Systems Santa Clara University Santa Clara CA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In distributed file systems (DFS), deadlocks present significant challenges that adversely affect both performance and reliability. Conventional methods for deadlock detection and resolution are typically resource-intensive, time-consuming, and often fail to provide timely solutions. This paper introduces an innovative approach that leverages the synergy of daemon processes and artificial intelligence (AI) to enhance deadlock detection and resolution mechanisms. Our proposed method deploys autonomous daemon processes to continuously monitor the system states, providing real-time insights into the operating environment. Additionally, we employ advanced AI algorithms to analyze these states, predict potential deadlocks, and proactively implement resolution strategies before system performance is compromised. Experimental results from our comprehensive testing reveal notable improvements in overall system performance, a significant reduction in deadlock occurrences, and enhanced reliability. These findings affirm the efficacy of the proposed approach and its potential to revolutionize deadlock management in distributed file systems, paving the way for more robust and efficient computing environments.

关键词： File systems System performance Heuristic algorithms Transforms System recovery Prediction algorithms Throughput Reliability Artificial intelligence Monitoring

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Study of an OPTO-ASIC for a Functionally Safe Speed Sensor Using the Spatial Frequency Filter Method

Study of an OPTO-ASIC for a Functionally Safe Speed Sensor U...

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IEEE Design and Diagnostics of Electronic Circuits and systems (DDECS)

作者： Frank Wasinski Werner Bonath Ubbo Ricklefs Michael Schwarz Josef Börcsök Dept. of Electrical Engineering and Information Technology THM University of Applied Sciences Giessen Germany ICAS Institute for Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (数字)9798331528010

ISBN: (纸本)9798331528027

For decades, speed sensors based on the spatial frequency filter method have successfully proven themselves in a wide range of applications. This makes the sensor concept particularly suitable for the area of functional safety. New filter variants extend the previous measuring range to very low speeds up to standstill detection. By combining the sensor with a safety system on chip for recording and analysing measured values, a functionally safe measuring chain can be established. In order to demonstrate the variability and performance of the new filters, an optical application-specific integrated circuit was designed, which forms the basis for further extensive tests. The core components are a large number of freely configurable photodiode channels, which were realised via a redundant structure, as well as two different transimpedance amplifier concepts. In the further course of the project, the ASIC will be analysed with regard to its desired performance parameters and its robustness.

关键词： Optical filters Semiconductor device measurement Optical design Stimulated emission Safety Topology System-on-chip Optical sensors Optical amplifiers Velocity measurement

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LiDAR Spoofing Attack Detection in Autonomous Vehicles

LiDAR Spoofing Attack Detection in Autonomous Vehicles

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Al Dosary, Duaa College of Computer and Information Technology University of Anbar Computer Networking Systems Dept. Ramadi Iraq Albaha University Faculty of Computer Science and Information Technology Computer Engineering and Science Department Albaha Saudi Arabia College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq

ISBN: (纸本)9781665441544

Detecting 3D objects is a core task for autonomous vehicles (AVs), as it allows them to drive safely and responsibly. To detect objects quickly and accurately, AVs have LiDAR sensors, which can capture 3D data from 360° in various conditions, including harsh weather. However, LiDAR sensors may face a spoofing attack via laser satirizing. This type of attack does not require physical contact with the sensors nor does it need to change them. It can mislead AVs by providing them with false information, which can then put passengers, pedestrians, and other vehicles in danger. Therefore, this paper proposes a model utilizing machine learning (i.e., decision trees) to protect AVs from such attacks. The performance of the proposed model indicates that it can detect LiDAR spoofing attacks. © 2022 IEEE.

关键词： Autonomous vehicles

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USDOT Tier-1 University Transportation Center for Advancing Cybersecurity Research and Education 1

USDOT Tier-1 University Transportation Center for Advancing ...

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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Zhang, Yunpeng Jack Wang, Kailai Song, Lingguang Lendasse, Amaury Song, Houbing Herbert Han, Zhu Verma, Rakesh Silva, Arlei Rubio-Medrano, Carlos Li, Zhixia Richard Zhang, Guohui University of Houston Dept. of Information Science Technology HoustonTX United States University of Houston Dept. of Construction Management HoustonTX United States University of Maryland Baltimore County Dept. of Information Systems BaltimoreMD United States University of Houston Dept. of Electrical & Computer Engineering HoustonTX United States University of Houston Dept. of Computer Science HoustonTX United States Rice University Dept. of Computer Science HoustonTX United States Texas A&m University-Corpus Christi Dept. of Computer Science Corpus ChristiTX United States University of Cincinnati Dept. of Civil and Architectural Engineering and Construction Management CincinnatiOH United States University of Hawai'i at Mānoa Dept. Civil and Environmental Engineering HonoluluHI United States

ISBN: (纸本)9798350319958

The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of 'Reducing Transportation Cybersecurity Risks.' CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity. © 2023 IEEE.

关键词： Technology transfer

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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework f...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Panchal, Barkha Bhatia, Jitendra Kumhar, Malaram Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350350548

The Internet of Medical Things (IoMT) revolutionizes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and KNN is 84.3%. The results shows that KNN performs better than other models by the factor of 9%. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Residual Blocks - Transformers Improving Cardiac MRI Segmentation in Deep Learning

Hybrid Residual Blocks - Transformers Improving Cardiac MRI ...

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Communications, information, Electronic and Energy systems (CIEES), International Conference on

作者： Maysaa Abd Ulkareem Naser Abbas H. Hassin Al-Asadi Dept. of Computer Information Systems University of Basrah Basrah Iraq Dept. of Computer Science Kufa University Kufa Iraq

Cardiovascular disease is one of the primary causes of mortality and disability. By assisting in early diagnosis, segmentation techniques based on deep learning might help to lessen their severity, although extremely high levels of accuracy are required. Introduce a novel and flexible architecture that raises the bar for semantic segmentation accuracy in this study. Our contributions focus on the merging of strong self-attention mechanisms obtained from Transformer with traditional convolutional neural network (CNN) architecture. This hybrid model demonstrates improved feature representation, contextual understanding, and pixel-wise predictions, setting new standards in semantic segmentation tasks. the average dice coefficients for ACDC dataset by our model, AVG DICE 92.52%, RV 90.77%, MYO 90.00%, LV 96.2S%

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Enhancing Diabetes Prediction using Hybrid Feature Selection and Ensemble Learning with AdaBoost 8

Enhancing Diabetes Prediction using Hybrid Feature Selection...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Thuraka, Bharadwaj Pasupuleti, Vikram Kodete, Chandra Shikhi Chigurupati, Ravi Swaroop Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin Northwest Missouri State University School of Computer Science and Information Systems MaryvilleMO64468 United States Eastern Illinois University School of Technology CharlestonIL61920 United States Dept. of Cse Bhimavaram India Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798350376425

This research relies on a feature selection method termed LasNO to analyse the progress of diabetes prediction. The ANOVA integrated with Lasso regularization identifies significant predictors like Age, BMI, DiabetesPedigreeFunction, and (Glucose). An AdaBoost model with the following characteristics was developed, and the results were exceptional performance metrics: 97.02% accuracy, 87.65% precision, and 82.5% recall. The results of the primary focus on glucose levels to differentiate between events with and without diabetes have been affirmed from the visual analyses. It also calls into attention that due to the appropriate cross-validation, LasNO is very successful on improving the interpretability and the possibility of generalization of the model. Such findings make it clear that efforts towards early detection of the condition and subsequent timely implementation of measures to contain the illness can go quite a long way in enhancing the quality of the lives of people that contract the disease. Further research is likely to be devoted to the assessing of the improved and broadened validation of the dataset as a critical approach for the enhancement and expansion of the model for use in clinics. As a result, this study aims to advance diabetes theragnostic possibilities and healthcare recommendations for better action based on ML approaches enhancing the accuracy of predictions. In this study, approaches to feature selection used for improving the performance while also making the models more understandable are the focus. The idea is to ensure that medical practitioners have reliable tools to undertake early detection and management of diabetes. Thus, the main objective of dealing with patients' Health-Related Quality of Life is to simultaneously increase the quality of patients' living while decreasing the costs of their treatment. © 2024 IEEE.

关键词： Prediction models

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