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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Elbattrawy, Ahmed N. El-Malek, Ahmed H. Abd Rabia, Sherif I. Zahra, Waheed K. Basic and Applied Sciences Institute New Borg El-Arab City Alexandria21934 Egypt Egypt-Japan University of Science and Technology School of Electronics Communications and Computer Engineering New Borg El-Arab City Alexandria21934 Egypt Alexandria University Faculty of Engineering Department of Engineering Mathematics and Physics Alexandria21544 Egypt

ISBN: (纸本)9798331517786

In this work, we investigate the security performance of an underlay cognitive radio network (CRN) with ambient backscatter communication (AmBC), where a backscattering device (BD) shares the spectrum and the receivers with the secondary user. Different from the related work, we consider a secondary user transmitter (ST) with multiple antenna in an AmBC-CRN with multiple receivers and multiple passive eavesdroppers. The ST performs joint antenna-user selection to enhance its security performance and overcome the performance degradation caused by BD interference. Considering the Nakagami-m fading model, closed-form expressions are derived for the secrecy outage probability for both the ST and the BD transmissions. Monte Carlo simulations are performed to validate the derived closed-form expressions. Numerical results show that employing joint antenna-user selection enhances the ST security performance by exploiting antenna and user diversity. © 2024 IEEE.

关键词： Radio communication

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Direct System Identification of Dynamical Networks with Partial Measurements: A Maximum Likelihood Approach

Direct System Identification of Dynamical Networks with Part...

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European Control Conference (ECC)

作者： João Victor Galvão Da Mata Anders Hansson Martin S. Andersen Department of Applied Mathematics and Computer Science Technical University of Denmark Department of Electrical Engineering Linköping University.

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper introduces a novel direct approach to system identification of dynamic networks with missing data based on maximum likelihood estimation. Dynamic networks generally present a singular probability density function, which poses a challenge in the estimation of their parameters. By leveraging knowledge about the network's interconnections, we show that it is possible to transform the problem into a more tractable form by applying linear transformations. This results in a nonsingular probability density function, enabling the application of maximum likelihood estimation techniques. Our preliminary numerical results suggest that when combined with global optimization algorithms or a suitable initialization strategy, we are able to obtain a good estimate of the dynamics of the internal systems.

关键词： Maximum likelihood estimation Sensitivity Heuristic algorithms Transfer functions Optimization methods Transforms Probability density function

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Image Reconstruction for Proton Therapy Range Verification via U-NETs 28th

Image Reconstruction for Proton Therapy Range Verification ...

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28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024

作者： Setterdahl, Lena M. Lionheart, William R. B. Holman, Sean Skjerdal, Kyrre Ratliff, Hunter N. Ytre-Hauge, Kristian Smeland Lathouwers, Danny Meric, Ilker Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Mathematics University of Manchester Manchester United Kingdom Department of Physics and Technology University of Bergen Bergen Norway Department of Radiation Science and Technology Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031669545

This study aims to investigate the capability of U-Nets in improving image reconstruction accuracy for proton range verification within the framework of the NOVO (Next generation imaging for real-time dose verification enabling adaptive proton therapy) project. NOVO aims to enhance the accuracy of proton range verification by imaging the distribution of prompt gamma-rays (PGs) and fast neutrons (FNs) produced by nuclear interactions within tissue. In this work, focus lies on FNs, leaving PGs as future work. A dataset consisting of Monte Carlo-based simple back-projection and ground truth images of FN production distributions in a homogeneous water phantom was utilized. Various U-Net models were trained to predict FN distributions, and a set of range landmark (RL) metrics were computed for evaluation. Linear regression models were established to correlate shifts in mean RL with true range shift magnitudes. Our findings demonstrate a strong linear correlation between the shifts in mean RL in U-Net predictions and the true range shift magnitudes. Multiple RL metrics, including weighted average, inflection point, edge, and peak, were explored. This study highlights the potential utility of U-Nets in enhancing image reconstruction accuracy for proton range verification. The observed correlations between RL shifts and true range shifts provide evidence of the ability of U-Nets to accurately predict images containing range information. Future research will focus on generating more realistic training data incorporating more clinically relevant phantoms, including tissue heterogeneities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Tissue

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Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Under Dynamic Lighting and Weather Conditions Using Electric Vehicles

arXiv

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arXiv 2024年

作者： Khalfin, Michael Volgren, Jack Jones, Matthew LeGoullon, Luke Siegel, Joshua Chung, Chan-Jin Department of Computational Applied Mathematics Rice University United States Department of Computer Science and Engineering Pennsylvania State University United States Department of Mathematics and Computer Science Willamette University United States Division of Computer Science and Engineering Louisiana State University United States Department of Computer Science and Engineering Michigan State University United States Department of Mathematics and Computer Science Lawrence Technological University United States

Self-driving vehicles have the potential to reduce accidents and fatalities on the road. Many production vehicles already come equipped with basic self-driving capabilities, but have trouble following lanes in adverse lighting and weather conditions. Therefore, we develop, analyze, and evaluate two vehicular lane-keeping algorithms under dynamic weather conditions using a combined deep learning- and hand-crafted approach and an end-to-end deep learning approach. We use image segmentation- and linear-regression based deep learning to drive the vehicle toward the center of the lane, measuring the amount of laps completed, average speed, and average steering error per lap. Our hybrid model completes more laps than our end-to-end deep learning model. In the future, we are interested in combining our algorithms to form one cohesive approach to lane-following. © 2024, CC BY.

关键词： computer vision

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Enhancing Urban Air Quality with Smart Vehicle Exhaust Management

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Operations Research Forum 2025年第2期6卷 1-24页

作者： Tyagi, Vaishali Dubey, Roshan Singh, Suraj Ram, Mangey Department of Applied Sciences & Humanities ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Computer Science & Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Mathematics Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

Air pollution is the contamination of the indoor or outdoor environment by any chemical, physical, or biological agent that modifies the natural characteristics of the atmosphere. Burning gasoline and diesel fuel creates harmful byproducts like nitrogen dioxide, carbon monoxide, hydrocarbons, benzene, and formaldehyde. This study proposes a sensor-based model to control vehicular pollution by monitoring emission levels and preventing vehicles from operating if their pollution exceeds government standards. The proposed model consists of a smoke sensor, particulate sensors, carbon monoxide sensors, along with Arduino based microcontroller, data logger, LCD display, and GPRS modem. Using the block diagram, a mathematical model has been developed and some differential equations by using Markov process have been generated. This Markov-based model gives the information about the failure causes and working of the proposed system. Some reliability matrices like availability, mean time between failure (MTBF), and sensitivity analysis have been evaluated with the help of Laplace transform and illustrated with the help of graphs. Also, the failure cases of the system and its cost analysis have been done along with all the numerical calculations required for the successful operation of the model. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Air pollution Cost analysis Failure cases Markov process Reliability Sensor system

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Representation with Minimized Max-Error in Optimal Piecewise Linear Approximation of Time Series Data 25th

Representation with Minimized Max-Error in Optimal Piecewi...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Zhao, Huanyu Li, Tongliang Wen, Shiting Shu, Zhenyu Deng, Ke Yang, Jian Pang, Chaoyi School of Computer Science and Technology Donghua University Shanghai China Institute of Applied Mathematics Hebei Academy of Sciences Shijiazhuang China Hebei Authentication Technology Engineering Research Center Shijiazhuang China Institute of Biology Hebei Academy of Sciences Shijiazhuang China School of Computer and Data Engineering NingboTech University Ningbo China RMIT University Melbourne Australia Macquarie University Sydney Australia

ISBN: (纸本)9789819605781

In the past two decades, Piecewise Linear Approximation under maximum error (max-error) bound (PLA∞) has been intensively studied for effective qualified representation and analysis of time series data. It divides a time series into fragments and then represents each fragment with a straight line to approximate the data points of that time slot. In this paper, to elevate the representation quality in the optimal PLA∞ results, we present a linear-time algorithm FindMin to construct the unique line representative of minimized maximum error (min-max error) for each fragment. Through extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution and achieves better performances than the state-of-the-art solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series analysis

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Analysis of Renewable Energy Harvesting Sources with Interaction of Piezoelectric Materials 2

Analysis of Renewable Energy Harvesting Sources with Interac...

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2nd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2023

作者： Yadav, Mohit Kumar, Surendra Yadav, Rajkumar Yadav, Dinesh Garg, Ramesh Kumar Kaushik, Ashish Ahlawat, Akash Chhabra, Deepak Maharshi Dayanand University Department of Applied Sciences Mathematics Uiet Rohtak124001 India University Institute of Engineering and Technology M.D. University Department of Applied Sciences Mathematics Rohtak124001 India University Institute of Engineering and Technology M.D. University Department of Computer Science & Engineering Rohtak124001 India Deenbandhu Chhotu Ram University of Science and Technology Department of Mechanical Engineering Sonipat131039 India Uiet Maharshi Dayanand University Department of Mechanical Engineering Rohtak124001 India

ISBN: (纸本)9798350326666

The results of enormous climate change and other industrial carbon emissions have been driven by the rise of nonrenewable traditional energy sources. The scientific community is looking at unconventional renewable techniques that can harvest energy in the external atmosphere. This research article provides a critical analysis of the growth in the field of piezoelectric energy harvesting. Recent advances in bio-inspired piezo-smart equipment are critically analyzed. A comprehensive overview of harvesting power from mechanical vibrations and its renewable sources, such as human body movements, flowing fluid, and circuitry arrangements of structures with piezoelectric materials have been discussed. Capturing and converting mechanical energy into useful electric power is the easiest and most prevalent way to harness the massive amounts of ambient energy around us. With the help of piezoelectric materials, vibrations induced by mechanical sources can convert into electrical energy. Various industries, including transportation, construction, aeronautical, smart systems, micro-fluids, biomedical devices, and implantable electronics, employ piezoelectric energy generating at the micro level. Piezoelectric energy generating technique's applications along with their achievements, and future possibilities are critically analyzed. This overview concisely presents the extensive range of piezoelectric materials for wireless electronic devices that use eco-friendly, renewable, and sustainable energy harvesting sources. © 2023 IEEE.

关键词： Energy harvesting

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Improving DL-Learner on a Malware Detection Use Case 36

Improving DL-Learner on a Malware Detection Use Case

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36th International Workshop on Description Logics, DL 2023

作者： Bisták, Tomáš Švec, Peter Kluka, Ján Šimko, Alexander Balogh, Štefan Homola, Martin Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava842 48 Slovakia Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovicova 3 Bratislava812 19 Slovakia

Automation of malware characterization has become increasingly important for early malware detection over the past decades. Since it is crucial to be able to perform malware detection transparently, explainable machine-learning methods may be considered particularly suitable for this application. In our previous work, we thus employed algorithms for learning description-logics concept expressions to obtain structured characterizations of malicious software. The results of our initial experiments, however, lead us to several inconsistencies in the behaviour of the studied algorithms included in DL-Learner, a widely adopted framework for concept learning. Hence, in this work, we make a couple of corrections to DL-Learner, along with a few further enhancements, and repeat the experiments to empirically assess the effects of our changes. The outcomes show that the modified algorithms can mark an improvement in terms of the predictability of their behaviour as well as evaluation metrics. © 2023 CEUR-WS. All rights reserved.

关键词： Malware

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Energy-Efficient Transmission in Wireless Networks Using Resnet with Gravitational Search Algorithm 2

Energy-Efficient Transmission in Wireless Networks Using Res...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rajakumari, S. Hc, Kantharaju Ahmad, Shadab Sharma, Rupa Rani Sudha, S. Ramesh, T. R.M.D. Engineering College Department of Mathematics India Vemana Institute of Technology Department of Ai & Ml Karnataka Bengaluru India Samara University Department of Electrical and Computer Engineering Afar region Ethiopia G.L Bajaj Institute of Technology and Management Department of Applied Science Uttar Pradesh Greater Noida India Sri Ranganathar Institute of Engineering and Technology Department of Ece Tamilnadu Coimbatore India R.M.K Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350371055

Although wireless networks are extremely important in modern communication, one of the most significant challenges they face is the quantity of energy that they use. Within this study, a new way of sending data over wireless networks is suggested. The gravitational search algorithm (GSA) and residue networks of the ResNet type are used together in this method. By using wireless networks, the main goal of this method is to find answers to problems that come up with using energy more efficiently. The creation and use of a hybrid ResNet-GSA model for wireless communication is one of the most important parts of our method. Because it follows the rules of gravity, GSA can quickly look through the solution space to find the right parameter values. At the same time, the network works better when the deep learning features of ResNet are used. The results of our research show that there are big improvements in how much energy is used compared to using traditional ways. If you want to cut down on the amount of energy used, the ResNet-GSA strategy that was suggested also works well. The findings demonstrate that optimisation techniques and deep learning methodologies are not only compatible with one another but also perform effectively together to make wireless networks more environmentally friendly. © 2024 IEEE.

关键词： Energy Efficiency Gravitational Search Algorithm Residual Networks Transmission Optimization

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A Fused Machine Learning Approach for Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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