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Space Division Multiple Access for Cellular V2X Communications

computers, Materials & Continua 2022年第10期73卷 1195-1206页

作者： Doaa Sami Khafaga Mohammad Zubair Khan Muhammad Awais Javed Amel Ali Alhussan Wael Said Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Information Taibah University Madinah Saudi Arabia Department of Electrical and Computer Engineering COMSATS University Islamabad45550Pakistan Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management *** safety applications,Basic Safety Messages(BSM)containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic *** are shared using mode 4 of Cellular V2X(C-V2X)communications in which resources are allocated in an ad hoc ***,the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network,thus reducing the reliability of safety ***,as vehicles choose the transmission resources in a distributed manner in mode 4 of CV2X,the packet collision problem is further *** paper presents a novel solution in the form of a Space Division Multiple Access(SDMA)protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node *** proposed protocol works by dividing road segments into clusters and *** sub-frames are allocated to a cluster and these sub-frames are reused after a certain *** a cluster,sub-channels are allocated to *** implement the proposed SDMA protocol and evaluate its performance in a highway vehicular *** results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling(SB-SPS)in terms of safety range and packet delay.

关键词： ITS vehicular network cellular V2X SDMA

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AMMONIA, METHANE, HYDROGEN AND METHANOL PRODUCED IN REMOTE RENEWABLE ENERGY HUBS: A COMPARATIVE QUANTITATIVE ANALYSIS 37

AMMONIA, METHANE, HYDROGEN AND METHANOL PRODUCED IN REMOTE R...

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37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024

作者： Larbanois, Antoine Dachet, Victor Dubois, Antoine Fonteneau, Raphaël Ernst, Damien Department of Computer Science and Electrical Engineering Liege University Liege Belgium LTCI Telecom Paris Institut Polytechnique de Paris Paris France

ISBN: (纸本)9798331307660

Remote renewable energy hubs (RREHs) for synthetic fuel production are engineering systems harvesting renewable energy where it is particularly abundant. They produce transportable synthetic fuels for export to distant load centers. This article aims to evaluate the production costs of different energy carriers, and includes a discussion on advantages and disadvantages in terms of technical performance. To do so, we extend the study of Berger et al. (2021) which focuses on methane (CH4) as energy carrier and introduce three new carriers: ammonia (NH3), hydrogen (H2) and methanol (CH3OH). The four different RREHs are located in the Algerian Sahara desert and must serve to the load center, Belgium, a constant electro-fuel demand of 10 TWh per year. The modelling and optimisation of these systems are performed using the modelling language GBOML (Graph-Based Optimisation Modelling Language). Our findings reveal that the three new RREHs, each with its respective carrier (ammonia, hydrogen, and methanol), are all more cost-effective than the methane-based system. Ammonia demonstrates the most favourable cost-to-energy exported ratio. © 2024 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024. All rights reserved.

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Smart Solar Forecasting: Machine Learning Approaches for Predicting Solar Power

Smart Solar Forecasting: Machine Learning Approaches for Pre...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Kumar, Madhav Borgohain, Santanu Panda, Kaibalya Prasad Thokchom, Surmila Panda, Gayadhar National Institute of Technology Department of Electrical Engineering Meghalaya India Pandit Deendayal Energy University Department of Electrical Engineering Gujarat Gandhinagar India National Institute of Technology Department of Computer Science and Engineering Meghalaya India Nitttr Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350350821

Solar energy is one of the most popular renewable energy sources in recent years. These solar systems not only reduce the need for electricity but also greatly improve the environment. Due to our excessive dependence on fossil fuels and their limited sources and the pollution caused by them, most people are turning to solar energy to power their homes and industries. It's critical to understand the location of the solar panel and the local weather since solar power efficiency is highly dependent on environmental factors. With the inherent unpredictability of photovoltaic power output, machine learning is a useful tool for predicting how weather conditions affect solar power plants, and there are various other viable prediction methods available. This research evaluates several meteorological characteristics for PV forecasting using numerous machine learning algorithms. Using the collected historical data set, a machine learning model is created. Necessary preprocessing techniques such as univariate and bivariate analysis are used. Machine learning algorithms benefit from data visualization as it makes it easier to understand features and create a robust classification model. Accuracy-related performance metrics such as MAE, MSE, and RMSE are used to compare the methods. We evaluate and contrast the various models' performances. This evaluation approach helps to choose the algorithm that best suits the task at hand. © 2024 IEEE.

关键词： Mean square error

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Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images

Few-Shot Learning Approach on Tuberculosis Classification Ba...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Maulana, Muhammad Fazil Pramana, A. A. G. Yogi Permana, Faiz Ihza Fudholi, Dzikri Rahadian Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350391213

Tuberculosis (TB) is caused by the bacterium Mycobacteriumtuberculosis, primarily affecting the lungs. Early detection is crucial for improving treatment effectiveness and reducing transmission risk. Artificial intelligence (AI), particularly through image classification of chest X-rays, can assist in TB detection. However, class imbalance in TB chest X-ray datasets presents a challenge for accurate classification. In this paper, we propose a few-shot learning (FSL) approach using the Prototypical Network algorithm to address this issue. We compare the performance of ResNet-18, ResNet-50, and VGG16 in feature extraction from the TBX11K Chest X-ray dataset. Experiments with the model demonstrate classification accuracy results of 98.93% for ResNet-18, 98.60% for ResNet-50, and 33.33 % for VGG16. These findings indicate that the proposed method performs well on certain feature extraction algorithms in mitigating data imbalance. Ultimately the implementation of few-shot learning in handling data imbalance proves to be a promising solution for enhancing TB detection accuracy. © 2024 IEEE.

关键词： Zero-shot learning

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Adaptive Offloading and Enhancement for Low-Light Video Analytics on Mobile Devices

Adaptive Offloading and Enhancement for Low-Light Video Anal...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： He, Yuanyi Yang, Peng Qin, Tian Hou, Jiawei Zhang, Ning Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China University of Windsor Department of Electrical and Computer Engineering WindsorON Canada

ISBN: (纸本)9798350351255

In this paper, we explore adaptive offloading and enhancement strategies for video analytics tasks on computing-constrained mobile devices in low-light conditions. We observe that the accuracy of low-light video analytics varies from different enhancement algorithms. The root cause could be the disparities in the effectiveness of enhancement algorithms for feature extraction in analytic models. Specifically, the difference in class activation maps (CAMs) between enhanced and low-light frames demonstrates a positive correlation with video analytics accuracy. Motivated by such observations, a novel enhancement quality assessment method is proposed on CAMs to evaluate the effectiveness of different enhancement algorithms for low-light videos. Then, we design a multi-edge system, which adaptively offloads and enhances low-light video analytics tasks from mobile devices. To achieve the trade-off between the enhancement quality and the latency for all system-served mobile devices, we propose a genetic-based scheduling algorithm, which can find a near-optimal solution in a reasonable time to meet the latency requirement. Thereby, the offloading strategies and the enhancement algorithms are properly selected under the condition of limited end-edge bandwidth and edge computation resources. Simulation experiments demonstrate the superiority of the proposed system, improving accuracy up to 20.83% compared to existing benchmarks. © 2024 IEEE.

关键词： Scheduling algorithms

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A Discriminative Approach To Unsupervised Domain Adaptation in Coarse-To-Fine Classifiers 33

A Discriminative Approach To Unsupervised Domain Adaptation ...

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Alkhouri, Ismail R. Awad, Akram S. Hatfield, Connor Atia, George K. University of Central Florida Department of Electrical and Computer Engineering OrlandoFL United States University of Central Florida Department of Computer Science OrlandoFL United States

ISBN: (纸本)9798350324112

Conventional machine learning models often exhibit poor performance when tested on data that comes from a different probability distribution than that of the training data. This phenomenon is known as 'domain shift'. To overcome this problem, domain adaptation (DA) methods have been proposed to reduce the discrepancy between the two domains and improve the performance on the target domain. However, most of the existing DA techniques for classification have focused on One Level Classifiers (OLCs), which are not suitable for data that follows a hierarchical classification structure. In this paper, we propose a discriminative DA approach for coarse-to-fine (C2F) classifiers, which provide both coarse and fine labels. Our approach learns an invariant feature representation of the data across domains for both the coarse and finer levels. Our experimental results on well-known digit datasets demonstrate that the proposed algorithm outperforms the base C2F model in terms of the classification accuracy of the target domain data. © 2023 IEEE.

关键词： Classification (of information)

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Parallel Implementation of the CCSDS Turbo Decoder on GPU

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China Communications 2024年第10期21卷 70-77页

作者： Liu Zhanxian Liu Rongke Zhang Haijun Wang Ning Sun Lei Wang Jianquan School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)***,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism *** mitigate this issue,we propose a padding method that inserts several bits before the information frame *** obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are *** presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small *** proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

关键词： CCSDS CUDA GPU parallel decoding turbo codes

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Cloud Security: Analyzing Threats and Challenges 25

Cloud Security: Analyzing Threats and Challenges

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Asassfeh, Mahmoud Rajallah Abu Elsoud, Esraa Albilasi, Sultan Mohammed Alqammaz, Abdullah Al-Shaikh, Ala'a Afaneh, Suha Doumi, Ahmad Bany Elrashidi, Ali Samara, Ghassan Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Jerash University Department of Computer Science Jerash Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The rapid embrace of cloud computing has changed the IT environment, providing unmatched scalability, adaptability, and cost-effectiveness, since cloud services brought in around 631.84 billion dollars globally in 2023, and they will expand at a rapid rate of 18.49% through 2029. With 45.58% of global revenues. Nevertheless, as companies move their essential operations and sensitive information to the cloud more and more, the shared nature of cloud environments creates important security challenges. This paper presents an extensive examination of the security vulnerabilities, challenges, strategies, and countermeasures pertinent to cloud computing since cloud computing includes several types of administration that are made available to service users online, such as programming, gathering, and coordinating equipment *** is necessary to safeguard this data from threats like denial of service attacks and unauthorized access. © 2024 IEEE.

关键词： Cloud security

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A microfluidic biosensor for the diagnosis ofchronic wasting disease

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Microsystems & Nanoengineering 2023年第4期9卷 263-279页

作者： Sura A.Muhsin Amjed Abdullah Estela kobashigawa Muthana Al-Amidie Sherri Russell Michael Z.Zhang Shuping Zhang Mahmoud Almasri University of Missouri–Columbia Electrical Engineering and Computer ScienceColumbiaMOUSA University of Missouri–Columbia College of Veterinary MedicineVeterinary Medical Diagnostic LaboratoryColumbiaMOUSA Missouri Department of Conservation ColumbiaMOUSA

Cervids are affected by a neurologic disease that is always fatal to individuals and has population effects. This disease iscalled chronic wasting disease (CWD) and is caused by a misfolded prion protein. The disease is transmitted viacontact with contaminated body fluids and tissue or exposure to the environment, such as drinking water or *** CWD diagnosis depends on ELISA screening of cervid lymph nodes and subsequent immunohistochemistry(IHC) confirmation of ELISA-positive results. The disease has proven to be difficult to control in part because ofsensitivity and specificity issues with the current test regimen. We have investigated an accurate, rapid, and low-costmicrofluidic microelectromechanical system (MEMS) biosensing device for the detection of CWD pathologic prions inretropharyngeal lymph nodes (RLNs), which is the current standard type of CWD diagnostic sample. The deviceconsists of three novel regions for concentrating, trapping, and detecting the prion. The detection region includes anarray of electrodes coated with a monoclonal antibody against pathologic prions. The experimental conditions wereoptimized using an engineered prion control antigen. Testing could be completed in less than 1 hour with highsensitivity and selectivity. The biosensor detected the engineered prion antigen at a 1:24 dilution, while ELISA detectedthe same antigen at a 1:8 dilution. The relative limit of detection (rLOD) of the biosensor was a 1:1000 dilution of aknown strong positive RLN sample, whereas ELISA showed a rLOD of 1:100 dilution. Thus, the biosensor was 10 timesmore sensitive than ELISA, which is the currently approved CWD diagnostic test. The biosensor’s specificity andselectivity were confirmed using known negative RPLN samples, a negative control antibody (monoclonal antibodyagainst bovine coronavirus BCV), and two negative control antigens (bluetongue virus and Epizootic hemorrhagicdisease virus). The biosensor’s ability to detect pathogenic prions was

关键词： fir diagnosis specificity

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Secure Energy Efficiency for ARIS Networks With Deep Learning: Active Beamforming and Position Optimization

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5282-5296页

作者： Wang, Dawei Wang, Zijun Zhao, Hongbo Zhou, Fuhui Alfarraj, Osama Yang, Weichao Mumtaz, Shahid Leung, Victor C. M. Northwestern Polytechnical University School of Electronics and Information Shaanxi Xi’an710072 China Beihang University School of Electronic and Information Engineering Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia Nottingham Trent University Department of Computer Science NottinghamNG1 4FQ United Kingdom Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Incorporating an active reconfigurable intelligent surface on an autonomous aerial vehicles (AAVs), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in AAVs, energy management emerges as a key challenge within AAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the AAV’s position and designing the active beamforming. For the AAV’s position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the AAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in AAV networks. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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