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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Kaddour, Hamza Olaveson, Israel G. Krome, Cameron J. Fouda, Mostafa M. Idaho State University Department of Electrical and Computer Engineering PocatelloID83209 United States Idaho FallsID83401 United States Ai Data and Visualization Ai Data and Visualization Idaho FallsID83415 United States

ISBN: (纸本)9798331530099

As 5G networks rapidly expand to support higher data rates, lower latency, and increased device density, the associated security risks are also growing. That is why the deployment of 5G networks introduces significant challenges in balancing Quality of Service (QoS) and security, especially in dynamic, high-demand environments. This paper presents a framework leveraging deep reinforcement learning (DRL) to optimize this tradeoff based on our generated QoS_5G_Sec dataset. We evaluated various DRL agents, including Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C), focusing on their ability to manage network parameters in real-time. Among the tested methods, the DQN model tuned using the Optuna hyperparameter optimization technique demonstrated the best overall performance. Our results show that this approach provides a robust solution for dynamically adjusting network parameters, improving both QoS and security in 5G network architectures. © 2024 IEEE.

关键词： Deep reinforcement learning

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Affective visualization Design: Leveraging the Emotional Impact of data

arXiv

引用

arXiv 2023年

作者： Lan, Xingyu Wu, Yanqiu Cao, Nan The Research Group of Computational and AI Communication Institute for Global Communications and Integrated Media China Intelligent Big Data Visualization Lab Tongji University China

In recent years, more and more researchers have reflected on the undervaluation of emotion in data visualization and highlighted the importance of considering human emotion in visualization design. Meanwhile, an increasing number of studies have been conducted to explore emotion-related factors. However, so far, this research area is still in its early stages and faces a set of challenges, such as the unclear definition of key concepts, the insufficient justification of why emotion is important in visualization design, and the lack of characterization of the design space of affective visualization design. To address these challenges, first, we conducted a literature review and identified three research lines that examined both emotion and data visualization. We clarified the differences between these research lines and kept 109 papers that studied or discussed how data visualization communicates and influences emotion. Then, we coded the 109 papers in terms of how they justified the legitimacy of considering emotion in visualization design (i.e., why emotion is important) and identified five argumentative perspectives. Based on these papers, we also identified 61 projects that practiced affective visualization design. We coded these design projects in three dimensions, including design fields (where), design tasks (what), and design methods (how), to explore the design space of affective visualization design. © 2023, CC BY.

关键词： data visualization

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YOLO for Radio Frequency Signal Classification

YOLO for Radio Frequency Signal Classification

引用

IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN

作者： T.Quach Anna Randall D. Reese AI Data & Visualization Idaho National Laboratory Idaho Falls ID USA

ISBN: (数字)9798350317640

ISBN: (纸本)9798350317657

Radio frequency signal classification plays a pivotal role in various applications, including spectrum management, wireless security, and cognitive radio. Extant signal classification methods require significant data throughput and are not multilabel. We propose a novel approach to radio frequency signal classification by leveraging the You Only Look Once (YOLO) object detection method. YOLO is a state-of-the-art deep learning model renowned for its real-time object detection capabilities in computer vision applications. We adapt YOLO for signal classification to enable the automatic and efficient identification of various signal types within a power spectral density image.

关键词： YOLO Radio frequency Deep learning Computer vision Computational modeling Pattern classification Dynamic spectrum access

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Altering 5G Network Parameters Using Deep Reinforcement Learning to Optimize QoS and Security

Altering 5G Network Parameters Using Deep Reinforcement Lear...

引用

Virtual Conference on Communications (VCC), IEEE

作者： Hamza Kaddour Israel G. Olaveson Cameron J. Krome Mostafa M. Fouda Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA AI Data & Visualization AI Data & Visualization Idaho Falls ID USA

ISBN: (数字)9798331530099

ISBN: (纸本)9798331530105

关键词： 5G mobile communication Quality of service Network architecture Deep reinforcement learning Hyperparameter optimization Stability analysis Real-time systems Security Tuning Optimization

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Exploiting Multi-Domain Features for Detection of Unclassified Electromagnetic Signals

Exploiting Multi-Domain Features for Detection of Unclassifi...

引用

MILCOM, Military Communications Conference

作者： Xue Wei Dola Saha Anna Quach Department of Electrical and Computer Engineering University at Albany SUNY AI Data and Visualization Idaho National Laboratory

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

Deep Learning based classification techniques have shown excellent performance in static environments, where the training and testing samples are drawn from the same distribution. However, real world scenarios often present samples that do not belong to the known set of classes chosen during training. This is quite common for electromagnetic signals, where it is impractical to assume that all possible waveforms are known a-priori, specially in scenarios like warfare. To address this problem, we propose a deep learning based adversarial model where the generator learns to generate waveform features that can deceive the discriminator model as true samples. We introduce domain knowledge of wireless signals by decomposing the signal into a lower dimensional unique feature set, which is used for classifying known versus unknown signals. We further introduce multiple domain representations of the signal to extract features and combine them together to accurately classify new waveforms as an unknown class. Our results show that combined features from multiple domains outperform any single domain representation, especially at low SNR regimes with fewer number of samples to classify.

关键词： Training Deep learning Wireless networks Electromagnetic scattering Feature extraction US Department of Defense Signal representation Electromagnetics Signal to noise ratio Testing

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Analyzing the Impact of Security Measures on Wi-Fi Quality of Service 2

Analyzing the Impact of Security Measures on Wi-Fi Quality o...

引用

2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, aiBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Quach, Anna T. Olaveson, Israel G. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Ai Data & Visualization Idaho National Laboratory Idaho FallsID United States

ISBN: (纸本)9798331529529

This study examines the relationship between Quality of Service (QoS) and security in Wi-Fi networks. Using a detailed dataset consisting of QoS metrics of file transfers under various security configurations, we analyze the impact of these settings on network performance. Our findings provide a quantification to accurately show that different encryption protocols, such as TKIP and CCMP, significantly affect QoS, with disabling security generally improving performance. The study provides a framework for optimizing security settings to enhance both security and QoS in future wireless networks. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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A Lightweight ai Model for Anomaly Detection in Wireless Networks 2

A Lightweight AI Model for Anomaly Detection in Wireless Net...

引用

2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, aiBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho National Laboratory Ai Data & Visualization Idaho FallsID United States

ISBN: (纸本)9798331529529

Detecting network anomalies is critical for wireless network security and reliability. Traditional ai methods often require substantial computational resources, particularly when deployed on cloud servers, leading to increased network load and latency. In contrast, deploying ai models directly on end devices enables real-time data processing at the source, facilitating faster anomaly detection and mitigation. We propose a lightweight hierarchical ai model where initial inferences are made by a simple model and outputs confidence scores. Using a modified TOPSIS-based method, we determine an optimal confidence score threshold. Inferences below this threshold are forwarded to a more complex model for accurate analysis, reducing overall computational demands while maintaining high detection performance. Our approach is tested on a 5 G network dataset, demonstrating competitive performance compared to other anomaly detection models. © 2024 IEEE.

关键词： 5G mobile communication systems

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Analyzing the Impact of Security Measures on Wi-Fi Quality of Service

Analyzing the Impact of Security Measures on Wi-Fi Quality o...

引用

Artificial Intelligence, Blockchain, and Internet of Things (aiBThings), IEEE International Conference on

作者： Thomas J. Kopcho Mostafa M. Fouda Cameron J. Krome Anna T. Quach Israel G. Olaveson Department of Computer Science Idaho State University Pocatello ID Department of Electrical and Computer Engineering Idaho State University Pocatello ID AI Data & Visualization Idaho National Laboratory Idaho Falls ID

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

关键词： Measurement Protocols Statistical analysis Wireless networks Quality of service Throughput Encryption Security Internet of Things Wireless fidelity

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A Lightweight ai Model for Anomaly Detection in Wireless Networks

A Lightweight AI Model for Anomaly Detection in Wireless Net...

引用

Artificial Intelligence, Blockchain, and Internet of Things (aiBThings), IEEE International Conference on

作者： Thomas J. Kopcho Mostafa M. Fouda Cameron J. Krome Department of Computer Science Idaho State University Pocatello ID Department of Electrical and Computer Engineering Idaho State University Pocatello ID AI Data & Visualization Idaho National Laboratory Idaho Falls ID

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

关键词： Computational modeling Wireless networks Real-time systems Servers Security Reliability Artificial intelligence Anomaly detection Load modeling Testing

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A generative artificial intelligence framework based on a molecular diffusion model for the design of metal–organic frameworks for carbon capture

arXiv

引用

arXiv 2023年

作者： Park, Hyun Yan, Xiaoli Zhu, Ruijie Huerta, Eliu A. Chaudhuri, Santanu Cooper, Donny Foster, Ian Tajkhorshid, Emad Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Visualization Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Metal–organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (ai), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates ai-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six ai-generated MOFs with CO2 capacities greater than 2m molg−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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